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b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..26a872a90e4934c929d168228125f2e43936c774 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/__init__.py @@ -0,0 +1,27 @@ +__all__ = [ + "NaT", + "NaTType", + "OutOfBoundsDatetime", + "Period", + "Timedelta", + "Timestamp", + "iNaT", + "Interval", +] + + +# Below imports needs to happen first to ensure pandas top level +# module gets monkeypatched with the pandas_datetime_CAPI +# see pandas_datetime_exec in pd_datetime.c +import pandas._libs.pandas_parser # isort: skip # type: ignore[reportUnusedImport] +import pandas._libs.pandas_datetime # noqa: F401 # isort: skip # type: ignore[reportUnusedImport] +from pandas._libs.interval import Interval +from pandas._libs.tslibs import ( + NaT, + NaTType, + OutOfBoundsDatetime, + Period, + Timedelta, + Timestamp, + iNaT, +) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/algos.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/algos.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..398693597f8cac51ed5d40a1c2c3b87d20620e1d --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/algos.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f2bd60cc1d7b27a3fb360675ae641ea48515a2585057704baa9bcd822edc151f +size 2180240 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/algos.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/algos.pyi new file mode 100644 index 0000000000000000000000000000000000000000..caf5425dfc7b44cbb2fea103e56d4584709eca1e --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/algos.pyi @@ -0,0 +1,416 @@ +from typing import Any + +import numpy as np + +from pandas._typing import npt + +class Infinity: + def __eq__(self, other) -> bool: ... + def __ne__(self, other) -> bool: ... + def __lt__(self, other) -> bool: ... + def __le__(self, other) -> bool: ... + def __gt__(self, other) -> bool: ... + def __ge__(self, other) -> bool: ... + +class NegInfinity: + def __eq__(self, other) -> bool: ... + def __ne__(self, other) -> bool: ... + def __lt__(self, other) -> bool: ... + def __le__(self, other) -> bool: ... + def __gt__(self, other) -> bool: ... + def __ge__(self, other) -> bool: ... + +def unique_deltas( + arr: np.ndarray, # const int64_t[:] +) -> np.ndarray: ... # np.ndarray[np.int64, ndim=1] +def is_lexsorted(list_of_arrays: list[npt.NDArray[np.int64]]) -> bool: ... +def groupsort_indexer( + index: np.ndarray, # const int64_t[:] + ngroups: int, +) -> tuple[ + np.ndarray, # ndarray[int64_t, ndim=1] + np.ndarray, # ndarray[int64_t, ndim=1] +]: ... +def kth_smallest( + arr: np.ndarray, # numeric[:] + k: int, +) -> Any: ... # numeric + +# ---------------------------------------------------------------------- +# Pairwise correlation/covariance + +def nancorr( + mat: npt.NDArray[np.float64], # const float64_t[:, :] + cov: bool = ..., + minp: int | None = ..., +) -> npt.NDArray[np.float64]: ... # ndarray[float64_t, ndim=2] +def nancorr_spearman( + mat: npt.NDArray[np.float64], # ndarray[float64_t, ndim=2] + minp: int = ..., +) -> npt.NDArray[np.float64]: ... # ndarray[float64_t, ndim=2] + +# ---------------------------------------------------------------------- + +def validate_limit(nobs: int | None, limit=...) -> int: ... +def get_fill_indexer( + mask: npt.NDArray[np.bool_], + limit: int | None = None, +) -> npt.NDArray[np.intp]: ... +def pad( + old: np.ndarray, # ndarray[numeric_object_t] + new: np.ndarray, # ndarray[numeric_object_t] + limit=..., +) -> npt.NDArray[np.intp]: ... # np.ndarray[np.intp, ndim=1] +def pad_inplace( + values: np.ndarray, # numeric_object_t[:] + mask: np.ndarray, # uint8_t[:] + limit=..., +) -> None: ... +def pad_2d_inplace( + values: np.ndarray, # numeric_object_t[:, :] + mask: np.ndarray, # const uint8_t[:, :] + limit=..., +) -> None: ... +def backfill( + old: np.ndarray, # ndarray[numeric_object_t] + new: np.ndarray, # ndarray[numeric_object_t] + limit=..., +) -> npt.NDArray[np.intp]: ... # np.ndarray[np.intp, ndim=1] +def backfill_inplace( + values: np.ndarray, # numeric_object_t[:] + mask: np.ndarray, # uint8_t[:] + limit=..., +) -> None: ... +def backfill_2d_inplace( + values: np.ndarray, # numeric_object_t[:, :] + mask: np.ndarray, # const uint8_t[:, :] + limit=..., +) -> None: ... +def is_monotonic( + arr: np.ndarray, # ndarray[numeric_object_t, ndim=1] + timelike: bool, +) -> tuple[bool, bool, bool]: ... + +# ---------------------------------------------------------------------- +# rank_1d, rank_2d +# ---------------------------------------------------------------------- + +def rank_1d( + values: np.ndarray, # ndarray[numeric_object_t, ndim=1] + labels: np.ndarray | None = ..., # const int64_t[:]=None + is_datetimelike: bool = ..., + ties_method=..., + ascending: bool = ..., + pct: bool = ..., + na_option=..., + mask: npt.NDArray[np.bool_] | None = ..., +) -> np.ndarray: ... # np.ndarray[float64_t, ndim=1] +def rank_2d( + in_arr: np.ndarray, # ndarray[numeric_object_t, ndim=2] + axis: int = ..., + is_datetimelike: bool = ..., + ties_method=..., + ascending: bool = ..., + na_option=..., + pct: bool = ..., +) -> np.ndarray: ... # np.ndarray[float64_t, ndim=1] +def diff_2d( + arr: np.ndarray, # ndarray[diff_t, ndim=2] + out: np.ndarray, # ndarray[out_t, ndim=2] + periods: int, + axis: int, + datetimelike: bool = ..., +) -> None: ... +def ensure_platform_int(arr: object) -> npt.NDArray[np.intp]: ... +def ensure_object(arr: object) -> npt.NDArray[np.object_]: ... +def ensure_float64(arr: object) -> npt.NDArray[np.float64]: ... +def ensure_int8(arr: object) -> npt.NDArray[np.int8]: ... +def ensure_int16(arr: object) -> npt.NDArray[np.int16]: ... +def ensure_int32(arr: object) -> npt.NDArray[np.int32]: ... +def ensure_int64(arr: object) -> npt.NDArray[np.int64]: ... +def ensure_uint64(arr: object) -> npt.NDArray[np.uint64]: ... +def take_1d_int8_int8( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_1d_int8_int32( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_1d_int8_int64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_1d_int8_float64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_1d_int16_int16( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_1d_int16_int32( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_1d_int16_int64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_1d_int16_float64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_1d_int32_int32( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_1d_int32_int64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_1d_int32_float64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_1d_int64_int64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_1d_int64_float64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_1d_float32_float32( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_1d_float32_float64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_1d_float64_float64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_1d_object_object( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_1d_bool_bool( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_1d_bool_object( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis0_int8_int8( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis0_int8_int32( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis0_int8_int64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis0_int8_float64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis0_int16_int16( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis0_int16_int32( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis0_int16_int64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis0_int16_float64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis0_int32_int32( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis0_int32_int64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis0_int32_float64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis0_int64_int64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis0_int64_float64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis0_float32_float32( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis0_float32_float64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis0_float64_float64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis0_object_object( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis0_bool_bool( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis0_bool_object( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis1_int8_int8( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis1_int8_int32( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis1_int8_int64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis1_int8_float64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis1_int16_int16( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis1_int16_int32( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis1_int16_int64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis1_int16_float64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis1_int32_int32( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis1_int32_int64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis1_int32_float64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis1_int64_int64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis1_int64_float64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis1_float32_float32( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis1_float32_float64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis1_float64_float64( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis1_object_object( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis1_bool_bool( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_axis1_bool_object( + values: np.ndarray, indexer: npt.NDArray[np.intp], out: np.ndarray, fill_value=... +) -> None: ... +def take_2d_multi_int8_int8( + values: np.ndarray, + indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]], + out: np.ndarray, + fill_value=..., +) -> None: ... +def take_2d_multi_int8_int32( + values: np.ndarray, + indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]], + out: np.ndarray, + fill_value=..., +) -> None: ... +def take_2d_multi_int8_int64( + values: np.ndarray, + indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]], + out: np.ndarray, + fill_value=..., +) -> None: ... +def take_2d_multi_int8_float64( + values: np.ndarray, + indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]], + out: np.ndarray, + fill_value=..., +) -> None: ... +def take_2d_multi_int16_int16( + values: np.ndarray, + indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]], + out: np.ndarray, + fill_value=..., +) -> None: ... +def take_2d_multi_int16_int32( + values: np.ndarray, + indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]], + out: np.ndarray, + fill_value=..., +) -> None: ... +def take_2d_multi_int16_int64( + values: np.ndarray, + indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]], + out: np.ndarray, + fill_value=..., +) -> None: ... +def take_2d_multi_int16_float64( + values: np.ndarray, + indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]], + out: np.ndarray, + fill_value=..., +) -> None: ... +def take_2d_multi_int32_int32( + values: np.ndarray, + indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]], + out: np.ndarray, + fill_value=..., +) -> None: ... +def take_2d_multi_int32_int64( + values: np.ndarray, + indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]], + out: np.ndarray, + fill_value=..., +) -> None: ... +def take_2d_multi_int32_float64( + values: np.ndarray, + indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]], + out: np.ndarray, + fill_value=..., +) -> None: ... +def take_2d_multi_int64_float64( + values: np.ndarray, + indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]], + out: np.ndarray, + fill_value=..., +) -> None: ... +def take_2d_multi_float32_float32( + values: np.ndarray, + indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]], + out: np.ndarray, + fill_value=..., +) -> None: ... +def take_2d_multi_float32_float64( + values: np.ndarray, + indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]], + out: np.ndarray, + fill_value=..., +) -> None: ... +def take_2d_multi_float64_float64( + values: np.ndarray, + indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]], + out: np.ndarray, + fill_value=..., +) -> None: ... +def take_2d_multi_object_object( + values: np.ndarray, + indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]], + out: np.ndarray, + fill_value=..., +) -> None: ... +def take_2d_multi_bool_bool( + values: np.ndarray, + indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]], + out: np.ndarray, + fill_value=..., +) -> None: ... +def take_2d_multi_bool_object( + values: np.ndarray, + indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]], + out: np.ndarray, + fill_value=..., +) -> None: ... +def take_2d_multi_int64_int64( + values: np.ndarray, + indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]], + out: np.ndarray, + fill_value=..., +) -> None: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/arrays.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/arrays.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..37cb8dda1062fed2eef19d5004e5ec2b09a1f019 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/arrays.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1c5265c026d4f95460fea88d7444b6bc37705a0e333d380331c021893354d658 +size 125392 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/arrays.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/arrays.pyi new file mode 100644 index 0000000000000000000000000000000000000000..86f69c3cdfc75f3101a8e0afe59f9b102d90ad79 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/arrays.pyi @@ -0,0 +1,40 @@ +from typing import Sequence + +import numpy as np + +from pandas._typing import ( + AxisInt, + DtypeObj, + Self, + Shape, +) + +class NDArrayBacked: + _dtype: DtypeObj + _ndarray: np.ndarray + def __init__(self, values: np.ndarray, dtype: DtypeObj) -> None: ... + @classmethod + def _simple_new(cls, values: np.ndarray, dtype: DtypeObj): ... + def _from_backing_data(self, values: np.ndarray): ... + def __setstate__(self, state): ... + def __len__(self) -> int: ... + @property + def shape(self) -> Shape: ... + @property + def ndim(self) -> int: ... + @property + def size(self) -> int: ... + @property + def nbytes(self) -> int: ... + def copy(self, order=...): ... + def delete(self, loc, axis=...): ... + def swapaxes(self, axis1, axis2): ... + def repeat(self, repeats: int | Sequence[int], axis: int | None = ...): ... + def reshape(self, *args, **kwargs): ... + def ravel(self, order=...): ... + @property + def T(self): ... + @classmethod + def _concat_same_type( + cls, to_concat: Sequence[Self], axis: AxisInt = ... + ) -> Self: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/byteswap.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/byteswap.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..1e2d684397cfc5b34dd293d51bc2412ee74890e6 Binary files /dev/null and b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/byteswap.cpython-310-x86_64-linux-gnu.so differ diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/byteswap.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/byteswap.pyi new file mode 100644 index 0000000000000000000000000000000000000000..bb0dbfc6a50b1bb7cd509dc5b3dfeed55ad70b09 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/byteswap.pyi @@ -0,0 +1,5 @@ +def read_float_with_byteswap(data: bytes, offset: int, byteswap: bool) -> float: ... +def read_double_with_byteswap(data: bytes, offset: int, byteswap: bool) -> float: ... +def read_uint16_with_byteswap(data: bytes, offset: int, byteswap: bool) -> int: ... +def read_uint32_with_byteswap(data: bytes, offset: int, byteswap: bool) -> int: ... +def read_uint64_with_byteswap(data: bytes, offset: int, byteswap: bool) -> int: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/groupby.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/groupby.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..8f70100d4569c0afac68b1d7f5261437f30ce050 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/groupby.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6662e0d859a88c98d46d24e3097df0f175b63d8d1f82b2e40ee30268dbf805b0 +size 2629960 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/groupby.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/groupby.pyi new file mode 100644 index 0000000000000000000000000000000000000000..eebd9331ab393d832c8c0099084a95a6aed0c460 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/groupby.pyi @@ -0,0 +1,217 @@ +from typing import Literal + +import numpy as np + +from pandas._typing import npt + +def group_median_float64( + out: np.ndarray, # ndarray[float64_t, ndim=2] + counts: npt.NDArray[np.int64], + values: np.ndarray, # ndarray[float64_t, ndim=2] + labels: npt.NDArray[np.int64], + min_count: int = ..., # Py_ssize_t + mask: np.ndarray | None = ..., + result_mask: np.ndarray | None = ..., +) -> None: ... +def group_cumprod( + out: np.ndarray, # float64_t[:, ::1] + values: np.ndarray, # const float64_t[:, :] + labels: np.ndarray, # const int64_t[:] + ngroups: int, + is_datetimelike: bool, + skipna: bool = ..., + mask: np.ndarray | None = ..., + result_mask: np.ndarray | None = ..., +) -> None: ... +def group_cumsum( + out: np.ndarray, # int64float_t[:, ::1] + values: np.ndarray, # ndarray[int64float_t, ndim=2] + labels: np.ndarray, # const int64_t[:] + ngroups: int, + is_datetimelike: bool, + skipna: bool = ..., + mask: np.ndarray | None = ..., + result_mask: np.ndarray | None = ..., +) -> None: ... +def group_shift_indexer( + out: np.ndarray, # int64_t[::1] + labels: np.ndarray, # const int64_t[:] + ngroups: int, + periods: int, +) -> None: ... +def group_fillna_indexer( + out: np.ndarray, # ndarray[intp_t] + labels: np.ndarray, # ndarray[int64_t] + sorted_labels: npt.NDArray[np.intp], + mask: npt.NDArray[np.uint8], + limit: int, # int64_t + dropna: bool, +) -> None: ... +def group_any_all( + out: np.ndarray, # uint8_t[::1] + values: np.ndarray, # const uint8_t[::1] + labels: np.ndarray, # const int64_t[:] + mask: np.ndarray, # const uint8_t[::1] + val_test: Literal["any", "all"], + skipna: bool, + result_mask: np.ndarray | None, +) -> None: ... +def group_sum( + out: np.ndarray, # complexfloatingintuint_t[:, ::1] + counts: np.ndarray, # int64_t[::1] + values: np.ndarray, # ndarray[complexfloatingintuint_t, ndim=2] + labels: np.ndarray, # const intp_t[:] + mask: np.ndarray | None, + result_mask: np.ndarray | None = ..., + min_count: int = ..., + is_datetimelike: bool = ..., + initial: object = ..., +) -> None: ... +def group_prod( + out: np.ndarray, # int64float_t[:, ::1] + counts: np.ndarray, # int64_t[::1] + values: np.ndarray, # ndarray[int64float_t, ndim=2] + labels: np.ndarray, # const intp_t[:] + mask: np.ndarray | None, + result_mask: np.ndarray | None = ..., + min_count: int = ..., +) -> None: ... +def group_var( + out: np.ndarray, # floating[:, ::1] + counts: np.ndarray, # int64_t[::1] + values: np.ndarray, # ndarray[floating, ndim=2] + labels: np.ndarray, # const intp_t[:] + min_count: int = ..., # Py_ssize_t + ddof: int = ..., # int64_t + mask: np.ndarray | None = ..., + result_mask: np.ndarray | None = ..., + is_datetimelike: bool = ..., + name: str = ..., +) -> None: ... +def group_skew( + out: np.ndarray, # float64_t[:, ::1] + counts: np.ndarray, # int64_t[::1] + values: np.ndarray, # ndarray[float64_T, ndim=2] + labels: np.ndarray, # const intp_t[::1] + mask: np.ndarray | None = ..., + result_mask: np.ndarray | None = ..., + skipna: bool = ..., +) -> None: ... +def group_mean( + out: np.ndarray, # floating[:, ::1] + counts: np.ndarray, # int64_t[::1] + values: np.ndarray, # ndarray[floating, ndim=2] + labels: np.ndarray, # const intp_t[:] + min_count: int = ..., # Py_ssize_t + is_datetimelike: bool = ..., # bint + mask: np.ndarray | None = ..., + result_mask: np.ndarray | None = ..., +) -> None: ... +def group_ohlc( + out: np.ndarray, # floatingintuint_t[:, ::1] + counts: np.ndarray, # int64_t[::1] + values: np.ndarray, # ndarray[floatingintuint_t, ndim=2] + labels: np.ndarray, # const intp_t[:] + min_count: int = ..., + mask: np.ndarray | None = ..., + result_mask: np.ndarray | None = ..., +) -> None: ... +def group_quantile( + out: npt.NDArray[np.float64], + values: np.ndarray, # ndarray[numeric, ndim=1] + labels: npt.NDArray[np.intp], + mask: npt.NDArray[np.uint8], + qs: npt.NDArray[np.float64], # const + starts: npt.NDArray[np.int64], + ends: npt.NDArray[np.int64], + interpolation: Literal["linear", "lower", "higher", "nearest", "midpoint"], + result_mask: np.ndarray | None, + is_datetimelike: bool, +) -> None: ... +def group_last( + out: np.ndarray, # rank_t[:, ::1] + counts: np.ndarray, # int64_t[::1] + values: np.ndarray, # ndarray[rank_t, ndim=2] + labels: np.ndarray, # const int64_t[:] + mask: npt.NDArray[np.bool_] | None, + result_mask: npt.NDArray[np.bool_] | None = ..., + min_count: int = ..., # Py_ssize_t + is_datetimelike: bool = ..., + skipna: bool = ..., +) -> None: ... +def group_nth( + out: np.ndarray, # rank_t[:, ::1] + counts: np.ndarray, # int64_t[::1] + values: np.ndarray, # ndarray[rank_t, ndim=2] + labels: np.ndarray, # const int64_t[:] + mask: npt.NDArray[np.bool_] | None, + result_mask: npt.NDArray[np.bool_] | None = ..., + min_count: int = ..., # int64_t + rank: int = ..., # int64_t + is_datetimelike: bool = ..., + skipna: bool = ..., +) -> None: ... +def group_rank( + out: np.ndarray, # float64_t[:, ::1] + values: np.ndarray, # ndarray[rank_t, ndim=2] + labels: np.ndarray, # const int64_t[:] + ngroups: int, + is_datetimelike: bool, + ties_method: Literal["average", "min", "max", "first", "dense"] = ..., + ascending: bool = ..., + pct: bool = ..., + na_option: Literal["keep", "top", "bottom"] = ..., + mask: npt.NDArray[np.bool_] | None = ..., +) -> None: ... +def group_max( + out: np.ndarray, # groupby_t[:, ::1] + counts: np.ndarray, # int64_t[::1] + values: np.ndarray, # ndarray[groupby_t, ndim=2] + labels: np.ndarray, # const int64_t[:] + min_count: int = ..., + is_datetimelike: bool = ..., + mask: np.ndarray | None = ..., + result_mask: np.ndarray | None = ..., +) -> None: ... +def group_min( + out: np.ndarray, # groupby_t[:, ::1] + counts: np.ndarray, # int64_t[::1] + values: np.ndarray, # ndarray[groupby_t, ndim=2] + labels: np.ndarray, # const int64_t[:] + min_count: int = ..., + is_datetimelike: bool = ..., + mask: np.ndarray | None = ..., + result_mask: np.ndarray | None = ..., +) -> None: ... +def group_idxmin_idxmax( + out: npt.NDArray[np.intp], + counts: npt.NDArray[np.int64], + values: np.ndarray, # ndarray[groupby_t, ndim=2] + labels: npt.NDArray[np.intp], + min_count: int = ..., + is_datetimelike: bool = ..., + mask: np.ndarray | None = ..., + name: str = ..., + skipna: bool = ..., + result_mask: np.ndarray | None = ..., +) -> None: ... +def group_cummin( + out: np.ndarray, # groupby_t[:, ::1] + values: np.ndarray, # ndarray[groupby_t, ndim=2] + labels: np.ndarray, # const int64_t[:] + ngroups: int, + is_datetimelike: bool, + mask: np.ndarray | None = ..., + result_mask: np.ndarray | None = ..., + skipna: bool = ..., +) -> None: ... +def group_cummax( + out: np.ndarray, # groupby_t[:, ::1] + values: np.ndarray, # ndarray[groupby_t, ndim=2] + labels: np.ndarray, # const int64_t[:] + ngroups: int, + is_datetimelike: bool, + mask: np.ndarray | None = ..., + result_mask: np.ndarray | None = ..., + skipna: bool = ..., +) -> None: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/hashing.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/hashing.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..7536785a83d64653501f14bdeb615ce58fbaf93f --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/hashing.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a5844d223cb2562fc4fe93f8ac5afdfc7c996fe61dd9bc391867440d8ca0bb97 +size 213168 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/hashing.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/hashing.pyi new file mode 100644 index 0000000000000000000000000000000000000000..8361026e4a87d462e04c53f7f1f8aee8a7f6ffe0 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/hashing.pyi @@ -0,0 +1,9 @@ +import numpy as np + +from pandas._typing import npt + +def hash_object_array( + arr: npt.NDArray[np.object_], + key: str, + encoding: str = ..., +) -> npt.NDArray[np.uint64]: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/hashtable.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/hashtable.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..08a7ee7991e4a48cc903d2bc2e6cc6d4f05b9122 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/hashtable.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c970bb293c370090976daae51eb3d7be5ed70cadeb6b0ae6de6201c3eb08b43 +size 2257520 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/hashtable.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/hashtable.pyi new file mode 100644 index 0000000000000000000000000000000000000000..555ec73acd9b2e9ccbb5ea145db77862e2c54dbf --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/hashtable.pyi @@ -0,0 +1,252 @@ +from typing import ( + Any, + Hashable, + Literal, +) + +import numpy as np + +from pandas._typing import npt + +def unique_label_indices( + labels: np.ndarray, # const int64_t[:] +) -> np.ndarray: ... + +class Factorizer: + count: int + uniques: Any + def __init__(self, size_hint: int) -> None: ... + def get_count(self) -> int: ... + def factorize( + self, + values: np.ndarray, + na_sentinel=..., + na_value=..., + mask=..., + ) -> npt.NDArray[np.intp]: ... + +class ObjectFactorizer(Factorizer): + table: PyObjectHashTable + uniques: ObjectVector + +class Int64Factorizer(Factorizer): + table: Int64HashTable + uniques: Int64Vector + +class UInt64Factorizer(Factorizer): + table: UInt64HashTable + uniques: UInt64Vector + +class Int32Factorizer(Factorizer): + table: Int32HashTable + uniques: Int32Vector + +class UInt32Factorizer(Factorizer): + table: UInt32HashTable + uniques: UInt32Vector + +class Int16Factorizer(Factorizer): + table: Int16HashTable + uniques: Int16Vector + +class UInt16Factorizer(Factorizer): + table: UInt16HashTable + uniques: UInt16Vector + +class Int8Factorizer(Factorizer): + table: Int8HashTable + uniques: Int8Vector + +class UInt8Factorizer(Factorizer): + table: UInt8HashTable + uniques: UInt8Vector + +class Float64Factorizer(Factorizer): + table: Float64HashTable + uniques: Float64Vector + +class Float32Factorizer(Factorizer): + table: Float32HashTable + uniques: Float32Vector + +class Complex64Factorizer(Factorizer): + table: Complex64HashTable + uniques: Complex64Vector + +class Complex128Factorizer(Factorizer): + table: Complex128HashTable + uniques: Complex128Vector + +class Int64Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.int64]: ... + +class Int32Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.int32]: ... + +class Int16Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.int16]: ... + +class Int8Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.int8]: ... + +class UInt64Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.uint64]: ... + +class UInt32Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.uint32]: ... + +class UInt16Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.uint16]: ... + +class UInt8Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.uint8]: ... + +class Float64Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.float64]: ... + +class Float32Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.float32]: ... + +class Complex128Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.complex128]: ... + +class Complex64Vector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.complex64]: ... + +class StringVector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.object_]: ... + +class ObjectVector: + def __init__(self, *args) -> None: ... + def __len__(self) -> int: ... + def to_array(self) -> npt.NDArray[np.object_]: ... + +class HashTable: + # NB: The base HashTable class does _not_ actually have these methods; + # we are putting them here for the sake of mypy to avoid + # reproducing them in each subclass below. + def __init__(self, size_hint: int = ..., uses_mask: bool = ...) -> None: ... + def __len__(self) -> int: ... + def __contains__(self, key: Hashable) -> bool: ... + def sizeof(self, deep: bool = ...) -> int: ... + def get_state(self) -> dict[str, int]: ... + # TODO: `val/key` type is subclass-specific + def get_item(self, val): ... # TODO: return type? + def set_item(self, key, val) -> None: ... + def get_na(self): ... # TODO: return type? + def set_na(self, val) -> None: ... + def map_locations( + self, + values: np.ndarray, # np.ndarray[subclass-specific] + mask: npt.NDArray[np.bool_] | None = ..., + ) -> None: ... + def lookup( + self, + values: np.ndarray, # np.ndarray[subclass-specific] + mask: npt.NDArray[np.bool_] | None = ..., + ) -> npt.NDArray[np.intp]: ... + def get_labels( + self, + values: np.ndarray, # np.ndarray[subclass-specific] + uniques, # SubclassTypeVector + count_prior: int = ..., + na_sentinel: int = ..., + na_value: object = ..., + mask=..., + ) -> npt.NDArray[np.intp]: ... + def unique( + self, + values: np.ndarray, # np.ndarray[subclass-specific] + return_inverse: bool = ..., + mask=..., + ) -> ( + tuple[ + np.ndarray, # np.ndarray[subclass-specific] + npt.NDArray[np.intp], + ] + | np.ndarray + ): ... # np.ndarray[subclass-specific] + def factorize( + self, + values: np.ndarray, # np.ndarray[subclass-specific] + na_sentinel: int = ..., + na_value: object = ..., + mask=..., + ignore_na: bool = True, + ) -> tuple[np.ndarray, npt.NDArray[np.intp]]: ... # np.ndarray[subclass-specific] + +class Complex128HashTable(HashTable): ... +class Complex64HashTable(HashTable): ... +class Float64HashTable(HashTable): ... +class Float32HashTable(HashTable): ... + +class Int64HashTable(HashTable): + # Only Int64HashTable has get_labels_groupby, map_keys_to_values + def get_labels_groupby( + self, + values: npt.NDArray[np.int64], # const int64_t[:] + ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.int64]]: ... + def map_keys_to_values( + self, + keys: npt.NDArray[np.int64], + values: npt.NDArray[np.int64], # const int64_t[:] + ) -> None: ... + +class Int32HashTable(HashTable): ... +class Int16HashTable(HashTable): ... +class Int8HashTable(HashTable): ... +class UInt64HashTable(HashTable): ... +class UInt32HashTable(HashTable): ... +class UInt16HashTable(HashTable): ... +class UInt8HashTable(HashTable): ... +class StringHashTable(HashTable): ... +class PyObjectHashTable(HashTable): ... +class IntpHashTable(HashTable): ... + +def duplicated( + values: np.ndarray, + keep: Literal["last", "first", False] = ..., + mask: npt.NDArray[np.bool_] | None = ..., +) -> npt.NDArray[np.bool_]: ... +def mode( + values: np.ndarray, dropna: bool, mask: npt.NDArray[np.bool_] | None = ... +) -> np.ndarray: ... +def value_count( + values: np.ndarray, + dropna: bool, + mask: npt.NDArray[np.bool_] | None = ..., +) -> tuple[np.ndarray, npt.NDArray[np.int64], int]: ... # np.ndarray[same-as-values] + +# arr and values should have same dtype +def ismember( + arr: np.ndarray, + values: np.ndarray, +) -> npt.NDArray[np.bool_]: ... +def object_hash(obj) -> int: ... +def objects_are_equal(a, b) -> bool: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/index.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/index.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..d9d0edae846142d64aad0bbe6b6c0d2e1729abaa --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/index.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:453349bb31bd4758057336ad43d3952dc870c6fd837280d7870b47cbfe67de24 +size 890304 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/index.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/index.pyi new file mode 100644 index 0000000000000000000000000000000000000000..9c3791a642768ccfb13f681a31ac993c606c2304 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/index.pyi @@ -0,0 +1,103 @@ +import numpy as np + +from pandas._typing import npt + +from pandas import MultiIndex +from pandas.core.arrays import ExtensionArray + +multiindex_nulls_shift: int + +class IndexEngine: + over_size_threshold: bool + def __init__(self, values: np.ndarray) -> None: ... + def __contains__(self, val: object) -> bool: ... + + # -> int | slice | np.ndarray[bool] + def get_loc(self, val: object) -> int | slice | np.ndarray: ... + def sizeof(self, deep: bool = ...) -> int: ... + def __sizeof__(self) -> int: ... + @property + def is_unique(self) -> bool: ... + @property + def is_monotonic_increasing(self) -> bool: ... + @property + def is_monotonic_decreasing(self) -> bool: ... + @property + def is_mapping_populated(self) -> bool: ... + def clear_mapping(self): ... + def get_indexer(self, values: np.ndarray) -> npt.NDArray[np.intp]: ... + def get_indexer_non_unique( + self, + targets: np.ndarray, + ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ... + +class MaskedIndexEngine(IndexEngine): + def __init__(self, values: object) -> None: ... + def get_indexer_non_unique( + self, targets: object + ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ... + +class Float64Engine(IndexEngine): ... +class Float32Engine(IndexEngine): ... +class Complex128Engine(IndexEngine): ... +class Complex64Engine(IndexEngine): ... +class Int64Engine(IndexEngine): ... +class Int32Engine(IndexEngine): ... +class Int16Engine(IndexEngine): ... +class Int8Engine(IndexEngine): ... +class UInt64Engine(IndexEngine): ... +class UInt32Engine(IndexEngine): ... +class UInt16Engine(IndexEngine): ... +class UInt8Engine(IndexEngine): ... +class ObjectEngine(IndexEngine): ... +class DatetimeEngine(Int64Engine): ... +class TimedeltaEngine(DatetimeEngine): ... +class PeriodEngine(Int64Engine): ... +class BoolEngine(UInt8Engine): ... +class MaskedFloat64Engine(MaskedIndexEngine): ... +class MaskedFloat32Engine(MaskedIndexEngine): ... +class MaskedComplex128Engine(MaskedIndexEngine): ... +class MaskedComplex64Engine(MaskedIndexEngine): ... +class MaskedInt64Engine(MaskedIndexEngine): ... +class MaskedInt32Engine(MaskedIndexEngine): ... +class MaskedInt16Engine(MaskedIndexEngine): ... +class MaskedInt8Engine(MaskedIndexEngine): ... +class MaskedUInt64Engine(MaskedIndexEngine): ... +class MaskedUInt32Engine(MaskedIndexEngine): ... +class MaskedUInt16Engine(MaskedIndexEngine): ... +class MaskedUInt8Engine(MaskedIndexEngine): ... +class MaskedBoolEngine(MaskedUInt8Engine): ... + +class StringObjectEngine(ObjectEngine): + def __init__(self, values: object, na_value) -> None: ... + +class BaseMultiIndexCodesEngine: + levels: list[np.ndarray] + offsets: np.ndarray # ndarray[uint64_t, ndim=1] + + def __init__( + self, + levels: list[np.ndarray], # all entries hashable + labels: list[np.ndarray], # all entries integer-dtyped + offsets: np.ndarray, # np.ndarray[np.uint64, ndim=1] + ) -> None: ... + def get_indexer(self, target: npt.NDArray[np.object_]) -> npt.NDArray[np.intp]: ... + def _extract_level_codes(self, target: MultiIndex) -> np.ndarray: ... + +class ExtensionEngine: + def __init__(self, values: ExtensionArray) -> None: ... + def __contains__(self, val: object) -> bool: ... + def get_loc(self, val: object) -> int | slice | np.ndarray: ... + def get_indexer(self, values: np.ndarray) -> npt.NDArray[np.intp]: ... + def get_indexer_non_unique( + self, + targets: np.ndarray, + ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ... + @property + def is_unique(self) -> bool: ... + @property + def is_monotonic_increasing(self) -> bool: ... + @property + def is_monotonic_decreasing(self) -> bool: ... + def sizeof(self, deep: bool = ...) -> int: ... + def clear_mapping(self): ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/indexing.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/indexing.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..341562b045543017dc4668fc61988e7ab60bb00a Binary files /dev/null and b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/indexing.cpython-310-x86_64-linux-gnu.so differ diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/indexing.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/indexing.pyi new file mode 100644 index 0000000000000000000000000000000000000000..3ae5c5044a2f75452fa57ba578af2c7b4c78ec96 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/indexing.pyi @@ -0,0 +1,17 @@ +from typing import ( + Generic, + TypeVar, +) + +from pandas.core.indexing import IndexingMixin + +_IndexingMixinT = TypeVar("_IndexingMixinT", bound=IndexingMixin) + +class NDFrameIndexerBase(Generic[_IndexingMixinT]): + name: str + # in practice obj is either a DataFrame or a Series + obj: _IndexingMixinT + + def __init__(self, name: str, obj: _IndexingMixinT) -> None: ... + @property + def ndim(self) -> int: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/internals.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/internals.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..7225b748787ca54aeddc38190f749d4d369b342d --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/internals.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:93be5133161c949795a98533fc7ff6ecc23218e17af1c3c0e0b36daa12457153 +size 403392 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/internals.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/internals.pyi new file mode 100644 index 0000000000000000000000000000000000000000..ffe6c7730bcdcfb8af1407bf55b59170c4093699 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/internals.pyi @@ -0,0 +1,94 @@ +from typing import ( + Iterator, + Sequence, + final, + overload, +) +import weakref + +import numpy as np + +from pandas._typing import ( + ArrayLike, + Self, + npt, +) + +from pandas import Index +from pandas.core.internals.blocks import Block as B + +def slice_len(slc: slice, objlen: int = ...) -> int: ... +def get_concat_blkno_indexers( + blknos_list: list[npt.NDArray[np.intp]], +) -> list[tuple[npt.NDArray[np.intp], BlockPlacement]]: ... +def get_blkno_indexers( + blknos: np.ndarray, # int64_t[:] + group: bool = ..., +) -> list[tuple[int, slice | np.ndarray]]: ... +def get_blkno_placements( + blknos: np.ndarray, + group: bool = ..., +) -> Iterator[tuple[int, BlockPlacement]]: ... +def update_blklocs_and_blknos( + blklocs: npt.NDArray[np.intp], + blknos: npt.NDArray[np.intp], + loc: int, + nblocks: int, +) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ... +@final +class BlockPlacement: + def __init__(self, val: int | slice | np.ndarray) -> None: ... + @property + def indexer(self) -> np.ndarray | slice: ... + @property + def as_array(self) -> np.ndarray: ... + @property + def as_slice(self) -> slice: ... + @property + def is_slice_like(self) -> bool: ... + @overload + def __getitem__( + self, loc: slice | Sequence[int] | npt.NDArray[np.intp] + ) -> BlockPlacement: ... + @overload + def __getitem__(self, loc: int) -> int: ... + def __iter__(self) -> Iterator[int]: ... + def __len__(self) -> int: ... + def delete(self, loc) -> BlockPlacement: ... + def add(self, other) -> BlockPlacement: ... + def append(self, others: list[BlockPlacement]) -> BlockPlacement: ... + def tile_for_unstack(self, factor: int) -> npt.NDArray[np.intp]: ... + +class Block: + _mgr_locs: BlockPlacement + ndim: int + values: ArrayLike + refs: BlockValuesRefs + def __init__( + self, + values: ArrayLike, + placement: BlockPlacement, + ndim: int, + refs: BlockValuesRefs | None = ..., + ) -> None: ... + def slice_block_rows(self, slicer: slice) -> Self: ... + +class BlockManager: + blocks: tuple[B, ...] + axes: list[Index] + _known_consolidated: bool + _is_consolidated: bool + _blknos: np.ndarray + _blklocs: np.ndarray + def __init__( + self, blocks: tuple[B, ...], axes: list[Index], verify_integrity=... + ) -> None: ... + def get_slice(self, slobj: slice, axis: int = ...) -> Self: ... + def _rebuild_blknos_and_blklocs(self) -> None: ... + +class BlockValuesRefs: + referenced_blocks: list[weakref.ref] + def __init__(self, blk: Block | None = ...) -> None: ... + def add_reference(self, blk: Block) -> None: ... + def add_index_reference(self, index: Index) -> None: ... + def has_reference(self) -> bool: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/interval.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/interval.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..7836ad8cea8ccc7e45db9e0127f39044ef1ce514 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/interval.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fd62b9cd62b0a56836dade53e3195b8b2a71ba4fd4775bc293b3959c9b365145 +size 1401712 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/interval.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/interval.pyi new file mode 100644 index 0000000000000000000000000000000000000000..587fdf84f2f85520713352bbcab29804c95621e5 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/interval.pyi @@ -0,0 +1,174 @@ +from typing import ( + Any, + Generic, + TypeVar, + overload, +) + +import numpy as np +import numpy.typing as npt + +from pandas._typing import ( + IntervalClosedType, + Timedelta, + Timestamp, +) + +VALID_CLOSED: frozenset[str] + +_OrderableScalarT = TypeVar("_OrderableScalarT", int, float) +_OrderableTimesT = TypeVar("_OrderableTimesT", Timestamp, Timedelta) +_OrderableT = TypeVar("_OrderableT", int, float, Timestamp, Timedelta) + +class _LengthDescriptor: + @overload + def __get__( + self, instance: Interval[_OrderableScalarT], owner: Any + ) -> _OrderableScalarT: ... + @overload + def __get__( + self, instance: Interval[_OrderableTimesT], owner: Any + ) -> Timedelta: ... + +class _MidDescriptor: + @overload + def __get__(self, instance: Interval[_OrderableScalarT], owner: Any) -> float: ... + @overload + def __get__( + self, instance: Interval[_OrderableTimesT], owner: Any + ) -> _OrderableTimesT: ... + +class IntervalMixin: + @property + def closed_left(self) -> bool: ... + @property + def closed_right(self) -> bool: ... + @property + def open_left(self) -> bool: ... + @property + def open_right(self) -> bool: ... + @property + def is_empty(self) -> bool: ... + def _check_closed_matches(self, other: IntervalMixin, name: str = ...) -> None: ... + +class Interval(IntervalMixin, Generic[_OrderableT]): + @property + def left(self: Interval[_OrderableT]) -> _OrderableT: ... + @property + def right(self: Interval[_OrderableT]) -> _OrderableT: ... + @property + def closed(self) -> IntervalClosedType: ... + mid: _MidDescriptor + length: _LengthDescriptor + def __init__( + self, + left: _OrderableT, + right: _OrderableT, + closed: IntervalClosedType = ..., + ) -> None: ... + def __hash__(self) -> int: ... + @overload + def __contains__( + self: Interval[Timedelta], key: Timedelta | Interval[Timedelta] + ) -> bool: ... + @overload + def __contains__( + self: Interval[Timestamp], key: Timestamp | Interval[Timestamp] + ) -> bool: ... + @overload + def __contains__( + self: Interval[_OrderableScalarT], + key: _OrderableScalarT | Interval[_OrderableScalarT], + ) -> bool: ... + @overload + def __add__( + self: Interval[_OrderableTimesT], y: Timedelta + ) -> Interval[_OrderableTimesT]: ... + @overload + def __add__( + self: Interval[int], y: _OrderableScalarT + ) -> Interval[_OrderableScalarT]: ... + @overload + def __add__(self: Interval[float], y: float) -> Interval[float]: ... + @overload + def __radd__( + self: Interval[_OrderableTimesT], y: Timedelta + ) -> Interval[_OrderableTimesT]: ... + @overload + def __radd__( + self: Interval[int], y: _OrderableScalarT + ) -> Interval[_OrderableScalarT]: ... + @overload + def __radd__(self: Interval[float], y: float) -> Interval[float]: ... + @overload + def __sub__( + self: Interval[_OrderableTimesT], y: Timedelta + ) -> Interval[_OrderableTimesT]: ... + @overload + def __sub__( + self: Interval[int], y: _OrderableScalarT + ) -> Interval[_OrderableScalarT]: ... + @overload + def __sub__(self: Interval[float], y: float) -> Interval[float]: ... + @overload + def __rsub__( + self: Interval[_OrderableTimesT], y: Timedelta + ) -> Interval[_OrderableTimesT]: ... + @overload + def __rsub__( + self: Interval[int], y: _OrderableScalarT + ) -> Interval[_OrderableScalarT]: ... + @overload + def __rsub__(self: Interval[float], y: float) -> Interval[float]: ... + @overload + def __mul__( + self: Interval[int], y: _OrderableScalarT + ) -> Interval[_OrderableScalarT]: ... + @overload + def __mul__(self: Interval[float], y: float) -> Interval[float]: ... + @overload + def __rmul__( + self: Interval[int], y: _OrderableScalarT + ) -> Interval[_OrderableScalarT]: ... + @overload + def __rmul__(self: Interval[float], y: float) -> Interval[float]: ... + @overload + def __truediv__( + self: Interval[int], y: _OrderableScalarT + ) -> Interval[_OrderableScalarT]: ... + @overload + def __truediv__(self: Interval[float], y: float) -> Interval[float]: ... + @overload + def __floordiv__( + self: Interval[int], y: _OrderableScalarT + ) -> Interval[_OrderableScalarT]: ... + @overload + def __floordiv__(self: Interval[float], y: float) -> Interval[float]: ... + def overlaps(self: Interval[_OrderableT], other: Interval[_OrderableT]) -> bool: ... + +def intervals_to_interval_bounds( + intervals: np.ndarray, validate_closed: bool = ... +) -> tuple[np.ndarray, np.ndarray, IntervalClosedType]: ... + +class IntervalTree(IntervalMixin): + def __init__( + self, + left: np.ndarray, + right: np.ndarray, + closed: IntervalClosedType = ..., + leaf_size: int = ..., + ) -> None: ... + @property + def mid(self) -> np.ndarray: ... + @property + def length(self) -> np.ndarray: ... + def get_indexer(self, target) -> npt.NDArray[np.intp]: ... + def get_indexer_non_unique( + self, target + ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ... + _na_count: int + @property + def is_overlapping(self) -> bool: ... + @property + def is_monotonic_increasing(self) -> bool: ... + def clear_mapping(self) -> None: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/join.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/join.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..c652b15d942150b28ef615b1a7728601e0ea5c65 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/join.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3d08ceffbcdbef7267538d48e639959c8a7570891a9e33fa2735ac2bd61c2b1b +size 1425168 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/join.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/join.pyi new file mode 100644 index 0000000000000000000000000000000000000000..1d4e8c90bc5593eae650319e9ca1b58cbd7eed73 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/join.pyi @@ -0,0 +1,79 @@ +import numpy as np + +from pandas._typing import npt + +def inner_join( + left: np.ndarray, # const intp_t[:] + right: np.ndarray, # const intp_t[:] + max_groups: int, + sort: bool = ..., +) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ... +def left_outer_join( + left: np.ndarray, # const intp_t[:] + right: np.ndarray, # const intp_t[:] + max_groups: int, + sort: bool = ..., +) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ... +def full_outer_join( + left: np.ndarray, # const intp_t[:] + right: np.ndarray, # const intp_t[:] + max_groups: int, +) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ... +def ffill_indexer( + indexer: np.ndarray, # const intp_t[:] +) -> npt.NDArray[np.intp]: ... +def left_join_indexer_unique( + left: np.ndarray, # ndarray[join_t] + right: np.ndarray, # ndarray[join_t] +) -> npt.NDArray[np.intp]: ... +def left_join_indexer( + left: np.ndarray, # ndarray[join_t] + right: np.ndarray, # ndarray[join_t] +) -> tuple[ + np.ndarray, # np.ndarray[join_t] + npt.NDArray[np.intp], + npt.NDArray[np.intp], +]: ... +def inner_join_indexer( + left: np.ndarray, # ndarray[join_t] + right: np.ndarray, # ndarray[join_t] +) -> tuple[ + np.ndarray, # np.ndarray[join_t] + npt.NDArray[np.intp], + npt.NDArray[np.intp], +]: ... +def outer_join_indexer( + left: np.ndarray, # ndarray[join_t] + right: np.ndarray, # ndarray[join_t] +) -> tuple[ + np.ndarray, # np.ndarray[join_t] + npt.NDArray[np.intp], + npt.NDArray[np.intp], +]: ... +def asof_join_backward_on_X_by_Y( + left_values: np.ndarray, # ndarray[numeric_t] + right_values: np.ndarray, # ndarray[numeric_t] + left_by_values: np.ndarray, # const int64_t[:] + right_by_values: np.ndarray, # const int64_t[:] + allow_exact_matches: bool = ..., + tolerance: np.number | float | None = ..., + use_hashtable: bool = ..., +) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ... +def asof_join_forward_on_X_by_Y( + left_values: np.ndarray, # ndarray[numeric_t] + right_values: np.ndarray, # ndarray[numeric_t] + left_by_values: np.ndarray, # const int64_t[:] + right_by_values: np.ndarray, # const int64_t[:] + allow_exact_matches: bool = ..., + tolerance: np.number | float | None = ..., + use_hashtable: bool = ..., +) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ... +def asof_join_nearest_on_X_by_Y( + left_values: np.ndarray, # ndarray[numeric_t] + right_values: np.ndarray, # ndarray[numeric_t] + left_by_values: np.ndarray, # const int64_t[:] + right_by_values: np.ndarray, # const int64_t[:] + allow_exact_matches: bool = ..., + tolerance: np.number | float | None = ..., + use_hashtable: bool = ..., +) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/json.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/json.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..8e23e2241f455840d8b4d4015d7cbdddd410122d Binary files /dev/null and b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/json.cpython-310-x86_64-linux-gnu.so differ diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/json.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/json.pyi new file mode 100644 index 0000000000000000000000000000000000000000..bc4fe68573b94290cfba2e66950c1b6d45ccf0dc --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/json.pyi @@ -0,0 +1,23 @@ +from typing import ( + Any, + Callable, +) + +def ujson_dumps( + obj: Any, + ensure_ascii: bool = ..., + double_precision: int = ..., + indent: int = ..., + orient: str = ..., + date_unit: str = ..., + iso_dates: bool = ..., + default_handler: None + | Callable[[Any], str | float | bool | list | dict | None] = ..., +) -> str: ... +def ujson_loads( + s: str, + precise_float: bool = ..., + numpy: bool = ..., + dtype: None = ..., + labelled: bool = ..., +) -> Any: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/lib.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/lib.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..fe06a1d4826bbc9083d9fb2a1760bde726ef041a --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/lib.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c96bf267ac948815d59c9ab5d0472ea581c9f918554500230c3863f10bad3d75 +size 867088 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/lib.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/lib.pyi new file mode 100644 index 0000000000000000000000000000000000000000..71a4d3ae2575ffd646568080485d9afada47d599 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/lib.pyi @@ -0,0 +1,216 @@ +# TODO(npdtypes): Many types specified here can be made more specific/accurate; +# the more specific versions are specified in comments +from decimal import Decimal +from typing import ( + Any, + Callable, + Final, + Generator, + Hashable, + Literal, + TypeAlias, + overload, +) + +import numpy as np + +from pandas._libs.interval import Interval +from pandas._libs.tslibs import Period +from pandas._typing import ( + ArrayLike, + DtypeObj, + TypeGuard, + npt, +) + +# placeholder until we can specify np.ndarray[object, ndim=2] +ndarray_obj_2d = np.ndarray + +from enum import Enum + +class _NoDefault(Enum): + no_default = ... + +no_default: Final = _NoDefault.no_default +NoDefault: TypeAlias = Literal[_NoDefault.no_default] + +i8max: int +u8max: int + +def is_np_dtype(dtype: object, kinds: str | None = ...) -> TypeGuard[np.dtype]: ... +def item_from_zerodim(val: object) -> object: ... +def infer_dtype(value: object, skipna: bool = ...) -> str: ... +def is_iterator(obj: object) -> bool: ... +def is_scalar(val: object) -> bool: ... +def is_list_like(obj: object, allow_sets: bool = ...) -> bool: ... +def is_pyarrow_array(obj: object) -> bool: ... +def is_period(val: object) -> TypeGuard[Period]: ... +def is_interval(obj: object) -> TypeGuard[Interval]: ... +def is_decimal(obj: object) -> TypeGuard[Decimal]: ... +def is_complex(obj: object) -> TypeGuard[complex]: ... +def is_bool(obj: object) -> TypeGuard[bool | np.bool_]: ... +def is_integer(obj: object) -> TypeGuard[int | np.integer]: ... +def is_int_or_none(obj) -> bool: ... +def is_float(obj: object) -> TypeGuard[float]: ... +def is_interval_array(values: np.ndarray) -> bool: ... +def is_datetime64_array(values: np.ndarray, skipna: bool = True) -> bool: ... +def is_timedelta_or_timedelta64_array( + values: np.ndarray, skipna: bool = True +) -> bool: ... +def is_datetime_with_singletz_array(values: np.ndarray) -> bool: ... +def is_time_array(values: np.ndarray, skipna: bool = ...): ... +def is_date_array(values: np.ndarray, skipna: bool = ...): ... +def is_datetime_array(values: np.ndarray, skipna: bool = ...): ... +def is_string_array(values: np.ndarray, skipna: bool = ...): ... +def is_float_array(values: np.ndarray): ... +def is_integer_array(values: np.ndarray, skipna: bool = ...): ... +def is_bool_array(values: np.ndarray, skipna: bool = ...): ... +def fast_multiget( + mapping: dict, + keys: np.ndarray, # object[:] + default=..., +) -> np.ndarray: ... +def fast_unique_multiple_list_gen(gen: Generator, sort: bool = ...) -> list: ... +def fast_unique_multiple_list(lists: list, sort: bool | None = ...) -> list: ... +def map_infer( + arr: np.ndarray, + f: Callable[[Any], Any], + convert: bool = ..., + ignore_na: bool = ..., +) -> np.ndarray: ... +@overload +def maybe_convert_objects( + objects: npt.NDArray[np.object_], + *, + try_float: bool = ..., + safe: bool = ..., + convert_numeric: bool = ..., + convert_non_numeric: Literal[False] = ..., + convert_string: Literal[False] = ..., + convert_to_nullable_dtype: Literal[False] = ..., + dtype_if_all_nat: DtypeObj | None = ..., +) -> npt.NDArray[np.object_ | np.number]: ... +@overload +def maybe_convert_objects( + objects: npt.NDArray[np.object_], + *, + try_float: bool = ..., + safe: bool = ..., + convert_numeric: bool = ..., + convert_non_numeric: bool = ..., + convert_string: bool = ..., + convert_to_nullable_dtype: Literal[True] = ..., + dtype_if_all_nat: DtypeObj | None = ..., +) -> ArrayLike: ... +@overload +def maybe_convert_objects( + objects: npt.NDArray[np.object_], + *, + try_float: bool = ..., + safe: bool = ..., + convert_numeric: bool = ..., + convert_non_numeric: bool = ..., + convert_string: bool = ..., + convert_to_nullable_dtype: bool = ..., + dtype_if_all_nat: DtypeObj | None = ..., +) -> ArrayLike: ... +@overload +def maybe_convert_numeric( + values: npt.NDArray[np.object_], + na_values: set, + convert_empty: bool = ..., + coerce_numeric: bool = ..., + convert_to_masked_nullable: Literal[False] = ..., +) -> tuple[np.ndarray, None]: ... +@overload +def maybe_convert_numeric( + values: npt.NDArray[np.object_], + na_values: set, + convert_empty: bool = ..., + coerce_numeric: bool = ..., + *, + convert_to_masked_nullable: Literal[True], +) -> tuple[np.ndarray, np.ndarray]: ... + +# TODO: restrict `arr`? +def ensure_string_array( + arr, + na_value: object = ..., + convert_na_value: bool = ..., + copy: bool = ..., + skipna: bool = ..., +) -> npt.NDArray[np.object_]: ... +def convert_nans_to_NA( + arr: npt.NDArray[np.object_], +) -> npt.NDArray[np.object_]: ... +def fast_zip(ndarrays: list) -> npt.NDArray[np.object_]: ... + +# TODO: can we be more specific about rows? +def to_object_array_tuples(rows: object) -> ndarray_obj_2d: ... +def tuples_to_object_array( + tuples: npt.NDArray[np.object_], +) -> ndarray_obj_2d: ... + +# TODO: can we be more specific about rows? +def to_object_array(rows: object, min_width: int = ...) -> ndarray_obj_2d: ... +def dicts_to_array(dicts: list, columns: list) -> ndarray_obj_2d: ... +def maybe_booleans_to_slice( + mask: npt.NDArray[np.uint8], +) -> slice | npt.NDArray[np.uint8]: ... +def maybe_indices_to_slice( + indices: npt.NDArray[np.intp], + max_len: int, +) -> slice | npt.NDArray[np.intp]: ... +def is_all_arraylike(obj: list) -> bool: ... + +# ----------------------------------------------------------------- +# Functions which in reality take memoryviews + +def memory_usage_of_objects(arr: np.ndarray) -> int: ... # object[:] # np.int64 +def map_infer_mask( + arr: np.ndarray, + f: Callable[[Any], Any], + mask: np.ndarray, # const uint8_t[:] + convert: bool = ..., + na_value: Any = ..., + dtype: np.dtype = ..., +) -> np.ndarray: ... +def indices_fast( + index: npt.NDArray[np.intp], + labels: np.ndarray, # const int64_t[:] + keys: list, + sorted_labels: list[npt.NDArray[np.int64]], +) -> dict[Hashable, npt.NDArray[np.intp]]: ... +def generate_slices( + labels: np.ndarray, ngroups: int # const intp_t[:] +) -> tuple[npt.NDArray[np.int64], npt.NDArray[np.int64]]: ... +def count_level_2d( + mask: np.ndarray, # ndarray[uint8_t, ndim=2, cast=True], + labels: np.ndarray, # const intp_t[:] + max_bin: int, +) -> np.ndarray: ... # np.ndarray[np.int64, ndim=2] +def get_level_sorter( + codes: np.ndarray, # const int64_t[:] + starts: np.ndarray, # const intp_t[:] +) -> np.ndarray: ... # np.ndarray[np.intp, ndim=1] +def generate_bins_dt64( + values: npt.NDArray[np.int64], + binner: np.ndarray, # const int64_t[:] + closed: object = ..., + hasnans: bool = ..., +) -> np.ndarray: ... # np.ndarray[np.int64, ndim=1] +def array_equivalent_object( + left: npt.NDArray[np.object_], + right: npt.NDArray[np.object_], +) -> bool: ... +def has_infs(arr: np.ndarray) -> bool: ... # const floating[:] +def has_only_ints_or_nan(arr: np.ndarray) -> bool: ... # const floating[:] +def get_reverse_indexer( + indexer: np.ndarray, # const intp_t[:] + length: int, +) -> npt.NDArray[np.intp]: ... +def is_bool_list(obj: list) -> bool: ... +def dtypes_all_equal(types: list[DtypeObj]) -> bool: ... +def is_range_indexer( + left: np.ndarray, n: int # np.ndarray[np.int64, ndim=1] +) -> bool: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/missing.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/missing.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..e8346d7907a0fb64077194d48ac6121b4ab794ea --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/missing.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f1ecf2be8a00c102f4259b6322356cd0918b886355d6dae073e6d516ae7497d5 +size 195344 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/missing.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/missing.pyi new file mode 100644 index 0000000000000000000000000000000000000000..282dcee3ed6cfdd5cd83e5c78a69c422831b4cac --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/missing.pyi @@ -0,0 +1,16 @@ +import numpy as np +from numpy import typing as npt + +class NAType: + def __new__(cls, *args, **kwargs): ... + +NA: NAType + +def is_matching_na( + left: object, right: object, nan_matches_none: bool = ... +) -> bool: ... +def isposinf_scalar(val: object) -> bool: ... +def isneginf_scalar(val: object) -> bool: ... +def checknull(val: object, inf_as_na: bool = ...) -> bool: ... +def isnaobj(arr: np.ndarray, inf_as_na: bool = ...) -> npt.NDArray[np.bool_]: ... +def is_numeric_na(values: np.ndarray) -> npt.NDArray[np.bool_]: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/ops.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/ops.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..3e5e29169a90e8adedf4d82546efa282d05d85f6 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/ops.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7772e1b01ca6bc9bef27435aa768bd090aeb51c6eee183081721a914ca4b02e5 +size 258384 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/ops.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/ops.pyi new file mode 100644 index 0000000000000000000000000000000000000000..6738a1dff4a9eb272f3e6c88a31b0bd7386aebd2 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/ops.pyi @@ -0,0 +1,51 @@ +from typing import ( + Any, + Callable, + Iterable, + Literal, + TypeAlias, + overload, +) + +import numpy as np + +from pandas._typing import npt + +_BinOp: TypeAlias = Callable[[Any, Any], Any] +_BoolOp: TypeAlias = Callable[[Any, Any], bool] + +def scalar_compare( + values: np.ndarray, # object[:] + val: object, + op: _BoolOp, # {operator.eq, operator.ne, ...} +) -> npt.NDArray[np.bool_]: ... +def vec_compare( + left: npt.NDArray[np.object_], + right: npt.NDArray[np.object_], + op: _BoolOp, # {operator.eq, operator.ne, ...} +) -> npt.NDArray[np.bool_]: ... +def scalar_binop( + values: np.ndarray, # object[:] + val: object, + op: _BinOp, # binary operator +) -> np.ndarray: ... +def vec_binop( + left: np.ndarray, # object[:] + right: np.ndarray, # object[:] + op: _BinOp, # binary operator +) -> np.ndarray: ... +@overload +def maybe_convert_bool( + arr: npt.NDArray[np.object_], + true_values: Iterable | None = None, + false_values: Iterable | None = None, + convert_to_masked_nullable: Literal[False] = ..., +) -> tuple[np.ndarray, None]: ... +@overload +def maybe_convert_bool( + arr: npt.NDArray[np.object_], + true_values: Iterable = ..., + false_values: Iterable = ..., + *, + convert_to_masked_nullable: Literal[True], +) -> tuple[np.ndarray, np.ndarray]: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/ops_dispatch.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/ops_dispatch.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..0c0fba5ce2335a0cb6e3b8ffc5667f6f395f7449 Binary files /dev/null and b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/ops_dispatch.cpython-310-x86_64-linux-gnu.so differ diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/ops_dispatch.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/ops_dispatch.pyi new file mode 100644 index 0000000000000000000000000000000000000000..91b5a4dbaaebc177191d3189f12e4e20d56ca0fa --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/ops_dispatch.pyi @@ -0,0 +1,5 @@ +import numpy as np + +def maybe_dispatch_ufunc_to_dunder_op( + self, ufunc: np.ufunc, method: str, *inputs, **kwargs +): ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/pandas_datetime.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/pandas_datetime.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..f19af041c19ed3db47fbe71aa32dc06936407b76 Binary files /dev/null and b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/pandas_datetime.cpython-310-x86_64-linux-gnu.so differ diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/pandas_parser.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/pandas_parser.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..981d010ab4c67d56b309bd168521351596213fdf Binary files /dev/null and b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/pandas_parser.cpython-310-x86_64-linux-gnu.so differ diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/parsers.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/parsers.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..57af156bfc7b56b8ff9c8890faa1cb79dbb28b85 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/parsers.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fd80bb61a89e519b1083ebc98e9eb73935514a8eb46ac4858b88e820058f4b95 +size 566272 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/parsers.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/parsers.pyi new file mode 100644 index 0000000000000000000000000000000000000000..253bb7303cefb81f61692c8d7bd9812a191d9ac5 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/parsers.pyi @@ -0,0 +1,77 @@ +from typing import ( + Hashable, + Literal, +) + +import numpy as np + +from pandas._typing import ( + ArrayLike, + Dtype, + npt, +) + +STR_NA_VALUES: set[str] +DEFAULT_BUFFER_HEURISTIC: int + +def sanitize_objects( + values: npt.NDArray[np.object_], + na_values: set, +) -> int: ... + +class TextReader: + unnamed_cols: set[str] + table_width: int # int64_t + leading_cols: int # int64_t + header: list[list[int]] # non-negative integers + def __init__( + self, + source, + delimiter: bytes | str = ..., # single-character only + header=..., + header_start: int = ..., # int64_t + header_end: int = ..., # uint64_t + index_col=..., + names=..., + tokenize_chunksize: int = ..., # int64_t + delim_whitespace: bool = ..., + converters=..., + skipinitialspace: bool = ..., + escapechar: bytes | str | None = ..., # single-character only + doublequote: bool = ..., + quotechar: str | bytes | None = ..., # at most 1 character + quoting: int = ..., + lineterminator: bytes | str | None = ..., # at most 1 character + comment=..., + decimal: bytes | str = ..., # single-character only + thousands: bytes | str | None = ..., # single-character only + dtype: Dtype | dict[Hashable, Dtype] = ..., + usecols=..., + error_bad_lines: bool = ..., + warn_bad_lines: bool = ..., + na_filter: bool = ..., + na_values=..., + na_fvalues=..., + keep_default_na: bool = ..., + true_values=..., + false_values=..., + allow_leading_cols: bool = ..., + skiprows=..., + skipfooter: int = ..., # int64_t + verbose: bool = ..., + float_precision: Literal["round_trip", "legacy", "high"] | None = ..., + skip_blank_lines: bool = ..., + encoding_errors: bytes | str = ..., + ) -> None: ... + def set_noconvert(self, i: int) -> None: ... + def remove_noconvert(self, i: int) -> None: ... + def close(self) -> None: ... + def read(self, rows: int | None = ...) -> dict[int, ArrayLike]: ... + def read_low_memory(self, rows: int | None) -> list[dict[int, ArrayLike]]: ... + +# _maybe_upcast, na_values are only exposed for testing +na_values: dict + +def _maybe_upcast( + arr, use_dtype_backend: bool = ..., dtype_backend: str = ... +) -> np.ndarray: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/properties.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/properties.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..904c8f08799738e066b048afbd1782e41cb9437e Binary files /dev/null and b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/properties.cpython-310-x86_64-linux-gnu.so differ diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/properties.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/properties.pyi new file mode 100644 index 0000000000000000000000000000000000000000..aaa44a0cf47bf8635727ea9f354227de72bbff29 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/properties.pyi @@ -0,0 +1,27 @@ +from typing import ( + Sequence, + overload, +) + +from pandas._typing import ( + AnyArrayLike, + DataFrame, + Index, + Series, +) + +# note: this is a lie to make type checkers happy (they special +# case property). cache_readonly uses attribute names similar to +# property (fget) but it does not provide fset and fdel. +cache_readonly = property + +class AxisProperty: + axis: int + def __init__(self, axis: int = ..., doc: str = ...) -> None: ... + @overload + def __get__(self, obj: DataFrame | Series, type) -> Index: ... + @overload + def __get__(self, obj: None, type) -> AxisProperty: ... + def __set__( + self, obj: DataFrame | Series, value: AnyArrayLike | Sequence + ) -> None: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/reshape.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/reshape.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..1583b1a3171dfab6c9ccb54aa322b465bfbc3aea --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/reshape.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8cbe5d21569103e83cc659ea0fbcb604479711feffc7cbc8f42d18d16fdec3e9 +size 312816 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/reshape.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/reshape.pyi new file mode 100644 index 0000000000000000000000000000000000000000..110687fcd0c313c45e8b025083fa5790fb9913b1 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/reshape.pyi @@ -0,0 +1,16 @@ +import numpy as np + +from pandas._typing import npt + +def unstack( + values: np.ndarray, # reshape_t[:, :] + mask: np.ndarray, # const uint8_t[:] + stride: int, + length: int, + width: int, + new_values: np.ndarray, # reshape_t[:, :] + new_mask: np.ndarray, # uint8_t[:, :] +) -> None: ... +def explode( + values: npt.NDArray[np.object_], +) -> tuple[npt.NDArray[np.object_], npt.NDArray[np.int64]]: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/sas.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/sas.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..ac3519cc463a210b712160173c2107160b6eeb7d --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/sas.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:579016d0b705ddc977b1de41f89e134278ae465f9a920372af9a2776cda6ec83 +size 262928 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/sas.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/sas.pyi new file mode 100644 index 0000000000000000000000000000000000000000..5d65e2b56b5916ed1e76e1409e4f75c652ee8fc9 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/sas.pyi @@ -0,0 +1,7 @@ +from pandas.io.sas.sas7bdat import SAS7BDATReader + +class Parser: + def __init__(self, parser: SAS7BDATReader) -> None: ... + def read(self, nrows: int) -> None: ... + +def get_subheader_index(signature: bytes) -> int: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/sparse.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/sparse.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..465ba5bd01e043556a6fc212eabdaa621e0e6b9f --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/sparse.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e175ba23d9cffbe843892a0cae168150c1634502c54b12897e9c8b1c28190df9 +size 1000880 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/sparse.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/sparse.pyi new file mode 100644 index 0000000000000000000000000000000000000000..536265b25425e9e7ecabd37f095115442a7cb209 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/sparse.pyi @@ -0,0 +1,51 @@ +from typing import Sequence + +import numpy as np + +from pandas._typing import ( + Self, + npt, +) + +class SparseIndex: + length: int + npoints: int + def __init__(self) -> None: ... + @property + def ngaps(self) -> int: ... + @property + def nbytes(self) -> int: ... + @property + def indices(self) -> npt.NDArray[np.int32]: ... + def equals(self, other) -> bool: ... + def lookup(self, index: int) -> np.int32: ... + def lookup_array(self, indexer: npt.NDArray[np.int32]) -> npt.NDArray[np.int32]: ... + def to_int_index(self) -> IntIndex: ... + def to_block_index(self) -> BlockIndex: ... + def intersect(self, y_: SparseIndex) -> Self: ... + def make_union(self, y_: SparseIndex) -> Self: ... + +class IntIndex(SparseIndex): + indices: npt.NDArray[np.int32] + def __init__( + self, length: int, indices: Sequence[int], check_integrity: bool = ... + ) -> None: ... + +class BlockIndex(SparseIndex): + nblocks: int + blocs: np.ndarray + blengths: np.ndarray + def __init__( + self, length: int, blocs: np.ndarray, blengths: np.ndarray + ) -> None: ... + + # Override to have correct parameters + def intersect(self, other: SparseIndex) -> Self: ... + def make_union(self, y: SparseIndex) -> Self: ... + +def make_mask_object_ndarray( + arr: npt.NDArray[np.object_], fill_value +) -> npt.NDArray[np.bool_]: ... +def get_blocks( + indices: npt.NDArray[np.int32], +) -> tuple[npt.NDArray[np.int32], npt.NDArray[np.int32]]: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/testing.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/testing.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..418957057d5898e7b0332c607a78b9faf1ce3489 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/testing.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:456fe124e1b71c1475a9e79a2994a4479bb135308d992d06200d53a0ad7d941e +size 119664 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/testing.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/testing.pyi new file mode 100644 index 0000000000000000000000000000000000000000..01da496975f512b204defb06fcd19a36e235f97a --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/testing.pyi @@ -0,0 +1,12 @@ +def assert_dict_equal(a, b, compare_keys: bool = ...): ... +def assert_almost_equal( + a, + b, + rtol: float = ..., + atol: float = ..., + check_dtype: bool = ..., + obj=..., + lobj=..., + robj=..., + index_values=..., +): ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslib.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslib.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..d4c93ee34a9f401c1308c8a5f757d79e97521671 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslib.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:da0b1d5a9e110945f9e9ef4818ec51c13eb6156c877acc105b062b2906968e60 +size 336720 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslib.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslib.pyi new file mode 100644 index 0000000000000000000000000000000000000000..5a340c1d88bc439663c2e482c9eb7270f8ebc5c6 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslib.pyi @@ -0,0 +1,37 @@ +from datetime import tzinfo + +import numpy as np + +from pandas._typing import npt + +def format_array_from_datetime( + values: npt.NDArray[np.int64], + tz: tzinfo | None = ..., + format: str | None = ..., + na_rep: str | float = ..., + reso: int = ..., # NPY_DATETIMEUNIT +) -> npt.NDArray[np.object_]: ... +def array_with_unit_to_datetime( + values: npt.NDArray[np.object_], + unit: str, + errors: str = ..., +) -> tuple[np.ndarray, tzinfo | None]: ... +def first_non_null(values: np.ndarray) -> int: ... +def array_to_datetime( + values: npt.NDArray[np.object_], + errors: str = ..., + dayfirst: bool = ..., + yearfirst: bool = ..., + utc: bool = ..., + creso: int = ..., +) -> tuple[np.ndarray, tzinfo | None]: ... + +# returned ndarray may be object dtype or datetime64[ns] + +def array_to_datetime_with_tz( + values: npt.NDArray[np.object_], + tz: tzinfo, + dayfirst: bool, + yearfirst: bool, + creso: int, +) -> npt.NDArray[np.int64]: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..88a9a259ac8ec87255b25b3e15375b1c92099999 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/__init__.py @@ -0,0 +1,87 @@ +__all__ = [ + "dtypes", + "localize_pydatetime", + "NaT", + "NaTType", + "iNaT", + "nat_strings", + "OutOfBoundsDatetime", + "OutOfBoundsTimedelta", + "IncompatibleFrequency", + "Period", + "Resolution", + "Timedelta", + "normalize_i8_timestamps", + "is_date_array_normalized", + "dt64arr_to_periodarr", + "delta_to_nanoseconds", + "ints_to_pydatetime", + "ints_to_pytimedelta", + "get_resolution", + "Timestamp", + "tz_convert_from_utc_single", + "tz_convert_from_utc", + "to_offset", + "Tick", + "BaseOffset", + "tz_compare", + "is_unitless", + "astype_overflowsafe", + "get_unit_from_dtype", + "periods_per_day", + "periods_per_second", + "guess_datetime_format", + "add_overflowsafe", + "get_supported_dtype", + "is_supported_dtype", +] + +from pandas._libs.tslibs import dtypes # pylint: disable=import-self +from pandas._libs.tslibs.conversion import localize_pydatetime +from pandas._libs.tslibs.dtypes import ( + Resolution, + periods_per_day, + periods_per_second, +) +from pandas._libs.tslibs.nattype import ( + NaT, + NaTType, + iNaT, + nat_strings, +) +from pandas._libs.tslibs.np_datetime import ( + OutOfBoundsDatetime, + OutOfBoundsTimedelta, + add_overflowsafe, + astype_overflowsafe, + get_supported_dtype, + is_supported_dtype, + is_unitless, + py_get_unit_from_dtype as get_unit_from_dtype, +) +from pandas._libs.tslibs.offsets import ( + BaseOffset, + Tick, + to_offset, +) +from pandas._libs.tslibs.parsing import guess_datetime_format +from pandas._libs.tslibs.period import ( + IncompatibleFrequency, + Period, +) +from pandas._libs.tslibs.timedeltas import ( + Timedelta, + delta_to_nanoseconds, + ints_to_pytimedelta, +) +from pandas._libs.tslibs.timestamps import Timestamp +from pandas._libs.tslibs.timezones import tz_compare +from pandas._libs.tslibs.tzconversion import tz_convert_from_utc_single +from pandas._libs.tslibs.vectorized import ( + dt64arr_to_periodarr, + get_resolution, + ints_to_pydatetime, + is_date_array_normalized, + normalize_i8_timestamps, + tz_convert_from_utc, +) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/base.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/base.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..71df370b808efdc3206ff0e741393e8834703812 Binary files /dev/null and b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/base.cpython-310-x86_64-linux-gnu.so differ diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/ccalendar.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/ccalendar.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..bb9d88adfc4cfa22b69954314c06c9bfd1452d1a Binary files /dev/null and b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/ccalendar.cpython-310-x86_64-linux-gnu.so differ diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/ccalendar.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/ccalendar.pyi new file mode 100644 index 0000000000000000000000000000000000000000..993f18a61d74aaa643e9790df70ede618b917223 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/ccalendar.pyi @@ -0,0 +1,12 @@ +DAYS: list[str] +MONTH_ALIASES: dict[int, str] +MONTH_NUMBERS: dict[str, int] +MONTHS: list[str] +int_to_weekday: dict[int, str] + +def get_firstbday(year: int, month: int) -> int: ... +def get_lastbday(year: int, month: int) -> int: ... +def get_day_of_year(year: int, month: int, day: int) -> int: ... +def get_iso_calendar(year: int, month: int, day: int) -> tuple[int, int, int]: ... +def get_week_of_year(year: int, month: int, day: int) -> int: ... +def get_days_in_month(year: int, month: int) -> int: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/conversion.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/conversion.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..f1805b7629f81558113c62b6f9b218fea3082d78 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/conversion.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:40290eaf506143c7ee7f0c93aff243e7a89eb7e22b891448a6af9ea896e936ec +size 304272 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/conversion.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/conversion.pyi new file mode 100644 index 0000000000000000000000000000000000000000..26affae577f4d3f4ecda2ac9c1bc0cb748a35d4d --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/conversion.pyi @@ -0,0 +1,14 @@ +from datetime import ( + datetime, + tzinfo, +) + +import numpy as np + +DT64NS_DTYPE: np.dtype +TD64NS_DTYPE: np.dtype + +def localize_pydatetime(dt: datetime, tz: tzinfo | None) -> datetime: ... +def cast_from_unit_vectorized( + values: np.ndarray, unit: str, out_unit: str = ... +) -> np.ndarray: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/dtypes.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/dtypes.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..e3ee60af86c0ede9ecb3042289d844760d463b84 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/dtypes.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f5c5545cad3f0cc2161afe8ab687378e500794fbae19834d934f88bc0ad7ce41 +size 182544 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/dtypes.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/dtypes.pyi new file mode 100644 index 0000000000000000000000000000000000000000..7fdeb88d498ac88b6d023e25409e8f756b12c07a --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/dtypes.pyi @@ -0,0 +1,83 @@ +from enum import Enum + +OFFSET_TO_PERIOD_FREQSTR: dict[str, str] + +def periods_per_day(reso: int = ...) -> int: ... +def periods_per_second(reso: int) -> int: ... +def abbrev_to_npy_unit(abbrev: str | None) -> int: ... +def freq_to_period_freqstr(freq_n: int, freq_name: str) -> str: ... + +class PeriodDtypeBase: + _dtype_code: int # PeriodDtypeCode + _n: int + + # actually __cinit__ + def __new__(cls, code: int, n: int): ... + @property + def _freq_group_code(self) -> int: ... + @property + def _resolution_obj(self) -> Resolution: ... + def _get_to_timestamp_base(self) -> int: ... + @property + def _freqstr(self) -> str: ... + def __hash__(self) -> int: ... + def _is_tick_like(self) -> bool: ... + @property + def _creso(self) -> int: ... + @property + def _td64_unit(self) -> str: ... + +class FreqGroup(Enum): + FR_ANN: int + FR_QTR: int + FR_MTH: int + FR_WK: int + FR_BUS: int + FR_DAY: int + FR_HR: int + FR_MIN: int + FR_SEC: int + FR_MS: int + FR_US: int + FR_NS: int + FR_UND: int + @staticmethod + def from_period_dtype_code(code: int) -> FreqGroup: ... + +class Resolution(Enum): + RESO_NS: int + RESO_US: int + RESO_MS: int + RESO_SEC: int + RESO_MIN: int + RESO_HR: int + RESO_DAY: int + RESO_MTH: int + RESO_QTR: int + RESO_YR: int + def __lt__(self, other: Resolution) -> bool: ... + def __ge__(self, other: Resolution) -> bool: ... + @property + def attrname(self) -> str: ... + @classmethod + def from_attrname(cls, attrname: str) -> Resolution: ... + @classmethod + def get_reso_from_freqstr(cls, freq: str) -> Resolution: ... + @property + def attr_abbrev(self) -> str: ... + +class NpyDatetimeUnit(Enum): + NPY_FR_Y: int + NPY_FR_M: int + NPY_FR_W: int + NPY_FR_D: int + NPY_FR_h: int + NPY_FR_m: int + NPY_FR_s: int + NPY_FR_ms: int + NPY_FR_us: int + NPY_FR_ns: int + NPY_FR_ps: int + NPY_FR_fs: int + NPY_FR_as: int + NPY_FR_GENERIC: int diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/fields.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/fields.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..22e531b66adfbde5ee0bbc0e4a2301d946c32f2e --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/fields.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f114548d632091f613cc6be05e3cbb48a78bd3c67fdda0202ed4475d318b2821 +size 336976 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/fields.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/fields.pyi new file mode 100644 index 0000000000000000000000000000000000000000..c6cfd44e9f6ab76ee4bc3be7c765569991514d18 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/fields.pyi @@ -0,0 +1,62 @@ +import numpy as np + +from pandas._typing import npt + +def build_field_sarray( + dtindex: npt.NDArray[np.int64], # const int64_t[:] + reso: int, # NPY_DATETIMEUNIT +) -> np.ndarray: ... +def month_position_check(fields, weekdays) -> str | None: ... +def get_date_name_field( + dtindex: npt.NDArray[np.int64], # const int64_t[:] + field: str, + locale: str | None = ..., + reso: int = ..., # NPY_DATETIMEUNIT +) -> npt.NDArray[np.object_]: ... +def get_start_end_field( + dtindex: npt.NDArray[np.int64], + field: str, + freqstr: str | None = ..., + month_kw: int = ..., + reso: int = ..., # NPY_DATETIMEUNIT +) -> npt.NDArray[np.bool_]: ... +def get_date_field( + dtindex: npt.NDArray[np.int64], # const int64_t[:] + field: str, + reso: int = ..., # NPY_DATETIMEUNIT +) -> npt.NDArray[np.int32]: ... +def get_timedelta_field( + tdindex: npt.NDArray[np.int64], # const int64_t[:] + field: str, + reso: int = ..., # NPY_DATETIMEUNIT +) -> npt.NDArray[np.int32]: ... +def get_timedelta_days( + tdindex: npt.NDArray[np.int64], # const int64_t[:] + reso: int = ..., # NPY_DATETIMEUNIT +) -> npt.NDArray[np.int64]: ... +def isleapyear_arr( + years: np.ndarray, +) -> npt.NDArray[np.bool_]: ... +def build_isocalendar_sarray( + dtindex: npt.NDArray[np.int64], # const int64_t[:] + reso: int, # NPY_DATETIMEUNIT +) -> np.ndarray: ... +def _get_locale_names(name_type: str, locale: str | None = ...): ... + +class RoundTo: + @property + def MINUS_INFTY(self) -> int: ... + @property + def PLUS_INFTY(self) -> int: ... + @property + def NEAREST_HALF_EVEN(self) -> int: ... + @property + def NEAREST_HALF_PLUS_INFTY(self) -> int: ... + @property + def NEAREST_HALF_MINUS_INFTY(self) -> int: ... + +def round_nsint64( + values: npt.NDArray[np.int64], + mode: RoundTo, + nanos: int, +) -> npt.NDArray[np.int64]: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/nattype.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/nattype.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..a7f11159b6ef59ad4ed04fd85e82947b0ac72d22 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/nattype.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6f31bad832b5ef56fec05de7d5e56fedeb7cc8f187ddf829a9390b6ade900eb4 +size 229520 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/nattype.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/nattype.pyi new file mode 100644 index 0000000000000000000000000000000000000000..bd86a6fdc2174a38bdd0845019e0afd6f2aecbf1 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/nattype.pyi @@ -0,0 +1,141 @@ +from datetime import ( + datetime, + timedelta, + tzinfo as _tzinfo, +) +import typing + +import numpy as np + +from pandas._libs.tslibs.period import Period +from pandas._typing import Self + +NaT: NaTType +iNaT: int +nat_strings: set[str] + +_NaTComparisonTypes: typing.TypeAlias = ( + datetime | timedelta | Period | np.datetime64 | np.timedelta64 +) + +class _NatComparison: + def __call__(self, other: _NaTComparisonTypes) -> bool: ... + +class NaTType: + _value: np.int64 + @property + def value(self) -> int: ... + @property + def asm8(self) -> np.datetime64: ... + def to_datetime64(self) -> np.datetime64: ... + def to_numpy( + self, dtype: np.dtype | str | None = ..., copy: bool = ... + ) -> np.datetime64 | np.timedelta64: ... + @property + def is_leap_year(self) -> bool: ... + @property + def is_month_start(self) -> bool: ... + @property + def is_quarter_start(self) -> bool: ... + @property + def is_year_start(self) -> bool: ... + @property + def is_month_end(self) -> bool: ... + @property + def is_quarter_end(self) -> bool: ... + @property + def is_year_end(self) -> bool: ... + @property + def day_of_year(self) -> float: ... + @property + def dayofyear(self) -> float: ... + @property + def days_in_month(self) -> float: ... + @property + def daysinmonth(self) -> float: ... + @property + def day_of_week(self) -> float: ... + @property + def dayofweek(self) -> float: ... + @property + def week(self) -> float: ... + @property + def weekofyear(self) -> float: ... + def day_name(self) -> float: ... + def month_name(self) -> float: ... + def weekday(self) -> float: ... + def isoweekday(self) -> float: ... + def total_seconds(self) -> float: ... + def today(self, *args, **kwargs) -> NaTType: ... + def now(self, *args, **kwargs) -> NaTType: ... + def to_pydatetime(self) -> NaTType: ... + def date(self) -> NaTType: ... + def round(self) -> NaTType: ... + def floor(self) -> NaTType: ... + def ceil(self) -> NaTType: ... + @property + def tzinfo(self) -> None: ... + @property + def tz(self) -> None: ... + def tz_convert(self, tz: _tzinfo | str | None) -> NaTType: ... + def tz_localize( + self, + tz: _tzinfo | str | None, + ambiguous: str = ..., + nonexistent: str = ..., + ) -> NaTType: ... + def replace( + self, + year: int | None = ..., + month: int | None = ..., + day: int | None = ..., + hour: int | None = ..., + minute: int | None = ..., + second: int | None = ..., + microsecond: int | None = ..., + nanosecond: int | None = ..., + tzinfo: _tzinfo | None = ..., + fold: int | None = ..., + ) -> NaTType: ... + @property + def year(self) -> float: ... + @property + def quarter(self) -> float: ... + @property + def month(self) -> float: ... + @property + def day(self) -> float: ... + @property + def hour(self) -> float: ... + @property + def minute(self) -> float: ... + @property + def second(self) -> float: ... + @property + def millisecond(self) -> float: ... + @property + def microsecond(self) -> float: ... + @property + def nanosecond(self) -> float: ... + # inject Timedelta properties + @property + def days(self) -> float: ... + @property + def microseconds(self) -> float: ... + @property + def nanoseconds(self) -> float: ... + # inject Period properties + @property + def qyear(self) -> float: ... + def __eq__(self, other: object) -> bool: ... + def __ne__(self, other: object) -> bool: ... + __lt__: _NatComparison + __le__: _NatComparison + __gt__: _NatComparison + __ge__: _NatComparison + def __sub__(self, other: Self | timedelta | datetime) -> Self: ... + def __rsub__(self, other: Self | timedelta | datetime) -> Self: ... + def __add__(self, other: Self | timedelta | datetime) -> Self: ... + def __radd__(self, other: Self | timedelta | datetime) -> Self: ... + def __hash__(self) -> int: ... + def as_unit(self, unit: str, round_ok: bool = ...) -> NaTType: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/np_datetime.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/np_datetime.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..963246a91f2e4b09b4a19ce3daf98d5eb51a1c0a --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/np_datetime.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:361fb220df3bffa802190bfe83d64b2c04324d7498150e2ead4491f4ca226d72 +size 140272 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/np_datetime.pyi b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/np_datetime.pyi new file mode 100644 index 0000000000000000000000000000000000000000..00ef35c50e53251d5ca6f6c6d5ad28a67a695a21 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/np_datetime.pyi @@ -0,0 +1,27 @@ +import numpy as np + +from pandas._typing import npt + +class OutOfBoundsDatetime(ValueError): ... +class OutOfBoundsTimedelta(ValueError): ... + +# only exposed for testing +def py_get_unit_from_dtype(dtype: np.dtype): ... +def py_td64_to_tdstruct(td64: int, unit: int) -> dict: ... +def astype_overflowsafe( + values: np.ndarray, + dtype: np.dtype, + copy: bool = ..., + round_ok: bool = ..., + is_coerce: bool = ..., +) -> np.ndarray: ... +def is_unitless(dtype: np.dtype) -> bool: ... +def compare_mismatched_resolutions( + left: np.ndarray, right: np.ndarray, op +) -> npt.NDArray[np.bool_]: ... +def add_overflowsafe( + left: npt.NDArray[np.int64], + right: npt.NDArray[np.int64], +) -> npt.NDArray[np.int64]: ... +def get_supported_dtype(dtype: np.dtype) -> np.dtype: ... +def is_supported_dtype(dtype: np.dtype) -> bool: ... diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/offsets.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/offsets.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..758f68693ecb3eacb880ae91c63766110d3ed130 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/offsets.cpython-310-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8e5fb3e5090f1b8b279994f9cb5a5eee4a5b24fa449c9b742065f494bde6f2fc +size 1053440 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/parsing.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/_libs/tslibs/parsing.cpython-310-x86_64-linux-gnu.so 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register_jitable +import numpy as np + +if TYPE_CHECKING: + from pandas._typing import npt + +from pandas.core._numba.kernels.shared import is_monotonic_increasing + + +@numba.jit(nopython=True, nogil=True, parallel=False) +def add_sum( + val: Any, + nobs: int, + sum_x: Any, + compensation: Any, + num_consecutive_same_value: int, + prev_value: Any, +) -> tuple[int, Any, Any, int, Any]: + if not np.isnan(val): + nobs += 1 + y = val - compensation + t = sum_x + y + compensation = t - sum_x - y + sum_x = t + + if val == prev_value: + num_consecutive_same_value += 1 + else: + num_consecutive_same_value = 1 + prev_value = val + + return nobs, sum_x, compensation, num_consecutive_same_value, prev_value + + +@numba.jit(nopython=True, nogil=True, parallel=False) +def remove_sum( + val: Any, nobs: int, sum_x: Any, compensation: Any +) -> tuple[int, Any, Any]: + if not np.isnan(val): + nobs -= 1 + y = -val - compensation + t = sum_x + y + compensation = t - sum_x - y + sum_x = t + return nobs, sum_x, compensation + + +@numba.jit(nopython=True, nogil=True, parallel=False) +def sliding_sum( + values: np.ndarray, + result_dtype: np.dtype, + start: np.ndarray, + end: np.ndarray, + min_periods: int, +) -> tuple[np.ndarray, list[int]]: + dtype = values.dtype + + na_val: object = np.nan + if dtype.kind == "i": + na_val = 0 + + N = len(start) + nobs = 0 + sum_x = 0 + compensation_add = 0 + compensation_remove = 0 + na_pos = [] + + is_monotonic_increasing_bounds = is_monotonic_increasing( + start + ) and is_monotonic_increasing(end) + + output = np.empty(N, dtype=result_dtype) + + for i in range(N): + s = start[i] + e = end[i] + if i == 0 or not is_monotonic_increasing_bounds: + prev_value = values[s] + num_consecutive_same_value = 0 + + for j in range(s, e): + val = values[j] + ( + nobs, + sum_x, + compensation_add, + num_consecutive_same_value, + prev_value, + ) = add_sum( + val, + nobs, + sum_x, + compensation_add, + num_consecutive_same_value, + prev_value, + ) + else: + for j in range(start[i - 1], s): + val = values[j] + nobs, sum_x, compensation_remove = remove_sum( + val, nobs, sum_x, compensation_remove + ) + + for j in range(end[i - 1], e): + val = values[j] + ( + nobs, + sum_x, + compensation_add, + num_consecutive_same_value, + prev_value, + ) = add_sum( + val, + nobs, + sum_x, + compensation_add, + num_consecutive_same_value, + prev_value, + ) + + if nobs == 0 == min_periods: + result: object = 0 + elif nobs >= min_periods: + if num_consecutive_same_value >= nobs: + result = prev_value * nobs + else: + result = sum_x + else: + result = na_val + if dtype.kind == "i": + na_pos.append(i) + + output[i] = result + + if not is_monotonic_increasing_bounds: + nobs = 0 + sum_x = 0 + compensation_remove = 0 + + return output, na_pos + + +# Mypy/pyright don't like the fact that the decorator is untyped +@register_jitable # type: ignore[misc] +def grouped_kahan_sum( + values: np.ndarray, + result_dtype: np.dtype, + labels: npt.NDArray[np.intp], + ngroups: int, +) -> tuple[ + np.ndarray, npt.NDArray[np.int64], np.ndarray, npt.NDArray[np.int64], np.ndarray +]: + N = len(labels) + + nobs_arr = np.zeros(ngroups, dtype=np.int64) + comp_arr = np.zeros(ngroups, dtype=values.dtype) + consecutive_counts = np.zeros(ngroups, dtype=np.int64) + prev_vals = np.zeros(ngroups, dtype=values.dtype) + output = np.zeros(ngroups, dtype=result_dtype) + + for i in range(N): + lab = labels[i] + val = values[i] + + if lab < 0: + continue + + sum_x = output[lab] + nobs = nobs_arr[lab] + compensation_add = comp_arr[lab] + num_consecutive_same_value = consecutive_counts[lab] + prev_value = prev_vals[lab] + + ( + nobs, + sum_x, + compensation_add, + num_consecutive_same_value, + prev_value, + ) = add_sum( + val, + nobs, + sum_x, + compensation_add, + num_consecutive_same_value, + prev_value, + ) + + output[lab] = sum_x + consecutive_counts[lab] = num_consecutive_same_value + prev_vals[lab] = prev_value + comp_arr[lab] = compensation_add + nobs_arr[lab] = nobs + return output, nobs_arr, comp_arr, consecutive_counts, prev_vals + + +@numba.jit(nopython=True, nogil=True, parallel=False) +def grouped_sum( + values: np.ndarray, + result_dtype: np.dtype, + labels: npt.NDArray[np.intp], + ngroups: int, + min_periods: int, +) -> tuple[np.ndarray, list[int]]: + na_pos = [] + + output, nobs_arr, comp_arr, consecutive_counts, prev_vals = grouped_kahan_sum( + values, result_dtype, labels, ngroups + ) + + # Post-processing, replace sums that don't satisfy min_periods + for lab in range(ngroups): + nobs = nobs_arr[lab] + num_consecutive_same_value = consecutive_counts[lab] + prev_value = prev_vals[lab] + sum_x = output[lab] + if nobs >= min_periods: + if num_consecutive_same_value >= nobs: + result = prev_value * nobs + else: + result = sum_x + else: + result = sum_x # Don't change val, will be replaced by nan later + na_pos.append(lab) + output[lab] = result + + return output, na_pos diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/_numba/kernels/var_.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/_numba/kernels/var_.py new file mode 100644 index 0000000000000000000000000000000000000000..c63d0b90b0fc3f4a4a532b9ce85e57d8bd823fb1 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/_numba/kernels/var_.py @@ -0,0 +1,245 @@ +""" +Numba 1D var kernels that can be shared by +* Dataframe / Series +* groupby +* rolling / expanding + +Mirrors pandas/_libs/window/aggregation.pyx +""" +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numba +import numpy as np + +if TYPE_CHECKING: + from pandas._typing import npt + +from pandas.core._numba.kernels.shared import is_monotonic_increasing + + +@numba.jit(nopython=True, nogil=True, parallel=False) +def add_var( + val: float, + nobs: int, + mean_x: float, + ssqdm_x: float, + compensation: float, + num_consecutive_same_value: int, + prev_value: float, +) -> tuple[int, float, float, float, int, float]: + if not np.isnan(val): + if val == prev_value: + num_consecutive_same_value += 1 + else: + num_consecutive_same_value = 1 + prev_value = val + + nobs += 1 + prev_mean = mean_x - compensation + y = val - compensation + t = y - mean_x + compensation = t + mean_x - y + delta = t + if nobs: + mean_x += delta / nobs + else: + mean_x = 0 + ssqdm_x += (val - prev_mean) * (val - mean_x) + return nobs, mean_x, ssqdm_x, compensation, num_consecutive_same_value, prev_value + + +@numba.jit(nopython=True, nogil=True, parallel=False) +def remove_var( + val: float, nobs: int, mean_x: float, ssqdm_x: float, compensation: float +) -> tuple[int, float, float, float]: + if not np.isnan(val): + nobs -= 1 + if nobs: + prev_mean = mean_x - compensation + y = val - compensation + t = y - mean_x + compensation = t + mean_x - y + delta = t + mean_x -= delta / nobs + ssqdm_x -= (val - prev_mean) * (val - mean_x) + else: + mean_x = 0 + ssqdm_x = 0 + return nobs, mean_x, ssqdm_x, compensation + + +@numba.jit(nopython=True, nogil=True, parallel=False) +def sliding_var( + values: np.ndarray, + result_dtype: np.dtype, + start: np.ndarray, + end: np.ndarray, + min_periods: int, + ddof: int = 1, +) -> tuple[np.ndarray, list[int]]: + N = len(start) + nobs = 0 + mean_x = 0.0 + ssqdm_x = 0.0 + compensation_add = 0.0 + compensation_remove = 0.0 + + min_periods = max(min_periods, 1) + is_monotonic_increasing_bounds = is_monotonic_increasing( + start + ) and is_monotonic_increasing(end) + + output = np.empty(N, dtype=result_dtype) + + for i in range(N): + s = start[i] + e = end[i] + if i == 0 or not is_monotonic_increasing_bounds: + prev_value = values[s] + num_consecutive_same_value = 0 + + for j in range(s, e): + val = values[j] + ( + nobs, + mean_x, + ssqdm_x, + compensation_add, + num_consecutive_same_value, + prev_value, + ) = add_var( + val, + nobs, + mean_x, + ssqdm_x, + compensation_add, + num_consecutive_same_value, + prev_value, + ) + else: + for j in range(start[i - 1], s): + val = values[j] + nobs, mean_x, ssqdm_x, compensation_remove = remove_var( + val, nobs, mean_x, ssqdm_x, compensation_remove + ) + + for j in range(end[i - 1], e): + val = values[j] + ( + nobs, + mean_x, + ssqdm_x, + compensation_add, + num_consecutive_same_value, + prev_value, + ) = add_var( + val, + nobs, + mean_x, + ssqdm_x, + compensation_add, + num_consecutive_same_value, + prev_value, + ) + + if nobs >= min_periods and nobs > ddof: + if nobs == 1 or num_consecutive_same_value >= nobs: + result = 0.0 + else: + result = ssqdm_x / (nobs - ddof) + else: + result = np.nan + + output[i] = result + + if not is_monotonic_increasing_bounds: + nobs = 0 + mean_x = 0.0 + ssqdm_x = 0.0 + compensation_remove = 0.0 + + # na_position is empty list since float64 can already hold nans + # Do list comprehension, since numba cannot figure out that na_pos is + # empty list of ints on its own + na_pos = [0 for i in range(0)] + return output, na_pos + + +@numba.jit(nopython=True, nogil=True, parallel=False) +def grouped_var( + values: np.ndarray, + result_dtype: np.dtype, + labels: npt.NDArray[np.intp], + ngroups: int, + min_periods: int, + ddof: int = 1, +) -> tuple[np.ndarray, list[int]]: + N = len(labels) + + nobs_arr = np.zeros(ngroups, dtype=np.int64) + comp_arr = np.zeros(ngroups, dtype=values.dtype) + consecutive_counts = np.zeros(ngroups, dtype=np.int64) + prev_vals = np.zeros(ngroups, dtype=values.dtype) + output = np.zeros(ngroups, dtype=result_dtype) + means = np.zeros(ngroups, dtype=result_dtype) + + for i in range(N): + lab = labels[i] + val = values[i] + + if lab < 0: + continue + + mean_x = means[lab] + ssqdm_x = output[lab] + nobs = nobs_arr[lab] + compensation_add = comp_arr[lab] + num_consecutive_same_value = consecutive_counts[lab] + prev_value = prev_vals[lab] + + ( + nobs, + mean_x, + ssqdm_x, + compensation_add, + num_consecutive_same_value, + prev_value, + ) = add_var( + val, + nobs, + mean_x, + ssqdm_x, + compensation_add, + num_consecutive_same_value, + prev_value, + ) + + output[lab] = ssqdm_x + means[lab] = mean_x + consecutive_counts[lab] = num_consecutive_same_value + prev_vals[lab] = prev_value + comp_arr[lab] = compensation_add + nobs_arr[lab] = nobs + + # Post-processing, replace vars that don't satisfy min_periods + for lab in range(ngroups): + nobs = nobs_arr[lab] + num_consecutive_same_value = consecutive_counts[lab] + ssqdm_x = output[lab] + if nobs >= min_periods and nobs > ddof: + if nobs == 1 or num_consecutive_same_value >= nobs: + result = 0.0 + else: + result = ssqdm_x / (nobs - ddof) + else: + result = np.nan + output[lab] = result + + # Second pass to get the std.dev + # na_position is empty list since float64 can already hold nans + # Do list comprehension, since numba cannot figure out that na_pos is + # empty list of ints on its own + na_pos = [0 for i in range(0)] + return output, na_pos diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a7655a013c6cf3fca754086fdeb29b806220d5e4 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/__init__.py @@ -0,0 +1,9 @@ +""" +core.array_algos is for algorithms that operate on ndarray and ExtensionArray. +These should: + +- Assume that any Index, Series, or DataFrame objects have already been unwrapped. +- Assume that any list arguments have already been cast to ndarray/EA. +- Not depend on Index, Series, or DataFrame, nor import any of these. +- May dispatch to ExtensionArray methods, but should not import from core.arrays. +""" diff --git 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0000000000000000000000000000000000000000..4934fa080ec2d8323fde40eb076accbb84603899 Binary files /dev/null and b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/__pycache__/transforms.cpython-310.pyc differ diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/datetimelike_accumulations.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/datetimelike_accumulations.py new file mode 100644 index 0000000000000000000000000000000000000000..825fe60ee6cf88a2186a5f501c8696cecaf2657d --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/datetimelike_accumulations.py @@ -0,0 +1,67 @@ +""" +datetimelke_accumulations.py is for accumulations of datetimelike extension arrays +""" + +from __future__ import annotations + +from typing import Callable + +import numpy as np + +from pandas._libs import iNaT + +from pandas.core.dtypes.missing import isna + + +def _cum_func( + func: Callable, + values: np.ndarray, + *, + skipna: bool = True, +): + """ + Accumulations for 1D datetimelike arrays. + + Parameters + ---------- + func : np.cumsum, np.maximum.accumulate, np.minimum.accumulate + values : np.ndarray + Numpy array with the values (can be of any dtype that support the + operation). Values is changed is modified inplace. + skipna : bool, default True + Whether to skip NA. + """ + try: + fill_value = { + np.maximum.accumulate: np.iinfo(np.int64).min, + np.cumsum: 0, + np.minimum.accumulate: np.iinfo(np.int64).max, + }[func] + except KeyError: + raise ValueError(f"No accumulation for {func} implemented on BaseMaskedArray") + + mask = isna(values) + y = values.view("i8") + y[mask] = fill_value + + if not skipna: + mask = np.maximum.accumulate(mask) + + result = func(y) + result[mask] = iNaT + + if values.dtype.kind in "mM": + return result.view(values.dtype.base) + return result + + +def cumsum(values: np.ndarray, *, skipna: bool = True) -> np.ndarray: + return _cum_func(np.cumsum, values, skipna=skipna) + + +def cummin(values: np.ndarray, *, skipna: bool = True): + return _cum_func(np.minimum.accumulate, values, skipna=skipna) + + +def cummax(values: np.ndarray, *, skipna: bool = True): + return _cum_func(np.maximum.accumulate, values, skipna=skipna) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/masked_accumulations.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/masked_accumulations.py new file mode 100644 index 0000000000000000000000000000000000000000..ad9e96d398a242dc64de2018b749fd2dbca7ed78 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/masked_accumulations.py @@ -0,0 +1,90 @@ +""" +masked_accumulations.py is for accumulation algorithms using a mask-based approach +for missing values. +""" + +from __future__ import annotations + +from typing import ( + TYPE_CHECKING, + Callable, +) + +import numpy as np + +if TYPE_CHECKING: + from pandas._typing import npt + + +def _cum_func( + func: Callable, + values: np.ndarray, + mask: npt.NDArray[np.bool_], + *, + skipna: bool = True, +): + """ + Accumulations for 1D masked array. + + We will modify values in place to replace NAs with the appropriate fill value. + + Parameters + ---------- + func : np.cumsum, np.cumprod, np.maximum.accumulate, np.minimum.accumulate + values : np.ndarray + Numpy array with the values (can be of any dtype that support the + operation). + mask : np.ndarray + Boolean numpy array (True values indicate missing values). + skipna : bool, default True + Whether to skip NA. + """ + dtype_info: np.iinfo | np.finfo + if values.dtype.kind == "f": + dtype_info = np.finfo(values.dtype.type) + elif values.dtype.kind in "iu": + dtype_info = np.iinfo(values.dtype.type) + elif values.dtype.kind == "b": + # Max value of bool is 1, but since we are setting into a boolean + # array, 255 is fine as well. Min value has to be 0 when setting + # into the boolean array. + dtype_info = np.iinfo(np.uint8) + else: + raise NotImplementedError( + f"No masked accumulation defined for dtype {values.dtype.type}" + ) + try: + fill_value = { + np.cumprod: 1, + np.maximum.accumulate: dtype_info.min, + np.cumsum: 0, + np.minimum.accumulate: dtype_info.max, + }[func] + except KeyError: + raise NotImplementedError( + f"No accumulation for {func} implemented on BaseMaskedArray" + ) + + values[mask] = fill_value + + if not skipna: + mask = np.maximum.accumulate(mask) + + values = func(values) + return values, mask + + +def cumsum(values: np.ndarray, mask: npt.NDArray[np.bool_], *, skipna: bool = True): + return _cum_func(np.cumsum, values, mask, skipna=skipna) + + +def cumprod(values: np.ndarray, mask: npt.NDArray[np.bool_], *, skipna: bool = True): + return _cum_func(np.cumprod, values, mask, skipna=skipna) + + +def cummin(values: np.ndarray, mask: npt.NDArray[np.bool_], *, skipna: bool = True): + return _cum_func(np.minimum.accumulate, values, mask, skipna=skipna) + + +def cummax(values: np.ndarray, mask: npt.NDArray[np.bool_], *, skipna: bool = True): + return _cum_func(np.maximum.accumulate, values, mask, skipna=skipna) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/masked_reductions.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/masked_reductions.py new file mode 100644 index 0000000000000000000000000000000000000000..6bf97729a79b1436fdf466eade06e0049984c1d0 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/masked_reductions.py @@ -0,0 +1,201 @@ +""" +masked_reductions.py is for reduction algorithms using a mask-based approach +for missing values. +""" +from __future__ import annotations + +from typing import ( + TYPE_CHECKING, + Callable, +) +import warnings + +import numpy as np + +from pandas._libs import missing as libmissing + +from pandas.core.nanops import check_below_min_count + +if TYPE_CHECKING: + from pandas._typing import ( + AxisInt, + npt, + ) + + +def _reductions( + func: Callable, + values: np.ndarray, + mask: npt.NDArray[np.bool_], + *, + skipna: bool = True, + min_count: int = 0, + axis: AxisInt | None = None, + **kwargs, +): + """ + Sum, mean or product for 1D masked array. + + Parameters + ---------- + func : np.sum or np.prod + values : np.ndarray + Numpy array with the values (can be of any dtype that support the + operation). + mask : np.ndarray[bool] + Boolean numpy array (True values indicate missing values). + skipna : bool, default True + Whether to skip NA. + min_count : int, default 0 + The required number of valid values to perform the operation. If fewer than + ``min_count`` non-NA values are present the result will be NA. + axis : int, optional, default None + """ + if not skipna: + if mask.any() or check_below_min_count(values.shape, None, min_count): + return libmissing.NA + else: + return func(values, axis=axis, **kwargs) + else: + if check_below_min_count(values.shape, mask, min_count) and ( + axis is None or values.ndim == 1 + ): + return libmissing.NA + + if values.dtype == np.dtype(object): + # object dtype does not support `where` without passing an initial + values = values[~mask] + return func(values, axis=axis, **kwargs) + return func(values, where=~mask, axis=axis, **kwargs) + + +def sum( + values: np.ndarray, + mask: npt.NDArray[np.bool_], + *, + skipna: bool = True, + min_count: int = 0, + axis: AxisInt | None = None, +): + return _reductions( + np.sum, values=values, mask=mask, skipna=skipna, min_count=min_count, axis=axis + ) + + +def prod( + values: np.ndarray, + mask: npt.NDArray[np.bool_], + *, + skipna: bool = True, + min_count: int = 0, + axis: AxisInt | None = None, +): + return _reductions( + np.prod, values=values, mask=mask, skipna=skipna, min_count=min_count, axis=axis + ) + + +def _minmax( + func: Callable, + values: np.ndarray, + mask: npt.NDArray[np.bool_], + *, + skipna: bool = True, + axis: AxisInt | None = None, +): + """ + Reduction for 1D masked array. + + Parameters + ---------- + func : np.min or np.max + values : np.ndarray + Numpy array with the values (can be of any dtype that support the + operation). + mask : np.ndarray[bool] + Boolean numpy array (True values indicate missing values). + skipna : bool, default True + Whether to skip NA. + axis : int, optional, default None + """ + if not skipna: + if mask.any() or not values.size: + # min/max with empty array raise in numpy, pandas returns NA + return libmissing.NA + else: + return func(values, axis=axis) + else: + subset = values[~mask] + if subset.size: + return func(subset, axis=axis) + else: + # min/max with empty array raise in numpy, pandas returns NA + return libmissing.NA + + +def min( + values: np.ndarray, + mask: npt.NDArray[np.bool_], + *, + skipna: bool = True, + axis: AxisInt | None = None, +): + return _minmax(np.min, values=values, mask=mask, skipna=skipna, axis=axis) + + +def max( + values: np.ndarray, + mask: npt.NDArray[np.bool_], + *, + skipna: bool = True, + axis: AxisInt | None = None, +): + return _minmax(np.max, values=values, mask=mask, skipna=skipna, axis=axis) + + +def mean( + values: np.ndarray, + mask: npt.NDArray[np.bool_], + *, + skipna: bool = True, + axis: AxisInt | None = None, +): + if not values.size or mask.all(): + return libmissing.NA + return _reductions(np.mean, values=values, mask=mask, skipna=skipna, axis=axis) + + +def var( + values: np.ndarray, + mask: npt.NDArray[np.bool_], + *, + skipna: bool = True, + axis: AxisInt | None = None, + ddof: int = 1, +): + if not values.size or mask.all(): + return libmissing.NA + + with warnings.catch_warnings(): + warnings.simplefilter("ignore", RuntimeWarning) + return _reductions( + np.var, values=values, mask=mask, skipna=skipna, axis=axis, ddof=ddof + ) + + +def std( + values: np.ndarray, + mask: npt.NDArray[np.bool_], + *, + skipna: bool = True, + axis: AxisInt | None = None, + ddof: int = 1, +): + if not values.size or mask.all(): + return libmissing.NA + + with warnings.catch_warnings(): + warnings.simplefilter("ignore", RuntimeWarning) + return _reductions( + np.std, values=values, mask=mask, skipna=skipna, axis=axis, ddof=ddof + ) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/putmask.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/putmask.py new file mode 100644 index 0000000000000000000000000000000000000000..f65d2d20e028e36b35a397d8ac973f184ce1412c --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/putmask.py @@ -0,0 +1,149 @@ +""" +EA-compatible analogue to np.putmask +""" +from __future__ import annotations + +from typing import ( + TYPE_CHECKING, + Any, +) + +import numpy as np + +from pandas._libs import lib + +from pandas.core.dtypes.cast import infer_dtype_from +from pandas.core.dtypes.common import is_list_like + +from pandas.core.arrays import ExtensionArray + +if TYPE_CHECKING: + from pandas._typing import ( + ArrayLike, + npt, + ) + + from pandas import MultiIndex + + +def putmask_inplace(values: ArrayLike, mask: npt.NDArray[np.bool_], value: Any) -> None: + """ + ExtensionArray-compatible implementation of np.putmask. The main + difference is we do not handle repeating or truncating like numpy. + + Parameters + ---------- + values: np.ndarray or ExtensionArray + mask : np.ndarray[bool] + We assume extract_bool_array has already been called. + value : Any + """ + + if ( + not isinstance(values, np.ndarray) + or (values.dtype == object and not lib.is_scalar(value)) + # GH#43424: np.putmask raises TypeError if we cannot cast between types with + # rule = "safe", a stricter guarantee we may not have here + or ( + isinstance(value, np.ndarray) and not np.can_cast(value.dtype, values.dtype) + ) + ): + # GH#19266 using np.putmask gives unexpected results with listlike value + # along with object dtype + if is_list_like(value) and len(value) == len(values): + values[mask] = value[mask] + else: + values[mask] = value + else: + # GH#37833 np.putmask is more performant than __setitem__ + np.putmask(values, mask, value) + + +def putmask_without_repeat( + values: np.ndarray, mask: npt.NDArray[np.bool_], new: Any +) -> None: + """ + np.putmask will truncate or repeat if `new` is a listlike with + len(new) != len(values). We require an exact match. + + Parameters + ---------- + values : np.ndarray + mask : np.ndarray[bool] + new : Any + """ + if getattr(new, "ndim", 0) >= 1: + new = new.astype(values.dtype, copy=False) + + # TODO: this prob needs some better checking for 2D cases + nlocs = mask.sum() + if nlocs > 0 and is_list_like(new) and getattr(new, "ndim", 1) == 1: + shape = np.shape(new) + # np.shape compat for if setitem_datetimelike_compat + # changed arraylike to list e.g. test_where_dt64_2d + if nlocs == shape[-1]: + # GH#30567 + # If length of ``new`` is less than the length of ``values``, + # `np.putmask` would first repeat the ``new`` array and then + # assign the masked values hence produces incorrect result. + # `np.place` on the other hand uses the ``new`` values at it is + # to place in the masked locations of ``values`` + np.place(values, mask, new) + # i.e. values[mask] = new + elif mask.shape[-1] == shape[-1] or shape[-1] == 1: + np.putmask(values, mask, new) + else: + raise ValueError("cannot assign mismatch length to masked array") + else: + np.putmask(values, mask, new) + + +def validate_putmask( + values: ArrayLike | MultiIndex, mask: np.ndarray +) -> tuple[npt.NDArray[np.bool_], bool]: + """ + Validate mask and check if this putmask operation is a no-op. + """ + mask = extract_bool_array(mask) + if mask.shape != values.shape: + raise ValueError("putmask: mask and data must be the same size") + + noop = not mask.any() + return mask, noop + + +def extract_bool_array(mask: ArrayLike) -> npt.NDArray[np.bool_]: + """ + If we have a SparseArray or BooleanArray, convert it to ndarray[bool]. + """ + if isinstance(mask, ExtensionArray): + # We could have BooleanArray, Sparse[bool], ... + # Except for BooleanArray, this is equivalent to just + # np.asarray(mask, dtype=bool) + mask = mask.to_numpy(dtype=bool, na_value=False) + + mask = np.asarray(mask, dtype=bool) + return mask + + +def setitem_datetimelike_compat(values: np.ndarray, num_set: int, other): + """ + Parameters + ---------- + values : np.ndarray + num_set : int + For putmask, this is mask.sum() + other : Any + """ + if values.dtype == object: + dtype, _ = infer_dtype_from(other) + + if lib.is_np_dtype(dtype, "mM"): + # https://github.com/numpy/numpy/issues/12550 + # timedelta64 will incorrectly cast to int + if not is_list_like(other): + other = [other] * num_set + else: + other = list(other) + + return other diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/quantile.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/quantile.py new file mode 100644 index 0000000000000000000000000000000000000000..5c933294fb944f04dd3e9a64e4731ea4349254f9 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/quantile.py @@ -0,0 +1,226 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np + +from pandas.core.dtypes.missing import ( + isna, + na_value_for_dtype, +) + +if TYPE_CHECKING: + from pandas._typing import ( + ArrayLike, + Scalar, + npt, + ) + + +def quantile_compat( + values: ArrayLike, qs: npt.NDArray[np.float64], interpolation: str +) -> ArrayLike: + """ + Compute the quantiles of the given values for each quantile in `qs`. + + Parameters + ---------- + values : np.ndarray or ExtensionArray + qs : np.ndarray[float64] + interpolation : str + + Returns + ------- + np.ndarray or ExtensionArray + """ + if isinstance(values, np.ndarray): + fill_value = na_value_for_dtype(values.dtype, compat=False) + mask = isna(values) + return quantile_with_mask(values, mask, fill_value, qs, interpolation) + else: + return values._quantile(qs, interpolation) + + +def quantile_with_mask( + values: np.ndarray, + mask: npt.NDArray[np.bool_], + fill_value, + qs: npt.NDArray[np.float64], + interpolation: str, +) -> np.ndarray: + """ + Compute the quantiles of the given values for each quantile in `qs`. + + Parameters + ---------- + values : np.ndarray + For ExtensionArray, this is _values_for_factorize()[0] + mask : np.ndarray[bool] + mask = isna(values) + For ExtensionArray, this is computed before calling _value_for_factorize + fill_value : Scalar + The value to interpret fill NA entries with + For ExtensionArray, this is _values_for_factorize()[1] + qs : np.ndarray[float64] + interpolation : str + Type of interpolation + + Returns + ------- + np.ndarray + + Notes + ----- + Assumes values is already 2D. For ExtensionArray this means np.atleast_2d + has been called on _values_for_factorize()[0] + + Quantile is computed along axis=1. + """ + assert values.shape == mask.shape + if values.ndim == 1: + # unsqueeze, operate, re-squeeze + values = np.atleast_2d(values) + mask = np.atleast_2d(mask) + res_values = quantile_with_mask(values, mask, fill_value, qs, interpolation) + return res_values[0] + + assert values.ndim == 2 + + is_empty = values.shape[1] == 0 + + if is_empty: + # create the array of na_values + # 2d len(values) * len(qs) + flat = np.array([fill_value] * len(qs)) + result = np.repeat(flat, len(values)).reshape(len(values), len(qs)) + else: + result = _nanpercentile( + values, + qs * 100.0, + na_value=fill_value, + mask=mask, + interpolation=interpolation, + ) + + result = np.asarray(result) + result = result.T + + return result + + +def _nanpercentile_1d( + values: np.ndarray, + mask: npt.NDArray[np.bool_], + qs: npt.NDArray[np.float64], + na_value: Scalar, + interpolation: str, +) -> Scalar | np.ndarray: + """ + Wrapper for np.percentile that skips missing values, specialized to + 1-dimensional case. + + Parameters + ---------- + values : array over which to find quantiles + mask : ndarray[bool] + locations in values that should be considered missing + qs : np.ndarray[float64] of quantile indices to find + na_value : scalar + value to return for empty or all-null values + interpolation : str + + Returns + ------- + quantiles : scalar or array + """ + # mask is Union[ExtensionArray, ndarray] + values = values[~mask] + + if len(values) == 0: + # Can't pass dtype=values.dtype here bc we might have na_value=np.nan + # with values.dtype=int64 see test_quantile_empty + # equiv: 'np.array([na_value] * len(qs))' but much faster + return np.full(len(qs), na_value) + + return np.percentile( + values, + qs, + # error: No overload variant of "percentile" matches argument + # types "ndarray[Any, Any]", "ndarray[Any, dtype[floating[_64Bit]]]" + # , "Dict[str, str]" [call-overload] + method=interpolation, # type: ignore[call-overload] + ) + + +def _nanpercentile( + values: np.ndarray, + qs: npt.NDArray[np.float64], + *, + na_value, + mask: npt.NDArray[np.bool_], + interpolation: str, +): + """ + Wrapper for np.percentile that skips missing values. + + Parameters + ---------- + values : np.ndarray[ndim=2] over which to find quantiles + qs : np.ndarray[float64] of quantile indices to find + na_value : scalar + value to return for empty or all-null values + mask : np.ndarray[bool] + locations in values that should be considered missing + interpolation : str + + Returns + ------- + quantiles : scalar or array + """ + + if values.dtype.kind in "mM": + # need to cast to integer to avoid rounding errors in numpy + result = _nanpercentile( + values.view("i8"), + qs=qs, + na_value=na_value.view("i8"), + mask=mask, + interpolation=interpolation, + ) + + # Note: we have to do `astype` and not view because in general we + # have float result at this point, not i8 + return result.astype(values.dtype) + + if mask.any(): + # Caller is responsible for ensuring mask shape match + assert mask.shape == values.shape + result = [ + _nanpercentile_1d(val, m, qs, na_value, interpolation=interpolation) + for (val, m) in zip(list(values), list(mask)) + ] + if values.dtype.kind == "f": + # preserve itemsize + result = np.asarray(result, dtype=values.dtype).T + else: + result = np.asarray(result).T + if ( + result.dtype != values.dtype + and not mask.all() + and (result == result.astype(values.dtype, copy=False)).all() + ): + # mask.all() will never get cast back to int + # e.g. values id integer dtype and result is floating dtype, + # only cast back to integer dtype if result values are all-integer. + result = result.astype(values.dtype, copy=False) + return result + else: + return np.percentile( + values, + qs, + axis=1, + # error: No overload variant of "percentile" matches argument types + # "ndarray[Any, Any]", "ndarray[Any, dtype[floating[_64Bit]]]", + # "int", "Dict[str, str]" [call-overload] + method=interpolation, # type: ignore[call-overload] + ) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/replace.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/replace.py new file mode 100644 index 0000000000000000000000000000000000000000..16239ed8c46970f38159ae12bc9a45da31ffd617 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/replace.py @@ -0,0 +1,155 @@ +""" +Methods used by Block.replace and related methods. +""" +from __future__ import annotations + +import operator +import re +from re import Pattern +from typing import ( + TYPE_CHECKING, + Any, +) + +import numpy as np + +from pandas.core.dtypes.common import ( + is_bool, + is_re, + is_re_compilable, +) +from pandas.core.dtypes.missing import isna + +if TYPE_CHECKING: + from pandas._typing import ( + ArrayLike, + Scalar, + npt, + ) + + +def should_use_regex(regex: bool, to_replace: Any) -> bool: + """ + Decide whether to treat `to_replace` as a regular expression. + """ + if is_re(to_replace): + regex = True + + regex = regex and is_re_compilable(to_replace) + + # Don't use regex if the pattern is empty. + regex = regex and re.compile(to_replace).pattern != "" + return regex + + +def compare_or_regex_search( + a: ArrayLike, b: Scalar | Pattern, regex: bool, mask: npt.NDArray[np.bool_] +) -> ArrayLike: + """ + Compare two array-like inputs of the same shape or two scalar values + + Calls operator.eq or re.search, depending on regex argument. If regex is + True, perform an element-wise regex matching. + + Parameters + ---------- + a : array-like + b : scalar or regex pattern + regex : bool + mask : np.ndarray[bool] + + Returns + ------- + mask : array-like of bool + """ + if isna(b): + return ~mask + + def _check_comparison_types( + result: ArrayLike | bool, a: ArrayLike, b: Scalar | Pattern + ): + """ + Raises an error if the two arrays (a,b) cannot be compared. + Otherwise, returns the comparison result as expected. + """ + if is_bool(result) and isinstance(a, np.ndarray): + type_names = [type(a).__name__, type(b).__name__] + + type_names[0] = f"ndarray(dtype={a.dtype})" + + raise TypeError( + f"Cannot compare types {repr(type_names[0])} and {repr(type_names[1])}" + ) + + if not regex or not should_use_regex(regex, b): + # TODO: should use missing.mask_missing? + op = lambda x: operator.eq(x, b) + else: + op = np.vectorize( + lambda x: bool(re.search(b, x)) + if isinstance(x, str) and isinstance(b, (str, Pattern)) + else False + ) + + # GH#32621 use mask to avoid comparing to NAs + if isinstance(a, np.ndarray) and mask is not None: + a = a[mask] + result = op(a) + + if isinstance(result, np.ndarray): + # The shape of the mask can differ to that of the result + # since we may compare only a subset of a's or b's elements + tmp = np.zeros(mask.shape, dtype=np.bool_) + np.place(tmp, mask, result) + result = tmp + else: + result = op(a) + + _check_comparison_types(result, a, b) + return result + + +def replace_regex( + values: ArrayLike, rx: re.Pattern, value, mask: npt.NDArray[np.bool_] | None +) -> None: + """ + Parameters + ---------- + values : ArrayLike + Object dtype. + rx : re.Pattern + value : Any + mask : np.ndarray[bool], optional + + Notes + ----- + Alters values in-place. + """ + + # deal with replacing values with objects (strings) that match but + # whose replacement is not a string (numeric, nan, object) + if isna(value) or not isinstance(value, str): + + def re_replacer(s): + if is_re(rx) and isinstance(s, str): + return value if rx.search(s) is not None else s + else: + return s + + else: + # value is guaranteed to be a string here, s can be either a string + # or null if it's null it gets returned + def re_replacer(s): + if is_re(rx) and isinstance(s, str): + return rx.sub(value, s) + else: + return s + + f = np.vectorize(re_replacer, otypes=[np.object_]) + + if mask is None: + values[:] = f(values) + else: + if values.ndim != mask.ndim: + mask = np.broadcast_to(mask, values.shape) + values[mask] = f(values[mask]) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/take.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/take.py new file mode 100644 index 0000000000000000000000000000000000000000..ac674e31586e72040a8e1313f232a00299b961ee --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/take.py @@ -0,0 +1,594 @@ +from __future__ import annotations + +import functools +from typing import ( + TYPE_CHECKING, + cast, + overload, +) + +import numpy as np + +from pandas._libs import ( + algos as libalgos, + lib, +) + +from pandas.core.dtypes.cast import maybe_promote +from pandas.core.dtypes.common import ( + ensure_platform_int, + is_1d_only_ea_dtype, +) +from pandas.core.dtypes.missing import na_value_for_dtype + +from pandas.core.construction import ensure_wrapped_if_datetimelike + +if TYPE_CHECKING: + from pandas._typing import ( + ArrayLike, + AxisInt, + npt, + ) + + from pandas.core.arrays._mixins import NDArrayBackedExtensionArray + from pandas.core.arrays.base import ExtensionArray + + +@overload +def take_nd( + arr: np.ndarray, + indexer, + axis: AxisInt = ..., + fill_value=..., + allow_fill: bool = ..., +) -> np.ndarray: + ... + + +@overload +def take_nd( + arr: ExtensionArray, + indexer, + axis: AxisInt = ..., + fill_value=..., + allow_fill: bool = ..., +) -> ArrayLike: + ... + + +def take_nd( + arr: ArrayLike, + indexer, + axis: AxisInt = 0, + fill_value=lib.no_default, + allow_fill: bool = True, +) -> ArrayLike: + """ + Specialized Cython take which sets NaN values in one pass + + This dispatches to ``take`` defined on ExtensionArrays. + + Note: this function assumes that the indexer is a valid(ated) indexer with + no out of bound indices. + + Parameters + ---------- + arr : np.ndarray or ExtensionArray + Input array. + indexer : ndarray + 1-D array of indices to take, subarrays corresponding to -1 value + indices are filed with fill_value + axis : int, default 0 + Axis to take from + fill_value : any, default np.nan + Fill value to replace -1 values with + allow_fill : bool, default True + If False, indexer is assumed to contain no -1 values so no filling + will be done. This short-circuits computation of a mask. Result is + undefined if allow_fill == False and -1 is present in indexer. + + Returns + ------- + subarray : np.ndarray or ExtensionArray + May be the same type as the input, or cast to an ndarray. + """ + if fill_value is lib.no_default: + fill_value = na_value_for_dtype(arr.dtype, compat=False) + elif lib.is_np_dtype(arr.dtype, "mM"): + dtype, fill_value = maybe_promote(arr.dtype, fill_value) + if arr.dtype != dtype: + # EA.take is strict about returning a new object of the same type + # so for that case cast upfront + arr = arr.astype(dtype) + + if not isinstance(arr, np.ndarray): + # i.e. ExtensionArray, + # includes for EA to catch DatetimeArray, TimedeltaArray + if not is_1d_only_ea_dtype(arr.dtype): + # i.e. DatetimeArray, TimedeltaArray + arr = cast("NDArrayBackedExtensionArray", arr) + return arr.take( + indexer, fill_value=fill_value, allow_fill=allow_fill, axis=axis + ) + + return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill) + + arr = np.asarray(arr) + return _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill) + + +def _take_nd_ndarray( + arr: np.ndarray, + indexer: npt.NDArray[np.intp] | None, + axis: AxisInt, + fill_value, + allow_fill: bool, +) -> np.ndarray: + if indexer is None: + indexer = np.arange(arr.shape[axis], dtype=np.intp) + dtype, fill_value = arr.dtype, arr.dtype.type() + else: + indexer = ensure_platform_int(indexer) + + dtype, fill_value, mask_info = _take_preprocess_indexer_and_fill_value( + arr, indexer, fill_value, allow_fill + ) + + flip_order = False + if arr.ndim == 2 and arr.flags.f_contiguous: + flip_order = True + + if flip_order: + arr = arr.T + axis = arr.ndim - axis - 1 + + # at this point, it's guaranteed that dtype can hold both the arr values + # and the fill_value + out_shape_ = list(arr.shape) + out_shape_[axis] = len(indexer) + out_shape = tuple(out_shape_) + if arr.flags.f_contiguous and axis == arr.ndim - 1: + # minor tweak that can make an order-of-magnitude difference + # for dataframes initialized directly from 2-d ndarrays + # (s.t. df.values is c-contiguous and df._mgr.blocks[0] is its + # f-contiguous transpose) + out = np.empty(out_shape, dtype=dtype, order="F") + else: + out = np.empty(out_shape, dtype=dtype) + + func = _get_take_nd_function( + arr.ndim, arr.dtype, out.dtype, axis=axis, mask_info=mask_info + ) + func(arr, indexer, out, fill_value) + + if flip_order: + out = out.T + return out + + +def take_1d( + arr: ArrayLike, + indexer: npt.NDArray[np.intp], + fill_value=None, + allow_fill: bool = True, + mask: npt.NDArray[np.bool_] | None = None, +) -> ArrayLike: + """ + Specialized version for 1D arrays. Differences compared to `take_nd`: + + - Assumes input array has already been converted to numpy array / EA + - Assumes indexer is already guaranteed to be intp dtype ndarray + - Only works for 1D arrays + + To ensure the lowest possible overhead. + + Note: similarly to `take_nd`, this function assumes that the indexer is + a valid(ated) indexer with no out of bound indices. + + Parameters + ---------- + arr : np.ndarray or ExtensionArray + Input array. + indexer : ndarray + 1-D array of indices to take (validated indices, intp dtype). + fill_value : any, default np.nan + Fill value to replace -1 values with + allow_fill : bool, default True + If False, indexer is assumed to contain no -1 values so no filling + will be done. This short-circuits computation of a mask. Result is + undefined if allow_fill == False and -1 is present in indexer. + mask : np.ndarray, optional, default None + If `allow_fill` is True, and the mask (where indexer == -1) is already + known, it can be passed to avoid recomputation. + """ + if not isinstance(arr, np.ndarray): + # ExtensionArray -> dispatch to their method + return arr.take(indexer, fill_value=fill_value, allow_fill=allow_fill) + + if not allow_fill: + return arr.take(indexer) + + dtype, fill_value, mask_info = _take_preprocess_indexer_and_fill_value( + arr, indexer, fill_value, True, mask + ) + + # at this point, it's guaranteed that dtype can hold both the arr values + # and the fill_value + out = np.empty(indexer.shape, dtype=dtype) + + func = _get_take_nd_function( + arr.ndim, arr.dtype, out.dtype, axis=0, mask_info=mask_info + ) + func(arr, indexer, out, fill_value) + + return out + + +def take_2d_multi( + arr: np.ndarray, + indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]], + fill_value=np.nan, +) -> np.ndarray: + """ + Specialized Cython take which sets NaN values in one pass. + """ + # This is only called from one place in DataFrame._reindex_multi, + # so we know indexer is well-behaved. + assert indexer is not None + assert indexer[0] is not None + assert indexer[1] is not None + + row_idx, col_idx = indexer + + row_idx = ensure_platform_int(row_idx) + col_idx = ensure_platform_int(col_idx) + indexer = row_idx, col_idx + mask_info = None + + # check for promotion based on types only (do this first because + # it's faster than computing a mask) + dtype, fill_value = maybe_promote(arr.dtype, fill_value) + if dtype != arr.dtype: + # check if promotion is actually required based on indexer + row_mask = row_idx == -1 + col_mask = col_idx == -1 + row_needs = row_mask.any() + col_needs = col_mask.any() + mask_info = (row_mask, col_mask), (row_needs, col_needs) + + if not (row_needs or col_needs): + # if not, then depromote, set fill_value to dummy + # (it won't be used but we don't want the cython code + # to crash when trying to cast it to dtype) + dtype, fill_value = arr.dtype, arr.dtype.type() + + # at this point, it's guaranteed that dtype can hold both the arr values + # and the fill_value + out_shape = len(row_idx), len(col_idx) + out = np.empty(out_shape, dtype=dtype) + + func = _take_2d_multi_dict.get((arr.dtype.name, out.dtype.name), None) + if func is None and arr.dtype != out.dtype: + func = _take_2d_multi_dict.get((out.dtype.name, out.dtype.name), None) + if func is not None: + func = _convert_wrapper(func, out.dtype) + + if func is not None: + func(arr, indexer, out=out, fill_value=fill_value) + else: + # test_reindex_multi + _take_2d_multi_object( + arr, indexer, out, fill_value=fill_value, mask_info=mask_info + ) + + return out + + +@functools.lru_cache +def _get_take_nd_function_cached( + ndim: int, arr_dtype: np.dtype, out_dtype: np.dtype, axis: AxisInt +): + """ + Part of _get_take_nd_function below that doesn't need `mask_info` and thus + can be cached (mask_info potentially contains a numpy ndarray which is not + hashable and thus cannot be used as argument for cached function). + """ + tup = (arr_dtype.name, out_dtype.name) + if ndim == 1: + func = _take_1d_dict.get(tup, None) + elif ndim == 2: + if axis == 0: + func = _take_2d_axis0_dict.get(tup, None) + else: + func = _take_2d_axis1_dict.get(tup, None) + if func is not None: + return func + + # We get here with string, uint, float16, and complex dtypes that could + # potentially be handled in algos_take_helper. + # Also a couple with (M8[ns], object) and (m8[ns], object) + tup = (out_dtype.name, out_dtype.name) + if ndim == 1: + func = _take_1d_dict.get(tup, None) + elif ndim == 2: + if axis == 0: + func = _take_2d_axis0_dict.get(tup, None) + else: + func = _take_2d_axis1_dict.get(tup, None) + if func is not None: + func = _convert_wrapper(func, out_dtype) + return func + + return None + + +def _get_take_nd_function( + ndim: int, + arr_dtype: np.dtype, + out_dtype: np.dtype, + axis: AxisInt = 0, + mask_info=None, +): + """ + Get the appropriate "take" implementation for the given dimension, axis + and dtypes. + """ + func = None + if ndim <= 2: + # for this part we don't need `mask_info` -> use the cached algo lookup + func = _get_take_nd_function_cached(ndim, arr_dtype, out_dtype, axis) + + if func is None: + + def func(arr, indexer, out, fill_value=np.nan) -> None: + indexer = ensure_platform_int(indexer) + _take_nd_object( + arr, indexer, out, axis=axis, fill_value=fill_value, mask_info=mask_info + ) + + return func + + +def _view_wrapper(f, arr_dtype=None, out_dtype=None, fill_wrap=None): + def wrapper( + arr: np.ndarray, indexer: np.ndarray, out: np.ndarray, fill_value=np.nan + ) -> None: + if arr_dtype is not None: + arr = arr.view(arr_dtype) + if out_dtype is not None: + out = out.view(out_dtype) + if fill_wrap is not None: + # FIXME: if we get here with dt64/td64 we need to be sure we have + # matching resos + if fill_value.dtype.kind == "m": + fill_value = fill_value.astype("m8[ns]") + else: + fill_value = fill_value.astype("M8[ns]") + fill_value = fill_wrap(fill_value) + + f(arr, indexer, out, fill_value=fill_value) + + return wrapper + + +def _convert_wrapper(f, conv_dtype): + def wrapper( + arr: np.ndarray, indexer: np.ndarray, out: np.ndarray, fill_value=np.nan + ) -> None: + if conv_dtype == object: + # GH#39755 avoid casting dt64/td64 to integers + arr = ensure_wrapped_if_datetimelike(arr) + arr = arr.astype(conv_dtype) + f(arr, indexer, out, fill_value=fill_value) + + return wrapper + + +_take_1d_dict = { + ("int8", "int8"): libalgos.take_1d_int8_int8, + ("int8", "int32"): libalgos.take_1d_int8_int32, + ("int8", "int64"): libalgos.take_1d_int8_int64, + ("int8", "float64"): libalgos.take_1d_int8_float64, + ("int16", "int16"): libalgos.take_1d_int16_int16, + ("int16", "int32"): libalgos.take_1d_int16_int32, + ("int16", "int64"): libalgos.take_1d_int16_int64, + ("int16", "float64"): libalgos.take_1d_int16_float64, + ("int32", "int32"): libalgos.take_1d_int32_int32, + ("int32", "int64"): libalgos.take_1d_int32_int64, + ("int32", "float64"): libalgos.take_1d_int32_float64, + ("int64", "int64"): libalgos.take_1d_int64_int64, + ("int64", "float64"): libalgos.take_1d_int64_float64, + ("float32", "float32"): libalgos.take_1d_float32_float32, + ("float32", "float64"): libalgos.take_1d_float32_float64, + ("float64", "float64"): libalgos.take_1d_float64_float64, + ("object", "object"): libalgos.take_1d_object_object, + ("bool", "bool"): _view_wrapper(libalgos.take_1d_bool_bool, np.uint8, np.uint8), + ("bool", "object"): _view_wrapper(libalgos.take_1d_bool_object, np.uint8, None), + ("datetime64[ns]", "datetime64[ns]"): _view_wrapper( + libalgos.take_1d_int64_int64, np.int64, np.int64, np.int64 + ), + ("timedelta64[ns]", "timedelta64[ns]"): _view_wrapper( + libalgos.take_1d_int64_int64, np.int64, np.int64, np.int64 + ), +} + +_take_2d_axis0_dict = { + ("int8", "int8"): libalgos.take_2d_axis0_int8_int8, + ("int8", "int32"): libalgos.take_2d_axis0_int8_int32, + ("int8", "int64"): libalgos.take_2d_axis0_int8_int64, + ("int8", "float64"): libalgos.take_2d_axis0_int8_float64, + ("int16", "int16"): libalgos.take_2d_axis0_int16_int16, + ("int16", "int32"): libalgos.take_2d_axis0_int16_int32, + ("int16", "int64"): libalgos.take_2d_axis0_int16_int64, + ("int16", "float64"): libalgos.take_2d_axis0_int16_float64, + ("int32", "int32"): libalgos.take_2d_axis0_int32_int32, + ("int32", "int64"): libalgos.take_2d_axis0_int32_int64, + ("int32", "float64"): libalgos.take_2d_axis0_int32_float64, + ("int64", "int64"): libalgos.take_2d_axis0_int64_int64, + ("int64", "float64"): libalgos.take_2d_axis0_int64_float64, + ("float32", "float32"): libalgos.take_2d_axis0_float32_float32, + ("float32", "float64"): libalgos.take_2d_axis0_float32_float64, + ("float64", "float64"): libalgos.take_2d_axis0_float64_float64, + ("object", "object"): libalgos.take_2d_axis0_object_object, + ("bool", "bool"): _view_wrapper( + libalgos.take_2d_axis0_bool_bool, np.uint8, np.uint8 + ), + ("bool", "object"): _view_wrapper( + libalgos.take_2d_axis0_bool_object, np.uint8, None + ), + ("datetime64[ns]", "datetime64[ns]"): _view_wrapper( + libalgos.take_2d_axis0_int64_int64, np.int64, np.int64, fill_wrap=np.int64 + ), + ("timedelta64[ns]", "timedelta64[ns]"): _view_wrapper( + libalgos.take_2d_axis0_int64_int64, np.int64, np.int64, fill_wrap=np.int64 + ), +} + +_take_2d_axis1_dict = { + ("int8", "int8"): libalgos.take_2d_axis1_int8_int8, + ("int8", "int32"): libalgos.take_2d_axis1_int8_int32, + ("int8", "int64"): libalgos.take_2d_axis1_int8_int64, + ("int8", "float64"): libalgos.take_2d_axis1_int8_float64, + ("int16", "int16"): libalgos.take_2d_axis1_int16_int16, + ("int16", "int32"): libalgos.take_2d_axis1_int16_int32, + ("int16", "int64"): libalgos.take_2d_axis1_int16_int64, + ("int16", "float64"): libalgos.take_2d_axis1_int16_float64, + ("int32", "int32"): libalgos.take_2d_axis1_int32_int32, + ("int32", "int64"): libalgos.take_2d_axis1_int32_int64, + ("int32", "float64"): libalgos.take_2d_axis1_int32_float64, + ("int64", "int64"): libalgos.take_2d_axis1_int64_int64, + ("int64", "float64"): libalgos.take_2d_axis1_int64_float64, + ("float32", "float32"): libalgos.take_2d_axis1_float32_float32, + ("float32", "float64"): libalgos.take_2d_axis1_float32_float64, + ("float64", "float64"): libalgos.take_2d_axis1_float64_float64, + ("object", "object"): libalgos.take_2d_axis1_object_object, + ("bool", "bool"): _view_wrapper( + libalgos.take_2d_axis1_bool_bool, np.uint8, np.uint8 + ), + ("bool", "object"): _view_wrapper( + libalgos.take_2d_axis1_bool_object, np.uint8, None + ), + ("datetime64[ns]", "datetime64[ns]"): _view_wrapper( + libalgos.take_2d_axis1_int64_int64, np.int64, np.int64, fill_wrap=np.int64 + ), + ("timedelta64[ns]", "timedelta64[ns]"): _view_wrapper( + libalgos.take_2d_axis1_int64_int64, np.int64, np.int64, fill_wrap=np.int64 + ), +} + +_take_2d_multi_dict = { + ("int8", "int8"): libalgos.take_2d_multi_int8_int8, + ("int8", "int32"): libalgos.take_2d_multi_int8_int32, + ("int8", "int64"): libalgos.take_2d_multi_int8_int64, + ("int8", "float64"): libalgos.take_2d_multi_int8_float64, + ("int16", "int16"): libalgos.take_2d_multi_int16_int16, + ("int16", "int32"): libalgos.take_2d_multi_int16_int32, + ("int16", "int64"): libalgos.take_2d_multi_int16_int64, + ("int16", "float64"): libalgos.take_2d_multi_int16_float64, + ("int32", "int32"): libalgos.take_2d_multi_int32_int32, + ("int32", "int64"): libalgos.take_2d_multi_int32_int64, + ("int32", "float64"): libalgos.take_2d_multi_int32_float64, + ("int64", "int64"): libalgos.take_2d_multi_int64_int64, + ("int64", "float64"): libalgos.take_2d_multi_int64_float64, + ("float32", "float32"): libalgos.take_2d_multi_float32_float32, + ("float32", "float64"): libalgos.take_2d_multi_float32_float64, + ("float64", "float64"): libalgos.take_2d_multi_float64_float64, + ("object", "object"): libalgos.take_2d_multi_object_object, + ("bool", "bool"): _view_wrapper( + libalgos.take_2d_multi_bool_bool, np.uint8, np.uint8 + ), + ("bool", "object"): _view_wrapper( + libalgos.take_2d_multi_bool_object, np.uint8, None + ), + ("datetime64[ns]", "datetime64[ns]"): _view_wrapper( + libalgos.take_2d_multi_int64_int64, np.int64, np.int64, fill_wrap=np.int64 + ), + ("timedelta64[ns]", "timedelta64[ns]"): _view_wrapper( + libalgos.take_2d_multi_int64_int64, np.int64, np.int64, fill_wrap=np.int64 + ), +} + + +def _take_nd_object( + arr: np.ndarray, + indexer: npt.NDArray[np.intp], + out: np.ndarray, + axis: AxisInt, + fill_value, + mask_info, +) -> None: + if mask_info is not None: + mask, needs_masking = mask_info + else: + mask = indexer == -1 + needs_masking = mask.any() + if arr.dtype != out.dtype: + arr = arr.astype(out.dtype) + if arr.shape[axis] > 0: + arr.take(indexer, axis=axis, out=out) + if needs_masking: + outindexer = [slice(None)] * arr.ndim + outindexer[axis] = mask + out[tuple(outindexer)] = fill_value + + +def _take_2d_multi_object( + arr: np.ndarray, + indexer: tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]], + out: np.ndarray, + fill_value, + mask_info, +) -> None: + # this is not ideal, performance-wise, but it's better than raising + # an exception (best to optimize in Cython to avoid getting here) + row_idx, col_idx = indexer # both np.intp + if mask_info is not None: + (row_mask, col_mask), (row_needs, col_needs) = mask_info + else: + row_mask = row_idx == -1 + col_mask = col_idx == -1 + row_needs = row_mask.any() + col_needs = col_mask.any() + if fill_value is not None: + if row_needs: + out[row_mask, :] = fill_value + if col_needs: + out[:, col_mask] = fill_value + for i, u_ in enumerate(row_idx): + if u_ != -1: + for j, v in enumerate(col_idx): + if v != -1: + out[i, j] = arr[u_, v] + + +def _take_preprocess_indexer_and_fill_value( + arr: np.ndarray, + indexer: npt.NDArray[np.intp], + fill_value, + allow_fill: bool, + mask: npt.NDArray[np.bool_] | None = None, +): + mask_info: tuple[np.ndarray | None, bool] | None = None + + if not allow_fill: + dtype, fill_value = arr.dtype, arr.dtype.type() + mask_info = None, False + else: + # check for promotion based on types only (do this first because + # it's faster than computing a mask) + dtype, fill_value = maybe_promote(arr.dtype, fill_value) + if dtype != arr.dtype: + # check if promotion is actually required based on indexer + if mask is not None: + needs_masking = True + else: + mask = indexer == -1 + needs_masking = bool(mask.any()) + mask_info = mask, needs_masking + if not needs_masking: + # if not, then depromote, set fill_value to dummy + # (it won't be used but we don't want the cython code + # to crash when trying to cast it to dtype) + dtype, fill_value = arr.dtype, arr.dtype.type() + + return dtype, fill_value, mask_info diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/transforms.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..ec67244949e3db92cc811b19cdfcd5d1dd2b4de8 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/array_algos/transforms.py @@ -0,0 +1,50 @@ +""" +transforms.py is for shape-preserving functions. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np + +if TYPE_CHECKING: + from pandas._typing import ( + AxisInt, + Scalar, + ) + + +def shift( + values: np.ndarray, periods: int, axis: AxisInt, fill_value: Scalar +) -> np.ndarray: + new_values = values + + if periods == 0 or values.size == 0: + return new_values.copy() + + # make sure array sent to np.roll is c_contiguous + f_ordered = values.flags.f_contiguous + if f_ordered: + new_values = new_values.T + axis = new_values.ndim - axis - 1 + + if new_values.size: + new_values = np.roll( + new_values, + np.intp(periods), + axis=axis, + ) + + axis_indexer = [slice(None)] * values.ndim + if periods > 0: + axis_indexer[axis] = slice(None, periods) + else: + axis_indexer[axis] = slice(periods, None) + new_values[tuple(axis_indexer)] = fill_value + + # restore original order + if f_ordered: + new_values = new_values.T + + return new_values diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/arrays/_ranges.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/arrays/_ranges.py new file mode 100644 index 0000000000000000000000000000000000000000..3e89391324ad4a90235da230250758662822678f --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/arrays/_ranges.py @@ -0,0 +1,207 @@ +""" +Helper functions to generate range-like data for DatetimeArray +(and possibly TimedeltaArray/PeriodArray) +""" +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np + +from pandas._libs.lib import i8max +from pandas._libs.tslibs import ( + BaseOffset, + OutOfBoundsDatetime, + Timedelta, + Timestamp, + iNaT, +) + +if TYPE_CHECKING: + from pandas._typing import npt + + +def generate_regular_range( + start: Timestamp | Timedelta | None, + end: Timestamp | Timedelta | None, + periods: int | None, + freq: BaseOffset, + unit: str = "ns", +) -> npt.NDArray[np.intp]: + """ + Generate a range of dates or timestamps with the spans between dates + described by the given `freq` DateOffset. + + Parameters + ---------- + start : Timedelta, Timestamp or None + First point of produced date range. + end : Timedelta, Timestamp or None + Last point of produced date range. + periods : int or None + Number of periods in produced date range. + freq : Tick + Describes space between dates in produced date range. + unit : str, default "ns" + The resolution the output is meant to represent. + + Returns + ------- + ndarray[np.int64] + Representing the given resolution. + """ + istart = start._value if start is not None else None + iend = end._value if end is not None else None + freq.nanos # raises if non-fixed frequency + td = Timedelta(freq) + b: int + e: int + try: + td = td.as_unit(unit, round_ok=False) + except ValueError as err: + raise ValueError( + f"freq={freq} is incompatible with unit={unit}. " + "Use a lower freq or a higher unit instead." + ) from err + stride = int(td._value) + + if periods is None and istart is not None and iend is not None: + b = istart + # cannot just use e = Timestamp(end) + 1 because arange breaks when + # stride is too large, see GH10887 + e = b + (iend - b) // stride * stride + stride // 2 + 1 + elif istart is not None and periods is not None: + b = istart + e = _generate_range_overflow_safe(b, periods, stride, side="start") + elif iend is not None and periods is not None: + e = iend + stride + b = _generate_range_overflow_safe(e, periods, stride, side="end") + else: + raise ValueError( + "at least 'start' or 'end' should be specified if a 'period' is given." + ) + + with np.errstate(over="raise"): + # If the range is sufficiently large, np.arange may overflow + # and incorrectly return an empty array if not caught. + try: + values = np.arange(b, e, stride, dtype=np.int64) + except FloatingPointError: + xdr = [b] + while xdr[-1] != e: + xdr.append(xdr[-1] + stride) + values = np.array(xdr[:-1], dtype=np.int64) + return values + + +def _generate_range_overflow_safe( + endpoint: int, periods: int, stride: int, side: str = "start" +) -> int: + """ + Calculate the second endpoint for passing to np.arange, checking + to avoid an integer overflow. Catch OverflowError and re-raise + as OutOfBoundsDatetime. + + Parameters + ---------- + endpoint : int + nanosecond timestamp of the known endpoint of the desired range + periods : int + number of periods in the desired range + stride : int + nanoseconds between periods in the desired range + side : {'start', 'end'} + which end of the range `endpoint` refers to + + Returns + ------- + other_end : int + + Raises + ------ + OutOfBoundsDatetime + """ + # GH#14187 raise instead of incorrectly wrapping around + assert side in ["start", "end"] + + i64max = np.uint64(i8max) + msg = f"Cannot generate range with {side}={endpoint} and periods={periods}" + + with np.errstate(over="raise"): + # if periods * strides cannot be multiplied within the *uint64* bounds, + # we cannot salvage the operation by recursing, so raise + try: + addend = np.uint64(periods) * np.uint64(np.abs(stride)) + except FloatingPointError as err: + raise OutOfBoundsDatetime(msg) from err + + if np.abs(addend) <= i64max: + # relatively easy case without casting concerns + return _generate_range_overflow_safe_signed(endpoint, periods, stride, side) + + elif (endpoint > 0 and side == "start" and stride > 0) or ( + endpoint < 0 < stride and side == "end" + ): + # no chance of not-overflowing + raise OutOfBoundsDatetime(msg) + + elif side == "end" and endpoint - stride <= i64max < endpoint: + # in _generate_regular_range we added `stride` thereby overflowing + # the bounds. Adjust to fix this. + return _generate_range_overflow_safe( + endpoint - stride, periods - 1, stride, side + ) + + # split into smaller pieces + mid_periods = periods // 2 + remaining = periods - mid_periods + assert 0 < remaining < periods, (remaining, periods, endpoint, stride) + + midpoint = int(_generate_range_overflow_safe(endpoint, mid_periods, stride, side)) + return _generate_range_overflow_safe(midpoint, remaining, stride, side) + + +def _generate_range_overflow_safe_signed( + endpoint: int, periods: int, stride: int, side: str +) -> int: + """ + A special case for _generate_range_overflow_safe where `periods * stride` + can be calculated without overflowing int64 bounds. + """ + assert side in ["start", "end"] + if side == "end": + stride *= -1 + + with np.errstate(over="raise"): + addend = np.int64(periods) * np.int64(stride) + try: + # easy case with no overflows + result = np.int64(endpoint) + addend + if result == iNaT: + # Putting this into a DatetimeArray/TimedeltaArray + # would incorrectly be interpreted as NaT + raise OverflowError + return int(result) + except (FloatingPointError, OverflowError): + # with endpoint negative and addend positive we risk + # FloatingPointError; with reversed signed we risk OverflowError + pass + + # if stride and endpoint had opposite signs, then endpoint + addend + # should never overflow. so they must have the same signs + assert (stride > 0 and endpoint >= 0) or (stride < 0 and endpoint <= 0) + + if stride > 0: + # watch out for very special case in which we just slightly + # exceed implementation bounds, but when passing the result to + # np.arange will get a result slightly within the bounds + + uresult = np.uint64(endpoint) + np.uint64(addend) + i64max = np.uint64(i8max) + assert uresult > i64max + if uresult <= i64max + np.uint64(stride): + return int(uresult) + + raise OutOfBoundsDatetime( + f"Cannot generate range with {side}={endpoint} and periods={periods}" + ) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/arrays/_utils.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/arrays/_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..6b46396d5efdfa4301a5362c8a5a71678345479b --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/arrays/_utils.py @@ -0,0 +1,63 @@ +from __future__ import annotations + +from typing import ( + TYPE_CHECKING, + Any, +) + +import numpy as np + +from pandas._libs import lib +from pandas.errors import LossySetitemError + +from pandas.core.dtypes.cast import np_can_hold_element +from pandas.core.dtypes.common import is_numeric_dtype + +if TYPE_CHECKING: + from pandas._typing import ( + ArrayLike, + npt, + ) + + +def to_numpy_dtype_inference( + arr: ArrayLike, dtype: npt.DTypeLike | None, na_value, hasna: bool +) -> tuple[npt.DTypeLike, Any]: + if dtype is None and is_numeric_dtype(arr.dtype): + dtype_given = False + if hasna: + if arr.dtype.kind == "b": + dtype = np.dtype(np.object_) + else: + if arr.dtype.kind in "iu": + dtype = np.dtype(np.float64) + else: + dtype = arr.dtype.numpy_dtype # type: ignore[union-attr] + if na_value is lib.no_default: + na_value = np.nan + else: + dtype = arr.dtype.numpy_dtype # type: ignore[union-attr] + elif dtype is not None: + dtype = np.dtype(dtype) + dtype_given = True + else: + dtype_given = True + + if na_value is lib.no_default: + if dtype is None or not hasna: + na_value = arr.dtype.na_value + elif dtype.kind == "f": # type: ignore[union-attr] + na_value = np.nan + elif dtype.kind == "M": # type: ignore[union-attr] + na_value = np.datetime64("nat") + elif dtype.kind == "m": # type: ignore[union-attr] + na_value = np.timedelta64("nat") + else: + na_value = arr.dtype.na_value + + if not dtype_given and hasna: + try: + np_can_hold_element(dtype, na_value) # type: ignore[arg-type] + except LossySetitemError: + dtype = np.dtype(np.object_) + return dtype, na_value diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/groupby/__pycache__/groupby.cpython-310.pyc b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/groupby/__pycache__/groupby.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f941c176d353b3d396c7e3fb2ccf30a363e44a66 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/groupby/__pycache__/groupby.cpython-310.pyc @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:03f1972bc48ce74b42d2a16f930abd23b52c31645ba1d3f0af668a610677a97a +size 159352 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/indexes/__pycache__/base.cpython-310.pyc b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/indexes/__pycache__/base.cpython-310.pyc new file mode 100644 index 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b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/methods/__pycache__/selectn.cpython-310.pyc differ diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/methods/describe.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/methods/describe.py new file mode 100644 index 0000000000000000000000000000000000000000..c620bb9d17976a9deeb682ff323476c484f283ff --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/methods/describe.py @@ -0,0 +1,416 @@ +""" +Module responsible for execution of NDFrame.describe() method. + +Method NDFrame.describe() delegates actual execution to function describe_ndframe(). +""" +from __future__ import annotations + +from abc import ( + ABC, + abstractmethod, +) +from typing import ( + TYPE_CHECKING, + Callable, + cast, +) + +import numpy as np + +from pandas._libs.tslibs import Timestamp +from pandas._typing import ( + DtypeObj, + NDFrameT, + npt, +) +from pandas.util._validators import validate_percentile + +from pandas.core.dtypes.common import ( + is_bool_dtype, + is_numeric_dtype, +) +from pandas.core.dtypes.dtypes import ( + ArrowDtype, + DatetimeTZDtype, + ExtensionDtype, +) + +from pandas.core.arrays.floating import Float64Dtype +from pandas.core.reshape.concat import concat + +from pandas.io.formats.format import format_percentiles + +if TYPE_CHECKING: + from collections.abc import ( + Hashable, + Sequence, + ) + + from pandas import ( + DataFrame, + Series, + ) + + +def describe_ndframe( + *, + obj: NDFrameT, + include: str | Sequence[str] | None, + exclude: str | Sequence[str] | None, + percentiles: Sequence[float] | np.ndarray | None, +) -> NDFrameT: + """Describe series or dataframe. + + Called from pandas.core.generic.NDFrame.describe() + + Parameters + ---------- + obj: DataFrame or Series + Either dataframe or series to be described. + include : 'all', list-like of dtypes or None (default), optional + A white list of data types to include in the result. Ignored for ``Series``. + exclude : list-like of dtypes or None (default), optional, + A black list of data types to omit from the result. Ignored for ``Series``. + percentiles : list-like of numbers, optional + The percentiles to include in the output. All should fall between 0 and 1. + The default is ``[.25, .5, .75]``, which returns the 25th, 50th, and + 75th percentiles. + + Returns + ------- + Dataframe or series description. + """ + percentiles = _refine_percentiles(percentiles) + + describer: NDFrameDescriberAbstract + if obj.ndim == 1: + describer = SeriesDescriber( + obj=cast("Series", obj), + ) + else: + describer = DataFrameDescriber( + obj=cast("DataFrame", obj), + include=include, + exclude=exclude, + ) + + result = describer.describe(percentiles=percentiles) + return cast(NDFrameT, result) + + +class NDFrameDescriberAbstract(ABC): + """Abstract class for describing dataframe or series. + + Parameters + ---------- + obj : Series or DataFrame + Object to be described. + """ + + def __init__(self, obj: DataFrame | Series) -> None: + self.obj = obj + + @abstractmethod + def describe(self, percentiles: Sequence[float] | np.ndarray) -> DataFrame | Series: + """Do describe either series or dataframe. + + Parameters + ---------- + percentiles : list-like of numbers + The percentiles to include in the output. + """ + + +class SeriesDescriber(NDFrameDescriberAbstract): + """Class responsible for creating series description.""" + + obj: Series + + def describe(self, percentiles: Sequence[float] | np.ndarray) -> Series: + describe_func = select_describe_func( + self.obj, + ) + return describe_func(self.obj, percentiles) + + +class DataFrameDescriber(NDFrameDescriberAbstract): + """Class responsible for creating dataobj description. + + Parameters + ---------- + obj : DataFrame + DataFrame to be described. + include : 'all', list-like of dtypes or None + A white list of data types to include in the result. + exclude : list-like of dtypes or None + A black list of data types to omit from the result. + """ + + obj: DataFrame + + def __init__( + self, + obj: DataFrame, + *, + include: str | Sequence[str] | None, + exclude: str | Sequence[str] | None, + ) -> None: + self.include = include + self.exclude = exclude + + if obj.ndim == 2 and obj.columns.size == 0: + raise ValueError("Cannot describe a DataFrame without columns") + + super().__init__(obj) + + def describe(self, percentiles: Sequence[float] | np.ndarray) -> DataFrame: + data = self._select_data() + + ldesc: list[Series] = [] + for _, series in data.items(): + describe_func = select_describe_func(series) + ldesc.append(describe_func(series, percentiles)) + + col_names = reorder_columns(ldesc) + d = concat( + [x.reindex(col_names, copy=False) for x in ldesc], + axis=1, + sort=False, + ) + d.columns = data.columns.copy() + return d + + def _select_data(self) -> DataFrame: + """Select columns to be described.""" + if (self.include is None) and (self.exclude is None): + # when some numerics are found, keep only numerics + default_include: list[npt.DTypeLike] = [np.number, "datetime"] + data = self.obj.select_dtypes(include=default_include) + if len(data.columns) == 0: + data = self.obj + elif self.include == "all": + if self.exclude is not None: + msg = "exclude must be None when include is 'all'" + raise ValueError(msg) + data = self.obj + else: + data = self.obj.select_dtypes( + include=self.include, + exclude=self.exclude, + ) + return data + + +def reorder_columns(ldesc: Sequence[Series]) -> list[Hashable]: + """Set a convenient order for rows for display.""" + names: list[Hashable] = [] + seen_names: set[Hashable] = set() + ldesc_indexes = sorted((x.index for x in ldesc), key=len) + for idxnames in ldesc_indexes: + for name in idxnames: + if name not in seen_names: + seen_names.add(name) + names.append(name) + return names + + +def describe_numeric_1d(series: Series, percentiles: Sequence[float]) -> Series: + """Describe series containing numerical data. + + Parameters + ---------- + series : Series + Series to be described. + percentiles : list-like of numbers + The percentiles to include in the output. + """ + from pandas import Series + + formatted_percentiles = format_percentiles(percentiles) + + stat_index = ["count", "mean", "std", "min"] + formatted_percentiles + ["max"] + d = ( + [series.count(), series.mean(), series.std(), series.min()] + + series.quantile(percentiles).tolist() + + [series.max()] + ) + # GH#48340 - always return float on non-complex numeric data + dtype: DtypeObj | None + if isinstance(series.dtype, ExtensionDtype): + if isinstance(series.dtype, ArrowDtype): + if series.dtype.kind == "m": + # GH53001: describe timedeltas with object dtype + dtype = None + else: + import pyarrow as pa + + dtype = ArrowDtype(pa.float64()) + else: + dtype = Float64Dtype() + elif series.dtype.kind in "iufb": + # i.e. numeric but exclude complex dtype + dtype = np.dtype("float") + else: + dtype = None + return Series(d, index=stat_index, name=series.name, dtype=dtype) + + +def describe_categorical_1d( + data: Series, + percentiles_ignored: Sequence[float], +) -> Series: + """Describe series containing categorical data. + + Parameters + ---------- + data : Series + Series to be described. + percentiles_ignored : list-like of numbers + Ignored, but in place to unify interface. + """ + names = ["count", "unique", "top", "freq"] + objcounts = data.value_counts() + count_unique = len(objcounts[objcounts != 0]) + if count_unique > 0: + top, freq = objcounts.index[0], objcounts.iloc[0] + dtype = None + else: + # If the DataFrame is empty, set 'top' and 'freq' to None + # to maintain output shape consistency + top, freq = np.nan, np.nan + dtype = "object" + + result = [data.count(), count_unique, top, freq] + + from pandas import Series + + return Series(result, index=names, name=data.name, dtype=dtype) + + +def describe_timestamp_as_categorical_1d( + data: Series, + percentiles_ignored: Sequence[float], +) -> Series: + """Describe series containing timestamp data treated as categorical. + + Parameters + ---------- + data : Series + Series to be described. + percentiles_ignored : list-like of numbers + Ignored, but in place to unify interface. + """ + names = ["count", "unique"] + objcounts = data.value_counts() + count_unique = len(objcounts[objcounts != 0]) + result: list[float | Timestamp] = [data.count(), count_unique] + dtype = None + if count_unique > 0: + top, freq = objcounts.index[0], objcounts.iloc[0] + tz = data.dt.tz + asint = data.dropna().values.view("i8") + top = Timestamp(top) + if top.tzinfo is not None and tz is not None: + # Don't tz_localize(None) if key is already tz-aware + top = top.tz_convert(tz) + else: + top = top.tz_localize(tz) + names += ["top", "freq", "first", "last"] + result += [ + top, + freq, + Timestamp(asint.min(), tz=tz), + Timestamp(asint.max(), tz=tz), + ] + + # If the DataFrame is empty, set 'top' and 'freq' to None + # to maintain output shape consistency + else: + names += ["top", "freq"] + result += [np.nan, np.nan] + dtype = "object" + + from pandas import Series + + return Series(result, index=names, name=data.name, dtype=dtype) + + +def describe_timestamp_1d(data: Series, percentiles: Sequence[float]) -> Series: + """Describe series containing datetime64 dtype. + + Parameters + ---------- + data : Series + Series to be described. + percentiles : list-like of numbers + The percentiles to include in the output. + """ + # GH-30164 + from pandas import Series + + formatted_percentiles = format_percentiles(percentiles) + + stat_index = ["count", "mean", "min"] + formatted_percentiles + ["max"] + d = ( + [data.count(), data.mean(), data.min()] + + data.quantile(percentiles).tolist() + + [data.max()] + ) + return Series(d, index=stat_index, name=data.name) + + +def select_describe_func( + data: Series, +) -> Callable: + """Select proper function for describing series based on data type. + + Parameters + ---------- + data : Series + Series to be described. + """ + if is_bool_dtype(data.dtype): + return describe_categorical_1d + elif is_numeric_dtype(data): + return describe_numeric_1d + elif data.dtype.kind == "M" or isinstance(data.dtype, DatetimeTZDtype): + return describe_timestamp_1d + elif data.dtype.kind == "m": + return describe_numeric_1d + else: + return describe_categorical_1d + + +def _refine_percentiles( + percentiles: Sequence[float] | np.ndarray | None, +) -> npt.NDArray[np.float64]: + """ + Ensure that percentiles are unique and sorted. + + Parameters + ---------- + percentiles : list-like of numbers, optional + The percentiles to include in the output. + """ + if percentiles is None: + return np.array([0.25, 0.5, 0.75]) + + # explicit conversion of `percentiles` to list + percentiles = list(percentiles) + + # get them all to be in [0, 1] + validate_percentile(percentiles) + + # median should always be included + if 0.5 not in percentiles: + percentiles.append(0.5) + + percentiles = np.asarray(percentiles) + + # sort and check for duplicates + unique_pcts = np.unique(percentiles) + assert percentiles is not None + if len(unique_pcts) < len(percentiles): + raise ValueError("percentiles cannot contain duplicates") + + return unique_pcts diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/methods/selectn.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/methods/selectn.py new file mode 100644 index 0000000000000000000000000000000000000000..a2f8ca94134b897bd3f7824a6cb45ca13516e5ff --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/methods/selectn.py @@ -0,0 +1,269 @@ +""" +Implementation of nlargest and nsmallest. +""" + +from __future__ import annotations + +from collections.abc import ( + Hashable, + Sequence, +) +from typing import ( + TYPE_CHECKING, + cast, + final, +) + +import numpy as np + +from pandas._libs import algos as libalgos + +from pandas.core.dtypes.common import ( + is_bool_dtype, + is_complex_dtype, + is_integer_dtype, + is_list_like, + is_numeric_dtype, + needs_i8_conversion, +) +from pandas.core.dtypes.dtypes import BaseMaskedDtype + +if TYPE_CHECKING: + from pandas._typing import ( + DtypeObj, + IndexLabel, + ) + + from pandas import ( + DataFrame, + Series, + ) + + +class SelectN: + def __init__(self, obj, n: int, keep: str) -> None: + self.obj = obj + self.n = n + self.keep = keep + + if self.keep not in ("first", "last", "all"): + raise ValueError('keep must be either "first", "last" or "all"') + + def compute(self, method: str) -> DataFrame | Series: + raise NotImplementedError + + @final + def nlargest(self): + return self.compute("nlargest") + + @final + def nsmallest(self): + return self.compute("nsmallest") + + @final + @staticmethod + def is_valid_dtype_n_method(dtype: DtypeObj) -> bool: + """ + Helper function to determine if dtype is valid for + nsmallest/nlargest methods + """ + if is_numeric_dtype(dtype): + return not is_complex_dtype(dtype) + return needs_i8_conversion(dtype) + + +class SelectNSeries(SelectN): + """ + Implement n largest/smallest for Series + + Parameters + ---------- + obj : Series + n : int + keep : {'first', 'last'}, default 'first' + + Returns + ------- + nordered : Series + """ + + def compute(self, method: str) -> Series: + from pandas.core.reshape.concat import concat + + n = self.n + dtype = self.obj.dtype + if not self.is_valid_dtype_n_method(dtype): + raise TypeError(f"Cannot use method '{method}' with dtype {dtype}") + + if n <= 0: + return self.obj[[]] + + dropped = self.obj.dropna() + nan_index = self.obj.drop(dropped.index) + + # slow method + if n >= len(self.obj): + ascending = method == "nsmallest" + return self.obj.sort_values(ascending=ascending).head(n) + + # fast method + new_dtype = dropped.dtype + + # Similar to algorithms._ensure_data + arr = dropped._values + if needs_i8_conversion(arr.dtype): + arr = arr.view("i8") + elif isinstance(arr.dtype, BaseMaskedDtype): + arr = arr._data + else: + arr = np.asarray(arr) + if arr.dtype.kind == "b": + arr = arr.view(np.uint8) + + if method == "nlargest": + arr = -arr + if is_integer_dtype(new_dtype): + # GH 21426: ensure reverse ordering at boundaries + arr -= 1 + + elif is_bool_dtype(new_dtype): + # GH 26154: ensure False is smaller than True + arr = 1 - (-arr) + + if self.keep == "last": + arr = arr[::-1] + + nbase = n + narr = len(arr) + n = min(n, narr) + + # arr passed into kth_smallest must be contiguous. We copy + # here because kth_smallest will modify its input + # avoid OOB access with kth_smallest_c when n <= 0 + if len(arr) > 0: + kth_val = libalgos.kth_smallest(arr.copy(order="C"), n - 1) + else: + kth_val = np.nan + (ns,) = np.nonzero(arr <= kth_val) + inds = ns[arr[ns].argsort(kind="mergesort")] + + if self.keep != "all": + inds = inds[:n] + findex = nbase + else: + if len(inds) < nbase <= len(nan_index) + len(inds): + findex = len(nan_index) + len(inds) + else: + findex = len(inds) + + if self.keep == "last": + # reverse indices + inds = narr - 1 - inds + + return concat([dropped.iloc[inds], nan_index]).iloc[:findex] + + +class SelectNFrame(SelectN): + """ + Implement n largest/smallest for DataFrame + + Parameters + ---------- + obj : DataFrame + n : int + keep : {'first', 'last'}, default 'first' + columns : list or str + + Returns + ------- + nordered : DataFrame + """ + + def __init__(self, obj: DataFrame, n: int, keep: str, columns: IndexLabel) -> None: + super().__init__(obj, n, keep) + if not is_list_like(columns) or isinstance(columns, tuple): + columns = [columns] + + columns = cast(Sequence[Hashable], columns) + columns = list(columns) + self.columns = columns + + def compute(self, method: str) -> DataFrame: + from pandas.core.api import Index + + n = self.n + frame = self.obj + columns = self.columns + + for column in columns: + dtype = frame[column].dtype + if not self.is_valid_dtype_n_method(dtype): + raise TypeError( + f"Column {repr(column)} has dtype {dtype}, " + f"cannot use method {repr(method)} with this dtype" + ) + + def get_indexer(current_indexer, other_indexer): + """ + Helper function to concat `current_indexer` and `other_indexer` + depending on `method` + """ + if method == "nsmallest": + return current_indexer.append(other_indexer) + else: + return other_indexer.append(current_indexer) + + # Below we save and reset the index in case index contains duplicates + original_index = frame.index + cur_frame = frame = frame.reset_index(drop=True) + cur_n = n + indexer = Index([], dtype=np.int64) + + for i, column in enumerate(columns): + # For each column we apply method to cur_frame[column]. + # If it's the last column or if we have the number of + # results desired we are done. + # Otherwise there are duplicates of the largest/smallest + # value and we need to look at the rest of the columns + # to determine which of the rows with the largest/smallest + # value in the column to keep. + series = cur_frame[column] + is_last_column = len(columns) - 1 == i + values = getattr(series, method)( + cur_n, keep=self.keep if is_last_column else "all" + ) + + if is_last_column or len(values) <= cur_n: + indexer = get_indexer(indexer, values.index) + break + + # Now find all values which are equal to + # the (nsmallest: largest)/(nlargest: smallest) + # from our series. + border_value = values == values[values.index[-1]] + + # Some of these values are among the top-n + # some aren't. + unsafe_values = values[border_value] + + # These values are definitely among the top-n + safe_values = values[~border_value] + indexer = get_indexer(indexer, safe_values.index) + + # Go on and separate the unsafe_values on the remaining + # columns. + cur_frame = cur_frame.loc[unsafe_values.index] + cur_n = n - len(indexer) + + frame = frame.take(indexer) + + # Restore the index on frame + frame.index = original_index.take(indexer) + + # If there is only one column, the frame is already sorted. + if len(columns) == 1: + return frame + + ascending = method == "nsmallest" + + return frame.sort_values(columns, ascending=ascending, kind="mergesort") diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/methods/to_dict.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/methods/to_dict.py new file mode 100644 index 0000000000000000000000000000000000000000..accbd92a91ed66fe4dfd1e3939fe8c498184285d --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/methods/to_dict.py @@ -0,0 +1,272 @@ +from __future__ import annotations + +from typing import ( + TYPE_CHECKING, + Literal, + overload, +) +import warnings + +import numpy as np + +from pandas._libs import ( + lib, + missing as libmissing, +) +from pandas.util._exceptions import find_stack_level + +from pandas.core.dtypes.cast import maybe_box_native +from pandas.core.dtypes.dtypes import ( + BaseMaskedDtype, + ExtensionDtype, +) + +from pandas.core import common as com + +if TYPE_CHECKING: + from pandas._typing import MutableMappingT + + from pandas import DataFrame + + +@overload +def to_dict( + df: DataFrame, + orient: Literal["dict", "list", "series", "split", "tight", "index"] = ..., + *, + into: type[MutableMappingT] | MutableMappingT, + index: bool = ..., +) -> MutableMappingT: + ... + + +@overload +def to_dict( + df: DataFrame, + orient: Literal["records"], + *, + into: type[MutableMappingT] | MutableMappingT, + index: bool = ..., +) -> list[MutableMappingT]: + ... + + +@overload +def to_dict( + df: DataFrame, + orient: Literal["dict", "list", "series", "split", "tight", "index"] = ..., + *, + into: type[dict] = ..., + index: bool = ..., +) -> dict: + ... + + +@overload +def to_dict( + df: DataFrame, + orient: Literal["records"], + *, + into: type[dict] = ..., + index: bool = ..., +) -> list[dict]: + ... + + +# error: Incompatible default for argument "into" (default has type "type[dict +# [Any, Any]]", argument has type "type[MutableMappingT] | MutableMappingT") +def to_dict( + df: DataFrame, + orient: Literal[ + "dict", "list", "series", "split", "tight", "records", "index" + ] = "dict", + *, + into: type[MutableMappingT] | MutableMappingT = dict, # type: ignore[assignment] + index: bool = True, +) -> MutableMappingT | list[MutableMappingT]: + """ + Convert the DataFrame to a dictionary. + + The type of the key-value pairs can be customized with the parameters + (see below). + + Parameters + ---------- + orient : str {'dict', 'list', 'series', 'split', 'tight', 'records', 'index'} + Determines the type of the values of the dictionary. + + - 'dict' (default) : dict like {column -> {index -> value}} + - 'list' : dict like {column -> [values]} + - 'series' : dict like {column -> Series(values)} + - 'split' : dict like + {'index' -> [index], 'columns' -> [columns], 'data' -> [values]} + - 'tight' : dict like + {'index' -> [index], 'columns' -> [columns], 'data' -> [values], + 'index_names' -> [index.names], 'column_names' -> [column.names]} + - 'records' : list like + [{column -> value}, ... , {column -> value}] + - 'index' : dict like {index -> {column -> value}} + + .. versionadded:: 1.4.0 + 'tight' as an allowed value for the ``orient`` argument + + into : class, default dict + The collections.abc.MutableMapping subclass used for all Mappings + in the return value. Can be the actual class or an empty + instance of the mapping type you want. If you want a + collections.defaultdict, you must pass it initialized. + + index : bool, default True + Whether to include the index item (and index_names item if `orient` + is 'tight') in the returned dictionary. Can only be ``False`` + when `orient` is 'split' or 'tight'. + + .. versionadded:: 2.0.0 + + Returns + ------- + dict, list or collections.abc.Mapping + Return a collections.abc.MutableMapping object representing the + DataFrame. The resulting transformation depends on the `orient` parameter. + """ + if not df.columns.is_unique: + warnings.warn( + "DataFrame columns are not unique, some columns will be omitted.", + UserWarning, + stacklevel=find_stack_level(), + ) + # GH16122 + into_c = com.standardize_mapping(into) + + # error: Incompatible types in assignment (expression has type "str", + # variable has type "Literal['dict', 'list', 'series', 'split', 'tight', + # 'records', 'index']") + orient = orient.lower() # type: ignore[assignment] + + if not index and orient not in ["split", "tight"]: + raise ValueError( + "'index=False' is only valid when 'orient' is 'split' or 'tight'" + ) + + if orient == "series": + # GH46470 Return quickly if orient series to avoid creating dtype objects + return into_c((k, v) for k, v in df.items()) + + box_native_indices = [ + i + for i, col_dtype in enumerate(df.dtypes.values) + if col_dtype == np.dtype(object) or isinstance(col_dtype, ExtensionDtype) + ] + box_na_values = [ + lib.no_default if not isinstance(col_dtype, BaseMaskedDtype) else libmissing.NA + for i, col_dtype in enumerate(df.dtypes.values) + ] + are_all_object_dtype_cols = len(box_native_indices) == len(df.dtypes) + + if orient == "dict": + return into_c((k, v.to_dict(into=into)) for k, v in df.items()) + + elif orient == "list": + object_dtype_indices_as_set: set[int] = set(box_native_indices) + return into_c( + ( + k, + list(map(maybe_box_native, v.to_numpy(na_value=box_na_values[i]))) + if i in object_dtype_indices_as_set + else list(map(maybe_box_native, v.to_numpy())), + ) + for i, (k, v) in enumerate(df.items()) + ) + + elif orient == "split": + data = df._create_data_for_split_and_tight_to_dict( + are_all_object_dtype_cols, box_native_indices + ) + + return into_c( + ((("index", df.index.tolist()),) if index else ()) + + ( + ("columns", df.columns.tolist()), + ("data", data), + ) + ) + + elif orient == "tight": + data = df._create_data_for_split_and_tight_to_dict( + are_all_object_dtype_cols, box_native_indices + ) + + return into_c( + ((("index", df.index.tolist()),) if index else ()) + + ( + ("columns", df.columns.tolist()), + ( + "data", + [ + list(map(maybe_box_native, t)) + for t in df.itertuples(index=False, name=None) + ], + ), + ) + + ((("index_names", list(df.index.names)),) if index else ()) + + (("column_names", list(df.columns.names)),) + ) + + elif orient == "records": + columns = df.columns.tolist() + if are_all_object_dtype_cols: + rows = ( + dict(zip(columns, row)) for row in df.itertuples(index=False, name=None) + ) + return [ + into_c((k, maybe_box_native(v)) for k, v in row.items()) for row in rows + ] + else: + data = [ + into_c(zip(columns, t)) for t in df.itertuples(index=False, name=None) + ] + if box_native_indices: + object_dtype_indices_as_set = set(box_native_indices) + object_dtype_cols = { + col + for i, col in enumerate(df.columns) + if i in object_dtype_indices_as_set + } + for row in data: + for col in object_dtype_cols: + row[col] = maybe_box_native(row[col]) + return data + + elif orient == "index": + if not df.index.is_unique: + raise ValueError("DataFrame index must be unique for orient='index'.") + columns = df.columns.tolist() + if are_all_object_dtype_cols: + return into_c( + (t[0], dict(zip(df.columns, map(maybe_box_native, t[1:])))) + for t in df.itertuples(name=None) + ) + elif box_native_indices: + object_dtype_indices_as_set = set(box_native_indices) + is_object_dtype_by_index = [ + i in object_dtype_indices_as_set for i in range(len(df.columns)) + ] + return into_c( + ( + t[0], + { + columns[i]: maybe_box_native(v) + if is_object_dtype_by_index[i] + else v + for i, v in enumerate(t[1:]) + }, + ) + for t in df.itertuples(name=None) + ) + else: + return into_c( + (t[0], dict(zip(df.columns, t[1:]))) for t in df.itertuples(name=None) + ) + + else: + raise ValueError(f"orient '{orient}' not understood") diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ae889a7fdbc24935c0884e8bbcdf56bde8946460 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/__init__.py @@ -0,0 +1,93 @@ +""" +Arithmetic operations for PandasObjects + +This is not a public API. +""" +from __future__ import annotations + +from pandas.core.ops.array_ops import ( + arithmetic_op, + comp_method_OBJECT_ARRAY, + comparison_op, + fill_binop, + get_array_op, + logical_op, + maybe_prepare_scalar_for_op, +) +from pandas.core.ops.common import ( + get_op_result_name, + unpack_zerodim_and_defer, +) +from pandas.core.ops.docstrings import make_flex_doc +from pandas.core.ops.invalid import invalid_comparison +from pandas.core.ops.mask_ops import ( + kleene_and, + kleene_or, + kleene_xor, +) +from pandas.core.roperator import ( + radd, + rand_, + rdiv, + rdivmod, + rfloordiv, + rmod, + rmul, + ror_, + rpow, + rsub, + rtruediv, + rxor, +) + +# ----------------------------------------------------------------------------- +# constants +ARITHMETIC_BINOPS: set[str] = { + "add", + "sub", + "mul", + "pow", + "mod", + "floordiv", + "truediv", + "divmod", + "radd", + "rsub", + "rmul", + "rpow", + "rmod", + "rfloordiv", + "rtruediv", + "rdivmod", +} + + +__all__ = [ + "ARITHMETIC_BINOPS", + "arithmetic_op", + "comparison_op", + "comp_method_OBJECT_ARRAY", + "invalid_comparison", + "fill_binop", + "kleene_and", + "kleene_or", + "kleene_xor", + "logical_op", + "make_flex_doc", + "radd", + "rand_", + "rdiv", + "rdivmod", + "rfloordiv", + "rmod", + "rmul", + "ror_", + "rpow", + "rsub", + "rtruediv", + "rxor", + "unpack_zerodim_and_defer", + "get_op_result_name", + "maybe_prepare_scalar_for_op", + "get_array_op", +] diff --git 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a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/array_ops.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/array_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..4b762a359d321ea61660e78ec63d392f8436939d --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/array_ops.py @@ -0,0 +1,604 @@ +""" +Functions for arithmetic and comparison operations on NumPy arrays and +ExtensionArrays. +""" +from __future__ import annotations + +import datetime +from functools import partial +import operator +from typing import ( + TYPE_CHECKING, + Any, +) +import warnings + +import numpy as np + +from pandas._libs import ( + NaT, + Timedelta, + Timestamp, + lib, + ops as libops, +) +from pandas._libs.tslibs import ( + BaseOffset, + get_supported_dtype, + is_supported_dtype, + is_unitless, +) +from pandas.util._exceptions import find_stack_level + +from pandas.core.dtypes.cast import ( + construct_1d_object_array_from_listlike, + find_common_type, +) +from pandas.core.dtypes.common import ( + ensure_object, + is_bool_dtype, + is_list_like, + is_numeric_v_string_like, + is_object_dtype, + is_scalar, +) +from pandas.core.dtypes.generic import ( + ABCExtensionArray, + ABCIndex, + ABCSeries, +) +from pandas.core.dtypes.missing import ( + isna, + notna, +) + +from pandas.core import roperator +from pandas.core.computation import expressions +from pandas.core.construction import ensure_wrapped_if_datetimelike +from pandas.core.ops import missing +from pandas.core.ops.dispatch import should_extension_dispatch +from pandas.core.ops.invalid import invalid_comparison + +if TYPE_CHECKING: + from pandas._typing import ( + ArrayLike, + Shape, + ) + +# ----------------------------------------------------------------------------- +# Masking NA values and fallbacks for operations numpy does not support + + +def fill_binop(left, right, fill_value): + """ + If a non-None fill_value is given, replace null entries in left and right + with this value, but only in positions where _one_ of left/right is null, + not both. + + Parameters + ---------- + left : array-like + right : array-like + fill_value : object + + Returns + ------- + left : array-like + right : array-like + + Notes + ----- + Makes copies if fill_value is not None and NAs are present. + """ + if fill_value is not None: + left_mask = isna(left) + right_mask = isna(right) + + # one but not both + mask = left_mask ^ right_mask + + if left_mask.any(): + # Avoid making a copy if we can + left = left.copy() + left[left_mask & mask] = fill_value + + if right_mask.any(): + # Avoid making a copy if we can + right = right.copy() + right[right_mask & mask] = fill_value + + return left, right + + +def comp_method_OBJECT_ARRAY(op, x, y): + if isinstance(y, list): + # e.g. test_tuple_categories + y = construct_1d_object_array_from_listlike(y) + + if isinstance(y, (np.ndarray, ABCSeries, ABCIndex)): + if not is_object_dtype(y.dtype): + y = y.astype(np.object_) + + if isinstance(y, (ABCSeries, ABCIndex)): + y = y._values + + if x.shape != y.shape: + raise ValueError("Shapes must match", x.shape, y.shape) + result = libops.vec_compare(x.ravel(), y.ravel(), op) + else: + result = libops.scalar_compare(x.ravel(), y, op) + return result.reshape(x.shape) + + +def _masked_arith_op(x: np.ndarray, y, op): + """ + If the given arithmetic operation fails, attempt it again on + only the non-null elements of the input array(s). + + Parameters + ---------- + x : np.ndarray + y : np.ndarray, Series, Index + op : binary operator + """ + # For Series `x` is 1D so ravel() is a no-op; calling it anyway makes + # the logic valid for both Series and DataFrame ops. + xrav = x.ravel() + + if isinstance(y, np.ndarray): + dtype = find_common_type([x.dtype, y.dtype]) + result = np.empty(x.size, dtype=dtype) + + if len(x) != len(y): + raise ValueError(x.shape, y.shape) + ymask = notna(y) + + # NB: ravel() is only safe since y is ndarray; for e.g. PeriodIndex + # we would get int64 dtype, see GH#19956 + yrav = y.ravel() + mask = notna(xrav) & ymask.ravel() + + # See GH#5284, GH#5035, GH#19448 for historical reference + if mask.any(): + result[mask] = op(xrav[mask], yrav[mask]) + + else: + if not is_scalar(y): + raise TypeError( + f"Cannot broadcast np.ndarray with operand of type { type(y) }" + ) + + # mask is only meaningful for x + result = np.empty(x.size, dtype=x.dtype) + mask = notna(xrav) + + # 1 ** np.nan is 1. So we have to unmask those. + if op is pow: + mask = np.where(x == 1, False, mask) + elif op is roperator.rpow: + mask = np.where(y == 1, False, mask) + + if mask.any(): + result[mask] = op(xrav[mask], y) + + np.putmask(result, ~mask, np.nan) + result = result.reshape(x.shape) # 2D compat + return result + + +def _na_arithmetic_op(left: np.ndarray, right, op, is_cmp: bool = False): + """ + Return the result of evaluating op on the passed in values. + + If native types are not compatible, try coercion to object dtype. + + Parameters + ---------- + left : np.ndarray + right : np.ndarray or scalar + Excludes DataFrame, Series, Index, ExtensionArray. + is_cmp : bool, default False + If this a comparison operation. + + Returns + ------- + array-like + + Raises + ------ + TypeError : invalid operation + """ + if isinstance(right, str): + # can never use numexpr + func = op + else: + func = partial(expressions.evaluate, op) + + try: + result = func(left, right) + except TypeError: + if not is_cmp and ( + left.dtype == object or getattr(right, "dtype", None) == object + ): + # For object dtype, fallback to a masked operation (only operating + # on the non-missing values) + # Don't do this for comparisons, as that will handle complex numbers + # incorrectly, see GH#32047 + result = _masked_arith_op(left, right, op) + else: + raise + + if is_cmp and (is_scalar(result) or result is NotImplemented): + # numpy returned a scalar instead of operating element-wise + # e.g. numeric array vs str + # TODO: can remove this after dropping some future numpy version? + return invalid_comparison(left, right, op) + + return missing.dispatch_fill_zeros(op, left, right, result) + + +def arithmetic_op(left: ArrayLike, right: Any, op): + """ + Evaluate an arithmetic operation `+`, `-`, `*`, `/`, `//`, `%`, `**`, ... + + Note: the caller is responsible for ensuring that numpy warnings are + suppressed (with np.errstate(all="ignore")) if needed. + + Parameters + ---------- + left : np.ndarray or ExtensionArray + right : object + Cannot be a DataFrame or Index. Series is *not* excluded. + op : {operator.add, operator.sub, ...} + Or one of the reversed variants from roperator. + + Returns + ------- + ndarray or ExtensionArray + Or a 2-tuple of these in the case of divmod or rdivmod. + """ + # NB: We assume that extract_array and ensure_wrapped_if_datetimelike + # have already been called on `left` and `right`, + # and `maybe_prepare_scalar_for_op` has already been called on `right` + # We need to special-case datetime64/timedelta64 dtypes (e.g. because numpy + # casts integer dtypes to timedelta64 when operating with timedelta64 - GH#22390) + + if ( + should_extension_dispatch(left, right) + or isinstance(right, (Timedelta, BaseOffset, Timestamp)) + or right is NaT + ): + # Timedelta/Timestamp and other custom scalars are included in the check + # because numexpr will fail on it, see GH#31457 + res_values = op(left, right) + else: + # TODO we should handle EAs consistently and move this check before the if/else + # (https://github.com/pandas-dev/pandas/issues/41165) + # error: Argument 2 to "_bool_arith_check" has incompatible type + # "Union[ExtensionArray, ndarray[Any, Any]]"; expected "ndarray[Any, Any]" + _bool_arith_check(op, left, right) # type: ignore[arg-type] + + # error: Argument 1 to "_na_arithmetic_op" has incompatible type + # "Union[ExtensionArray, ndarray[Any, Any]]"; expected "ndarray[Any, Any]" + res_values = _na_arithmetic_op(left, right, op) # type: ignore[arg-type] + + return res_values + + +def comparison_op(left: ArrayLike, right: Any, op) -> ArrayLike: + """ + Evaluate a comparison operation `=`, `!=`, `>=`, `>`, `<=`, or `<`. + + Note: the caller is responsible for ensuring that numpy warnings are + suppressed (with np.errstate(all="ignore")) if needed. + + Parameters + ---------- + left : np.ndarray or ExtensionArray + right : object + Cannot be a DataFrame, Series, or Index. + op : {operator.eq, operator.ne, operator.gt, operator.ge, operator.lt, operator.le} + + Returns + ------- + ndarray or ExtensionArray + """ + # NB: We assume extract_array has already been called on left and right + lvalues = ensure_wrapped_if_datetimelike(left) + rvalues = ensure_wrapped_if_datetimelike(right) + + rvalues = lib.item_from_zerodim(rvalues) + if isinstance(rvalues, list): + # We don't catch tuple here bc we may be comparing e.g. MultiIndex + # to a tuple that represents a single entry, see test_compare_tuple_strs + rvalues = np.asarray(rvalues) + + if isinstance(rvalues, (np.ndarray, ABCExtensionArray)): + # TODO: make this treatment consistent across ops and classes. + # We are not catching all listlikes here (e.g. frozenset, tuple) + # The ambiguous case is object-dtype. See GH#27803 + if len(lvalues) != len(rvalues): + raise ValueError( + "Lengths must match to compare", lvalues.shape, rvalues.shape + ) + + if should_extension_dispatch(lvalues, rvalues) or ( + (isinstance(rvalues, (Timedelta, BaseOffset, Timestamp)) or right is NaT) + and lvalues.dtype != object + ): + # Call the method on lvalues + res_values = op(lvalues, rvalues) + + elif is_scalar(rvalues) and isna(rvalues): # TODO: but not pd.NA? + # numpy does not like comparisons vs None + if op is operator.ne: + res_values = np.ones(lvalues.shape, dtype=bool) + else: + res_values = np.zeros(lvalues.shape, dtype=bool) + + elif is_numeric_v_string_like(lvalues, rvalues): + # GH#36377 going through the numexpr path would incorrectly raise + return invalid_comparison(lvalues, rvalues, op) + + elif lvalues.dtype == object or isinstance(rvalues, str): + res_values = comp_method_OBJECT_ARRAY(op, lvalues, rvalues) + + else: + res_values = _na_arithmetic_op(lvalues, rvalues, op, is_cmp=True) + + return res_values + + +def na_logical_op(x: np.ndarray, y, op): + try: + # For exposition, write: + # yarr = isinstance(y, np.ndarray) + # yint = is_integer(y) or (yarr and y.dtype.kind == "i") + # ybool = is_bool(y) or (yarr and y.dtype.kind == "b") + # xint = x.dtype.kind == "i" + # xbool = x.dtype.kind == "b" + # Then Cases where this goes through without raising include: + # (xint or xbool) and (yint or bool) + result = op(x, y) + except TypeError: + if isinstance(y, np.ndarray): + # bool-bool dtype operations should be OK, should not get here + assert not (x.dtype.kind == "b" and y.dtype.kind == "b") + x = ensure_object(x) + y = ensure_object(y) + result = libops.vec_binop(x.ravel(), y.ravel(), op) + else: + # let null fall thru + assert lib.is_scalar(y) + if not isna(y): + y = bool(y) + try: + result = libops.scalar_binop(x, y, op) + except ( + TypeError, + ValueError, + AttributeError, + OverflowError, + NotImplementedError, + ) as err: + typ = type(y).__name__ + raise TypeError( + f"Cannot perform '{op.__name__}' with a dtyped [{x.dtype}] array " + f"and scalar of type [{typ}]" + ) from err + + return result.reshape(x.shape) + + +def logical_op(left: ArrayLike, right: Any, op) -> ArrayLike: + """ + Evaluate a logical operation `|`, `&`, or `^`. + + Parameters + ---------- + left : np.ndarray or ExtensionArray + right : object + Cannot be a DataFrame, Series, or Index. + op : {operator.and_, operator.or_, operator.xor} + Or one of the reversed variants from roperator. + + Returns + ------- + ndarray or ExtensionArray + """ + + def fill_bool(x, left=None): + # if `left` is specifically not-boolean, we do not cast to bool + if x.dtype.kind in "cfO": + # dtypes that can hold NA + mask = isna(x) + if mask.any(): + x = x.astype(object) + x[mask] = False + + if left is None or left.dtype.kind == "b": + x = x.astype(bool) + return x + + right = lib.item_from_zerodim(right) + if is_list_like(right) and not hasattr(right, "dtype"): + # e.g. list, tuple + warnings.warn( + "Logical ops (and, or, xor) between Pandas objects and dtype-less " + "sequences (e.g. list, tuple) are deprecated and will raise in a " + "future version. Wrap the object in a Series, Index, or np.array " + "before operating instead.", + FutureWarning, + stacklevel=find_stack_level(), + ) + right = construct_1d_object_array_from_listlike(right) + + # NB: We assume extract_array has already been called on left and right + lvalues = ensure_wrapped_if_datetimelike(left) + rvalues = right + + if should_extension_dispatch(lvalues, rvalues): + # Call the method on lvalues + res_values = op(lvalues, rvalues) + + else: + if isinstance(rvalues, np.ndarray): + is_other_int_dtype = rvalues.dtype.kind in "iu" + if not is_other_int_dtype: + rvalues = fill_bool(rvalues, lvalues) + + else: + # i.e. scalar + is_other_int_dtype = lib.is_integer(rvalues) + + res_values = na_logical_op(lvalues, rvalues, op) + + # For int vs int `^`, `|`, `&` are bitwise operators and return + # integer dtypes. Otherwise these are boolean ops + if not (left.dtype.kind in "iu" and is_other_int_dtype): + res_values = fill_bool(res_values) + + return res_values + + +def get_array_op(op): + """ + Return a binary array operation corresponding to the given operator op. + + Parameters + ---------- + op : function + Binary operator from operator or roperator module. + + Returns + ------- + functools.partial + """ + if isinstance(op, partial): + # We get here via dispatch_to_series in DataFrame case + # e.g. test_rolling_consistency_var_debiasing_factors + return op + + op_name = op.__name__.strip("_").lstrip("r") + if op_name == "arith_op": + # Reached via DataFrame._combine_frame i.e. flex methods + # e.g. test_df_add_flex_filled_mixed_dtypes + return op + + if op_name in {"eq", "ne", "lt", "le", "gt", "ge"}: + return partial(comparison_op, op=op) + elif op_name in {"and", "or", "xor", "rand", "ror", "rxor"}: + return partial(logical_op, op=op) + elif op_name in { + "add", + "sub", + "mul", + "truediv", + "floordiv", + "mod", + "divmod", + "pow", + }: + return partial(arithmetic_op, op=op) + else: + raise NotImplementedError(op_name) + + +def maybe_prepare_scalar_for_op(obj, shape: Shape): + """ + Cast non-pandas objects to pandas types to unify behavior of arithmetic + and comparison operations. + + Parameters + ---------- + obj: object + shape : tuple[int] + + Returns + ------- + out : object + + Notes + ----- + Be careful to call this *after* determining the `name` attribute to be + attached to the result of the arithmetic operation. + """ + if type(obj) is datetime.timedelta: + # GH#22390 cast up to Timedelta to rely on Timedelta + # implementation; otherwise operation against numeric-dtype + # raises TypeError + return Timedelta(obj) + elif type(obj) is datetime.datetime: + # cast up to Timestamp to rely on Timestamp implementation, see Timedelta above + return Timestamp(obj) + elif isinstance(obj, np.datetime64): + # GH#28080 numpy casts integer-dtype to datetime64 when doing + # array[int] + datetime64, which we do not allow + if isna(obj): + from pandas.core.arrays import DatetimeArray + + # Avoid possible ambiguities with pd.NaT + # GH 52295 + if is_unitless(obj.dtype): + obj = obj.astype("datetime64[ns]") + elif not is_supported_dtype(obj.dtype): + new_dtype = get_supported_dtype(obj.dtype) + obj = obj.astype(new_dtype) + right = np.broadcast_to(obj, shape) + return DatetimeArray._simple_new(right, dtype=right.dtype) + + return Timestamp(obj) + + elif isinstance(obj, np.timedelta64): + if isna(obj): + from pandas.core.arrays import TimedeltaArray + + # wrapping timedelta64("NaT") in Timedelta returns NaT, + # which would incorrectly be treated as a datetime-NaT, so + # we broadcast and wrap in a TimedeltaArray + # GH 52295 + if is_unitless(obj.dtype): + obj = obj.astype("timedelta64[ns]") + elif not is_supported_dtype(obj.dtype): + new_dtype = get_supported_dtype(obj.dtype) + obj = obj.astype(new_dtype) + right = np.broadcast_to(obj, shape) + return TimedeltaArray._simple_new(right, dtype=right.dtype) + + # In particular non-nanosecond timedelta64 needs to be cast to + # nanoseconds, or else we get undesired behavior like + # np.timedelta64(3, 'D') / 2 == np.timedelta64(1, 'D') + return Timedelta(obj) + + # We want NumPy numeric scalars to behave like Python scalars + # post NEP 50 + elif isinstance(obj, np.integer): + return int(obj) + + elif isinstance(obj, np.floating): + return float(obj) + + return obj + + +_BOOL_OP_NOT_ALLOWED = { + operator.truediv, + roperator.rtruediv, + operator.floordiv, + roperator.rfloordiv, + operator.pow, + roperator.rpow, +} + + +def _bool_arith_check(op, a: np.ndarray, b): + """ + In contrast to numpy, pandas raises an error for certain operations + with booleans. + """ + if op in _BOOL_OP_NOT_ALLOWED: + if a.dtype.kind == "b" and (is_bool_dtype(b) or lib.is_bool(b)): + op_name = op.__name__.strip("_").lstrip("r") + raise NotImplementedError( + f"operator '{op_name}' not implemented for bool dtypes" + ) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/common.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/common.py new file mode 100644 index 0000000000000000000000000000000000000000..559977bacf881552d546e7704d4cf4b12b4a32fe --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/common.py @@ -0,0 +1,146 @@ +""" +Boilerplate functions used in defining binary operations. +""" +from __future__ import annotations + +from functools import wraps +from typing import ( + TYPE_CHECKING, + Callable, +) + +from pandas._libs.lib import item_from_zerodim +from pandas._libs.missing import is_matching_na + +from pandas.core.dtypes.generic import ( + ABCIndex, + ABCSeries, +) + +if TYPE_CHECKING: + from pandas._typing import F + + +def unpack_zerodim_and_defer(name: str) -> Callable[[F], F]: + """ + Boilerplate for pandas conventions in arithmetic and comparison methods. + + Parameters + ---------- + name : str + + Returns + ------- + decorator + """ + + def wrapper(method: F) -> F: + return _unpack_zerodim_and_defer(method, name) + + return wrapper + + +def _unpack_zerodim_and_defer(method, name: str): + """ + Boilerplate for pandas conventions in arithmetic and comparison methods. + + Ensure method returns NotImplemented when operating against "senior" + classes. Ensure zero-dimensional ndarrays are always unpacked. + + Parameters + ---------- + method : binary method + name : str + + Returns + ------- + method + """ + stripped_name = name.removeprefix("__").removesuffix("__") + is_cmp = stripped_name in {"eq", "ne", "lt", "le", "gt", "ge"} + + @wraps(method) + def new_method(self, other): + if is_cmp and isinstance(self, ABCIndex) and isinstance(other, ABCSeries): + # For comparison ops, Index does *not* defer to Series + pass + else: + prio = getattr(other, "__pandas_priority__", None) + if prio is not None: + if prio > self.__pandas_priority__: + # e.g. other is DataFrame while self is Index/Series/EA + return NotImplemented + + other = item_from_zerodim(other) + + return method(self, other) + + return new_method + + +def get_op_result_name(left, right): + """ + Find the appropriate name to pin to an operation result. This result + should always be either an Index or a Series. + + Parameters + ---------- + left : {Series, Index} + right : object + + Returns + ------- + name : object + Usually a string + """ + if isinstance(right, (ABCSeries, ABCIndex)): + name = _maybe_match_name(left, right) + else: + name = left.name + return name + + +def _maybe_match_name(a, b): + """ + Try to find a name to attach to the result of an operation between + a and b. If only one of these has a `name` attribute, return that + name. Otherwise return a consensus name if they match or None if + they have different names. + + Parameters + ---------- + a : object + b : object + + Returns + ------- + name : str or None + + See Also + -------- + pandas.core.common.consensus_name_attr + """ + a_has = hasattr(a, "name") + b_has = hasattr(b, "name") + if a_has and b_has: + try: + if a.name == b.name: + return a.name + elif is_matching_na(a.name, b.name): + # e.g. both are np.nan + return a.name + else: + return None + except TypeError: + # pd.NA + if is_matching_na(a.name, b.name): + return a.name + return None + except ValueError: + # e.g. np.int64(1) vs (np.int64(1), np.int64(2)) + return None + elif a_has: + return a.name + elif b_has: + return b.name + return None diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/dispatch.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/dispatch.py new file mode 100644 index 0000000000000000000000000000000000000000..a939fdd3d041e9f99dde7ea40fd7aa0572d0d9b7 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/dispatch.py @@ -0,0 +1,30 @@ +""" +Functions for defining unary operations. +""" +from __future__ import annotations + +from typing import ( + TYPE_CHECKING, + Any, +) + +from pandas.core.dtypes.generic import ABCExtensionArray + +if TYPE_CHECKING: + from pandas._typing import ArrayLike + + +def should_extension_dispatch(left: ArrayLike, right: Any) -> bool: + """ + Identify cases where Series operation should dispatch to ExtensionArray method. + + Parameters + ---------- + left : np.ndarray or ExtensionArray + right : object + + Returns + ------- + bool + """ + return isinstance(left, ABCExtensionArray) or isinstance(right, ABCExtensionArray) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/docstrings.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/docstrings.py new file mode 100644 index 0000000000000000000000000000000000000000..bd2e532536d8491af44631e52982217a04ef5b17 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/docstrings.py @@ -0,0 +1,772 @@ +""" +Templating for ops docstrings +""" +from __future__ import annotations + + +def make_flex_doc(op_name: str, typ: str) -> str: + """ + Make the appropriate substitutions for the given operation and class-typ + into either _flex_doc_SERIES or _flex_doc_FRAME to return the docstring + to attach to a generated method. + + Parameters + ---------- + op_name : str {'__add__', '__sub__', ... '__eq__', '__ne__', ...} + typ : str {series, 'dataframe']} + + Returns + ------- + doc : str + """ + op_name = op_name.replace("__", "") + op_desc = _op_descriptions[op_name] + + op_desc_op = op_desc["op"] + assert op_desc_op is not None # for mypy + if op_name.startswith("r"): + equiv = f"other {op_desc_op} {typ}" + elif op_name == "divmod": + equiv = f"{op_name}({typ}, other)" + else: + equiv = f"{typ} {op_desc_op} other" + + if typ == "series": + base_doc = _flex_doc_SERIES + if op_desc["reverse"]: + base_doc += _see_also_reverse_SERIES.format( + reverse=op_desc["reverse"], see_also_desc=op_desc["see_also_desc"] + ) + doc_no_examples = base_doc.format( + desc=op_desc["desc"], + op_name=op_name, + equiv=equiv, + series_returns=op_desc["series_returns"], + ) + ser_example = op_desc["series_examples"] + if ser_example: + doc = doc_no_examples + ser_example + else: + doc = doc_no_examples + elif typ == "dataframe": + if op_name in ["eq", "ne", "le", "lt", "ge", "gt"]: + base_doc = _flex_comp_doc_FRAME + doc = _flex_comp_doc_FRAME.format( + op_name=op_name, + desc=op_desc["desc"], + ) + else: + base_doc = _flex_doc_FRAME + doc = base_doc.format( + desc=op_desc["desc"], + op_name=op_name, + equiv=equiv, + reverse=op_desc["reverse"], + ) + else: + raise AssertionError("Invalid typ argument.") + return doc + + +_common_examples_algebra_SERIES = """ +Examples +-------- +>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) +>>> a +a 1.0 +b 1.0 +c 1.0 +d NaN +dtype: float64 +>>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) +>>> b +a 1.0 +b NaN +d 1.0 +e NaN +dtype: float64""" + +_common_examples_comparison_SERIES = """ +Examples +-------- +>>> a = pd.Series([1, 1, 1, np.nan, 1], index=['a', 'b', 'c', 'd', 'e']) +>>> a +a 1.0 +b 1.0 +c 1.0 +d NaN +e 1.0 +dtype: float64 +>>> b = pd.Series([0, 1, 2, np.nan, 1], index=['a', 'b', 'c', 'd', 'f']) +>>> b +a 0.0 +b 1.0 +c 2.0 +d NaN +f 1.0 +dtype: float64""" + +_add_example_SERIES = ( + _common_examples_algebra_SERIES + + """ +>>> a.add(b, fill_value=0) +a 2.0 +b 1.0 +c 1.0 +d 1.0 +e NaN +dtype: float64 +""" +) + +_sub_example_SERIES = ( + _common_examples_algebra_SERIES + + """ +>>> a.subtract(b, fill_value=0) +a 0.0 +b 1.0 +c 1.0 +d -1.0 +e NaN +dtype: float64 +""" +) + +_mul_example_SERIES = ( + _common_examples_algebra_SERIES + + """ +>>> a.multiply(b, fill_value=0) +a 1.0 +b 0.0 +c 0.0 +d 0.0 +e NaN +dtype: float64 +""" +) + +_div_example_SERIES = ( + _common_examples_algebra_SERIES + + """ +>>> a.divide(b, fill_value=0) +a 1.0 +b inf +c inf +d 0.0 +e NaN +dtype: float64 +""" +) + +_floordiv_example_SERIES = ( + _common_examples_algebra_SERIES + + """ +>>> a.floordiv(b, fill_value=0) +a 1.0 +b inf +c inf +d 0.0 +e NaN +dtype: float64 +""" +) + +_divmod_example_SERIES = ( + _common_examples_algebra_SERIES + + """ +>>> a.divmod(b, fill_value=0) +(a 1.0 + b inf + c inf + d 0.0 + e NaN + dtype: float64, + a 0.0 + b NaN + c NaN + d 0.0 + e NaN + dtype: float64) +""" +) + +_mod_example_SERIES = ( + _common_examples_algebra_SERIES + + """ +>>> a.mod(b, fill_value=0) +a 0.0 +b NaN +c NaN +d 0.0 +e NaN +dtype: float64 +""" +) +_pow_example_SERIES = ( + _common_examples_algebra_SERIES + + """ +>>> a.pow(b, fill_value=0) +a 1.0 +b 1.0 +c 1.0 +d 0.0 +e NaN +dtype: float64 +""" +) + +_ne_example_SERIES = ( + _common_examples_algebra_SERIES + + """ +>>> a.ne(b, fill_value=0) +a False +b True +c True +d True +e True +dtype: bool +""" +) + +_eq_example_SERIES = ( + _common_examples_algebra_SERIES + + """ +>>> a.eq(b, fill_value=0) +a True +b False +c False +d False +e False +dtype: bool +""" +) + +_lt_example_SERIES = ( + _common_examples_comparison_SERIES + + """ +>>> a.lt(b, fill_value=0) +a False +b False +c True +d False +e False +f True +dtype: bool +""" +) + +_le_example_SERIES = ( + _common_examples_comparison_SERIES + + """ +>>> a.le(b, fill_value=0) +a False +b True +c True +d False +e False +f True +dtype: bool +""" +) + +_gt_example_SERIES = ( + _common_examples_comparison_SERIES + + """ +>>> a.gt(b, fill_value=0) +a True +b False +c False +d False +e True +f False +dtype: bool +""" +) + +_ge_example_SERIES = ( + _common_examples_comparison_SERIES + + """ +>>> a.ge(b, fill_value=0) +a True +b True +c False +d False +e True +f False +dtype: bool +""" +) + +_returns_series = """Series\n The result of the operation.""" + +_returns_tuple = """2-Tuple of Series\n The result of the operation.""" + +_op_descriptions: dict[str, dict[str, str | None]] = { + # Arithmetic Operators + "add": { + "op": "+", + "desc": "Addition", + "reverse": "radd", + "series_examples": _add_example_SERIES, + "series_returns": _returns_series, + }, + "sub": { + "op": "-", + "desc": "Subtraction", + "reverse": "rsub", + "series_examples": _sub_example_SERIES, + "series_returns": _returns_series, + }, + "mul": { + "op": "*", + "desc": "Multiplication", + "reverse": "rmul", + "series_examples": _mul_example_SERIES, + "series_returns": _returns_series, + "df_examples": None, + }, + "mod": { + "op": "%", + "desc": "Modulo", + "reverse": "rmod", + "series_examples": _mod_example_SERIES, + "series_returns": _returns_series, + }, + "pow": { + "op": "**", + "desc": "Exponential power", + "reverse": "rpow", + "series_examples": _pow_example_SERIES, + "series_returns": _returns_series, + "df_examples": None, + }, + "truediv": { + "op": "/", + "desc": "Floating division", + "reverse": "rtruediv", + "series_examples": _div_example_SERIES, + "series_returns": _returns_series, + "df_examples": None, + }, + "floordiv": { + "op": "//", + "desc": "Integer division", + "reverse": "rfloordiv", + "series_examples": _floordiv_example_SERIES, + "series_returns": _returns_series, + "df_examples": None, + }, + "divmod": { + "op": "divmod", + "desc": "Integer division and modulo", + "reverse": "rdivmod", + "series_examples": _divmod_example_SERIES, + "series_returns": _returns_tuple, + "df_examples": None, + }, + # Comparison Operators + "eq": { + "op": "==", + "desc": "Equal to", + "reverse": None, + "series_examples": _eq_example_SERIES, + "series_returns": _returns_series, + }, + "ne": { + "op": "!=", + "desc": "Not equal to", + "reverse": None, + "series_examples": _ne_example_SERIES, + "series_returns": _returns_series, + }, + "lt": { + "op": "<", + "desc": "Less than", + "reverse": None, + "series_examples": _lt_example_SERIES, + "series_returns": _returns_series, + }, + "le": { + "op": "<=", + "desc": "Less than or equal to", + "reverse": None, + "series_examples": _le_example_SERIES, + "series_returns": _returns_series, + }, + "gt": { + "op": ">", + "desc": "Greater than", + "reverse": None, + "series_examples": _gt_example_SERIES, + "series_returns": _returns_series, + }, + "ge": { + "op": ">=", + "desc": "Greater than or equal to", + "reverse": None, + "series_examples": _ge_example_SERIES, + "series_returns": _returns_series, + }, +} + +_py_num_ref = """see + `Python documentation + `_ + for more details""" +_op_names = list(_op_descriptions.keys()) +for key in _op_names: + reverse_op = _op_descriptions[key]["reverse"] + if reverse_op is not None: + _op_descriptions[reverse_op] = _op_descriptions[key].copy() + _op_descriptions[reverse_op]["reverse"] = key + _op_descriptions[key][ + "see_also_desc" + ] = f"Reverse of the {_op_descriptions[key]['desc']} operator, {_py_num_ref}" + _op_descriptions[reverse_op][ + "see_also_desc" + ] = f"Element-wise {_op_descriptions[key]['desc']}, {_py_num_ref}" + +_flex_doc_SERIES = """ +Return {desc} of series and other, element-wise (binary operator `{op_name}`). + +Equivalent to ``{equiv}``, but with support to substitute a fill_value for +missing data in either one of the inputs. + +Parameters +---------- +other : Series or scalar value +level : int or name + Broadcast across a level, matching Index values on the + passed MultiIndex level. +fill_value : None or float value, default None (NaN) + Fill existing missing (NaN) values, and any new element needed for + successful Series alignment, with this value before computation. + If data in both corresponding Series locations is missing + the result of filling (at that location) will be missing. +axis : {{0 or 'index'}} + Unused. Parameter needed for compatibility with DataFrame. + +Returns +------- +{series_returns} +""" + +_see_also_reverse_SERIES = """ +See Also +-------- +Series.{reverse} : {see_also_desc}. +""" + +_flex_doc_FRAME = """ +Get {desc} of dataframe and other, element-wise (binary operator `{op_name}`). + +Equivalent to ``{equiv}``, but with support to substitute a fill_value +for missing data in one of the inputs. With reverse version, `{reverse}`. + +Among flexible wrappers (`add`, `sub`, `mul`, `div`, `floordiv`, `mod`, `pow`) to +arithmetic operators: `+`, `-`, `*`, `/`, `//`, `%`, `**`. + +Parameters +---------- +other : scalar, sequence, Series, dict or DataFrame + Any single or multiple element data structure, or list-like object. +axis : {{0 or 'index', 1 or 'columns'}} + Whether to compare by the index (0 or 'index') or columns. + (1 or 'columns'). For Series input, axis to match Series index on. +level : int or label + Broadcast across a level, matching Index values on the + passed MultiIndex level. +fill_value : float or None, default None + Fill existing missing (NaN) values, and any new element needed for + successful DataFrame alignment, with this value before computation. + If data in both corresponding DataFrame locations is missing + the result will be missing. + +Returns +------- +DataFrame + Result of the arithmetic operation. + +See Also +-------- +DataFrame.add : Add DataFrames. +DataFrame.sub : Subtract DataFrames. +DataFrame.mul : Multiply DataFrames. +DataFrame.div : Divide DataFrames (float division). +DataFrame.truediv : Divide DataFrames (float division). +DataFrame.floordiv : Divide DataFrames (integer division). +DataFrame.mod : Calculate modulo (remainder after division). +DataFrame.pow : Calculate exponential power. + +Notes +----- +Mismatched indices will be unioned together. + +Examples +-------- +>>> df = pd.DataFrame({{'angles': [0, 3, 4], +... 'degrees': [360, 180, 360]}}, +... index=['circle', 'triangle', 'rectangle']) +>>> df + angles degrees +circle 0 360 +triangle 3 180 +rectangle 4 360 + +Add a scalar with operator version which return the same +results. + +>>> df + 1 + angles degrees +circle 1 361 +triangle 4 181 +rectangle 5 361 + +>>> df.add(1) + angles degrees +circle 1 361 +triangle 4 181 +rectangle 5 361 + +Divide by constant with reverse version. + +>>> df.div(10) + angles degrees +circle 0.0 36.0 +triangle 0.3 18.0 +rectangle 0.4 36.0 + +>>> df.rdiv(10) + angles degrees +circle inf 0.027778 +triangle 3.333333 0.055556 +rectangle 2.500000 0.027778 + +Subtract a list and Series by axis with operator version. + +>>> df - [1, 2] + angles degrees +circle -1 358 +triangle 2 178 +rectangle 3 358 + +>>> df.sub([1, 2], axis='columns') + angles degrees +circle -1 358 +triangle 2 178 +rectangle 3 358 + +>>> df.sub(pd.Series([1, 1, 1], index=['circle', 'triangle', 'rectangle']), +... axis='index') + angles degrees +circle -1 359 +triangle 2 179 +rectangle 3 359 + +Multiply a dictionary by axis. + +>>> df.mul({{'angles': 0, 'degrees': 2}}) + angles degrees +circle 0 720 +triangle 0 360 +rectangle 0 720 + +>>> df.mul({{'circle': 0, 'triangle': 2, 'rectangle': 3}}, axis='index') + angles degrees +circle 0 0 +triangle 6 360 +rectangle 12 1080 + +Multiply a DataFrame of different shape with operator version. + +>>> other = pd.DataFrame({{'angles': [0, 3, 4]}}, +... index=['circle', 'triangle', 'rectangle']) +>>> other + angles +circle 0 +triangle 3 +rectangle 4 + +>>> df * other + angles degrees +circle 0 NaN +triangle 9 NaN +rectangle 16 NaN + +>>> df.mul(other, fill_value=0) + angles degrees +circle 0 0.0 +triangle 9 0.0 +rectangle 16 0.0 + +Divide by a MultiIndex by level. + +>>> df_multindex = pd.DataFrame({{'angles': [0, 3, 4, 4, 5, 6], +... 'degrees': [360, 180, 360, 360, 540, 720]}}, +... index=[['A', 'A', 'A', 'B', 'B', 'B'], +... ['circle', 'triangle', 'rectangle', +... 'square', 'pentagon', 'hexagon']]) +>>> df_multindex + angles degrees +A circle 0 360 + triangle 3 180 + rectangle 4 360 +B square 4 360 + pentagon 5 540 + hexagon 6 720 + +>>> df.div(df_multindex, level=1, fill_value=0) + angles degrees +A circle NaN 1.0 + triangle 1.0 1.0 + rectangle 1.0 1.0 +B square 0.0 0.0 + pentagon 0.0 0.0 + hexagon 0.0 0.0 +""" + +_flex_comp_doc_FRAME = """ +Get {desc} of dataframe and other, element-wise (binary operator `{op_name}`). + +Among flexible wrappers (`eq`, `ne`, `le`, `lt`, `ge`, `gt`) to comparison +operators. + +Equivalent to `==`, `!=`, `<=`, `<`, `>=`, `>` with support to choose axis +(rows or columns) and level for comparison. + +Parameters +---------- +other : scalar, sequence, Series, or DataFrame + Any single or multiple element data structure, or list-like object. +axis : {{0 or 'index', 1 or 'columns'}}, default 'columns' + Whether to compare by the index (0 or 'index') or columns + (1 or 'columns'). +level : int or label + Broadcast across a level, matching Index values on the passed + MultiIndex level. + +Returns +------- +DataFrame of bool + Result of the comparison. + +See Also +-------- +DataFrame.eq : Compare DataFrames for equality elementwise. +DataFrame.ne : Compare DataFrames for inequality elementwise. +DataFrame.le : Compare DataFrames for less than inequality + or equality elementwise. +DataFrame.lt : Compare DataFrames for strictly less than + inequality elementwise. +DataFrame.ge : Compare DataFrames for greater than inequality + or equality elementwise. +DataFrame.gt : Compare DataFrames for strictly greater than + inequality elementwise. + +Notes +----- +Mismatched indices will be unioned together. +`NaN` values are considered different (i.e. `NaN` != `NaN`). + +Examples +-------- +>>> df = pd.DataFrame({{'cost': [250, 150, 100], +... 'revenue': [100, 250, 300]}}, +... index=['A', 'B', 'C']) +>>> df + cost revenue +A 250 100 +B 150 250 +C 100 300 + +Comparison with a scalar, using either the operator or method: + +>>> df == 100 + cost revenue +A False True +B False False +C True False + +>>> df.eq(100) + cost revenue +A False True +B False False +C True False + +When `other` is a :class:`Series`, the columns of a DataFrame are aligned +with the index of `other` and broadcast: + +>>> df != pd.Series([100, 250], index=["cost", "revenue"]) + cost revenue +A True True +B True False +C False True + +Use the method to control the broadcast axis: + +>>> df.ne(pd.Series([100, 300], index=["A", "D"]), axis='index') + cost revenue +A True False +B True True +C True True +D True True + +When comparing to an arbitrary sequence, the number of columns must +match the number elements in `other`: + +>>> df == [250, 100] + cost revenue +A True True +B False False +C False False + +Use the method to control the axis: + +>>> df.eq([250, 250, 100], axis='index') + cost revenue +A True False +B False True +C True False + +Compare to a DataFrame of different shape. + +>>> other = pd.DataFrame({{'revenue': [300, 250, 100, 150]}}, +... index=['A', 'B', 'C', 'D']) +>>> other + revenue +A 300 +B 250 +C 100 +D 150 + +>>> df.gt(other) + cost revenue +A False False +B False False +C False True +D False False + +Compare to a MultiIndex by level. + +>>> df_multindex = pd.DataFrame({{'cost': [250, 150, 100, 150, 300, 220], +... 'revenue': [100, 250, 300, 200, 175, 225]}}, +... index=[['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2'], +... ['A', 'B', 'C', 'A', 'B', 'C']]) +>>> df_multindex + cost revenue +Q1 A 250 100 + B 150 250 + C 100 300 +Q2 A 150 200 + B 300 175 + C 220 225 + +>>> df.le(df_multindex, level=1) + cost revenue +Q1 A True True + B True True + C True True +Q2 A False True + B True False + C True False +""" diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/invalid.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/invalid.py new file mode 100644 index 0000000000000000000000000000000000000000..e5ae6d359ac2205b01706211382d116b29176c7a --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/invalid.py @@ -0,0 +1,62 @@ +""" +Templates for invalid operations. +""" +from __future__ import annotations + +import operator +from typing import TYPE_CHECKING + +import numpy as np + +if TYPE_CHECKING: + from pandas._typing import npt + + +def invalid_comparison(left, right, op) -> npt.NDArray[np.bool_]: + """ + If a comparison has mismatched types and is not necessarily meaningful, + follow python3 conventions by: + + - returning all-False for equality + - returning all-True for inequality + - raising TypeError otherwise + + Parameters + ---------- + left : array-like + right : scalar, array-like + op : operator.{eq, ne, lt, le, gt} + + Raises + ------ + TypeError : on inequality comparisons + """ + if op is operator.eq: + res_values = np.zeros(left.shape, dtype=bool) + elif op is operator.ne: + res_values = np.ones(left.shape, dtype=bool) + else: + typ = type(right).__name__ + raise TypeError(f"Invalid comparison between dtype={left.dtype} and {typ}") + return res_values + + +def make_invalid_op(name: str): + """ + Return a binary method that always raises a TypeError. + + Parameters + ---------- + name : str + + Returns + ------- + invalid_op : function + """ + + def invalid_op(self, other=None): + typ = type(self).__name__ + raise TypeError(f"cannot perform {name} with this index type: {typ}") + + invalid_op.__name__ = name + return invalid_op diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/mask_ops.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/mask_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..adc1f63c568bf579f31b13446f4614435d443df1 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/mask_ops.py @@ -0,0 +1,189 @@ +""" +Ops for masked arrays. +""" +from __future__ import annotations + +import numpy as np + +from pandas._libs import ( + lib, + missing as libmissing, +) + + +def kleene_or( + left: bool | np.ndarray | libmissing.NAType, + right: bool | np.ndarray | libmissing.NAType, + left_mask: np.ndarray | None, + right_mask: np.ndarray | None, +): + """ + Boolean ``or`` using Kleene logic. + + Values are NA where we have ``NA | NA`` or ``NA | False``. + ``NA | True`` is considered True. + + Parameters + ---------- + left, right : ndarray, NA, or bool + The values of the array. + left_mask, right_mask : ndarray, optional + The masks. Only one of these may be None, which implies that + the associated `left` or `right` value is a scalar. + + Returns + ------- + result, mask: ndarray[bool] + The result of the logical or, and the new mask. + """ + # To reduce the number of cases, we ensure that `left` & `left_mask` + # always come from an array, not a scalar. This is safe, since + # A | B == B | A + if left_mask is None: + return kleene_or(right, left, right_mask, left_mask) + + if not isinstance(left, np.ndarray): + raise TypeError("Either `left` or `right` need to be a np.ndarray.") + + raise_for_nan(right, method="or") + + if right is libmissing.NA: + result = left.copy() + else: + result = left | right + + if right_mask is not None: + # output is unknown where (False & NA), (NA & False), (NA & NA) + left_false = ~(left | left_mask) + right_false = ~(right | right_mask) + mask = ( + (left_false & right_mask) + | (right_false & left_mask) + | (left_mask & right_mask) + ) + else: + if right is True: + mask = np.zeros_like(left_mask) + elif right is libmissing.NA: + mask = (~left & ~left_mask) | left_mask + else: + # False + mask = left_mask.copy() + + return result, mask + + +def kleene_xor( + left: bool | np.ndarray | libmissing.NAType, + right: bool | np.ndarray | libmissing.NAType, + left_mask: np.ndarray | None, + right_mask: np.ndarray | None, +): + """ + Boolean ``xor`` using Kleene logic. + + This is the same as ``or``, with the following adjustments + + * True, True -> False + * True, NA -> NA + + Parameters + ---------- + left, right : ndarray, NA, or bool + The values of the array. + left_mask, right_mask : ndarray, optional + The masks. Only one of these may be None, which implies that + the associated `left` or `right` value is a scalar. + + Returns + ------- + result, mask: ndarray[bool] + The result of the logical xor, and the new mask. + """ + # To reduce the number of cases, we ensure that `left` & `left_mask` + # always come from an array, not a scalar. This is safe, since + # A ^ B == B ^ A + if left_mask is None: + return kleene_xor(right, left, right_mask, left_mask) + + if not isinstance(left, np.ndarray): + raise TypeError("Either `left` or `right` need to be a np.ndarray.") + + raise_for_nan(right, method="xor") + if right is libmissing.NA: + result = np.zeros_like(left) + else: + result = left ^ right + + if right_mask is None: + if right is libmissing.NA: + mask = np.ones_like(left_mask) + else: + mask = left_mask.copy() + else: + mask = left_mask | right_mask + + return result, mask + + +def kleene_and( + left: bool | libmissing.NAType | np.ndarray, + right: bool | libmissing.NAType | np.ndarray, + left_mask: np.ndarray | None, + right_mask: np.ndarray | None, +): + """ + Boolean ``and`` using Kleene logic. + + Values are ``NA`` for ``NA & NA`` or ``True & NA``. + + Parameters + ---------- + left, right : ndarray, NA, or bool + The values of the array. + left_mask, right_mask : ndarray, optional + The masks. Only one of these may be None, which implies that + the associated `left` or `right` value is a scalar. + + Returns + ------- + result, mask: ndarray[bool] + The result of the logical xor, and the new mask. + """ + # To reduce the number of cases, we ensure that `left` & `left_mask` + # always come from an array, not a scalar. This is safe, since + # A & B == B & A + if left_mask is None: + return kleene_and(right, left, right_mask, left_mask) + + if not isinstance(left, np.ndarray): + raise TypeError("Either `left` or `right` need to be a np.ndarray.") + raise_for_nan(right, method="and") + + if right is libmissing.NA: + result = np.zeros_like(left) + else: + result = left & right + + if right_mask is None: + # Scalar `right` + if right is libmissing.NA: + mask = (left & ~left_mask) | left_mask + + else: + mask = left_mask.copy() + if right is False: + # unmask everything + mask[:] = False + else: + # unmask where either left or right is False + left_false = ~(left | left_mask) + right_false = ~(right | right_mask) + mask = (left_mask & ~right_false) | (right_mask & ~left_false) + + return result, mask + + +def raise_for_nan(value, method: str) -> None: + if lib.is_float(value) and np.isnan(value): + raise ValueError(f"Cannot perform logical '{method}' with floating NaN") diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/missing.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/missing.py new file mode 100644 index 0000000000000000000000000000000000000000..fc685935a35fceab74012912d3c3cae65b9c1818 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/ops/missing.py @@ -0,0 +1,176 @@ +""" +Missing data handling for arithmetic operations. + +In particular, pandas conventions regarding division by zero differ +from numpy in the following ways: + 1) np.array([-1, 0, 1], dtype=dtype1) // np.array([0, 0, 0], dtype=dtype2) + gives [nan, nan, nan] for most dtype combinations, and [0, 0, 0] for + the remaining pairs + (the remaining being dtype1==dtype2==intN and dtype==dtype2==uintN). + + pandas convention is to return [-inf, nan, inf] for all dtype + combinations. + + Note: the numpy behavior described here is py3-specific. + + 2) np.array([-1, 0, 1], dtype=dtype1) % np.array([0, 0, 0], dtype=dtype2) + gives precisely the same results as the // operation. + + pandas convention is to return [nan, nan, nan] for all dtype + combinations. + + 3) divmod behavior consistent with 1) and 2). +""" +from __future__ import annotations + +import operator + +import numpy as np + +from pandas.core import roperator + + +def _fill_zeros(result: np.ndarray, x, y): + """ + If this is a reversed op, then flip x,y + + If we have an integer value (or array in y) + and we have 0's, fill them with np.nan, + return the result. + + Mask the nan's from x. + """ + if result.dtype.kind == "f": + return result + + is_variable_type = hasattr(y, "dtype") + is_scalar_type = not isinstance(y, np.ndarray) + + if not is_variable_type and not is_scalar_type: + # e.g. test_series_ops_name_retention with mod we get here with list/tuple + return result + + if is_scalar_type: + y = np.array(y) + + if y.dtype.kind in "iu": + ymask = y == 0 + if ymask.any(): + # GH#7325, mask and nans must be broadcastable + mask = ymask & ~np.isnan(result) + + # GH#9308 doing ravel on result and mask can improve putmask perf, + # but can also make unwanted copies. + result = result.astype("float64", copy=False) + + np.putmask(result, mask, np.nan) + + return result + + +def mask_zero_div_zero(x, y, result: np.ndarray) -> np.ndarray: + """ + Set results of 0 // 0 to np.nan, regardless of the dtypes + of the numerator or the denominator. + + Parameters + ---------- + x : ndarray + y : ndarray + result : ndarray + + Returns + ------- + ndarray + The filled result. + + Examples + -------- + >>> x = np.array([1, 0, -1], dtype=np.int64) + >>> x + array([ 1, 0, -1]) + >>> y = 0 # int 0; numpy behavior is different with float + >>> result = x // y + >>> result # raw numpy result does not fill division by zero + array([0, 0, 0]) + >>> mask_zero_div_zero(x, y, result) + array([ inf, nan, -inf]) + """ + + if not hasattr(y, "dtype"): + # e.g. scalar, tuple + y = np.array(y) + if not hasattr(x, "dtype"): + # e.g scalar, tuple + x = np.array(x) + + zmask = y == 0 + + if zmask.any(): + # Flip sign if necessary for -0.0 + zneg_mask = zmask & np.signbit(y) + zpos_mask = zmask & ~zneg_mask + + x_lt0 = x < 0 + x_gt0 = x > 0 + nan_mask = zmask & (x == 0) + neginf_mask = (zpos_mask & x_lt0) | (zneg_mask & x_gt0) + posinf_mask = (zpos_mask & x_gt0) | (zneg_mask & x_lt0) + + if nan_mask.any() or neginf_mask.any() or posinf_mask.any(): + # Fill negative/0 with -inf, positive/0 with +inf, 0/0 with NaN + result = result.astype("float64", copy=False) + + result[nan_mask] = np.nan + result[posinf_mask] = np.inf + result[neginf_mask] = -np.inf + + return result + + +def dispatch_fill_zeros(op, left, right, result): + """ + Call _fill_zeros with the appropriate fill value depending on the operation, + with special logic for divmod and rdivmod. + + Parameters + ---------- + op : function (operator.add, operator.div, ...) + left : object (np.ndarray for non-reversed ops) + We have excluded ExtensionArrays here + right : object (np.ndarray for reversed ops) + We have excluded ExtensionArrays here + result : ndarray + + Returns + ------- + result : np.ndarray + + Notes + ----- + For divmod and rdivmod, the `result` parameter and returned `result` + is a 2-tuple of ndarray objects. + """ + if op is divmod: + result = ( + mask_zero_div_zero(left, right, result[0]), + _fill_zeros(result[1], left, right), + ) + elif op is roperator.rdivmod: + result = ( + mask_zero_div_zero(right, left, result[0]), + _fill_zeros(result[1], right, left), + ) + elif op is operator.floordiv: + # Note: no need to do this for truediv; in py3 numpy behaves the way + # we want. + result = mask_zero_div_zero(left, right, result) + elif op is roperator.rfloordiv: + # Note: no need to do this for rtruediv; in py3 numpy behaves the way + # we want. + result = mask_zero_div_zero(right, left, result) + elif op is operator.mod: + result = _fill_zeros(result, left, right) + elif op is roperator.rmod: + result = _fill_zeros(result, right, left) + return result diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/strings/__pycache__/accessor.cpython-310.pyc b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/strings/__pycache__/accessor.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b0da9f2a1fffd34290f952df860f8af1a2207e28 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/core/strings/__pycache__/accessor.cpython-310.pyc @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d54c62280e727ee08327944f0417f7a1a78ce75535f2664c5c43f879d2e8ee12 +size 100945 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/io/__pycache__/pytables.cpython-310.pyc b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/io/__pycache__/pytables.cpython-310.pyc new 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+size 102750 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/api/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/api/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/api/test_api.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/api/test_api.py new file mode 100644 index 0000000000000000000000000000000000000000..60bcb97aaa3642be064bcacd130edf2084c4a55c --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/api/test_api.py @@ -0,0 +1,383 @@ +from __future__ import annotations + +import pytest + +import pandas as pd +from pandas import api +import pandas._testing as tm +from pandas.api import ( + extensions as api_extensions, + indexers as api_indexers, + interchange as api_interchange, + types as api_types, + typing as api_typing, +) + + +class Base: + def check(self, namespace, expected, ignored=None): + # see which names are in the namespace, minus optional + # ignored ones + # compare vs the expected + + result = sorted( + f for f in dir(namespace) if not f.startswith("__") and f != "annotations" + ) + if ignored is not None: + result = sorted(set(result) - set(ignored)) + + expected = sorted(expected) + tm.assert_almost_equal(result, expected) + + +class TestPDApi(Base): + # these are optionally imported based on testing + # & need to be ignored + ignored = ["tests", "locale", "conftest", "_version_meson"] + + # top-level sub-packages + public_lib = [ + "api", + "arrays", + "options", + "test", + "testing", + "errors", + "plotting", + "io", + "tseries", + ] + private_lib = ["compat", "core", "pandas", "util", "_built_with_meson"] + + # misc + misc = ["IndexSlice", "NaT", "NA"] + + # top-level classes + classes = [ + "ArrowDtype", + "Categorical", + "CategoricalIndex", + "DataFrame", + "DateOffset", + "DatetimeIndex", + "ExcelFile", + "ExcelWriter", + "Flags", + "Grouper", + "HDFStore", + "Index", + "MultiIndex", + "Period", + "PeriodIndex", + "RangeIndex", + "Series", + "SparseDtype", + "StringDtype", + "Timedelta", + "TimedeltaIndex", + "Timestamp", + "Interval", + "IntervalIndex", + "CategoricalDtype", + "PeriodDtype", + "IntervalDtype", + "DatetimeTZDtype", + "BooleanDtype", + "Int8Dtype", + "Int16Dtype", + "Int32Dtype", + "Int64Dtype", + "UInt8Dtype", + "UInt16Dtype", + "UInt32Dtype", + "UInt64Dtype", + "Float32Dtype", + "Float64Dtype", + "NamedAgg", + ] + + # these are already deprecated; awaiting removal + deprecated_classes: list[str] = [] + + # external modules exposed in pandas namespace + modules: list[str] = [] + + # top-level functions + funcs = [ + "array", + "bdate_range", + "concat", + "crosstab", + "cut", + "date_range", + "interval_range", + "eval", + "factorize", + "get_dummies", + "from_dummies", + "infer_freq", + "isna", + "isnull", + "lreshape", + "melt", + "notna", + "notnull", + "offsets", + "merge", + "merge_ordered", + "merge_asof", + "period_range", + "pivot", + "pivot_table", + "qcut", + "show_versions", + "timedelta_range", + "unique", + "value_counts", + "wide_to_long", + ] + + # top-level option funcs + funcs_option = [ + "reset_option", + "describe_option", + "get_option", + "option_context", + "set_option", + "set_eng_float_format", + ] + + # top-level read_* funcs + funcs_read = [ + "read_clipboard", + "read_csv", + "read_excel", + "read_fwf", + "read_gbq", + "read_hdf", + "read_html", + "read_xml", + "read_json", + "read_pickle", + "read_sas", + "read_sql", + "read_sql_query", + "read_sql_table", + "read_stata", + "read_table", + "read_feather", + "read_parquet", + "read_orc", + "read_spss", + ] + + # top-level json funcs + funcs_json = ["json_normalize"] + + # top-level to_* funcs + funcs_to = ["to_datetime", "to_numeric", "to_pickle", "to_timedelta"] + + # top-level to deprecate in the future + deprecated_funcs_in_future: list[str] = [] + + # these are already deprecated; awaiting removal + deprecated_funcs: list[str] = [] + + # private modules in pandas namespace + private_modules = [ + "_config", + "_libs", + "_is_numpy_dev", + "_pandas_datetime_CAPI", + "_pandas_parser_CAPI", + "_testing", + "_typing", + ] + if not pd._built_with_meson: + private_modules.append("_version") + + def test_api(self): + checkthese = ( + self.public_lib + + self.private_lib + + self.misc + + self.modules + + self.classes + + self.funcs + + self.funcs_option + + self.funcs_read + + self.funcs_json + + self.funcs_to + + self.private_modules + ) + self.check(namespace=pd, expected=checkthese, ignored=self.ignored) + + def test_api_all(self): + expected = set( + self.public_lib + + self.misc + + self.modules + + self.classes + + self.funcs + + self.funcs_option + + self.funcs_read + + self.funcs_json + + self.funcs_to + ) - set(self.deprecated_classes) + actual = set(pd.__all__) + + extraneous = actual - expected + assert not extraneous + + missing = expected - actual + assert not missing + + def test_depr(self): + deprecated_list = ( + self.deprecated_classes + + self.deprecated_funcs + + self.deprecated_funcs_in_future + ) + for depr in deprecated_list: + with tm.assert_produces_warning(FutureWarning): + _ = getattr(pd, depr) + + +class TestApi(Base): + allowed_api_dirs = [ + "types", + "extensions", + "indexers", + "interchange", + "typing", + ] + allowed_typing = [ + "DataFrameGroupBy", + "DatetimeIndexResamplerGroupby", + "Expanding", + "ExpandingGroupby", + "ExponentialMovingWindow", + "ExponentialMovingWindowGroupby", + "JsonReader", + "NaTType", + "NAType", + "PeriodIndexResamplerGroupby", + "Resampler", + "Rolling", + "RollingGroupby", + "SeriesGroupBy", + "StataReader", + "TimedeltaIndexResamplerGroupby", + "TimeGrouper", + "Window", + ] + allowed_api_types = [ + "is_any_real_numeric_dtype", + "is_array_like", + "is_bool", + "is_bool_dtype", + "is_categorical_dtype", + "is_complex", + "is_complex_dtype", + "is_datetime64_any_dtype", + "is_datetime64_dtype", + "is_datetime64_ns_dtype", + "is_datetime64tz_dtype", + "is_dict_like", + "is_dtype_equal", + "is_extension_array_dtype", + "is_file_like", + "is_float", + "is_float_dtype", + "is_hashable", + "is_int64_dtype", + "is_integer", + "is_integer_dtype", + "is_interval", + "is_interval_dtype", + "is_iterator", + "is_list_like", + "is_named_tuple", + "is_number", + "is_numeric_dtype", + "is_object_dtype", + "is_period_dtype", + "is_re", + "is_re_compilable", + "is_scalar", + "is_signed_integer_dtype", + "is_sparse", + "is_string_dtype", + "is_timedelta64_dtype", + "is_timedelta64_ns_dtype", + "is_unsigned_integer_dtype", + "pandas_dtype", + "infer_dtype", + "union_categoricals", + "CategoricalDtype", + "DatetimeTZDtype", + "IntervalDtype", + "PeriodDtype", + ] + allowed_api_interchange = ["from_dataframe", "DataFrame"] + allowed_api_indexers = [ + "check_array_indexer", + "BaseIndexer", + "FixedForwardWindowIndexer", + "VariableOffsetWindowIndexer", + ] + allowed_api_extensions = [ + "no_default", + "ExtensionDtype", + "register_extension_dtype", + "register_dataframe_accessor", + "register_index_accessor", + "register_series_accessor", + "take", + "ExtensionArray", + "ExtensionScalarOpsMixin", + ] + + def test_api(self): + self.check(api, self.allowed_api_dirs) + + def test_api_typing(self): + self.check(api_typing, self.allowed_typing) + + def test_api_types(self): + self.check(api_types, self.allowed_api_types) + + def test_api_interchange(self): + self.check(api_interchange, self.allowed_api_interchange) + + def test_api_indexers(self): + self.check(api_indexers, self.allowed_api_indexers) + + def test_api_extensions(self): + self.check(api_extensions, self.allowed_api_extensions) + + +class TestTesting(Base): + funcs = [ + "assert_frame_equal", + "assert_series_equal", + "assert_index_equal", + "assert_extension_array_equal", + ] + + def test_testing(self): + from pandas import testing + + self.check(testing, self.funcs) + + def test_util_in_top_level(self): + with pytest.raises(AttributeError, match="foo"): + pd.util.foo + + +def test_pandas_array_alias(): + msg = "PandasArray has been renamed NumpyExtensionArray" + with tm.assert_produces_warning(FutureWarning, match=msg): + res = pd.arrays.PandasArray + + assert res is pd.arrays.NumpyExtensionArray diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/api/test_types.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/api/test_types.py new file mode 100644 index 0000000000000000000000000000000000000000..fbaa6e7e18bcaa9a574b741b5361818f1be01ecf --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/api/test_types.py @@ -0,0 +1,62 @@ +from __future__ import annotations + +import pandas._testing as tm +from pandas.api import types +from pandas.tests.api.test_api import Base + + +class TestTypes(Base): + allowed = [ + "is_any_real_numeric_dtype", + "is_bool", + "is_bool_dtype", + "is_categorical_dtype", + "is_complex", + "is_complex_dtype", + "is_datetime64_any_dtype", + "is_datetime64_dtype", + "is_datetime64_ns_dtype", + "is_datetime64tz_dtype", + "is_dtype_equal", + "is_float", + "is_float_dtype", + "is_int64_dtype", + "is_integer", + "is_integer_dtype", + "is_number", + "is_numeric_dtype", + "is_object_dtype", + "is_scalar", + "is_sparse", + "is_string_dtype", + "is_signed_integer_dtype", + "is_timedelta64_dtype", + "is_timedelta64_ns_dtype", + "is_unsigned_integer_dtype", + "is_period_dtype", + "is_interval", + "is_interval_dtype", + "is_re", + "is_re_compilable", + "is_dict_like", + "is_iterator", + "is_file_like", + "is_list_like", + "is_hashable", + "is_array_like", + "is_named_tuple", + "pandas_dtype", + "union_categoricals", + "infer_dtype", + "is_extension_array_dtype", + ] + deprecated: list[str] = [] + dtypes = ["CategoricalDtype", "DatetimeTZDtype", "PeriodDtype", "IntervalDtype"] + + def test_types(self): + self.check(types, self.allowed + self.dtypes + self.deprecated) + + def test_deprecated_from_api_types(self): + for t in self.deprecated: + with tm.assert_produces_warning(FutureWarning): + getattr(types, t)(1) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/common.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/common.py new file mode 100644 index 0000000000000000000000000000000000000000..b608df1554154f4723a0147ea02c04c780839c65 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/common.py @@ -0,0 +1,155 @@ +""" +Assertion helpers for arithmetic tests. +""" +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Index, + Series, + array, +) +import pandas._testing as tm +from pandas.core.arrays import ( + BooleanArray, + NumpyExtensionArray, +) + + +def assert_cannot_add(left, right, msg="cannot add"): + """ + Helper to assert that left and right cannot be added. + + Parameters + ---------- + left : object + right : object + msg : str, default "cannot add" + """ + with pytest.raises(TypeError, match=msg): + left + right + with pytest.raises(TypeError, match=msg): + right + left + + +def assert_invalid_addsub_type(left, right, msg=None): + """ + Helper to assert that left and right can be neither added nor subtracted. + + Parameters + ---------- + left : object + right : object + msg : str or None, default None + """ + with pytest.raises(TypeError, match=msg): + left + right + with pytest.raises(TypeError, match=msg): + right + left + with pytest.raises(TypeError, match=msg): + left - right + with pytest.raises(TypeError, match=msg): + right - left + + +def get_upcast_box(left, right, is_cmp: bool = False): + """ + Get the box to use for 'expected' in an arithmetic or comparison operation. + + Parameters + left : Any + right : Any + is_cmp : bool, default False + Whether the operation is a comparison method. + """ + + if isinstance(left, DataFrame) or isinstance(right, DataFrame): + return DataFrame + if isinstance(left, Series) or isinstance(right, Series): + if is_cmp and isinstance(left, Index): + # Index does not defer for comparisons + return np.array + return Series + if isinstance(left, Index) or isinstance(right, Index): + if is_cmp: + return np.array + return Index + return tm.to_array + + +def assert_invalid_comparison(left, right, box): + """ + Assert that comparison operations with mismatched types behave correctly. + + Parameters + ---------- + left : np.ndarray, ExtensionArray, Index, or Series + right : object + box : {pd.DataFrame, pd.Series, pd.Index, pd.array, tm.to_array} + """ + # Not for tznaive-tzaware comparison + + # Note: not quite the same as how we do this for tm.box_expected + xbox = box if box not in [Index, array] else np.array + + def xbox2(x): + # Eventually we'd like this to be tighter, but for now we'll + # just exclude NumpyExtensionArray[bool] + if isinstance(x, NumpyExtensionArray): + return x._ndarray + if isinstance(x, BooleanArray): + # NB: we are assuming no pd.NAs for now + return x.astype(bool) + return x + + # rev_box: box to use for reversed comparisons + rev_box = xbox + if isinstance(right, Index) and isinstance(left, Series): + rev_box = np.array + + result = xbox2(left == right) + expected = xbox(np.zeros(result.shape, dtype=np.bool_)) + + tm.assert_equal(result, expected) + + result = xbox2(right == left) + tm.assert_equal(result, rev_box(expected)) + + result = xbox2(left != right) + tm.assert_equal(result, ~expected) + + result = xbox2(right != left) + tm.assert_equal(result, rev_box(~expected)) + + msg = "|".join( + [ + "Invalid comparison between", + "Cannot compare type", + "not supported between", + "invalid type promotion", + ( + # GH#36706 npdev 1.20.0 2020-09-28 + r"The DTypes and " + r" do not have a common DType. " + "For example they cannot be stored in a single array unless the " + "dtype is `object`." + ), + ] + ) + with pytest.raises(TypeError, match=msg): + left < right + with pytest.raises(TypeError, match=msg): + left <= right + with pytest.raises(TypeError, match=msg): + left > right + with pytest.raises(TypeError, match=msg): + left >= right + with pytest.raises(TypeError, match=msg): + right < left + with pytest.raises(TypeError, match=msg): + right <= left + with pytest.raises(TypeError, match=msg): + right > left + with pytest.raises(TypeError, match=msg): + right >= left diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/conftest.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..c7703b34a5e38e7a3887d727b0a8c954016ad836 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/conftest.py @@ -0,0 +1,139 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import Index + + +@pytest.fixture(params=[1, np.array(1, dtype=np.int64)]) +def one(request): + """ + Several variants of integer value 1. The zero-dim integer array + behaves like an integer. + + This fixture can be used to check that datetimelike indexes handle + addition and subtraction of integers and zero-dimensional arrays + of integers. + + Examples + -------- + dti = pd.date_range('2016-01-01', periods=2, freq='h') + dti + DatetimeIndex(['2016-01-01 00:00:00', '2016-01-01 01:00:00'], + dtype='datetime64[ns]', freq='h') + dti + one + DatetimeIndex(['2016-01-01 01:00:00', '2016-01-01 02:00:00'], + dtype='datetime64[ns]', freq='h') + """ + return request.param + + +zeros = [ + box_cls([0] * 5, dtype=dtype) + for box_cls in [Index, np.array, pd.array] + for dtype in [np.int64, np.uint64, np.float64] +] +zeros.extend([box_cls([-0.0] * 5, dtype=np.float64) for box_cls in [Index, np.array]]) +zeros.extend([np.array(0, dtype=dtype) for dtype in [np.int64, np.uint64, np.float64]]) +zeros.extend([np.array(-0.0, dtype=np.float64)]) +zeros.extend([0, 0.0, -0.0]) + + +@pytest.fixture(params=zeros) +def zero(request): + """ + Several types of scalar zeros and length 5 vectors of zeros. + + This fixture can be used to check that numeric-dtype indexes handle + division by any zero numeric-dtype. + + Uses vector of length 5 for broadcasting with `numeric_idx` fixture, + which creates numeric-dtype vectors also of length 5. + + Examples + -------- + arr = RangeIndex(5) + arr / zeros + Index([nan, inf, inf, inf, inf], dtype='float64') + """ + return request.param + + +# ------------------------------------------------------------------ +# Scalar Fixtures + + +@pytest.fixture( + params=[ + pd.Timedelta("10m7s").to_pytimedelta(), + pd.Timedelta("10m7s"), + pd.Timedelta("10m7s").to_timedelta64(), + ], + ids=lambda x: type(x).__name__, +) +def scalar_td(request): + """ + Several variants of Timedelta scalars representing 10 minutes and 7 seconds. + """ + return request.param + + +@pytest.fixture( + params=[ + pd.offsets.Day(3), + pd.offsets.Hour(72), + pd.Timedelta(days=3).to_pytimedelta(), + pd.Timedelta("72:00:00"), + np.timedelta64(3, "D"), + np.timedelta64(72, "h"), + ], + ids=lambda x: type(x).__name__, +) +def three_days(request): + """ + Several timedelta-like and DateOffset objects that each represent + a 3-day timedelta + """ + return request.param + + +@pytest.fixture( + params=[ + pd.offsets.Hour(2), + pd.offsets.Minute(120), + pd.Timedelta(hours=2).to_pytimedelta(), + pd.Timedelta(seconds=2 * 3600), + np.timedelta64(2, "h"), + np.timedelta64(120, "m"), + ], + ids=lambda x: type(x).__name__, +) +def two_hours(request): + """ + Several timedelta-like and DateOffset objects that each represent + a 2-hour timedelta + """ + return request.param + + +_common_mismatch = [ + pd.offsets.YearBegin(2), + pd.offsets.MonthBegin(1), + pd.offsets.Minute(), +] + + +@pytest.fixture( + params=[ + np.timedelta64(4, "h"), + pd.Timedelta(hours=23).to_pytimedelta(), + pd.Timedelta("23:00:00"), + ] + + _common_mismatch +) +def not_daily(request): + """ + Several timedelta-like and DateOffset instances that are _not_ + compatible with Daily frequencies. + """ + return request.param diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_array_ops.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_array_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..2c347d965bbf7353a6a4e81ca955341f8041b6de --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_array_ops.py @@ -0,0 +1,39 @@ +import operator + +import numpy as np +import pytest + +import pandas._testing as tm +from pandas.core.ops.array_ops import ( + comparison_op, + na_logical_op, +) + + +def test_na_logical_op_2d(): + left = np.arange(8).reshape(4, 2) + right = left.astype(object) + right[0, 0] = np.nan + + # Check that we fall back to the vec_binop branch + with pytest.raises(TypeError, match="unsupported operand type"): + operator.or_(left, right) + + result = na_logical_op(left, right, operator.or_) + expected = right + tm.assert_numpy_array_equal(result, expected) + + +def test_object_comparison_2d(): + left = np.arange(9).reshape(3, 3).astype(object) + right = left.T + + result = comparison_op(left, right, operator.eq) + expected = np.eye(3).astype(bool) + tm.assert_numpy_array_equal(result, expected) + + # Ensure that cython doesn't raise on non-writeable arg, which + # we can get from np.broadcast_to + right.flags.writeable = False + result = comparison_op(left, right, operator.ne) + tm.assert_numpy_array_equal(result, ~expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_categorical.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_categorical.py new file mode 100644 index 0000000000000000000000000000000000000000..d6f3a13ce670596a12ca10b9e8d02d69d63c96fb --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_categorical.py @@ -0,0 +1,25 @@ +import numpy as np + +from pandas import ( + Categorical, + Series, +) +import pandas._testing as tm + + +class TestCategoricalComparisons: + def test_categorical_nan_equality(self): + cat = Series(Categorical(["a", "b", "c", np.nan])) + expected = Series([True, True, True, False]) + result = cat == cat + tm.assert_series_equal(result, expected) + + def test_categorical_tuple_equality(self): + # GH 18050 + ser = Series([(0, 0), (0, 1), (0, 0), (1, 0), (1, 1)]) + expected = Series([True, False, True, False, False]) + result = ser == (0, 0) + tm.assert_series_equal(result, expected) + + result = ser.astype("category") == (0, 0) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_datetime64.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_datetime64.py new file mode 100644 index 0000000000000000000000000000000000000000..a468449efd507fae37f3fcb15f64a3e1bf551f93 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_datetime64.py @@ -0,0 +1,2469 @@ +# Arithmetic tests for DataFrame/Series/Index/Array classes that should +# behave identically. +# Specifically for datetime64 and datetime64tz dtypes +from datetime import ( + datetime, + time, + timedelta, +) +from itertools import ( + product, + starmap, +) +import operator + +import numpy as np +import pytest +import pytz + +from pandas._libs.tslibs.conversion import localize_pydatetime +from pandas._libs.tslibs.offsets import shift_months +from pandas.errors import PerformanceWarning + +import pandas as pd +from pandas import ( + DateOffset, + DatetimeIndex, + NaT, + Period, + Series, + Timedelta, + TimedeltaIndex, + Timestamp, + date_range, +) +import pandas._testing as tm +from pandas.core import roperator +from pandas.tests.arithmetic.common import ( + assert_cannot_add, + assert_invalid_addsub_type, + assert_invalid_comparison, + get_upcast_box, +) + +# ------------------------------------------------------------------ +# Comparisons + + +class TestDatetime64ArrayLikeComparisons: + # Comparison tests for datetime64 vectors fully parametrized over + # DataFrame/Series/DatetimeIndex/DatetimeArray. Ideally all comparison + # tests will eventually end up here. + + def test_compare_zerodim(self, tz_naive_fixture, box_with_array): + # Test comparison with zero-dimensional array is unboxed + tz = tz_naive_fixture + box = box_with_array + dti = date_range("20130101", periods=3, tz=tz) + + other = np.array(dti.to_numpy()[0]) + + dtarr = tm.box_expected(dti, box) + xbox = get_upcast_box(dtarr, other, True) + result = dtarr <= other + expected = np.array([True, False, False]) + expected = tm.box_expected(expected, xbox) + tm.assert_equal(result, expected) + + @pytest.mark.parametrize( + "other", + [ + "foo", + -1, + 99, + 4.0, + object(), + timedelta(days=2), + # GH#19800, GH#19301 datetime.date comparison raises to + # match DatetimeIndex/Timestamp. This also matches the behavior + # of stdlib datetime.datetime + datetime(2001, 1, 1).date(), + # GH#19301 None and NaN are *not* cast to NaT for comparisons + None, + np.nan, + ], + ) + def test_dt64arr_cmp_scalar_invalid(self, other, tz_naive_fixture, box_with_array): + # GH#22074, GH#15966 + tz = tz_naive_fixture + + rng = date_range("1/1/2000", periods=10, tz=tz) + dtarr = tm.box_expected(rng, box_with_array) + assert_invalid_comparison(dtarr, other, box_with_array) + + @pytest.mark.parametrize( + "other", + [ + # GH#4968 invalid date/int comparisons + list(range(10)), + np.arange(10), + np.arange(10).astype(np.float32), + np.arange(10).astype(object), + pd.timedelta_range("1ns", periods=10).array, + np.array(pd.timedelta_range("1ns", periods=10)), + list(pd.timedelta_range("1ns", periods=10)), + pd.timedelta_range("1 Day", periods=10).astype(object), + pd.period_range("1971-01-01", freq="D", periods=10).array, + pd.period_range("1971-01-01", freq="D", periods=10).astype(object), + ], + ) + def test_dt64arr_cmp_arraylike_invalid( + self, other, tz_naive_fixture, box_with_array + ): + tz = tz_naive_fixture + + dta = date_range("1970-01-01", freq="ns", periods=10, tz=tz)._data + obj = tm.box_expected(dta, box_with_array) + assert_invalid_comparison(obj, other, box_with_array) + + def test_dt64arr_cmp_mixed_invalid(self, tz_naive_fixture): + tz = tz_naive_fixture + + dta = date_range("1970-01-01", freq="h", periods=5, tz=tz)._data + + other = np.array([0, 1, 2, dta[3], Timedelta(days=1)]) + result = dta == other + expected = np.array([False, False, False, True, False]) + tm.assert_numpy_array_equal(result, expected) + + result = dta != other + tm.assert_numpy_array_equal(result, ~expected) + + msg = "Invalid comparison between|Cannot compare type|not supported between" + with pytest.raises(TypeError, match=msg): + dta < other + with pytest.raises(TypeError, match=msg): + dta > other + with pytest.raises(TypeError, match=msg): + dta <= other + with pytest.raises(TypeError, match=msg): + dta >= other + + def test_dt64arr_nat_comparison(self, tz_naive_fixture, box_with_array): + # GH#22242, GH#22163 DataFrame considered NaT == ts incorrectly + tz = tz_naive_fixture + box = box_with_array + + ts = Timestamp("2021-01-01", tz=tz) + ser = Series([ts, NaT]) + + obj = tm.box_expected(ser, box) + xbox = get_upcast_box(obj, ts, True) + + expected = Series([True, False], dtype=np.bool_) + expected = tm.box_expected(expected, xbox) + + result = obj == ts + tm.assert_equal(result, expected) + + +class TestDatetime64SeriesComparison: + # TODO: moved from tests.series.test_operators; needs cleanup + + @pytest.mark.parametrize( + "pair", + [ + ( + [Timestamp("2011-01-01"), NaT, Timestamp("2011-01-03")], + [NaT, NaT, Timestamp("2011-01-03")], + ), + ( + [Timedelta("1 days"), NaT, Timedelta("3 days")], + [NaT, NaT, Timedelta("3 days")], + ), + ( + [Period("2011-01", freq="M"), NaT, Period("2011-03", freq="M")], + [NaT, NaT, Period("2011-03", freq="M")], + ), + ], + ) + @pytest.mark.parametrize("reverse", [True, False]) + @pytest.mark.parametrize("dtype", [None, object]) + @pytest.mark.parametrize( + "op, expected", + [ + (operator.eq, Series([False, False, True])), + (operator.ne, Series([True, True, False])), + (operator.lt, Series([False, False, False])), + (operator.gt, Series([False, False, False])), + (operator.ge, Series([False, False, True])), + (operator.le, Series([False, False, True])), + ], + ) + def test_nat_comparisons( + self, + dtype, + index_or_series, + reverse, + pair, + op, + expected, + ): + box = index_or_series + lhs, rhs = pair + if reverse: + # add lhs / rhs switched data + lhs, rhs = rhs, lhs + + left = Series(lhs, dtype=dtype) + right = box(rhs, dtype=dtype) + + result = op(left, right) + + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "data", + [ + [Timestamp("2011-01-01"), NaT, Timestamp("2011-01-03")], + [Timedelta("1 days"), NaT, Timedelta("3 days")], + [Period("2011-01", freq="M"), NaT, Period("2011-03", freq="M")], + ], + ) + @pytest.mark.parametrize("dtype", [None, object]) + def test_nat_comparisons_scalar(self, dtype, data, box_with_array): + box = box_with_array + + left = Series(data, dtype=dtype) + left = tm.box_expected(left, box) + xbox = get_upcast_box(left, NaT, True) + + expected = [False, False, False] + expected = tm.box_expected(expected, xbox) + if box is pd.array and dtype is object: + expected = pd.array(expected, dtype="bool") + + tm.assert_equal(left == NaT, expected) + tm.assert_equal(NaT == left, expected) + + expected = [True, True, True] + expected = tm.box_expected(expected, xbox) + if box is pd.array and dtype is object: + expected = pd.array(expected, dtype="bool") + tm.assert_equal(left != NaT, expected) + tm.assert_equal(NaT != left, expected) + + expected = [False, False, False] + expected = tm.box_expected(expected, xbox) + if box is pd.array and dtype is object: + expected = pd.array(expected, dtype="bool") + tm.assert_equal(left < NaT, expected) + tm.assert_equal(NaT > left, expected) + tm.assert_equal(left <= NaT, expected) + tm.assert_equal(NaT >= left, expected) + + tm.assert_equal(left > NaT, expected) + tm.assert_equal(NaT < left, expected) + tm.assert_equal(left >= NaT, expected) + tm.assert_equal(NaT <= left, expected) + + @pytest.mark.parametrize("val", [datetime(2000, 1, 4), datetime(2000, 1, 5)]) + def test_series_comparison_scalars(self, val): + series = Series(date_range("1/1/2000", periods=10)) + + result = series > val + expected = Series([x > val for x in series]) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "left,right", [("lt", "gt"), ("le", "ge"), ("eq", "eq"), ("ne", "ne")] + ) + def test_timestamp_compare_series(self, left, right): + # see gh-4982 + # Make sure we can compare Timestamps on the right AND left hand side. + ser = Series(date_range("20010101", periods=10), name="dates") + s_nat = ser.copy(deep=True) + + ser[0] = Timestamp("nat") + ser[3] = Timestamp("nat") + + left_f = getattr(operator, left) + right_f = getattr(operator, right) + + # No NaT + expected = left_f(ser, Timestamp("20010109")) + result = right_f(Timestamp("20010109"), ser) + tm.assert_series_equal(result, expected) + + # NaT + expected = left_f(ser, Timestamp("nat")) + result = right_f(Timestamp("nat"), ser) + tm.assert_series_equal(result, expected) + + # Compare to Timestamp with series containing NaT + expected = left_f(s_nat, Timestamp("20010109")) + result = right_f(Timestamp("20010109"), s_nat) + tm.assert_series_equal(result, expected) + + # Compare to NaT with series containing NaT + expected = left_f(s_nat, NaT) + result = right_f(NaT, s_nat) + tm.assert_series_equal(result, expected) + + def test_dt64arr_timestamp_equality(self, box_with_array): + # GH#11034 + box = box_with_array + + ser = Series([Timestamp("2000-01-29 01:59:00"), Timestamp("2000-01-30"), NaT]) + ser = tm.box_expected(ser, box) + xbox = get_upcast_box(ser, ser, True) + + result = ser != ser + expected = tm.box_expected([False, False, True], xbox) + tm.assert_equal(result, expected) + + if box is pd.DataFrame: + # alignment for frame vs series comparisons deprecated + # in GH#46795 enforced 2.0 + with pytest.raises(ValueError, match="not aligned"): + ser != ser[0] + + else: + result = ser != ser[0] + expected = tm.box_expected([False, True, True], xbox) + tm.assert_equal(result, expected) + + if box is pd.DataFrame: + # alignment for frame vs series comparisons deprecated + # in GH#46795 enforced 2.0 + with pytest.raises(ValueError, match="not aligned"): + ser != ser[2] + else: + result = ser != ser[2] + expected = tm.box_expected([True, True, True], xbox) + tm.assert_equal(result, expected) + + result = ser == ser + expected = tm.box_expected([True, True, False], xbox) + tm.assert_equal(result, expected) + + if box is pd.DataFrame: + # alignment for frame vs series comparisons deprecated + # in GH#46795 enforced 2.0 + with pytest.raises(ValueError, match="not aligned"): + ser == ser[0] + else: + result = ser == ser[0] + expected = tm.box_expected([True, False, False], xbox) + tm.assert_equal(result, expected) + + if box is pd.DataFrame: + # alignment for frame vs series comparisons deprecated + # in GH#46795 enforced 2.0 + with pytest.raises(ValueError, match="not aligned"): + ser == ser[2] + else: + result = ser == ser[2] + expected = tm.box_expected([False, False, False], xbox) + tm.assert_equal(result, expected) + + @pytest.mark.parametrize( + "datetimelike", + [ + Timestamp("20130101"), + datetime(2013, 1, 1), + np.datetime64("2013-01-01T00:00", "ns"), + ], + ) + @pytest.mark.parametrize( + "op,expected", + [ + (operator.lt, [True, False, False, False]), + (operator.le, [True, True, False, False]), + (operator.eq, [False, True, False, False]), + (operator.gt, [False, False, False, True]), + ], + ) + def test_dt64_compare_datetime_scalar(self, datetimelike, op, expected): + # GH#17965, test for ability to compare datetime64[ns] columns + # to datetimelike + ser = Series( + [ + Timestamp("20120101"), + Timestamp("20130101"), + np.nan, + Timestamp("20130103"), + ], + name="A", + ) + result = op(ser, datetimelike) + expected = Series(expected, name="A") + tm.assert_series_equal(result, expected) + + +class TestDatetimeIndexComparisons: + # TODO: moved from tests.indexes.test_base; parametrize and de-duplicate + def test_comparators(self, comparison_op): + index = date_range("2020-01-01", periods=10) + element = index[len(index) // 2] + element = Timestamp(element).to_datetime64() + + arr = np.array(index) + arr_result = comparison_op(arr, element) + index_result = comparison_op(index, element) + + assert isinstance(index_result, np.ndarray) + tm.assert_numpy_array_equal(arr_result, index_result) + + @pytest.mark.parametrize( + "other", + [datetime(2016, 1, 1), Timestamp("2016-01-01"), np.datetime64("2016-01-01")], + ) + def test_dti_cmp_datetimelike(self, other, tz_naive_fixture): + tz = tz_naive_fixture + dti = date_range("2016-01-01", periods=2, tz=tz) + if tz is not None: + if isinstance(other, np.datetime64): + pytest.skip(f"{type(other).__name__} is not tz aware") + other = localize_pydatetime(other, dti.tzinfo) + + result = dti == other + expected = np.array([True, False]) + tm.assert_numpy_array_equal(result, expected) + + result = dti > other + expected = np.array([False, True]) + tm.assert_numpy_array_equal(result, expected) + + result = dti >= other + expected = np.array([True, True]) + tm.assert_numpy_array_equal(result, expected) + + result = dti < other + expected = np.array([False, False]) + tm.assert_numpy_array_equal(result, expected) + + result = dti <= other + expected = np.array([True, False]) + tm.assert_numpy_array_equal(result, expected) + + @pytest.mark.parametrize("dtype", [None, object]) + def test_dti_cmp_nat(self, dtype, box_with_array): + left = DatetimeIndex([Timestamp("2011-01-01"), NaT, Timestamp("2011-01-03")]) + right = DatetimeIndex([NaT, NaT, Timestamp("2011-01-03")]) + + left = tm.box_expected(left, box_with_array) + right = tm.box_expected(right, box_with_array) + xbox = get_upcast_box(left, right, True) + + lhs, rhs = left, right + if dtype is object: + lhs, rhs = left.astype(object), right.astype(object) + + result = rhs == lhs + expected = np.array([False, False, True]) + expected = tm.box_expected(expected, xbox) + tm.assert_equal(result, expected) + + result = lhs != rhs + expected = np.array([True, True, False]) + expected = tm.box_expected(expected, xbox) + tm.assert_equal(result, expected) + + expected = np.array([False, False, False]) + expected = tm.box_expected(expected, xbox) + tm.assert_equal(lhs == NaT, expected) + tm.assert_equal(NaT == rhs, expected) + + expected = np.array([True, True, True]) + expected = tm.box_expected(expected, xbox) + tm.assert_equal(lhs != NaT, expected) + tm.assert_equal(NaT != lhs, expected) + + expected = np.array([False, False, False]) + expected = tm.box_expected(expected, xbox) + tm.assert_equal(lhs < NaT, expected) + tm.assert_equal(NaT > lhs, expected) + + def test_dti_cmp_nat_behaves_like_float_cmp_nan(self): + fidx1 = pd.Index([1.0, np.nan, 3.0, np.nan, 5.0, 7.0]) + fidx2 = pd.Index([2.0, 3.0, np.nan, np.nan, 6.0, 7.0]) + + didx1 = DatetimeIndex( + ["2014-01-01", NaT, "2014-03-01", NaT, "2014-05-01", "2014-07-01"] + ) + didx2 = DatetimeIndex( + ["2014-02-01", "2014-03-01", NaT, NaT, "2014-06-01", "2014-07-01"] + ) + darr = np.array( + [ + np.datetime64("2014-02-01 00:00"), + np.datetime64("2014-03-01 00:00"), + np.datetime64("nat"), + np.datetime64("nat"), + np.datetime64("2014-06-01 00:00"), + np.datetime64("2014-07-01 00:00"), + ] + ) + + cases = [(fidx1, fidx2), (didx1, didx2), (didx1, darr)] + + # Check pd.NaT is handles as the same as np.nan + with tm.assert_produces_warning(None): + for idx1, idx2 in cases: + result = idx1 < idx2 + expected = np.array([True, False, False, False, True, False]) + tm.assert_numpy_array_equal(result, expected) + + result = idx2 > idx1 + expected = np.array([True, False, False, False, True, False]) + tm.assert_numpy_array_equal(result, expected) + + result = idx1 <= idx2 + expected = np.array([True, False, False, False, True, True]) + tm.assert_numpy_array_equal(result, expected) + + result = idx2 >= idx1 + expected = np.array([True, False, False, False, True, True]) + tm.assert_numpy_array_equal(result, expected) + + result = idx1 == idx2 + expected = np.array([False, False, False, False, False, True]) + tm.assert_numpy_array_equal(result, expected) + + result = idx1 != idx2 + expected = np.array([True, True, True, True, True, False]) + tm.assert_numpy_array_equal(result, expected) + + with tm.assert_produces_warning(None): + for idx1, val in [(fidx1, np.nan), (didx1, NaT)]: + result = idx1 < val + expected = np.array([False, False, False, False, False, False]) + tm.assert_numpy_array_equal(result, expected) + result = idx1 > val + tm.assert_numpy_array_equal(result, expected) + + result = idx1 <= val + tm.assert_numpy_array_equal(result, expected) + result = idx1 >= val + tm.assert_numpy_array_equal(result, expected) + + result = idx1 == val + tm.assert_numpy_array_equal(result, expected) + + result = idx1 != val + expected = np.array([True, True, True, True, True, True]) + tm.assert_numpy_array_equal(result, expected) + + # Check pd.NaT is handles as the same as np.nan + with tm.assert_produces_warning(None): + for idx1, val in [(fidx1, 3), (didx1, datetime(2014, 3, 1))]: + result = idx1 < val + expected = np.array([True, False, False, False, False, False]) + tm.assert_numpy_array_equal(result, expected) + result = idx1 > val + expected = np.array([False, False, False, False, True, True]) + tm.assert_numpy_array_equal(result, expected) + + result = idx1 <= val + expected = np.array([True, False, True, False, False, False]) + tm.assert_numpy_array_equal(result, expected) + result = idx1 >= val + expected = np.array([False, False, True, False, True, True]) + tm.assert_numpy_array_equal(result, expected) + + result = idx1 == val + expected = np.array([False, False, True, False, False, False]) + tm.assert_numpy_array_equal(result, expected) + + result = idx1 != val + expected = np.array([True, True, False, True, True, True]) + tm.assert_numpy_array_equal(result, expected) + + def test_comparison_tzawareness_compat(self, comparison_op, box_with_array): + # GH#18162 + op = comparison_op + box = box_with_array + + dr = date_range("2016-01-01", periods=6) + dz = dr.tz_localize("US/Pacific") + + dr = tm.box_expected(dr, box) + dz = tm.box_expected(dz, box) + + if box is pd.DataFrame: + tolist = lambda x: x.astype(object).values.tolist()[0] + else: + tolist = list + + if op not in [operator.eq, operator.ne]: + msg = ( + r"Invalid comparison between dtype=datetime64\[ns.*\] " + "and (Timestamp|DatetimeArray|list|ndarray)" + ) + with pytest.raises(TypeError, match=msg): + op(dr, dz) + + with pytest.raises(TypeError, match=msg): + op(dr, tolist(dz)) + with pytest.raises(TypeError, match=msg): + op(dr, np.array(tolist(dz), dtype=object)) + with pytest.raises(TypeError, match=msg): + op(dz, dr) + + with pytest.raises(TypeError, match=msg): + op(dz, tolist(dr)) + with pytest.raises(TypeError, match=msg): + op(dz, np.array(tolist(dr), dtype=object)) + + # The aware==aware and naive==naive comparisons should *not* raise + assert np.all(dr == dr) + assert np.all(dr == tolist(dr)) + assert np.all(tolist(dr) == dr) + assert np.all(np.array(tolist(dr), dtype=object) == dr) + assert np.all(dr == np.array(tolist(dr), dtype=object)) + + assert np.all(dz == dz) + assert np.all(dz == tolist(dz)) + assert np.all(tolist(dz) == dz) + assert np.all(np.array(tolist(dz), dtype=object) == dz) + assert np.all(dz == np.array(tolist(dz), dtype=object)) + + def test_comparison_tzawareness_compat_scalars(self, comparison_op, box_with_array): + # GH#18162 + op = comparison_op + + dr = date_range("2016-01-01", periods=6) + dz = dr.tz_localize("US/Pacific") + + dr = tm.box_expected(dr, box_with_array) + dz = tm.box_expected(dz, box_with_array) + + # Check comparisons against scalar Timestamps + ts = Timestamp("2000-03-14 01:59") + ts_tz = Timestamp("2000-03-14 01:59", tz="Europe/Amsterdam") + + assert np.all(dr > ts) + msg = r"Invalid comparison between dtype=datetime64\[ns.*\] and Timestamp" + if op not in [operator.eq, operator.ne]: + with pytest.raises(TypeError, match=msg): + op(dr, ts_tz) + + assert np.all(dz > ts_tz) + if op not in [operator.eq, operator.ne]: + with pytest.raises(TypeError, match=msg): + op(dz, ts) + + if op not in [operator.eq, operator.ne]: + # GH#12601: Check comparison against Timestamps and DatetimeIndex + with pytest.raises(TypeError, match=msg): + op(ts, dz) + + @pytest.mark.parametrize( + "other", + [datetime(2016, 1, 1), Timestamp("2016-01-01"), np.datetime64("2016-01-01")], + ) + # Bug in NumPy? https://github.com/numpy/numpy/issues/13841 + # Raising in __eq__ will fallback to NumPy, which warns, fails, + # then re-raises the original exception. So we just need to ignore. + @pytest.mark.filterwarnings("ignore:elementwise comp:DeprecationWarning") + def test_scalar_comparison_tzawareness( + self, comparison_op, other, tz_aware_fixture, box_with_array + ): + op = comparison_op + tz = tz_aware_fixture + dti = date_range("2016-01-01", periods=2, tz=tz) + + dtarr = tm.box_expected(dti, box_with_array) + xbox = get_upcast_box(dtarr, other, True) + if op in [operator.eq, operator.ne]: + exbool = op is operator.ne + expected = np.array([exbool, exbool], dtype=bool) + expected = tm.box_expected(expected, xbox) + + result = op(dtarr, other) + tm.assert_equal(result, expected) + + result = op(other, dtarr) + tm.assert_equal(result, expected) + else: + msg = ( + r"Invalid comparison between dtype=datetime64\[ns, .*\] " + f"and {type(other).__name__}" + ) + with pytest.raises(TypeError, match=msg): + op(dtarr, other) + with pytest.raises(TypeError, match=msg): + op(other, dtarr) + + def test_nat_comparison_tzawareness(self, comparison_op): + # GH#19276 + # tzaware DatetimeIndex should not raise when compared to NaT + op = comparison_op + + dti = DatetimeIndex( + ["2014-01-01", NaT, "2014-03-01", NaT, "2014-05-01", "2014-07-01"] + ) + expected = np.array([op == operator.ne] * len(dti)) + result = op(dti, NaT) + tm.assert_numpy_array_equal(result, expected) + + result = op(dti.tz_localize("US/Pacific"), NaT) + tm.assert_numpy_array_equal(result, expected) + + def test_dti_cmp_str(self, tz_naive_fixture): + # GH#22074 + # regardless of tz, we expect these comparisons are valid + tz = tz_naive_fixture + rng = date_range("1/1/2000", periods=10, tz=tz) + other = "1/1/2000" + + result = rng == other + expected = np.array([True] + [False] * 9) + tm.assert_numpy_array_equal(result, expected) + + result = rng != other + expected = np.array([False] + [True] * 9) + tm.assert_numpy_array_equal(result, expected) + + result = rng < other + expected = np.array([False] * 10) + tm.assert_numpy_array_equal(result, expected) + + result = rng <= other + expected = np.array([True] + [False] * 9) + tm.assert_numpy_array_equal(result, expected) + + result = rng > other + expected = np.array([False] + [True] * 9) + tm.assert_numpy_array_equal(result, expected) + + result = rng >= other + expected = np.array([True] * 10) + tm.assert_numpy_array_equal(result, expected) + + def test_dti_cmp_list(self): + rng = date_range("1/1/2000", periods=10) + + result = rng == list(rng) + expected = rng == rng + tm.assert_numpy_array_equal(result, expected) + + @pytest.mark.parametrize( + "other", + [ + pd.timedelta_range("1D", periods=10), + pd.timedelta_range("1D", periods=10).to_series(), + pd.timedelta_range("1D", periods=10).asi8.view("m8[ns]"), + ], + ids=lambda x: type(x).__name__, + ) + def test_dti_cmp_tdi_tzawareness(self, other): + # GH#22074 + # reversion test that we _don't_ call _assert_tzawareness_compat + # when comparing against TimedeltaIndex + dti = date_range("2000-01-01", periods=10, tz="Asia/Tokyo") + + result = dti == other + expected = np.array([False] * 10) + tm.assert_numpy_array_equal(result, expected) + + result = dti != other + expected = np.array([True] * 10) + tm.assert_numpy_array_equal(result, expected) + msg = "Invalid comparison between" + with pytest.raises(TypeError, match=msg): + dti < other + with pytest.raises(TypeError, match=msg): + dti <= other + with pytest.raises(TypeError, match=msg): + dti > other + with pytest.raises(TypeError, match=msg): + dti >= other + + def test_dti_cmp_object_dtype(self): + # GH#22074 + dti = date_range("2000-01-01", periods=10, tz="Asia/Tokyo") + + other = dti.astype("O") + + result = dti == other + expected = np.array([True] * 10) + tm.assert_numpy_array_equal(result, expected) + + other = dti.tz_localize(None) + result = dti != other + tm.assert_numpy_array_equal(result, expected) + + other = np.array(list(dti[:5]) + [Timedelta(days=1)] * 5) + result = dti == other + expected = np.array([True] * 5 + [False] * 5) + tm.assert_numpy_array_equal(result, expected) + msg = ">=' not supported between instances of 'Timestamp' and 'Timedelta'" + with pytest.raises(TypeError, match=msg): + dti >= other + + +# ------------------------------------------------------------------ +# Arithmetic + + +class TestDatetime64Arithmetic: + # This class is intended for "finished" tests that are fully parametrized + # over DataFrame/Series/Index/DatetimeArray + + # ------------------------------------------------------------- + # Addition/Subtraction of timedelta-like + + @pytest.mark.arm_slow + def test_dt64arr_add_timedeltalike_scalar( + self, tz_naive_fixture, two_hours, box_with_array + ): + # GH#22005, GH#22163 check DataFrame doesn't raise TypeError + tz = tz_naive_fixture + + rng = date_range("2000-01-01", "2000-02-01", tz=tz) + expected = date_range("2000-01-01 02:00", "2000-02-01 02:00", tz=tz) + + rng = tm.box_expected(rng, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + result = rng + two_hours + tm.assert_equal(result, expected) + + result = two_hours + rng + tm.assert_equal(result, expected) + + rng += two_hours + tm.assert_equal(rng, expected) + + def test_dt64arr_sub_timedeltalike_scalar( + self, tz_naive_fixture, two_hours, box_with_array + ): + tz = tz_naive_fixture + + rng = date_range("2000-01-01", "2000-02-01", tz=tz) + expected = date_range("1999-12-31 22:00", "2000-01-31 22:00", tz=tz) + + rng = tm.box_expected(rng, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + result = rng - two_hours + tm.assert_equal(result, expected) + + rng -= two_hours + tm.assert_equal(rng, expected) + + def test_dt64_array_sub_dt_with_different_timezone(self, box_with_array): + t1 = date_range("20130101", periods=3).tz_localize("US/Eastern") + t1 = tm.box_expected(t1, box_with_array) + t2 = Timestamp("20130101").tz_localize("CET") + tnaive = Timestamp(20130101) + + result = t1 - t2 + expected = TimedeltaIndex( + ["0 days 06:00:00", "1 days 06:00:00", "2 days 06:00:00"] + ) + expected = tm.box_expected(expected, box_with_array) + tm.assert_equal(result, expected) + + result = t2 - t1 + expected = TimedeltaIndex( + ["-1 days +18:00:00", "-2 days +18:00:00", "-3 days +18:00:00"] + ) + expected = tm.box_expected(expected, box_with_array) + tm.assert_equal(result, expected) + + msg = "Cannot subtract tz-naive and tz-aware datetime-like objects" + with pytest.raises(TypeError, match=msg): + t1 - tnaive + + with pytest.raises(TypeError, match=msg): + tnaive - t1 + + def test_dt64_array_sub_dt64_array_with_different_timezone(self, box_with_array): + t1 = date_range("20130101", periods=3).tz_localize("US/Eastern") + t1 = tm.box_expected(t1, box_with_array) + t2 = date_range("20130101", periods=3).tz_localize("CET") + t2 = tm.box_expected(t2, box_with_array) + tnaive = date_range("20130101", periods=3) + + result = t1 - t2 + expected = TimedeltaIndex( + ["0 days 06:00:00", "0 days 06:00:00", "0 days 06:00:00"] + ) + expected = tm.box_expected(expected, box_with_array) + tm.assert_equal(result, expected) + + result = t2 - t1 + expected = TimedeltaIndex( + ["-1 days +18:00:00", "-1 days +18:00:00", "-1 days +18:00:00"] + ) + expected = tm.box_expected(expected, box_with_array) + tm.assert_equal(result, expected) + + msg = "Cannot subtract tz-naive and tz-aware datetime-like objects" + with pytest.raises(TypeError, match=msg): + t1 - tnaive + + with pytest.raises(TypeError, match=msg): + tnaive - t1 + + def test_dt64arr_add_sub_td64_nat(self, box_with_array, tz_naive_fixture): + # GH#23320 special handling for timedelta64("NaT") + tz = tz_naive_fixture + + dti = date_range("1994-04-01", periods=9, tz=tz, freq="QS") + other = np.timedelta64("NaT") + expected = DatetimeIndex(["NaT"] * 9, tz=tz).as_unit("ns") + + obj = tm.box_expected(dti, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + result = obj + other + tm.assert_equal(result, expected) + result = other + obj + tm.assert_equal(result, expected) + result = obj - other + tm.assert_equal(result, expected) + msg = "cannot subtract" + with pytest.raises(TypeError, match=msg): + other - obj + + def test_dt64arr_add_sub_td64ndarray(self, tz_naive_fixture, box_with_array): + tz = tz_naive_fixture + dti = date_range("2016-01-01", periods=3, tz=tz) + tdi = TimedeltaIndex(["-1 Day", "-1 Day", "-1 Day"]) + tdarr = tdi.values + + expected = date_range("2015-12-31", "2016-01-02", periods=3, tz=tz) + + dtarr = tm.box_expected(dti, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + result = dtarr + tdarr + tm.assert_equal(result, expected) + result = tdarr + dtarr + tm.assert_equal(result, expected) + + expected = date_range("2016-01-02", "2016-01-04", periods=3, tz=tz) + expected = tm.box_expected(expected, box_with_array) + + result = dtarr - tdarr + tm.assert_equal(result, expected) + msg = "cannot subtract|(bad|unsupported) operand type for unary" + with pytest.raises(TypeError, match=msg): + tdarr - dtarr + + # ----------------------------------------------------------------- + # Subtraction of datetime-like scalars + + @pytest.mark.parametrize( + "ts", + [ + Timestamp("2013-01-01"), + Timestamp("2013-01-01").to_pydatetime(), + Timestamp("2013-01-01").to_datetime64(), + # GH#7996, GH#22163 ensure non-nano datetime64 is converted to nano + # for DataFrame operation + np.datetime64("2013-01-01", "D"), + ], + ) + def test_dt64arr_sub_dtscalar(self, box_with_array, ts): + # GH#8554, GH#22163 DataFrame op should _not_ return dt64 dtype + idx = date_range("2013-01-01", periods=3)._with_freq(None) + idx = tm.box_expected(idx, box_with_array) + + expected = TimedeltaIndex(["0 Days", "1 Day", "2 Days"]) + expected = tm.box_expected(expected, box_with_array) + + result = idx - ts + tm.assert_equal(result, expected) + + result = ts - idx + tm.assert_equal(result, -expected) + tm.assert_equal(result, -expected) + + def test_dt64arr_sub_timestamp_tzaware(self, box_with_array): + ser = date_range("2014-03-17", periods=2, freq="D", tz="US/Eastern") + ser = ser._with_freq(None) + ts = ser[0] + + ser = tm.box_expected(ser, box_with_array) + + delta_series = Series([np.timedelta64(0, "D"), np.timedelta64(1, "D")]) + expected = tm.box_expected(delta_series, box_with_array) + + tm.assert_equal(ser - ts, expected) + tm.assert_equal(ts - ser, -expected) + + def test_dt64arr_sub_NaT(self, box_with_array, unit): + # GH#18808 + dti = DatetimeIndex([NaT, Timestamp("19900315")]).as_unit(unit) + ser = tm.box_expected(dti, box_with_array) + + result = ser - NaT + expected = Series([NaT, NaT], dtype=f"timedelta64[{unit}]") + expected = tm.box_expected(expected, box_with_array) + tm.assert_equal(result, expected) + + dti_tz = dti.tz_localize("Asia/Tokyo") + ser_tz = tm.box_expected(dti_tz, box_with_array) + + result = ser_tz - NaT + expected = Series([NaT, NaT], dtype=f"timedelta64[{unit}]") + expected = tm.box_expected(expected, box_with_array) + tm.assert_equal(result, expected) + + # ------------------------------------------------------------- + # Subtraction of datetime-like array-like + + def test_dt64arr_sub_dt64object_array(self, box_with_array, tz_naive_fixture): + dti = date_range("2016-01-01", periods=3, tz=tz_naive_fixture) + expected = dti - dti + + obj = tm.box_expected(dti, box_with_array) + expected = tm.box_expected(expected, box_with_array).astype(object) + + with tm.assert_produces_warning(PerformanceWarning): + result = obj - obj.astype(object) + tm.assert_equal(result, expected) + + def test_dt64arr_naive_sub_dt64ndarray(self, box_with_array): + dti = date_range("2016-01-01", periods=3, tz=None) + dt64vals = dti.values + + dtarr = tm.box_expected(dti, box_with_array) + + expected = dtarr - dtarr + result = dtarr - dt64vals + tm.assert_equal(result, expected) + result = dt64vals - dtarr + tm.assert_equal(result, expected) + + def test_dt64arr_aware_sub_dt64ndarray_raises( + self, tz_aware_fixture, box_with_array + ): + tz = tz_aware_fixture + dti = date_range("2016-01-01", periods=3, tz=tz) + dt64vals = dti.values + + dtarr = tm.box_expected(dti, box_with_array) + msg = "Cannot subtract tz-naive and tz-aware datetime" + with pytest.raises(TypeError, match=msg): + dtarr - dt64vals + with pytest.raises(TypeError, match=msg): + dt64vals - dtarr + + # ------------------------------------------------------------- + # Addition of datetime-like others (invalid) + + def test_dt64arr_add_dtlike_raises(self, tz_naive_fixture, box_with_array): + # GH#22163 ensure DataFrame doesn't cast Timestamp to i8 + # GH#9631 + tz = tz_naive_fixture + + dti = date_range("2016-01-01", periods=3, tz=tz) + if tz is None: + dti2 = dti.tz_localize("US/Eastern") + else: + dti2 = dti.tz_localize(None) + dtarr = tm.box_expected(dti, box_with_array) + + assert_cannot_add(dtarr, dti.values) + assert_cannot_add(dtarr, dti) + assert_cannot_add(dtarr, dtarr) + assert_cannot_add(dtarr, dti[0]) + assert_cannot_add(dtarr, dti[0].to_pydatetime()) + assert_cannot_add(dtarr, dti[0].to_datetime64()) + assert_cannot_add(dtarr, dti2[0]) + assert_cannot_add(dtarr, dti2[0].to_pydatetime()) + assert_cannot_add(dtarr, np.datetime64("2011-01-01", "D")) + + # ------------------------------------------------------------- + # Other Invalid Addition/Subtraction + + # Note: freq here includes both Tick and non-Tick offsets; this is + # relevant because historically integer-addition was allowed if we had + # a freq. + @pytest.mark.parametrize("freq", ["h", "D", "W", "2ME", "MS", "QE", "B", None]) + @pytest.mark.parametrize("dtype", [None, "uint8"]) + def test_dt64arr_addsub_intlike( + self, request, dtype, index_or_series_or_array, freq, tz_naive_fixture + ): + # GH#19959, GH#19123, GH#19012 + # GH#55860 use index_or_series_or_array instead of box_with_array + # bc DataFrame alignment makes it inapplicable + tz = tz_naive_fixture + + if freq is None: + dti = DatetimeIndex(["NaT", "2017-04-05 06:07:08"], tz=tz) + else: + dti = date_range("2016-01-01", periods=2, freq=freq, tz=tz) + + obj = index_or_series_or_array(dti) + other = np.array([4, -1]) + if dtype is not None: + other = other.astype(dtype) + + msg = "|".join( + [ + "Addition/subtraction of integers", + "cannot subtract DatetimeArray from", + # IntegerArray + "can only perform ops with numeric values", + "unsupported operand type.*Categorical", + r"unsupported operand type\(s\) for -: 'int' and 'Timestamp'", + ] + ) + assert_invalid_addsub_type(obj, 1, msg) + assert_invalid_addsub_type(obj, np.int64(2), msg) + assert_invalid_addsub_type(obj, np.array(3, dtype=np.int64), msg) + assert_invalid_addsub_type(obj, other, msg) + assert_invalid_addsub_type(obj, np.array(other), msg) + assert_invalid_addsub_type(obj, pd.array(other), msg) + assert_invalid_addsub_type(obj, pd.Categorical(other), msg) + assert_invalid_addsub_type(obj, pd.Index(other), msg) + assert_invalid_addsub_type(obj, Series(other), msg) + + @pytest.mark.parametrize( + "other", + [ + 3.14, + np.array([2.0, 3.0]), + # GH#13078 datetime +/- Period is invalid + Period("2011-01-01", freq="D"), + # https://github.com/pandas-dev/pandas/issues/10329 + time(1, 2, 3), + ], + ) + @pytest.mark.parametrize("dti_freq", [None, "D"]) + def test_dt64arr_add_sub_invalid(self, dti_freq, other, box_with_array): + dti = DatetimeIndex(["2011-01-01", "2011-01-02"], freq=dti_freq) + dtarr = tm.box_expected(dti, box_with_array) + msg = "|".join( + [ + "unsupported operand type", + "cannot (add|subtract)", + "cannot use operands with types", + "ufunc '?(add|subtract)'? cannot use operands with types", + "Concatenation operation is not implemented for NumPy arrays", + ] + ) + assert_invalid_addsub_type(dtarr, other, msg) + + @pytest.mark.parametrize("pi_freq", ["D", "W", "Q", "h"]) + @pytest.mark.parametrize("dti_freq", [None, "D"]) + def test_dt64arr_add_sub_parr( + self, dti_freq, pi_freq, box_with_array, box_with_array2 + ): + # GH#20049 subtracting PeriodIndex should raise TypeError + dti = DatetimeIndex(["2011-01-01", "2011-01-02"], freq=dti_freq) + pi = dti.to_period(pi_freq) + + dtarr = tm.box_expected(dti, box_with_array) + parr = tm.box_expected(pi, box_with_array2) + msg = "|".join( + [ + "cannot (add|subtract)", + "unsupported operand", + "descriptor.*requires", + "ufunc.*cannot use operands", + ] + ) + assert_invalid_addsub_type(dtarr, parr, msg) + + @pytest.mark.filterwarnings("ignore::pandas.errors.PerformanceWarning") + def test_dt64arr_addsub_time_objects_raises(self, box_with_array, tz_naive_fixture): + # https://github.com/pandas-dev/pandas/issues/10329 + + tz = tz_naive_fixture + + obj1 = date_range("2012-01-01", periods=3, tz=tz) + obj2 = [time(i, i, i) for i in range(3)] + + obj1 = tm.box_expected(obj1, box_with_array) + obj2 = tm.box_expected(obj2, box_with_array) + + msg = "|".join( + [ + "unsupported operand", + "cannot subtract DatetimeArray from ndarray", + ] + ) + # pandas.errors.PerformanceWarning: Non-vectorized DateOffset being + # applied to Series or DatetimeIndex + # we aren't testing that here, so ignore. + assert_invalid_addsub_type(obj1, obj2, msg=msg) + + # ------------------------------------------------------------- + # Other invalid operations + + @pytest.mark.parametrize( + "dt64_series", + [ + Series([Timestamp("19900315"), Timestamp("19900315")]), + Series([NaT, Timestamp("19900315")]), + Series([NaT, NaT], dtype="datetime64[ns]"), + ], + ) + @pytest.mark.parametrize("one", [1, 1.0, np.array(1)]) + def test_dt64_mul_div_numeric_invalid(self, one, dt64_series, box_with_array): + obj = tm.box_expected(dt64_series, box_with_array) + + msg = "cannot perform .* with this index type" + + # multiplication + with pytest.raises(TypeError, match=msg): + obj * one + with pytest.raises(TypeError, match=msg): + one * obj + + # division + with pytest.raises(TypeError, match=msg): + obj / one + with pytest.raises(TypeError, match=msg): + one / obj + + +class TestDatetime64DateOffsetArithmetic: + # ------------------------------------------------------------- + # Tick DateOffsets + + # TODO: parametrize over timezone? + @pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"]) + def test_dt64arr_series_add_tick_DateOffset(self, box_with_array, unit): + # GH#4532 + # operate with pd.offsets + ser = Series( + [Timestamp("20130101 9:01"), Timestamp("20130101 9:02")] + ).dt.as_unit(unit) + expected = Series( + [Timestamp("20130101 9:01:05"), Timestamp("20130101 9:02:05")] + ).dt.as_unit(unit) + + ser = tm.box_expected(ser, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + result = ser + pd.offsets.Second(5) + tm.assert_equal(result, expected) + + result2 = pd.offsets.Second(5) + ser + tm.assert_equal(result2, expected) + + def test_dt64arr_series_sub_tick_DateOffset(self, box_with_array): + # GH#4532 + # operate with pd.offsets + ser = Series([Timestamp("20130101 9:01"), Timestamp("20130101 9:02")]) + expected = Series( + [Timestamp("20130101 9:00:55"), Timestamp("20130101 9:01:55")] + ) + + ser = tm.box_expected(ser, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + result = ser - pd.offsets.Second(5) + tm.assert_equal(result, expected) + + result2 = -pd.offsets.Second(5) + ser + tm.assert_equal(result2, expected) + msg = "(bad|unsupported) operand type for unary" + with pytest.raises(TypeError, match=msg): + pd.offsets.Second(5) - ser + + @pytest.mark.parametrize( + "cls_name", ["Day", "Hour", "Minute", "Second", "Milli", "Micro", "Nano"] + ) + def test_dt64arr_add_sub_tick_DateOffset_smoke(self, cls_name, box_with_array): + # GH#4532 + # smoke tests for valid DateOffsets + ser = Series([Timestamp("20130101 9:01"), Timestamp("20130101 9:02")]) + ser = tm.box_expected(ser, box_with_array) + + offset_cls = getattr(pd.offsets, cls_name) + ser + offset_cls(5) + offset_cls(5) + ser + ser - offset_cls(5) + + def test_dti_add_tick_tzaware(self, tz_aware_fixture, box_with_array): + # GH#21610, GH#22163 ensure DataFrame doesn't return object-dtype + tz = tz_aware_fixture + if tz == "US/Pacific": + dates = date_range("2012-11-01", periods=3, tz=tz) + offset = dates + pd.offsets.Hour(5) + assert dates[0] + pd.offsets.Hour(5) == offset[0] + + dates = date_range("2010-11-01 00:00", periods=3, tz=tz, freq="h") + expected = DatetimeIndex( + ["2010-11-01 05:00", "2010-11-01 06:00", "2010-11-01 07:00"], + freq="h", + tz=tz, + ).as_unit("ns") + + dates = tm.box_expected(dates, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + for scalar in [pd.offsets.Hour(5), np.timedelta64(5, "h"), timedelta(hours=5)]: + offset = dates + scalar + tm.assert_equal(offset, expected) + offset = scalar + dates + tm.assert_equal(offset, expected) + + roundtrip = offset - scalar + tm.assert_equal(roundtrip, dates) + + msg = "|".join( + ["bad operand type for unary -", "cannot subtract DatetimeArray"] + ) + with pytest.raises(TypeError, match=msg): + scalar - dates + + # ------------------------------------------------------------- + # RelativeDelta DateOffsets + + @pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"]) + def test_dt64arr_add_sub_relativedelta_offsets(self, box_with_array, unit): + # GH#10699 + vec = DatetimeIndex( + [ + Timestamp("2000-01-05 00:15:00"), + Timestamp("2000-01-31 00:23:00"), + Timestamp("2000-01-01"), + Timestamp("2000-03-31"), + Timestamp("2000-02-29"), + Timestamp("2000-12-31"), + Timestamp("2000-05-15"), + Timestamp("2001-06-15"), + ] + ).as_unit(unit) + vec = tm.box_expected(vec, box_with_array) + vec_items = vec.iloc[0] if box_with_array is pd.DataFrame else vec + + # DateOffset relativedelta fastpath + relative_kwargs = [ + ("years", 2), + ("months", 5), + ("days", 3), + ("hours", 5), + ("minutes", 10), + ("seconds", 2), + ("microseconds", 5), + ] + for i, (offset_unit, value) in enumerate(relative_kwargs): + off = DateOffset(**{offset_unit: value}) + + exp_unit = unit + if offset_unit == "microseconds" and unit != "ns": + exp_unit = "us" + + # TODO(GH#55564): as_unit will be unnecessary + expected = DatetimeIndex([x + off for x in vec_items]).as_unit(exp_unit) + expected = tm.box_expected(expected, box_with_array) + tm.assert_equal(expected, vec + off) + + expected = DatetimeIndex([x - off for x in vec_items]).as_unit(exp_unit) + expected = tm.box_expected(expected, box_with_array) + tm.assert_equal(expected, vec - off) + + off = DateOffset(**dict(relative_kwargs[: i + 1])) + + expected = DatetimeIndex([x + off for x in vec_items]).as_unit(exp_unit) + expected = tm.box_expected(expected, box_with_array) + tm.assert_equal(expected, vec + off) + + expected = DatetimeIndex([x - off for x in vec_items]).as_unit(exp_unit) + expected = tm.box_expected(expected, box_with_array) + tm.assert_equal(expected, vec - off) + msg = "(bad|unsupported) operand type for unary" + with pytest.raises(TypeError, match=msg): + off - vec + + # ------------------------------------------------------------- + # Non-Tick, Non-RelativeDelta DateOffsets + + # TODO: redundant with test_dt64arr_add_sub_DateOffset? that includes + # tz-aware cases which this does not + @pytest.mark.filterwarnings("ignore::pandas.errors.PerformanceWarning") + @pytest.mark.parametrize( + "cls_and_kwargs", + [ + "YearBegin", + ("YearBegin", {"month": 5}), + "YearEnd", + ("YearEnd", {"month": 5}), + "MonthBegin", + "MonthEnd", + "SemiMonthEnd", + "SemiMonthBegin", + "Week", + ("Week", {"weekday": 3}), + "Week", + ("Week", {"weekday": 6}), + "BusinessDay", + "BDay", + "QuarterEnd", + "QuarterBegin", + "CustomBusinessDay", + "CDay", + "CBMonthEnd", + "CBMonthBegin", + "BMonthBegin", + "BMonthEnd", + "BusinessHour", + "BYearBegin", + "BYearEnd", + "BQuarterBegin", + ("LastWeekOfMonth", {"weekday": 2}), + ( + "FY5253Quarter", + { + "qtr_with_extra_week": 1, + "startingMonth": 1, + "weekday": 2, + "variation": "nearest", + }, + ), + ("FY5253", {"weekday": 0, "startingMonth": 2, "variation": "nearest"}), + ("WeekOfMonth", {"weekday": 2, "week": 2}), + "Easter", + ("DateOffset", {"day": 4}), + ("DateOffset", {"month": 5}), + ], + ) + @pytest.mark.parametrize("normalize", [True, False]) + @pytest.mark.parametrize("n", [0, 5]) + @pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"]) + @pytest.mark.parametrize("tz", [None, "US/Central"]) + def test_dt64arr_add_sub_DateOffsets( + self, box_with_array, n, normalize, cls_and_kwargs, unit, tz + ): + # GH#10699 + # assert vectorized operation matches pointwise operations + + if isinstance(cls_and_kwargs, tuple): + # If cls_name param is a tuple, then 2nd entry is kwargs for + # the offset constructor + cls_name, kwargs = cls_and_kwargs + else: + cls_name = cls_and_kwargs + kwargs = {} + + if n == 0 and cls_name in [ + "WeekOfMonth", + "LastWeekOfMonth", + "FY5253Quarter", + "FY5253", + ]: + # passing n = 0 is invalid for these offset classes + return + + vec = ( + DatetimeIndex( + [ + Timestamp("2000-01-05 00:15:00"), + Timestamp("2000-01-31 00:23:00"), + Timestamp("2000-01-01"), + Timestamp("2000-03-31"), + Timestamp("2000-02-29"), + Timestamp("2000-12-31"), + Timestamp("2000-05-15"), + Timestamp("2001-06-15"), + ] + ) + .as_unit(unit) + .tz_localize(tz) + ) + vec = tm.box_expected(vec, box_with_array) + vec_items = vec.iloc[0] if box_with_array is pd.DataFrame else vec + + offset_cls = getattr(pd.offsets, cls_name) + offset = offset_cls(n, normalize=normalize, **kwargs) + + # TODO(GH#55564): as_unit will be unnecessary + expected = DatetimeIndex([x + offset for x in vec_items]).as_unit(unit) + expected = tm.box_expected(expected, box_with_array) + tm.assert_equal(expected, vec + offset) + tm.assert_equal(expected, offset + vec) + + expected = DatetimeIndex([x - offset for x in vec_items]).as_unit(unit) + expected = tm.box_expected(expected, box_with_array) + tm.assert_equal(expected, vec - offset) + + expected = DatetimeIndex([offset + x for x in vec_items]).as_unit(unit) + expected = tm.box_expected(expected, box_with_array) + tm.assert_equal(expected, offset + vec) + msg = "(bad|unsupported) operand type for unary" + with pytest.raises(TypeError, match=msg): + offset - vec + + @pytest.mark.parametrize( + "other", + [ + np.array([pd.offsets.MonthEnd(), pd.offsets.Day(n=2)]), + np.array([pd.offsets.DateOffset(years=1), pd.offsets.MonthEnd()]), + np.array( # matching offsets + [pd.offsets.DateOffset(years=1), pd.offsets.DateOffset(years=1)] + ), + ], + ) + @pytest.mark.parametrize("op", [operator.add, roperator.radd, operator.sub]) + def test_dt64arr_add_sub_offset_array( + self, tz_naive_fixture, box_with_array, op, other + ): + # GH#18849 + # GH#10699 array of offsets + + tz = tz_naive_fixture + dti = date_range("2017-01-01", periods=2, tz=tz) + dtarr = tm.box_expected(dti, box_with_array) + + expected = DatetimeIndex([op(dti[n], other[n]) for n in range(len(dti))]) + expected = tm.box_expected(expected, box_with_array).astype(object) + + with tm.assert_produces_warning(PerformanceWarning): + res = op(dtarr, other) + tm.assert_equal(res, expected) + + # Same thing but boxing other + other = tm.box_expected(other, box_with_array) + if box_with_array is pd.array and op is roperator.radd: + # We expect a NumpyExtensionArray, not ndarray[object] here + expected = pd.array(expected, dtype=object) + with tm.assert_produces_warning(PerformanceWarning): + res = op(dtarr, other) + tm.assert_equal(res, expected) + + @pytest.mark.parametrize( + "op, offset, exp, exp_freq", + [ + ( + "__add__", + DateOffset(months=3, days=10), + [ + Timestamp("2014-04-11"), + Timestamp("2015-04-11"), + Timestamp("2016-04-11"), + Timestamp("2017-04-11"), + ], + None, + ), + ( + "__add__", + DateOffset(months=3), + [ + Timestamp("2014-04-01"), + Timestamp("2015-04-01"), + Timestamp("2016-04-01"), + Timestamp("2017-04-01"), + ], + "YS-APR", + ), + ( + "__sub__", + DateOffset(months=3, days=10), + [ + Timestamp("2013-09-21"), + Timestamp("2014-09-21"), + Timestamp("2015-09-21"), + Timestamp("2016-09-21"), + ], + None, + ), + ( + "__sub__", + DateOffset(months=3), + [ + Timestamp("2013-10-01"), + Timestamp("2014-10-01"), + Timestamp("2015-10-01"), + Timestamp("2016-10-01"), + ], + "YS-OCT", + ), + ], + ) + def test_dti_add_sub_nonzero_mth_offset( + self, op, offset, exp, exp_freq, tz_aware_fixture, box_with_array + ): + # GH 26258 + tz = tz_aware_fixture + date = date_range(start="01 Jan 2014", end="01 Jan 2017", freq="YS", tz=tz) + date = tm.box_expected(date, box_with_array, False) + mth = getattr(date, op) + result = mth(offset) + + expected = DatetimeIndex(exp, tz=tz).as_unit("ns") + expected = tm.box_expected(expected, box_with_array, False) + tm.assert_equal(result, expected) + + def test_dt64arr_series_add_DateOffset_with_milli(self): + # GH 57529 + dti = DatetimeIndex( + [ + "2000-01-01 00:00:00.012345678", + "2000-01-31 00:00:00.012345678", + "2000-02-29 00:00:00.012345678", + ], + dtype="datetime64[ns]", + ) + result = dti + DateOffset(milliseconds=4) + expected = DatetimeIndex( + [ + "2000-01-01 00:00:00.016345678", + "2000-01-31 00:00:00.016345678", + "2000-02-29 00:00:00.016345678", + ], + dtype="datetime64[ns]", + ) + tm.assert_index_equal(result, expected) + + result = dti + DateOffset(days=1, milliseconds=4) + expected = DatetimeIndex( + [ + "2000-01-02 00:00:00.016345678", + "2000-02-01 00:00:00.016345678", + "2000-03-01 00:00:00.016345678", + ], + dtype="datetime64[ns]", + ) + tm.assert_index_equal(result, expected) + + +class TestDatetime64OverflowHandling: + # TODO: box + de-duplicate + + def test_dt64_overflow_masking(self, box_with_array): + # GH#25317 + left = Series([Timestamp("1969-12-31")], dtype="M8[ns]") + right = Series([NaT]) + + left = tm.box_expected(left, box_with_array) + right = tm.box_expected(right, box_with_array) + + expected = TimedeltaIndex([NaT], dtype="m8[ns]") + expected = tm.box_expected(expected, box_with_array) + + result = left - right + tm.assert_equal(result, expected) + + def test_dt64_series_arith_overflow(self): + # GH#12534, fixed by GH#19024 + dt = Timestamp("1700-01-31") + td = Timedelta("20000 Days") + dti = date_range("1949-09-30", freq="100YE", periods=4) + ser = Series(dti) + msg = "Overflow in int64 addition" + with pytest.raises(OverflowError, match=msg): + ser - dt + with pytest.raises(OverflowError, match=msg): + dt - ser + with pytest.raises(OverflowError, match=msg): + ser + td + with pytest.raises(OverflowError, match=msg): + td + ser + + ser.iloc[-1] = NaT + expected = Series( + ["2004-10-03", "2104-10-04", "2204-10-04", "NaT"], dtype="datetime64[ns]" + ) + res = ser + td + tm.assert_series_equal(res, expected) + res = td + ser + tm.assert_series_equal(res, expected) + + ser.iloc[1:] = NaT + expected = Series(["91279 Days", "NaT", "NaT", "NaT"], dtype="timedelta64[ns]") + res = ser - dt + tm.assert_series_equal(res, expected) + res = dt - ser + tm.assert_series_equal(res, -expected) + + def test_datetimeindex_sub_timestamp_overflow(self): + dtimax = pd.to_datetime(["2021-12-28 17:19", Timestamp.max]).as_unit("ns") + dtimin = pd.to_datetime(["2021-12-28 17:19", Timestamp.min]).as_unit("ns") + + tsneg = Timestamp("1950-01-01").as_unit("ns") + ts_neg_variants = [ + tsneg, + tsneg.to_pydatetime(), + tsneg.to_datetime64().astype("datetime64[ns]"), + tsneg.to_datetime64().astype("datetime64[D]"), + ] + + tspos = Timestamp("1980-01-01").as_unit("ns") + ts_pos_variants = [ + tspos, + tspos.to_pydatetime(), + tspos.to_datetime64().astype("datetime64[ns]"), + tspos.to_datetime64().astype("datetime64[D]"), + ] + msg = "Overflow in int64 addition" + for variant in ts_neg_variants: + with pytest.raises(OverflowError, match=msg): + dtimax - variant + + expected = Timestamp.max._value - tspos._value + for variant in ts_pos_variants: + res = dtimax - variant + assert res[1]._value == expected + + expected = Timestamp.min._value - tsneg._value + for variant in ts_neg_variants: + res = dtimin - variant + assert res[1]._value == expected + + for variant in ts_pos_variants: + with pytest.raises(OverflowError, match=msg): + dtimin - variant + + def test_datetimeindex_sub_datetimeindex_overflow(self): + # GH#22492, GH#22508 + dtimax = pd.to_datetime(["2021-12-28 17:19", Timestamp.max]).as_unit("ns") + dtimin = pd.to_datetime(["2021-12-28 17:19", Timestamp.min]).as_unit("ns") + + ts_neg = pd.to_datetime(["1950-01-01", "1950-01-01"]).as_unit("ns") + ts_pos = pd.to_datetime(["1980-01-01", "1980-01-01"]).as_unit("ns") + + # General tests + expected = Timestamp.max._value - ts_pos[1]._value + result = dtimax - ts_pos + assert result[1]._value == expected + + expected = Timestamp.min._value - ts_neg[1]._value + result = dtimin - ts_neg + assert result[1]._value == expected + msg = "Overflow in int64 addition" + with pytest.raises(OverflowError, match=msg): + dtimax - ts_neg + + with pytest.raises(OverflowError, match=msg): + dtimin - ts_pos + + # Edge cases + tmin = pd.to_datetime([Timestamp.min]) + t1 = tmin + Timedelta.max + Timedelta("1us") + with pytest.raises(OverflowError, match=msg): + t1 - tmin + + tmax = pd.to_datetime([Timestamp.max]) + t2 = tmax + Timedelta.min - Timedelta("1us") + with pytest.raises(OverflowError, match=msg): + tmax - t2 + + +class TestTimestampSeriesArithmetic: + def test_empty_series_add_sub(self, box_with_array): + # GH#13844 + a = Series(dtype="M8[ns]") + b = Series(dtype="m8[ns]") + a = box_with_array(a) + b = box_with_array(b) + tm.assert_equal(a, a + b) + tm.assert_equal(a, a - b) + tm.assert_equal(a, b + a) + msg = "cannot subtract" + with pytest.raises(TypeError, match=msg): + b - a + + def test_operators_datetimelike(self): + # ## timedelta64 ### + td1 = Series([timedelta(minutes=5, seconds=3)] * 3) + td1.iloc[2] = np.nan + + # ## datetime64 ### + dt1 = Series( + [ + Timestamp("20111230"), + Timestamp("20120101"), + Timestamp("20120103"), + ] + ) + dt1.iloc[2] = np.nan + dt2 = Series( + [ + Timestamp("20111231"), + Timestamp("20120102"), + Timestamp("20120104"), + ] + ) + dt1 - dt2 + dt2 - dt1 + + # datetime64 with timetimedelta + dt1 + td1 + td1 + dt1 + dt1 - td1 + + # timetimedelta with datetime64 + td1 + dt1 + dt1 + td1 + + def test_dt64ser_sub_datetime_dtype(self, unit): + ts = Timestamp(datetime(1993, 1, 7, 13, 30, 00)) + dt = datetime(1993, 6, 22, 13, 30) + ser = Series([ts], dtype=f"M8[{unit}]") + result = ser - dt + + # the expected unit is the max of `unit` and the unit imputed to `dt`, + # which is "us" + exp_unit = tm.get_finest_unit(unit, "us") + assert result.dtype == f"timedelta64[{exp_unit}]" + + # ------------------------------------------------------------- + # TODO: This next block of tests came from tests.series.test_operators, + # needs to be de-duplicated and parametrized over `box` classes + + @pytest.mark.parametrize( + "left, right, op_fail", + [ + [ + [Timestamp("20111230"), Timestamp("20120101"), NaT], + [Timestamp("20111231"), Timestamp("20120102"), Timestamp("20120104")], + ["__sub__", "__rsub__"], + ], + [ + [Timestamp("20111230"), Timestamp("20120101"), NaT], + [timedelta(minutes=5, seconds=3), timedelta(minutes=5, seconds=3), NaT], + ["__add__", "__radd__", "__sub__"], + ], + [ + [ + Timestamp("20111230", tz="US/Eastern"), + Timestamp("20111230", tz="US/Eastern"), + NaT, + ], + [timedelta(minutes=5, seconds=3), NaT, timedelta(minutes=5, seconds=3)], + ["__add__", "__radd__", "__sub__"], + ], + ], + ) + def test_operators_datetimelike_invalid( + self, left, right, op_fail, all_arithmetic_operators + ): + # these are all TypeError ops + op_str = all_arithmetic_operators + arg1 = Series(left) + arg2 = Series(right) + # check that we are getting a TypeError + # with 'operate' (from core/ops.py) for the ops that are not + # defined + op = getattr(arg1, op_str, None) + # Previously, _validate_for_numeric_binop in core/indexes/base.py + # did this for us. + if op_str not in op_fail: + with pytest.raises( + TypeError, match="operate|[cC]annot|unsupported operand" + ): + op(arg2) + else: + # Smoke test + op(arg2) + + def test_sub_single_tz(self, unit): + # GH#12290 + s1 = Series([Timestamp("2016-02-10", tz="America/Sao_Paulo")]).dt.as_unit(unit) + s2 = Series([Timestamp("2016-02-08", tz="America/Sao_Paulo")]).dt.as_unit(unit) + result = s1 - s2 + expected = Series([Timedelta("2days")]).dt.as_unit(unit) + tm.assert_series_equal(result, expected) + result = s2 - s1 + expected = Series([Timedelta("-2days")]).dt.as_unit(unit) + tm.assert_series_equal(result, expected) + + def test_dt64tz_series_sub_dtitz(self): + # GH#19071 subtracting tzaware DatetimeIndex from tzaware Series + # (with same tz) raises, fixed by #19024 + dti = date_range("1999-09-30", periods=10, tz="US/Pacific") + ser = Series(dti) + expected = Series(TimedeltaIndex(["0days"] * 10)) + + res = dti - ser + tm.assert_series_equal(res, expected) + res = ser - dti + tm.assert_series_equal(res, expected) + + def test_sub_datetime_compat(self, unit): + # see GH#14088 + ser = Series([datetime(2016, 8, 23, 12, tzinfo=pytz.utc), NaT]).dt.as_unit(unit) + dt = datetime(2016, 8, 22, 12, tzinfo=pytz.utc) + # The datetime object has "us" so we upcast lower units + exp_unit = tm.get_finest_unit(unit, "us") + exp = Series([Timedelta("1 days"), NaT]).dt.as_unit(exp_unit) + result = ser - dt + tm.assert_series_equal(result, exp) + result2 = ser - Timestamp(dt) + tm.assert_series_equal(result2, exp) + + def test_dt64_series_add_mixed_tick_DateOffset(self): + # GH#4532 + # operate with pd.offsets + s = Series([Timestamp("20130101 9:01"), Timestamp("20130101 9:02")]) + + result = s + pd.offsets.Milli(5) + result2 = pd.offsets.Milli(5) + s + expected = Series( + [Timestamp("20130101 9:01:00.005"), Timestamp("20130101 9:02:00.005")] + ) + tm.assert_series_equal(result, expected) + tm.assert_series_equal(result2, expected) + + result = s + pd.offsets.Minute(5) + pd.offsets.Milli(5) + expected = Series( + [Timestamp("20130101 9:06:00.005"), Timestamp("20130101 9:07:00.005")] + ) + tm.assert_series_equal(result, expected) + + def test_datetime64_ops_nat(self, unit): + # GH#11349 + datetime_series = Series([NaT, Timestamp("19900315")]).dt.as_unit(unit) + nat_series_dtype_timestamp = Series([NaT, NaT], dtype=f"datetime64[{unit}]") + single_nat_dtype_datetime = Series([NaT], dtype=f"datetime64[{unit}]") + + # subtraction + tm.assert_series_equal(-NaT + datetime_series, nat_series_dtype_timestamp) + msg = "bad operand type for unary -: 'DatetimeArray'" + with pytest.raises(TypeError, match=msg): + -single_nat_dtype_datetime + datetime_series + + tm.assert_series_equal( + -NaT + nat_series_dtype_timestamp, nat_series_dtype_timestamp + ) + with pytest.raises(TypeError, match=msg): + -single_nat_dtype_datetime + nat_series_dtype_timestamp + + # addition + tm.assert_series_equal( + nat_series_dtype_timestamp + NaT, nat_series_dtype_timestamp + ) + tm.assert_series_equal( + NaT + nat_series_dtype_timestamp, nat_series_dtype_timestamp + ) + + tm.assert_series_equal( + nat_series_dtype_timestamp + NaT, nat_series_dtype_timestamp + ) + tm.assert_series_equal( + NaT + nat_series_dtype_timestamp, nat_series_dtype_timestamp + ) + + # ------------------------------------------------------------- + # Timezone-Centric Tests + + def test_operators_datetimelike_with_timezones(self): + tz = "US/Eastern" + dt1 = Series(date_range("2000-01-01 09:00:00", periods=5, tz=tz), name="foo") + dt2 = dt1.copy() + dt2.iloc[2] = np.nan + + td1 = Series(pd.timedelta_range("1 days 1 min", periods=5, freq="h")) + td2 = td1.copy() + td2.iloc[1] = np.nan + assert td2._values.freq is None + + result = dt1 + td1[0] + exp = (dt1.dt.tz_localize(None) + td1[0]).dt.tz_localize(tz) + tm.assert_series_equal(result, exp) + + result = dt2 + td2[0] + exp = (dt2.dt.tz_localize(None) + td2[0]).dt.tz_localize(tz) + tm.assert_series_equal(result, exp) + + # odd numpy behavior with scalar timedeltas + result = td1[0] + dt1 + exp = (dt1.dt.tz_localize(None) + td1[0]).dt.tz_localize(tz) + tm.assert_series_equal(result, exp) + + result = td2[0] + dt2 + exp = (dt2.dt.tz_localize(None) + td2[0]).dt.tz_localize(tz) + tm.assert_series_equal(result, exp) + + result = dt1 - td1[0] + exp = (dt1.dt.tz_localize(None) - td1[0]).dt.tz_localize(tz) + tm.assert_series_equal(result, exp) + msg = "(bad|unsupported) operand type for unary" + with pytest.raises(TypeError, match=msg): + td1[0] - dt1 + + result = dt2 - td2[0] + exp = (dt2.dt.tz_localize(None) - td2[0]).dt.tz_localize(tz) + tm.assert_series_equal(result, exp) + with pytest.raises(TypeError, match=msg): + td2[0] - dt2 + + result = dt1 + td1 + exp = (dt1.dt.tz_localize(None) + td1).dt.tz_localize(tz) + tm.assert_series_equal(result, exp) + + result = dt2 + td2 + exp = (dt2.dt.tz_localize(None) + td2).dt.tz_localize(tz) + tm.assert_series_equal(result, exp) + + result = dt1 - td1 + exp = (dt1.dt.tz_localize(None) - td1).dt.tz_localize(tz) + tm.assert_series_equal(result, exp) + + result = dt2 - td2 + exp = (dt2.dt.tz_localize(None) - td2).dt.tz_localize(tz) + tm.assert_series_equal(result, exp) + msg = "cannot (add|subtract)" + with pytest.raises(TypeError, match=msg): + td1 - dt1 + with pytest.raises(TypeError, match=msg): + td2 - dt2 + + +class TestDatetimeIndexArithmetic: + # ------------------------------------------------------------- + # Binary operations DatetimeIndex and TimedeltaIndex/array + + def test_dti_add_tdi(self, tz_naive_fixture): + # GH#17558 + tz = tz_naive_fixture + dti = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10) + tdi = pd.timedelta_range("0 days", periods=10) + expected = date_range("2017-01-01", periods=10, tz=tz) + expected = expected._with_freq(None) + + # add with TimedeltaIndex + result = dti + tdi + tm.assert_index_equal(result, expected) + + result = tdi + dti + tm.assert_index_equal(result, expected) + + # add with timedelta64 array + result = dti + tdi.values + tm.assert_index_equal(result, expected) + + result = tdi.values + dti + tm.assert_index_equal(result, expected) + + def test_dti_iadd_tdi(self, tz_naive_fixture): + # GH#17558 + tz = tz_naive_fixture + dti = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10) + tdi = pd.timedelta_range("0 days", periods=10) + expected = date_range("2017-01-01", periods=10, tz=tz) + expected = expected._with_freq(None) + + # iadd with TimedeltaIndex + result = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10) + result += tdi + tm.assert_index_equal(result, expected) + + result = pd.timedelta_range("0 days", periods=10) + result += dti + tm.assert_index_equal(result, expected) + + # iadd with timedelta64 array + result = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10) + result += tdi.values + tm.assert_index_equal(result, expected) + + result = pd.timedelta_range("0 days", periods=10) + result += dti + tm.assert_index_equal(result, expected) + + def test_dti_sub_tdi(self, tz_naive_fixture): + # GH#17558 + tz = tz_naive_fixture + dti = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10) + tdi = pd.timedelta_range("0 days", periods=10) + expected = date_range("2017-01-01", periods=10, tz=tz, freq="-1D") + expected = expected._with_freq(None) + + # sub with TimedeltaIndex + result = dti - tdi + tm.assert_index_equal(result, expected) + + msg = "cannot subtract .*TimedeltaArray" + with pytest.raises(TypeError, match=msg): + tdi - dti + + # sub with timedelta64 array + result = dti - tdi.values + tm.assert_index_equal(result, expected) + + msg = "cannot subtract a datelike from a TimedeltaArray" + with pytest.raises(TypeError, match=msg): + tdi.values - dti + + def test_dti_isub_tdi(self, tz_naive_fixture, unit): + # GH#17558 + tz = tz_naive_fixture + dti = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10).as_unit(unit) + tdi = pd.timedelta_range("0 days", periods=10, unit=unit) + expected = date_range("2017-01-01", periods=10, tz=tz, freq="-1D", unit=unit) + expected = expected._with_freq(None) + + # isub with TimedeltaIndex + result = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10).as_unit(unit) + result -= tdi + tm.assert_index_equal(result, expected) + + # DTA.__isub__ GH#43904 + dta = dti._data.copy() + dta -= tdi + tm.assert_datetime_array_equal(dta, expected._data) + + out = dti._data.copy() + np.subtract(out, tdi, out=out) + tm.assert_datetime_array_equal(out, expected._data) + + msg = "cannot subtract a datelike from a TimedeltaArray" + with pytest.raises(TypeError, match=msg): + tdi -= dti + + # isub with timedelta64 array + result = DatetimeIndex([Timestamp("2017-01-01", tz=tz)] * 10).as_unit(unit) + result -= tdi.values + tm.assert_index_equal(result, expected) + + with pytest.raises(TypeError, match=msg): + tdi.values -= dti + + with pytest.raises(TypeError, match=msg): + tdi._values -= dti + + # ------------------------------------------------------------- + # Binary Operations DatetimeIndex and datetime-like + # TODO: A couple other tests belong in this section. Move them in + # A PR where there isn't already a giant diff. + + # ------------------------------------------------------------- + + def test_dta_add_sub_index(self, tz_naive_fixture): + # Check that DatetimeArray defers to Index classes + dti = date_range("20130101", periods=3, tz=tz_naive_fixture) + dta = dti.array + result = dta - dti + expected = dti - dti + tm.assert_index_equal(result, expected) + + tdi = result + result = dta + tdi + expected = dti + tdi + tm.assert_index_equal(result, expected) + + result = dta - tdi + expected = dti - tdi + tm.assert_index_equal(result, expected) + + def test_sub_dti_dti(self, unit): + # previously performed setop (deprecated in 0.16.0), now changed to + # return subtraction -> TimeDeltaIndex (GH ...) + + dti = date_range("20130101", periods=3, unit=unit) + dti_tz = date_range("20130101", periods=3, unit=unit).tz_localize("US/Eastern") + expected = TimedeltaIndex([0, 0, 0]).as_unit(unit) + + result = dti - dti + tm.assert_index_equal(result, expected) + + result = dti_tz - dti_tz + tm.assert_index_equal(result, expected) + msg = "Cannot subtract tz-naive and tz-aware datetime-like objects" + with pytest.raises(TypeError, match=msg): + dti_tz - dti + + with pytest.raises(TypeError, match=msg): + dti - dti_tz + + # isub + dti -= dti + tm.assert_index_equal(dti, expected) + + # different length raises ValueError + dti1 = date_range("20130101", periods=3, unit=unit) + dti2 = date_range("20130101", periods=4, unit=unit) + msg = "cannot add indices of unequal length" + with pytest.raises(ValueError, match=msg): + dti1 - dti2 + + # NaN propagation + dti1 = DatetimeIndex(["2012-01-01", np.nan, "2012-01-03"]).as_unit(unit) + dti2 = DatetimeIndex(["2012-01-02", "2012-01-03", np.nan]).as_unit(unit) + expected = TimedeltaIndex(["1 days", np.nan, np.nan]).as_unit(unit) + result = dti2 - dti1 + tm.assert_index_equal(result, expected) + + # ------------------------------------------------------------------- + # TODO: Most of this block is moved from series or frame tests, needs + # cleanup, box-parametrization, and de-duplication + + @pytest.mark.parametrize("op", [operator.add, operator.sub]) + def test_timedelta64_equal_timedelta_supported_ops(self, op, box_with_array): + ser = Series( + [ + Timestamp("20130301"), + Timestamp("20130228 23:00:00"), + Timestamp("20130228 22:00:00"), + Timestamp("20130228 21:00:00"), + ] + ) + obj = box_with_array(ser) + + intervals = ["D", "h", "m", "s", "us"] + + def timedelta64(*args): + # see casting notes in NumPy gh-12927 + return np.sum(list(starmap(np.timedelta64, zip(args, intervals)))) + + for d, h, m, s, us in product(*([range(2)] * 5)): + nptd = timedelta64(d, h, m, s, us) + pytd = timedelta(days=d, hours=h, minutes=m, seconds=s, microseconds=us) + lhs = op(obj, nptd) + rhs = op(obj, pytd) + + tm.assert_equal(lhs, rhs) + + def test_ops_nat_mixed_datetime64_timedelta64(self): + # GH#11349 + timedelta_series = Series([NaT, Timedelta("1s")]) + datetime_series = Series([NaT, Timestamp("19900315")]) + nat_series_dtype_timedelta = Series([NaT, NaT], dtype="timedelta64[ns]") + nat_series_dtype_timestamp = Series([NaT, NaT], dtype="datetime64[ns]") + single_nat_dtype_datetime = Series([NaT], dtype="datetime64[ns]") + single_nat_dtype_timedelta = Series([NaT], dtype="timedelta64[ns]") + + # subtraction + tm.assert_series_equal( + datetime_series - single_nat_dtype_datetime, nat_series_dtype_timedelta + ) + + tm.assert_series_equal( + datetime_series - single_nat_dtype_timedelta, nat_series_dtype_timestamp + ) + tm.assert_series_equal( + -single_nat_dtype_timedelta + datetime_series, nat_series_dtype_timestamp + ) + + # without a Series wrapping the NaT, it is ambiguous + # whether it is a datetime64 or timedelta64 + # defaults to interpreting it as timedelta64 + tm.assert_series_equal( + nat_series_dtype_timestamp - single_nat_dtype_datetime, + nat_series_dtype_timedelta, + ) + + tm.assert_series_equal( + nat_series_dtype_timestamp - single_nat_dtype_timedelta, + nat_series_dtype_timestamp, + ) + tm.assert_series_equal( + -single_nat_dtype_timedelta + nat_series_dtype_timestamp, + nat_series_dtype_timestamp, + ) + msg = "cannot subtract a datelike" + with pytest.raises(TypeError, match=msg): + timedelta_series - single_nat_dtype_datetime + + # addition + tm.assert_series_equal( + nat_series_dtype_timestamp + single_nat_dtype_timedelta, + nat_series_dtype_timestamp, + ) + tm.assert_series_equal( + single_nat_dtype_timedelta + nat_series_dtype_timestamp, + nat_series_dtype_timestamp, + ) + + tm.assert_series_equal( + nat_series_dtype_timestamp + single_nat_dtype_timedelta, + nat_series_dtype_timestamp, + ) + tm.assert_series_equal( + single_nat_dtype_timedelta + nat_series_dtype_timestamp, + nat_series_dtype_timestamp, + ) + + tm.assert_series_equal( + nat_series_dtype_timedelta + single_nat_dtype_datetime, + nat_series_dtype_timestamp, + ) + tm.assert_series_equal( + single_nat_dtype_datetime + nat_series_dtype_timedelta, + nat_series_dtype_timestamp, + ) + + def test_ufunc_coercions(self, unit): + idx = date_range("2011-01-01", periods=3, freq="2D", name="x", unit=unit) + + delta = np.timedelta64(1, "D") + exp = date_range("2011-01-02", periods=3, freq="2D", name="x", unit=unit) + for result in [idx + delta, np.add(idx, delta)]: + assert isinstance(result, DatetimeIndex) + tm.assert_index_equal(result, exp) + assert result.freq == "2D" + + exp = date_range("2010-12-31", periods=3, freq="2D", name="x", unit=unit) + + for result in [idx - delta, np.subtract(idx, delta)]: + assert isinstance(result, DatetimeIndex) + tm.assert_index_equal(result, exp) + assert result.freq == "2D" + + # When adding/subtracting an ndarray (which has no .freq), the result + # does not infer freq + idx = idx._with_freq(None) + delta = np.array( + [np.timedelta64(1, "D"), np.timedelta64(2, "D"), np.timedelta64(3, "D")] + ) + exp = DatetimeIndex( + ["2011-01-02", "2011-01-05", "2011-01-08"], name="x" + ).as_unit(unit) + + for result in [idx + delta, np.add(idx, delta)]: + tm.assert_index_equal(result, exp) + assert result.freq == exp.freq + + exp = DatetimeIndex( + ["2010-12-31", "2011-01-01", "2011-01-02"], name="x" + ).as_unit(unit) + for result in [idx - delta, np.subtract(idx, delta)]: + assert isinstance(result, DatetimeIndex) + tm.assert_index_equal(result, exp) + assert result.freq == exp.freq + + def test_dti_add_series(self, tz_naive_fixture, names): + # GH#13905 + tz = tz_naive_fixture + index = DatetimeIndex( + ["2016-06-28 05:30", "2016-06-28 05:31"], tz=tz, name=names[0] + ).as_unit("ns") + ser = Series([Timedelta(seconds=5)] * 2, index=index, name=names[1]) + expected = Series(index + Timedelta(seconds=5), index=index, name=names[2]) + + # passing name arg isn't enough when names[2] is None + expected.name = names[2] + assert expected.dtype == index.dtype + result = ser + index + tm.assert_series_equal(result, expected) + result2 = index + ser + tm.assert_series_equal(result2, expected) + + expected = index + Timedelta(seconds=5) + result3 = ser.values + index + tm.assert_index_equal(result3, expected) + result4 = index + ser.values + tm.assert_index_equal(result4, expected) + + @pytest.mark.parametrize("op", [operator.add, roperator.radd, operator.sub]) + def test_dti_addsub_offset_arraylike( + self, tz_naive_fixture, names, op, index_or_series + ): + # GH#18849, GH#19744 + other_box = index_or_series + + tz = tz_naive_fixture + dti = date_range("2017-01-01", periods=2, tz=tz, name=names[0]) + other = other_box([pd.offsets.MonthEnd(), pd.offsets.Day(n=2)], name=names[1]) + + xbox = get_upcast_box(dti, other) + + with tm.assert_produces_warning(PerformanceWarning): + res = op(dti, other) + + expected = DatetimeIndex( + [op(dti[n], other[n]) for n in range(len(dti))], name=names[2], freq="infer" + ) + expected = tm.box_expected(expected, xbox).astype(object) + tm.assert_equal(res, expected) + + @pytest.mark.parametrize("other_box", [pd.Index, np.array]) + def test_dti_addsub_object_arraylike( + self, tz_naive_fixture, box_with_array, other_box + ): + tz = tz_naive_fixture + + dti = date_range("2017-01-01", periods=2, tz=tz) + dtarr = tm.box_expected(dti, box_with_array) + other = other_box([pd.offsets.MonthEnd(), Timedelta(days=4)]) + xbox = get_upcast_box(dtarr, other) + + expected = DatetimeIndex(["2017-01-31", "2017-01-06"], tz=tz_naive_fixture) + expected = tm.box_expected(expected, xbox).astype(object) + + with tm.assert_produces_warning(PerformanceWarning): + result = dtarr + other + tm.assert_equal(result, expected) + + expected = DatetimeIndex(["2016-12-31", "2016-12-29"], tz=tz_naive_fixture) + expected = tm.box_expected(expected, xbox).astype(object) + + with tm.assert_produces_warning(PerformanceWarning): + result = dtarr - other + tm.assert_equal(result, expected) + + +@pytest.mark.parametrize("years", [-1, 0, 1]) +@pytest.mark.parametrize("months", [-2, 0, 2]) +@pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"]) +def test_shift_months(years, months, unit): + dti = DatetimeIndex( + [ + Timestamp("2000-01-05 00:15:00"), + Timestamp("2000-01-31 00:23:00"), + Timestamp("2000-01-01"), + Timestamp("2000-02-29"), + Timestamp("2000-12-31"), + ] + ).as_unit(unit) + shifted = shift_months(dti.asi8, years * 12 + months, reso=dti._data._creso) + shifted_dt64 = shifted.view(f"M8[{dti.unit}]") + actual = DatetimeIndex(shifted_dt64) + + raw = [x + pd.offsets.DateOffset(years=years, months=months) for x in dti] + expected = DatetimeIndex(raw).as_unit(dti.unit) + tm.assert_index_equal(actual, expected) + + +def test_dt64arr_addsub_object_dtype_2d(): + # block-wise DataFrame operations will require operating on 2D + # DatetimeArray/TimedeltaArray, so check that specifically. + dti = date_range("1994-02-13", freq="2W", periods=4) + dta = dti._data.reshape((4, 1)) + + other = np.array([[pd.offsets.Day(n)] for n in range(4)]) + assert other.shape == dta.shape + + with tm.assert_produces_warning(PerformanceWarning): + result = dta + other + with tm.assert_produces_warning(PerformanceWarning): + expected = (dta[:, 0] + other[:, 0]).reshape(-1, 1) + + tm.assert_numpy_array_equal(result, expected) + + with tm.assert_produces_warning(PerformanceWarning): + # Case where we expect to get a TimedeltaArray back + result2 = dta - dta.astype(object) + + assert result2.shape == (4, 1) + assert all(td._value == 0 for td in result2.ravel()) + + +def test_non_nano_dt64_addsub_np_nat_scalars(): + # GH 52295 + ser = Series([1233242342344, 232432434324, 332434242344], dtype="datetime64[ms]") + result = ser - np.datetime64("nat", "ms") + expected = Series([NaT] * 3, dtype="timedelta64[ms]") + tm.assert_series_equal(result, expected) + + result = ser + np.timedelta64("nat", "ms") + expected = Series([NaT] * 3, dtype="datetime64[ms]") + tm.assert_series_equal(result, expected) + + +def test_non_nano_dt64_addsub_np_nat_scalars_unitless(): + # GH 52295 + # TODO: Can we default to the ser unit? + ser = Series([1233242342344, 232432434324, 332434242344], dtype="datetime64[ms]") + result = ser - np.datetime64("nat") + expected = Series([NaT] * 3, dtype="timedelta64[ns]") + tm.assert_series_equal(result, expected) + + result = ser + np.timedelta64("nat") + expected = Series([NaT] * 3, dtype="datetime64[ns]") + tm.assert_series_equal(result, expected) + + +def test_non_nano_dt64_addsub_np_nat_scalars_unsupported_unit(): + # GH 52295 + ser = Series([12332, 23243, 33243], dtype="datetime64[s]") + result = ser - np.datetime64("nat", "D") + expected = Series([NaT] * 3, dtype="timedelta64[s]") + tm.assert_series_equal(result, expected) + + result = ser + np.timedelta64("nat", "D") + expected = Series([NaT] * 3, dtype="datetime64[s]") + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_interval.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_interval.py new file mode 100644 index 0000000000000000000000000000000000000000..0e316cf419cb0d3be489f474a9c6d889e668e7c9 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_interval.py @@ -0,0 +1,306 @@ +import operator + +import numpy as np +import pytest + +from pandas.core.dtypes.common import is_list_like + +import pandas as pd +from pandas import ( + Categorical, + Index, + Interval, + IntervalIndex, + Period, + Series, + Timedelta, + Timestamp, + date_range, + period_range, + timedelta_range, +) +import pandas._testing as tm +from pandas.core.arrays import ( + BooleanArray, + IntervalArray, +) +from pandas.tests.arithmetic.common import get_upcast_box + + +@pytest.fixture( + params=[ + (Index([0, 2, 4, 4]), Index([1, 3, 5, 8])), + (Index([0.0, 1.0, 2.0, np.nan]), Index([1.0, 2.0, 3.0, np.nan])), + ( + timedelta_range("0 days", periods=3).insert(3, pd.NaT), + timedelta_range("1 day", periods=3).insert(3, pd.NaT), + ), + ( + date_range("20170101", periods=3).insert(3, pd.NaT), + date_range("20170102", periods=3).insert(3, pd.NaT), + ), + ( + date_range("20170101", periods=3, tz="US/Eastern").insert(3, pd.NaT), + date_range("20170102", periods=3, tz="US/Eastern").insert(3, pd.NaT), + ), + ], + ids=lambda x: str(x[0].dtype), +) +def left_right_dtypes(request): + """ + Fixture for building an IntervalArray from various dtypes + """ + return request.param + + +@pytest.fixture +def interval_array(left_right_dtypes): + """ + Fixture to generate an IntervalArray of various dtypes containing NA if possible + """ + left, right = left_right_dtypes + return IntervalArray.from_arrays(left, right) + + +def create_categorical_intervals(left, right, closed="right"): + return Categorical(IntervalIndex.from_arrays(left, right, closed)) + + +def create_series_intervals(left, right, closed="right"): + return Series(IntervalArray.from_arrays(left, right, closed)) + + +def create_series_categorical_intervals(left, right, closed="right"): + return Series(Categorical(IntervalIndex.from_arrays(left, right, closed))) + + +class TestComparison: + @pytest.fixture(params=[operator.eq, operator.ne]) + def op(self, request): + return request.param + + @pytest.fixture( + params=[ + IntervalArray.from_arrays, + IntervalIndex.from_arrays, + create_categorical_intervals, + create_series_intervals, + create_series_categorical_intervals, + ], + ids=[ + "IntervalArray", + "IntervalIndex", + "Categorical[Interval]", + "Series[Interval]", + "Series[Categorical[Interval]]", + ], + ) + def interval_constructor(self, request): + """ + Fixture for all pandas native interval constructors. + To be used as the LHS of IntervalArray comparisons. + """ + return request.param + + def elementwise_comparison(self, op, interval_array, other): + """ + Helper that performs elementwise comparisons between `array` and `other` + """ + other = other if is_list_like(other) else [other] * len(interval_array) + expected = np.array([op(x, y) for x, y in zip(interval_array, other)]) + if isinstance(other, Series): + return Series(expected, index=other.index) + return expected + + def test_compare_scalar_interval(self, op, interval_array): + # matches first interval + other = interval_array[0] + result = op(interval_array, other) + expected = self.elementwise_comparison(op, interval_array, other) + tm.assert_numpy_array_equal(result, expected) + + # matches on a single endpoint but not both + other = Interval(interval_array.left[0], interval_array.right[1]) + result = op(interval_array, other) + expected = self.elementwise_comparison(op, interval_array, other) + tm.assert_numpy_array_equal(result, expected) + + def test_compare_scalar_interval_mixed_closed(self, op, closed, other_closed): + interval_array = IntervalArray.from_arrays(range(2), range(1, 3), closed=closed) + other = Interval(0, 1, closed=other_closed) + + result = op(interval_array, other) + expected = self.elementwise_comparison(op, interval_array, other) + tm.assert_numpy_array_equal(result, expected) + + def test_compare_scalar_na(self, op, interval_array, nulls_fixture, box_with_array): + box = box_with_array + obj = tm.box_expected(interval_array, box) + result = op(obj, nulls_fixture) + + if nulls_fixture is pd.NA: + # GH#31882 + exp = np.ones(interval_array.shape, dtype=bool) + expected = BooleanArray(exp, exp) + else: + expected = self.elementwise_comparison(op, interval_array, nulls_fixture) + + if not (box is Index and nulls_fixture is pd.NA): + # don't cast expected from BooleanArray to ndarray[object] + xbox = get_upcast_box(obj, nulls_fixture, True) + expected = tm.box_expected(expected, xbox) + + tm.assert_equal(result, expected) + + rev = op(nulls_fixture, obj) + tm.assert_equal(rev, expected) + + @pytest.mark.parametrize( + "other", + [ + 0, + 1.0, + True, + "foo", + Timestamp("2017-01-01"), + Timestamp("2017-01-01", tz="US/Eastern"), + Timedelta("0 days"), + Period("2017-01-01", "D"), + ], + ) + def test_compare_scalar_other(self, op, interval_array, other): + result = op(interval_array, other) + expected = self.elementwise_comparison(op, interval_array, other) + tm.assert_numpy_array_equal(result, expected) + + def test_compare_list_like_interval(self, op, interval_array, interval_constructor): + # same endpoints + other = interval_constructor(interval_array.left, interval_array.right) + result = op(interval_array, other) + expected = self.elementwise_comparison(op, interval_array, other) + tm.assert_equal(result, expected) + + # different endpoints + other = interval_constructor( + interval_array.left[::-1], interval_array.right[::-1] + ) + result = op(interval_array, other) + expected = self.elementwise_comparison(op, interval_array, other) + tm.assert_equal(result, expected) + + # all nan endpoints + other = interval_constructor([np.nan] * 4, [np.nan] * 4) + result = op(interval_array, other) + expected = self.elementwise_comparison(op, interval_array, other) + tm.assert_equal(result, expected) + + def test_compare_list_like_interval_mixed_closed( + self, op, interval_constructor, closed, other_closed + ): + interval_array = IntervalArray.from_arrays(range(2), range(1, 3), closed=closed) + other = interval_constructor(range(2), range(1, 3), closed=other_closed) + + result = op(interval_array, other) + expected = self.elementwise_comparison(op, interval_array, other) + tm.assert_equal(result, expected) + + @pytest.mark.parametrize( + "other", + [ + ( + Interval(0, 1), + Interval(Timedelta("1 day"), Timedelta("2 days")), + Interval(4, 5, "both"), + Interval(10, 20, "neither"), + ), + (0, 1.5, Timestamp("20170103"), np.nan), + ( + Timestamp("20170102", tz="US/Eastern"), + Timedelta("2 days"), + "baz", + pd.NaT, + ), + ], + ) + def test_compare_list_like_object(self, op, interval_array, other): + result = op(interval_array, other) + expected = self.elementwise_comparison(op, interval_array, other) + tm.assert_numpy_array_equal(result, expected) + + def test_compare_list_like_nan(self, op, interval_array, nulls_fixture): + other = [nulls_fixture] * 4 + result = op(interval_array, other) + expected = self.elementwise_comparison(op, interval_array, other) + + tm.assert_equal(result, expected) + + @pytest.mark.parametrize( + "other", + [ + np.arange(4, dtype="int64"), + np.arange(4, dtype="float64"), + date_range("2017-01-01", periods=4), + date_range("2017-01-01", periods=4, tz="US/Eastern"), + timedelta_range("0 days", periods=4), + period_range("2017-01-01", periods=4, freq="D"), + Categorical(list("abab")), + Categorical(date_range("2017-01-01", periods=4)), + pd.array(list("abcd")), + pd.array(["foo", 3.14, None, object()], dtype=object), + ], + ids=lambda x: str(x.dtype), + ) + def test_compare_list_like_other(self, op, interval_array, other): + result = op(interval_array, other) + expected = self.elementwise_comparison(op, interval_array, other) + tm.assert_numpy_array_equal(result, expected) + + @pytest.mark.parametrize("length", [1, 3, 5]) + @pytest.mark.parametrize("other_constructor", [IntervalArray, list]) + def test_compare_length_mismatch_errors(self, op, other_constructor, length): + interval_array = IntervalArray.from_arrays(range(4), range(1, 5)) + other = other_constructor([Interval(0, 1)] * length) + with pytest.raises(ValueError, match="Lengths must match to compare"): + op(interval_array, other) + + @pytest.mark.parametrize( + "constructor, expected_type, assert_func", + [ + (IntervalIndex, np.array, tm.assert_numpy_array_equal), + (Series, Series, tm.assert_series_equal), + ], + ) + def test_index_series_compat(self, op, constructor, expected_type, assert_func): + # IntervalIndex/Series that rely on IntervalArray for comparisons + breaks = range(4) + index = constructor(IntervalIndex.from_breaks(breaks)) + + # scalar comparisons + other = index[0] + result = op(index, other) + expected = expected_type(self.elementwise_comparison(op, index, other)) + assert_func(result, expected) + + other = breaks[0] + result = op(index, other) + expected = expected_type(self.elementwise_comparison(op, index, other)) + assert_func(result, expected) + + # list-like comparisons + other = IntervalArray.from_breaks(breaks) + result = op(index, other) + expected = expected_type(self.elementwise_comparison(op, index, other)) + assert_func(result, expected) + + other = [index[0], breaks[0], "foo"] + result = op(index, other) + expected = expected_type(self.elementwise_comparison(op, index, other)) + assert_func(result, expected) + + @pytest.mark.parametrize("scalars", ["a", False, 1, 1.0, None]) + def test_comparison_operations(self, scalars): + # GH #28981 + expected = Series([False, False]) + s = Series([Interval(0, 1), Interval(1, 2)], dtype="interval") + result = s == scalars + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_numeric.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_numeric.py new file mode 100644 index 0000000000000000000000000000000000000000..d8c1786b6b422c32a0f396b43f51d75b2b3ffe25 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_numeric.py @@ -0,0 +1,1567 @@ +# Arithmetic tests for DataFrame/Series/Index/Array classes that should +# behave identically. +# Specifically for numeric dtypes +from __future__ import annotations + +from collections import abc +from datetime import timedelta +from decimal import Decimal +import operator + +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Index, + RangeIndex, + Series, + Timedelta, + TimedeltaIndex, + array, + date_range, +) +import pandas._testing as tm +from pandas.core import ops +from pandas.core.computation import expressions as expr +from pandas.tests.arithmetic.common import ( + assert_invalid_addsub_type, + assert_invalid_comparison, +) + + +@pytest.fixture(autouse=True, params=[0, 1000000], ids=["numexpr", "python"]) +def switch_numexpr_min_elements(request, monkeypatch): + with monkeypatch.context() as m: + m.setattr(expr, "_MIN_ELEMENTS", request.param) + yield request.param + + +@pytest.fixture(params=[Index, Series, tm.to_array]) +def box_pandas_1d_array(request): + """ + Fixture to test behavior for Index, Series and tm.to_array classes + """ + return request.param + + +@pytest.fixture( + params=[ + # TODO: add more dtypes here + Index(np.arange(5, dtype="float64")), + Index(np.arange(5, dtype="int64")), + Index(np.arange(5, dtype="uint64")), + RangeIndex(5), + ], + ids=lambda x: type(x).__name__, +) +def numeric_idx(request): + """ + Several types of numeric-dtypes Index objects + """ + return request.param + + +@pytest.fixture( + params=[Index, Series, tm.to_array, np.array, list], ids=lambda x: x.__name__ +) +def box_1d_array(request): + """ + Fixture to test behavior for Index, Series, tm.to_array, numpy Array and list + classes + """ + return request.param + + +def adjust_negative_zero(zero, expected): + """ + Helper to adjust the expected result if we are dividing by -0.0 + as opposed to 0.0 + """ + if np.signbit(np.array(zero)).any(): + # All entries in the `zero` fixture should be either + # all-negative or no-negative. + assert np.signbit(np.array(zero)).all() + + expected *= -1 + + return expected + + +def compare_op(series, other, op): + left = np.abs(series) if op in (ops.rpow, operator.pow) else series + right = np.abs(other) if op in (ops.rpow, operator.pow) else other + + cython_or_numpy = op(left, right) + python = left.combine(right, op) + if isinstance(other, Series) and not other.index.equals(series.index): + python.index = python.index._with_freq(None) + tm.assert_series_equal(cython_or_numpy, python) + + +# TODO: remove this kludge once mypy stops giving false positives here +# List comprehension has incompatible type List[PandasObject]; expected List[RangeIndex] +# See GH#29725 +_ldtypes = ["i1", "i2", "i4", "i8", "u1", "u2", "u4", "u8", "f2", "f4", "f8"] +lefts: list[Index | Series] = [RangeIndex(10, 40, 10)] +lefts.extend([Series([10, 20, 30], dtype=dtype) for dtype in _ldtypes]) +lefts.extend([Index([10, 20, 30], dtype=dtype) for dtype in _ldtypes if dtype != "f2"]) + +# ------------------------------------------------------------------ +# Comparisons + + +class TestNumericComparisons: + def test_operator_series_comparison_zerorank(self): + # GH#13006 + result = np.float64(0) > Series([1, 2, 3]) + expected = 0.0 > Series([1, 2, 3]) + tm.assert_series_equal(result, expected) + result = Series([1, 2, 3]) < np.float64(0) + expected = Series([1, 2, 3]) < 0.0 + tm.assert_series_equal(result, expected) + result = np.array([0, 1, 2])[0] > Series([0, 1, 2]) + expected = 0.0 > Series([1, 2, 3]) + tm.assert_series_equal(result, expected) + + def test_df_numeric_cmp_dt64_raises(self, box_with_array, fixed_now_ts): + # GH#8932, GH#22163 + ts = fixed_now_ts + obj = np.array(range(5)) + obj = tm.box_expected(obj, box_with_array) + + assert_invalid_comparison(obj, ts, box_with_array) + + def test_compare_invalid(self): + # GH#8058 + # ops testing + a = Series(np.random.default_rng(2).standard_normal(5), name=0) + b = Series(np.random.default_rng(2).standard_normal(5)) + b.name = pd.Timestamp("2000-01-01") + tm.assert_series_equal(a / b, 1 / (b / a)) + + def test_numeric_cmp_string_numexpr_path(self, box_with_array, monkeypatch): + # GH#36377, GH#35700 + box = box_with_array + xbox = box if box is not Index else np.ndarray + + obj = Series(np.random.default_rng(2).standard_normal(51)) + obj = tm.box_expected(obj, box, transpose=False) + with monkeypatch.context() as m: + m.setattr(expr, "_MIN_ELEMENTS", 50) + result = obj == "a" + + expected = Series(np.zeros(51, dtype=bool)) + expected = tm.box_expected(expected, xbox, transpose=False) + tm.assert_equal(result, expected) + + with monkeypatch.context() as m: + m.setattr(expr, "_MIN_ELEMENTS", 50) + result = obj != "a" + tm.assert_equal(result, ~expected) + + msg = "Invalid comparison between dtype=float64 and str" + with pytest.raises(TypeError, match=msg): + obj < "a" + + +# ------------------------------------------------------------------ +# Numeric dtypes Arithmetic with Datetime/Timedelta Scalar + + +class TestNumericArraylikeArithmeticWithDatetimeLike: + @pytest.mark.parametrize("box_cls", [np.array, Index, Series]) + @pytest.mark.parametrize( + "left", lefts, ids=lambda x: type(x).__name__ + str(x.dtype) + ) + def test_mul_td64arr(self, left, box_cls): + # GH#22390 + right = np.array([1, 2, 3], dtype="m8[s]") + right = box_cls(right) + + expected = TimedeltaIndex(["10s", "40s", "90s"], dtype=right.dtype) + + if isinstance(left, Series) or box_cls is Series: + expected = Series(expected) + assert expected.dtype == right.dtype + + result = left * right + tm.assert_equal(result, expected) + + result = right * left + tm.assert_equal(result, expected) + + @pytest.mark.parametrize("box_cls", [np.array, Index, Series]) + @pytest.mark.parametrize( + "left", lefts, ids=lambda x: type(x).__name__ + str(x.dtype) + ) + def test_div_td64arr(self, left, box_cls): + # GH#22390 + right = np.array([10, 40, 90], dtype="m8[s]") + right = box_cls(right) + + expected = TimedeltaIndex(["1s", "2s", "3s"], dtype=right.dtype) + if isinstance(left, Series) or box_cls is Series: + expected = Series(expected) + assert expected.dtype == right.dtype + + result = right / left + tm.assert_equal(result, expected) + + result = right // left + tm.assert_equal(result, expected) + + # (true_) needed for min-versions build 2022-12-26 + msg = "ufunc '(true_)?divide' cannot use operands with types" + with pytest.raises(TypeError, match=msg): + left / right + + msg = "ufunc 'floor_divide' cannot use operands with types" + with pytest.raises(TypeError, match=msg): + left // right + + # TODO: also test Tick objects; + # see test_numeric_arr_rdiv_tdscalar for note on these failing + @pytest.mark.parametrize( + "scalar_td", + [ + Timedelta(days=1), + Timedelta(days=1).to_timedelta64(), + Timedelta(days=1).to_pytimedelta(), + Timedelta(days=1).to_timedelta64().astype("timedelta64[s]"), + Timedelta(days=1).to_timedelta64().astype("timedelta64[ms]"), + ], + ids=lambda x: type(x).__name__, + ) + def test_numeric_arr_mul_tdscalar(self, scalar_td, numeric_idx, box_with_array): + # GH#19333 + box = box_with_array + index = numeric_idx + expected = TimedeltaIndex([Timedelta(days=n) for n in range(len(index))]) + if isinstance(scalar_td, np.timedelta64): + dtype = scalar_td.dtype + expected = expected.astype(dtype) + elif type(scalar_td) is timedelta: + expected = expected.astype("m8[us]") + + index = tm.box_expected(index, box) + expected = tm.box_expected(expected, box) + + result = index * scalar_td + tm.assert_equal(result, expected) + + commute = scalar_td * index + tm.assert_equal(commute, expected) + + @pytest.mark.parametrize( + "scalar_td", + [ + Timedelta(days=1), + Timedelta(days=1).to_timedelta64(), + Timedelta(days=1).to_pytimedelta(), + ], + ids=lambda x: type(x).__name__, + ) + @pytest.mark.parametrize("dtype", [np.int64, np.float64]) + def test_numeric_arr_mul_tdscalar_numexpr_path( + self, dtype, scalar_td, box_with_array + ): + # GH#44772 for the float64 case + box = box_with_array + + arr_i8 = np.arange(2 * 10**4).astype(np.int64, copy=False) + arr = arr_i8.astype(dtype, copy=False) + obj = tm.box_expected(arr, box, transpose=False) + + expected = arr_i8.view("timedelta64[D]").astype("timedelta64[ns]") + if type(scalar_td) is timedelta: + expected = expected.astype("timedelta64[us]") + + expected = tm.box_expected(expected, box, transpose=False) + + result = obj * scalar_td + tm.assert_equal(result, expected) + + result = scalar_td * obj + tm.assert_equal(result, expected) + + def test_numeric_arr_rdiv_tdscalar(self, three_days, numeric_idx, box_with_array): + box = box_with_array + + index = numeric_idx[1:3] + + expected = TimedeltaIndex(["3 Days", "36 Hours"]) + if isinstance(three_days, np.timedelta64): + dtype = three_days.dtype + if dtype < np.dtype("m8[s]"): + # i.e. resolution is lower -> use lowest supported resolution + dtype = np.dtype("m8[s]") + expected = expected.astype(dtype) + elif type(three_days) is timedelta: + expected = expected.astype("m8[us]") + elif isinstance( + three_days, + (pd.offsets.Day, pd.offsets.Hour, pd.offsets.Minute, pd.offsets.Second), + ): + # closest reso is Second + expected = expected.astype("m8[s]") + + index = tm.box_expected(index, box) + expected = tm.box_expected(expected, box) + + result = three_days / index + tm.assert_equal(result, expected) + + msg = "cannot use operands with types dtype" + with pytest.raises(TypeError, match=msg): + index / three_days + + @pytest.mark.parametrize( + "other", + [ + Timedelta(hours=31), + Timedelta(hours=31).to_pytimedelta(), + Timedelta(hours=31).to_timedelta64(), + Timedelta(hours=31).to_timedelta64().astype("m8[h]"), + np.timedelta64("NaT"), + np.timedelta64("NaT", "D"), + pd.offsets.Minute(3), + pd.offsets.Second(0), + # GH#28080 numeric+datetimelike should raise; Timestamp used + # to raise NullFrequencyError but that behavior was removed in 1.0 + pd.Timestamp("2021-01-01", tz="Asia/Tokyo"), + pd.Timestamp("2021-01-01"), + pd.Timestamp("2021-01-01").to_pydatetime(), + pd.Timestamp("2021-01-01", tz="UTC").to_pydatetime(), + pd.Timestamp("2021-01-01").to_datetime64(), + np.datetime64("NaT", "ns"), + pd.NaT, + ], + ids=repr, + ) + def test_add_sub_datetimedeltalike_invalid( + self, numeric_idx, other, box_with_array + ): + box = box_with_array + + left = tm.box_expected(numeric_idx, box) + msg = "|".join( + [ + "unsupported operand type", + "Addition/subtraction of integers and integer-arrays", + "Instead of adding/subtracting", + "cannot use operands with types dtype", + "Concatenation operation is not implemented for NumPy arrays", + "Cannot (add|subtract) NaT (to|from) ndarray", + # pd.array vs np.datetime64 case + r"operand type\(s\) all returned NotImplemented from __array_ufunc__", + "can only perform ops with numeric values", + "cannot subtract DatetimeArray from ndarray", + # pd.Timedelta(1) + Index([0, 1, 2]) + "Cannot add or subtract Timedelta from integers", + ] + ) + assert_invalid_addsub_type(left, other, msg) + + +# ------------------------------------------------------------------ +# Arithmetic + + +class TestDivisionByZero: + def test_div_zero(self, zero, numeric_idx): + idx = numeric_idx + + expected = Index([np.nan, np.inf, np.inf, np.inf, np.inf], dtype=np.float64) + # We only adjust for Index, because Series does not yet apply + # the adjustment correctly. + expected2 = adjust_negative_zero(zero, expected) + + result = idx / zero + tm.assert_index_equal(result, expected2) + ser_compat = Series(idx).astype("i8") / np.array(zero).astype("i8") + tm.assert_series_equal(ser_compat, Series(expected)) + + def test_floordiv_zero(self, zero, numeric_idx): + idx = numeric_idx + + expected = Index([np.nan, np.inf, np.inf, np.inf, np.inf], dtype=np.float64) + # We only adjust for Index, because Series does not yet apply + # the adjustment correctly. + expected2 = adjust_negative_zero(zero, expected) + + result = idx // zero + tm.assert_index_equal(result, expected2) + ser_compat = Series(idx).astype("i8") // np.array(zero).astype("i8") + tm.assert_series_equal(ser_compat, Series(expected)) + + def test_mod_zero(self, zero, numeric_idx): + idx = numeric_idx + + expected = Index([np.nan, np.nan, np.nan, np.nan, np.nan], dtype=np.float64) + result = idx % zero + tm.assert_index_equal(result, expected) + ser_compat = Series(idx).astype("i8") % np.array(zero).astype("i8") + tm.assert_series_equal(ser_compat, Series(result)) + + def test_divmod_zero(self, zero, numeric_idx): + idx = numeric_idx + + exleft = Index([np.nan, np.inf, np.inf, np.inf, np.inf], dtype=np.float64) + exright = Index([np.nan, np.nan, np.nan, np.nan, np.nan], dtype=np.float64) + exleft = adjust_negative_zero(zero, exleft) + + result = divmod(idx, zero) + tm.assert_index_equal(result[0], exleft) + tm.assert_index_equal(result[1], exright) + + @pytest.mark.parametrize("op", [operator.truediv, operator.floordiv]) + def test_div_negative_zero(self, zero, numeric_idx, op): + # Check that -1 / -0.0 returns np.inf, not -np.inf + if numeric_idx.dtype == np.uint64: + pytest.skip(f"Div by negative 0 not relevant for {numeric_idx.dtype}") + idx = numeric_idx - 3 + + expected = Index([-np.inf, -np.inf, -np.inf, np.nan, np.inf], dtype=np.float64) + expected = adjust_negative_zero(zero, expected) + + result = op(idx, zero) + tm.assert_index_equal(result, expected) + + # ------------------------------------------------------------------ + + @pytest.mark.parametrize("dtype1", [np.int64, np.float64, np.uint64]) + def test_ser_div_ser( + self, + switch_numexpr_min_elements, + dtype1, + any_real_numpy_dtype, + ): + # no longer do integer div for any ops, but deal with the 0's + dtype2 = any_real_numpy_dtype + + first = Series([3, 4, 5, 8], name="first").astype(dtype1) + second = Series([0, 0, 0, 3], name="second").astype(dtype2) + + with np.errstate(all="ignore"): + expected = Series( + first.values.astype(np.float64) / second.values, + dtype="float64", + name=None, + ) + expected.iloc[0:3] = np.inf + if first.dtype == "int64" and second.dtype == "float32": + # when using numexpr, the casting rules are slightly different + # and int64/float32 combo results in float32 instead of float64 + if expr.USE_NUMEXPR and switch_numexpr_min_elements == 0: + expected = expected.astype("float32") + + result = first / second + tm.assert_series_equal(result, expected) + assert not result.equals(second / first) + + @pytest.mark.parametrize("dtype1", [np.int64, np.float64, np.uint64]) + def test_ser_divmod_zero(self, dtype1, any_real_numpy_dtype): + # GH#26987 + dtype2 = any_real_numpy_dtype + left = Series([1, 1]).astype(dtype1) + right = Series([0, 2]).astype(dtype2) + + # GH#27321 pandas convention is to set 1 // 0 to np.inf, as opposed + # to numpy which sets to np.nan; patch `expected[0]` below + expected = left // right, left % right + expected = list(expected) + expected[0] = expected[0].astype(np.float64) + expected[0][0] = np.inf + result = divmod(left, right) + + tm.assert_series_equal(result[0], expected[0]) + tm.assert_series_equal(result[1], expected[1]) + + # rdivmod case + result = divmod(left.values, right) + tm.assert_series_equal(result[0], expected[0]) + tm.assert_series_equal(result[1], expected[1]) + + def test_ser_divmod_inf(self): + left = Series([np.inf, 1.0]) + right = Series([np.inf, 2.0]) + + expected = left // right, left % right + result = divmod(left, right) + + tm.assert_series_equal(result[0], expected[0]) + tm.assert_series_equal(result[1], expected[1]) + + # rdivmod case + result = divmod(left.values, right) + tm.assert_series_equal(result[0], expected[0]) + tm.assert_series_equal(result[1], expected[1]) + + def test_rdiv_zero_compat(self): + # GH#8674 + zero_array = np.array([0] * 5) + data = np.random.default_rng(2).standard_normal(5) + expected = Series([0.0] * 5) + + result = zero_array / Series(data) + tm.assert_series_equal(result, expected) + + result = Series(zero_array) / data + tm.assert_series_equal(result, expected) + + result = Series(zero_array) / Series(data) + tm.assert_series_equal(result, expected) + + def test_div_zero_inf_signs(self): + # GH#9144, inf signing + ser = Series([-1, 0, 1], name="first") + expected = Series([-np.inf, np.nan, np.inf], name="first") + + result = ser / 0 + tm.assert_series_equal(result, expected) + + def test_rdiv_zero(self): + # GH#9144 + ser = Series([-1, 0, 1], name="first") + expected = Series([0.0, np.nan, 0.0], name="first") + + result = 0 / ser + tm.assert_series_equal(result, expected) + + def test_floordiv_div(self): + # GH#9144 + ser = Series([-1, 0, 1], name="first") + + result = ser // 0 + expected = Series([-np.inf, np.nan, np.inf], name="first") + tm.assert_series_equal(result, expected) + + def test_df_div_zero_df(self): + # integer div, but deal with the 0's (GH#9144) + df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]}) + result = df / df + + first = Series([1.0, 1.0, 1.0, 1.0]) + second = Series([np.nan, np.nan, np.nan, 1]) + expected = pd.DataFrame({"first": first, "second": second}) + tm.assert_frame_equal(result, expected) + + def test_df_div_zero_array(self): + # integer div, but deal with the 0's (GH#9144) + df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]}) + + first = Series([1.0, 1.0, 1.0, 1.0]) + second = Series([np.nan, np.nan, np.nan, 1]) + expected = pd.DataFrame({"first": first, "second": second}) + + with np.errstate(all="ignore"): + arr = df.values.astype("float") / df.values + result = pd.DataFrame(arr, index=df.index, columns=df.columns) + tm.assert_frame_equal(result, expected) + + def test_df_div_zero_int(self): + # integer div, but deal with the 0's (GH#9144) + df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]}) + + result = df / 0 + expected = pd.DataFrame(np.inf, index=df.index, columns=df.columns) + expected.iloc[0:3, 1] = np.nan + tm.assert_frame_equal(result, expected) + + # numpy has a slightly different (wrong) treatment + with np.errstate(all="ignore"): + arr = df.values.astype("float64") / 0 + result2 = pd.DataFrame(arr, index=df.index, columns=df.columns) + tm.assert_frame_equal(result2, expected) + + def test_df_div_zero_series_does_not_commute(self): + # integer div, but deal with the 0's (GH#9144) + df = pd.DataFrame(np.random.default_rng(2).standard_normal((10, 5))) + ser = df[0] + res = ser / df + res2 = df / ser + assert not res.fillna(0).equals(res2.fillna(0)) + + # ------------------------------------------------------------------ + # Mod By Zero + + def test_df_mod_zero_df(self, using_array_manager): + # GH#3590, modulo as ints + df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]}) + # this is technically wrong, as the integer portion is coerced to float + first = Series([0, 0, 0, 0]) + if not using_array_manager: + # INFO(ArrayManager) BlockManager doesn't preserve dtype per column + # while ArrayManager performs op column-wisedoes and thus preserves + # dtype if possible + first = first.astype("float64") + second = Series([np.nan, np.nan, np.nan, 0]) + expected = pd.DataFrame({"first": first, "second": second}) + result = df % df + tm.assert_frame_equal(result, expected) + + # GH#38939 If we dont pass copy=False, df is consolidated and + # result["first"] is float64 instead of int64 + df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]}, copy=False) + first = Series([0, 0, 0, 0], dtype="int64") + second = Series([np.nan, np.nan, np.nan, 0]) + expected = pd.DataFrame({"first": first, "second": second}) + result = df % df + tm.assert_frame_equal(result, expected) + + def test_df_mod_zero_array(self): + # GH#3590, modulo as ints + df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]}) + + # this is technically wrong, as the integer portion is coerced to float + # ### + first = Series([0, 0, 0, 0], dtype="float64") + second = Series([np.nan, np.nan, np.nan, 0]) + expected = pd.DataFrame({"first": first, "second": second}) + + # numpy has a slightly different (wrong) treatment + with np.errstate(all="ignore"): + arr = df.values % df.values + result2 = pd.DataFrame(arr, index=df.index, columns=df.columns, dtype="float64") + result2.iloc[0:3, 1] = np.nan + tm.assert_frame_equal(result2, expected) + + def test_df_mod_zero_int(self): + # GH#3590, modulo as ints + df = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]}) + + result = df % 0 + expected = pd.DataFrame(np.nan, index=df.index, columns=df.columns) + tm.assert_frame_equal(result, expected) + + # numpy has a slightly different (wrong) treatment + with np.errstate(all="ignore"): + arr = df.values.astype("float64") % 0 + result2 = pd.DataFrame(arr, index=df.index, columns=df.columns) + tm.assert_frame_equal(result2, expected) + + def test_df_mod_zero_series_does_not_commute(self): + # GH#3590, modulo as ints + # not commutative with series + df = pd.DataFrame(np.random.default_rng(2).standard_normal((10, 5))) + ser = df[0] + res = ser % df + res2 = df % ser + assert not res.fillna(0).equals(res2.fillna(0)) + + +class TestMultiplicationDivision: + # __mul__, __rmul__, __div__, __rdiv__, __floordiv__, __rfloordiv__ + # for non-timestamp/timedelta/period dtypes + + def test_divide_decimal(self, box_with_array): + # resolves issue GH#9787 + box = box_with_array + ser = Series([Decimal(10)]) + expected = Series([Decimal(5)]) + + ser = tm.box_expected(ser, box) + expected = tm.box_expected(expected, box) + + result = ser / Decimal(2) + + tm.assert_equal(result, expected) + + result = ser // Decimal(2) + tm.assert_equal(result, expected) + + def test_div_equiv_binop(self): + # Test Series.div as well as Series.__div__ + # float/integer issue + # GH#7785 + first = Series([1, 0], name="first") + second = Series([-0.01, -0.02], name="second") + expected = Series([-0.01, -np.inf]) + + result = second.div(first) + tm.assert_series_equal(result, expected, check_names=False) + + result = second / first + tm.assert_series_equal(result, expected) + + def test_div_int(self, numeric_idx): + idx = numeric_idx + result = idx / 1 + expected = idx.astype("float64") + tm.assert_index_equal(result, expected) + + result = idx / 2 + expected = Index(idx.values / 2) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize("op", [operator.mul, ops.rmul, operator.floordiv]) + def test_mul_int_identity(self, op, numeric_idx, box_with_array): + idx = numeric_idx + idx = tm.box_expected(idx, box_with_array) + + result = op(idx, 1) + tm.assert_equal(result, idx) + + def test_mul_int_array(self, numeric_idx): + idx = numeric_idx + didx = idx * idx + + result = idx * np.array(5, dtype="int64") + tm.assert_index_equal(result, idx * 5) + + arr_dtype = "uint64" if idx.dtype == np.uint64 else "int64" + result = idx * np.arange(5, dtype=arr_dtype) + tm.assert_index_equal(result, didx) + + def test_mul_int_series(self, numeric_idx): + idx = numeric_idx + didx = idx * idx + + arr_dtype = "uint64" if idx.dtype == np.uint64 else "int64" + result = idx * Series(np.arange(5, dtype=arr_dtype)) + tm.assert_series_equal(result, Series(didx)) + + def test_mul_float_series(self, numeric_idx): + idx = numeric_idx + rng5 = np.arange(5, dtype="float64") + + result = idx * Series(rng5 + 0.1) + expected = Series(rng5 * (rng5 + 0.1)) + tm.assert_series_equal(result, expected) + + def test_mul_index(self, numeric_idx): + idx = numeric_idx + + result = idx * idx + tm.assert_index_equal(result, idx**2) + + def test_mul_datelike_raises(self, numeric_idx): + idx = numeric_idx + msg = "cannot perform __rmul__ with this index type" + with pytest.raises(TypeError, match=msg): + idx * date_range("20130101", periods=5) + + def test_mul_size_mismatch_raises(self, numeric_idx): + idx = numeric_idx + msg = "operands could not be broadcast together" + with pytest.raises(ValueError, match=msg): + idx * idx[0:3] + with pytest.raises(ValueError, match=msg): + idx * np.array([1, 2]) + + @pytest.mark.parametrize("op", [operator.pow, ops.rpow]) + def test_pow_float(self, op, numeric_idx, box_with_array): + # test power calculations both ways, GH#14973 + box = box_with_array + idx = numeric_idx + expected = Index(op(idx.values, 2.0)) + + idx = tm.box_expected(idx, box) + expected = tm.box_expected(expected, box) + + result = op(idx, 2.0) + tm.assert_equal(result, expected) + + def test_modulo(self, numeric_idx, box_with_array): + # GH#9244 + box = box_with_array + idx = numeric_idx + expected = Index(idx.values % 2) + + idx = tm.box_expected(idx, box) + expected = tm.box_expected(expected, box) + + result = idx % 2 + tm.assert_equal(result, expected) + + def test_divmod_scalar(self, numeric_idx): + idx = numeric_idx + + result = divmod(idx, 2) + with np.errstate(all="ignore"): + div, mod = divmod(idx.values, 2) + + expected = Index(div), Index(mod) + for r, e in zip(result, expected): + tm.assert_index_equal(r, e) + + def test_divmod_ndarray(self, numeric_idx): + idx = numeric_idx + other = np.ones(idx.values.shape, dtype=idx.values.dtype) * 2 + + result = divmod(idx, other) + with np.errstate(all="ignore"): + div, mod = divmod(idx.values, other) + + expected = Index(div), Index(mod) + for r, e in zip(result, expected): + tm.assert_index_equal(r, e) + + def test_divmod_series(self, numeric_idx): + idx = numeric_idx + other = np.ones(idx.values.shape, dtype=idx.values.dtype) * 2 + + result = divmod(idx, Series(other)) + with np.errstate(all="ignore"): + div, mod = divmod(idx.values, other) + + expected = Series(div), Series(mod) + for r, e in zip(result, expected): + tm.assert_series_equal(r, e) + + @pytest.mark.parametrize("other", [np.nan, 7, -23, 2.718, -3.14, np.inf]) + def test_ops_np_scalar(self, other): + vals = np.random.default_rng(2).standard_normal((5, 3)) + f = lambda x: pd.DataFrame( + x, index=list("ABCDE"), columns=["jim", "joe", "jolie"] + ) + + df = f(vals) + + tm.assert_frame_equal(df / np.array(other), f(vals / other)) + tm.assert_frame_equal(np.array(other) * df, f(vals * other)) + tm.assert_frame_equal(df + np.array(other), f(vals + other)) + tm.assert_frame_equal(np.array(other) - df, f(other - vals)) + + # TODO: This came from series.test.test_operators, needs cleanup + def test_operators_frame(self): + # rpow does not work with DataFrame + ts = Series( + np.arange(10, dtype=np.float64), + index=date_range("2020-01-01", periods=10), + name="ts", + ) + ts.name = "ts" + + df = pd.DataFrame({"A": ts}) + + tm.assert_series_equal(ts + ts, ts + df["A"], check_names=False) + tm.assert_series_equal(ts**ts, ts ** df["A"], check_names=False) + tm.assert_series_equal(ts < ts, ts < df["A"], check_names=False) + tm.assert_series_equal(ts / ts, ts / df["A"], check_names=False) + + # TODO: this came from tests.series.test_analytics, needs cleanup and + # de-duplication with test_modulo above + def test_modulo2(self): + with np.errstate(all="ignore"): + # GH#3590, modulo as ints + p = pd.DataFrame({"first": [3, 4, 5, 8], "second": [0, 0, 0, 3]}) + result = p["first"] % p["second"] + expected = Series(p["first"].values % p["second"].values, dtype="float64") + expected.iloc[0:3] = np.nan + tm.assert_series_equal(result, expected) + + result = p["first"] % 0 + expected = Series(np.nan, index=p.index, name="first") + tm.assert_series_equal(result, expected) + + p = p.astype("float64") + result = p["first"] % p["second"] + expected = Series(p["first"].values % p["second"].values) + tm.assert_series_equal(result, expected) + + p = p.astype("float64") + result = p["first"] % p["second"] + result2 = p["second"] % p["first"] + assert not result.equals(result2) + + def test_modulo_zero_int(self): + # GH#9144 + with np.errstate(all="ignore"): + s = Series([0, 1]) + + result = s % 0 + expected = Series([np.nan, np.nan]) + tm.assert_series_equal(result, expected) + + result = 0 % s + expected = Series([np.nan, 0.0]) + tm.assert_series_equal(result, expected) + + +class TestAdditionSubtraction: + # __add__, __sub__, __radd__, __rsub__, __iadd__, __isub__ + # for non-timestamp/timedelta/period dtypes + + @pytest.mark.parametrize( + "first, second, expected", + [ + ( + Series([1, 2, 3], index=list("ABC"), name="x"), + Series([2, 2, 2], index=list("ABD"), name="x"), + Series([3.0, 4.0, np.nan, np.nan], index=list("ABCD"), name="x"), + ), + ( + Series([1, 2, 3], index=list("ABC"), name="x"), + Series([2, 2, 2, 2], index=list("ABCD"), name="x"), + Series([3, 4, 5, np.nan], index=list("ABCD"), name="x"), + ), + ], + ) + def test_add_series(self, first, second, expected): + # GH#1134 + tm.assert_series_equal(first + second, expected) + tm.assert_series_equal(second + first, expected) + + @pytest.mark.parametrize( + "first, second, expected", + [ + ( + pd.DataFrame({"x": [1, 2, 3]}, index=list("ABC")), + pd.DataFrame({"x": [2, 2, 2]}, index=list("ABD")), + pd.DataFrame({"x": [3.0, 4.0, np.nan, np.nan]}, index=list("ABCD")), + ), + ( + pd.DataFrame({"x": [1, 2, 3]}, index=list("ABC")), + pd.DataFrame({"x": [2, 2, 2, 2]}, index=list("ABCD")), + pd.DataFrame({"x": [3, 4, 5, np.nan]}, index=list("ABCD")), + ), + ], + ) + def test_add_frames(self, first, second, expected): + # GH#1134 + tm.assert_frame_equal(first + second, expected) + tm.assert_frame_equal(second + first, expected) + + # TODO: This came from series.test.test_operators, needs cleanup + def test_series_frame_radd_bug(self, fixed_now_ts): + # GH#353 + vals = Series([str(i) for i in range(5)]) + result = "foo_" + vals + expected = vals.map(lambda x: "foo_" + x) + tm.assert_series_equal(result, expected) + + frame = pd.DataFrame({"vals": vals}) + result = "foo_" + frame + expected = pd.DataFrame({"vals": vals.map(lambda x: "foo_" + x)}) + tm.assert_frame_equal(result, expected) + + ts = Series( + np.arange(10, dtype=np.float64), + index=date_range("2020-01-01", periods=10), + name="ts", + ) + + # really raise this time + fix_now = fixed_now_ts.to_pydatetime() + msg = "|".join( + [ + "unsupported operand type", + # wrong error message, see https://github.com/numpy/numpy/issues/18832 + "Concatenation operation", + ] + ) + with pytest.raises(TypeError, match=msg): + fix_now + ts + + with pytest.raises(TypeError, match=msg): + ts + fix_now + + # TODO: This came from series.test.test_operators, needs cleanup + def test_datetime64_with_index(self): + # arithmetic integer ops with an index + ser = Series(np.random.default_rng(2).standard_normal(5)) + expected = ser - ser.index.to_series() + result = ser - ser.index + tm.assert_series_equal(result, expected) + + # GH#4629 + # arithmetic datetime64 ops with an index + ser = Series( + date_range("20130101", periods=5), + index=date_range("20130101", periods=5), + ) + expected = ser - ser.index.to_series() + result = ser - ser.index + tm.assert_series_equal(result, expected) + + msg = "cannot subtract PeriodArray from DatetimeArray" + with pytest.raises(TypeError, match=msg): + # GH#18850 + result = ser - ser.index.to_period() + + df = pd.DataFrame( + np.random.default_rng(2).standard_normal((5, 2)), + index=date_range("20130101", periods=5), + ) + df["date"] = pd.Timestamp("20130102") + df["expected"] = df["date"] - df.index.to_series() + df["result"] = df["date"] - df.index + tm.assert_series_equal(df["result"], df["expected"], check_names=False) + + # TODO: taken from tests.frame.test_operators, needs cleanup + def test_frame_operators(self, float_frame): + frame = float_frame + + garbage = np.random.default_rng(2).random(4) + colSeries = Series(garbage, index=np.array(frame.columns)) + + idSum = frame + frame + seriesSum = frame + colSeries + + for col, series in idSum.items(): + for idx, val in series.items(): + origVal = frame[col][idx] * 2 + if not np.isnan(val): + assert val == origVal + else: + assert np.isnan(origVal) + + for col, series in seriesSum.items(): + for idx, val in series.items(): + origVal = frame[col][idx] + colSeries[col] + if not np.isnan(val): + assert val == origVal + else: + assert np.isnan(origVal) + + def test_frame_operators_col_align(self, float_frame): + frame2 = pd.DataFrame(float_frame, columns=["D", "C", "B", "A"]) + added = frame2 + frame2 + expected = frame2 * 2 + tm.assert_frame_equal(added, expected) + + def test_frame_operators_none_to_nan(self): + df = pd.DataFrame({"a": ["a", None, "b"]}) + tm.assert_frame_equal(df + df, pd.DataFrame({"a": ["aa", np.nan, "bb"]})) + + @pytest.mark.parametrize("dtype", ("float", "int64")) + def test_frame_operators_empty_like(self, dtype): + # Test for issue #10181 + frames = [ + pd.DataFrame(dtype=dtype), + pd.DataFrame(columns=["A"], dtype=dtype), + pd.DataFrame(index=[0], dtype=dtype), + ] + for df in frames: + assert (df + df).equals(df) + tm.assert_frame_equal(df + df, df) + + @pytest.mark.parametrize( + "func", + [lambda x: x * 2, lambda x: x[::2], lambda x: 5], + ids=["multiply", "slice", "constant"], + ) + def test_series_operators_arithmetic(self, all_arithmetic_functions, func): + op = all_arithmetic_functions + series = Series( + np.arange(10, dtype=np.float64), + index=date_range("2020-01-01", periods=10), + name="ts", + ) + other = func(series) + compare_op(series, other, op) + + @pytest.mark.parametrize( + "func", [lambda x: x + 1, lambda x: 5], ids=["add", "constant"] + ) + def test_series_operators_compare(self, comparison_op, func): + op = comparison_op + series = Series( + np.arange(10, dtype=np.float64), + index=date_range("2020-01-01", periods=10), + name="ts", + ) + other = func(series) + compare_op(series, other, op) + + @pytest.mark.parametrize( + "func", + [lambda x: x * 2, lambda x: x[::2], lambda x: 5], + ids=["multiply", "slice", "constant"], + ) + def test_divmod(self, func): + series = Series( + np.arange(10, dtype=np.float64), + index=date_range("2020-01-01", periods=10), + name="ts", + ) + other = func(series) + results = divmod(series, other) + if isinstance(other, abc.Iterable) and len(series) != len(other): + # if the lengths don't match, this is the test where we use + # `tser[::2]`. Pad every other value in `other_np` with nan. + other_np = [] + for n in other: + other_np.append(n) + other_np.append(np.nan) + else: + other_np = other + other_np = np.asarray(other_np) + with np.errstate(all="ignore"): + expecteds = divmod(series.values, np.asarray(other_np)) + + for result, expected in zip(results, expecteds): + # check the values, name, and index separately + tm.assert_almost_equal(np.asarray(result), expected) + + assert result.name == series.name + tm.assert_index_equal(result.index, series.index._with_freq(None)) + + def test_series_divmod_zero(self): + # Check that divmod uses pandas convention for division by zero, + # which does not match numpy. + # pandas convention has + # 1/0 == np.inf + # -1/0 == -np.inf + # 1/-0.0 == -np.inf + # -1/-0.0 == np.inf + tser = Series( + np.arange(1, 11, dtype=np.float64), + index=date_range("2020-01-01", periods=10), + name="ts", + ) + other = tser * 0 + + result = divmod(tser, other) + exp1 = Series([np.inf] * len(tser), index=tser.index, name="ts") + exp2 = Series([np.nan] * len(tser), index=tser.index, name="ts") + tm.assert_series_equal(result[0], exp1) + tm.assert_series_equal(result[1], exp2) + + +class TestUFuncCompat: + # TODO: add more dtypes + @pytest.mark.parametrize("holder", [Index, RangeIndex, Series]) + @pytest.mark.parametrize("dtype", [np.int64, np.uint64, np.float64]) + def test_ufunc_compat(self, holder, dtype): + box = Series if holder is Series else Index + + if holder is RangeIndex: + if dtype != np.int64: + pytest.skip(f"dtype {dtype} not relevant for RangeIndex") + idx = RangeIndex(0, 5, name="foo") + else: + idx = holder(np.arange(5, dtype=dtype), name="foo") + result = np.sin(idx) + expected = box(np.sin(np.arange(5, dtype=dtype)), name="foo") + tm.assert_equal(result, expected) + + # TODO: add more dtypes + @pytest.mark.parametrize("holder", [Index, Series]) + @pytest.mark.parametrize("dtype", [np.int64, np.uint64, np.float64]) + def test_ufunc_coercions(self, holder, dtype): + idx = holder([1, 2, 3, 4, 5], dtype=dtype, name="x") + box = Series if holder is Series else Index + + result = np.sqrt(idx) + assert result.dtype == "f8" and isinstance(result, box) + exp = Index(np.sqrt(np.array([1, 2, 3, 4, 5], dtype=np.float64)), name="x") + exp = tm.box_expected(exp, box) + tm.assert_equal(result, exp) + + result = np.divide(idx, 2.0) + assert result.dtype == "f8" and isinstance(result, box) + exp = Index([0.5, 1.0, 1.5, 2.0, 2.5], dtype=np.float64, name="x") + exp = tm.box_expected(exp, box) + tm.assert_equal(result, exp) + + # _evaluate_numeric_binop + result = idx + 2.0 + assert result.dtype == "f8" and isinstance(result, box) + exp = Index([3.0, 4.0, 5.0, 6.0, 7.0], dtype=np.float64, name="x") + exp = tm.box_expected(exp, box) + tm.assert_equal(result, exp) + + result = idx - 2.0 + assert result.dtype == "f8" and isinstance(result, box) + exp = Index([-1.0, 0.0, 1.0, 2.0, 3.0], dtype=np.float64, name="x") + exp = tm.box_expected(exp, box) + tm.assert_equal(result, exp) + + result = idx * 1.0 + assert result.dtype == "f8" and isinstance(result, box) + exp = Index([1.0, 2.0, 3.0, 4.0, 5.0], dtype=np.float64, name="x") + exp = tm.box_expected(exp, box) + tm.assert_equal(result, exp) + + result = idx / 2.0 + assert result.dtype == "f8" and isinstance(result, box) + exp = Index([0.5, 1.0, 1.5, 2.0, 2.5], dtype=np.float64, name="x") + exp = tm.box_expected(exp, box) + tm.assert_equal(result, exp) + + # TODO: add more dtypes + @pytest.mark.parametrize("holder", [Index, Series]) + @pytest.mark.parametrize("dtype", [np.int64, np.uint64, np.float64]) + def test_ufunc_multiple_return_values(self, holder, dtype): + obj = holder([1, 2, 3], dtype=dtype, name="x") + box = Series if holder is Series else Index + + result = np.modf(obj) + assert isinstance(result, tuple) + exp1 = Index([0.0, 0.0, 0.0], dtype=np.float64, name="x") + exp2 = Index([1.0, 2.0, 3.0], dtype=np.float64, name="x") + tm.assert_equal(result[0], tm.box_expected(exp1, box)) + tm.assert_equal(result[1], tm.box_expected(exp2, box)) + + def test_ufunc_at(self): + s = Series([0, 1, 2], index=[1, 2, 3], name="x") + np.add.at(s, [0, 2], 10) + expected = Series([10, 1, 12], index=[1, 2, 3], name="x") + tm.assert_series_equal(s, expected) + + +class TestObjectDtypeEquivalence: + # Tests that arithmetic operations match operations executed elementwise + + @pytest.mark.parametrize("dtype", [None, object]) + def test_numarr_with_dtype_add_nan(self, dtype, box_with_array): + box = box_with_array + ser = Series([1, 2, 3], dtype=dtype) + expected = Series([np.nan, np.nan, np.nan], dtype=dtype) + + ser = tm.box_expected(ser, box) + expected = tm.box_expected(expected, box) + + result = np.nan + ser + tm.assert_equal(result, expected) + + result = ser + np.nan + tm.assert_equal(result, expected) + + @pytest.mark.parametrize("dtype", [None, object]) + def test_numarr_with_dtype_add_int(self, dtype, box_with_array): + box = box_with_array + ser = Series([1, 2, 3], dtype=dtype) + expected = Series([2, 3, 4], dtype=dtype) + + ser = tm.box_expected(ser, box) + expected = tm.box_expected(expected, box) + + result = 1 + ser + tm.assert_equal(result, expected) + + result = ser + 1 + tm.assert_equal(result, expected) + + # TODO: moved from tests.series.test_operators; needs cleanup + @pytest.mark.parametrize( + "op", + [operator.add, operator.sub, operator.mul, operator.truediv, operator.floordiv], + ) + def test_operators_reverse_object(self, op): + # GH#56 + arr = Series( + np.random.default_rng(2).standard_normal(10), + index=np.arange(10), + dtype=object, + ) + + result = op(1.0, arr) + expected = op(1.0, arr.astype(float)) + tm.assert_series_equal(result.astype(float), expected) + + +class TestNumericArithmeticUnsorted: + # Tests in this class have been moved from type-specific test modules + # but not yet sorted, parametrized, and de-duplicated + @pytest.mark.parametrize( + "op", + [ + operator.add, + operator.sub, + operator.mul, + operator.floordiv, + operator.truediv, + ], + ) + @pytest.mark.parametrize( + "idx1", + [ + RangeIndex(0, 10, 1), + RangeIndex(0, 20, 2), + RangeIndex(-10, 10, 2), + RangeIndex(5, -5, -1), + ], + ) + @pytest.mark.parametrize( + "idx2", + [ + RangeIndex(0, 10, 1), + RangeIndex(0, 20, 2), + RangeIndex(-10, 10, 2), + RangeIndex(5, -5, -1), + ], + ) + def test_binops_index(self, op, idx1, idx2): + idx1 = idx1._rename("foo") + idx2 = idx2._rename("bar") + result = op(idx1, idx2) + expected = op(Index(idx1.to_numpy()), Index(idx2.to_numpy())) + tm.assert_index_equal(result, expected, exact="equiv") + + @pytest.mark.parametrize( + "op", + [ + operator.add, + operator.sub, + operator.mul, + operator.floordiv, + operator.truediv, + ], + ) + @pytest.mark.parametrize( + "idx", + [ + RangeIndex(0, 10, 1), + RangeIndex(0, 20, 2), + RangeIndex(-10, 10, 2), + RangeIndex(5, -5, -1), + ], + ) + @pytest.mark.parametrize("scalar", [-1, 1, 2]) + def test_binops_index_scalar(self, op, idx, scalar): + result = op(idx, scalar) + expected = op(Index(idx.to_numpy()), scalar) + tm.assert_index_equal(result, expected, exact="equiv") + + @pytest.mark.parametrize("idx1", [RangeIndex(0, 10, 1), RangeIndex(0, 20, 2)]) + @pytest.mark.parametrize("idx2", [RangeIndex(0, 10, 1), RangeIndex(0, 20, 2)]) + def test_binops_index_pow(self, idx1, idx2): + # numpy does not allow powers of negative integers so test separately + # https://github.com/numpy/numpy/pull/8127 + idx1 = idx1._rename("foo") + idx2 = idx2._rename("bar") + result = pow(idx1, idx2) + expected = pow(Index(idx1.to_numpy()), Index(idx2.to_numpy())) + tm.assert_index_equal(result, expected, exact="equiv") + + @pytest.mark.parametrize("idx", [RangeIndex(0, 10, 1), RangeIndex(0, 20, 2)]) + @pytest.mark.parametrize("scalar", [1, 2]) + def test_binops_index_scalar_pow(self, idx, scalar): + # numpy does not allow powers of negative integers so test separately + # https://github.com/numpy/numpy/pull/8127 + result = pow(idx, scalar) + expected = pow(Index(idx.to_numpy()), scalar) + tm.assert_index_equal(result, expected, exact="equiv") + + # TODO: divmod? + @pytest.mark.parametrize( + "op", + [ + operator.add, + operator.sub, + operator.mul, + operator.floordiv, + operator.truediv, + operator.pow, + operator.mod, + ], + ) + def test_arithmetic_with_frame_or_series(self, op): + # check that we return NotImplemented when operating with Series + # or DataFrame + index = RangeIndex(5) + other = Series(np.random.default_rng(2).standard_normal(5)) + + expected = op(Series(index), other) + result = op(index, other) + tm.assert_series_equal(result, expected) + + other = pd.DataFrame(np.random.default_rng(2).standard_normal((2, 5))) + expected = op(pd.DataFrame([index, index]), other) + result = op(index, other) + tm.assert_frame_equal(result, expected) + + def test_numeric_compat2(self): + # validate that we are handling the RangeIndex overrides to numeric ops + # and returning RangeIndex where possible + + idx = RangeIndex(0, 10, 2) + + result = idx * 2 + expected = RangeIndex(0, 20, 4) + tm.assert_index_equal(result, expected, exact=True) + + result = idx + 2 + expected = RangeIndex(2, 12, 2) + tm.assert_index_equal(result, expected, exact=True) + + result = idx - 2 + expected = RangeIndex(-2, 8, 2) + tm.assert_index_equal(result, expected, exact=True) + + result = idx / 2 + expected = RangeIndex(0, 5, 1).astype("float64") + tm.assert_index_equal(result, expected, exact=True) + + result = idx / 4 + expected = RangeIndex(0, 10, 2) / 4 + tm.assert_index_equal(result, expected, exact=True) + + result = idx // 1 + expected = idx + tm.assert_index_equal(result, expected, exact=True) + + # __mul__ + result = idx * idx + expected = Index(idx.values * idx.values) + tm.assert_index_equal(result, expected, exact=True) + + # __pow__ + idx = RangeIndex(0, 1000, 2) + result = idx**2 + expected = Index(idx._values) ** 2 + tm.assert_index_equal(Index(result.values), expected, exact=True) + + @pytest.mark.parametrize( + "idx, div, expected", + [ + # TODO: add more dtypes + (RangeIndex(0, 1000, 2), 2, RangeIndex(0, 500, 1)), + (RangeIndex(-99, -201, -3), -3, RangeIndex(33, 67, 1)), + ( + RangeIndex(0, 1000, 1), + 2, + Index(RangeIndex(0, 1000, 1)._values) // 2, + ), + ( + RangeIndex(0, 100, 1), + 2.0, + Index(RangeIndex(0, 100, 1)._values) // 2.0, + ), + (RangeIndex(0), 50, RangeIndex(0)), + (RangeIndex(2, 4, 2), 3, RangeIndex(0, 1, 1)), + (RangeIndex(-5, -10, -6), 4, RangeIndex(-2, -1, 1)), + (RangeIndex(-100, -200, 3), 2, RangeIndex(0)), + ], + ) + def test_numeric_compat2_floordiv(self, idx, div, expected): + # __floordiv__ + tm.assert_index_equal(idx // div, expected, exact=True) + + @pytest.mark.parametrize("dtype", [np.int64, np.float64]) + @pytest.mark.parametrize("delta", [1, 0, -1]) + def test_addsub_arithmetic(self, dtype, delta): + # GH#8142 + delta = dtype(delta) + index = Index([10, 11, 12], dtype=dtype) + result = index + delta + expected = Index(index.values + delta, dtype=dtype) + tm.assert_index_equal(result, expected) + + # this subtraction used to fail + result = index - delta + expected = Index(index.values - delta, dtype=dtype) + tm.assert_index_equal(result, expected) + + tm.assert_index_equal(index + index, 2 * index) + tm.assert_index_equal(index - index, 0 * index) + assert not (index - index).empty + + def test_pow_nan_with_zero(self, box_with_array): + left = Index([np.nan, np.nan, np.nan]) + right = Index([0, 0, 0]) + expected = Index([1.0, 1.0, 1.0]) + + left = tm.box_expected(left, box_with_array) + right = tm.box_expected(right, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + result = left**right + tm.assert_equal(result, expected) + + +def test_fill_value_inf_masking(): + # GH #27464 make sure we mask 0/1 with Inf and not NaN + df = pd.DataFrame({"A": [0, 1, 2], "B": [1.1, None, 1.1]}) + + other = pd.DataFrame({"A": [1.1, 1.2, 1.3]}, index=[0, 2, 3]) + + result = df.rfloordiv(other, fill_value=1) + + expected = pd.DataFrame( + {"A": [np.inf, 1.0, 0.0, 1.0], "B": [0.0, np.nan, 0.0, np.nan]} + ) + tm.assert_frame_equal(result, expected) + + +def test_dataframe_div_silenced(): + # GH#26793 + pdf1 = pd.DataFrame( + { + "A": np.arange(10), + "B": [np.nan, 1, 2, 3, 4] * 2, + "C": [np.nan] * 10, + "D": np.arange(10), + }, + index=list("abcdefghij"), + columns=list("ABCD"), + ) + pdf2 = pd.DataFrame( + np.random.default_rng(2).standard_normal((10, 4)), + index=list("abcdefghjk"), + columns=list("ABCX"), + ) + with tm.assert_produces_warning(None): + pdf1.div(pdf2, fill_value=0) + + +@pytest.mark.parametrize( + "data, expected_data", + [([0, 1, 2], [0, 2, 4])], +) +def test_integer_array_add_list_like( + box_pandas_1d_array, box_1d_array, data, expected_data +): + # GH22606 Verify operators with IntegerArray and list-likes + arr = array(data, dtype="Int64") + container = box_pandas_1d_array(arr) + left = container + box_1d_array(data) + right = box_1d_array(data) + container + + if Series in [box_1d_array, box_pandas_1d_array]: + cls = Series + elif Index in [box_1d_array, box_pandas_1d_array]: + cls = Index + else: + cls = array + + expected = cls(expected_data, dtype="Int64") + + tm.assert_equal(left, expected) + tm.assert_equal(right, expected) + + +def test_sub_multiindex_swapped_levels(): + # GH 9952 + df = pd.DataFrame( + {"a": np.random.default_rng(2).standard_normal(6)}, + index=pd.MultiIndex.from_product( + [["a", "b"], [0, 1, 2]], names=["levA", "levB"] + ), + ) + df2 = df.copy() + df2.index = df2.index.swaplevel(0, 1) + result = df - df2 + expected = pd.DataFrame([0.0] * 6, columns=["a"], index=df.index) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("power", [1, 2, 5]) +@pytest.mark.parametrize("string_size", [0, 1, 2, 5]) +def test_empty_str_comparison(power, string_size): + # GH 37348 + a = np.array(range(10**power)) + right = pd.DataFrame(a, dtype=np.int64) + left = " " * string_size + + result = right == left + expected = pd.DataFrame(np.zeros(right.shape, dtype=bool)) + tm.assert_frame_equal(result, expected) + + +def test_series_add_sub_with_UInt64(): + # GH 22023 + series1 = Series([1, 2, 3]) + series2 = Series([2, 1, 3], dtype="UInt64") + + result = series1 + series2 + expected = Series([3, 3, 6], dtype="Float64") + tm.assert_series_equal(result, expected) + + result = series1 - series2 + expected = Series([-1, 1, 0], dtype="Float64") + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_object.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_object.py new file mode 100644 index 0000000000000000000000000000000000000000..44e485d40ba536ba08fabb8d2f5aa4e439177010 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_object.py @@ -0,0 +1,414 @@ +# Arithmetic tests for DataFrame/Series/Index/Array classes that should +# behave identically. +# Specifically for object dtype +import datetime +from decimal import Decimal +import operator + +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +import pandas as pd +from pandas import ( + Series, + Timestamp, + option_context, +) +import pandas._testing as tm +from pandas.core import ops + +# ------------------------------------------------------------------ +# Comparisons + + +class TestObjectComparisons: + def test_comparison_object_numeric_nas(self, comparison_op): + ser = Series(np.random.default_rng(2).standard_normal(10), dtype=object) + shifted = ser.shift(2) + + func = comparison_op + + result = func(ser, shifted) + expected = func(ser.astype(float), shifted.astype(float)) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "infer_string", [False, pytest.param(True, marks=td.skip_if_no("pyarrow"))] + ) + def test_object_comparisons(self, infer_string): + with option_context("future.infer_string", infer_string): + ser = Series(["a", "b", np.nan, "c", "a"]) + + result = ser == "a" + expected = Series([True, False, False, False, True]) + tm.assert_series_equal(result, expected) + + result = ser < "a" + expected = Series([False, False, False, False, False]) + tm.assert_series_equal(result, expected) + + result = ser != "a" + expected = -(ser == "a") + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("dtype", [None, object]) + def test_more_na_comparisons(self, dtype): + left = Series(["a", np.nan, "c"], dtype=dtype) + right = Series(["a", np.nan, "d"], dtype=dtype) + + result = left == right + expected = Series([True, False, False]) + tm.assert_series_equal(result, expected) + + result = left != right + expected = Series([False, True, True]) + tm.assert_series_equal(result, expected) + + result = left == np.nan + expected = Series([False, False, False]) + tm.assert_series_equal(result, expected) + + result = left != np.nan + expected = Series([True, True, True]) + tm.assert_series_equal(result, expected) + + +# ------------------------------------------------------------------ +# Arithmetic + + +class TestArithmetic: + def test_add_period_to_array_of_offset(self): + # GH#50162 + per = pd.Period("2012-1-1", freq="D") + pi = pd.period_range("2012-1-1", periods=10, freq="D") + idx = per - pi + + expected = pd.Index([x + per for x in idx], dtype=object) + result = idx + per + tm.assert_index_equal(result, expected) + + result = per + idx + tm.assert_index_equal(result, expected) + + # TODO: parametrize + def test_pow_ops_object(self): + # GH#22922 + # pow is weird with masking & 1, so testing here + a = Series([1, np.nan, 1, np.nan], dtype=object) + b = Series([1, np.nan, np.nan, 1], dtype=object) + result = a**b + expected = Series(a.values**b.values, dtype=object) + tm.assert_series_equal(result, expected) + + result = b**a + expected = Series(b.values**a.values, dtype=object) + + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("op", [operator.add, ops.radd]) + @pytest.mark.parametrize("other", ["category", "Int64"]) + def test_add_extension_scalar(self, other, box_with_array, op): + # GH#22378 + # Check that scalars satisfying is_extension_array_dtype(obj) + # do not incorrectly try to dispatch to an ExtensionArray operation + + arr = Series(["a", "b", "c"]) + expected = Series([op(x, other) for x in arr]) + + arr = tm.box_expected(arr, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + result = op(arr, other) + tm.assert_equal(result, expected) + + def test_objarr_add_str(self, box_with_array): + ser = Series(["x", np.nan, "x"]) + expected = Series(["xa", np.nan, "xa"]) + + ser = tm.box_expected(ser, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + result = ser + "a" + tm.assert_equal(result, expected) + + def test_objarr_radd_str(self, box_with_array): + ser = Series(["x", np.nan, "x"]) + expected = Series(["ax", np.nan, "ax"]) + + ser = tm.box_expected(ser, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + result = "a" + ser + tm.assert_equal(result, expected) + + @pytest.mark.parametrize( + "data", + [ + [1, 2, 3], + [1.1, 2.2, 3.3], + [Timestamp("2011-01-01"), Timestamp("2011-01-02"), pd.NaT], + ["x", "y", 1], + ], + ) + @pytest.mark.parametrize("dtype", [None, object]) + def test_objarr_radd_str_invalid(self, dtype, data, box_with_array): + ser = Series(data, dtype=dtype) + + ser = tm.box_expected(ser, box_with_array) + msg = "|".join( + [ + "can only concatenate str", + "did not contain a loop with signature matching types", + "unsupported operand type", + "must be str", + ] + ) + with pytest.raises(TypeError, match=msg): + "foo_" + ser + + @pytest.mark.parametrize("op", [operator.add, ops.radd, operator.sub, ops.rsub]) + def test_objarr_add_invalid(self, op, box_with_array): + # invalid ops + box = box_with_array + + obj_ser = Series(list("abc"), dtype=object, name="objects") + + obj_ser = tm.box_expected(obj_ser, box) + msg = "|".join( + [ + "can only concatenate str", + "unsupported operand type", + "must be str", + "has no kernel", + "operation 'add' not supported", + "operation 'radd' not supported", + "operation 'sub' not supported", + "operation 'rsub' not supported", + ] + ) + with pytest.raises(Exception, match=msg): + op(obj_ser, 1) + with pytest.raises(Exception, match=msg): + op(obj_ser, np.array(1, dtype=np.int64)) + + # TODO: Moved from tests.series.test_operators; needs cleanup + def test_operators_na_handling(self): + ser = Series(["foo", "bar", "baz", np.nan]) + result = "prefix_" + ser + expected = Series(["prefix_foo", "prefix_bar", "prefix_baz", np.nan]) + tm.assert_series_equal(result, expected) + + result = ser + "_suffix" + expected = Series(["foo_suffix", "bar_suffix", "baz_suffix", np.nan]) + tm.assert_series_equal(result, expected) + + # TODO: parametrize over box + @pytest.mark.parametrize("dtype", [None, object]) + def test_series_with_dtype_radd_timedelta(self, dtype): + # note this test is _not_ aimed at timedelta64-dtyped Series + # as of 2.0 we retain object dtype when ser.dtype == object + ser = Series( + [pd.Timedelta("1 days"), pd.Timedelta("2 days"), pd.Timedelta("3 days")], + dtype=dtype, + ) + expected = Series( + [pd.Timedelta("4 days"), pd.Timedelta("5 days"), pd.Timedelta("6 days")], + dtype=dtype, + ) + + result = pd.Timedelta("3 days") + ser + tm.assert_series_equal(result, expected) + + result = ser + pd.Timedelta("3 days") + tm.assert_series_equal(result, expected) + + # TODO: cleanup & parametrize over box + def test_mixed_timezone_series_ops_object(self): + # GH#13043 + ser = Series( + [ + Timestamp("2015-01-01", tz="US/Eastern"), + Timestamp("2015-01-01", tz="Asia/Tokyo"), + ], + name="xxx", + ) + assert ser.dtype == object + + exp = Series( + [ + Timestamp("2015-01-02", tz="US/Eastern"), + Timestamp("2015-01-02", tz="Asia/Tokyo"), + ], + name="xxx", + ) + tm.assert_series_equal(ser + pd.Timedelta("1 days"), exp) + tm.assert_series_equal(pd.Timedelta("1 days") + ser, exp) + + # object series & object series + ser2 = Series( + [ + Timestamp("2015-01-03", tz="US/Eastern"), + Timestamp("2015-01-05", tz="Asia/Tokyo"), + ], + name="xxx", + ) + assert ser2.dtype == object + exp = Series( + [pd.Timedelta("2 days"), pd.Timedelta("4 days")], name="xxx", dtype=object + ) + tm.assert_series_equal(ser2 - ser, exp) + tm.assert_series_equal(ser - ser2, -exp) + + ser = Series( + [pd.Timedelta("01:00:00"), pd.Timedelta("02:00:00")], + name="xxx", + dtype=object, + ) + assert ser.dtype == object + + exp = Series( + [pd.Timedelta("01:30:00"), pd.Timedelta("02:30:00")], + name="xxx", + dtype=object, + ) + tm.assert_series_equal(ser + pd.Timedelta("00:30:00"), exp) + tm.assert_series_equal(pd.Timedelta("00:30:00") + ser, exp) + + # TODO: cleanup & parametrize over box + def test_iadd_preserves_name(self): + # GH#17067, GH#19723 __iadd__ and __isub__ should preserve index name + ser = Series([1, 2, 3]) + ser.index.name = "foo" + + ser.index += 1 + assert ser.index.name == "foo" + + ser.index -= 1 + assert ser.index.name == "foo" + + def test_add_string(self): + # from bug report + index = pd.Index(["a", "b", "c"]) + index2 = index + "foo" + + assert "a" not in index2 + assert "afoo" in index2 + + def test_iadd_string(self): + index = pd.Index(["a", "b", "c"]) + # doesn't fail test unless there is a check before `+=` + assert "a" in index + + index += "_x" + assert "a_x" in index + + def test_add(self): + index = pd.Index([str(i) for i in range(10)]) + expected = pd.Index(index.values * 2) + tm.assert_index_equal(index + index, expected) + tm.assert_index_equal(index + index.tolist(), expected) + tm.assert_index_equal(index.tolist() + index, expected) + + # test add and radd + index = pd.Index(list("abc")) + expected = pd.Index(["a1", "b1", "c1"]) + tm.assert_index_equal(index + "1", expected) + expected = pd.Index(["1a", "1b", "1c"]) + tm.assert_index_equal("1" + index, expected) + + def test_sub_fail(self): + index = pd.Index([str(i) for i in range(10)]) + + msg = "unsupported operand type|Cannot broadcast|sub' not supported" + with pytest.raises(TypeError, match=msg): + index - "a" + with pytest.raises(TypeError, match=msg): + index - index + with pytest.raises(TypeError, match=msg): + index - index.tolist() + with pytest.raises(TypeError, match=msg): + index.tolist() - index + + def test_sub_object(self): + # GH#19369 + index = pd.Index([Decimal(1), Decimal(2)]) + expected = pd.Index([Decimal(0), Decimal(1)]) + + result = index - Decimal(1) + tm.assert_index_equal(result, expected) + + result = index - pd.Index([Decimal(1), Decimal(1)]) + tm.assert_index_equal(result, expected) + + msg = "unsupported operand type" + with pytest.raises(TypeError, match=msg): + index - "foo" + + with pytest.raises(TypeError, match=msg): + index - np.array([2, "foo"], dtype=object) + + def test_rsub_object(self, fixed_now_ts): + # GH#19369 + index = pd.Index([Decimal(1), Decimal(2)]) + expected = pd.Index([Decimal(1), Decimal(0)]) + + result = Decimal(2) - index + tm.assert_index_equal(result, expected) + + result = np.array([Decimal(2), Decimal(2)]) - index + tm.assert_index_equal(result, expected) + + msg = "unsupported operand type" + with pytest.raises(TypeError, match=msg): + "foo" - index + + with pytest.raises(TypeError, match=msg): + np.array([True, fixed_now_ts]) - index + + +class MyIndex(pd.Index): + # Simple index subclass that tracks ops calls. + + _calls: int + + @classmethod + def _simple_new(cls, values, name=None, dtype=None): + result = object.__new__(cls) + result._data = values + result._name = name + result._calls = 0 + result._reset_identity() + + return result + + def __add__(self, other): + self._calls += 1 + return self._simple_new(self._data) + + def __radd__(self, other): + return self.__add__(other) + + +@pytest.mark.parametrize( + "other", + [ + [datetime.timedelta(1), datetime.timedelta(2)], + [datetime.datetime(2000, 1, 1), datetime.datetime(2000, 1, 2)], + [pd.Period("2000"), pd.Period("2001")], + ["a", "b"], + ], + ids=["timedelta", "datetime", "period", "object"], +) +def test_index_ops_defer_to_unknown_subclasses(other): + # https://github.com/pandas-dev/pandas/issues/31109 + values = np.array( + [datetime.date(2000, 1, 1), datetime.date(2000, 1, 2)], dtype=object + ) + a = MyIndex._simple_new(values) + other = pd.Index(other) + result = other + a + assert isinstance(result, MyIndex) + assert a._calls == 1 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_period.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_period.py new file mode 100644 index 0000000000000000000000000000000000000000..5535fe8ff928d10b994bd6556229e0163a358ab0 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_period.py @@ -0,0 +1,1675 @@ +# Arithmetic tests for DataFrame/Series/Index/Array classes that should +# behave identically. +# Specifically for Period dtype +import operator + +import numpy as np +import pytest + +from pandas._libs.tslibs import ( + IncompatibleFrequency, + Period, + Timestamp, + to_offset, +) +from pandas.errors import PerformanceWarning + +import pandas as pd +from pandas import ( + PeriodIndex, + Series, + Timedelta, + TimedeltaIndex, + period_range, +) +import pandas._testing as tm +from pandas.core import ops +from pandas.core.arrays import TimedeltaArray +from pandas.tests.arithmetic.common import ( + assert_invalid_addsub_type, + assert_invalid_comparison, + get_upcast_box, +) + +_common_mismatch = [ + pd.offsets.YearBegin(2), + pd.offsets.MonthBegin(1), + pd.offsets.Minute(), +] + + +@pytest.fixture( + params=[ + Timedelta(minutes=30).to_pytimedelta(), + np.timedelta64(30, "s"), + Timedelta(seconds=30), + ] + + _common_mismatch +) +def not_hourly(request): + """ + Several timedelta-like and DateOffset instances that are _not_ + compatible with Hourly frequencies. + """ + return request.param + + +@pytest.fixture( + params=[ + np.timedelta64(365, "D"), + Timedelta(days=365).to_pytimedelta(), + Timedelta(days=365), + ] + + _common_mismatch +) +def mismatched_freq(request): + """ + Several timedelta-like and DateOffset instances that are _not_ + compatible with Monthly or Annual frequencies. + """ + return request.param + + +# ------------------------------------------------------------------ +# Comparisons + + +class TestPeriodArrayLikeComparisons: + # Comparison tests for PeriodDtype vectors fully parametrized over + # DataFrame/Series/PeriodIndex/PeriodArray. Ideally all comparison + # tests will eventually end up here. + + @pytest.mark.parametrize("other", ["2017", Period("2017", freq="D")]) + def test_eq_scalar(self, other, box_with_array): + idx = PeriodIndex(["2017", "2017", "2018"], freq="D") + idx = tm.box_expected(idx, box_with_array) + xbox = get_upcast_box(idx, other, True) + + expected = np.array([True, True, False]) + expected = tm.box_expected(expected, xbox) + + result = idx == other + + tm.assert_equal(result, expected) + + def test_compare_zerodim(self, box_with_array): + # GH#26689 make sure we unbox zero-dimensional arrays + + pi = period_range("2000", periods=4) + other = np.array(pi.to_numpy()[0]) + + pi = tm.box_expected(pi, box_with_array) + xbox = get_upcast_box(pi, other, True) + + result = pi <= other + expected = np.array([True, False, False, False]) + expected = tm.box_expected(expected, xbox) + tm.assert_equal(result, expected) + + @pytest.mark.parametrize( + "scalar", + [ + "foo", + Timestamp("2021-01-01"), + Timedelta(days=4), + 9, + 9.5, + 2000, # specifically don't consider 2000 to match Period("2000", "D") + False, + None, + ], + ) + def test_compare_invalid_scalar(self, box_with_array, scalar): + # GH#28980 + # comparison with scalar that cannot be interpreted as a Period + pi = period_range("2000", periods=4) + parr = tm.box_expected(pi, box_with_array) + assert_invalid_comparison(parr, scalar, box_with_array) + + @pytest.mark.parametrize( + "other", + [ + pd.date_range("2000", periods=4).array, + pd.timedelta_range("1D", periods=4).array, + np.arange(4), + np.arange(4).astype(np.float64), + list(range(4)), + # match Period semantics by not treating integers as Periods + [2000, 2001, 2002, 2003], + np.arange(2000, 2004), + np.arange(2000, 2004).astype(object), + pd.Index([2000, 2001, 2002, 2003]), + ], + ) + def test_compare_invalid_listlike(self, box_with_array, other): + pi = period_range("2000", periods=4) + parr = tm.box_expected(pi, box_with_array) + assert_invalid_comparison(parr, other, box_with_array) + + @pytest.mark.parametrize("other_box", [list, np.array, lambda x: x.astype(object)]) + def test_compare_object_dtype(self, box_with_array, other_box): + pi = period_range("2000", periods=5) + parr = tm.box_expected(pi, box_with_array) + + other = other_box(pi) + xbox = get_upcast_box(parr, other, True) + + expected = np.array([True, True, True, True, True]) + expected = tm.box_expected(expected, xbox) + + result = parr == other + tm.assert_equal(result, expected) + result = parr <= other + tm.assert_equal(result, expected) + result = parr >= other + tm.assert_equal(result, expected) + + result = parr != other + tm.assert_equal(result, ~expected) + result = parr < other + tm.assert_equal(result, ~expected) + result = parr > other + tm.assert_equal(result, ~expected) + + other = other_box(pi[::-1]) + + expected = np.array([False, False, True, False, False]) + expected = tm.box_expected(expected, xbox) + result = parr == other + tm.assert_equal(result, expected) + + expected = np.array([True, True, True, False, False]) + expected = tm.box_expected(expected, xbox) + result = parr <= other + tm.assert_equal(result, expected) + + expected = np.array([False, False, True, True, True]) + expected = tm.box_expected(expected, xbox) + result = parr >= other + tm.assert_equal(result, expected) + + expected = np.array([True, True, False, True, True]) + expected = tm.box_expected(expected, xbox) + result = parr != other + tm.assert_equal(result, expected) + + expected = np.array([True, True, False, False, False]) + expected = tm.box_expected(expected, xbox) + result = parr < other + tm.assert_equal(result, expected) + + expected = np.array([False, False, False, True, True]) + expected = tm.box_expected(expected, xbox) + result = parr > other + tm.assert_equal(result, expected) + + +class TestPeriodIndexComparisons: + # TODO: parameterize over boxes + + def test_pi_cmp_period(self): + idx = period_range("2007-01", periods=20, freq="M") + per = idx[10] + + result = idx < per + exp = idx.values < idx.values[10] + tm.assert_numpy_array_equal(result, exp) + + # Tests Period.__richcmp__ against ndarray[object, ndim=2] + result = idx.values.reshape(10, 2) < per + tm.assert_numpy_array_equal(result, exp.reshape(10, 2)) + + # Tests Period.__richcmp__ against ndarray[object, ndim=0] + result = idx < np.array(per) + tm.assert_numpy_array_equal(result, exp) + + # TODO: moved from test_datetime64; de-duplicate with version below + def test_parr_cmp_period_scalar2(self, box_with_array): + pi = period_range("2000-01-01", periods=10, freq="D") + + val = pi[3] + expected = [x > val for x in pi] + + ser = tm.box_expected(pi, box_with_array) + xbox = get_upcast_box(ser, val, True) + + expected = tm.box_expected(expected, xbox) + result = ser > val + tm.assert_equal(result, expected) + + val = pi[5] + result = ser > val + expected = [x > val for x in pi] + expected = tm.box_expected(expected, xbox) + tm.assert_equal(result, expected) + + @pytest.mark.parametrize("freq", ["M", "2M", "3M"]) + def test_parr_cmp_period_scalar(self, freq, box_with_array): + # GH#13200 + base = PeriodIndex(["2011-01", "2011-02", "2011-03", "2011-04"], freq=freq) + base = tm.box_expected(base, box_with_array) + per = Period("2011-02", freq=freq) + xbox = get_upcast_box(base, per, True) + + exp = np.array([False, True, False, False]) + exp = tm.box_expected(exp, xbox) + tm.assert_equal(base == per, exp) + tm.assert_equal(per == base, exp) + + exp = np.array([True, False, True, True]) + exp = tm.box_expected(exp, xbox) + tm.assert_equal(base != per, exp) + tm.assert_equal(per != base, exp) + + exp = np.array([False, False, True, True]) + exp = tm.box_expected(exp, xbox) + tm.assert_equal(base > per, exp) + tm.assert_equal(per < base, exp) + + exp = np.array([True, False, False, False]) + exp = tm.box_expected(exp, xbox) + tm.assert_equal(base < per, exp) + tm.assert_equal(per > base, exp) + + exp = np.array([False, True, True, True]) + exp = tm.box_expected(exp, xbox) + tm.assert_equal(base >= per, exp) + tm.assert_equal(per <= base, exp) + + exp = np.array([True, True, False, False]) + exp = tm.box_expected(exp, xbox) + tm.assert_equal(base <= per, exp) + tm.assert_equal(per >= base, exp) + + @pytest.mark.parametrize("freq", ["M", "2M", "3M"]) + def test_parr_cmp_pi(self, freq, box_with_array): + # GH#13200 + base = PeriodIndex(["2011-01", "2011-02", "2011-03", "2011-04"], freq=freq) + base = tm.box_expected(base, box_with_array) + + # TODO: could also box idx? + idx = PeriodIndex(["2011-02", "2011-01", "2011-03", "2011-05"], freq=freq) + + xbox = get_upcast_box(base, idx, True) + + exp = np.array([False, False, True, False]) + exp = tm.box_expected(exp, xbox) + tm.assert_equal(base == idx, exp) + + exp = np.array([True, True, False, True]) + exp = tm.box_expected(exp, xbox) + tm.assert_equal(base != idx, exp) + + exp = np.array([False, True, False, False]) + exp = tm.box_expected(exp, xbox) + tm.assert_equal(base > idx, exp) + + exp = np.array([True, False, False, True]) + exp = tm.box_expected(exp, xbox) + tm.assert_equal(base < idx, exp) + + exp = np.array([False, True, True, False]) + exp = tm.box_expected(exp, xbox) + tm.assert_equal(base >= idx, exp) + + exp = np.array([True, False, True, True]) + exp = tm.box_expected(exp, xbox) + tm.assert_equal(base <= idx, exp) + + @pytest.mark.parametrize("freq", ["M", "2M", "3M"]) + def test_parr_cmp_pi_mismatched_freq(self, freq, box_with_array): + # GH#13200 + # different base freq + base = PeriodIndex(["2011-01", "2011-02", "2011-03", "2011-04"], freq=freq) + base = tm.box_expected(base, box_with_array) + + msg = rf"Invalid comparison between dtype=period\[{freq}\] and Period" + with pytest.raises(TypeError, match=msg): + base <= Period("2011", freq="Y") + + with pytest.raises(TypeError, match=msg): + Period("2011", freq="Y") >= base + + # TODO: Could parametrize over boxes for idx? + idx = PeriodIndex(["2011", "2012", "2013", "2014"], freq="Y") + rev_msg = r"Invalid comparison between dtype=period\[Y-DEC\] and PeriodArray" + idx_msg = rev_msg if box_with_array in [tm.to_array, pd.array] else msg + with pytest.raises(TypeError, match=idx_msg): + base <= idx + + # Different frequency + msg = rf"Invalid comparison between dtype=period\[{freq}\] and Period" + with pytest.raises(TypeError, match=msg): + base <= Period("2011", freq="4M") + + with pytest.raises(TypeError, match=msg): + Period("2011", freq="4M") >= base + + idx = PeriodIndex(["2011", "2012", "2013", "2014"], freq="4M") + rev_msg = r"Invalid comparison between dtype=period\[4M\] and PeriodArray" + idx_msg = rev_msg if box_with_array in [tm.to_array, pd.array] else msg + with pytest.raises(TypeError, match=idx_msg): + base <= idx + + @pytest.mark.parametrize("freq", ["M", "2M", "3M"]) + def test_pi_cmp_nat(self, freq): + idx1 = PeriodIndex(["2011-01", "2011-02", "NaT", "2011-05"], freq=freq) + per = idx1[1] + + result = idx1 > per + exp = np.array([False, False, False, True]) + tm.assert_numpy_array_equal(result, exp) + result = per < idx1 + tm.assert_numpy_array_equal(result, exp) + + result = idx1 == pd.NaT + exp = np.array([False, False, False, False]) + tm.assert_numpy_array_equal(result, exp) + result = pd.NaT == idx1 + tm.assert_numpy_array_equal(result, exp) + + result = idx1 != pd.NaT + exp = np.array([True, True, True, True]) + tm.assert_numpy_array_equal(result, exp) + result = pd.NaT != idx1 + tm.assert_numpy_array_equal(result, exp) + + idx2 = PeriodIndex(["2011-02", "2011-01", "2011-04", "NaT"], freq=freq) + result = idx1 < idx2 + exp = np.array([True, False, False, False]) + tm.assert_numpy_array_equal(result, exp) + + result = idx1 == idx2 + exp = np.array([False, False, False, False]) + tm.assert_numpy_array_equal(result, exp) + + result = idx1 != idx2 + exp = np.array([True, True, True, True]) + tm.assert_numpy_array_equal(result, exp) + + result = idx1 == idx1 + exp = np.array([True, True, False, True]) + tm.assert_numpy_array_equal(result, exp) + + result = idx1 != idx1 + exp = np.array([False, False, True, False]) + tm.assert_numpy_array_equal(result, exp) + + @pytest.mark.parametrize("freq", ["M", "2M", "3M"]) + def test_pi_cmp_nat_mismatched_freq_raises(self, freq): + idx1 = PeriodIndex(["2011-01", "2011-02", "NaT", "2011-05"], freq=freq) + + diff = PeriodIndex(["2011-02", "2011-01", "2011-04", "NaT"], freq="4M") + msg = rf"Invalid comparison between dtype=period\[{freq}\] and PeriodArray" + with pytest.raises(TypeError, match=msg): + idx1 > diff + + result = idx1 == diff + expected = np.array([False, False, False, False], dtype=bool) + tm.assert_numpy_array_equal(result, expected) + + # TODO: De-duplicate with test_pi_cmp_nat + @pytest.mark.parametrize("dtype", [object, None]) + def test_comp_nat(self, dtype): + left = PeriodIndex([Period("2011-01-01"), pd.NaT, Period("2011-01-03")]) + right = PeriodIndex([pd.NaT, pd.NaT, Period("2011-01-03")]) + + if dtype is not None: + left = left.astype(dtype) + right = right.astype(dtype) + + result = left == right + expected = np.array([False, False, True]) + tm.assert_numpy_array_equal(result, expected) + + result = left != right + expected = np.array([True, True, False]) + tm.assert_numpy_array_equal(result, expected) + + expected = np.array([False, False, False]) + tm.assert_numpy_array_equal(left == pd.NaT, expected) + tm.assert_numpy_array_equal(pd.NaT == right, expected) + + expected = np.array([True, True, True]) + tm.assert_numpy_array_equal(left != pd.NaT, expected) + tm.assert_numpy_array_equal(pd.NaT != left, expected) + + expected = np.array([False, False, False]) + tm.assert_numpy_array_equal(left < pd.NaT, expected) + tm.assert_numpy_array_equal(pd.NaT > left, expected) + + +class TestPeriodSeriesComparisons: + def test_cmp_series_period_series_mixed_freq(self): + # GH#13200 + base = Series( + [ + Period("2011", freq="Y"), + Period("2011-02", freq="M"), + Period("2013", freq="Y"), + Period("2011-04", freq="M"), + ] + ) + + ser = Series( + [ + Period("2012", freq="Y"), + Period("2011-01", freq="M"), + Period("2013", freq="Y"), + Period("2011-05", freq="M"), + ] + ) + + exp = Series([False, False, True, False]) + tm.assert_series_equal(base == ser, exp) + + exp = Series([True, True, False, True]) + tm.assert_series_equal(base != ser, exp) + + exp = Series([False, True, False, False]) + tm.assert_series_equal(base > ser, exp) + + exp = Series([True, False, False, True]) + tm.assert_series_equal(base < ser, exp) + + exp = Series([False, True, True, False]) + tm.assert_series_equal(base >= ser, exp) + + exp = Series([True, False, True, True]) + tm.assert_series_equal(base <= ser, exp) + + +class TestPeriodIndexSeriesComparisonConsistency: + """Test PeriodIndex and Period Series Ops consistency""" + + # TODO: needs parametrization+de-duplication + + def _check(self, values, func, expected): + # Test PeriodIndex and Period Series Ops consistency + + idx = PeriodIndex(values) + result = func(idx) + + # check that we don't pass an unwanted type to tm.assert_equal + assert isinstance(expected, (pd.Index, np.ndarray)) + tm.assert_equal(result, expected) + + s = Series(values) + result = func(s) + + exp = Series(expected, name=values.name) + tm.assert_series_equal(result, exp) + + def test_pi_comp_period(self): + idx = PeriodIndex( + ["2011-01", "2011-02", "2011-03", "2011-04"], freq="M", name="idx" + ) + per = idx[2] + + f = lambda x: x == per + exp = np.array([False, False, True, False], dtype=np.bool_) + self._check(idx, f, exp) + f = lambda x: per == x + self._check(idx, f, exp) + + f = lambda x: x != per + exp = np.array([True, True, False, True], dtype=np.bool_) + self._check(idx, f, exp) + f = lambda x: per != x + self._check(idx, f, exp) + + f = lambda x: per >= x + exp = np.array([True, True, True, False], dtype=np.bool_) + self._check(idx, f, exp) + + f = lambda x: x > per + exp = np.array([False, False, False, True], dtype=np.bool_) + self._check(idx, f, exp) + + f = lambda x: per >= x + exp = np.array([True, True, True, False], dtype=np.bool_) + self._check(idx, f, exp) + + def test_pi_comp_period_nat(self): + idx = PeriodIndex( + ["2011-01", "NaT", "2011-03", "2011-04"], freq="M", name="idx" + ) + per = idx[2] + + f = lambda x: x == per + exp = np.array([False, False, True, False], dtype=np.bool_) + self._check(idx, f, exp) + f = lambda x: per == x + self._check(idx, f, exp) + + f = lambda x: x == pd.NaT + exp = np.array([False, False, False, False], dtype=np.bool_) + self._check(idx, f, exp) + f = lambda x: pd.NaT == x + self._check(idx, f, exp) + + f = lambda x: x != per + exp = np.array([True, True, False, True], dtype=np.bool_) + self._check(idx, f, exp) + f = lambda x: per != x + self._check(idx, f, exp) + + f = lambda x: x != pd.NaT + exp = np.array([True, True, True, True], dtype=np.bool_) + self._check(idx, f, exp) + f = lambda x: pd.NaT != x + self._check(idx, f, exp) + + f = lambda x: per >= x + exp = np.array([True, False, True, False], dtype=np.bool_) + self._check(idx, f, exp) + + f = lambda x: x < per + exp = np.array([True, False, False, False], dtype=np.bool_) + self._check(idx, f, exp) + + f = lambda x: x > pd.NaT + exp = np.array([False, False, False, False], dtype=np.bool_) + self._check(idx, f, exp) + + f = lambda x: pd.NaT >= x + exp = np.array([False, False, False, False], dtype=np.bool_) + self._check(idx, f, exp) + + +# ------------------------------------------------------------------ +# Arithmetic + + +class TestPeriodFrameArithmetic: + def test_ops_frame_period(self): + # GH#13043 + df = pd.DataFrame( + { + "A": [Period("2015-01", freq="M"), Period("2015-02", freq="M")], + "B": [Period("2014-01", freq="M"), Period("2014-02", freq="M")], + } + ) + assert df["A"].dtype == "Period[M]" + assert df["B"].dtype == "Period[M]" + + p = Period("2015-03", freq="M") + off = p.freq + # dtype will be object because of original dtype + exp = pd.DataFrame( + { + "A": np.array([2 * off, 1 * off], dtype=object), + "B": np.array([14 * off, 13 * off], dtype=object), + } + ) + tm.assert_frame_equal(p - df, exp) + tm.assert_frame_equal(df - p, -1 * exp) + + df2 = pd.DataFrame( + { + "A": [Period("2015-05", freq="M"), Period("2015-06", freq="M")], + "B": [Period("2015-05", freq="M"), Period("2015-06", freq="M")], + } + ) + assert df2["A"].dtype == "Period[M]" + assert df2["B"].dtype == "Period[M]" + + exp = pd.DataFrame( + { + "A": np.array([4 * off, 4 * off], dtype=object), + "B": np.array([16 * off, 16 * off], dtype=object), + } + ) + tm.assert_frame_equal(df2 - df, exp) + tm.assert_frame_equal(df - df2, -1 * exp) + + +class TestPeriodIndexArithmetic: + # --------------------------------------------------------------- + # __add__/__sub__ with PeriodIndex + # PeriodIndex + other is defined for integers and timedelta-like others + # PeriodIndex - other is defined for integers, timedelta-like others, + # and PeriodIndex (with matching freq) + + def test_parr_add_iadd_parr_raises(self, box_with_array): + rng = period_range("1/1/2000", freq="D", periods=5) + other = period_range("1/6/2000", freq="D", periods=5) + # TODO: parametrize over boxes for other? + + rng = tm.box_expected(rng, box_with_array) + # An earlier implementation of PeriodIndex addition performed + # a set operation (union). This has since been changed to + # raise a TypeError. See GH#14164 and GH#13077 for historical + # reference. + msg = r"unsupported operand type\(s\) for \+: .* and .*" + with pytest.raises(TypeError, match=msg): + rng + other + + with pytest.raises(TypeError, match=msg): + rng += other + + def test_pi_sub_isub_pi(self): + # GH#20049 + # For historical reference see GH#14164, GH#13077. + # PeriodIndex subtraction originally performed set difference, + # then changed to raise TypeError before being implemented in GH#20049 + rng = period_range("1/1/2000", freq="D", periods=5) + other = period_range("1/6/2000", freq="D", periods=5) + + off = rng.freq + expected = pd.Index([-5 * off] * 5) + result = rng - other + tm.assert_index_equal(result, expected) + + rng -= other + tm.assert_index_equal(rng, expected) + + def test_pi_sub_pi_with_nat(self): + rng = period_range("1/1/2000", freq="D", periods=5) + other = rng[1:].insert(0, pd.NaT) + assert other[1:].equals(rng[1:]) + + result = rng - other + off = rng.freq + expected = pd.Index([pd.NaT, 0 * off, 0 * off, 0 * off, 0 * off]) + tm.assert_index_equal(result, expected) + + def test_parr_sub_pi_mismatched_freq(self, box_with_array, box_with_array2): + rng = period_range("1/1/2000", freq="D", periods=5) + other = period_range("1/6/2000", freq="h", periods=5) + + rng = tm.box_expected(rng, box_with_array) + other = tm.box_expected(other, box_with_array2) + msg = r"Input has different freq=[hD] from PeriodArray\(freq=[Dh]\)" + with pytest.raises(IncompatibleFrequency, match=msg): + rng - other + + @pytest.mark.parametrize("n", [1, 2, 3, 4]) + def test_sub_n_gt_1_ticks(self, tick_classes, n): + # GH 23878 + p1_d = "19910905" + p2_d = "19920406" + p1 = PeriodIndex([p1_d], freq=tick_classes(n)) + p2 = PeriodIndex([p2_d], freq=tick_classes(n)) + + expected = PeriodIndex([p2_d], freq=p2.freq.base) - PeriodIndex( + [p1_d], freq=p1.freq.base + ) + + tm.assert_index_equal((p2 - p1), expected) + + @pytest.mark.parametrize("n", [1, 2, 3, 4]) + @pytest.mark.parametrize( + "offset, kwd_name", + [ + (pd.offsets.YearEnd, "month"), + (pd.offsets.QuarterEnd, "startingMonth"), + (pd.offsets.MonthEnd, None), + (pd.offsets.Week, "weekday"), + ], + ) + def test_sub_n_gt_1_offsets(self, offset, kwd_name, n): + # GH 23878 + kwds = {kwd_name: 3} if kwd_name is not None else {} + p1_d = "19910905" + p2_d = "19920406" + freq = offset(n, normalize=False, **kwds) + p1 = PeriodIndex([p1_d], freq=freq) + p2 = PeriodIndex([p2_d], freq=freq) + + result = p2 - p1 + expected = PeriodIndex([p2_d], freq=freq.base) - PeriodIndex( + [p1_d], freq=freq.base + ) + + tm.assert_index_equal(result, expected) + + # ------------------------------------------------------------- + # Invalid Operations + + @pytest.mark.parametrize( + "other", + [ + # datetime scalars + Timestamp("2016-01-01"), + Timestamp("2016-01-01").to_pydatetime(), + Timestamp("2016-01-01").to_datetime64(), + # datetime-like arrays + pd.date_range("2016-01-01", periods=3, freq="h"), + pd.date_range("2016-01-01", periods=3, tz="Europe/Brussels"), + pd.date_range("2016-01-01", periods=3, freq="s")._data, + pd.date_range("2016-01-01", periods=3, tz="Asia/Tokyo")._data, + # Miscellaneous invalid types + 3.14, + np.array([2.0, 3.0, 4.0]), + ], + ) + def test_parr_add_sub_invalid(self, other, box_with_array): + # GH#23215 + rng = period_range("1/1/2000", freq="D", periods=3) + rng = tm.box_expected(rng, box_with_array) + + msg = "|".join( + [ + r"(:?cannot add PeriodArray and .*)", + r"(:?cannot subtract .* from (:?a\s)?.*)", + r"(:?unsupported operand type\(s\) for \+: .* and .*)", + r"unsupported operand type\(s\) for [+-]: .* and .*", + ] + ) + assert_invalid_addsub_type(rng, other, msg) + with pytest.raises(TypeError, match=msg): + rng + other + with pytest.raises(TypeError, match=msg): + other + rng + with pytest.raises(TypeError, match=msg): + rng - other + with pytest.raises(TypeError, match=msg): + other - rng + + # ----------------------------------------------------------------- + # __add__/__sub__ with ndarray[datetime64] and ndarray[timedelta64] + + def test_pi_add_sub_td64_array_non_tick_raises(self): + rng = period_range("1/1/2000", freq="Q", periods=3) + tdi = TimedeltaIndex(["-1 Day", "-1 Day", "-1 Day"]) + tdarr = tdi.values + + msg = r"Cannot add or subtract timedelta64\[ns\] dtype from period\[Q-DEC\]" + with pytest.raises(TypeError, match=msg): + rng + tdarr + with pytest.raises(TypeError, match=msg): + tdarr + rng + + with pytest.raises(TypeError, match=msg): + rng - tdarr + msg = r"cannot subtract PeriodArray from TimedeltaArray" + with pytest.raises(TypeError, match=msg): + tdarr - rng + + def test_pi_add_sub_td64_array_tick(self): + # PeriodIndex + Timedelta-like is allowed only with + # tick-like frequencies + rng = period_range("1/1/2000", freq="90D", periods=3) + tdi = TimedeltaIndex(["-1 Day", "-1 Day", "-1 Day"]) + tdarr = tdi.values + + expected = period_range("12/31/1999", freq="90D", periods=3) + result = rng + tdi + tm.assert_index_equal(result, expected) + result = rng + tdarr + tm.assert_index_equal(result, expected) + result = tdi + rng + tm.assert_index_equal(result, expected) + result = tdarr + rng + tm.assert_index_equal(result, expected) + + expected = period_range("1/2/2000", freq="90D", periods=3) + + result = rng - tdi + tm.assert_index_equal(result, expected) + result = rng - tdarr + tm.assert_index_equal(result, expected) + + msg = r"cannot subtract .* from .*" + with pytest.raises(TypeError, match=msg): + tdarr - rng + + with pytest.raises(TypeError, match=msg): + tdi - rng + + @pytest.mark.parametrize("pi_freq", ["D", "W", "Q", "h"]) + @pytest.mark.parametrize("tdi_freq", [None, "h"]) + def test_parr_sub_td64array(self, box_with_array, tdi_freq, pi_freq): + box = box_with_array + xbox = box if box not in [pd.array, tm.to_array] else pd.Index + + tdi = TimedeltaIndex(["1 hours", "2 hours"], freq=tdi_freq) + dti = Timestamp("2018-03-07 17:16:40") + tdi + pi = dti.to_period(pi_freq) + + # TODO: parametrize over box for pi? + td64obj = tm.box_expected(tdi, box) + + if pi_freq == "h": + result = pi - td64obj + expected = (pi.to_timestamp("s") - tdi).to_period(pi_freq) + expected = tm.box_expected(expected, xbox) + tm.assert_equal(result, expected) + + # Subtract from scalar + result = pi[0] - td64obj + expected = (pi[0].to_timestamp("s") - tdi).to_period(pi_freq) + expected = tm.box_expected(expected, box) + tm.assert_equal(result, expected) + + elif pi_freq == "D": + # Tick, but non-compatible + msg = ( + "Cannot add/subtract timedelta-like from PeriodArray that is " + "not an integer multiple of the PeriodArray's freq." + ) + with pytest.raises(IncompatibleFrequency, match=msg): + pi - td64obj + + with pytest.raises(IncompatibleFrequency, match=msg): + pi[0] - td64obj + + else: + # With non-Tick freq, we could not add timedelta64 array regardless + # of what its resolution is + msg = "Cannot add or subtract timedelta64" + with pytest.raises(TypeError, match=msg): + pi - td64obj + with pytest.raises(TypeError, match=msg): + pi[0] - td64obj + + # ----------------------------------------------------------------- + # operations with array/Index of DateOffset objects + + @pytest.mark.parametrize("box", [np.array, pd.Index]) + def test_pi_add_offset_array(self, box): + # GH#18849 + pi = PeriodIndex([Period("2015Q1"), Period("2016Q2")]) + offs = box( + [ + pd.offsets.QuarterEnd(n=1, startingMonth=12), + pd.offsets.QuarterEnd(n=-2, startingMonth=12), + ] + ) + expected = PeriodIndex([Period("2015Q2"), Period("2015Q4")]).astype(object) + + with tm.assert_produces_warning(PerformanceWarning): + res = pi + offs + tm.assert_index_equal(res, expected) + + with tm.assert_produces_warning(PerformanceWarning): + res2 = offs + pi + tm.assert_index_equal(res2, expected) + + unanchored = np.array([pd.offsets.Hour(n=1), pd.offsets.Minute(n=-2)]) + # addition/subtraction ops with incompatible offsets should issue + # a PerformanceWarning and _then_ raise a TypeError. + msg = r"Input cannot be converted to Period\(freq=Q-DEC\)" + with pytest.raises(IncompatibleFrequency, match=msg): + with tm.assert_produces_warning(PerformanceWarning): + pi + unanchored + with pytest.raises(IncompatibleFrequency, match=msg): + with tm.assert_produces_warning(PerformanceWarning): + unanchored + pi + + @pytest.mark.parametrize("box", [np.array, pd.Index]) + def test_pi_sub_offset_array(self, box): + # GH#18824 + pi = PeriodIndex([Period("2015Q1"), Period("2016Q2")]) + other = box( + [ + pd.offsets.QuarterEnd(n=1, startingMonth=12), + pd.offsets.QuarterEnd(n=-2, startingMonth=12), + ] + ) + + expected = PeriodIndex([pi[n] - other[n] for n in range(len(pi))]) + expected = expected.astype(object) + + with tm.assert_produces_warning(PerformanceWarning): + res = pi - other + tm.assert_index_equal(res, expected) + + anchored = box([pd.offsets.MonthEnd(), pd.offsets.Day(n=2)]) + + # addition/subtraction ops with anchored offsets should issue + # a PerformanceWarning and _then_ raise a TypeError. + msg = r"Input has different freq=-1M from Period\(freq=Q-DEC\)" + with pytest.raises(IncompatibleFrequency, match=msg): + with tm.assert_produces_warning(PerformanceWarning): + pi - anchored + with pytest.raises(IncompatibleFrequency, match=msg): + with tm.assert_produces_warning(PerformanceWarning): + anchored - pi + + def test_pi_add_iadd_int(self, one): + # Variants of `one` for #19012 + rng = period_range("2000-01-01 09:00", freq="h", periods=10) + result = rng + one + expected = period_range("2000-01-01 10:00", freq="h", periods=10) + tm.assert_index_equal(result, expected) + rng += one + tm.assert_index_equal(rng, expected) + + def test_pi_sub_isub_int(self, one): + """ + PeriodIndex.__sub__ and __isub__ with several representations of + the integer 1, e.g. int, np.int64, np.uint8, ... + """ + rng = period_range("2000-01-01 09:00", freq="h", periods=10) + result = rng - one + expected = period_range("2000-01-01 08:00", freq="h", periods=10) + tm.assert_index_equal(result, expected) + rng -= one + tm.assert_index_equal(rng, expected) + + @pytest.mark.parametrize("five", [5, np.array(5, dtype=np.int64)]) + def test_pi_sub_intlike(self, five): + rng = period_range("2007-01", periods=50) + + result = rng - five + exp = rng + (-five) + tm.assert_index_equal(result, exp) + + def test_pi_add_sub_int_array_freqn_gt1(self): + # GH#47209 test adding array of ints when freq.n > 1 matches + # scalar behavior + pi = period_range("2016-01-01", periods=10, freq="2D") + arr = np.arange(10) + result = pi + arr + expected = pd.Index([x + y for x, y in zip(pi, arr)]) + tm.assert_index_equal(result, expected) + + result = pi - arr + expected = pd.Index([x - y for x, y in zip(pi, arr)]) + tm.assert_index_equal(result, expected) + + def test_pi_sub_isub_offset(self): + # offset + # DateOffset + rng = period_range("2014", "2024", freq="Y") + result = rng - pd.offsets.YearEnd(5) + expected = period_range("2009", "2019", freq="Y") + tm.assert_index_equal(result, expected) + rng -= pd.offsets.YearEnd(5) + tm.assert_index_equal(rng, expected) + + rng = period_range("2014-01", "2016-12", freq="M") + result = rng - pd.offsets.MonthEnd(5) + expected = period_range("2013-08", "2016-07", freq="M") + tm.assert_index_equal(result, expected) + + rng -= pd.offsets.MonthEnd(5) + tm.assert_index_equal(rng, expected) + + @pytest.mark.parametrize("transpose", [True, False]) + def test_pi_add_offset_n_gt1(self, box_with_array, transpose): + # GH#23215 + # add offset to PeriodIndex with freq.n > 1 + + per = Period("2016-01", freq="2M") + pi = PeriodIndex([per]) + + expected = PeriodIndex(["2016-03"], freq="2M") + + pi = tm.box_expected(pi, box_with_array, transpose=transpose) + expected = tm.box_expected(expected, box_with_array, transpose=transpose) + + result = pi + per.freq + tm.assert_equal(result, expected) + + result = per.freq + pi + tm.assert_equal(result, expected) + + def test_pi_add_offset_n_gt1_not_divisible(self, box_with_array): + # GH#23215 + # PeriodIndex with freq.n > 1 add offset with offset.n % freq.n != 0 + pi = PeriodIndex(["2016-01"], freq="2M") + expected = PeriodIndex(["2016-04"], freq="2M") + + pi = tm.box_expected(pi, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + result = pi + to_offset("3ME") + tm.assert_equal(result, expected) + + result = to_offset("3ME") + pi + tm.assert_equal(result, expected) + + # --------------------------------------------------------------- + # __add__/__sub__ with integer arrays + + @pytest.mark.parametrize("int_holder", [np.array, pd.Index]) + @pytest.mark.parametrize("op", [operator.add, ops.radd]) + def test_pi_add_intarray(self, int_holder, op): + # GH#19959 + pi = PeriodIndex([Period("2015Q1"), Period("NaT")]) + other = int_holder([4, -1]) + + result = op(pi, other) + expected = PeriodIndex([Period("2016Q1"), Period("NaT")]) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize("int_holder", [np.array, pd.Index]) + def test_pi_sub_intarray(self, int_holder): + # GH#19959 + pi = PeriodIndex([Period("2015Q1"), Period("NaT")]) + other = int_holder([4, -1]) + + result = pi - other + expected = PeriodIndex([Period("2014Q1"), Period("NaT")]) + tm.assert_index_equal(result, expected) + + msg = r"bad operand type for unary -: 'PeriodArray'" + with pytest.raises(TypeError, match=msg): + other - pi + + # --------------------------------------------------------------- + # Timedelta-like (timedelta, timedelta64, Timedelta, Tick) + # TODO: Some of these are misnomers because of non-Tick DateOffsets + + def test_parr_add_timedeltalike_minute_gt1(self, three_days, box_with_array): + # GH#23031 adding a time-delta-like offset to a PeriodArray that has + # minute frequency with n != 1. A more general case is tested below + # in test_pi_add_timedeltalike_tick_gt1, but here we write out the + # expected result more explicitly. + other = three_days + rng = period_range("2014-05-01", periods=3, freq="2D") + rng = tm.box_expected(rng, box_with_array) + + expected = PeriodIndex(["2014-05-04", "2014-05-06", "2014-05-08"], freq="2D") + expected = tm.box_expected(expected, box_with_array) + + result = rng + other + tm.assert_equal(result, expected) + + result = other + rng + tm.assert_equal(result, expected) + + # subtraction + expected = PeriodIndex(["2014-04-28", "2014-04-30", "2014-05-02"], freq="2D") + expected = tm.box_expected(expected, box_with_array) + result = rng - other + tm.assert_equal(result, expected) + + msg = "|".join( + [ + r"bad operand type for unary -: 'PeriodArray'", + r"cannot subtract PeriodArray from timedelta64\[[hD]\]", + ] + ) + with pytest.raises(TypeError, match=msg): + other - rng + + @pytest.mark.parametrize("freqstr", ["5ns", "5us", "5ms", "5s", "5min", "5h", "5d"]) + def test_parr_add_timedeltalike_tick_gt1(self, three_days, freqstr, box_with_array): + # GH#23031 adding a time-delta-like offset to a PeriodArray that has + # tick-like frequency with n != 1 + other = three_days + rng = period_range("2014-05-01", periods=6, freq=freqstr) + first = rng[0] + rng = tm.box_expected(rng, box_with_array) + + expected = period_range(first + other, periods=6, freq=freqstr) + expected = tm.box_expected(expected, box_with_array) + + result = rng + other + tm.assert_equal(result, expected) + + result = other + rng + tm.assert_equal(result, expected) + + # subtraction + expected = period_range(first - other, periods=6, freq=freqstr) + expected = tm.box_expected(expected, box_with_array) + result = rng - other + tm.assert_equal(result, expected) + msg = "|".join( + [ + r"bad operand type for unary -: 'PeriodArray'", + r"cannot subtract PeriodArray from timedelta64\[[hD]\]", + ] + ) + with pytest.raises(TypeError, match=msg): + other - rng + + def test_pi_add_iadd_timedeltalike_daily(self, three_days): + # Tick + other = three_days + rng = period_range("2014-05-01", "2014-05-15", freq="D") + expected = period_range("2014-05-04", "2014-05-18", freq="D") + + result = rng + other + tm.assert_index_equal(result, expected) + + rng += other + tm.assert_index_equal(rng, expected) + + def test_pi_sub_isub_timedeltalike_daily(self, three_days): + # Tick-like 3 Days + other = three_days + rng = period_range("2014-05-01", "2014-05-15", freq="D") + expected = period_range("2014-04-28", "2014-05-12", freq="D") + + result = rng - other + tm.assert_index_equal(result, expected) + + rng -= other + tm.assert_index_equal(rng, expected) + + def test_parr_add_sub_timedeltalike_freq_mismatch_daily( + self, not_daily, box_with_array + ): + other = not_daily + rng = period_range("2014-05-01", "2014-05-15", freq="D") + rng = tm.box_expected(rng, box_with_array) + + msg = "|".join( + [ + # non-timedelta-like DateOffset + "Input has different freq(=.+)? from Period.*?\\(freq=D\\)", + # timedelta/td64/Timedelta but not a multiple of 24H + "Cannot add/subtract timedelta-like from PeriodArray that is " + "not an integer multiple of the PeriodArray's freq.", + ] + ) + with pytest.raises(IncompatibleFrequency, match=msg): + rng + other + with pytest.raises(IncompatibleFrequency, match=msg): + rng += other + with pytest.raises(IncompatibleFrequency, match=msg): + rng - other + with pytest.raises(IncompatibleFrequency, match=msg): + rng -= other + + def test_pi_add_iadd_timedeltalike_hourly(self, two_hours): + other = two_hours + rng = period_range("2014-01-01 10:00", "2014-01-05 10:00", freq="h") + expected = period_range("2014-01-01 12:00", "2014-01-05 12:00", freq="h") + + result = rng + other + tm.assert_index_equal(result, expected) + + rng += other + tm.assert_index_equal(rng, expected) + + def test_parr_add_timedeltalike_mismatched_freq_hourly( + self, not_hourly, box_with_array + ): + other = not_hourly + rng = period_range("2014-01-01 10:00", "2014-01-05 10:00", freq="h") + rng = tm.box_expected(rng, box_with_array) + msg = "|".join( + [ + # non-timedelta-like DateOffset + "Input has different freq(=.+)? from Period.*?\\(freq=h\\)", + # timedelta/td64/Timedelta but not a multiple of 24H + "Cannot add/subtract timedelta-like from PeriodArray that is " + "not an integer multiple of the PeriodArray's freq.", + ] + ) + + with pytest.raises(IncompatibleFrequency, match=msg): + rng + other + + with pytest.raises(IncompatibleFrequency, match=msg): + rng += other + + def test_pi_sub_isub_timedeltalike_hourly(self, two_hours): + other = two_hours + rng = period_range("2014-01-01 10:00", "2014-01-05 10:00", freq="h") + expected = period_range("2014-01-01 08:00", "2014-01-05 08:00", freq="h") + + result = rng - other + tm.assert_index_equal(result, expected) + + rng -= other + tm.assert_index_equal(rng, expected) + + def test_add_iadd_timedeltalike_annual(self): + # offset + # DateOffset + rng = period_range("2014", "2024", freq="Y") + result = rng + pd.offsets.YearEnd(5) + expected = period_range("2019", "2029", freq="Y") + tm.assert_index_equal(result, expected) + rng += pd.offsets.YearEnd(5) + tm.assert_index_equal(rng, expected) + + def test_pi_add_sub_timedeltalike_freq_mismatch_annual(self, mismatched_freq): + other = mismatched_freq + rng = period_range("2014", "2024", freq="Y") + msg = "Input has different freq(=.+)? from Period.*?\\(freq=Y-DEC\\)" + with pytest.raises(IncompatibleFrequency, match=msg): + rng + other + with pytest.raises(IncompatibleFrequency, match=msg): + rng += other + with pytest.raises(IncompatibleFrequency, match=msg): + rng - other + with pytest.raises(IncompatibleFrequency, match=msg): + rng -= other + + def test_pi_add_iadd_timedeltalike_M(self): + rng = period_range("2014-01", "2016-12", freq="M") + expected = period_range("2014-06", "2017-05", freq="M") + + result = rng + pd.offsets.MonthEnd(5) + tm.assert_index_equal(result, expected) + + rng += pd.offsets.MonthEnd(5) + tm.assert_index_equal(rng, expected) + + def test_pi_add_sub_timedeltalike_freq_mismatch_monthly(self, mismatched_freq): + other = mismatched_freq + rng = period_range("2014-01", "2016-12", freq="M") + msg = "Input has different freq(=.+)? from Period.*?\\(freq=M\\)" + with pytest.raises(IncompatibleFrequency, match=msg): + rng + other + with pytest.raises(IncompatibleFrequency, match=msg): + rng += other + with pytest.raises(IncompatibleFrequency, match=msg): + rng - other + with pytest.raises(IncompatibleFrequency, match=msg): + rng -= other + + @pytest.mark.parametrize("transpose", [True, False]) + def test_parr_add_sub_td64_nat(self, box_with_array, transpose): + # GH#23320 special handling for timedelta64("NaT") + pi = period_range("1994-04-01", periods=9, freq="19D") + other = np.timedelta64("NaT") + expected = PeriodIndex(["NaT"] * 9, freq="19D") + + obj = tm.box_expected(pi, box_with_array, transpose=transpose) + expected = tm.box_expected(expected, box_with_array, transpose=transpose) + + result = obj + other + tm.assert_equal(result, expected) + result = other + obj + tm.assert_equal(result, expected) + result = obj - other + tm.assert_equal(result, expected) + msg = r"cannot subtract .* from .*" + with pytest.raises(TypeError, match=msg): + other - obj + + @pytest.mark.parametrize( + "other", + [ + np.array(["NaT"] * 9, dtype="m8[ns]"), + TimedeltaArray._from_sequence(["NaT"] * 9, dtype="m8[ns]"), + ], + ) + def test_parr_add_sub_tdt64_nat_array(self, box_with_array, other): + pi = period_range("1994-04-01", periods=9, freq="19D") + expected = PeriodIndex(["NaT"] * 9, freq="19D") + + obj = tm.box_expected(pi, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + result = obj + other + tm.assert_equal(result, expected) + result = other + obj + tm.assert_equal(result, expected) + result = obj - other + tm.assert_equal(result, expected) + msg = r"cannot subtract .* from .*" + with pytest.raises(TypeError, match=msg): + other - obj + + # some but not *all* NaT + other = other.copy() + other[0] = np.timedelta64(0, "ns") + expected = PeriodIndex([pi[0]] + ["NaT"] * 8, freq="19D") + expected = tm.box_expected(expected, box_with_array) + + result = obj + other + tm.assert_equal(result, expected) + result = other + obj + tm.assert_equal(result, expected) + result = obj - other + tm.assert_equal(result, expected) + with pytest.raises(TypeError, match=msg): + other - obj + + # --------------------------------------------------------------- + # Unsorted + + def test_parr_add_sub_index(self): + # Check that PeriodArray defers to Index on arithmetic ops + pi = period_range("2000-12-31", periods=3) + parr = pi.array + + result = parr - pi + expected = pi - pi + tm.assert_index_equal(result, expected) + + def test_parr_add_sub_object_array(self): + pi = period_range("2000-12-31", periods=3, freq="D") + parr = pi.array + + other = np.array([Timedelta(days=1), pd.offsets.Day(2), 3]) + + with tm.assert_produces_warning(PerformanceWarning): + result = parr + other + + expected = PeriodIndex( + ["2001-01-01", "2001-01-03", "2001-01-05"], freq="D" + )._data.astype(object) + tm.assert_equal(result, expected) + + with tm.assert_produces_warning(PerformanceWarning): + result = parr - other + + expected = PeriodIndex(["2000-12-30"] * 3, freq="D")._data.astype(object) + tm.assert_equal(result, expected) + + def test_period_add_timestamp_raises(self, box_with_array): + # GH#17983 + ts = Timestamp("2017") + per = Period("2017", freq="M") + + arr = pd.Index([per], dtype="Period[M]") + arr = tm.box_expected(arr, box_with_array) + + msg = "cannot add PeriodArray and Timestamp" + with pytest.raises(TypeError, match=msg): + arr + ts + with pytest.raises(TypeError, match=msg): + ts + arr + msg = "cannot add PeriodArray and DatetimeArray" + with pytest.raises(TypeError, match=msg): + arr + Series([ts]) + with pytest.raises(TypeError, match=msg): + Series([ts]) + arr + with pytest.raises(TypeError, match=msg): + arr + pd.Index([ts]) + with pytest.raises(TypeError, match=msg): + pd.Index([ts]) + arr + + if box_with_array is pd.DataFrame: + msg = "cannot add PeriodArray and DatetimeArray" + else: + msg = r"unsupported operand type\(s\) for \+: 'Period' and 'DatetimeArray" + with pytest.raises(TypeError, match=msg): + arr + pd.DataFrame([ts]) + if box_with_array is pd.DataFrame: + msg = "cannot add PeriodArray and DatetimeArray" + else: + msg = r"unsupported operand type\(s\) for \+: 'DatetimeArray' and 'Period'" + with pytest.raises(TypeError, match=msg): + pd.DataFrame([ts]) + arr + + +class TestPeriodSeriesArithmetic: + def test_parr_add_timedeltalike_scalar(self, three_days, box_with_array): + # GH#13043 + ser = Series( + [Period("2015-01-01", freq="D"), Period("2015-01-02", freq="D")], + name="xxx", + ) + assert ser.dtype == "Period[D]" + + expected = Series( + [Period("2015-01-04", freq="D"), Period("2015-01-05", freq="D")], + name="xxx", + ) + + obj = tm.box_expected(ser, box_with_array) + if box_with_array is pd.DataFrame: + assert (obj.dtypes == "Period[D]").all() + + expected = tm.box_expected(expected, box_with_array) + + result = obj + three_days + tm.assert_equal(result, expected) + + result = three_days + obj + tm.assert_equal(result, expected) + + def test_ops_series_period(self): + # GH#13043 + ser = Series( + [Period("2015-01-01", freq="D"), Period("2015-01-02", freq="D")], + name="xxx", + ) + assert ser.dtype == "Period[D]" + + per = Period("2015-01-10", freq="D") + off = per.freq + # dtype will be object because of original dtype + expected = Series([9 * off, 8 * off], name="xxx", dtype=object) + tm.assert_series_equal(per - ser, expected) + tm.assert_series_equal(ser - per, -1 * expected) + + s2 = Series( + [Period("2015-01-05", freq="D"), Period("2015-01-04", freq="D")], + name="xxx", + ) + assert s2.dtype == "Period[D]" + + expected = Series([4 * off, 2 * off], name="xxx", dtype=object) + tm.assert_series_equal(s2 - ser, expected) + tm.assert_series_equal(ser - s2, -1 * expected) + + +class TestPeriodIndexSeriesMethods: + """Test PeriodIndex and Period Series Ops consistency""" + + def _check(self, values, func, expected): + idx = PeriodIndex(values) + result = func(idx) + tm.assert_equal(result, expected) + + ser = Series(values) + result = func(ser) + + exp = Series(expected, name=values.name) + tm.assert_series_equal(result, exp) + + def test_pi_ops(self): + idx = PeriodIndex( + ["2011-01", "2011-02", "2011-03", "2011-04"], freq="M", name="idx" + ) + + expected = PeriodIndex( + ["2011-03", "2011-04", "2011-05", "2011-06"], freq="M", name="idx" + ) + + self._check(idx, lambda x: x + 2, expected) + self._check(idx, lambda x: 2 + x, expected) + + self._check(idx + 2, lambda x: x - 2, idx) + + result = idx - Period("2011-01", freq="M") + off = idx.freq + exp = pd.Index([0 * off, 1 * off, 2 * off, 3 * off], name="idx") + tm.assert_index_equal(result, exp) + + result = Period("2011-01", freq="M") - idx + exp = pd.Index([0 * off, -1 * off, -2 * off, -3 * off], name="idx") + tm.assert_index_equal(result, exp) + + @pytest.mark.parametrize("ng", ["str", 1.5]) + @pytest.mark.parametrize( + "func", + [ + lambda obj, ng: obj + ng, + lambda obj, ng: ng + obj, + lambda obj, ng: obj - ng, + lambda obj, ng: ng - obj, + lambda obj, ng: np.add(obj, ng), + lambda obj, ng: np.add(ng, obj), + lambda obj, ng: np.subtract(obj, ng), + lambda obj, ng: np.subtract(ng, obj), + ], + ) + def test_parr_ops_errors(self, ng, func, box_with_array): + idx = PeriodIndex( + ["2011-01", "2011-02", "2011-03", "2011-04"], freq="M", name="idx" + ) + obj = tm.box_expected(idx, box_with_array) + msg = "|".join( + [ + r"unsupported operand type\(s\)", + "can only concatenate", + r"must be str", + "object to str implicitly", + ] + ) + + with pytest.raises(TypeError, match=msg): + func(obj, ng) + + def test_pi_ops_nat(self): + idx = PeriodIndex( + ["2011-01", "2011-02", "NaT", "2011-04"], freq="M", name="idx" + ) + expected = PeriodIndex( + ["2011-03", "2011-04", "NaT", "2011-06"], freq="M", name="idx" + ) + + self._check(idx, lambda x: x + 2, expected) + self._check(idx, lambda x: 2 + x, expected) + self._check(idx, lambda x: np.add(x, 2), expected) + + self._check(idx + 2, lambda x: x - 2, idx) + self._check(idx + 2, lambda x: np.subtract(x, 2), idx) + + # freq with mult + idx = PeriodIndex( + ["2011-01", "2011-02", "NaT", "2011-04"], freq="2M", name="idx" + ) + expected = PeriodIndex( + ["2011-07", "2011-08", "NaT", "2011-10"], freq="2M", name="idx" + ) + + self._check(idx, lambda x: x + 3, expected) + self._check(idx, lambda x: 3 + x, expected) + self._check(idx, lambda x: np.add(x, 3), expected) + + self._check(idx + 3, lambda x: x - 3, idx) + self._check(idx + 3, lambda x: np.subtract(x, 3), idx) + + def test_pi_ops_array_int(self): + idx = PeriodIndex( + ["2011-01", "2011-02", "NaT", "2011-04"], freq="M", name="idx" + ) + f = lambda x: x + np.array([1, 2, 3, 4]) + exp = PeriodIndex( + ["2011-02", "2011-04", "NaT", "2011-08"], freq="M", name="idx" + ) + self._check(idx, f, exp) + + f = lambda x: np.add(x, np.array([4, -1, 1, 2])) + exp = PeriodIndex( + ["2011-05", "2011-01", "NaT", "2011-06"], freq="M", name="idx" + ) + self._check(idx, f, exp) + + f = lambda x: x - np.array([1, 2, 3, 4]) + exp = PeriodIndex( + ["2010-12", "2010-12", "NaT", "2010-12"], freq="M", name="idx" + ) + self._check(idx, f, exp) + + f = lambda x: np.subtract(x, np.array([3, 2, 3, -2])) + exp = PeriodIndex( + ["2010-10", "2010-12", "NaT", "2011-06"], freq="M", name="idx" + ) + self._check(idx, f, exp) + + def test_pi_ops_offset(self): + idx = PeriodIndex( + ["2011-01-01", "2011-02-01", "2011-03-01", "2011-04-01"], + freq="D", + name="idx", + ) + f = lambda x: x + pd.offsets.Day() + exp = PeriodIndex( + ["2011-01-02", "2011-02-02", "2011-03-02", "2011-04-02"], + freq="D", + name="idx", + ) + self._check(idx, f, exp) + + f = lambda x: x + pd.offsets.Day(2) + exp = PeriodIndex( + ["2011-01-03", "2011-02-03", "2011-03-03", "2011-04-03"], + freq="D", + name="idx", + ) + self._check(idx, f, exp) + + f = lambda x: x - pd.offsets.Day(2) + exp = PeriodIndex( + ["2010-12-30", "2011-01-30", "2011-02-27", "2011-03-30"], + freq="D", + name="idx", + ) + self._check(idx, f, exp) + + def test_pi_offset_errors(self): + idx = PeriodIndex( + ["2011-01-01", "2011-02-01", "2011-03-01", "2011-04-01"], + freq="D", + name="idx", + ) + ser = Series(idx) + + msg = ( + "Cannot add/subtract timedelta-like from PeriodArray that is not " + "an integer multiple of the PeriodArray's freq" + ) + for obj in [idx, ser]: + with pytest.raises(IncompatibleFrequency, match=msg): + obj + pd.offsets.Hour(2) + + with pytest.raises(IncompatibleFrequency, match=msg): + pd.offsets.Hour(2) + obj + + with pytest.raises(IncompatibleFrequency, match=msg): + obj - pd.offsets.Hour(2) + + def test_pi_sub_period(self): + # GH#13071 + idx = PeriodIndex( + ["2011-01", "2011-02", "2011-03", "2011-04"], freq="M", name="idx" + ) + + result = idx - Period("2012-01", freq="M") + off = idx.freq + exp = pd.Index([-12 * off, -11 * off, -10 * off, -9 * off], name="idx") + tm.assert_index_equal(result, exp) + + result = np.subtract(idx, Period("2012-01", freq="M")) + tm.assert_index_equal(result, exp) + + result = Period("2012-01", freq="M") - idx + exp = pd.Index([12 * off, 11 * off, 10 * off, 9 * off], name="idx") + tm.assert_index_equal(result, exp) + + result = np.subtract(Period("2012-01", freq="M"), idx) + tm.assert_index_equal(result, exp) + + exp = TimedeltaIndex([np.nan, np.nan, np.nan, np.nan], name="idx") + result = idx - Period("NaT", freq="M") + tm.assert_index_equal(result, exp) + assert result.freq == exp.freq + + result = Period("NaT", freq="M") - idx + tm.assert_index_equal(result, exp) + assert result.freq == exp.freq + + def test_pi_sub_pdnat(self): + # GH#13071, GH#19389 + idx = PeriodIndex( + ["2011-01", "2011-02", "NaT", "2011-04"], freq="M", name="idx" + ) + exp = TimedeltaIndex([pd.NaT] * 4, name="idx") + tm.assert_index_equal(pd.NaT - idx, exp) + tm.assert_index_equal(idx - pd.NaT, exp) + + def test_pi_sub_period_nat(self): + # GH#13071 + idx = PeriodIndex( + ["2011-01", "NaT", "2011-03", "2011-04"], freq="M", name="idx" + ) + + result = idx - Period("2012-01", freq="M") + off = idx.freq + exp = pd.Index([-12 * off, pd.NaT, -10 * off, -9 * off], name="idx") + tm.assert_index_equal(result, exp) + + result = Period("2012-01", freq="M") - idx + exp = pd.Index([12 * off, pd.NaT, 10 * off, 9 * off], name="idx") + tm.assert_index_equal(result, exp) + + exp = TimedeltaIndex([np.nan, np.nan, np.nan, np.nan], name="idx") + tm.assert_index_equal(idx - Period("NaT", freq="M"), exp) + tm.assert_index_equal(Period("NaT", freq="M") - idx, exp) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_timedelta64.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_timedelta64.py new file mode 100644 index 0000000000000000000000000000000000000000..d02e827d435cf16c806b5130f5949143f51c15e3 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/arithmetic/test_timedelta64.py @@ -0,0 +1,2179 @@ +# Arithmetic tests for DataFrame/Series/Index/Array classes that should +# behave identically. +from datetime import ( + datetime, + timedelta, +) + +import numpy as np +import pytest + +from pandas.errors import ( + OutOfBoundsDatetime, + PerformanceWarning, +) + +import pandas as pd +from pandas import ( + DataFrame, + DatetimeIndex, + Index, + NaT, + Series, + Timedelta, + TimedeltaIndex, + Timestamp, + offsets, + timedelta_range, +) +import pandas._testing as tm +from pandas.core.arrays import NumpyExtensionArray +from pandas.tests.arithmetic.common import ( + assert_invalid_addsub_type, + assert_invalid_comparison, + get_upcast_box, +) + + +def assert_dtype(obj, expected_dtype): + """ + Helper to check the dtype for a Series, Index, or single-column DataFrame. + """ + dtype = tm.get_dtype(obj) + + assert dtype == expected_dtype + + +def get_expected_name(box, names): + if box is DataFrame: + # Since we are operating with a DataFrame and a non-DataFrame, + # the non-DataFrame is cast to Series and its name ignored. + exname = names[0] + elif box in [tm.to_array, pd.array]: + exname = names[1] + else: + exname = names[2] + return exname + + +# ------------------------------------------------------------------ +# Timedelta64[ns] dtype Comparisons + + +class TestTimedelta64ArrayLikeComparisons: + # Comparison tests for timedelta64[ns] vectors fully parametrized over + # DataFrame/Series/TimedeltaIndex/TimedeltaArray. Ideally all comparison + # tests will eventually end up here. + + def test_compare_timedelta64_zerodim(self, box_with_array): + # GH#26689 should unbox when comparing with zerodim array + box = box_with_array + xbox = box_with_array if box_with_array not in [Index, pd.array] else np.ndarray + + tdi = timedelta_range("2h", periods=4) + other = np.array(tdi.to_numpy()[0]) + + tdi = tm.box_expected(tdi, box) + res = tdi <= other + expected = np.array([True, False, False, False]) + expected = tm.box_expected(expected, xbox) + tm.assert_equal(res, expected) + + @pytest.mark.parametrize( + "td_scalar", + [ + timedelta(days=1), + Timedelta(days=1), + Timedelta(days=1).to_timedelta64(), + offsets.Hour(24), + ], + ) + def test_compare_timedeltalike_scalar(self, box_with_array, td_scalar): + # regression test for GH#5963 + box = box_with_array + xbox = box if box not in [Index, pd.array] else np.ndarray + + ser = Series([timedelta(days=1), timedelta(days=2)]) + ser = tm.box_expected(ser, box) + actual = ser > td_scalar + expected = Series([False, True]) + expected = tm.box_expected(expected, xbox) + tm.assert_equal(actual, expected) + + @pytest.mark.parametrize( + "invalid", + [ + 345600000000000, + "a", + Timestamp("2021-01-01"), + Timestamp("2021-01-01").now("UTC"), + Timestamp("2021-01-01").now().to_datetime64(), + Timestamp("2021-01-01").now().to_pydatetime(), + Timestamp("2021-01-01").date(), + np.array(4), # zero-dim mismatched dtype + ], + ) + def test_td64_comparisons_invalid(self, box_with_array, invalid): + # GH#13624 for str + box = box_with_array + + rng = timedelta_range("1 days", periods=10) + obj = tm.box_expected(rng, box) + + assert_invalid_comparison(obj, invalid, box) + + @pytest.mark.parametrize( + "other", + [ + list(range(10)), + np.arange(10), + np.arange(10).astype(np.float32), + np.arange(10).astype(object), + pd.date_range("1970-01-01", periods=10, tz="UTC").array, + np.array(pd.date_range("1970-01-01", periods=10)), + list(pd.date_range("1970-01-01", periods=10)), + pd.date_range("1970-01-01", periods=10).astype(object), + pd.period_range("1971-01-01", freq="D", periods=10).array, + pd.period_range("1971-01-01", freq="D", periods=10).astype(object), + ], + ) + def test_td64arr_cmp_arraylike_invalid(self, other, box_with_array): + # We don't parametrize this over box_with_array because listlike + # other plays poorly with assert_invalid_comparison reversed checks + + rng = timedelta_range("1 days", periods=10)._data + rng = tm.box_expected(rng, box_with_array) + assert_invalid_comparison(rng, other, box_with_array) + + def test_td64arr_cmp_mixed_invalid(self): + rng = timedelta_range("1 days", periods=5)._data + other = np.array([0, 1, 2, rng[3], Timestamp("2021-01-01")]) + + result = rng == other + expected = np.array([False, False, False, True, False]) + tm.assert_numpy_array_equal(result, expected) + + result = rng != other + tm.assert_numpy_array_equal(result, ~expected) + + msg = "Invalid comparison between|Cannot compare type|not supported between" + with pytest.raises(TypeError, match=msg): + rng < other + with pytest.raises(TypeError, match=msg): + rng > other + with pytest.raises(TypeError, match=msg): + rng <= other + with pytest.raises(TypeError, match=msg): + rng >= other + + +class TestTimedelta64ArrayComparisons: + # TODO: All of these need to be parametrized over box + + @pytest.mark.parametrize("dtype", [None, object]) + def test_comp_nat(self, dtype): + left = TimedeltaIndex([Timedelta("1 days"), NaT, Timedelta("3 days")]) + right = TimedeltaIndex([NaT, NaT, Timedelta("3 days")]) + + lhs, rhs = left, right + if dtype is object: + lhs, rhs = left.astype(object), right.astype(object) + + result = rhs == lhs + expected = np.array([False, False, True]) + tm.assert_numpy_array_equal(result, expected) + + result = rhs != lhs + expected = np.array([True, True, False]) + tm.assert_numpy_array_equal(result, expected) + + expected = np.array([False, False, False]) + tm.assert_numpy_array_equal(lhs == NaT, expected) + tm.assert_numpy_array_equal(NaT == rhs, expected) + + expected = np.array([True, True, True]) + tm.assert_numpy_array_equal(lhs != NaT, expected) + tm.assert_numpy_array_equal(NaT != lhs, expected) + + expected = np.array([False, False, False]) + tm.assert_numpy_array_equal(lhs < NaT, expected) + tm.assert_numpy_array_equal(NaT > lhs, expected) + + @pytest.mark.parametrize( + "idx2", + [ + TimedeltaIndex( + ["2 day", "2 day", NaT, NaT, "1 day 00:00:02", "5 days 00:00:03"] + ), + np.array( + [ + np.timedelta64(2, "D"), + np.timedelta64(2, "D"), + np.timedelta64("nat"), + np.timedelta64("nat"), + np.timedelta64(1, "D") + np.timedelta64(2, "s"), + np.timedelta64(5, "D") + np.timedelta64(3, "s"), + ] + ), + ], + ) + def test_comparisons_nat(self, idx2): + idx1 = TimedeltaIndex( + [ + "1 day", + NaT, + "1 day 00:00:01", + NaT, + "1 day 00:00:01", + "5 day 00:00:03", + ] + ) + # Check pd.NaT is handles as the same as np.nan + result = idx1 < idx2 + expected = np.array([True, False, False, False, True, False]) + tm.assert_numpy_array_equal(result, expected) + + result = idx2 > idx1 + expected = np.array([True, False, False, False, True, False]) + tm.assert_numpy_array_equal(result, expected) + + result = idx1 <= idx2 + expected = np.array([True, False, False, False, True, True]) + tm.assert_numpy_array_equal(result, expected) + + result = idx2 >= idx1 + expected = np.array([True, False, False, False, True, True]) + tm.assert_numpy_array_equal(result, expected) + + result = idx1 == idx2 + expected = np.array([False, False, False, False, False, True]) + tm.assert_numpy_array_equal(result, expected) + + result = idx1 != idx2 + expected = np.array([True, True, True, True, True, False]) + tm.assert_numpy_array_equal(result, expected) + + # TODO: better name + def test_comparisons_coverage(self): + rng = timedelta_range("1 days", periods=10) + + result = rng < rng[3] + expected = np.array([True, True, True] + [False] * 7) + tm.assert_numpy_array_equal(result, expected) + + result = rng == list(rng) + exp = rng == rng + tm.assert_numpy_array_equal(result, exp) + + +# ------------------------------------------------------------------ +# Timedelta64[ns] dtype Arithmetic Operations + + +class TestTimedelta64ArithmeticUnsorted: + # Tests moved from type-specific test files but not + # yet sorted/parametrized/de-duplicated + + def test_ufunc_coercions(self): + # normal ops are also tested in tseries/test_timedeltas.py + idx = TimedeltaIndex(["2h", "4h", "6h", "8h", "10h"], freq="2h", name="x") + + for result in [idx * 2, np.multiply(idx, 2)]: + assert isinstance(result, TimedeltaIndex) + exp = TimedeltaIndex(["4h", "8h", "12h", "16h", "20h"], freq="4h", name="x") + tm.assert_index_equal(result, exp) + assert result.freq == "4h" + + for result in [idx / 2, np.divide(idx, 2)]: + assert isinstance(result, TimedeltaIndex) + exp = TimedeltaIndex(["1h", "2h", "3h", "4h", "5h"], freq="h", name="x") + tm.assert_index_equal(result, exp) + assert result.freq == "h" + + for result in [-idx, np.negative(idx)]: + assert isinstance(result, TimedeltaIndex) + exp = TimedeltaIndex( + ["-2h", "-4h", "-6h", "-8h", "-10h"], freq="-2h", name="x" + ) + tm.assert_index_equal(result, exp) + assert result.freq == "-2h" + + idx = TimedeltaIndex(["-2h", "-1h", "0h", "1h", "2h"], freq="h", name="x") + for result in [abs(idx), np.absolute(idx)]: + assert isinstance(result, TimedeltaIndex) + exp = TimedeltaIndex(["2h", "1h", "0h", "1h", "2h"], freq=None, name="x") + tm.assert_index_equal(result, exp) + assert result.freq is None + + def test_subtraction_ops(self): + # with datetimes/timedelta and tdi/dti + tdi = TimedeltaIndex(["1 days", NaT, "2 days"], name="foo") + dti = pd.date_range("20130101", periods=3, name="bar") + td = Timedelta("1 days") + dt = Timestamp("20130101") + + msg = "cannot subtract a datelike from a TimedeltaArray" + with pytest.raises(TypeError, match=msg): + tdi - dt + with pytest.raises(TypeError, match=msg): + tdi - dti + + msg = r"unsupported operand type\(s\) for -" + with pytest.raises(TypeError, match=msg): + td - dt + + msg = "(bad|unsupported) operand type for unary" + with pytest.raises(TypeError, match=msg): + td - dti + + result = dt - dti + expected = TimedeltaIndex(["0 days", "-1 days", "-2 days"], name="bar") + tm.assert_index_equal(result, expected) + + result = dti - dt + expected = TimedeltaIndex(["0 days", "1 days", "2 days"], name="bar") + tm.assert_index_equal(result, expected) + + result = tdi - td + expected = TimedeltaIndex(["0 days", NaT, "1 days"], name="foo") + tm.assert_index_equal(result, expected) + + result = td - tdi + expected = TimedeltaIndex(["0 days", NaT, "-1 days"], name="foo") + tm.assert_index_equal(result, expected) + + result = dti - td + expected = DatetimeIndex( + ["20121231", "20130101", "20130102"], dtype="M8[ns]", freq="D", name="bar" + ) + tm.assert_index_equal(result, expected) + + result = dt - tdi + expected = DatetimeIndex( + ["20121231", NaT, "20121230"], dtype="M8[ns]", name="foo" + ) + tm.assert_index_equal(result, expected) + + def test_subtraction_ops_with_tz(self, box_with_array): + # check that dt/dti subtraction ops with tz are validated + dti = pd.date_range("20130101", periods=3) + dti = tm.box_expected(dti, box_with_array) + ts = Timestamp("20130101") + dt = ts.to_pydatetime() + dti_tz = pd.date_range("20130101", periods=3).tz_localize("US/Eastern") + dti_tz = tm.box_expected(dti_tz, box_with_array) + ts_tz = Timestamp("20130101").tz_localize("US/Eastern") + ts_tz2 = Timestamp("20130101").tz_localize("CET") + dt_tz = ts_tz.to_pydatetime() + td = Timedelta("1 days") + + def _check(result, expected): + assert result == expected + assert isinstance(result, Timedelta) + + # scalars + result = ts - ts + expected = Timedelta("0 days") + _check(result, expected) + + result = dt_tz - ts_tz + expected = Timedelta("0 days") + _check(result, expected) + + result = ts_tz - dt_tz + expected = Timedelta("0 days") + _check(result, expected) + + # tz mismatches + msg = "Cannot subtract tz-naive and tz-aware datetime-like objects." + with pytest.raises(TypeError, match=msg): + dt_tz - ts + msg = "can't subtract offset-naive and offset-aware datetimes" + with pytest.raises(TypeError, match=msg): + dt_tz - dt + msg = "can't subtract offset-naive and offset-aware datetimes" + with pytest.raises(TypeError, match=msg): + dt - dt_tz + msg = "Cannot subtract tz-naive and tz-aware datetime-like objects." + with pytest.raises(TypeError, match=msg): + ts - dt_tz + with pytest.raises(TypeError, match=msg): + ts_tz2 - ts + with pytest.raises(TypeError, match=msg): + ts_tz2 - dt + + msg = "Cannot subtract tz-naive and tz-aware" + # with dti + with pytest.raises(TypeError, match=msg): + dti - ts_tz + with pytest.raises(TypeError, match=msg): + dti_tz - ts + + result = dti_tz - dt_tz + expected = TimedeltaIndex(["0 days", "1 days", "2 days"]) + expected = tm.box_expected(expected, box_with_array) + tm.assert_equal(result, expected) + + result = dt_tz - dti_tz + expected = TimedeltaIndex(["0 days", "-1 days", "-2 days"]) + expected = tm.box_expected(expected, box_with_array) + tm.assert_equal(result, expected) + + result = dti_tz - ts_tz + expected = TimedeltaIndex(["0 days", "1 days", "2 days"]) + expected = tm.box_expected(expected, box_with_array) + tm.assert_equal(result, expected) + + result = ts_tz - dti_tz + expected = TimedeltaIndex(["0 days", "-1 days", "-2 days"]) + expected = tm.box_expected(expected, box_with_array) + tm.assert_equal(result, expected) + + result = td - td + expected = Timedelta("0 days") + _check(result, expected) + + result = dti_tz - td + expected = DatetimeIndex( + ["20121231", "20130101", "20130102"], tz="US/Eastern" + ).as_unit("ns") + expected = tm.box_expected(expected, box_with_array) + tm.assert_equal(result, expected) + + def test_dti_tdi_numeric_ops(self): + # These are normally union/diff set-like ops + tdi = TimedeltaIndex(["1 days", NaT, "2 days"], name="foo") + dti = pd.date_range("20130101", periods=3, name="bar") + + result = tdi - tdi + expected = TimedeltaIndex(["0 days", NaT, "0 days"], name="foo") + tm.assert_index_equal(result, expected) + + result = tdi + tdi + expected = TimedeltaIndex(["2 days", NaT, "4 days"], name="foo") + tm.assert_index_equal(result, expected) + + result = dti - tdi # name will be reset + expected = DatetimeIndex(["20121231", NaT, "20130101"], dtype="M8[ns]") + tm.assert_index_equal(result, expected) + + def test_addition_ops(self): + # with datetimes/timedelta and tdi/dti + tdi = TimedeltaIndex(["1 days", NaT, "2 days"], name="foo") + dti = pd.date_range("20130101", periods=3, name="bar") + td = Timedelta("1 days") + dt = Timestamp("20130101") + + result = tdi + dt + expected = DatetimeIndex( + ["20130102", NaT, "20130103"], dtype="M8[ns]", name="foo" + ) + tm.assert_index_equal(result, expected) + + result = dt + tdi + expected = DatetimeIndex( + ["20130102", NaT, "20130103"], dtype="M8[ns]", name="foo" + ) + tm.assert_index_equal(result, expected) + + result = td + tdi + expected = TimedeltaIndex(["2 days", NaT, "3 days"], name="foo") + tm.assert_index_equal(result, expected) + + result = tdi + td + expected = TimedeltaIndex(["2 days", NaT, "3 days"], name="foo") + tm.assert_index_equal(result, expected) + + # unequal length + msg = "cannot add indices of unequal length" + with pytest.raises(ValueError, match=msg): + tdi + dti[0:1] + with pytest.raises(ValueError, match=msg): + tdi[0:1] + dti + + # random indexes + msg = "Addition/subtraction of integers and integer-arrays" + with pytest.raises(TypeError, match=msg): + tdi + Index([1, 2, 3], dtype=np.int64) + + # this is a union! + # FIXME: don't leave commented-out + # pytest.raises(TypeError, lambda : Index([1,2,3]) + tdi) + + result = tdi + dti # name will be reset + expected = DatetimeIndex(["20130102", NaT, "20130105"], dtype="M8[ns]") + tm.assert_index_equal(result, expected) + + result = dti + tdi # name will be reset + expected = DatetimeIndex(["20130102", NaT, "20130105"], dtype="M8[ns]") + tm.assert_index_equal(result, expected) + + result = dt + td + expected = Timestamp("20130102") + assert result == expected + + result = td + dt + expected = Timestamp("20130102") + assert result == expected + + # TODO: Needs more informative name, probably split up into + # more targeted tests + @pytest.mark.parametrize("freq", ["D", "B"]) + def test_timedelta(self, freq): + index = pd.date_range("1/1/2000", periods=50, freq=freq) + + shifted = index + timedelta(1) + back = shifted + timedelta(-1) + back = back._with_freq("infer") + tm.assert_index_equal(index, back) + + if freq == "D": + expected = pd.tseries.offsets.Day(1) + assert index.freq == expected + assert shifted.freq == expected + assert back.freq == expected + else: # freq == 'B' + assert index.freq == pd.tseries.offsets.BusinessDay(1) + assert shifted.freq is None + assert back.freq == pd.tseries.offsets.BusinessDay(1) + + result = index - timedelta(1) + expected = index + timedelta(-1) + tm.assert_index_equal(result, expected) + + def test_timedelta_tick_arithmetic(self): + # GH#4134, buggy with timedeltas + rng = pd.date_range("2013", "2014") + s = Series(rng) + result1 = rng - offsets.Hour(1) + result2 = DatetimeIndex(s - np.timedelta64(100000000)) + result3 = rng - np.timedelta64(100000000) + result4 = DatetimeIndex(s - offsets.Hour(1)) + + assert result1.freq == rng.freq + result1 = result1._with_freq(None) + tm.assert_index_equal(result1, result4) + + assert result3.freq == rng.freq + result3 = result3._with_freq(None) + tm.assert_index_equal(result2, result3) + + def test_tda_add_sub_index(self): + # Check that TimedeltaArray defers to Index on arithmetic ops + tdi = TimedeltaIndex(["1 days", NaT, "2 days"]) + tda = tdi.array + + dti = pd.date_range("1999-12-31", periods=3, freq="D") + + result = tda + dti + expected = tdi + dti + tm.assert_index_equal(result, expected) + + result = tda + tdi + expected = tdi + tdi + tm.assert_index_equal(result, expected) + + result = tda - tdi + expected = tdi - tdi + tm.assert_index_equal(result, expected) + + def test_tda_add_dt64_object_array(self, box_with_array, tz_naive_fixture): + # Result should be cast back to DatetimeArray + box = box_with_array + + dti = pd.date_range("2016-01-01", periods=3, tz=tz_naive_fixture) + dti = dti._with_freq(None) + tdi = dti - dti + + obj = tm.box_expected(tdi, box) + other = tm.box_expected(dti, box) + + with tm.assert_produces_warning(PerformanceWarning): + result = obj + other.astype(object) + tm.assert_equal(result, other.astype(object)) + + # ------------------------------------------------------------- + # Binary operations TimedeltaIndex and timedelta-like + + def test_tdi_iadd_timedeltalike(self, two_hours, box_with_array): + # only test adding/sub offsets as + is now numeric + rng = timedelta_range("1 days", "10 days") + expected = timedelta_range("1 days 02:00:00", "10 days 02:00:00", freq="D") + + rng = tm.box_expected(rng, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + orig_rng = rng + rng += two_hours + tm.assert_equal(rng, expected) + if box_with_array is not Index: + # Check that operation is actually inplace + tm.assert_equal(orig_rng, expected) + + def test_tdi_isub_timedeltalike(self, two_hours, box_with_array): + # only test adding/sub offsets as - is now numeric + rng = timedelta_range("1 days", "10 days") + expected = timedelta_range("0 days 22:00:00", "9 days 22:00:00") + + rng = tm.box_expected(rng, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + orig_rng = rng + rng -= two_hours + tm.assert_equal(rng, expected) + if box_with_array is not Index: + # Check that operation is actually inplace + tm.assert_equal(orig_rng, expected) + + # ------------------------------------------------------------- + + def test_tdi_ops_attributes(self): + rng = timedelta_range("2 days", periods=5, freq="2D", name="x") + + result = rng + 1 * rng.freq + exp = timedelta_range("4 days", periods=5, freq="2D", name="x") + tm.assert_index_equal(result, exp) + assert result.freq == "2D" + + result = rng - 2 * rng.freq + exp = timedelta_range("-2 days", periods=5, freq="2D", name="x") + tm.assert_index_equal(result, exp) + assert result.freq == "2D" + + result = rng * 2 + exp = timedelta_range("4 days", periods=5, freq="4D", name="x") + tm.assert_index_equal(result, exp) + assert result.freq == "4D" + + result = rng / 2 + exp = timedelta_range("1 days", periods=5, freq="D", name="x") + tm.assert_index_equal(result, exp) + assert result.freq == "D" + + result = -rng + exp = timedelta_range("-2 days", periods=5, freq="-2D", name="x") + tm.assert_index_equal(result, exp) + assert result.freq == "-2D" + + rng = timedelta_range("-2 days", periods=5, freq="D", name="x") + + result = abs(rng) + exp = TimedeltaIndex( + ["2 days", "1 days", "0 days", "1 days", "2 days"], name="x" + ) + tm.assert_index_equal(result, exp) + assert result.freq is None + + +class TestAddSubNaTMasking: + # TODO: parametrize over boxes + + @pytest.mark.parametrize("str_ts", ["1950-01-01", "1980-01-01"]) + def test_tdarr_add_timestamp_nat_masking(self, box_with_array, str_ts): + # GH#17991 checking for overflow-masking with NaT + tdinat = pd.to_timedelta(["24658 days 11:15:00", "NaT"]) + tdobj = tm.box_expected(tdinat, box_with_array) + + ts = Timestamp(str_ts) + ts_variants = [ + ts, + ts.to_pydatetime(), + ts.to_datetime64().astype("datetime64[ns]"), + ts.to_datetime64().astype("datetime64[D]"), + ] + + for variant in ts_variants: + res = tdobj + variant + if box_with_array is DataFrame: + assert res.iloc[1, 1] is NaT + else: + assert res[1] is NaT + + def test_tdi_add_overflow(self): + # See GH#14068 + # preliminary test scalar analogue of vectorized tests below + # TODO: Make raised error message more informative and test + with pytest.raises(OutOfBoundsDatetime, match="10155196800000000000"): + pd.to_timedelta(106580, "D") + Timestamp("2000") + with pytest.raises(OutOfBoundsDatetime, match="10155196800000000000"): + Timestamp("2000") + pd.to_timedelta(106580, "D") + + _NaT = NaT._value + 1 + msg = "Overflow in int64 addition" + with pytest.raises(OverflowError, match=msg): + pd.to_timedelta([106580], "D") + Timestamp("2000") + with pytest.raises(OverflowError, match=msg): + Timestamp("2000") + pd.to_timedelta([106580], "D") + with pytest.raises(OverflowError, match=msg): + pd.to_timedelta([_NaT]) - Timedelta("1 days") + with pytest.raises(OverflowError, match=msg): + pd.to_timedelta(["5 days", _NaT]) - Timedelta("1 days") + with pytest.raises(OverflowError, match=msg): + ( + pd.to_timedelta([_NaT, "5 days", "1 hours"]) + - pd.to_timedelta(["7 seconds", _NaT, "4 hours"]) + ) + + # These should not overflow! + exp = TimedeltaIndex([NaT]) + result = pd.to_timedelta([NaT]) - Timedelta("1 days") + tm.assert_index_equal(result, exp) + + exp = TimedeltaIndex(["4 days", NaT]) + result = pd.to_timedelta(["5 days", NaT]) - Timedelta("1 days") + tm.assert_index_equal(result, exp) + + exp = TimedeltaIndex([NaT, NaT, "5 hours"]) + result = pd.to_timedelta([NaT, "5 days", "1 hours"]) + pd.to_timedelta( + ["7 seconds", NaT, "4 hours"] + ) + tm.assert_index_equal(result, exp) + + +class TestTimedeltaArraylikeAddSubOps: + # Tests for timedelta64[ns] __add__, __sub__, __radd__, __rsub__ + + def test_sub_nat_retain_unit(self): + ser = pd.to_timedelta(Series(["00:00:01"])).astype("m8[s]") + + result = ser - NaT + expected = Series([NaT], dtype="m8[s]") + tm.assert_series_equal(result, expected) + + # TODO: moved from tests.indexes.timedeltas.test_arithmetic; needs + # parametrization+de-duplication + def test_timedelta_ops_with_missing_values(self): + # setup + s1 = pd.to_timedelta(Series(["00:00:01"])) + s2 = pd.to_timedelta(Series(["00:00:02"])) + + sn = pd.to_timedelta(Series([NaT], dtype="m8[ns]")) + + df1 = DataFrame(["00:00:01"]).apply(pd.to_timedelta) + df2 = DataFrame(["00:00:02"]).apply(pd.to_timedelta) + + dfn = DataFrame([NaT._value]).apply(pd.to_timedelta) + + scalar1 = pd.to_timedelta("00:00:01") + scalar2 = pd.to_timedelta("00:00:02") + timedelta_NaT = pd.to_timedelta("NaT") + + actual = scalar1 + scalar1 + assert actual == scalar2 + actual = scalar2 - scalar1 + assert actual == scalar1 + + actual = s1 + s1 + tm.assert_series_equal(actual, s2) + actual = s2 - s1 + tm.assert_series_equal(actual, s1) + + actual = s1 + scalar1 + tm.assert_series_equal(actual, s2) + actual = scalar1 + s1 + tm.assert_series_equal(actual, s2) + actual = s2 - scalar1 + tm.assert_series_equal(actual, s1) + actual = -scalar1 + s2 + tm.assert_series_equal(actual, s1) + + actual = s1 + timedelta_NaT + tm.assert_series_equal(actual, sn) + actual = timedelta_NaT + s1 + tm.assert_series_equal(actual, sn) + actual = s1 - timedelta_NaT + tm.assert_series_equal(actual, sn) + actual = -timedelta_NaT + s1 + tm.assert_series_equal(actual, sn) + + msg = "unsupported operand type" + with pytest.raises(TypeError, match=msg): + s1 + np.nan + with pytest.raises(TypeError, match=msg): + np.nan + s1 + with pytest.raises(TypeError, match=msg): + s1 - np.nan + with pytest.raises(TypeError, match=msg): + -np.nan + s1 + + actual = s1 + NaT + tm.assert_series_equal(actual, sn) + actual = s2 - NaT + tm.assert_series_equal(actual, sn) + + actual = s1 + df1 + tm.assert_frame_equal(actual, df2) + actual = s2 - df1 + tm.assert_frame_equal(actual, df1) + actual = df1 + s1 + tm.assert_frame_equal(actual, df2) + actual = df2 - s1 + tm.assert_frame_equal(actual, df1) + + actual = df1 + df1 + tm.assert_frame_equal(actual, df2) + actual = df2 - df1 + tm.assert_frame_equal(actual, df1) + + actual = df1 + scalar1 + tm.assert_frame_equal(actual, df2) + actual = df2 - scalar1 + tm.assert_frame_equal(actual, df1) + + actual = df1 + timedelta_NaT + tm.assert_frame_equal(actual, dfn) + actual = df1 - timedelta_NaT + tm.assert_frame_equal(actual, dfn) + + msg = "cannot subtract a datelike from|unsupported operand type" + with pytest.raises(TypeError, match=msg): + df1 + np.nan + with pytest.raises(TypeError, match=msg): + df1 - np.nan + + actual = df1 + NaT # NaT is datetime, not timedelta + tm.assert_frame_equal(actual, dfn) + actual = df1 - NaT + tm.assert_frame_equal(actual, dfn) + + # TODO: moved from tests.series.test_operators, needs splitting, cleanup, + # de-duplication, box-parametrization... + def test_operators_timedelta64(self): + # series ops + v1 = pd.date_range("2012-1-1", periods=3, freq="D") + v2 = pd.date_range("2012-1-2", periods=3, freq="D") + rs = Series(v2) - Series(v1) + xp = Series(1e9 * 3600 * 24, rs.index).astype("int64").astype("timedelta64[ns]") + tm.assert_series_equal(rs, xp) + assert rs.dtype == "timedelta64[ns]" + + df = DataFrame({"A": v1}) + td = Series([timedelta(days=i) for i in range(3)]) + assert td.dtype == "timedelta64[ns]" + + # series on the rhs + result = df["A"] - df["A"].shift() + assert result.dtype == "timedelta64[ns]" + + result = df["A"] + td + assert result.dtype == "M8[ns]" + + # scalar Timestamp on rhs + maxa = df["A"].max() + assert isinstance(maxa, Timestamp) + + resultb = df["A"] - df["A"].max() + assert resultb.dtype == "timedelta64[ns]" + + # timestamp on lhs + result = resultb + df["A"] + values = [Timestamp("20111230"), Timestamp("20120101"), Timestamp("20120103")] + expected = Series(values, dtype="M8[ns]", name="A") + tm.assert_series_equal(result, expected) + + # datetimes on rhs + result = df["A"] - datetime(2001, 1, 1) + expected = Series([timedelta(days=4017 + i) for i in range(3)], name="A") + tm.assert_series_equal(result, expected) + assert result.dtype == "m8[ns]" + + d = datetime(2001, 1, 1, 3, 4) + resulta = df["A"] - d + assert resulta.dtype == "m8[ns]" + + # roundtrip + resultb = resulta + d + tm.assert_series_equal(df["A"], resultb) + + # timedeltas on rhs + td = timedelta(days=1) + resulta = df["A"] + td + resultb = resulta - td + tm.assert_series_equal(resultb, df["A"]) + assert resultb.dtype == "M8[ns]" + + # roundtrip + td = timedelta(minutes=5, seconds=3) + resulta = df["A"] + td + resultb = resulta - td + tm.assert_series_equal(df["A"], resultb) + assert resultb.dtype == "M8[ns]" + + # inplace + value = rs[2] + np.timedelta64(timedelta(minutes=5, seconds=1)) + rs[2] += np.timedelta64(timedelta(minutes=5, seconds=1)) + assert rs[2] == value + + def test_timedelta64_ops_nat(self): + # GH 11349 + timedelta_series = Series([NaT, Timedelta("1s")]) + nat_series_dtype_timedelta = Series([NaT, NaT], dtype="timedelta64[ns]") + single_nat_dtype_timedelta = Series([NaT], dtype="timedelta64[ns]") + + # subtraction + tm.assert_series_equal(timedelta_series - NaT, nat_series_dtype_timedelta) + tm.assert_series_equal(-NaT + timedelta_series, nat_series_dtype_timedelta) + + tm.assert_series_equal( + timedelta_series - single_nat_dtype_timedelta, nat_series_dtype_timedelta + ) + tm.assert_series_equal( + -single_nat_dtype_timedelta + timedelta_series, nat_series_dtype_timedelta + ) + + # addition + tm.assert_series_equal( + nat_series_dtype_timedelta + NaT, nat_series_dtype_timedelta + ) + tm.assert_series_equal( + NaT + nat_series_dtype_timedelta, nat_series_dtype_timedelta + ) + + tm.assert_series_equal( + nat_series_dtype_timedelta + single_nat_dtype_timedelta, + nat_series_dtype_timedelta, + ) + tm.assert_series_equal( + single_nat_dtype_timedelta + nat_series_dtype_timedelta, + nat_series_dtype_timedelta, + ) + + tm.assert_series_equal(timedelta_series + NaT, nat_series_dtype_timedelta) + tm.assert_series_equal(NaT + timedelta_series, nat_series_dtype_timedelta) + + tm.assert_series_equal( + timedelta_series + single_nat_dtype_timedelta, nat_series_dtype_timedelta + ) + tm.assert_series_equal( + single_nat_dtype_timedelta + timedelta_series, nat_series_dtype_timedelta + ) + + tm.assert_series_equal( + nat_series_dtype_timedelta + NaT, nat_series_dtype_timedelta + ) + tm.assert_series_equal( + NaT + nat_series_dtype_timedelta, nat_series_dtype_timedelta + ) + + tm.assert_series_equal( + nat_series_dtype_timedelta + single_nat_dtype_timedelta, + nat_series_dtype_timedelta, + ) + tm.assert_series_equal( + single_nat_dtype_timedelta + nat_series_dtype_timedelta, + nat_series_dtype_timedelta, + ) + + # multiplication + tm.assert_series_equal( + nat_series_dtype_timedelta * 1.0, nat_series_dtype_timedelta + ) + tm.assert_series_equal( + 1.0 * nat_series_dtype_timedelta, nat_series_dtype_timedelta + ) + + tm.assert_series_equal(timedelta_series * 1, timedelta_series) + tm.assert_series_equal(1 * timedelta_series, timedelta_series) + + tm.assert_series_equal(timedelta_series * 1.5, Series([NaT, Timedelta("1.5s")])) + tm.assert_series_equal(1.5 * timedelta_series, Series([NaT, Timedelta("1.5s")])) + + tm.assert_series_equal(timedelta_series * np.nan, nat_series_dtype_timedelta) + tm.assert_series_equal(np.nan * timedelta_series, nat_series_dtype_timedelta) + + # division + tm.assert_series_equal(timedelta_series / 2, Series([NaT, Timedelta("0.5s")])) + tm.assert_series_equal(timedelta_series / 2.0, Series([NaT, Timedelta("0.5s")])) + tm.assert_series_equal(timedelta_series / np.nan, nat_series_dtype_timedelta) + + # ------------------------------------------------------------- + # Binary operations td64 arraylike and datetime-like + + @pytest.mark.parametrize("cls", [Timestamp, datetime, np.datetime64]) + def test_td64arr_add_sub_datetimelike_scalar( + self, cls, box_with_array, tz_naive_fixture + ): + # GH#11925, GH#29558, GH#23215 + tz = tz_naive_fixture + + dt_scalar = Timestamp("2012-01-01", tz=tz) + if cls is datetime: + ts = dt_scalar.to_pydatetime() + elif cls is np.datetime64: + if tz_naive_fixture is not None: + pytest.skip(f"{cls} doesn support {tz_naive_fixture}") + ts = dt_scalar.to_datetime64() + else: + ts = dt_scalar + + tdi = timedelta_range("1 day", periods=3) + expected = pd.date_range("2012-01-02", periods=3, tz=tz) + + tdarr = tm.box_expected(tdi, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + tm.assert_equal(ts + tdarr, expected) + tm.assert_equal(tdarr + ts, expected) + + expected2 = pd.date_range("2011-12-31", periods=3, freq="-1D", tz=tz) + expected2 = tm.box_expected(expected2, box_with_array) + + tm.assert_equal(ts - tdarr, expected2) + tm.assert_equal(ts + (-tdarr), expected2) + + msg = "cannot subtract a datelike" + with pytest.raises(TypeError, match=msg): + tdarr - ts + + def test_td64arr_add_datetime64_nat(self, box_with_array): + # GH#23215 + other = np.datetime64("NaT") + + tdi = timedelta_range("1 day", periods=3) + expected = DatetimeIndex(["NaT", "NaT", "NaT"], dtype="M8[ns]") + + tdser = tm.box_expected(tdi, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + tm.assert_equal(tdser + other, expected) + tm.assert_equal(other + tdser, expected) + + def test_td64arr_sub_dt64_array(self, box_with_array): + dti = pd.date_range("2016-01-01", periods=3) + tdi = TimedeltaIndex(["-1 Day"] * 3) + dtarr = dti.values + expected = DatetimeIndex(dtarr) - tdi + + tdi = tm.box_expected(tdi, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + msg = "cannot subtract a datelike from" + with pytest.raises(TypeError, match=msg): + tdi - dtarr + + # TimedeltaIndex.__rsub__ + result = dtarr - tdi + tm.assert_equal(result, expected) + + def test_td64arr_add_dt64_array(self, box_with_array): + dti = pd.date_range("2016-01-01", periods=3) + tdi = TimedeltaIndex(["-1 Day"] * 3) + dtarr = dti.values + expected = DatetimeIndex(dtarr) + tdi + + tdi = tm.box_expected(tdi, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + result = tdi + dtarr + tm.assert_equal(result, expected) + result = dtarr + tdi + tm.assert_equal(result, expected) + + # ------------------------------------------------------------------ + # Invalid __add__/__sub__ operations + + @pytest.mark.parametrize("pi_freq", ["D", "W", "Q", "h"]) + @pytest.mark.parametrize("tdi_freq", [None, "h"]) + def test_td64arr_sub_periodlike( + self, box_with_array, box_with_array2, tdi_freq, pi_freq + ): + # GH#20049 subtracting PeriodIndex should raise TypeError + tdi = TimedeltaIndex(["1 hours", "2 hours"], freq=tdi_freq) + dti = Timestamp("2018-03-07 17:16:40") + tdi + pi = dti.to_period(pi_freq) + per = pi[0] + + tdi = tm.box_expected(tdi, box_with_array) + pi = tm.box_expected(pi, box_with_array2) + msg = "cannot subtract|unsupported operand type" + with pytest.raises(TypeError, match=msg): + tdi - pi + + # GH#13078 subtraction of Period scalar not supported + with pytest.raises(TypeError, match=msg): + tdi - per + + @pytest.mark.parametrize( + "other", + [ + # GH#12624 for str case + "a", + # GH#19123 + 1, + 1.5, + np.array(2), + ], + ) + def test_td64arr_addsub_numeric_scalar_invalid(self, box_with_array, other): + # vector-like others are tested in test_td64arr_add_sub_numeric_arr_invalid + tdser = Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]") + tdarr = tm.box_expected(tdser, box_with_array) + + assert_invalid_addsub_type(tdarr, other) + + @pytest.mark.parametrize( + "vec", + [ + np.array([1, 2, 3]), + Index([1, 2, 3]), + Series([1, 2, 3]), + DataFrame([[1, 2, 3]]), + ], + ids=lambda x: type(x).__name__, + ) + def test_td64arr_addsub_numeric_arr_invalid( + self, box_with_array, vec, any_real_numpy_dtype + ): + tdser = Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]") + tdarr = tm.box_expected(tdser, box_with_array) + + vector = vec.astype(any_real_numpy_dtype) + assert_invalid_addsub_type(tdarr, vector) + + def test_td64arr_add_sub_int(self, box_with_array, one): + # Variants of `one` for #19012, deprecated GH#22535 + rng = timedelta_range("1 days 09:00:00", freq="h", periods=10) + tdarr = tm.box_expected(rng, box_with_array) + + msg = "Addition/subtraction of integers" + assert_invalid_addsub_type(tdarr, one, msg) + + # TODO: get inplace ops into assert_invalid_addsub_type + with pytest.raises(TypeError, match=msg): + tdarr += one + with pytest.raises(TypeError, match=msg): + tdarr -= one + + def test_td64arr_add_sub_integer_array(self, box_with_array): + # GH#19959, deprecated GH#22535 + # GH#22696 for DataFrame case, check that we don't dispatch to numpy + # implementation, which treats int64 as m8[ns] + box = box_with_array + xbox = np.ndarray if box is pd.array else box + + rng = timedelta_range("1 days 09:00:00", freq="h", periods=3) + tdarr = tm.box_expected(rng, box) + other = tm.box_expected([4, 3, 2], xbox) + + msg = "Addition/subtraction of integers and integer-arrays" + assert_invalid_addsub_type(tdarr, other, msg) + + def test_td64arr_addsub_integer_array_no_freq(self, box_with_array): + # GH#19959 + box = box_with_array + xbox = np.ndarray if box is pd.array else box + + tdi = TimedeltaIndex(["1 Day", "NaT", "3 Hours"]) + tdarr = tm.box_expected(tdi, box) + other = tm.box_expected([14, -1, 16], xbox) + + msg = "Addition/subtraction of integers" + assert_invalid_addsub_type(tdarr, other, msg) + + # ------------------------------------------------------------------ + # Operations with timedelta-like others + + def test_td64arr_add_sub_td64_array(self, box_with_array): + box = box_with_array + dti = pd.date_range("2016-01-01", periods=3) + tdi = dti - dti.shift(1) + tdarr = tdi.values + + expected = 2 * tdi + tdi = tm.box_expected(tdi, box) + expected = tm.box_expected(expected, box) + + result = tdi + tdarr + tm.assert_equal(result, expected) + result = tdarr + tdi + tm.assert_equal(result, expected) + + expected_sub = 0 * tdi + result = tdi - tdarr + tm.assert_equal(result, expected_sub) + result = tdarr - tdi + tm.assert_equal(result, expected_sub) + + def test_td64arr_add_sub_tdi(self, box_with_array, names): + # GH#17250 make sure result dtype is correct + # GH#19043 make sure names are propagated correctly + box = box_with_array + exname = get_expected_name(box, names) + + tdi = TimedeltaIndex(["0 days", "1 day"], name=names[1]) + tdi = np.array(tdi) if box in [tm.to_array, pd.array] else tdi + ser = Series([Timedelta(hours=3), Timedelta(hours=4)], name=names[0]) + expected = Series([Timedelta(hours=3), Timedelta(days=1, hours=4)], name=exname) + + ser = tm.box_expected(ser, box) + expected = tm.box_expected(expected, box) + + result = tdi + ser + tm.assert_equal(result, expected) + assert_dtype(result, "timedelta64[ns]") + + result = ser + tdi + tm.assert_equal(result, expected) + assert_dtype(result, "timedelta64[ns]") + + expected = Series( + [Timedelta(hours=-3), Timedelta(days=1, hours=-4)], name=exname + ) + expected = tm.box_expected(expected, box) + + result = tdi - ser + tm.assert_equal(result, expected) + assert_dtype(result, "timedelta64[ns]") + + result = ser - tdi + tm.assert_equal(result, -expected) + assert_dtype(result, "timedelta64[ns]") + + @pytest.mark.parametrize("tdnat", [np.timedelta64("NaT"), NaT]) + def test_td64arr_add_sub_td64_nat(self, box_with_array, tdnat): + # GH#18808, GH#23320 special handling for timedelta64("NaT") + box = box_with_array + tdi = TimedeltaIndex([NaT, Timedelta("1s")]) + expected = TimedeltaIndex(["NaT"] * 2) + + obj = tm.box_expected(tdi, box) + expected = tm.box_expected(expected, box) + + result = obj + tdnat + tm.assert_equal(result, expected) + result = tdnat + obj + tm.assert_equal(result, expected) + result = obj - tdnat + tm.assert_equal(result, expected) + result = tdnat - obj + tm.assert_equal(result, expected) + + def test_td64arr_add_timedeltalike(self, two_hours, box_with_array): + # only test adding/sub offsets as + is now numeric + # GH#10699 for Tick cases + box = box_with_array + rng = timedelta_range("1 days", "10 days") + expected = timedelta_range("1 days 02:00:00", "10 days 02:00:00", freq="D") + rng = tm.box_expected(rng, box) + expected = tm.box_expected(expected, box) + + result = rng + two_hours + tm.assert_equal(result, expected) + + result = two_hours + rng + tm.assert_equal(result, expected) + + def test_td64arr_sub_timedeltalike(self, two_hours, box_with_array): + # only test adding/sub offsets as - is now numeric + # GH#10699 for Tick cases + box = box_with_array + rng = timedelta_range("1 days", "10 days") + expected = timedelta_range("0 days 22:00:00", "9 days 22:00:00") + + rng = tm.box_expected(rng, box) + expected = tm.box_expected(expected, box) + + result = rng - two_hours + tm.assert_equal(result, expected) + + result = two_hours - rng + tm.assert_equal(result, -expected) + + # ------------------------------------------------------------------ + # __add__/__sub__ with DateOffsets and arrays of DateOffsets + + def test_td64arr_add_sub_offset_index(self, names, box_with_array): + # GH#18849, GH#19744 + box = box_with_array + exname = get_expected_name(box, names) + + tdi = TimedeltaIndex(["1 days 00:00:00", "3 days 04:00:00"], name=names[0]) + other = Index([offsets.Hour(n=1), offsets.Minute(n=-2)], name=names[1]) + other = np.array(other) if box in [tm.to_array, pd.array] else other + + expected = TimedeltaIndex( + [tdi[n] + other[n] for n in range(len(tdi))], freq="infer", name=exname + ) + expected_sub = TimedeltaIndex( + [tdi[n] - other[n] for n in range(len(tdi))], freq="infer", name=exname + ) + + tdi = tm.box_expected(tdi, box) + expected = tm.box_expected(expected, box).astype(object, copy=False) + expected_sub = tm.box_expected(expected_sub, box).astype(object, copy=False) + + with tm.assert_produces_warning(PerformanceWarning): + res = tdi + other + tm.assert_equal(res, expected) + + with tm.assert_produces_warning(PerformanceWarning): + res2 = other + tdi + tm.assert_equal(res2, expected) + + with tm.assert_produces_warning(PerformanceWarning): + res_sub = tdi - other + tm.assert_equal(res_sub, expected_sub) + + def test_td64arr_add_sub_offset_array(self, box_with_array): + # GH#18849, GH#18824 + box = box_with_array + tdi = TimedeltaIndex(["1 days 00:00:00", "3 days 04:00:00"]) + other = np.array([offsets.Hour(n=1), offsets.Minute(n=-2)]) + + expected = TimedeltaIndex( + [tdi[n] + other[n] for n in range(len(tdi))], freq="infer" + ) + expected_sub = TimedeltaIndex( + [tdi[n] - other[n] for n in range(len(tdi))], freq="infer" + ) + + tdi = tm.box_expected(tdi, box) + expected = tm.box_expected(expected, box).astype(object) + + with tm.assert_produces_warning(PerformanceWarning): + res = tdi + other + tm.assert_equal(res, expected) + + with tm.assert_produces_warning(PerformanceWarning): + res2 = other + tdi + tm.assert_equal(res2, expected) + + expected_sub = tm.box_expected(expected_sub, box_with_array).astype(object) + with tm.assert_produces_warning(PerformanceWarning): + res_sub = tdi - other + tm.assert_equal(res_sub, expected_sub) + + def test_td64arr_with_offset_series(self, names, box_with_array): + # GH#18849 + box = box_with_array + box2 = Series if box in [Index, tm.to_array, pd.array] else box + exname = get_expected_name(box, names) + + tdi = TimedeltaIndex(["1 days 00:00:00", "3 days 04:00:00"], name=names[0]) + other = Series([offsets.Hour(n=1), offsets.Minute(n=-2)], name=names[1]) + + expected_add = Series( + [tdi[n] + other[n] for n in range(len(tdi))], name=exname, dtype=object + ) + obj = tm.box_expected(tdi, box) + expected_add = tm.box_expected(expected_add, box2).astype(object) + + with tm.assert_produces_warning(PerformanceWarning): + res = obj + other + tm.assert_equal(res, expected_add) + + with tm.assert_produces_warning(PerformanceWarning): + res2 = other + obj + tm.assert_equal(res2, expected_add) + + expected_sub = Series( + [tdi[n] - other[n] for n in range(len(tdi))], name=exname, dtype=object + ) + expected_sub = tm.box_expected(expected_sub, box2).astype(object) + + with tm.assert_produces_warning(PerformanceWarning): + res3 = obj - other + tm.assert_equal(res3, expected_sub) + + @pytest.mark.parametrize("obox", [np.array, Index, Series]) + def test_td64arr_addsub_anchored_offset_arraylike(self, obox, box_with_array): + # GH#18824 + tdi = TimedeltaIndex(["1 days 00:00:00", "3 days 04:00:00"]) + tdi = tm.box_expected(tdi, box_with_array) + + anchored = obox([offsets.MonthEnd(), offsets.Day(n=2)]) + + # addition/subtraction ops with anchored offsets should issue + # a PerformanceWarning and _then_ raise a TypeError. + msg = "has incorrect type|cannot add the type MonthEnd" + with pytest.raises(TypeError, match=msg): + with tm.assert_produces_warning(PerformanceWarning): + tdi + anchored + with pytest.raises(TypeError, match=msg): + with tm.assert_produces_warning(PerformanceWarning): + anchored + tdi + with pytest.raises(TypeError, match=msg): + with tm.assert_produces_warning(PerformanceWarning): + tdi - anchored + with pytest.raises(TypeError, match=msg): + with tm.assert_produces_warning(PerformanceWarning): + anchored - tdi + + # ------------------------------------------------------------------ + # Unsorted + + def test_td64arr_add_sub_object_array(self, box_with_array): + box = box_with_array + xbox = np.ndarray if box is pd.array else box + + tdi = timedelta_range("1 day", periods=3, freq="D") + tdarr = tm.box_expected(tdi, box) + + other = np.array([Timedelta(days=1), offsets.Day(2), Timestamp("2000-01-04")]) + + with tm.assert_produces_warning(PerformanceWarning): + result = tdarr + other + + expected = Index( + [Timedelta(days=2), Timedelta(days=4), Timestamp("2000-01-07")] + ) + expected = tm.box_expected(expected, xbox).astype(object) + tm.assert_equal(result, expected) + + msg = "unsupported operand type|cannot subtract a datelike" + with pytest.raises(TypeError, match=msg): + with tm.assert_produces_warning(PerformanceWarning): + tdarr - other + + with tm.assert_produces_warning(PerformanceWarning): + result = other - tdarr + + expected = Index([Timedelta(0), Timedelta(0), Timestamp("2000-01-01")]) + expected = tm.box_expected(expected, xbox).astype(object) + tm.assert_equal(result, expected) + + +class TestTimedeltaArraylikeMulDivOps: + # Tests for timedelta64[ns] + # __mul__, __rmul__, __div__, __rdiv__, __floordiv__, __rfloordiv__ + + # ------------------------------------------------------------------ + # Multiplication + # organized with scalar others first, then array-like + + def test_td64arr_mul_int(self, box_with_array): + idx = TimedeltaIndex(np.arange(5, dtype="int64")) + idx = tm.box_expected(idx, box_with_array) + + result = idx * 1 + tm.assert_equal(result, idx) + + result = 1 * idx + tm.assert_equal(result, idx) + + def test_td64arr_mul_tdlike_scalar_raises(self, two_hours, box_with_array): + rng = timedelta_range("1 days", "10 days", name="foo") + rng = tm.box_expected(rng, box_with_array) + msg = "|".join( + [ + "argument must be an integer", + "cannot use operands with types dtype", + "Cannot multiply with", + ] + ) + with pytest.raises(TypeError, match=msg): + rng * two_hours + + def test_tdi_mul_int_array_zerodim(self, box_with_array): + rng5 = np.arange(5, dtype="int64") + idx = TimedeltaIndex(rng5) + expected = TimedeltaIndex(rng5 * 5) + + idx = tm.box_expected(idx, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + result = idx * np.array(5, dtype="int64") + tm.assert_equal(result, expected) + + def test_tdi_mul_int_array(self, box_with_array): + rng5 = np.arange(5, dtype="int64") + idx = TimedeltaIndex(rng5) + expected = TimedeltaIndex(rng5**2) + + idx = tm.box_expected(idx, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + result = idx * rng5 + tm.assert_equal(result, expected) + + def test_tdi_mul_int_series(self, box_with_array): + box = box_with_array + xbox = Series if box in [Index, tm.to_array, pd.array] else box + + idx = TimedeltaIndex(np.arange(5, dtype="int64")) + expected = TimedeltaIndex(np.arange(5, dtype="int64") ** 2) + + idx = tm.box_expected(idx, box) + expected = tm.box_expected(expected, xbox) + + result = idx * Series(np.arange(5, dtype="int64")) + tm.assert_equal(result, expected) + + def test_tdi_mul_float_series(self, box_with_array): + box = box_with_array + xbox = Series if box in [Index, tm.to_array, pd.array] else box + + idx = TimedeltaIndex(np.arange(5, dtype="int64")) + idx = tm.box_expected(idx, box) + + rng5f = np.arange(5, dtype="float64") + expected = TimedeltaIndex(rng5f * (rng5f + 1.0)) + expected = tm.box_expected(expected, xbox) + + result = idx * Series(rng5f + 1.0) + tm.assert_equal(result, expected) + + # TODO: Put Series/DataFrame in others? + @pytest.mark.parametrize( + "other", + [ + np.arange(1, 11), + Index(np.arange(1, 11), np.int64), + Index(range(1, 11), np.uint64), + Index(range(1, 11), np.float64), + pd.RangeIndex(1, 11), + ], + ids=lambda x: type(x).__name__, + ) + def test_tdi_rmul_arraylike(self, other, box_with_array): + box = box_with_array + + tdi = TimedeltaIndex(["1 Day"] * 10) + expected = timedelta_range("1 days", "10 days")._with_freq(None) + + tdi = tm.box_expected(tdi, box) + xbox = get_upcast_box(tdi, other) + + expected = tm.box_expected(expected, xbox) + + result = other * tdi + tm.assert_equal(result, expected) + commute = tdi * other + tm.assert_equal(commute, expected) + + # ------------------------------------------------------------------ + # __div__, __rdiv__ + + def test_td64arr_div_nat_invalid(self, box_with_array): + # don't allow division by NaT (maybe could in the future) + rng = timedelta_range("1 days", "10 days", name="foo") + rng = tm.box_expected(rng, box_with_array) + + with pytest.raises(TypeError, match="unsupported operand type"): + rng / NaT + with pytest.raises(TypeError, match="Cannot divide NaTType by"): + NaT / rng + + dt64nat = np.datetime64("NaT", "ns") + msg = "|".join( + [ + # 'divide' on npdev as of 2021-12-18 + "ufunc '(true_divide|divide)' cannot use operands", + "cannot perform __r?truediv__", + "Cannot divide datetime64 by TimedeltaArray", + ] + ) + with pytest.raises(TypeError, match=msg): + rng / dt64nat + with pytest.raises(TypeError, match=msg): + dt64nat / rng + + def test_td64arr_div_td64nat(self, box_with_array): + # GH#23829 + box = box_with_array + xbox = np.ndarray if box is pd.array else box + + rng = timedelta_range("1 days", "10 days") + rng = tm.box_expected(rng, box) + + other = np.timedelta64("NaT") + + expected = np.array([np.nan] * 10) + expected = tm.box_expected(expected, xbox) + + result = rng / other + tm.assert_equal(result, expected) + + result = other / rng + tm.assert_equal(result, expected) + + def test_td64arr_div_int(self, box_with_array): + idx = TimedeltaIndex(np.arange(5, dtype="int64")) + idx = tm.box_expected(idx, box_with_array) + + result = idx / 1 + tm.assert_equal(result, idx) + + with pytest.raises(TypeError, match="Cannot divide"): + # GH#23829 + 1 / idx + + def test_td64arr_div_tdlike_scalar(self, two_hours, box_with_array): + # GH#20088, GH#22163 ensure DataFrame returns correct dtype + box = box_with_array + xbox = np.ndarray if box is pd.array else box + + rng = timedelta_range("1 days", "10 days", name="foo") + expected = Index((np.arange(10) + 1) * 12, dtype=np.float64, name="foo") + + rng = tm.box_expected(rng, box) + expected = tm.box_expected(expected, xbox) + + result = rng / two_hours + tm.assert_equal(result, expected) + + result = two_hours / rng + expected = 1 / expected + tm.assert_equal(result, expected) + + @pytest.mark.parametrize("m", [1, 3, 10]) + @pytest.mark.parametrize("unit", ["D", "h", "m", "s", "ms", "us", "ns"]) + def test_td64arr_div_td64_scalar(self, m, unit, box_with_array): + box = box_with_array + xbox = np.ndarray if box is pd.array else box + + ser = Series([Timedelta(days=59)] * 3) + ser[2] = np.nan + flat = ser + ser = tm.box_expected(ser, box) + + # op + expected = Series([x / np.timedelta64(m, unit) for x in flat]) + expected = tm.box_expected(expected, xbox) + result = ser / np.timedelta64(m, unit) + tm.assert_equal(result, expected) + + # reverse op + expected = Series([Timedelta(np.timedelta64(m, unit)) / x for x in flat]) + expected = tm.box_expected(expected, xbox) + result = np.timedelta64(m, unit) / ser + tm.assert_equal(result, expected) + + def test_td64arr_div_tdlike_scalar_with_nat(self, two_hours, box_with_array): + box = box_with_array + xbox = np.ndarray if box is pd.array else box + + rng = TimedeltaIndex(["1 days", NaT, "2 days"], name="foo") + expected = Index([12, np.nan, 24], dtype=np.float64, name="foo") + + rng = tm.box_expected(rng, box) + expected = tm.box_expected(expected, xbox) + + result = rng / two_hours + tm.assert_equal(result, expected) + + result = two_hours / rng + expected = 1 / expected + tm.assert_equal(result, expected) + + def test_td64arr_div_td64_ndarray(self, box_with_array): + # GH#22631 + box = box_with_array + xbox = np.ndarray if box is pd.array else box + + rng = TimedeltaIndex(["1 days", NaT, "2 days"]) + expected = Index([12, np.nan, 24], dtype=np.float64) + + rng = tm.box_expected(rng, box) + expected = tm.box_expected(expected, xbox) + + other = np.array([2, 4, 2], dtype="m8[h]") + result = rng / other + tm.assert_equal(result, expected) + + result = rng / tm.box_expected(other, box) + tm.assert_equal(result, expected) + + result = rng / other.astype(object) + tm.assert_equal(result, expected.astype(object)) + + result = rng / list(other) + tm.assert_equal(result, expected) + + # reversed op + expected = 1 / expected + result = other / rng + tm.assert_equal(result, expected) + + result = tm.box_expected(other, box) / rng + tm.assert_equal(result, expected) + + result = other.astype(object) / rng + tm.assert_equal(result, expected) + + result = list(other) / rng + tm.assert_equal(result, expected) + + def test_tdarr_div_length_mismatch(self, box_with_array): + rng = TimedeltaIndex(["1 days", NaT, "2 days"]) + mismatched = [1, 2, 3, 4] + + rng = tm.box_expected(rng, box_with_array) + msg = "Cannot divide vectors|Unable to coerce to Series" + for obj in [mismatched, mismatched[:2]]: + # one shorter, one longer + for other in [obj, np.array(obj), Index(obj)]: + with pytest.raises(ValueError, match=msg): + rng / other + with pytest.raises(ValueError, match=msg): + other / rng + + def test_td64_div_object_mixed_result(self, box_with_array): + # Case where we having a NaT in the result inseat of timedelta64("NaT") + # is misleading + orig = timedelta_range("1 Day", periods=3).insert(1, NaT) + tdi = tm.box_expected(orig, box_with_array, transpose=False) + + other = np.array([orig[0], 1.5, 2.0, orig[2]], dtype=object) + other = tm.box_expected(other, box_with_array, transpose=False) + + res = tdi / other + + expected = Index([1.0, np.timedelta64("NaT", "ns"), orig[0], 1.5], dtype=object) + expected = tm.box_expected(expected, box_with_array, transpose=False) + if isinstance(expected, NumpyExtensionArray): + expected = expected.to_numpy() + tm.assert_equal(res, expected) + if box_with_array is DataFrame: + # We have a np.timedelta64(NaT), not pd.NaT + assert isinstance(res.iloc[1, 0], np.timedelta64) + + res = tdi // other + + expected = Index([1, np.timedelta64("NaT", "ns"), orig[0], 1], dtype=object) + expected = tm.box_expected(expected, box_with_array, transpose=False) + if isinstance(expected, NumpyExtensionArray): + expected = expected.to_numpy() + tm.assert_equal(res, expected) + if box_with_array is DataFrame: + # We have a np.timedelta64(NaT), not pd.NaT + assert isinstance(res.iloc[1, 0], np.timedelta64) + + # ------------------------------------------------------------------ + # __floordiv__, __rfloordiv__ + + def test_td64arr_floordiv_td64arr_with_nat( + self, box_with_array, using_array_manager + ): + # GH#35529 + box = box_with_array + xbox = np.ndarray if box is pd.array else box + + left = Series([1000, 222330, 30], dtype="timedelta64[ns]") + right = Series([1000, 222330, None], dtype="timedelta64[ns]") + + left = tm.box_expected(left, box) + right = tm.box_expected(right, box) + + expected = np.array([1.0, 1.0, np.nan], dtype=np.float64) + expected = tm.box_expected(expected, xbox) + if box is DataFrame and using_array_manager: + # INFO(ArrayManager) floordiv returns integer, and ArrayManager + # performs ops column-wise and thus preserves int64 dtype for + # columns without missing values + expected[[0, 1]] = expected[[0, 1]].astype("int64") + + with tm.maybe_produces_warning( + RuntimeWarning, box is pd.array, check_stacklevel=False + ): + result = left // right + + tm.assert_equal(result, expected) + + # case that goes through __rfloordiv__ with arraylike + with tm.maybe_produces_warning( + RuntimeWarning, box is pd.array, check_stacklevel=False + ): + result = np.asarray(left) // right + tm.assert_equal(result, expected) + + @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning") + def test_td64arr_floordiv_tdscalar(self, box_with_array, scalar_td): + # GH#18831, GH#19125 + box = box_with_array + xbox = np.ndarray if box is pd.array else box + td = Timedelta("5m3s") # i.e. (scalar_td - 1sec) / 2 + + td1 = Series([td, td, NaT], dtype="m8[ns]") + td1 = tm.box_expected(td1, box, transpose=False) + + expected = Series([0, 0, np.nan]) + expected = tm.box_expected(expected, xbox, transpose=False) + + result = td1 // scalar_td + tm.assert_equal(result, expected) + + # Reversed op + expected = Series([2, 2, np.nan]) + expected = tm.box_expected(expected, xbox, transpose=False) + + result = scalar_td // td1 + tm.assert_equal(result, expected) + + # same thing buts let's be explicit about calling __rfloordiv__ + result = td1.__rfloordiv__(scalar_td) + tm.assert_equal(result, expected) + + def test_td64arr_floordiv_int(self, box_with_array): + idx = TimedeltaIndex(np.arange(5, dtype="int64")) + idx = tm.box_expected(idx, box_with_array) + result = idx // 1 + tm.assert_equal(result, idx) + + pattern = "floor_divide cannot use operands|Cannot divide int by Timedelta*" + with pytest.raises(TypeError, match=pattern): + 1 // idx + + # ------------------------------------------------------------------ + # mod, divmod + # TODO: operations with timedelta-like arrays, numeric arrays, + # reversed ops + + def test_td64arr_mod_tdscalar(self, box_with_array, three_days): + tdi = timedelta_range("1 Day", "9 days") + tdarr = tm.box_expected(tdi, box_with_array) + + expected = TimedeltaIndex(["1 Day", "2 Days", "0 Days"] * 3) + expected = tm.box_expected(expected, box_with_array) + + result = tdarr % three_days + tm.assert_equal(result, expected) + + warn = None + if box_with_array is DataFrame and isinstance(three_days, pd.DateOffset): + warn = PerformanceWarning + # TODO: making expected be object here a result of DataFrame.__divmod__ + # being defined in a naive way that does not dispatch to the underlying + # array's __divmod__ + expected = expected.astype(object) + + with tm.assert_produces_warning(warn): + result = divmod(tdarr, three_days) + + tm.assert_equal(result[1], expected) + tm.assert_equal(result[0], tdarr // three_days) + + def test_td64arr_mod_int(self, box_with_array): + tdi = timedelta_range("1 ns", "10 ns", periods=10) + tdarr = tm.box_expected(tdi, box_with_array) + + expected = TimedeltaIndex(["1 ns", "0 ns"] * 5) + expected = tm.box_expected(expected, box_with_array) + + result = tdarr % 2 + tm.assert_equal(result, expected) + + msg = "Cannot divide int by" + with pytest.raises(TypeError, match=msg): + 2 % tdarr + + result = divmod(tdarr, 2) + tm.assert_equal(result[1], expected) + tm.assert_equal(result[0], tdarr // 2) + + def test_td64arr_rmod_tdscalar(self, box_with_array, three_days): + tdi = timedelta_range("1 Day", "9 days") + tdarr = tm.box_expected(tdi, box_with_array) + + expected = ["0 Days", "1 Day", "0 Days"] + ["3 Days"] * 6 + expected = TimedeltaIndex(expected) + expected = tm.box_expected(expected, box_with_array) + + result = three_days % tdarr + tm.assert_equal(result, expected) + + result = divmod(three_days, tdarr) + tm.assert_equal(result[1], expected) + tm.assert_equal(result[0], three_days // tdarr) + + # ------------------------------------------------------------------ + # Operations with invalid others + + def test_td64arr_mul_tdscalar_invalid(self, box_with_array, scalar_td): + td1 = Series([timedelta(minutes=5, seconds=3)] * 3) + td1.iloc[2] = np.nan + + td1 = tm.box_expected(td1, box_with_array) + + # check that we are getting a TypeError + # with 'operate' (from core/ops.py) for the ops that are not + # defined + pattern = "operate|unsupported|cannot|not supported" + with pytest.raises(TypeError, match=pattern): + td1 * scalar_td + with pytest.raises(TypeError, match=pattern): + scalar_td * td1 + + def test_td64arr_mul_too_short_raises(self, box_with_array): + idx = TimedeltaIndex(np.arange(5, dtype="int64")) + idx = tm.box_expected(idx, box_with_array) + msg = "|".join( + [ + "cannot use operands with types dtype", + "Cannot multiply with unequal lengths", + "Unable to coerce to Series", + ] + ) + with pytest.raises(TypeError, match=msg): + # length check before dtype check + idx * idx[:3] + with pytest.raises(ValueError, match=msg): + idx * np.array([1, 2]) + + def test_td64arr_mul_td64arr_raises(self, box_with_array): + idx = TimedeltaIndex(np.arange(5, dtype="int64")) + idx = tm.box_expected(idx, box_with_array) + msg = "cannot use operands with types dtype" + with pytest.raises(TypeError, match=msg): + idx * idx + + # ------------------------------------------------------------------ + # Operations with numeric others + + def test_td64arr_mul_numeric_scalar(self, box_with_array, one): + # GH#4521 + # divide/multiply by integers + tdser = Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]") + expected = Series(["-59 Days", "-59 Days", "NaT"], dtype="timedelta64[ns]") + + tdser = tm.box_expected(tdser, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + result = tdser * (-one) + tm.assert_equal(result, expected) + result = (-one) * tdser + tm.assert_equal(result, expected) + + expected = Series(["118 Days", "118 Days", "NaT"], dtype="timedelta64[ns]") + expected = tm.box_expected(expected, box_with_array) + + result = tdser * (2 * one) + tm.assert_equal(result, expected) + result = (2 * one) * tdser + tm.assert_equal(result, expected) + + @pytest.mark.parametrize("two", [2, 2.0, np.array(2), np.array(2.0)]) + def test_td64arr_div_numeric_scalar(self, box_with_array, two): + # GH#4521 + # divide/multiply by integers + tdser = Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]") + expected = Series(["29.5D", "29.5D", "NaT"], dtype="timedelta64[ns]") + + tdser = tm.box_expected(tdser, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + result = tdser / two + tm.assert_equal(result, expected) + + with pytest.raises(TypeError, match="Cannot divide"): + two / tdser + + @pytest.mark.parametrize("two", [2, 2.0, np.array(2), np.array(2.0)]) + def test_td64arr_floordiv_numeric_scalar(self, box_with_array, two): + tdser = Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]") + expected = Series(["29.5D", "29.5D", "NaT"], dtype="timedelta64[ns]") + + tdser = tm.box_expected(tdser, box_with_array) + expected = tm.box_expected(expected, box_with_array) + + result = tdser // two + tm.assert_equal(result, expected) + + with pytest.raises(TypeError, match="Cannot divide"): + two // tdser + + @pytest.mark.parametrize( + "vector", + [np.array([20, 30, 40]), Index([20, 30, 40]), Series([20, 30, 40])], + ids=lambda x: type(x).__name__, + ) + def test_td64arr_rmul_numeric_array( + self, + box_with_array, + vector, + any_real_numpy_dtype, + ): + # GH#4521 + # divide/multiply by integers + + tdser = Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]") + vector = vector.astype(any_real_numpy_dtype) + + expected = Series(["1180 Days", "1770 Days", "NaT"], dtype="timedelta64[ns]") + + tdser = tm.box_expected(tdser, box_with_array) + xbox = get_upcast_box(tdser, vector) + + expected = tm.box_expected(expected, xbox) + + result = tdser * vector + tm.assert_equal(result, expected) + + result = vector * tdser + tm.assert_equal(result, expected) + + @pytest.mark.parametrize( + "vector", + [np.array([20, 30, 40]), Index([20, 30, 40]), Series([20, 30, 40])], + ids=lambda x: type(x).__name__, + ) + def test_td64arr_div_numeric_array( + self, box_with_array, vector, any_real_numpy_dtype + ): + # GH#4521 + # divide/multiply by integers + + tdser = Series(["59 Days", "59 Days", "NaT"], dtype="m8[ns]") + vector = vector.astype(any_real_numpy_dtype) + + expected = Series(["2.95D", "1D 23h 12m", "NaT"], dtype="timedelta64[ns]") + + tdser = tm.box_expected(tdser, box_with_array) + xbox = get_upcast_box(tdser, vector) + expected = tm.box_expected(expected, xbox) + + result = tdser / vector + tm.assert_equal(result, expected) + + pattern = "|".join( + [ + "true_divide'? cannot use operands", + "cannot perform __div__", + "cannot perform __truediv__", + "unsupported operand", + "Cannot divide", + "ufunc 'divide' cannot use operands with types", + ] + ) + with pytest.raises(TypeError, match=pattern): + vector / tdser + + result = tdser / vector.astype(object) + if box_with_array is DataFrame: + expected = [tdser.iloc[0, n] / vector[n] for n in range(len(vector))] + expected = tm.box_expected(expected, xbox).astype(object) + # We specifically expect timedelta64("NaT") here, not pd.NA + msg = "The 'downcast' keyword in fillna" + with tm.assert_produces_warning(FutureWarning, match=msg): + expected[2] = expected[2].fillna( + np.timedelta64("NaT", "ns"), downcast=False + ) + else: + expected = [tdser[n] / vector[n] for n in range(len(tdser))] + expected = [ + x if x is not NaT else np.timedelta64("NaT", "ns") for x in expected + ] + if xbox is tm.to_array: + expected = tm.to_array(expected).astype(object) + else: + expected = xbox(expected, dtype=object) + + tm.assert_equal(result, expected) + + with pytest.raises(TypeError, match=pattern): + vector.astype(object) / tdser + + def test_td64arr_mul_int_series(self, box_with_array, names): + # GH#19042 test for correct name attachment + box = box_with_array + exname = get_expected_name(box, names) + + tdi = TimedeltaIndex( + ["0days", "1day", "2days", "3days", "4days"], name=names[0] + ) + # TODO: Should we be parametrizing over types for `ser` too? + ser = Series([0, 1, 2, 3, 4], dtype=np.int64, name=names[1]) + + expected = Series( + ["0days", "1day", "4days", "9days", "16days"], + dtype="timedelta64[ns]", + name=exname, + ) + + tdi = tm.box_expected(tdi, box) + xbox = get_upcast_box(tdi, ser) + + expected = tm.box_expected(expected, xbox) + + result = ser * tdi + tm.assert_equal(result, expected) + + result = tdi * ser + tm.assert_equal(result, expected) + + # TODO: Should we be parametrizing over types for `ser` too? + def test_float_series_rdiv_td64arr(self, box_with_array, names): + # GH#19042 test for correct name attachment + box = box_with_array + tdi = TimedeltaIndex( + ["0days", "1day", "2days", "3days", "4days"], name=names[0] + ) + ser = Series([1.5, 3, 4.5, 6, 7.5], dtype=np.float64, name=names[1]) + + xname = names[2] if box not in [tm.to_array, pd.array] else names[1] + expected = Series( + [tdi[n] / ser[n] for n in range(len(ser))], + dtype="timedelta64[ns]", + name=xname, + ) + + tdi = tm.box_expected(tdi, box) + xbox = get_upcast_box(tdi, ser) + expected = tm.box_expected(expected, xbox) + + result = ser.__rtruediv__(tdi) + if box is DataFrame: + assert result is NotImplemented + else: + tm.assert_equal(result, expected) + + def test_td64arr_all_nat_div_object_dtype_numeric(self, box_with_array): + # GH#39750 make sure we infer the result as td64 + tdi = TimedeltaIndex([NaT, NaT]) + + left = tm.box_expected(tdi, box_with_array) + right = np.array([2, 2.0], dtype=object) + + tdnat = np.timedelta64("NaT", "ns") + expected = Index([tdnat] * 2, dtype=object) + if box_with_array is not Index: + expected = tm.box_expected(expected, box_with_array).astype(object) + if box_with_array in [Series, DataFrame]: + msg = "The 'downcast' keyword in fillna is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = expected.fillna(tdnat, downcast=False) # GH#18463 + + result = left / right + tm.assert_equal(result, expected) + + result = left // right + tm.assert_equal(result, expected) + + +class TestTimedelta64ArrayLikeArithmetic: + # Arithmetic tests for timedelta64[ns] vectors fully parametrized over + # DataFrame/Series/TimedeltaIndex/TimedeltaArray. Ideally all arithmetic + # tests will eventually end up here. + + def test_td64arr_pow_invalid(self, scalar_td, box_with_array): + td1 = Series([timedelta(minutes=5, seconds=3)] * 3) + td1.iloc[2] = np.nan + + td1 = tm.box_expected(td1, box_with_array) + + # check that we are getting a TypeError + # with 'operate' (from core/ops.py) for the ops that are not + # defined + pattern = "operate|unsupported|cannot|not supported" + with pytest.raises(TypeError, match=pattern): + scalar_td**td1 + + with pytest.raises(TypeError, match=pattern): + td1**scalar_td + + +def test_add_timestamp_to_timedelta(): + # GH: 35897 + timestamp = Timestamp("2021-01-01") + result = timestamp + timedelta_range("0s", "1s", periods=31) + expected = DatetimeIndex( + [ + timestamp + + ( + pd.to_timedelta("0.033333333s") * i + + pd.to_timedelta("0.000000001s") * divmod(i, 3)[0] + ) + for i in range(31) + ] + ) + tm.assert_index_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/construction/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/construction/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/construction/test_extract_array.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/construction/test_extract_array.py new file mode 100644 index 0000000000000000000000000000000000000000..4dd3eda8c995ce022e9d46b907323e79bcd679f8 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/construction/test_extract_array.py @@ -0,0 +1,18 @@ +from pandas import Index +import pandas._testing as tm +from pandas.core.construction import extract_array + + +def test_extract_array_rangeindex(): + ri = Index(range(5)) + + expected = ri._values + res = extract_array(ri, extract_numpy=True, extract_range=True) + tm.assert_numpy_array_equal(res, expected) + res = extract_array(ri, extract_numpy=False, extract_range=True) + tm.assert_numpy_array_equal(res, expected) + + res = extract_array(ri, extract_numpy=True, extract_range=False) + tm.assert_index_equal(res, ri) + res = extract_array(ri, extract_numpy=False, extract_range=False) + tm.assert_index_equal(res, ri) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/test_array.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/test_array.py new file mode 100644 index 0000000000000000000000000000000000000000..0dabec6014b0dfff04874ab67f0c7bd830dad3f3 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/test_array.py @@ -0,0 +1,218 @@ +import numpy as np +import pytest + +from pandas.compat.numpy import np_version_gt2 + +from pandas import ( + DataFrame, + Series, + date_range, +) +import pandas._testing as tm +from pandas.tests.copy_view.util import get_array + +# ----------------------------------------------------------------------------- +# Copy/view behaviour for accessing underlying array of Series/DataFrame + + +@pytest.mark.parametrize( + "method", + [ + lambda ser: ser.values, + lambda ser: np.asarray(ser), + lambda ser: np.array(ser, copy=False), + ], + ids=["values", "asarray", "array"], +) +def test_series_values(using_copy_on_write, method): + ser = Series([1, 2, 3], name="name") + ser_orig = ser.copy() + + arr = method(ser) + + if using_copy_on_write: + # .values still gives a view but is read-only + assert np.shares_memory(arr, get_array(ser, "name")) + assert arr.flags.writeable is False + + # mutating series through arr therefore doesn't work + with pytest.raises(ValueError, match="read-only"): + arr[0] = 0 + tm.assert_series_equal(ser, ser_orig) + + # mutating the series itself still works + ser.iloc[0] = 0 + assert ser.values[0] == 0 + else: + assert arr.flags.writeable is True + arr[0] = 0 + assert ser.iloc[0] == 0 + + +@pytest.mark.parametrize( + "method", + [ + lambda df: df.values, + lambda df: np.asarray(df), + lambda ser: np.array(ser, copy=False), + ], + ids=["values", "asarray", "array"], +) +def test_dataframe_values(using_copy_on_write, using_array_manager, method): + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + df_orig = df.copy() + + arr = method(df) + + if using_copy_on_write: + # .values still gives a view but is read-only + assert np.shares_memory(arr, get_array(df, "a")) + assert arr.flags.writeable is False + + # mutating series through arr therefore doesn't work + with pytest.raises(ValueError, match="read-only"): + arr[0, 0] = 0 + tm.assert_frame_equal(df, df_orig) + + # mutating the series itself still works + df.iloc[0, 0] = 0 + assert df.values[0, 0] == 0 + else: + assert arr.flags.writeable is True + arr[0, 0] = 0 + if not using_array_manager: + assert df.iloc[0, 0] == 0 + else: + tm.assert_frame_equal(df, df_orig) + + +def test_series_to_numpy(using_copy_on_write): + ser = Series([1, 2, 3], name="name") + ser_orig = ser.copy() + + # default: copy=False, no dtype or NAs + arr = ser.to_numpy() + if using_copy_on_write: + # to_numpy still gives a view but is read-only + assert np.shares_memory(arr, get_array(ser, "name")) + assert arr.flags.writeable is False + + # mutating series through arr therefore doesn't work + with pytest.raises(ValueError, match="read-only"): + arr[0] = 0 + tm.assert_series_equal(ser, ser_orig) + + # mutating the series itself still works + ser.iloc[0] = 0 + assert ser.values[0] == 0 + else: + assert arr.flags.writeable is True + arr[0] = 0 + assert ser.iloc[0] == 0 + + # specify copy=True gives a writeable array + ser = Series([1, 2, 3], name="name") + arr = ser.to_numpy(copy=True) + assert not np.shares_memory(arr, get_array(ser, "name")) + assert arr.flags.writeable is True + + # specifying a dtype that already causes a copy also gives a writeable array + ser = Series([1, 2, 3], name="name") + arr = ser.to_numpy(dtype="float64") + assert not np.shares_memory(arr, get_array(ser, "name")) + assert arr.flags.writeable is True + + +@pytest.mark.parametrize("order", ["F", "C"]) +def test_ravel_read_only(using_copy_on_write, order): + ser = Series([1, 2, 3]) + with tm.assert_produces_warning(FutureWarning, match="is deprecated"): + arr = ser.ravel(order=order) + if using_copy_on_write: + assert arr.flags.writeable is False + assert np.shares_memory(get_array(ser), arr) + + +def test_series_array_ea_dtypes(using_copy_on_write): + ser = Series([1, 2, 3], dtype="Int64") + arr = np.asarray(ser, dtype="int64") + assert np.shares_memory(arr, get_array(ser)) + if using_copy_on_write: + assert arr.flags.writeable is False + else: + assert arr.flags.writeable is True + + arr = np.asarray(ser) + assert np.shares_memory(arr, get_array(ser)) + if using_copy_on_write: + assert arr.flags.writeable is False + else: + assert arr.flags.writeable is True + + +def test_dataframe_array_ea_dtypes(using_copy_on_write): + df = DataFrame({"a": [1, 2, 3]}, dtype="Int64") + arr = np.asarray(df, dtype="int64") + assert np.shares_memory(arr, get_array(df, "a")) + if using_copy_on_write: + assert arr.flags.writeable is False + else: + assert arr.flags.writeable is True + + arr = np.asarray(df) + assert np.shares_memory(arr, get_array(df, "a")) + if using_copy_on_write: + assert arr.flags.writeable is False + else: + assert arr.flags.writeable is True + + +def test_dataframe_array_string_dtype(using_copy_on_write, using_array_manager): + df = DataFrame({"a": ["a", "b"]}, dtype="string") + arr = np.asarray(df) + if not using_array_manager: + assert np.shares_memory(arr, get_array(df, "a")) + if using_copy_on_write: + assert arr.flags.writeable is False + else: + assert arr.flags.writeable is True + + +def test_dataframe_multiple_numpy_dtypes(): + df = DataFrame({"a": [1, 2, 3], "b": 1.5}) + arr = np.asarray(df) + assert not np.shares_memory(arr, get_array(df, "a")) + assert arr.flags.writeable is True + + if np_version_gt2: + # copy=False semantics are only supported in NumPy>=2. + + msg = "Starting with NumPy 2.0, the behavior of the 'copy' keyword has changed" + with pytest.raises(FutureWarning, match=msg): + arr = np.array(df, copy=False) + + arr = np.array(df, copy=True) + assert arr.flags.writeable is True + + +def test_dataframe_single_block_copy_true(): + # the copy=False/None cases are tested above in test_dataframe_values + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + arr = np.array(df, copy=True) + assert not np.shares_memory(arr, get_array(df, "a")) + assert arr.flags.writeable is True + + +def test_values_is_ea(using_copy_on_write): + df = DataFrame({"a": date_range("2012-01-01", periods=3)}) + arr = np.asarray(df) + if using_copy_on_write: + assert arr.flags.writeable is False + else: + assert arr.flags.writeable is True + + +def test_empty_dataframe(): + df = DataFrame() + arr = np.asarray(df) + assert arr.flags.writeable is True diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/test_astype.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/test_astype.py new file mode 100644 index 0000000000000000000000000000000000000000..45fc3333c49a7722db200985d8232b9470baa205 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/test_astype.py @@ -0,0 +1,287 @@ +import pickle + +import numpy as np +import pytest + +from pandas.compat import HAS_PYARROW +from pandas.compat.pyarrow import pa_version_under12p0 +import pandas.util._test_decorators as td + +import pandas as pd +from pandas import ( + DataFrame, + Series, + Timestamp, + date_range, +) +import pandas._testing as tm +from pandas.tests.copy_view.util import get_array + + +def test_astype_single_dtype(using_copy_on_write): + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": 1.5}) + df_orig = df.copy() + df2 = df.astype("float64") + + if using_copy_on_write: + assert np.shares_memory(get_array(df2, "c"), get_array(df, "c")) + assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a")) + else: + assert not np.shares_memory(get_array(df2, "c"), get_array(df, "c")) + assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a")) + + # mutating df2 triggers a copy-on-write for that column/block + df2.iloc[0, 2] = 5.5 + if using_copy_on_write: + assert not np.shares_memory(get_array(df2, "c"), get_array(df, "c")) + tm.assert_frame_equal(df, df_orig) + + # mutating parent also doesn't update result + df2 = df.astype("float64") + df.iloc[0, 2] = 5.5 + tm.assert_frame_equal(df2, df_orig.astype("float64")) + + +@pytest.mark.parametrize("dtype", ["int64", "Int64"]) +@pytest.mark.parametrize("new_dtype", ["int64", "Int64", "int64[pyarrow]"]) +def test_astype_avoids_copy(using_copy_on_write, dtype, new_dtype): + if new_dtype == "int64[pyarrow]": + pytest.importorskip("pyarrow") + df = DataFrame({"a": [1, 2, 3]}, dtype=dtype) + df_orig = df.copy() + df2 = df.astype(new_dtype) + + if using_copy_on_write: + assert np.shares_memory(get_array(df2, "a"), get_array(df, "a")) + else: + assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a")) + + # mutating df2 triggers a copy-on-write for that column/block + df2.iloc[0, 0] = 10 + if using_copy_on_write: + assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a")) + tm.assert_frame_equal(df, df_orig) + + # mutating parent also doesn't update result + df2 = df.astype(new_dtype) + df.iloc[0, 0] = 100 + tm.assert_frame_equal(df2, df_orig.astype(new_dtype)) + + +@pytest.mark.parametrize("dtype", ["float64", "int32", "Int32", "int32[pyarrow]"]) +def test_astype_different_target_dtype(using_copy_on_write, dtype): + if dtype == "int32[pyarrow]": + pytest.importorskip("pyarrow") + df = DataFrame({"a": [1, 2, 3]}) + df_orig = df.copy() + df2 = df.astype(dtype) + + assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a")) + if using_copy_on_write: + assert df2._mgr._has_no_reference(0) + + df2.iloc[0, 0] = 5 + tm.assert_frame_equal(df, df_orig) + + # mutating parent also doesn't update result + df2 = df.astype(dtype) + df.iloc[0, 0] = 100 + tm.assert_frame_equal(df2, df_orig.astype(dtype)) + + +@td.skip_array_manager_invalid_test +def test_astype_numpy_to_ea(): + ser = Series([1, 2, 3]) + with pd.option_context("mode.copy_on_write", True): + result = ser.astype("Int64") + assert np.shares_memory(get_array(ser), get_array(result)) + + +@pytest.mark.parametrize( + "dtype, new_dtype", [("object", "string"), ("string", "object")] +) +def test_astype_string_and_object(using_copy_on_write, dtype, new_dtype): + df = DataFrame({"a": ["a", "b", "c"]}, dtype=dtype) + df_orig = df.copy() + df2 = df.astype(new_dtype) + + if using_copy_on_write: + assert np.shares_memory(get_array(df2, "a"), get_array(df, "a")) + else: + assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a")) + + df2.iloc[0, 0] = "x" + tm.assert_frame_equal(df, df_orig) + + +@pytest.mark.parametrize( + "dtype, new_dtype", [("object", "string"), ("string", "object")] +) +def test_astype_string_and_object_update_original( + using_copy_on_write, dtype, new_dtype +): + df = DataFrame({"a": ["a", "b", "c"]}, dtype=dtype) + df2 = df.astype(new_dtype) + df_orig = df2.copy() + + if using_copy_on_write: + assert np.shares_memory(get_array(df2, "a"), get_array(df, "a")) + else: + assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a")) + + df.iloc[0, 0] = "x" + tm.assert_frame_equal(df2, df_orig) + + +def test_astype_str_copy_on_pickle_roundrip(): + # TODO(infer_string) this test can be removed after 3.0 (once str is the default) + # https://github.com/pandas-dev/pandas/issues/54654 + # ensure_string_array may alter array inplace + base = Series(np.array([(1, 2), None, 1], dtype="object")) + base_copy = pickle.loads(pickle.dumps(base)) + base_copy.astype(str) + tm.assert_series_equal(base, base_copy) + + +def test_astype_string_copy_on_pickle_roundrip(any_string_dtype): + # https://github.com/pandas-dev/pandas/issues/54654 + # ensure_string_array may alter array inplace + base = Series(np.array([(1, 2), None, 1], dtype="object")) + base_copy = pickle.loads(pickle.dumps(base)) + base_copy.astype(any_string_dtype) + tm.assert_series_equal(base, base_copy) + + +def test_astype_string_read_only_on_pickle_roundrip(any_string_dtype): + # https://github.com/pandas-dev/pandas/issues/54654 + # ensure_string_array may alter read-only array inplace + base = Series(np.array([(1, 2), None, 1], dtype="object")) + base_copy = pickle.loads(pickle.dumps(base)) + base_copy._values.flags.writeable = False + base_copy.astype(any_string_dtype) + tm.assert_series_equal(base, base_copy) + + +def test_astype_dict_dtypes(using_copy_on_write): + df = DataFrame( + {"a": [1, 2, 3], "b": [4, 5, 6], "c": Series([1.5, 1.5, 1.5], dtype="float64")} + ) + df_orig = df.copy() + df2 = df.astype({"a": "float64", "c": "float64"}) + + if using_copy_on_write: + assert np.shares_memory(get_array(df2, "c"), get_array(df, "c")) + assert np.shares_memory(get_array(df2, "b"), get_array(df, "b")) + assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a")) + else: + assert not np.shares_memory(get_array(df2, "c"), get_array(df, "c")) + assert not np.shares_memory(get_array(df2, "b"), get_array(df, "b")) + assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a")) + + # mutating df2 triggers a copy-on-write for that column/block + df2.iloc[0, 2] = 5.5 + if using_copy_on_write: + assert not np.shares_memory(get_array(df2, "c"), get_array(df, "c")) + + df2.iloc[0, 1] = 10 + if using_copy_on_write: + assert not np.shares_memory(get_array(df2, "b"), get_array(df, "b")) + tm.assert_frame_equal(df, df_orig) + + +def test_astype_different_datetime_resos(using_copy_on_write): + df = DataFrame({"a": date_range("2019-12-31", periods=2, freq="D")}) + result = df.astype("datetime64[ms]") + + assert not np.shares_memory(get_array(df, "a"), get_array(result, "a")) + if using_copy_on_write: + assert result._mgr._has_no_reference(0) + + +def test_astype_different_timezones(using_copy_on_write): + df = DataFrame( + {"a": date_range("2019-12-31", periods=5, freq="D", tz="US/Pacific")} + ) + result = df.astype("datetime64[ns, Europe/Berlin]") + if using_copy_on_write: + assert not result._mgr._has_no_reference(0) + assert np.shares_memory(get_array(df, "a"), get_array(result, "a")) + + +def test_astype_different_timezones_different_reso(using_copy_on_write): + df = DataFrame( + {"a": date_range("2019-12-31", periods=5, freq="D", tz="US/Pacific")} + ) + result = df.astype("datetime64[ms, Europe/Berlin]") + if using_copy_on_write: + assert result._mgr._has_no_reference(0) + assert not np.shares_memory(get_array(df, "a"), get_array(result, "a")) + + +def test_astype_arrow_timestamp(using_copy_on_write): + pytest.importorskip("pyarrow") + df = DataFrame( + { + "a": [ + Timestamp("2020-01-01 01:01:01.000001"), + Timestamp("2020-01-01 01:01:01.000001"), + ] + }, + dtype="M8[ns]", + ) + result = df.astype("timestamp[ns][pyarrow]") + if using_copy_on_write: + assert not result._mgr._has_no_reference(0) + if pa_version_under12p0: + assert not np.shares_memory( + get_array(df, "a"), get_array(result, "a")._pa_array + ) + else: + assert np.shares_memory( + get_array(df, "a"), get_array(result, "a")._pa_array + ) + + +def test_convert_dtypes_infer_objects(using_copy_on_write): + ser = Series(["a", "b", "c"]) + ser_orig = ser.copy() + result = ser.convert_dtypes( + convert_integer=False, + convert_boolean=False, + convert_floating=False, + convert_string=False, + ) + + if using_copy_on_write: + assert tm.shares_memory(get_array(ser), get_array(result)) + else: + assert not np.shares_memory(get_array(ser), get_array(result)) + + result.iloc[0] = "x" + tm.assert_series_equal(ser, ser_orig) + + +def test_convert_dtypes(using_copy_on_write, using_infer_string): + df = DataFrame({"a": ["a", "b"], "b": [1, 2], "c": [1.5, 2.5], "d": [True, False]}) + df_orig = df.copy() + df2 = df.convert_dtypes() + + if using_copy_on_write: + if using_infer_string and HAS_PYARROW: + # TODO the default nullable string dtype still uses python storage + # this should be changed to pyarrow if installed + assert not tm.shares_memory(get_array(df2, "a"), get_array(df, "a")) + else: + assert tm.shares_memory(get_array(df2, "a"), get_array(df, "a")) + assert tm.shares_memory(get_array(df2, "d"), get_array(df, "d")) + assert tm.shares_memory(get_array(df2, "b"), get_array(df, "b")) + assert tm.shares_memory(get_array(df2, "c"), get_array(df, "c")) + else: + assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a")) + assert not np.shares_memory(get_array(df2, "b"), get_array(df, "b")) + assert not np.shares_memory(get_array(df2, "c"), get_array(df, "c")) + assert not np.shares_memory(get_array(df2, "d"), get_array(df, "d")) + + df2.iloc[0, 0] = "x" + df2.iloc[0, 1] = 10 + tm.assert_frame_equal(df, df_orig) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/test_chained_assignment_deprecation.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/test_chained_assignment_deprecation.py new file mode 100644 index 0000000000000000000000000000000000000000..49f5254f5bab44fa871b80eb898bde689974aff7 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/test_chained_assignment_deprecation.py @@ -0,0 +1,260 @@ +import numpy as np +import pytest + +from pandas.compat import ( + PY311, + WARNING_CHECK_DISABLED, +) +from pandas.errors import ( + ChainedAssignmentError, + SettingWithCopyWarning, +) + +from pandas import ( + DataFrame, + option_context, +) +import pandas._testing as tm + + +def test_methods_iloc_warn(using_copy_on_write): + if not using_copy_on_write: + df = DataFrame({"a": [1, 2, 3], "b": 1}) + with tm.assert_cow_warning(match="A value"): + df.iloc[:, 0].replace(1, 5, inplace=True) + + with tm.assert_cow_warning(match="A value"): + df.iloc[:, 0].fillna(1, inplace=True) + + with tm.assert_cow_warning(match="A value"): + df.iloc[:, 0].interpolate(inplace=True) + + with tm.assert_cow_warning(match="A value"): + df.iloc[:, 0].ffill(inplace=True) + + with tm.assert_cow_warning(match="A value"): + df.iloc[:, 0].bfill(inplace=True) + + +@pytest.mark.parametrize( + "func, args", + [ + ("replace", (4, 5)), + ("fillna", (1,)), + ("interpolate", ()), + ("bfill", ()), + ("ffill", ()), + ], +) +def test_methods_iloc_getitem_item_cache( + func, args, using_copy_on_write, warn_copy_on_write +): + # ensure we don't incorrectly raise chained assignment warning because + # of the item cache / iloc not setting the item cache + df_orig = DataFrame({"a": [1, 2, 3], "b": 1}) + + df = df_orig.copy() + ser = df.iloc[:, 0] + getattr(ser, func)(*args, inplace=True) + + # parent that holds item_cache is dead, so don't increase ref count + df = df_orig.copy() + ser = df.copy()["a"] + getattr(ser, func)(*args, inplace=True) + + df = df_orig.copy() + df["a"] # populate the item_cache + ser = df.iloc[:, 0] # iloc creates a new object + getattr(ser, func)(*args, inplace=True) + + df = df_orig.copy() + df["a"] # populate the item_cache + ser = df["a"] + getattr(ser, func)(*args, inplace=True) + + df = df_orig.copy() + df["a"] # populate the item_cache + # TODO(CoW-warn) because of the usage of *args, this doesn't warn on Py3.11+ + if using_copy_on_write: + with tm.raises_chained_assignment_error(not PY311): + getattr(df["a"], func)(*args, inplace=True) + else: + with tm.assert_cow_warning(not PY311, match="A value"): + getattr(df["a"], func)(*args, inplace=True) + + df = df_orig.copy() + ser = df["a"] # populate the item_cache and keep ref + if using_copy_on_write: + with tm.raises_chained_assignment_error(not PY311): + getattr(df["a"], func)(*args, inplace=True) + else: + # ideally also warns on the default mode, but the ser' _cacher + # messes up the refcount + even in warning mode this doesn't trigger + # the warning of Py3.1+ (see above) + with tm.assert_cow_warning(warn_copy_on_write and not PY311, match="A value"): + getattr(df["a"], func)(*args, inplace=True) + + +def test_methods_iloc_getitem_item_cache_fillna( + using_copy_on_write, warn_copy_on_write +): + # ensure we don't incorrectly raise chained assignment warning because + # of the item cache / iloc not setting the item cache + df_orig = DataFrame({"a": [1, 2, 3], "b": 1}) + + df = df_orig.copy() + ser = df.iloc[:, 0] + ser.fillna(1, inplace=True) + + # parent that holds item_cache is dead, so don't increase ref count + df = df_orig.copy() + ser = df.copy()["a"] + ser.fillna(1, inplace=True) + + df = df_orig.copy() + df["a"] # populate the item_cache + ser = df.iloc[:, 0] # iloc creates a new object + ser.fillna(1, inplace=True) + + df = df_orig.copy() + df["a"] # populate the item_cache + ser = df["a"] + ser.fillna(1, inplace=True) + + df = df_orig.copy() + df["a"] # populate the item_cache + if using_copy_on_write: + with tm.raises_chained_assignment_error(): + df["a"].fillna(1, inplace=True) + else: + with tm.assert_cow_warning(match="A value"): + df["a"].fillna(1, inplace=True) + + df = df_orig.copy() + ser = df["a"] # populate the item_cache and keep ref + if using_copy_on_write: + with tm.raises_chained_assignment_error(): + df["a"].fillna(1, inplace=True) + else: + # TODO(CoW-warn) ideally also warns on the default mode, but the ser' _cacher + # messes up the refcount + with tm.assert_cow_warning(warn_copy_on_write, match="A value"): + df["a"].fillna(1, inplace=True) + + +# TODO(CoW-warn) expand the cases +@pytest.mark.parametrize( + "indexer", [0, [0, 1], slice(0, 2), np.array([True, False, True])] +) +def test_series_setitem(indexer, using_copy_on_write, warn_copy_on_write): + # ensure we only get a single warning for those typical cases of chained + # assignment + df = DataFrame({"a": [1, 2, 3], "b": 1}) + + # using custom check instead of tm.assert_produces_warning because that doesn't + # fail if multiple warnings are raised + if WARNING_CHECK_DISABLED: + return + + with pytest.warns() as record: + df["a"][indexer] = 0 + assert len(record) == 1 + if using_copy_on_write: + assert record[0].category == ChainedAssignmentError + else: + assert record[0].category == FutureWarning + assert "ChainedAssignmentError" in record[0].message.args[0] + + +@pytest.mark.filterwarnings("ignore::pandas.errors.SettingWithCopyWarning") +@pytest.mark.parametrize( + "indexer", ["a", ["a", "b"], slice(0, 2), np.array([True, False, True])] +) +def test_frame_setitem(indexer, using_copy_on_write): + df = DataFrame({"a": [1, 2, 3, 4, 5], "b": 1}) + + extra_warnings = () if using_copy_on_write else (SettingWithCopyWarning,) + + with option_context("chained_assignment", "warn"): + with tm.raises_chained_assignment_error(extra_warnings=extra_warnings): + df[0:3][indexer] = 10 + + +@pytest.mark.parametrize( + "indexer", [0, [0, 1], slice(0, 2), np.array([True, False, True])] +) +def test_series_iloc_setitem(indexer): + df = DataFrame({"a": [1, 2, 3], "b": 1}) + + with option_context("chained_assignment", "warn"): + with tm.raises_chained_assignment_error(): + df["a"].iloc[indexer] = 0 + + +@pytest.mark.parametrize( + "indexer", [0, [0, 1], slice(0, 2), np.array([True, False, True])] +) +def test_frame_iloc_setitem(indexer, using_copy_on_write): + df = DataFrame({"a": [1, 2, 3, 4, 5], "b": 1}) + + extra_warnings = () if using_copy_on_write else (SettingWithCopyWarning,) + + with option_context("chained_assignment", "warn"): + with tm.raises_chained_assignment_error(extra_warnings=extra_warnings): + df[0:3].iloc[indexer] = 10 + + +@pytest.mark.parametrize( + "indexer", [0, [0, 1], slice(0, 2), np.array([True, False, True])] +) +def test_series_loc_setitem(indexer): + df = DataFrame({"a": [1, 2, 3], "b": 1}) + + with option_context("chained_assignment", "warn"): + with tm.raises_chained_assignment_error(): + df["a"].loc[indexer] = 0 + + +@pytest.mark.parametrize( + "indexer", [0, [0, 1], (0, "a"), slice(0, 2), np.array([True, False, True])] +) +def test_frame_loc_setitem(indexer, using_copy_on_write): + df = DataFrame({"a": [1, 2, 3, 4, 5], "b": 1}) + + extra_warnings = () if using_copy_on_write else (SettingWithCopyWarning,) + + with option_context("chained_assignment", "warn"): + with tm.raises_chained_assignment_error(extra_warnings=extra_warnings): + df[0:3].loc[indexer] = 10 + + +def test_series_at_setitem(): + df = DataFrame({"a": [1, 2, 3], "b": 1}) + + with option_context("chained_assignment", "warn"): + with tm.raises_chained_assignment_error(): + df["a"].at[0] = 0 + + +def test_frame_at_setitem(): + df = DataFrame({"a": [1, 2, 3, 4, 5], "b": 1}) + + with option_context("chained_assignment", "warn"): + with tm.raises_chained_assignment_error(): + df[0:3].at[0, "a"] = 10 + + +def test_series_iat_setitem(): + df = DataFrame({"a": [1, 2, 3], "b": 1}) + + with option_context("chained_assignment", "warn"): + with tm.raises_chained_assignment_error(): + df["a"].iat[0] = 0 + + +def test_frame_iat_setitem(): + df = DataFrame({"a": [1, 2, 3, 4, 5], "b": 1}) + + with option_context("chained_assignment", "warn"): + with tm.raises_chained_assignment_error(): + df[0:3].iat[0, 0] = 10 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/test_clip.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/test_clip.py new file mode 100644 index 0000000000000000000000000000000000000000..5c5abdae7a021497d69c61c6b1e75e47a39d00b2 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/test_clip.py @@ -0,0 +1,106 @@ +import numpy as np + +from pandas.compat import WARNING_CHECK_DISABLED + +from pandas import ( + DataFrame, + option_context, +) +import pandas._testing as tm +from pandas.tests.copy_view.util import get_array + + +def test_clip_inplace_reference(using_copy_on_write, warn_copy_on_write): + df = DataFrame({"a": [1.5, 2, 3]}) + df_copy = df.copy() + arr_a = get_array(df, "a") + view = df[:] + if warn_copy_on_write: + with tm.assert_cow_warning(): + df.clip(lower=2, inplace=True) + else: + df.clip(lower=2, inplace=True) + + if using_copy_on_write: + assert not np.shares_memory(get_array(df, "a"), arr_a) + assert df._mgr._has_no_reference(0) + assert view._mgr._has_no_reference(0) + tm.assert_frame_equal(df_copy, view) + else: + assert np.shares_memory(get_array(df, "a"), arr_a) + + +def test_clip_inplace_reference_no_op(using_copy_on_write): + df = DataFrame({"a": [1.5, 2, 3]}) + df_copy = df.copy() + arr_a = get_array(df, "a") + view = df[:] + df.clip(lower=0, inplace=True) + + assert np.shares_memory(get_array(df, "a"), arr_a) + + if using_copy_on_write: + assert not df._mgr._has_no_reference(0) + assert not view._mgr._has_no_reference(0) + tm.assert_frame_equal(df_copy, view) + + +def test_clip_inplace(using_copy_on_write): + df = DataFrame({"a": [1.5, 2, 3]}) + arr_a = get_array(df, "a") + df.clip(lower=2, inplace=True) + + assert np.shares_memory(get_array(df, "a"), arr_a) + + if using_copy_on_write: + assert df._mgr._has_no_reference(0) + + +def test_clip(using_copy_on_write): + df = DataFrame({"a": [1.5, 2, 3]}) + df_orig = df.copy() + df2 = df.clip(lower=2) + + assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a")) + + if using_copy_on_write: + assert df._mgr._has_no_reference(0) + tm.assert_frame_equal(df_orig, df) + + +def test_clip_no_op(using_copy_on_write): + df = DataFrame({"a": [1.5, 2, 3]}) + df2 = df.clip(lower=0) + + if using_copy_on_write: + assert not df._mgr._has_no_reference(0) + assert np.shares_memory(get_array(df2, "a"), get_array(df, "a")) + else: + assert not np.shares_memory(get_array(df2, "a"), get_array(df, "a")) + + +def test_clip_chained_inplace(using_copy_on_write): + df = DataFrame({"a": [1, 4, 2], "b": 1}) + df_orig = df.copy() + if using_copy_on_write: + with tm.raises_chained_assignment_error(): + df["a"].clip(1, 2, inplace=True) + tm.assert_frame_equal(df, df_orig) + + with tm.raises_chained_assignment_error(): + df[["a"]].clip(1, 2, inplace=True) + tm.assert_frame_equal(df, df_orig) + else: + with tm.assert_produces_warning( + FutureWarning if not WARNING_CHECK_DISABLED else None, + match="inplace method", + ): + df["a"].clip(1, 2, inplace=True) + + with tm.assert_produces_warning(None): + with option_context("mode.chained_assignment", None): + df[["a"]].clip(1, 2, inplace=True) + + with tm.assert_produces_warning(None): + with option_context("mode.chained_assignment", None): + df[df["a"] > 1].clip(1, 2, inplace=True) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/test_core_functionalities.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/test_core_functionalities.py new file mode 100644 index 0000000000000000000000000000000000000000..8dc80c5cc0e0eadbe792e114d48593d95df17907 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/test_core_functionalities.py @@ -0,0 +1,106 @@ +import numpy as np +import pytest + +from pandas import DataFrame +import pandas._testing as tm +from pandas.tests.copy_view.util import get_array + + +def test_assigning_to_same_variable_removes_references(using_copy_on_write): + df = DataFrame({"a": [1, 2, 3]}) + df = df.reset_index() + if using_copy_on_write: + assert df._mgr._has_no_reference(1) + arr = get_array(df, "a") + df.iloc[0, 1] = 100 # Write into a + + assert np.shares_memory(arr, get_array(df, "a")) + + +def test_setitem_dont_track_unnecessary_references(using_copy_on_write): + df = DataFrame({"a": [1, 2, 3], "b": 1, "c": 1}) + + df["b"] = 100 + arr = get_array(df, "a") + # We split the block in setitem, if we are not careful the new blocks will + # reference each other triggering a copy + df.iloc[0, 0] = 100 + assert np.shares_memory(arr, get_array(df, "a")) + + +def test_setitem_with_view_copies(using_copy_on_write, warn_copy_on_write): + df = DataFrame({"a": [1, 2, 3], "b": 1, "c": 1}) + view = df[:] + expected = df.copy() + + df["b"] = 100 + arr = get_array(df, "a") + with tm.assert_cow_warning(warn_copy_on_write): + df.iloc[0, 0] = 100 # Check that we correctly track reference + if using_copy_on_write: + assert not np.shares_memory(arr, get_array(df, "a")) + tm.assert_frame_equal(view, expected) + + +def test_setitem_with_view_invalidated_does_not_copy( + using_copy_on_write, warn_copy_on_write, request +): + df = DataFrame({"a": [1, 2, 3], "b": 1, "c": 1}) + view = df[:] + + df["b"] = 100 + arr = get_array(df, "a") + view = None # noqa: F841 + # TODO(CoW-warn) false positive? -> block gets split because of `df["b"] = 100` + # which introduces additional refs, even when those of `view` go out of scopes + with tm.assert_cow_warning(warn_copy_on_write): + df.iloc[0, 0] = 100 + if using_copy_on_write: + # Setitem split the block. Since the old block shared data with view + # all the new blocks are referencing view and each other. When view + # goes out of scope, they don't share data with any other block, + # so we should not trigger a copy + mark = pytest.mark.xfail( + reason="blk.delete does not track references correctly" + ) + request.applymarker(mark) + assert np.shares_memory(arr, get_array(df, "a")) + + +def test_out_of_scope(using_copy_on_write): + def func(): + df = DataFrame({"a": [1, 2], "b": 1.5, "c": 1}) + # create some subset + result = df[["a", "b"]] + return result + + result = func() + if using_copy_on_write: + assert not result._mgr.blocks[0].refs.has_reference() + assert not result._mgr.blocks[1].refs.has_reference() + + +def test_delete(using_copy_on_write): + df = DataFrame( + np.random.default_rng(2).standard_normal((4, 3)), columns=["a", "b", "c"] + ) + del df["b"] + if using_copy_on_write: + assert not df._mgr.blocks[0].refs.has_reference() + assert not df._mgr.blocks[1].refs.has_reference() + + df = df[["a"]] + if using_copy_on_write: + assert not df._mgr.blocks[0].refs.has_reference() + + +def test_delete_reference(using_copy_on_write): + df = DataFrame( + np.random.default_rng(2).standard_normal((4, 3)), columns=["a", "b", "c"] + ) + x = df[:] + del df["b"] + if using_copy_on_write: + assert df._mgr.blocks[0].refs.has_reference() + assert df._mgr.blocks[1].refs.has_reference() + assert x._mgr.blocks[0].refs.has_reference() diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/test_functions.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/test_functions.py new file mode 100644 index 0000000000000000000000000000000000000000..ce444ac3571fa64866e43fc982577e6f6f28fe5d --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/test_functions.py @@ -0,0 +1,397 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Index, + Series, + concat, + merge, +) +import pandas._testing as tm +from pandas.tests.copy_view.util import get_array + + +def test_concat_frames(using_copy_on_write): + df = DataFrame({"b": ["a"] * 3}, dtype=object) + df2 = DataFrame({"a": ["a"] * 3}, dtype=object) + df_orig = df.copy() + result = concat([df, df2], axis=1) + + if using_copy_on_write: + assert np.shares_memory(get_array(result, "b"), get_array(df, "b")) + assert np.shares_memory(get_array(result, "a"), get_array(df2, "a")) + else: + assert not np.shares_memory(get_array(result, "b"), get_array(df, "b")) + assert not np.shares_memory(get_array(result, "a"), get_array(df2, "a")) + + result.iloc[0, 0] = "d" + if using_copy_on_write: + assert not np.shares_memory(get_array(result, "b"), get_array(df, "b")) + assert np.shares_memory(get_array(result, "a"), get_array(df2, "a")) + + result.iloc[0, 1] = "d" + if using_copy_on_write: + assert not np.shares_memory(get_array(result, "a"), get_array(df2, "a")) + tm.assert_frame_equal(df, df_orig) + + +def test_concat_frames_updating_input(using_copy_on_write): + df = DataFrame({"b": ["a"] * 3}, dtype=object) + df2 = DataFrame({"a": ["a"] * 3}, dtype=object) + result = concat([df, df2], axis=1) + + if using_copy_on_write: + assert np.shares_memory(get_array(result, "b"), get_array(df, "b")) + assert np.shares_memory(get_array(result, "a"), get_array(df2, "a")) + else: + assert not np.shares_memory(get_array(result, "b"), get_array(df, "b")) + assert not np.shares_memory(get_array(result, "a"), get_array(df2, "a")) + + expected = result.copy() + df.iloc[0, 0] = "d" + if using_copy_on_write: + assert not np.shares_memory(get_array(result, "b"), get_array(df, "b")) + assert np.shares_memory(get_array(result, "a"), get_array(df2, "a")) + + df2.iloc[0, 0] = "d" + if using_copy_on_write: + assert not np.shares_memory(get_array(result, "a"), get_array(df2, "a")) + tm.assert_frame_equal(result, expected) + + +def test_concat_series(using_copy_on_write): + ser = Series([1, 2], name="a") + ser2 = Series([3, 4], name="b") + ser_orig = ser.copy() + ser2_orig = ser2.copy() + result = concat([ser, ser2], axis=1) + + if using_copy_on_write: + assert np.shares_memory(get_array(result, "a"), ser.values) + assert np.shares_memory(get_array(result, "b"), ser2.values) + else: + assert not np.shares_memory(get_array(result, "a"), ser.values) + assert not np.shares_memory(get_array(result, "b"), ser2.values) + + result.iloc[0, 0] = 100 + if using_copy_on_write: + assert not np.shares_memory(get_array(result, "a"), ser.values) + assert np.shares_memory(get_array(result, "b"), ser2.values) + + result.iloc[0, 1] = 1000 + if using_copy_on_write: + assert not np.shares_memory(get_array(result, "b"), ser2.values) + tm.assert_series_equal(ser, ser_orig) + tm.assert_series_equal(ser2, ser2_orig) + + +def test_concat_frames_chained(using_copy_on_write): + df1 = DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3]}) + df2 = DataFrame({"c": [4, 5, 6]}) + df3 = DataFrame({"d": [4, 5, 6]}) + result = concat([concat([df1, df2], axis=1), df3], axis=1) + expected = result.copy() + + if using_copy_on_write: + assert np.shares_memory(get_array(result, "a"), get_array(df1, "a")) + assert np.shares_memory(get_array(result, "c"), get_array(df2, "c")) + assert np.shares_memory(get_array(result, "d"), get_array(df3, "d")) + else: + assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a")) + assert not np.shares_memory(get_array(result, "c"), get_array(df2, "c")) + assert not np.shares_memory(get_array(result, "d"), get_array(df3, "d")) + + df1.iloc[0, 0] = 100 + if using_copy_on_write: + assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a")) + + tm.assert_frame_equal(result, expected) + + +def test_concat_series_chained(using_copy_on_write): + ser1 = Series([1, 2, 3], name="a") + ser2 = Series([4, 5, 6], name="c") + ser3 = Series([4, 5, 6], name="d") + result = concat([concat([ser1, ser2], axis=1), ser3], axis=1) + expected = result.copy() + + if using_copy_on_write: + assert np.shares_memory(get_array(result, "a"), get_array(ser1, "a")) + assert np.shares_memory(get_array(result, "c"), get_array(ser2, "c")) + assert np.shares_memory(get_array(result, "d"), get_array(ser3, "d")) + else: + assert not np.shares_memory(get_array(result, "a"), get_array(ser1, "a")) + assert not np.shares_memory(get_array(result, "c"), get_array(ser2, "c")) + assert not np.shares_memory(get_array(result, "d"), get_array(ser3, "d")) + + ser1.iloc[0] = 100 + if using_copy_on_write: + assert not np.shares_memory(get_array(result, "a"), get_array(ser1, "a")) + + tm.assert_frame_equal(result, expected) + + +def test_concat_series_updating_input(using_copy_on_write): + ser = Series([1, 2], name="a") + ser2 = Series([3, 4], name="b") + expected = DataFrame({"a": [1, 2], "b": [3, 4]}) + result = concat([ser, ser2], axis=1) + + if using_copy_on_write: + assert np.shares_memory(get_array(result, "a"), get_array(ser, "a")) + assert np.shares_memory(get_array(result, "b"), get_array(ser2, "b")) + else: + assert not np.shares_memory(get_array(result, "a"), get_array(ser, "a")) + assert not np.shares_memory(get_array(result, "b"), get_array(ser2, "b")) + + ser.iloc[0] = 100 + if using_copy_on_write: + assert not np.shares_memory(get_array(result, "a"), get_array(ser, "a")) + assert np.shares_memory(get_array(result, "b"), get_array(ser2, "b")) + tm.assert_frame_equal(result, expected) + + ser2.iloc[0] = 1000 + if using_copy_on_write: + assert not np.shares_memory(get_array(result, "b"), get_array(ser2, "b")) + tm.assert_frame_equal(result, expected) + + +def test_concat_mixed_series_frame(using_copy_on_write): + df = DataFrame({"a": [1, 2, 3], "c": 1}) + ser = Series([4, 5, 6], name="d") + result = concat([df, ser], axis=1) + expected = result.copy() + + if using_copy_on_write: + assert np.shares_memory(get_array(result, "a"), get_array(df, "a")) + assert np.shares_memory(get_array(result, "c"), get_array(df, "c")) + assert np.shares_memory(get_array(result, "d"), get_array(ser, "d")) + else: + assert not np.shares_memory(get_array(result, "a"), get_array(df, "a")) + assert not np.shares_memory(get_array(result, "c"), get_array(df, "c")) + assert not np.shares_memory(get_array(result, "d"), get_array(ser, "d")) + + ser.iloc[0] = 100 + if using_copy_on_write: + assert not np.shares_memory(get_array(result, "d"), get_array(ser, "d")) + + df.iloc[0, 0] = 100 + if using_copy_on_write: + assert not np.shares_memory(get_array(result, "a"), get_array(df, "a")) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("copy", [True, None, False]) +def test_concat_copy_keyword(using_copy_on_write, copy): + df = DataFrame({"a": [1, 2]}) + df2 = DataFrame({"b": [1.5, 2.5]}) + + result = concat([df, df2], axis=1, copy=copy) + + if using_copy_on_write or copy is False: + assert np.shares_memory(get_array(df, "a"), get_array(result, "a")) + assert np.shares_memory(get_array(df2, "b"), get_array(result, "b")) + else: + assert not np.shares_memory(get_array(df, "a"), get_array(result, "a")) + assert not np.shares_memory(get_array(df2, "b"), get_array(result, "b")) + + +@pytest.mark.parametrize( + "func", + [ + lambda df1, df2, **kwargs: df1.merge(df2, **kwargs), + lambda df1, df2, **kwargs: merge(df1, df2, **kwargs), + ], +) +def test_merge_on_key(using_copy_on_write, func): + df1 = DataFrame({"key": Series(["a", "b", "c"], dtype=object), "a": [1, 2, 3]}) + df2 = DataFrame({"key": Series(["a", "b", "c"], dtype=object), "b": [4, 5, 6]}) + df1_orig = df1.copy() + df2_orig = df2.copy() + + result = func(df1, df2, on="key") + + if using_copy_on_write: + assert np.shares_memory(get_array(result, "a"), get_array(df1, "a")) + assert np.shares_memory(get_array(result, "b"), get_array(df2, "b")) + assert np.shares_memory(get_array(result, "key"), get_array(df1, "key")) + assert not np.shares_memory(get_array(result, "key"), get_array(df2, "key")) + else: + assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a")) + assert not np.shares_memory(get_array(result, "b"), get_array(df2, "b")) + + result.iloc[0, 1] = 0 + if using_copy_on_write: + assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a")) + assert np.shares_memory(get_array(result, "b"), get_array(df2, "b")) + + result.iloc[0, 2] = 0 + if using_copy_on_write: + assert not np.shares_memory(get_array(result, "b"), get_array(df2, "b")) + tm.assert_frame_equal(df1, df1_orig) + tm.assert_frame_equal(df2, df2_orig) + + +def test_merge_on_index(using_copy_on_write): + df1 = DataFrame({"a": [1, 2, 3]}) + df2 = DataFrame({"b": [4, 5, 6]}) + df1_orig = df1.copy() + df2_orig = df2.copy() + + result = merge(df1, df2, left_index=True, right_index=True) + + if using_copy_on_write: + assert np.shares_memory(get_array(result, "a"), get_array(df1, "a")) + assert np.shares_memory(get_array(result, "b"), get_array(df2, "b")) + else: + assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a")) + assert not np.shares_memory(get_array(result, "b"), get_array(df2, "b")) + + result.iloc[0, 0] = 0 + if using_copy_on_write: + assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a")) + assert np.shares_memory(get_array(result, "b"), get_array(df2, "b")) + + result.iloc[0, 1] = 0 + if using_copy_on_write: + assert not np.shares_memory(get_array(result, "b"), get_array(df2, "b")) + tm.assert_frame_equal(df1, df1_orig) + tm.assert_frame_equal(df2, df2_orig) + + +@pytest.mark.parametrize( + "func, how", + [ + (lambda df1, df2, **kwargs: merge(df2, df1, on="key", **kwargs), "right"), + (lambda df1, df2, **kwargs: merge(df1, df2, on="key", **kwargs), "left"), + ], +) +def test_merge_on_key_enlarging_one(using_copy_on_write, func, how): + df1 = DataFrame({"key": Series(["a", "b", "c"], dtype=object), "a": [1, 2, 3]}) + df2 = DataFrame({"key": Series(["a", "b"], dtype=object), "b": [4, 5]}) + df1_orig = df1.copy() + df2_orig = df2.copy() + + result = func(df1, df2, how=how) + + if using_copy_on_write: + assert np.shares_memory(get_array(result, "a"), get_array(df1, "a")) + assert not np.shares_memory(get_array(result, "b"), get_array(df2, "b")) + assert df2._mgr._has_no_reference(1) + assert df2._mgr._has_no_reference(0) + assert np.shares_memory(get_array(result, "key"), get_array(df1, "key")) is ( + how == "left" + ) + assert not np.shares_memory(get_array(result, "key"), get_array(df2, "key")) + else: + assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a")) + assert not np.shares_memory(get_array(result, "b"), get_array(df2, "b")) + + if how == "left": + result.iloc[0, 1] = 0 + else: + result.iloc[0, 2] = 0 + if using_copy_on_write: + assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a")) + tm.assert_frame_equal(df1, df1_orig) + tm.assert_frame_equal(df2, df2_orig) + + +@pytest.mark.parametrize("copy", [True, None, False]) +def test_merge_copy_keyword(using_copy_on_write, copy): + df = DataFrame({"a": [1, 2]}) + df2 = DataFrame({"b": [3, 4.5]}) + + result = df.merge(df2, copy=copy, left_index=True, right_index=True) + + if using_copy_on_write or copy is False: + assert np.shares_memory(get_array(df, "a"), get_array(result, "a")) + assert np.shares_memory(get_array(df2, "b"), get_array(result, "b")) + else: + assert not np.shares_memory(get_array(df, "a"), get_array(result, "a")) + assert not np.shares_memory(get_array(df2, "b"), get_array(result, "b")) + + +@pytest.mark.parametrize("dtype", [object, "str"]) +def test_join_on_key(dtype, using_copy_on_write): + df_index = Index(["a", "b", "c"], name="key", dtype=dtype) + + df1 = DataFrame({"a": [1, 2, 3]}, index=df_index.copy(deep=True)) + df2 = DataFrame({"b": [4, 5, 6]}, index=df_index.copy(deep=True)) + + df1_orig = df1.copy() + df2_orig = df2.copy() + + result = df1.join(df2, on="key") + + if using_copy_on_write: + assert np.shares_memory(get_array(result, "a"), get_array(df1, "a")) + assert np.shares_memory(get_array(result, "b"), get_array(df2, "b")) + assert tm.shares_memory(get_array(result.index), get_array(df1.index)) + assert not np.shares_memory(get_array(result.index), get_array(df2.index)) + else: + assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a")) + assert not np.shares_memory(get_array(result, "b"), get_array(df2, "b")) + + result.iloc[0, 0] = 0 + if using_copy_on_write: + assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a")) + assert np.shares_memory(get_array(result, "b"), get_array(df2, "b")) + + result.iloc[0, 1] = 0 + if using_copy_on_write: + assert not np.shares_memory(get_array(result, "b"), get_array(df2, "b")) + + tm.assert_frame_equal(df1, df1_orig) + tm.assert_frame_equal(df2, df2_orig) + + +def test_join_multiple_dataframes_on_key(using_copy_on_write): + df_index = Index(["a", "b", "c"], name="key", dtype=object) + + df1 = DataFrame({"a": [1, 2, 3]}, index=df_index.copy(deep=True)) + dfs_list = [ + DataFrame({"b": [4, 5, 6]}, index=df_index.copy(deep=True)), + DataFrame({"c": [7, 8, 9]}, index=df_index.copy(deep=True)), + ] + + df1_orig = df1.copy() + dfs_list_orig = [df.copy() for df in dfs_list] + + result = df1.join(dfs_list) + + if using_copy_on_write: + assert np.shares_memory(get_array(result, "a"), get_array(df1, "a")) + assert np.shares_memory(get_array(result, "b"), get_array(dfs_list[0], "b")) + assert np.shares_memory(get_array(result, "c"), get_array(dfs_list[1], "c")) + assert np.shares_memory(get_array(result.index), get_array(df1.index)) + assert not np.shares_memory( + get_array(result.index), get_array(dfs_list[0].index) + ) + assert not np.shares_memory( + get_array(result.index), get_array(dfs_list[1].index) + ) + else: + assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a")) + assert not np.shares_memory(get_array(result, "b"), get_array(dfs_list[0], "b")) + assert not np.shares_memory(get_array(result, "c"), get_array(dfs_list[1], "c")) + + result.iloc[0, 0] = 0 + if using_copy_on_write: + assert not np.shares_memory(get_array(result, "a"), get_array(df1, "a")) + assert np.shares_memory(get_array(result, "b"), get_array(dfs_list[0], "b")) + assert np.shares_memory(get_array(result, "c"), get_array(dfs_list[1], "c")) + + result.iloc[0, 1] = 0 + if using_copy_on_write: + assert not np.shares_memory(get_array(result, "b"), get_array(dfs_list[0], "b")) + assert np.shares_memory(get_array(result, "c"), get_array(dfs_list[1], "c")) + + result.iloc[0, 2] = 0 + if using_copy_on_write: + assert not np.shares_memory(get_array(result, "c"), get_array(dfs_list[1], "c")) + + tm.assert_frame_equal(df1, df1_orig) + for df, df_orig in zip(dfs_list, dfs_list_orig): + tm.assert_frame_equal(df, df_orig) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/test_internals.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/test_internals.py new file mode 100644 index 0000000000000000000000000000000000000000..8526d385888974a8c369faec9c3d26d7f35d0d89 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/copy_view/test_internals.py @@ -0,0 +1,154 @@ +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +import pandas as pd +from pandas import ( + DataFrame, + Series, +) +import pandas._testing as tm +from pandas.tests.copy_view.util import get_array + + +@td.skip_array_manager_invalid_test +def test_consolidate(using_copy_on_write): + # create unconsolidated DataFrame + df = DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3]}) + df["c"] = [4, 5, 6] + + # take a viewing subset + subset = df[:] + + # each block of subset references a block of df + assert all(blk.refs.has_reference() for blk in subset._mgr.blocks) + + # consolidate the two int64 blocks + subset._consolidate_inplace() + + # the float64 block still references the parent one because it still a view + assert subset._mgr.blocks[0].refs.has_reference() + # equivalent of assert np.shares_memory(df["b"].values, subset["b"].values) + # but avoids caching df["b"] + assert np.shares_memory(get_array(df, "b"), get_array(subset, "b")) + + # the new consolidated int64 block does not reference another + assert not subset._mgr.blocks[1].refs.has_reference() + + # the parent dataframe now also only is linked for the float column + assert not df._mgr.blocks[0].refs.has_reference() + assert df._mgr.blocks[1].refs.has_reference() + assert not df._mgr.blocks[2].refs.has_reference() + + # and modifying subset still doesn't modify parent + if using_copy_on_write: + subset.iloc[0, 1] = 0.0 + assert not df._mgr.blocks[1].refs.has_reference() + assert df.loc[0, "b"] == 0.1 + + +@pytest.mark.single_cpu +@td.skip_array_manager_invalid_test +def test_switch_options(): + # ensure we can switch the value of the option within one session + # (assuming data is constructed after switching) + + # using the option_context to ensure we set back to global option value + # after running the test + with pd.option_context("mode.copy_on_write", False): + df = DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3]}) + subset = df[:] + subset.iloc[0, 0] = 0 + # df updated with CoW disabled + assert df.iloc[0, 0] == 0 + + pd.options.mode.copy_on_write = True + df = DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3]}) + subset = df[:] + subset.iloc[0, 0] = 0 + # df not updated with CoW enabled + assert df.iloc[0, 0] == 1 + + pd.options.mode.copy_on_write = False + df = DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3]}) + subset = df[:] + subset.iloc[0, 0] = 0 + # df updated with CoW disabled + assert df.iloc[0, 0] == 0 + + +@td.skip_array_manager_invalid_test +@pytest.mark.parametrize("dtype", [np.intp, np.int8]) +@pytest.mark.parametrize( + "locs, arr", + [ + ([0], np.array([-1, -2, -3])), + ([1], np.array([-1, -2, -3])), + ([5], np.array([-1, -2, -3])), + ([0, 1], np.array([[-1, -2, -3], [-4, -5, -6]]).T), + ([0, 2], np.array([[-1, -2, -3], [-4, -5, -6]]).T), + ([0, 1, 2], np.array([[-1, -2, -3], [-4, -5, -6], [-4, -5, -6]]).T), + ([1, 2], np.array([[-1, -2, -3], [-4, -5, -6]]).T), + ([1, 3], np.array([[-1, -2, -3], [-4, -5, -6]]).T), + ([1, 3], np.array([[-1, -2, -3], [-4, -5, -6]]).T), + ], +) +def test_iset_splits_blocks_inplace(using_copy_on_write, locs, arr, dtype): + # Nothing currently calls iset with + # more than 1 loc with inplace=True (only happens with inplace=False) + # but ensure that it works + df = DataFrame( + { + "a": [1, 2, 3], + "b": [4, 5, 6], + "c": [7, 8, 9], + "d": [10, 11, 12], + "e": [13, 14, 15], + "f": Series(["a", "b", "c"], dtype=object), + }, + ) + arr = arr.astype(dtype) + df_orig = df.copy() + df2 = df.copy(deep=None) # Trigger a CoW (if enabled, otherwise makes copy) + df2._mgr.iset(locs, arr, inplace=True) + + tm.assert_frame_equal(df, df_orig) + + if using_copy_on_write: + for i, col in enumerate(df.columns): + if i not in locs: + assert np.shares_memory(get_array(df, col), get_array(df2, col)) + else: + for col in df.columns: + assert not np.shares_memory(get_array(df, col), get_array(df2, col)) + + +def test_exponential_backoff(): + # GH#55518 + df = DataFrame({"a": [1, 2, 3]}) + for i in range(490): + df.copy(deep=False) + + assert len(df._mgr.blocks[0].refs.referenced_blocks) == 491 + + df = DataFrame({"a": [1, 2, 3]}) + dfs = [df.copy(deep=False) for i in range(510)] + + for i in range(20): + df.copy(deep=False) + assert len(df._mgr.blocks[0].refs.referenced_blocks) == 531 + assert df._mgr.blocks[0].refs.clear_counter == 1000 + + for i in range(500): + df.copy(deep=False) + + # Don't reduce since we still have over 500 objects alive + assert df._mgr.blocks[0].refs.clear_counter == 1000 + + dfs = dfs[:300] + for i in range(500): + df.copy(deep=False) + + # Reduce since there are less than 500 objects alive + assert df._mgr.blocks[0].refs.clear_counter == 500 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/constructors/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/constructors/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/constructors/test_from_dict.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/constructors/test_from_dict.py new file mode 100644 index 0000000000000000000000000000000000000000..845174bbf600e0211d4514ff6b713e0b3b40d756 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/constructors/test_from_dict.py @@ -0,0 +1,223 @@ +from collections import OrderedDict + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Index, + MultiIndex, + RangeIndex, + Series, +) +import pandas._testing as tm + + +class TestFromDict: + # Note: these tests are specific to the from_dict method, not for + # passing dictionaries to DataFrame.__init__ + + def test_constructor_list_of_odicts(self): + data = [ + OrderedDict([["a", 1.5], ["b", 3], ["c", 4], ["d", 6]]), + OrderedDict([["a", 1.5], ["b", 3], ["d", 6]]), + OrderedDict([["a", 1.5], ["d", 6]]), + OrderedDict(), + OrderedDict([["a", 1.5], ["b", 3], ["c", 4]]), + OrderedDict([["b", 3], ["c", 4], ["d", 6]]), + ] + + result = DataFrame(data) + expected = DataFrame.from_dict( + dict(zip(range(len(data)), data)), orient="index" + ) + tm.assert_frame_equal(result, expected.reindex(result.index)) + + def test_constructor_single_row(self): + data = [OrderedDict([["a", 1.5], ["b", 3], ["c", 4], ["d", 6]])] + + result = DataFrame(data) + expected = DataFrame.from_dict(dict(zip([0], data)), orient="index").reindex( + result.index + ) + tm.assert_frame_equal(result, expected) + + def test_constructor_list_of_series(self): + data = [ + OrderedDict([["a", 1.5], ["b", 3.0], ["c", 4.0]]), + OrderedDict([["a", 1.5], ["b", 3.0], ["c", 6.0]]), + ] + sdict = OrderedDict(zip(["x", "y"], data)) + idx = Index(["a", "b", "c"]) + + # all named + data2 = [ + Series([1.5, 3, 4], idx, dtype="O", name="x"), + Series([1.5, 3, 6], idx, name="y"), + ] + result = DataFrame(data2) + expected = DataFrame.from_dict(sdict, orient="index") + tm.assert_frame_equal(result, expected) + + # some unnamed + data2 = [ + Series([1.5, 3, 4], idx, dtype="O", name="x"), + Series([1.5, 3, 6], idx), + ] + result = DataFrame(data2) + + sdict = OrderedDict(zip(["x", "Unnamed 0"], data)) + expected = DataFrame.from_dict(sdict, orient="index") + tm.assert_frame_equal(result, expected) + + # none named + data = [ + OrderedDict([["a", 1.5], ["b", 3], ["c", 4], ["d", 6]]), + OrderedDict([["a", 1.5], ["b", 3], ["d", 6]]), + OrderedDict([["a", 1.5], ["d", 6]]), + OrderedDict(), + OrderedDict([["a", 1.5], ["b", 3], ["c", 4]]), + OrderedDict([["b", 3], ["c", 4], ["d", 6]]), + ] + data = [Series(d) for d in data] + + result = DataFrame(data) + sdict = OrderedDict(zip(range(len(data)), data)) + expected = DataFrame.from_dict(sdict, orient="index") + tm.assert_frame_equal(result, expected.reindex(result.index)) + + result2 = DataFrame(data, index=np.arange(6, dtype=np.int64)) + tm.assert_frame_equal(result, result2) + + result = DataFrame([Series(dtype=object)]) + expected = DataFrame(index=[0]) + tm.assert_frame_equal(result, expected) + + data = [ + OrderedDict([["a", 1.5], ["b", 3.0], ["c", 4.0]]), + OrderedDict([["a", 1.5], ["b", 3.0], ["c", 6.0]]), + ] + sdict = OrderedDict(zip(range(len(data)), data)) + + idx = Index(["a", "b", "c"]) + data2 = [Series([1.5, 3, 4], idx, dtype="O"), Series([1.5, 3, 6], idx)] + result = DataFrame(data2) + expected = DataFrame.from_dict(sdict, orient="index") + tm.assert_frame_equal(result, expected) + + def test_constructor_orient(self, float_string_frame): + data_dict = float_string_frame.T._series + recons = DataFrame.from_dict(data_dict, orient="index") + expected = float_string_frame.reindex(index=recons.index) + tm.assert_frame_equal(recons, expected) + + # dict of sequence + a = {"hi": [32, 3, 3], "there": [3, 5, 3]} + rs = DataFrame.from_dict(a, orient="index") + xp = DataFrame.from_dict(a).T.reindex(list(a.keys())) + tm.assert_frame_equal(rs, xp) + + def test_constructor_from_ordered_dict(self): + # GH#8425 + a = OrderedDict( + [ + ("one", OrderedDict([("col_a", "foo1"), ("col_b", "bar1")])), + ("two", OrderedDict([("col_a", "foo2"), ("col_b", "bar2")])), + ("three", OrderedDict([("col_a", "foo3"), ("col_b", "bar3")])), + ] + ) + expected = DataFrame.from_dict(a, orient="columns").T + result = DataFrame.from_dict(a, orient="index") + tm.assert_frame_equal(result, expected) + + def test_from_dict_columns_parameter(self): + # GH#18529 + # Test new columns parameter for from_dict that was added to make + # from_items(..., orient='index', columns=[...]) easier to replicate + result = DataFrame.from_dict( + OrderedDict([("A", [1, 2]), ("B", [4, 5])]), + orient="index", + columns=["one", "two"], + ) + expected = DataFrame([[1, 2], [4, 5]], index=["A", "B"], columns=["one", "two"]) + tm.assert_frame_equal(result, expected) + + msg = "cannot use columns parameter with orient='columns'" + with pytest.raises(ValueError, match=msg): + DataFrame.from_dict( + {"A": [1, 2], "B": [4, 5]}, + orient="columns", + columns=["one", "two"], + ) + with pytest.raises(ValueError, match=msg): + DataFrame.from_dict({"A": [1, 2], "B": [4, 5]}, columns=["one", "two"]) + + @pytest.mark.parametrize( + "data_dict, orient, expected", + [ + ({}, "index", RangeIndex(0)), + ( + [{("a",): 1}, {("a",): 2}], + "columns", + Index([("a",)], tupleize_cols=False), + ), + ( + [OrderedDict([(("a",), 1), (("b",), 2)])], + "columns", + Index([("a",), ("b",)], tupleize_cols=False), + ), + ([{("a", "b"): 1}], "columns", Index([("a", "b")], tupleize_cols=False)), + ], + ) + def test_constructor_from_dict_tuples(self, data_dict, orient, expected): + # GH#16769 + df = DataFrame.from_dict(data_dict, orient) + result = df.columns + tm.assert_index_equal(result, expected) + + def test_frame_dict_constructor_empty_series(self): + s1 = Series( + [1, 2, 3, 4], index=MultiIndex.from_tuples([(1, 2), (1, 3), (2, 2), (2, 4)]) + ) + s2 = Series( + [1, 2, 3, 4], index=MultiIndex.from_tuples([(1, 2), (1, 3), (3, 2), (3, 4)]) + ) + s3 = Series(dtype=object) + + # it works! + DataFrame({"foo": s1, "bar": s2, "baz": s3}) + DataFrame.from_dict({"foo": s1, "baz": s3, "bar": s2}) + + def test_from_dict_scalars_requires_index(self): + msg = "If using all scalar values, you must pass an index" + with pytest.raises(ValueError, match=msg): + DataFrame.from_dict(OrderedDict([("b", 8), ("a", 5), ("a", 6)])) + + def test_from_dict_orient_invalid(self): + msg = ( + "Expected 'index', 'columns' or 'tight' for orient parameter. " + "Got 'abc' instead" + ) + with pytest.raises(ValueError, match=msg): + DataFrame.from_dict({"foo": 1, "baz": 3, "bar": 2}, orient="abc") + + def test_from_dict_order_with_single_column(self): + data = { + "alpha": { + "value2": 123, + "value1": 532, + "animal": 222, + "plant": False, + "name": "test", + } + } + result = DataFrame.from_dict( + data, + orient="columns", + ) + expected = DataFrame( + [[123], [532], [222], [False], ["test"]], + index=["value2", "value1", "animal", "plant", "name"], + columns=["alpha"], + ) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/constructors/test_from_records.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/constructors/test_from_records.py new file mode 100644 index 0000000000000000000000000000000000000000..58e47ba48f89445ee888c61055ad5a397a192f4a --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/constructors/test_from_records.py @@ -0,0 +1,503 @@ +from collections.abc import Iterator +from datetime import datetime +from decimal import Decimal + +import numpy as np +import pytest +import pytz + +from pandas._config import using_string_dtype + +from pandas.compat import is_platform_little_endian + +from pandas import ( + CategoricalIndex, + DataFrame, + Index, + Interval, + RangeIndex, + Series, + date_range, +) +import pandas._testing as tm + + +class TestFromRecords: + def test_from_records_dt64tz_frame(self): + # GH#51162 don't lose tz when calling from_records with DataFrame input + dti = date_range("2016-01-01", periods=10, tz="US/Pacific") + df = DataFrame({i: dti for i in range(4)}) + with tm.assert_produces_warning(FutureWarning): + res = DataFrame.from_records(df) + tm.assert_frame_equal(res, df) + + def test_from_records_with_datetimes(self): + # this may fail on certain platforms because of a numpy issue + # related GH#6140 + if not is_platform_little_endian(): + pytest.skip("known failure of test on non-little endian") + + # construction with a null in a recarray + # GH#6140 + expected = DataFrame({"EXPIRY": [datetime(2005, 3, 1, 0, 0), None]}) + + arrdata = [np.array([datetime(2005, 3, 1, 0, 0), None])] + dtypes = [("EXPIRY", " None: + self.args = args + + def __getitem__(self, i): + return self.args[i] + + def __iter__(self) -> Iterator: + return iter(self.args) + + recs = [Record(1, 2, 3), Record(4, 5, 6), Record(7, 8, 9)] + tups = [tuple(rec) for rec in recs] + + result = DataFrame.from_records(recs) + expected = DataFrame.from_records(tups) + tm.assert_frame_equal(result, expected) + + def test_from_records_len0_with_columns(self): + # GH#2633 + result = DataFrame.from_records([], index="foo", columns=["foo", "bar"]) + expected = Index(["bar"]) + + assert len(result) == 0 + assert result.index.name == "foo" + tm.assert_index_equal(result.columns, expected) + + def test_from_records_series_list_dict(self): + # GH#27358 + expected = DataFrame([[{"a": 1, "b": 2}, {"a": 3, "b": 4}]]).T + data = Series([[{"a": 1, "b": 2}], [{"a": 3, "b": 4}]]) + result = DataFrame.from_records(data) + tm.assert_frame_equal(result, expected) + + def test_from_records_series_categorical_index(self): + # GH#32805 + index = CategoricalIndex( + [Interval(-20, -10), Interval(-10, 0), Interval(0, 10)] + ) + series_of_dicts = Series([{"a": 1}, {"a": 2}, {"b": 3}], index=index) + frame = DataFrame.from_records(series_of_dicts, index=index) + expected = DataFrame( + {"a": [1, 2, np.nan], "b": [np.nan, np.nan, 3]}, index=index + ) + tm.assert_frame_equal(frame, expected) + + def test_frame_from_records_utc(self): + rec = {"datum": 1.5, "begin_time": datetime(2006, 4, 27, tzinfo=pytz.utc)} + + # it works + DataFrame.from_records([rec], index="begin_time") + + def test_from_records_to_records(self): + # from numpy documentation + arr = np.zeros((2,), dtype=("i4,f4,S10")) + arr[:] = [(1, 2.0, "Hello"), (2, 3.0, "World")] + + DataFrame.from_records(arr) + + index = Index(np.arange(len(arr))[::-1]) + indexed_frame = DataFrame.from_records(arr, index=index) + tm.assert_index_equal(indexed_frame.index, index) + + # without names, it should go to last ditch + arr2 = np.zeros((2, 3)) + tm.assert_frame_equal(DataFrame.from_records(arr2), DataFrame(arr2)) + + # wrong length + msg = "|".join( + [ + r"Length of values \(2\) does not match length of index \(1\)", + ] + ) + with pytest.raises(ValueError, match=msg): + DataFrame.from_records(arr, index=index[:-1]) + + indexed_frame = DataFrame.from_records(arr, index="f1") + + # what to do? + records = indexed_frame.to_records() + assert len(records.dtype.names) == 3 + + records = indexed_frame.to_records(index=False) + assert len(records.dtype.names) == 2 + assert "index" not in records.dtype.names + + def test_from_records_nones(self): + tuples = [(1, 2, None, 3), (1, 2, None, 3), (None, 2, 5, 3)] + + df = DataFrame.from_records(tuples, columns=["a", "b", "c", "d"]) + assert np.isnan(df["c"][0]) + + def test_from_records_iterator(self): + arr = np.array( + [(1.0, 1.0, 2, 2), (3.0, 3.0, 4, 4), (5.0, 5.0, 6, 6), (7.0, 7.0, 8, 8)], + dtype=[ + ("x", np.float64), + ("u", np.float32), + ("y", np.int64), + ("z", np.int32), + ], + ) + df = DataFrame.from_records(iter(arr), nrows=2) + xp = DataFrame( + { + "x": np.array([1.0, 3.0], dtype=np.float64), + "u": np.array([1.0, 3.0], dtype=np.float32), + "y": np.array([2, 4], dtype=np.int64), + "z": np.array([2, 4], dtype=np.int32), + } + ) + tm.assert_frame_equal(df.reindex_like(xp), xp) + + # no dtypes specified here, so just compare with the default + arr = [(1.0, 2), (3.0, 4), (5.0, 6), (7.0, 8)] + df = DataFrame.from_records(iter(arr), columns=["x", "y"], nrows=2) + tm.assert_frame_equal(df, xp.reindex(columns=["x", "y"]), check_dtype=False) + + def test_from_records_tuples_generator(self): + def tuple_generator(length): + for i in range(length): + letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" + yield (i, letters[i % len(letters)], i / length) + + columns_names = ["Integer", "String", "Float"] + columns = [ + [i[j] for i in tuple_generator(10)] for j in range(len(columns_names)) + ] + data = {"Integer": columns[0], "String": columns[1], "Float": columns[2]} + expected = DataFrame(data, columns=columns_names) + + generator = tuple_generator(10) + result = DataFrame.from_records(generator, columns=columns_names) + tm.assert_frame_equal(result, expected) + + def test_from_records_lists_generator(self): + def list_generator(length): + for i in range(length): + letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" + yield [i, letters[i % len(letters)], i / length] + + columns_names = ["Integer", "String", "Float"] + columns = [ + [i[j] for i in list_generator(10)] for j in range(len(columns_names)) + ] + data = {"Integer": columns[0], "String": columns[1], "Float": columns[2]} + expected = DataFrame(data, columns=columns_names) + + generator = list_generator(10) + result = DataFrame.from_records(generator, columns=columns_names) + tm.assert_frame_equal(result, expected) + + def test_from_records_columns_not_modified(self): + tuples = [(1, 2, 3), (1, 2, 3), (2, 5, 3)] + + columns = ["a", "b", "c"] + original_columns = list(columns) + + DataFrame.from_records(tuples, columns=columns, index="a") + + assert columns == original_columns + + def test_from_records_decimal(self): + tuples = [(Decimal("1.5"),), (Decimal("2.5"),), (None,)] + + df = DataFrame.from_records(tuples, columns=["a"]) + assert df["a"].dtype == object + + df = DataFrame.from_records(tuples, columns=["a"], coerce_float=True) + assert df["a"].dtype == np.float64 + assert np.isnan(df["a"].values[-1]) + + def test_from_records_duplicates(self): + result = DataFrame.from_records([(1, 2, 3), (4, 5, 6)], columns=["a", "b", "a"]) + + expected = DataFrame([(1, 2, 3), (4, 5, 6)], columns=["a", "b", "a"]) + + tm.assert_frame_equal(result, expected) + + def test_from_records_set_index_name(self): + def create_dict(order_id): + return { + "order_id": order_id, + "quantity": np.random.default_rng(2).integers(1, 10), + "price": np.random.default_rng(2).integers(1, 10), + } + + documents = [create_dict(i) for i in range(10)] + # demo missing data + documents.append({"order_id": 10, "quantity": 5}) + + result = DataFrame.from_records(documents, index="order_id") + assert result.index.name == "order_id" + + # MultiIndex + result = DataFrame.from_records(documents, index=["order_id", "quantity"]) + assert result.index.names == ("order_id", "quantity") + + def test_from_records_misc_brokenness(self): + # GH#2179 + + data = {1: ["foo"], 2: ["bar"]} + + result = DataFrame.from_records(data, columns=["a", "b"]) + exp = DataFrame(data, columns=["a", "b"]) + tm.assert_frame_equal(result, exp) + + # overlap in index/index_names + + data = {"a": [1, 2, 3], "b": [4, 5, 6]} + + result = DataFrame.from_records(data, index=["a", "b", "c"]) + exp = DataFrame(data, index=["a", "b", "c"]) + tm.assert_frame_equal(result, exp) + + def test_from_records_misc_brokenness2(self): + # GH#2623 + rows = [] + rows.append([datetime(2010, 1, 1), 1]) + rows.append([datetime(2010, 1, 2), "hi"]) # test col upconverts to obj + result = DataFrame.from_records(rows, columns=["date", "test"]) + expected = DataFrame( + {"date": [row[0] for row in rows], "test": [row[1] for row in rows]} + ) + tm.assert_frame_equal(result, expected) + assert result.dtypes["test"] == np.dtype(object) + + def test_from_records_misc_brokenness3(self): + rows = [] + rows.append([datetime(2010, 1, 1), 1]) + rows.append([datetime(2010, 1, 2), 1]) + result = DataFrame.from_records(rows, columns=["date", "test"]) + expected = DataFrame( + {"date": [row[0] for row in rows], "test": [row[1] for row in rows]} + ) + tm.assert_frame_equal(result, expected) + + def test_from_records_empty(self): + # GH#3562 + result = DataFrame.from_records([], columns=["a", "b", "c"]) + expected = DataFrame(columns=["a", "b", "c"]) + tm.assert_frame_equal(result, expected) + + result = DataFrame.from_records([], columns=["a", "b", "b"]) + expected = DataFrame(columns=["a", "b", "b"]) + tm.assert_frame_equal(result, expected) + + def test_from_records_empty_with_nonempty_fields_gh3682(self): + a = np.array([(1, 2)], dtype=[("id", np.int64), ("value", np.int64)]) + df = DataFrame.from_records(a, index="id") + + ex_index = Index([1], name="id") + expected = DataFrame({"value": [2]}, index=ex_index, columns=["value"]) + tm.assert_frame_equal(df, expected) + + b = a[:0] + df2 = DataFrame.from_records(b, index="id") + tm.assert_frame_equal(df2, df.iloc[:0]) + + def test_from_records_empty2(self): + # GH#42456 + dtype = [("prop", int)] + shape = (0, len(dtype)) + arr = np.empty(shape, dtype=dtype) + + result = DataFrame.from_records(arr) + expected = DataFrame({"prop": np.array([], dtype=int)}) + tm.assert_frame_equal(result, expected) + + alt = DataFrame(arr) + tm.assert_frame_equal(alt, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_where.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_where.py new file mode 100644 index 0000000000000000000000000000000000000000..356257bbfec9804336159becf86212d2afeeed0d --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_where.py @@ -0,0 +1,1104 @@ +from datetime import datetime + +from hypothesis import given +import numpy as np +import pytest + +from pandas.core.dtypes.common import is_scalar + +import pandas as pd +from pandas import ( + DataFrame, + DatetimeIndex, + Index, + Series, + StringDtype, + Timestamp, + date_range, + isna, +) +import pandas._testing as tm +from pandas._testing._hypothesis import OPTIONAL_ONE_OF_ALL + + +@pytest.fixture(params=["default", "float_string", "mixed_float", "mixed_int"]) +def where_frame(request, float_string_frame, mixed_float_frame, mixed_int_frame): + if request.param == "default": + return DataFrame( + np.random.default_rng(2).standard_normal((5, 3)), columns=["A", "B", "C"] + ) + if request.param == "float_string": + return float_string_frame + if request.param == "mixed_float": + return mixed_float_frame + if request.param == "mixed_int": + return mixed_int_frame + + +def _safe_add(df): + # only add to the numeric items + def is_ok(s): + return ( + issubclass(s.dtype.type, (np.integer, np.floating)) and s.dtype != "uint8" + ) + + return DataFrame(dict((c, s + 1) if is_ok(s) else (c, s) for c, s in df.items())) + + +class TestDataFrameIndexingWhere: + def test_where_get(self, where_frame, float_string_frame): + def _check_get(df, cond, check_dtypes=True): + other1 = _safe_add(df) + rs = df.where(cond, other1) + rs2 = df.where(cond.values, other1) + for k, v in rs.items(): + exp = Series(np.where(cond[k], df[k], other1[k]), index=v.index) + tm.assert_series_equal(v, exp, check_names=False) + tm.assert_frame_equal(rs, rs2) + + # dtypes + if check_dtypes: + assert (rs.dtypes == df.dtypes).all() + + # check getting + df = where_frame + if df is float_string_frame: + msg = ( + "'>' not supported between instances of 'str' and 'int'" + "|Invalid comparison" + ) + with pytest.raises(TypeError, match=msg): + df > 0 + return + cond = df > 0 + _check_get(df, cond) + + def test_where_upcasting(self): + # upcasting case (GH # 2794) + df = DataFrame( + { + c: Series([1] * 3, dtype=c) + for c in ["float32", "float64", "int32", "int64"] + } + ) + df.iloc[1, :] = 0 + result = df.dtypes + expected = Series( + [ + np.dtype("float32"), + np.dtype("float64"), + np.dtype("int32"), + np.dtype("int64"), + ], + index=["float32", "float64", "int32", "int64"], + ) + + # when we don't preserve boolean casts + # + # expected = Series({ 'float32' : 1, 'float64' : 3 }) + + tm.assert_series_equal(result, expected) + + @pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning") + def test_where_alignment(self, where_frame, float_string_frame): + # aligning + def _check_align(df, cond, other, check_dtypes=True): + rs = df.where(cond, other) + for i, k in enumerate(rs.columns): + result = rs[k] + d = df[k].values + c = cond[k].reindex(df[k].index).fillna(False).values + + if is_scalar(other): + o = other + elif isinstance(other, np.ndarray): + o = Series(other[:, i], index=result.index).values + else: + o = other[k].values + + new_values = d if c.all() else np.where(c, d, o) + expected = Series(new_values, index=result.index, name=k) + + # since we can't always have the correct numpy dtype + # as numpy doesn't know how to downcast, don't check + tm.assert_series_equal(result, expected, check_dtype=False) + + # dtypes + # can't check dtype when other is an ndarray + + if check_dtypes and not isinstance(other, np.ndarray): + assert (rs.dtypes == df.dtypes).all() + + df = where_frame + if df is float_string_frame: + msg = ( + "'>' not supported between instances of 'str' and 'int'" + "|Invalid comparison" + ) + with pytest.raises(TypeError, match=msg): + df > 0 + return + + # other is a frame + cond = (df > 0)[1:] + _check_align(df, cond, _safe_add(df)) + + # check other is ndarray + cond = df > 0 + _check_align(df, cond, (_safe_add(df).values)) + + # integers are upcast, so don't check the dtypes + cond = df > 0 + check_dtypes = all(not issubclass(s.type, np.integer) for s in df.dtypes) + _check_align(df, cond, np.nan, check_dtypes=check_dtypes) + + # Ignore deprecation warning in Python 3.12 for inverting a bool + @pytest.mark.filterwarnings("ignore::DeprecationWarning") + def test_where_invalid(self): + # invalid conditions + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 3)), columns=["A", "B", "C"] + ) + cond = df > 0 + + err1 = (df + 1).values[0:2, :] + msg = "other must be the same shape as self when an ndarray" + with pytest.raises(ValueError, match=msg): + df.where(cond, err1) + + err2 = cond.iloc[:2, :].values + other1 = _safe_add(df) + msg = "Array conditional must be same shape as self" + with pytest.raises(ValueError, match=msg): + df.where(err2, other1) + + with pytest.raises(ValueError, match=msg): + df.mask(True) + with pytest.raises(ValueError, match=msg): + df.mask(0) + + @pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning") + def test_where_set(self, where_frame, float_string_frame, mixed_int_frame): + # where inplace + + def _check_set(df, cond, check_dtypes=True): + dfi = df.copy() + econd = cond.reindex_like(df).fillna(True).infer_objects(copy=False) + expected = dfi.mask(~econd) + + return_value = dfi.where(cond, np.nan, inplace=True) + assert return_value is None + tm.assert_frame_equal(dfi, expected) + + # dtypes (and confirm upcasts)x + if check_dtypes: + for k, v in df.dtypes.items(): + if issubclass(v.type, np.integer) and not cond[k].all(): + v = np.dtype("float64") + assert dfi[k].dtype == v + + df = where_frame + if df is float_string_frame: + msg = ( + "'>' not supported between instances of 'str' and 'int'" + "|Invalid comparison" + ) + with pytest.raises(TypeError, match=msg): + df > 0 + return + if df is mixed_int_frame: + df = df.astype("float64") + + cond = df > 0 + _check_set(df, cond) + + cond = df >= 0 + _check_set(df, cond) + + # aligning + cond = (df >= 0)[1:] + _check_set(df, cond) + + def test_where_series_slicing(self): + # GH 10218 + # test DataFrame.where with Series slicing + df = DataFrame({"a": range(3), "b": range(4, 7)}) + result = df.where(df["a"] == 1) + expected = df[df["a"] == 1].reindex(df.index) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("klass", [list, tuple, np.array]) + def test_where_array_like(self, klass): + # see gh-15414 + df = DataFrame({"a": [1, 2, 3]}) + cond = [[False], [True], [True]] + expected = DataFrame({"a": [np.nan, 2, 3]}) + + result = df.where(klass(cond)) + tm.assert_frame_equal(result, expected) + + df["b"] = 2 + expected["b"] = [2, np.nan, 2] + cond = [[False, True], [True, False], [True, True]] + + result = df.where(klass(cond)) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "cond", + [ + [[1], [0], [1]], + Series([[2], [5], [7]]), + DataFrame({"a": [2, 5, 7]}), + [["True"], ["False"], ["True"]], + [[Timestamp("2017-01-01")], [pd.NaT], [Timestamp("2017-01-02")]], + ], + ) + def test_where_invalid_input_single(self, cond): + # see gh-15414: only boolean arrays accepted + df = DataFrame({"a": [1, 2, 3]}) + msg = "Boolean array expected for the condition" + + with pytest.raises(ValueError, match=msg): + df.where(cond) + + @pytest.mark.parametrize( + "cond", + [ + [[0, 1], [1, 0], [1, 1]], + Series([[0, 2], [5, 0], [4, 7]]), + [["False", "True"], ["True", "False"], ["True", "True"]], + DataFrame({"a": [2, 5, 7], "b": [4, 8, 9]}), + [ + [pd.NaT, Timestamp("2017-01-01")], + [Timestamp("2017-01-02"), pd.NaT], + [Timestamp("2017-01-03"), Timestamp("2017-01-03")], + ], + ], + ) + def test_where_invalid_input_multiple(self, cond): + # see gh-15414: only boolean arrays accepted + df = DataFrame({"a": [1, 2, 3], "b": [2, 2, 2]}) + msg = "Boolean array expected for the condition" + + with pytest.raises(ValueError, match=msg): + df.where(cond) + + def test_where_dataframe_col_match(self): + df = DataFrame([[1, 2, 3], [4, 5, 6]]) + cond = DataFrame([[True, False, True], [False, False, True]]) + + result = df.where(cond) + expected = DataFrame([[1.0, np.nan, 3], [np.nan, np.nan, 6]]) + tm.assert_frame_equal(result, expected) + + # this *does* align, though has no matching columns + cond.columns = ["a", "b", "c"] + result = df.where(cond) + expected = DataFrame(np.nan, index=df.index, columns=df.columns) + tm.assert_frame_equal(result, expected) + + def test_where_ndframe_align(self): + msg = "Array conditional must be same shape as self" + df = DataFrame([[1, 2, 3], [4, 5, 6]]) + + cond = [True] + with pytest.raises(ValueError, match=msg): + df.where(cond) + + expected = DataFrame([[1, 2, 3], [np.nan, np.nan, np.nan]]) + + out = df.where(Series(cond)) + tm.assert_frame_equal(out, expected) + + cond = np.array([False, True, False, True]) + with pytest.raises(ValueError, match=msg): + df.where(cond) + + expected = DataFrame([[np.nan, np.nan, np.nan], [4, 5, 6]]) + + out = df.where(Series(cond)) + tm.assert_frame_equal(out, expected) + + def test_where_bug(self): + # see gh-2793 + df = DataFrame( + {"a": [1.0, 2.0, 3.0, 4.0], "b": [4.0, 3.0, 2.0, 1.0]}, dtype="float64" + ) + expected = DataFrame( + {"a": [np.nan, np.nan, 3.0, 4.0], "b": [4.0, 3.0, np.nan, np.nan]}, + dtype="float64", + ) + result = df.where(df > 2, np.nan) + tm.assert_frame_equal(result, expected) + + result = df.copy() + return_value = result.where(result > 2, np.nan, inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + + def test_where_bug_mixed(self, any_signed_int_numpy_dtype): + # see gh-2793 + df = DataFrame( + { + "a": np.array([1, 2, 3, 4], dtype=any_signed_int_numpy_dtype), + "b": np.array([4.0, 3.0, 2.0, 1.0], dtype="float64"), + } + ) + + expected = DataFrame( + {"a": [-1, -1, 3, 4], "b": [4.0, 3.0, -1, -1]}, + ).astype({"a": any_signed_int_numpy_dtype, "b": "float64"}) + + result = df.where(df > 2, -1) + tm.assert_frame_equal(result, expected) + + result = df.copy() + return_value = result.where(result > 2, -1, inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + + def test_where_bug_transposition(self): + # see gh-7506 + a = DataFrame({0: [1, 2], 1: [3, 4], 2: [5, 6]}) + b = DataFrame({0: [np.nan, 8], 1: [9, np.nan], 2: [np.nan, np.nan]}) + do_not_replace = b.isna() | (a > b) + + expected = a.copy() + expected[~do_not_replace] = b + + msg = "Downcasting behavior in Series and DataFrame methods 'where'" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = a.where(do_not_replace, b) + tm.assert_frame_equal(result, expected) + + a = DataFrame({0: [4, 6], 1: [1, 0]}) + b = DataFrame({0: [np.nan, 3], 1: [3, np.nan]}) + do_not_replace = b.isna() | (a > b) + + expected = a.copy() + expected[~do_not_replace] = b + + with tm.assert_produces_warning(FutureWarning, match=msg): + result = a.where(do_not_replace, b) + tm.assert_frame_equal(result, expected) + + def test_where_datetime(self): + # GH 3311 + df = DataFrame( + { + "A": date_range("20130102", periods=5), + "B": date_range("20130104", periods=5), + "C": np.random.default_rng(2).standard_normal(5), + } + ) + + stamp = datetime(2013, 1, 3) + msg = "'>' not supported between instances of 'float' and 'datetime.datetime'" + with pytest.raises(TypeError, match=msg): + df > stamp + + result = df[df.iloc[:, :-1] > stamp] + + expected = df.copy() + expected.loc[[0, 1], "A"] = np.nan + + expected.loc[:, "C"] = np.nan + tm.assert_frame_equal(result, expected) + + def test_where_none(self): + # GH 4667 + # setting with None changes dtype + df = DataFrame({"series": Series(range(10))}).astype(float) + df[df > 7] = None + expected = DataFrame( + {"series": Series([0, 1, 2, 3, 4, 5, 6, 7, np.nan, np.nan])} + ) + tm.assert_frame_equal(df, expected) + + # GH 7656 + df = DataFrame( + [ + {"A": 1, "B": np.nan, "C": "Test"}, + {"A": np.nan, "B": "Test", "C": np.nan}, + ] + ) + + orig = df.copy() + + mask = ~isna(df) + df.where(mask, None, inplace=True) + expected = DataFrame( + { + "A": [1.0, np.nan], + "B": [None, "Test"], + "C": ["Test", None], + } + ) + tm.assert_frame_equal(df, expected) + + df = orig.copy() + df[~mask] = None + tm.assert_frame_equal(df, expected) + + def test_where_empty_df_and_empty_cond_having_non_bool_dtypes(self): + # see gh-21947 + df = DataFrame(columns=["a"]) + cond = df + assert (cond.dtypes == object).all() + + result = df.where(cond) + tm.assert_frame_equal(result, df) + + def test_where_align(self): + def create(): + df = DataFrame(np.random.default_rng(2).standard_normal((10, 3))) + df.iloc[3:5, 0] = np.nan + df.iloc[4:6, 1] = np.nan + df.iloc[5:8, 2] = np.nan + return df + + # series + df = create() + expected = df.fillna(df.mean()) + result = df.where(pd.notna(df), df.mean(), axis="columns") + tm.assert_frame_equal(result, expected) + + return_value = df.where(pd.notna(df), df.mean(), inplace=True, axis="columns") + assert return_value is None + tm.assert_frame_equal(df, expected) + + df = create().fillna(0) + expected = df.apply(lambda x, y: x.where(x > 0, y), y=df[0]) + result = df.where(df > 0, df[0], axis="index") + tm.assert_frame_equal(result, expected) + result = df.where(df > 0, df[0], axis="rows") + tm.assert_frame_equal(result, expected) + + # frame + df = create() + expected = df.fillna(1) + result = df.where( + pd.notna(df), DataFrame(1, index=df.index, columns=df.columns) + ) + tm.assert_frame_equal(result, expected) + + def test_where_complex(self): + # GH 6345 + expected = DataFrame([[1 + 1j, 2], [np.nan, 4 + 1j]], columns=["a", "b"]) + df = DataFrame([[1 + 1j, 2], [5 + 1j, 4 + 1j]], columns=["a", "b"]) + df[df.abs() >= 5] = np.nan + tm.assert_frame_equal(df, expected) + + def test_where_axis(self): + # GH 9736 + df = DataFrame(np.random.default_rng(2).standard_normal((2, 2))) + mask = DataFrame([[False, False], [False, False]]) + ser = Series([0, 1]) + + expected = DataFrame([[0, 0], [1, 1]], dtype="float64") + result = df.where(mask, ser, axis="index") + tm.assert_frame_equal(result, expected) + + result = df.copy() + return_value = result.where(mask, ser, axis="index", inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + + expected = DataFrame([[0, 1], [0, 1]], dtype="float64") + result = df.where(mask, ser, axis="columns") + tm.assert_frame_equal(result, expected) + + result = df.copy() + return_value = result.where(mask, ser, axis="columns", inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + + def test_where_axis_with_upcast(self): + # Upcast needed + df = DataFrame([[1, 2], [3, 4]], dtype="int64") + mask = DataFrame([[False, False], [False, False]]) + ser = Series([0, np.nan]) + + expected = DataFrame([[0, 0], [np.nan, np.nan]], dtype="float64") + result = df.where(mask, ser, axis="index") + tm.assert_frame_equal(result, expected) + + result = df.copy() + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + return_value = result.where(mask, ser, axis="index", inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + + expected = DataFrame([[0, np.nan], [0, np.nan]]) + result = df.where(mask, ser, axis="columns") + tm.assert_frame_equal(result, expected) + + expected = DataFrame( + { + 0: np.array([0, 0], dtype="int64"), + 1: np.array([np.nan, np.nan], dtype="float64"), + } + ) + result = df.copy() + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + return_value = result.where(mask, ser, axis="columns", inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + + def test_where_axis_multiple_dtypes(self): + # Multiple dtypes (=> multiple Blocks) + df = pd.concat( + [ + DataFrame(np.random.default_rng(2).standard_normal((10, 2))), + DataFrame( + np.random.default_rng(2).integers(0, 10, size=(10, 2)), + dtype="int64", + ), + ], + ignore_index=True, + axis=1, + ) + mask = DataFrame(False, columns=df.columns, index=df.index) + s1 = Series(1, index=df.columns) + s2 = Series(2, index=df.index) + + result = df.where(mask, s1, axis="columns") + expected = DataFrame(1.0, columns=df.columns, index=df.index) + expected[2] = expected[2].astype("int64") + expected[3] = expected[3].astype("int64") + tm.assert_frame_equal(result, expected) + + result = df.copy() + return_value = result.where(mask, s1, axis="columns", inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + + result = df.where(mask, s2, axis="index") + expected = DataFrame(2.0, columns=df.columns, index=df.index) + expected[2] = expected[2].astype("int64") + expected[3] = expected[3].astype("int64") + tm.assert_frame_equal(result, expected) + + result = df.copy() + return_value = result.where(mask, s2, axis="index", inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + + # DataFrame vs DataFrame + d1 = df.copy().drop(1, axis=0) + # Explicit cast to avoid implicit cast when setting value to np.nan + expected = df.copy().astype("float") + expected.loc[1, :] = np.nan + + result = df.where(mask, d1) + tm.assert_frame_equal(result, expected) + result = df.where(mask, d1, axis="index") + tm.assert_frame_equal(result, expected) + result = df.copy() + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + return_value = result.where(mask, d1, inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + result = df.copy() + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + return_value = result.where(mask, d1, inplace=True, axis="index") + assert return_value is None + tm.assert_frame_equal(result, expected) + + d2 = df.copy().drop(1, axis=1) + expected = df.copy() + expected.loc[:, 1] = np.nan + + result = df.where(mask, d2) + tm.assert_frame_equal(result, expected) + result = df.where(mask, d2, axis="columns") + tm.assert_frame_equal(result, expected) + result = df.copy() + return_value = result.where(mask, d2, inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + result = df.copy() + return_value = result.where(mask, d2, inplace=True, axis="columns") + assert return_value is None + tm.assert_frame_equal(result, expected) + + def test_where_callable(self): + # GH 12533 + df = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) + result = df.where(lambda x: x > 4, lambda x: x + 1) + exp = DataFrame([[2, 3, 4], [5, 5, 6], [7, 8, 9]]) + tm.assert_frame_equal(result, exp) + tm.assert_frame_equal(result, df.where(df > 4, df + 1)) + + # return ndarray and scalar + result = df.where(lambda x: (x % 2 == 0).values, lambda x: 99) + exp = DataFrame([[99, 2, 99], [4, 99, 6], [99, 8, 99]]) + tm.assert_frame_equal(result, exp) + tm.assert_frame_equal(result, df.where(df % 2 == 0, 99)) + + # chain + result = (df + 2).where(lambda x: x > 8, lambda x: x + 10) + exp = DataFrame([[13, 14, 15], [16, 17, 18], [9, 10, 11]]) + tm.assert_frame_equal(result, exp) + tm.assert_frame_equal(result, (df + 2).where((df + 2) > 8, (df + 2) + 10)) + + def test_where_tz_values(self, tz_naive_fixture, frame_or_series): + obj1 = DataFrame( + DatetimeIndex(["20150101", "20150102", "20150103"], tz=tz_naive_fixture), + columns=["date"], + ) + obj2 = DataFrame( + DatetimeIndex(["20150103", "20150104", "20150105"], tz=tz_naive_fixture), + columns=["date"], + ) + mask = DataFrame([True, True, False], columns=["date"]) + exp = DataFrame( + DatetimeIndex(["20150101", "20150102", "20150105"], tz=tz_naive_fixture), + columns=["date"], + ) + if frame_or_series is Series: + obj1 = obj1["date"] + obj2 = obj2["date"] + mask = mask["date"] + exp = exp["date"] + + result = obj1.where(mask, obj2) + tm.assert_equal(exp, result) + + def test_df_where_change_dtype(self): + # GH#16979 + df = DataFrame(np.arange(2 * 3).reshape(2, 3), columns=list("ABC")) + mask = np.array([[True, False, False], [False, False, True]]) + + result = df.where(mask) + expected = DataFrame( + [[0, np.nan, np.nan], [np.nan, np.nan, 5]], columns=list("ABC") + ) + + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("kwargs", [{}, {"other": None}]) + def test_df_where_with_category(self, kwargs): + # GH#16979 + data = np.arange(2 * 3, dtype=np.int64).reshape(2, 3) + df = DataFrame(data, columns=list("ABC")) + mask = np.array([[True, False, False], [False, False, True]]) + + # change type to category + df.A = df.A.astype("category") + df.B = df.B.astype("category") + df.C = df.C.astype("category") + + result = df.where(mask, **kwargs) + A = pd.Categorical([0, np.nan], categories=[0, 3]) + B = pd.Categorical([np.nan, np.nan], categories=[1, 4]) + C = pd.Categorical([np.nan, 5], categories=[2, 5]) + expected = DataFrame({"A": A, "B": B, "C": C}) + + tm.assert_frame_equal(result, expected) + + # Check Series.where while we're here + result = df.A.where(mask[:, 0], **kwargs) + expected = Series(A, name="A") + + tm.assert_series_equal(result, expected) + + def test_where_categorical_filtering(self): + # GH#22609 Verify filtering operations on DataFrames with categorical Series + df = DataFrame(data=[[0, 0], [1, 1]], columns=["a", "b"]) + df["b"] = df["b"].astype("category") + + result = df.where(df["a"] > 0) + # Explicitly cast to 'float' to avoid implicit cast when setting np.nan + expected = df.copy().astype({"a": "float"}) + expected.loc[0, :] = np.nan + + tm.assert_equal(result, expected) + + def test_where_ea_other(self): + # GH#38729/GH#38742 + df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) + arr = pd.array([7, pd.NA, 9]) + ser = Series(arr) + mask = np.ones(df.shape, dtype=bool) + mask[1, :] = False + + # TODO: ideally we would get Int64 instead of object + result = df.where(mask, ser, axis=0) + expected = DataFrame({"A": [1, np.nan, 3], "B": [4, np.nan, 6]}) + tm.assert_frame_equal(result, expected) + + ser2 = Series(arr[:2], index=["A", "B"]) + expected = DataFrame({"A": [1, 7, 3], "B": [4, np.nan, 6]}) + result = df.where(mask, ser2, axis=1) + tm.assert_frame_equal(result, expected) + + def test_where_interval_noop(self): + # GH#44181 + df = DataFrame([pd.Interval(0, 0)]) + res = df.where(df.notna()) + tm.assert_frame_equal(res, df) + + ser = df[0] + res = ser.where(ser.notna()) + tm.assert_series_equal(res, ser) + + def test_where_interval_fullop_downcast(self, frame_or_series): + # GH#45768 + obj = frame_or_series([pd.Interval(0, 0)] * 2) + other = frame_or_series([1.0, 2.0]) + + msg = "Downcasting behavior in Series and DataFrame methods 'where'" + with tm.assert_produces_warning(FutureWarning, match=msg): + res = obj.where(~obj.notna(), other) + + # since all entries are being changed, we will downcast result + # from object to ints (not floats) + tm.assert_equal(res, other.astype(np.int64)) + + # unlike where, Block.putmask does not downcast + with tm.assert_produces_warning( + FutureWarning, match="Setting an item of incompatible dtype" + ): + obj.mask(obj.notna(), other, inplace=True) + tm.assert_equal(obj, other.astype(object)) + + @pytest.mark.parametrize( + "dtype", + [ + "timedelta64[ns]", + "datetime64[ns]", + "datetime64[ns, Asia/Tokyo]", + "Period[D]", + ], + ) + def test_where_datetimelike_noop(self, dtype): + # GH#45135, analogue to GH#44181 for Period don't raise on no-op + # For td64/dt64/dt64tz we already don't raise, but also are + # checking that we don't unnecessarily upcast to object. + with tm.assert_produces_warning(FutureWarning, match="is deprecated"): + ser = Series(np.arange(3) * 10**9, dtype=np.int64).view(dtype) + df = ser.to_frame() + mask = np.array([False, False, False]) + + res = ser.where(~mask, "foo") + tm.assert_series_equal(res, ser) + + mask2 = mask.reshape(-1, 1) + res2 = df.where(~mask2, "foo") + tm.assert_frame_equal(res2, df) + + res3 = ser.mask(mask, "foo") + tm.assert_series_equal(res3, ser) + + res4 = df.mask(mask2, "foo") + tm.assert_frame_equal(res4, df) + + # opposite case where we are replacing *all* values -> we downcast + # from object dtype # GH#45768 + msg = "Downcasting behavior in Series and DataFrame methods 'where'" + with tm.assert_produces_warning(FutureWarning, match=msg): + res5 = df.where(mask2, 4) + expected = DataFrame(4, index=df.index, columns=df.columns) + tm.assert_frame_equal(res5, expected) + + # unlike where, Block.putmask does not downcast + with tm.assert_produces_warning( + FutureWarning, match="Setting an item of incompatible dtype" + ): + df.mask(~mask2, 4, inplace=True) + tm.assert_frame_equal(df, expected.astype(object)) + + +def test_where_int_downcasting_deprecated(): + # GH#44597 + arr = np.arange(6).astype(np.int16).reshape(3, 2) + df = DataFrame(arr) + + mask = np.zeros(arr.shape, dtype=bool) + mask[:, 0] = True + + res = df.where(mask, 2**17) + + expected = DataFrame({0: arr[:, 0], 1: np.array([2**17] * 3, dtype=np.int32)}) + tm.assert_frame_equal(res, expected) + + +def test_where_copies_with_noop(frame_or_series): + # GH-39595 + result = frame_or_series([1, 2, 3, 4]) + expected = result.copy() + col = result[0] if frame_or_series is DataFrame else result + + where_res = result.where(col < 5) + where_res *= 2 + + tm.assert_equal(result, expected) + + where_res = result.where(col > 5, [1, 2, 3, 4]) + where_res *= 2 + + tm.assert_equal(result, expected) + + +def test_where_string_dtype(frame_or_series): + # GH40824 + obj = frame_or_series( + ["a", "b", "c", "d"], index=["id1", "id2", "id3", "id4"], dtype=StringDtype() + ) + filtered_obj = frame_or_series( + ["b", "c"], index=["id2", "id3"], dtype=StringDtype() + ) + filter_ser = Series([False, True, True, False]) + + result = obj.where(filter_ser, filtered_obj) + expected = frame_or_series( + [pd.NA, "b", "c", pd.NA], + index=["id1", "id2", "id3", "id4"], + dtype=StringDtype(), + ) + tm.assert_equal(result, expected) + + result = obj.mask(~filter_ser, filtered_obj) + tm.assert_equal(result, expected) + + obj.mask(~filter_ser, filtered_obj, inplace=True) + tm.assert_equal(result, expected) + + +def test_where_bool_comparison(): + # GH 10336 + df_mask = DataFrame( + {"AAA": [True] * 4, "BBB": [False] * 4, "CCC": [True, False, True, False]} + ) + result = df_mask.where(df_mask == False) # noqa: E712 + expected = DataFrame( + { + "AAA": np.array([np.nan] * 4, dtype=object), + "BBB": [False] * 4, + "CCC": [np.nan, False, np.nan, False], + } + ) + tm.assert_frame_equal(result, expected) + + +def test_where_none_nan_coerce(): + # GH 15613 + expected = DataFrame( + { + "A": [Timestamp("20130101"), pd.NaT, Timestamp("20130103")], + "B": [1, 2, np.nan], + } + ) + result = expected.where(expected.notnull(), None) + tm.assert_frame_equal(result, expected) + + +def test_where_duplicate_axes_mixed_dtypes(): + # GH 25399, verify manually masking is not affected anymore by dtype of column for + # duplicate axes. + result = DataFrame(data=[[0, np.nan]], columns=Index(["A", "A"])) + index, columns = result.axes + mask = DataFrame(data=[[True, True]], columns=columns, index=index) + a = result.astype(object).where(mask) + b = result.astype("f8").where(mask) + c = result.T.where(mask.T).T + d = result.where(mask) # used to fail with "cannot reindex from a duplicate axis" + tm.assert_frame_equal(a.astype("f8"), b.astype("f8")) + tm.assert_frame_equal(b.astype("f8"), c.astype("f8")) + tm.assert_frame_equal(c.astype("f8"), d.astype("f8")) + + +def test_where_columns_casting(): + # GH 42295 + + df = DataFrame({"a": [1.0, 2.0], "b": [3, np.nan]}) + expected = df.copy() + result = df.where(pd.notnull(df), None) + # make sure dtypes don't change + tm.assert_frame_equal(expected, result) + + +@pytest.mark.parametrize("as_cat", [True, False]) +def test_where_period_invalid_na(frame_or_series, as_cat, request): + # GH#44697 + idx = pd.period_range("2016-01-01", periods=3, freq="D") + if as_cat: + idx = idx.astype("category") + obj = frame_or_series(idx) + + # NA value that we should *not* cast to Period dtype + tdnat = pd.NaT.to_numpy("m8[ns]") + + mask = np.array([True, True, False], ndmin=obj.ndim).T + + if as_cat: + msg = ( + r"Cannot setitem on a Categorical with a new category \(NaT\), " + "set the categories first" + ) + else: + msg = "value should be a 'Period'" + + if as_cat: + with pytest.raises(TypeError, match=msg): + obj.where(mask, tdnat) + + with pytest.raises(TypeError, match=msg): + obj.mask(mask, tdnat) + + with pytest.raises(TypeError, match=msg): + obj.mask(mask, tdnat, inplace=True) + + else: + # With PeriodDtype, ser[i] = tdnat coerces instead of raising, + # so for consistency, ser[mask] = tdnat must as well + expected = obj.astype(object).where(mask, tdnat) + result = obj.where(mask, tdnat) + tm.assert_equal(result, expected) + + expected = obj.astype(object).mask(mask, tdnat) + result = obj.mask(mask, tdnat) + tm.assert_equal(result, expected) + + with tm.assert_produces_warning( + FutureWarning, match="Setting an item of incompatible dtype" + ): + obj.mask(mask, tdnat, inplace=True) + tm.assert_equal(obj, expected) + + +def test_where_nullable_invalid_na(frame_or_series, any_numeric_ea_dtype): + # GH#44697 + arr = pd.array([1, 2, 3], dtype=any_numeric_ea_dtype) + obj = frame_or_series(arr) + + mask = np.array([True, True, False], ndmin=obj.ndim).T + + msg = r"Invalid value '.*' for dtype '(U?Int|Float)\d{1,2}'" + + for null in tm.NP_NAT_OBJECTS + [pd.NaT]: + # NaT is an NA value that we should *not* cast to pd.NA dtype + with pytest.raises(TypeError, match=msg): + obj.where(mask, null) + + with pytest.raises(TypeError, match=msg): + obj.mask(mask, null) + + +@given(data=OPTIONAL_ONE_OF_ALL) +def test_where_inplace_casting(data): + # GH 22051 + df = DataFrame({"a": data}) + df_copy = df.where(pd.notnull(df), None).copy() + df.where(pd.notnull(df), None, inplace=True) + tm.assert_equal(df, df_copy) + + +def test_where_downcast_to_td64(): + ser = Series([1, 2, 3]) + + mask = np.array([False, False, False]) + + td = pd.Timedelta(days=1) + + msg = "Downcasting behavior in Series and DataFrame methods 'where'" + with tm.assert_produces_warning(FutureWarning, match=msg): + res = ser.where(mask, td) + expected = Series([td, td, td], dtype="m8[ns]") + tm.assert_series_equal(res, expected) + + with pd.option_context("future.no_silent_downcasting", True): + with tm.assert_produces_warning(None, match=msg): + res2 = ser.where(mask, td) + expected2 = expected.astype(object) + tm.assert_series_equal(res2, expected2) + + +def _check_where_equivalences(df, mask, other, expected): + # similar to tests.series.indexing.test_setitem.SetitemCastingEquivalences + # but with DataFrame in mind and less fleshed-out + res = df.where(mask, other) + tm.assert_frame_equal(res, expected) + + res = df.mask(~mask, other) + tm.assert_frame_equal(res, expected) + + # Note: frame.mask(~mask, other, inplace=True) takes some more work bc + # Block.putmask does *not* downcast. The change to 'expected' here + # is specific to the cases in test_where_dt64_2d. + df = df.copy() + df.mask(~mask, other, inplace=True) + if not mask.all(): + # with mask.all(), Block.putmask is a no-op, so does not downcast + expected = expected.copy() + expected["A"] = expected["A"].astype(object) + tm.assert_frame_equal(df, expected) + + +def test_where_dt64_2d(): + dti = date_range("2016-01-01", periods=6) + dta = dti._data.reshape(3, 2) + other = dta - dta[0, 0] + + df = DataFrame(dta, columns=["A", "B"]) + + mask = np.asarray(df.isna()).copy() + mask[:, 1] = True + + # setting all of one column, none of the other + expected = DataFrame({"A": other[:, 0], "B": dta[:, 1]}) + with tm.assert_produces_warning( + FutureWarning, match="Setting an item of incompatible dtype" + ): + _check_where_equivalences(df, mask, other, expected) + + # setting part of one column, none of the other + mask[1, 0] = True + expected = DataFrame( + { + "A": np.array([other[0, 0], dta[1, 0], other[2, 0]], dtype=object), + "B": dta[:, 1], + } + ) + with tm.assert_produces_warning( + FutureWarning, match="Setting an item of incompatible dtype" + ): + _check_where_equivalences(df, mask, other, expected) + + # setting nothing in either column + mask[:] = True + expected = df + _check_where_equivalences(df, mask, other, expected) + + +def test_where_producing_ea_cond_for_np_dtype(): + # GH#44014 + df = DataFrame({"a": Series([1, pd.NA, 2], dtype="Int64"), "b": [1, 2, 3]}) + result = df.where(lambda x: x.apply(lambda y: y > 1, axis=1)) + expected = DataFrame( + {"a": Series([pd.NA, pd.NA, 2], dtype="Int64"), "b": [np.nan, 2, 3]} + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "replacement", [0.001, True, "snake", None, datetime(2022, 5, 4)] +) +def test_where_int_overflow(replacement): + # GH 31687 + df = DataFrame([[1.0, 2e25, "nine"], [np.nan, 0.1, None]]) + result = df.where(pd.notnull(df), replacement) + expected = DataFrame([[1.0, 2e25, "nine"], [replacement, 0.1, replacement]]) + + tm.assert_frame_equal(result, expected) + + +def test_where_inplace_no_other(): + # GH#51685 + df = DataFrame({"a": [1.0, 2.0], "b": ["x", "y"]}) + cond = DataFrame({"a": [True, False], "b": [False, True]}) + df.where(cond, inplace=True) + expected = DataFrame({"a": [1, np.nan], "b": [np.nan, "y"]}) + tm.assert_frame_equal(df, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_xs.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_xs.py new file mode 100644 index 0000000000000000000000000000000000000000..2aa27d1d6a5489933459bb877fbbbba1d5a1d98b --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_xs.py @@ -0,0 +1,444 @@ +import re + +import numpy as np +import pytest + +from pandas.errors import SettingWithCopyError + +from pandas import ( + DataFrame, + Index, + IndexSlice, + MultiIndex, + Series, + concat, +) +import pandas._testing as tm + +from pandas.tseries.offsets import BDay + + +@pytest.fixture +def four_level_index_dataframe(): + arr = np.array( + [ + [-0.5109, -2.3358, -0.4645, 0.05076, 0.364], + [0.4473, 1.4152, 0.2834, 1.00661, 0.1744], + [-0.6662, -0.5243, -0.358, 0.89145, 2.5838], + ] + ) + index = MultiIndex( + levels=[["a", "x"], ["b", "q"], [10.0032, 20.0, 30.0], [3, 4, 5]], + codes=[[0, 0, 1], [0, 1, 1], [0, 1, 2], [2, 1, 0]], + names=["one", "two", "three", "four"], + ) + return DataFrame(arr, index=index, columns=list("ABCDE")) + + +class TestXS: + def test_xs( + self, float_frame, datetime_frame, using_copy_on_write, warn_copy_on_write + ): + float_frame_orig = float_frame.copy() + idx = float_frame.index[5] + xs = float_frame.xs(idx) + for item, value in xs.items(): + if np.isnan(value): + assert np.isnan(float_frame[item][idx]) + else: + assert value == float_frame[item][idx] + + # mixed-type xs + test_data = {"A": {"1": 1, "2": 2}, "B": {"1": "1", "2": "2", "3": "3"}} + frame = DataFrame(test_data) + xs = frame.xs("1") + assert xs.dtype == np.object_ + assert xs["A"] == 1 + assert xs["B"] == "1" + + with pytest.raises( + KeyError, match=re.escape("Timestamp('1999-12-31 00:00:00')") + ): + datetime_frame.xs(datetime_frame.index[0] - BDay()) + + # xs get column + series = float_frame.xs("A", axis=1) + expected = float_frame["A"] + tm.assert_series_equal(series, expected) + + # view is returned if possible + series = float_frame.xs("A", axis=1) + with tm.assert_cow_warning(warn_copy_on_write): + series[:] = 5 + if using_copy_on_write: + # but with CoW the view shouldn't propagate mutations + tm.assert_series_equal(float_frame["A"], float_frame_orig["A"]) + assert not (expected == 5).all() + else: + assert (expected == 5).all() + + def test_xs_corner(self): + # pathological mixed-type reordering case + df = DataFrame(index=[0], columns=Index([], dtype="str")) + df["A"] = 1.0 + df["B"] = "foo" + df["C"] = 2.0 + df["D"] = "bar" + df["E"] = 3.0 + + xs = df.xs(0) + exp = Series([1.0, "foo", 2.0, "bar", 3.0], index=list("ABCDE"), name=0) + tm.assert_series_equal(xs, exp) + + # no columns but Index(dtype=object) + df = DataFrame(index=["a", "b", "c"]) + result = df.xs("a") + expected = Series([], name="a", dtype=np.float64) + tm.assert_series_equal(result, expected) + + def test_xs_duplicates(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 2)), + index=["b", "b", "c", "b", "a"], + ) + + cross = df.xs("c") + exp = df.iloc[2] + tm.assert_series_equal(cross, exp) + + def test_xs_keep_level(self): + df = DataFrame( + { + "day": {0: "sat", 1: "sun"}, + "flavour": {0: "strawberry", 1: "strawberry"}, + "sales": {0: 10, 1: 12}, + "year": {0: 2008, 1: 2008}, + } + ).set_index(["year", "flavour", "day"]) + result = df.xs("sat", level="day", drop_level=False) + expected = df[:1] + tm.assert_frame_equal(result, expected) + + result = df.xs((2008, "sat"), level=["year", "day"], drop_level=False) + tm.assert_frame_equal(result, expected) + + def test_xs_view( + self, using_array_manager, using_copy_on_write, warn_copy_on_write + ): + # in 0.14 this will return a view if possible a copy otherwise, but + # this is numpy dependent + + dm = DataFrame(np.arange(20.0).reshape(4, 5), index=range(4), columns=range(5)) + df_orig = dm.copy() + + if using_copy_on_write: + with tm.raises_chained_assignment_error(): + dm.xs(2)[:] = 20 + tm.assert_frame_equal(dm, df_orig) + elif using_array_manager: + # INFO(ArrayManager) with ArrayManager getting a row as a view is + # not possible + msg = r"\nA value is trying to be set on a copy of a slice from a DataFrame" + with pytest.raises(SettingWithCopyError, match=msg): + dm.xs(2)[:] = 20 + assert not (dm.xs(2) == 20).any() + else: + with tm.raises_chained_assignment_error(): + dm.xs(2)[:] = 20 + assert (dm.xs(2) == 20).all() + + +class TestXSWithMultiIndex: + def test_xs_doc_example(self): + # TODO: more descriptive name + # based on example in advanced.rst + arrays = [ + ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"], + ["one", "two", "one", "two", "one", "two", "one", "two"], + ] + tuples = list(zip(*arrays)) + + index = MultiIndex.from_tuples(tuples, names=["first", "second"]) + df = DataFrame( + np.random.default_rng(2).standard_normal((3, 8)), + index=["A", "B", "C"], + columns=index, + ) + + result = df.xs(("one", "bar"), level=("second", "first"), axis=1) + + expected = df.iloc[:, [0]] + tm.assert_frame_equal(result, expected) + + def test_xs_integer_key(self): + # see GH#2107 + dates = range(20111201, 20111205) + ids = list("abcde") + index = MultiIndex.from_product([dates, ids], names=["date", "secid"]) + df = DataFrame( + np.random.default_rng(2).standard_normal((len(index), 3)), + index, + ["X", "Y", "Z"], + ) + + result = df.xs(20111201, level="date") + expected = df.loc[20111201, :] + tm.assert_frame_equal(result, expected) + + def test_xs_level(self, multiindex_dataframe_random_data): + df = multiindex_dataframe_random_data + result = df.xs("two", level="second") + expected = df[df.index.get_level_values(1) == "two"] + expected.index = Index(["foo", "bar", "baz", "qux"], name="first") + tm.assert_frame_equal(result, expected) + + def test_xs_level_eq_2(self): + arr = np.random.default_rng(2).standard_normal((3, 5)) + index = MultiIndex( + levels=[["a", "p", "x"], ["b", "q", "y"], ["c", "r", "z"]], + codes=[[2, 0, 1], [2, 0, 1], [2, 0, 1]], + ) + df = DataFrame(arr, index=index) + expected = DataFrame(arr[1:2], index=[["a"], ["b"]]) + result = df.xs("c", level=2) + tm.assert_frame_equal(result, expected) + + def test_xs_setting_with_copy_error( + self, + multiindex_dataframe_random_data, + using_copy_on_write, + warn_copy_on_write, + ): + # this is a copy in 0.14 + df = multiindex_dataframe_random_data + df_orig = df.copy() + result = df.xs("two", level="second") + + if using_copy_on_write or warn_copy_on_write: + result[:] = 10 + else: + # setting this will give a SettingWithCopyError + # as we are trying to write a view + msg = "A value is trying to be set on a copy of a slice from a DataFrame" + with pytest.raises(SettingWithCopyError, match=msg): + result[:] = 10 + tm.assert_frame_equal(df, df_orig) + + def test_xs_setting_with_copy_error_multiple( + self, four_level_index_dataframe, using_copy_on_write, warn_copy_on_write + ): + # this is a copy in 0.14 + df = four_level_index_dataframe + df_orig = df.copy() + result = df.xs(("a", 4), level=["one", "four"]) + + if using_copy_on_write or warn_copy_on_write: + result[:] = 10 + else: + # setting this will give a SettingWithCopyError + # as we are trying to write a view + msg = "A value is trying to be set on a copy of a slice from a DataFrame" + with pytest.raises(SettingWithCopyError, match=msg): + result[:] = 10 + tm.assert_frame_equal(df, df_orig) + + @pytest.mark.parametrize("key, level", [("one", "second"), (["one"], ["second"])]) + def test_xs_with_duplicates(self, key, level, multiindex_dataframe_random_data): + # see GH#13719 + frame = multiindex_dataframe_random_data + df = concat([frame] * 2) + assert df.index.is_unique is False + expected = concat([frame.xs("one", level="second")] * 2) + + if isinstance(key, list): + result = df.xs(tuple(key), level=level) + else: + result = df.xs(key, level=level) + tm.assert_frame_equal(result, expected) + + def test_xs_missing_values_in_index(self): + # see GH#6574 + # missing values in returned index should be preserved + acc = [ + ("a", "abcde", 1), + ("b", "bbcde", 2), + ("y", "yzcde", 25), + ("z", "xbcde", 24), + ("z", None, 26), + ("z", "zbcde", 25), + ("z", "ybcde", 26), + ] + df = DataFrame(acc, columns=["a1", "a2", "cnt"]).set_index(["a1", "a2"]) + expected = DataFrame( + {"cnt": [24, 26, 25, 26]}, + index=Index(["xbcde", np.nan, "zbcde", "ybcde"], name="a2"), + ) + + result = df.xs("z", level="a1") + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "key, level, exp_arr, exp_index", + [ + ("a", "lvl0", lambda x: x[:, 0:2], Index(["bar", "foo"], name="lvl1")), + ("foo", "lvl1", lambda x: x[:, 1:2], Index(["a"], name="lvl0")), + ], + ) + def test_xs_named_levels_axis_eq_1(self, key, level, exp_arr, exp_index): + # see GH#2903 + arr = np.random.default_rng(2).standard_normal((4, 4)) + index = MultiIndex( + levels=[["a", "b"], ["bar", "foo", "hello", "world"]], + codes=[[0, 0, 1, 1], [0, 1, 2, 3]], + names=["lvl0", "lvl1"], + ) + df = DataFrame(arr, columns=index) + result = df.xs(key, level=level, axis=1) + expected = DataFrame(exp_arr(arr), columns=exp_index) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "indexer", + [ + lambda df: df.xs(("a", 4), level=["one", "four"]), + lambda df: df.xs("a").xs(4, level="four"), + ], + ) + def test_xs_level_multiple(self, indexer, four_level_index_dataframe): + df = four_level_index_dataframe + expected_values = [[0.4473, 1.4152, 0.2834, 1.00661, 0.1744]] + expected_index = MultiIndex( + levels=[["q"], [20.0]], codes=[[0], [0]], names=["two", "three"] + ) + expected = DataFrame( + expected_values, index=expected_index, columns=list("ABCDE") + ) + result = indexer(df) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "indexer", [lambda df: df.xs("a", level=0), lambda df: df.xs("a")] + ) + def test_xs_level0(self, indexer, four_level_index_dataframe): + df = four_level_index_dataframe + expected_values = [ + [-0.5109, -2.3358, -0.4645, 0.05076, 0.364], + [0.4473, 1.4152, 0.2834, 1.00661, 0.1744], + ] + expected_index = MultiIndex( + levels=[["b", "q"], [10.0032, 20.0], [4, 5]], + codes=[[0, 1], [0, 1], [1, 0]], + names=["two", "three", "four"], + ) + expected = DataFrame( + expected_values, index=expected_index, columns=list("ABCDE") + ) + + result = indexer(df) + tm.assert_frame_equal(result, expected) + + def test_xs_values(self, multiindex_dataframe_random_data): + df = multiindex_dataframe_random_data + result = df.xs(("bar", "two")).values + expected = df.values[4] + tm.assert_almost_equal(result, expected) + + def test_xs_loc_equality(self, multiindex_dataframe_random_data): + df = multiindex_dataframe_random_data + result = df.xs(("bar", "two")) + expected = df.loc[("bar", "two")] + tm.assert_series_equal(result, expected) + + def test_xs_IndexSlice_argument_not_implemented(self, frame_or_series): + # GH#35301 + + index = MultiIndex( + levels=[[("foo", "bar", 0), ("foo", "baz", 0), ("foo", "qux", 0)], [0, 1]], + codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]], + ) + + obj = DataFrame(np.random.default_rng(2).standard_normal((6, 4)), index=index) + if frame_or_series is Series: + obj = obj[0] + + expected = obj.iloc[-2:].droplevel(0) + + result = obj.xs(IndexSlice[("foo", "qux", 0), :]) + tm.assert_equal(result, expected) + + result = obj.loc[IndexSlice[("foo", "qux", 0), :]] + tm.assert_equal(result, expected) + + def test_xs_levels_raises(self, frame_or_series): + obj = DataFrame({"A": [1, 2, 3]}) + if frame_or_series is Series: + obj = obj["A"] + + msg = "Index must be a MultiIndex" + with pytest.raises(TypeError, match=msg): + obj.xs(0, level="as") + + def test_xs_multiindex_droplevel_false(self): + # GH#19056 + mi = MultiIndex.from_tuples( + [("a", "x"), ("a", "y"), ("b", "x")], names=["level1", "level2"] + ) + df = DataFrame([[1, 2, 3]], columns=mi) + result = df.xs("a", axis=1, drop_level=False) + expected = DataFrame( + [[1, 2]], + columns=MultiIndex.from_tuples( + [("a", "x"), ("a", "y")], names=["level1", "level2"] + ), + ) + tm.assert_frame_equal(result, expected) + + def test_xs_droplevel_false(self): + # GH#19056 + df = DataFrame([[1, 2, 3]], columns=Index(["a", "b", "c"])) + result = df.xs("a", axis=1, drop_level=False) + expected = DataFrame({"a": [1]}) + tm.assert_frame_equal(result, expected) + + def test_xs_droplevel_false_view( + self, using_array_manager, using_copy_on_write, warn_copy_on_write + ): + # GH#37832 + df = DataFrame([[1, 2, 3]], columns=Index(["a", "b", "c"])) + result = df.xs("a", axis=1, drop_level=False) + # check that result still views the same data as df + assert np.shares_memory(result.iloc[:, 0]._values, df.iloc[:, 0]._values) + + with tm.assert_cow_warning(warn_copy_on_write): + df.iloc[0, 0] = 2 + if using_copy_on_write: + # with copy on write the subset is never modified + expected = DataFrame({"a": [1]}) + else: + # modifying original df also modifies result when having a single block + expected = DataFrame({"a": [2]}) + tm.assert_frame_equal(result, expected) + + # with mixed dataframe, modifying the parent doesn't modify result + # TODO the "split" path behaves differently here as with single block + df = DataFrame([[1, 2.5, "a"]], columns=Index(["a", "b", "c"])) + result = df.xs("a", axis=1, drop_level=False) + df.iloc[0, 0] = 2 + if using_copy_on_write: + # with copy on write the subset is never modified + expected = DataFrame({"a": [1]}) + elif using_array_manager: + # Here the behavior is consistent + expected = DataFrame({"a": [2]}) + else: + # FIXME: iloc does not update the array inplace using + # "split" path + expected = DataFrame({"a": [1]}) + tm.assert_frame_equal(result, expected) + + def test_xs_list_indexer_droplevel_false(self): + # GH#41760 + mi = MultiIndex.from_tuples([("x", "m", "a"), ("x", "n", "b"), ("y", "o", "c")]) + df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=mi) + with pytest.raises(KeyError, match="y"): + df.xs(("x", "y"), drop_level=False, axis=1) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..245594bfdc9e72ff5cb3a4799e9055c7cd6b5a3e --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/__init__.py @@ -0,0 +1,7 @@ +""" +Test files dedicated to individual (stand-alone) DataFrame methods + +Ideally these files/tests should correspond 1-to-1 with tests.series.methods + +These may also present opportunities for sharing/de-duplicating test code. +""" diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_add_prefix_suffix.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_add_prefix_suffix.py new file mode 100644 index 0000000000000000000000000000000000000000..92d7cdd7990e168721610b7f52f653a69ac1e078 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_add_prefix_suffix.py @@ -0,0 +1,49 @@ +import pytest + +from pandas import Index +import pandas._testing as tm + + +def test_add_prefix_suffix(float_frame): + with_prefix = float_frame.add_prefix("foo#") + expected = Index([f"foo#{c}" for c in float_frame.columns]) + tm.assert_index_equal(with_prefix.columns, expected) + + with_suffix = float_frame.add_suffix("#foo") + expected = Index([f"{c}#foo" for c in float_frame.columns]) + tm.assert_index_equal(with_suffix.columns, expected) + + with_pct_prefix = float_frame.add_prefix("%") + expected = Index([f"%{c}" for c in float_frame.columns]) + tm.assert_index_equal(with_pct_prefix.columns, expected) + + with_pct_suffix = float_frame.add_suffix("%") + expected = Index([f"{c}%" for c in float_frame.columns]) + tm.assert_index_equal(with_pct_suffix.columns, expected) + + +def test_add_prefix_suffix_axis(float_frame): + # GH 47819 + with_prefix = float_frame.add_prefix("foo#", axis=0) + expected = Index([f"foo#{c}" for c in float_frame.index]) + tm.assert_index_equal(with_prefix.index, expected) + + with_prefix = float_frame.add_prefix("foo#", axis=1) + expected = Index([f"foo#{c}" for c in float_frame.columns]) + tm.assert_index_equal(with_prefix.columns, expected) + + with_pct_suffix = float_frame.add_suffix("#foo", axis=0) + expected = Index([f"{c}#foo" for c in float_frame.index]) + tm.assert_index_equal(with_pct_suffix.index, expected) + + with_pct_suffix = float_frame.add_suffix("#foo", axis=1) + expected = Index([f"{c}#foo" for c in float_frame.columns]) + tm.assert_index_equal(with_pct_suffix.columns, expected) + + +def test_add_prefix_suffix_invalid_axis(float_frame): + with pytest.raises(ValueError, match="No axis named 2 for object type DataFrame"): + float_frame.add_prefix("foo#", axis=2) + + with pytest.raises(ValueError, match="No axis named 2 for object type DataFrame"): + float_frame.add_suffix("foo#", axis=2) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_align.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_align.py new file mode 100644 index 0000000000000000000000000000000000000000..5a9c47866dae8102fdf51541aaae5c61eeb7e84c --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_align.py @@ -0,0 +1,484 @@ +from datetime import timezone + +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + Index, + Series, + date_range, +) +import pandas._testing as tm + + +class TestDataFrameAlign: + def test_align_asfreq_method_raises(self): + df = DataFrame({"A": [1, np.nan, 2]}) + msg = "Invalid fill method" + msg2 = "The 'method', 'limit', and 'fill_axis' keywords" + with pytest.raises(ValueError, match=msg): + with tm.assert_produces_warning(FutureWarning, match=msg2): + df.align(df.iloc[::-1], method="asfreq") + + def test_frame_align_aware(self): + idx1 = date_range("2001", periods=5, freq="h", tz="US/Eastern") + idx2 = date_range("2001", periods=5, freq="2h", tz="US/Eastern") + df1 = DataFrame(np.random.default_rng(2).standard_normal((len(idx1), 3)), idx1) + df2 = DataFrame(np.random.default_rng(2).standard_normal((len(idx2), 3)), idx2) + new1, new2 = df1.align(df2) + assert df1.index.tz == new1.index.tz + assert df2.index.tz == new2.index.tz + + # different timezones convert to UTC + + # frame with frame + df1_central = df1.tz_convert("US/Central") + new1, new2 = df1.align(df1_central) + assert new1.index.tz is timezone.utc + assert new2.index.tz is timezone.utc + + # frame with Series + new1, new2 = df1.align(df1_central[0], axis=0) + assert new1.index.tz is timezone.utc + assert new2.index.tz is timezone.utc + + df1[0].align(df1_central, axis=0) + assert new1.index.tz is timezone.utc + assert new2.index.tz is timezone.utc + + def test_align_float(self, float_frame, using_copy_on_write): + af, bf = float_frame.align(float_frame) + assert af._mgr is not float_frame._mgr + + af, bf = float_frame.align(float_frame, copy=False) + if not using_copy_on_write: + assert af._mgr is float_frame._mgr + else: + assert af._mgr is not float_frame._mgr + + # axis = 0 + other = float_frame.iloc[:-5, :3] + af, bf = float_frame.align(other, axis=0, fill_value=-1) + + tm.assert_index_equal(bf.columns, other.columns) + + # test fill value + join_idx = float_frame.index.join(other.index) + diff_a = float_frame.index.difference(join_idx) + diff_a_vals = af.reindex(diff_a).values + assert (diff_a_vals == -1).all() + + af, bf = float_frame.align(other, join="right", axis=0) + tm.assert_index_equal(bf.columns, other.columns) + tm.assert_index_equal(bf.index, other.index) + tm.assert_index_equal(af.index, other.index) + + # axis = 1 + other = float_frame.iloc[:-5, :3].copy() + af, bf = float_frame.align(other, axis=1) + tm.assert_index_equal(bf.columns, float_frame.columns) + tm.assert_index_equal(bf.index, other.index) + + # test fill value + join_idx = float_frame.index.join(other.index) + diff_a = float_frame.index.difference(join_idx) + diff_a_vals = af.reindex(diff_a).values + + assert (diff_a_vals == -1).all() + + af, bf = float_frame.align(other, join="inner", axis=1) + tm.assert_index_equal(bf.columns, other.columns) + + msg = ( + "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align " + "are deprecated" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + af, bf = float_frame.align(other, join="inner", axis=1, method="pad") + tm.assert_index_equal(bf.columns, other.columns) + + msg = ( + "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align " + "are deprecated" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + af, bf = float_frame.align( + other.iloc[:, 0], join="inner", axis=1, method=None, fill_value=None + ) + tm.assert_index_equal(bf.index, Index([]).astype(bf.index.dtype)) + + msg = ( + "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align " + "are deprecated" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + af, bf = float_frame.align( + other.iloc[:, 0], join="inner", axis=1, method=None, fill_value=0 + ) + tm.assert_index_equal(bf.index, Index([]).astype(bf.index.dtype)) + + # Try to align DataFrame to Series along bad axis + msg = "No axis named 2 for object type DataFrame" + with pytest.raises(ValueError, match=msg): + float_frame.align(af.iloc[0, :3], join="inner", axis=2) + + def test_align_frame_with_series(self, float_frame): + # align dataframe to series with broadcast or not + idx = float_frame.index + s = Series(range(len(idx)), index=idx) + + left, right = float_frame.align(s, axis=0) + tm.assert_index_equal(left.index, float_frame.index) + tm.assert_index_equal(right.index, float_frame.index) + assert isinstance(right, Series) + + msg = "The 'broadcast_axis' keyword in DataFrame.align is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + left, right = float_frame.align(s, broadcast_axis=1) + tm.assert_index_equal(left.index, float_frame.index) + expected = {c: s for c in float_frame.columns} + expected = DataFrame( + expected, index=float_frame.index, columns=float_frame.columns + ) + tm.assert_frame_equal(right, expected) + + def test_align_series_condition(self): + # see gh-9558 + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + result = df[df["a"] == 2] + expected = DataFrame([[2, 5]], index=[1], columns=["a", "b"]) + tm.assert_frame_equal(result, expected) + + result = df.where(df["a"] == 2, 0) + expected = DataFrame({"a": [0, 2, 0], "b": [0, 5, 0]}) + tm.assert_frame_equal(result, expected) + + def test_align_int(self, int_frame): + # test other non-float types + other = DataFrame(index=range(5), columns=["A", "B", "C"]) + + msg = ( + "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align " + "are deprecated" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + af, bf = int_frame.align(other, join="inner", axis=1, method="pad") + tm.assert_index_equal(bf.columns, other.columns) + + def test_align_mixed_type(self, float_string_frame): + msg = ( + "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align " + "are deprecated" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + af, bf = float_string_frame.align( + float_string_frame, join="inner", axis=1, method="pad" + ) + tm.assert_index_equal(bf.columns, float_string_frame.columns) + + def test_align_mixed_float(self, mixed_float_frame): + # mixed floats/ints + other = DataFrame(index=range(5), columns=["A", "B", "C"]) + + msg = ( + "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align " + "are deprecated" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + af, bf = mixed_float_frame.align( + other.iloc[:, 0], join="inner", axis=1, method=None, fill_value=0 + ) + tm.assert_index_equal(bf.index, Index([])) + + def test_align_mixed_int(self, mixed_int_frame): + other = DataFrame(index=range(5), columns=["A", "B", "C"]) + + msg = ( + "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align " + "are deprecated" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + af, bf = mixed_int_frame.align( + other.iloc[:, 0], join="inner", axis=1, method=None, fill_value=0 + ) + tm.assert_index_equal(bf.index, Index([])) + + @pytest.mark.parametrize( + "l_ordered,r_ordered,expected", + [ + [True, True, pd.CategoricalIndex], + [True, False, Index], + [False, True, Index], + [False, False, pd.CategoricalIndex], + ], + ) + def test_align_categorical(self, l_ordered, r_ordered, expected): + # GH-28397 + df_1 = DataFrame( + { + "A": np.arange(6, dtype="int64"), + "B": Series(list("aabbca")).astype( + pd.CategoricalDtype(list("cab"), ordered=l_ordered) + ), + } + ).set_index("B") + df_2 = DataFrame( + { + "A": np.arange(5, dtype="int64"), + "B": Series(list("babca")).astype( + pd.CategoricalDtype(list("cab"), ordered=r_ordered) + ), + } + ).set_index("B") + + aligned_1, aligned_2 = df_1.align(df_2) + assert isinstance(aligned_1.index, expected) + assert isinstance(aligned_2.index, expected) + tm.assert_index_equal(aligned_1.index, aligned_2.index) + + def test_align_multiindex(self): + # GH#10665 + # same test cases as test_align_multiindex in test_series.py + + midx = pd.MultiIndex.from_product( + [range(2), range(3), range(2)], names=("a", "b", "c") + ) + idx = Index(range(2), name="b") + df1 = DataFrame(np.arange(12, dtype="int64"), index=midx) + df2 = DataFrame(np.arange(2, dtype="int64"), index=idx) + + # these must be the same results (but flipped) + res1l, res1r = df1.align(df2, join="left") + res2l, res2r = df2.align(df1, join="right") + + expl = df1 + tm.assert_frame_equal(expl, res1l) + tm.assert_frame_equal(expl, res2r) + expr = DataFrame([0, 0, 1, 1, np.nan, np.nan] * 2, index=midx) + tm.assert_frame_equal(expr, res1r) + tm.assert_frame_equal(expr, res2l) + + res1l, res1r = df1.align(df2, join="right") + res2l, res2r = df2.align(df1, join="left") + + exp_idx = pd.MultiIndex.from_product( + [range(2), range(2), range(2)], names=("a", "b", "c") + ) + expl = DataFrame([0, 1, 2, 3, 6, 7, 8, 9], index=exp_idx) + tm.assert_frame_equal(expl, res1l) + tm.assert_frame_equal(expl, res2r) + expr = DataFrame([0, 0, 1, 1] * 2, index=exp_idx) + tm.assert_frame_equal(expr, res1r) + tm.assert_frame_equal(expr, res2l) + + def test_align_series_combinations(self): + df = DataFrame({"a": [1, 3, 5], "b": [1, 3, 5]}, index=list("ACE")) + s = Series([1, 2, 4], index=list("ABD"), name="x") + + # frame + series + res1, res2 = df.align(s, axis=0) + exp1 = DataFrame( + {"a": [1, np.nan, 3, np.nan, 5], "b": [1, np.nan, 3, np.nan, 5]}, + index=list("ABCDE"), + ) + exp2 = Series([1, 2, np.nan, 4, np.nan], index=list("ABCDE"), name="x") + + tm.assert_frame_equal(res1, exp1) + tm.assert_series_equal(res2, exp2) + + # series + frame + res1, res2 = s.align(df) + tm.assert_series_equal(res1, exp2) + tm.assert_frame_equal(res2, exp1) + + def test_multiindex_align_to_series_with_common_index_level(self): + # GH-46001 + foo_index = Index([1, 2, 3], name="foo") + bar_index = Index([1, 2], name="bar") + + series = Series([1, 2], index=bar_index, name="foo_series") + df = DataFrame( + {"col": np.arange(6)}, + index=pd.MultiIndex.from_product([foo_index, bar_index]), + ) + + expected_r = Series([1, 2] * 3, index=df.index, name="foo_series") + result_l, result_r = df.align(series, axis=0) + + tm.assert_frame_equal(result_l, df) + tm.assert_series_equal(result_r, expected_r) + + def test_multiindex_align_to_series_with_common_index_level_missing_in_left(self): + # GH-46001 + foo_index = Index([1, 2, 3], name="foo") + bar_index = Index([1, 2], name="bar") + + series = Series( + [1, 2, 3, 4], index=Index([1, 2, 3, 4], name="bar"), name="foo_series" + ) + df = DataFrame( + {"col": np.arange(6)}, + index=pd.MultiIndex.from_product([foo_index, bar_index]), + ) + + expected_r = Series([1, 2] * 3, index=df.index, name="foo_series") + result_l, result_r = df.align(series, axis=0) + + tm.assert_frame_equal(result_l, df) + tm.assert_series_equal(result_r, expected_r) + + def test_multiindex_align_to_series_with_common_index_level_missing_in_right(self): + # GH-46001 + foo_index = Index([1, 2, 3], name="foo") + bar_index = Index([1, 2, 3, 4], name="bar") + + series = Series([1, 2], index=Index([1, 2], name="bar"), name="foo_series") + df = DataFrame( + {"col": np.arange(12)}, + index=pd.MultiIndex.from_product([foo_index, bar_index]), + ) + + expected_r = Series( + [1, 2, np.nan, np.nan] * 3, index=df.index, name="foo_series" + ) + result_l, result_r = df.align(series, axis=0) + + tm.assert_frame_equal(result_l, df) + tm.assert_series_equal(result_r, expected_r) + + def test_multiindex_align_to_series_with_common_index_level_missing_in_both(self): + # GH-46001 + foo_index = Index([1, 2, 3], name="foo") + bar_index = Index([1, 3, 4], name="bar") + + series = Series( + [1, 2, 3], index=Index([1, 2, 4], name="bar"), name="foo_series" + ) + df = DataFrame( + {"col": np.arange(9)}, + index=pd.MultiIndex.from_product([foo_index, bar_index]), + ) + + expected_r = Series([1, np.nan, 3] * 3, index=df.index, name="foo_series") + result_l, result_r = df.align(series, axis=0) + + tm.assert_frame_equal(result_l, df) + tm.assert_series_equal(result_r, expected_r) + + def test_multiindex_align_to_series_with_common_index_level_non_unique_cols(self): + # GH-46001 + foo_index = Index([1, 2, 3], name="foo") + bar_index = Index([1, 2], name="bar") + + series = Series([1, 2], index=bar_index, name="foo_series") + df = DataFrame( + np.arange(18).reshape(6, 3), + index=pd.MultiIndex.from_product([foo_index, bar_index]), + ) + df.columns = ["cfoo", "cbar", "cfoo"] + + expected = Series([1, 2] * 3, index=df.index, name="foo_series") + result_left, result_right = df.align(series, axis=0) + + tm.assert_series_equal(result_right, expected) + tm.assert_index_equal(result_left.columns, df.columns) + + def test_missing_axis_specification_exception(self): + df = DataFrame(np.arange(50).reshape((10, 5))) + series = Series(np.arange(5)) + + with pytest.raises(ValueError, match=r"axis=0 or 1"): + df.align(series) + + @pytest.mark.parametrize("method", ["pad", "bfill"]) + @pytest.mark.parametrize("axis", [0, 1, None]) + @pytest.mark.parametrize("fill_axis", [0, 1]) + @pytest.mark.parametrize("how", ["inner", "outer", "left", "right"]) + @pytest.mark.parametrize( + "left_slice", + [ + [slice(4), slice(10)], + [slice(0), slice(0)], + ], + ) + @pytest.mark.parametrize( + "right_slice", + [ + [slice(2, None), slice(6, None)], + [slice(0), slice(0)], + ], + ) + @pytest.mark.parametrize("limit", [1, None]) + def test_align_fill_method( + self, how, method, axis, fill_axis, float_frame, left_slice, right_slice, limit + ): + frame = float_frame + left = frame.iloc[left_slice[0], left_slice[1]] + right = frame.iloc[right_slice[0], right_slice[1]] + + msg = ( + "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align " + "are deprecated" + ) + + with tm.assert_produces_warning(FutureWarning, match=msg): + aa, ab = left.align( + right, + axis=axis, + join=how, + method=method, + limit=limit, + fill_axis=fill_axis, + ) + + join_index, join_columns = None, None + + ea, eb = left, right + if axis is None or axis == 0: + join_index = left.index.join(right.index, how=how) + ea = ea.reindex(index=join_index) + eb = eb.reindex(index=join_index) + + if axis is None or axis == 1: + join_columns = left.columns.join(right.columns, how=how) + ea = ea.reindex(columns=join_columns) + eb = eb.reindex(columns=join_columns) + + msg = "DataFrame.fillna with 'method' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + ea = ea.fillna(axis=fill_axis, method=method, limit=limit) + eb = eb.fillna(axis=fill_axis, method=method, limit=limit) + + tm.assert_frame_equal(aa, ea) + tm.assert_frame_equal(ab, eb) + + def test_align_series_check_copy(self): + # GH# + df = DataFrame({0: [1, 2]}) + ser = Series([1], name=0) + expected = ser.copy() + result, other = df.align(ser, axis=1) + ser.iloc[0] = 100 + tm.assert_series_equal(other, expected) + + def test_align_identical_different_object(self): + # GH#51032 + df = DataFrame({"a": [1, 2]}) + ser = Series([3, 4]) + result, result2 = df.align(ser, axis=0) + tm.assert_frame_equal(result, df) + tm.assert_series_equal(result2, ser) + assert df is not result + assert ser is not result2 + + def test_align_identical_different_object_columns(self): + # GH#51032 + df = DataFrame({"a": [1, 2]}) + ser = Series([1], index=["a"]) + result, result2 = df.align(ser, axis=1) + tm.assert_frame_equal(result, df) + tm.assert_series_equal(result2, ser) + assert df is not result + assert ser is not result2 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_asfreq.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_asfreq.py new file mode 100644 index 0000000000000000000000000000000000000000..ef72ca1ac86b9a6eb395a5a64bbfb99aef76a02a --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_asfreq.py @@ -0,0 +1,263 @@ +from datetime import datetime + +import numpy as np +import pytest + +from pandas._libs.tslibs.offsets import MonthEnd + +from pandas import ( + DataFrame, + DatetimeIndex, + Series, + date_range, + period_range, + to_datetime, +) +import pandas._testing as tm + +from pandas.tseries import offsets + + +class TestAsFreq: + @pytest.fixture(params=["s", "ms", "us", "ns"]) + def unit(self, request): + return request.param + + def test_asfreq2(self, frame_or_series): + ts = frame_or_series( + [0.0, 1.0, 2.0], + index=DatetimeIndex( + [ + datetime(2009, 10, 30), + datetime(2009, 11, 30), + datetime(2009, 12, 31), + ], + dtype="M8[ns]", + freq="BME", + ), + ) + + daily_ts = ts.asfreq("B") + monthly_ts = daily_ts.asfreq("BME") + tm.assert_equal(monthly_ts, ts) + + daily_ts = ts.asfreq("B", method="pad") + monthly_ts = daily_ts.asfreq("BME") + tm.assert_equal(monthly_ts, ts) + + daily_ts = ts.asfreq(offsets.BDay()) + monthly_ts = daily_ts.asfreq(offsets.BMonthEnd()) + tm.assert_equal(monthly_ts, ts) + + result = ts[:0].asfreq("ME") + assert len(result) == 0 + assert result is not ts + + if frame_or_series is Series: + daily_ts = ts.asfreq("D", fill_value=-1) + result = daily_ts.value_counts().sort_index() + expected = Series( + [60, 1, 1, 1], index=[-1.0, 2.0, 1.0, 0.0], name="count" + ).sort_index() + tm.assert_series_equal(result, expected) + + def test_asfreq_datetimeindex_empty(self, frame_or_series): + # GH#14320 + index = DatetimeIndex(["2016-09-29 11:00"]) + expected = frame_or_series(index=index, dtype=object).asfreq("h") + result = frame_or_series([3], index=index.copy()).asfreq("h") + tm.assert_index_equal(expected.index, result.index) + + @pytest.mark.parametrize("tz", ["US/Eastern", "dateutil/US/Eastern"]) + def test_tz_aware_asfreq_smoke(self, tz, frame_or_series): + dr = date_range("2011-12-01", "2012-07-20", freq="D", tz=tz) + + obj = frame_or_series( + np.random.default_rng(2).standard_normal(len(dr)), index=dr + ) + + # it works! + obj.asfreq("min") + + def test_asfreq_normalize(self, frame_or_series): + rng = date_range("1/1/2000 09:30", periods=20) + norm = date_range("1/1/2000", periods=20) + + vals = np.random.default_rng(2).standard_normal((20, 3)) + + obj = DataFrame(vals, index=rng) + expected = DataFrame(vals, index=norm) + if frame_or_series is Series: + obj = obj[0] + expected = expected[0] + + result = obj.asfreq("D", normalize=True) + tm.assert_equal(result, expected) + + def test_asfreq_keep_index_name(self, frame_or_series): + # GH#9854 + index_name = "bar" + index = date_range("20130101", periods=20, name=index_name) + obj = DataFrame(list(range(20)), columns=["foo"], index=index) + obj = tm.get_obj(obj, frame_or_series) + + assert index_name == obj.index.name + assert index_name == obj.asfreq("10D").index.name + + def test_asfreq_ts(self, frame_or_series): + index = period_range(freq="Y", start="1/1/2001", end="12/31/2010") + obj = DataFrame( + np.random.default_rng(2).standard_normal((len(index), 3)), index=index + ) + obj = tm.get_obj(obj, frame_or_series) + + result = obj.asfreq("D", how="end") + exp_index = index.asfreq("D", how="end") + assert len(result) == len(obj) + tm.assert_index_equal(result.index, exp_index) + + result = obj.asfreq("D", how="start") + exp_index = index.asfreq("D", how="start") + assert len(result) == len(obj) + tm.assert_index_equal(result.index, exp_index) + + def test_asfreq_resample_set_correct_freq(self, frame_or_series): + # GH#5613 + # we test if .asfreq() and .resample() set the correct value for .freq + dti = to_datetime(["2012-01-01", "2012-01-02", "2012-01-03"]) + obj = DataFrame({"col": [1, 2, 3]}, index=dti) + obj = tm.get_obj(obj, frame_or_series) + + # testing the settings before calling .asfreq() and .resample() + assert obj.index.freq is None + assert obj.index.inferred_freq == "D" + + # does .asfreq() set .freq correctly? + assert obj.asfreq("D").index.freq == "D" + + # does .resample() set .freq correctly? + assert obj.resample("D").asfreq().index.freq == "D" + + def test_asfreq_empty(self, datetime_frame): + # test does not blow up on length-0 DataFrame + zero_length = datetime_frame.reindex([]) + result = zero_length.asfreq("BME") + assert result is not zero_length + + def test_asfreq(self, datetime_frame): + offset_monthly = datetime_frame.asfreq(offsets.BMonthEnd()) + rule_monthly = datetime_frame.asfreq("BME") + + tm.assert_frame_equal(offset_monthly, rule_monthly) + + rule_monthly.asfreq("B", method="pad") + # TODO: actually check that this worked. + + # don't forget! + rule_monthly.asfreq("B", method="pad") + + def test_asfreq_datetimeindex(self): + df = DataFrame( + {"A": [1, 2, 3]}, + index=[datetime(2011, 11, 1), datetime(2011, 11, 2), datetime(2011, 11, 3)], + ) + df = df.asfreq("B") + assert isinstance(df.index, DatetimeIndex) + + ts = df["A"].asfreq("B") + assert isinstance(ts.index, DatetimeIndex) + + def test_asfreq_fillvalue(self): + # test for fill value during upsampling, related to issue 3715 + + # setup + rng = date_range("1/1/2016", periods=10, freq="2s") + # Explicit cast to 'float' to avoid implicit cast when setting None + ts = Series(np.arange(len(rng)), index=rng, dtype="float") + df = DataFrame({"one": ts}) + + # insert pre-existing missing value + df.loc["2016-01-01 00:00:08", "one"] = None + + actual_df = df.asfreq(freq="1s", fill_value=9.0) + expected_df = df.asfreq(freq="1s").fillna(9.0) + expected_df.loc["2016-01-01 00:00:08", "one"] = None + tm.assert_frame_equal(expected_df, actual_df) + + expected_series = ts.asfreq(freq="1s").fillna(9.0) + actual_series = ts.asfreq(freq="1s", fill_value=9.0) + tm.assert_series_equal(expected_series, actual_series) + + def test_asfreq_with_date_object_index(self, frame_or_series): + rng = date_range("1/1/2000", periods=20) + ts = frame_or_series(np.random.default_rng(2).standard_normal(20), index=rng) + + ts2 = ts.copy() + ts2.index = [x.date() for x in ts2.index] + + result = ts2.asfreq("4h", method="ffill") + expected = ts.asfreq("4h", method="ffill") + tm.assert_equal(result, expected) + + def test_asfreq_with_unsorted_index(self, frame_or_series): + # GH#39805 + # Test that rows are not dropped when the datetime index is out of order + index = to_datetime(["2021-01-04", "2021-01-02", "2021-01-03", "2021-01-01"]) + result = frame_or_series(range(4), index=index) + + expected = result.reindex(sorted(index)) + expected.index = expected.index._with_freq("infer") + + result = result.asfreq("D") + tm.assert_equal(result, expected) + + def test_asfreq_after_normalize(self, unit): + # https://github.com/pandas-dev/pandas/issues/50727 + result = DatetimeIndex( + date_range("2000", periods=2).as_unit(unit).normalize(), freq="D" + ) + expected = DatetimeIndex(["2000-01-01", "2000-01-02"], freq="D").as_unit(unit) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize( + "freq, freq_half", + [ + ("2ME", "ME"), + (MonthEnd(2), MonthEnd(1)), + ], + ) + def test_asfreq_2ME(self, freq, freq_half): + index = date_range("1/1/2000", periods=6, freq=freq_half) + df = DataFrame({"s": Series([0.0, 1.0, 2.0, 3.0, 4.0, 5.0], index=index)}) + expected = df.asfreq(freq=freq) + + index = date_range("1/1/2000", periods=3, freq=freq) + result = DataFrame({"s": Series([0.0, 2.0, 4.0], index=index)}) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "freq, freq_depr", + [ + ("2ME", "2M"), + ("2QE", "2Q"), + ("2QE-SEP", "2Q-SEP"), + ("1BQE", "1BQ"), + ("2BQE-SEP", "2BQ-SEP"), + ("1YE", "1Y"), + ("2YE-MAR", "2Y-MAR"), + ("1YE", "1A"), + ("2YE-MAR", "2A-MAR"), + ("2BYE-MAR", "2BA-MAR"), + ], + ) + def test_asfreq_frequency_M_Q_Y_A_deprecated(self, freq, freq_depr): + # GH#9586, #55978 + depr_msg = f"'{freq_depr[1:]}' is deprecated and will be removed " + f"in a future version, please use '{freq[1:]}' instead." + + index = date_range("1/1/2000", periods=4, freq=f"{freq[1:]}") + df = DataFrame({"s": Series([0.0, 1.0, 2.0, 3.0], index=index)}) + expected = df.asfreq(freq=freq) + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + result = df.asfreq(freq=freq_depr) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_asof.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_asof.py new file mode 100644 index 0000000000000000000000000000000000000000..4a8adf89b3aef83001f6bb7669d8a9eae12529ea --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_asof.py @@ -0,0 +1,198 @@ +import numpy as np +import pytest + +from pandas._libs.tslibs import IncompatibleFrequency + +from pandas import ( + DataFrame, + Period, + Series, + Timestamp, + date_range, + period_range, + to_datetime, +) +import pandas._testing as tm + + +@pytest.fixture +def date_range_frame(): + """ + Fixture for DataFrame of ints with date_range index + + Columns are ['A', 'B']. + """ + N = 50 + rng = date_range("1/1/1990", periods=N, freq="53s") + return DataFrame({"A": np.arange(N), "B": np.arange(N)}, index=rng) + + +class TestFrameAsof: + def test_basic(self, date_range_frame): + # Explicitly cast to float to avoid implicit cast when setting np.nan + df = date_range_frame.astype({"A": "float"}) + N = 50 + df.loc[df.index[15:30], "A"] = np.nan + dates = date_range("1/1/1990", periods=N * 3, freq="25s") + + result = df.asof(dates) + assert result.notna().all(1).all() + lb = df.index[14] + ub = df.index[30] + + dates = list(dates) + + result = df.asof(dates) + assert result.notna().all(1).all() + + mask = (result.index >= lb) & (result.index < ub) + rs = result[mask] + assert (rs == 14).all(1).all() + + def test_subset(self, date_range_frame): + N = 10 + # explicitly cast to float to avoid implicit upcast when setting to np.nan + df = date_range_frame.iloc[:N].copy().astype({"A": "float"}) + df.loc[df.index[4:8], "A"] = np.nan + dates = date_range("1/1/1990", periods=N * 3, freq="25s") + + # with a subset of A should be the same + result = df.asof(dates, subset="A") + expected = df.asof(dates) + tm.assert_frame_equal(result, expected) + + # same with A/B + result = df.asof(dates, subset=["A", "B"]) + expected = df.asof(dates) + tm.assert_frame_equal(result, expected) + + # B gives df.asof + result = df.asof(dates, subset="B") + expected = df.resample("25s", closed="right").ffill().reindex(dates) + expected.iloc[20:] = 9 + # no "missing", so "B" can retain int dtype (df["A"].dtype platform-dependent) + expected["B"] = expected["B"].astype(df["B"].dtype) + + tm.assert_frame_equal(result, expected) + + def test_missing(self, date_range_frame): + # GH 15118 + # no match found - `where` value before earliest date in index + N = 10 + # Cast to 'float64' to avoid upcast when introducing nan in df.asof + df = date_range_frame.iloc[:N].copy().astype("float64") + + result = df.asof("1989-12-31") + + expected = Series( + index=["A", "B"], name=Timestamp("1989-12-31"), dtype=np.float64 + ) + tm.assert_series_equal(result, expected) + + result = df.asof(to_datetime(["1989-12-31"])) + expected = DataFrame( + index=to_datetime(["1989-12-31"]), columns=["A", "B"], dtype="float64" + ) + tm.assert_frame_equal(result, expected) + + # Check that we handle PeriodIndex correctly, dont end up with + # period.ordinal for series name + df = df.to_period("D") + result = df.asof("1989-12-31") + assert isinstance(result.name, Period) + + def test_asof_all_nans(self, frame_or_series): + # GH 15713 + # DataFrame/Series is all nans + result = frame_or_series([np.nan]).asof([0]) + expected = frame_or_series([np.nan]) + tm.assert_equal(result, expected) + + def test_all_nans(self, date_range_frame): + # GH 15713 + # DataFrame is all nans + + # testing non-default indexes, multiple inputs + N = 150 + rng = date_range_frame.index + dates = date_range("1/1/1990", periods=N, freq="25s") + result = DataFrame(np.nan, index=rng, columns=["A"]).asof(dates) + expected = DataFrame(np.nan, index=dates, columns=["A"]) + tm.assert_frame_equal(result, expected) + + # testing multiple columns + dates = date_range("1/1/1990", periods=N, freq="25s") + result = DataFrame(np.nan, index=rng, columns=["A", "B", "C"]).asof(dates) + expected = DataFrame(np.nan, index=dates, columns=["A", "B", "C"]) + tm.assert_frame_equal(result, expected) + + # testing scalar input + result = DataFrame(np.nan, index=[1, 2], columns=["A", "B"]).asof([3]) + expected = DataFrame(np.nan, index=[3], columns=["A", "B"]) + tm.assert_frame_equal(result, expected) + + result = DataFrame(np.nan, index=[1, 2], columns=["A", "B"]).asof(3) + expected = Series(np.nan, index=["A", "B"], name=3) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "stamp,expected", + [ + ( + Timestamp("2018-01-01 23:22:43.325+00:00"), + Series(2, name=Timestamp("2018-01-01 23:22:43.325+00:00")), + ), + ( + Timestamp("2018-01-01 22:33:20.682+01:00"), + Series(1, name=Timestamp("2018-01-01 22:33:20.682+01:00")), + ), + ], + ) + def test_time_zone_aware_index(self, stamp, expected): + # GH21194 + # Testing awareness of DataFrame index considering different + # UTC and timezone + df = DataFrame( + data=[1, 2], + index=[ + Timestamp("2018-01-01 21:00:05.001+00:00"), + Timestamp("2018-01-01 22:35:10.550+00:00"), + ], + ) + + result = df.asof(stamp) + tm.assert_series_equal(result, expected) + + def test_is_copy(self, date_range_frame): + # GH-27357, GH-30784: ensure the result of asof is an actual copy and + # doesn't track the parent dataframe / doesn't give SettingWithCopy warnings + df = date_range_frame.astype({"A": "float"}) + N = 50 + df.loc[df.index[15:30], "A"] = np.nan + dates = date_range("1/1/1990", periods=N * 3, freq="25s") + + result = df.asof(dates) + + with tm.assert_produces_warning(None): + result["C"] = 1 + + def test_asof_periodindex_mismatched_freq(self): + N = 50 + rng = period_range("1/1/1990", periods=N, freq="h") + df = DataFrame(np.random.default_rng(2).standard_normal(N), index=rng) + + # Mismatched freq + msg = "Input has different freq" + with pytest.raises(IncompatibleFrequency, match=msg): + df.asof(rng.asfreq("D")) + + def test_asof_preserves_bool_dtype(self): + # GH#16063 was casting bools to floats + dti = date_range("2017-01-01", freq="MS", periods=4) + ser = Series([True, False, True], index=dti[:-1]) + + ts = dti[-1] + res = ser.asof([ts]) + + expected = Series([True], index=[ts]) + tm.assert_series_equal(res, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_assign.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_assign.py new file mode 100644 index 0000000000000000000000000000000000000000..0ae501d43e74252a420acf96b9428ab4b8f5f211 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_assign.py @@ -0,0 +1,84 @@ +import pytest + +from pandas import DataFrame +import pandas._testing as tm + + +class TestAssign: + def test_assign(self): + df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) + original = df.copy() + result = df.assign(C=df.B / df.A) + expected = df.copy() + expected["C"] = [4, 2.5, 2] + tm.assert_frame_equal(result, expected) + + # lambda syntax + result = df.assign(C=lambda x: x.B / x.A) + tm.assert_frame_equal(result, expected) + + # original is unmodified + tm.assert_frame_equal(df, original) + + # Non-Series array-like + result = df.assign(C=[4, 2.5, 2]) + tm.assert_frame_equal(result, expected) + # original is unmodified + tm.assert_frame_equal(df, original) + + result = df.assign(B=df.B / df.A) + expected = expected.drop("B", axis=1).rename(columns={"C": "B"}) + tm.assert_frame_equal(result, expected) + + # overwrite + result = df.assign(A=df.A + df.B) + expected = df.copy() + expected["A"] = [5, 7, 9] + tm.assert_frame_equal(result, expected) + + # lambda + result = df.assign(A=lambda x: x.A + x.B) + tm.assert_frame_equal(result, expected) + + def test_assign_multiple(self): + df = DataFrame([[1, 4], [2, 5], [3, 6]], columns=["A", "B"]) + result = df.assign(C=[7, 8, 9], D=df.A, E=lambda x: x.B) + expected = DataFrame( + [[1, 4, 7, 1, 4], [2, 5, 8, 2, 5], [3, 6, 9, 3, 6]], columns=list("ABCDE") + ) + tm.assert_frame_equal(result, expected) + + def test_assign_order(self): + # GH 9818 + df = DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) + result = df.assign(D=df.A + df.B, C=df.A - df.B) + + expected = DataFrame([[1, 2, 3, -1], [3, 4, 7, -1]], columns=list("ABDC")) + tm.assert_frame_equal(result, expected) + result = df.assign(C=df.A - df.B, D=df.A + df.B) + + expected = DataFrame([[1, 2, -1, 3], [3, 4, -1, 7]], columns=list("ABCD")) + + tm.assert_frame_equal(result, expected) + + def test_assign_bad(self): + df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) + + # non-keyword argument + msg = r"assign\(\) takes 1 positional argument but 2 were given" + with pytest.raises(TypeError, match=msg): + df.assign(lambda x: x.A) + msg = "'DataFrame' object has no attribute 'C'" + with pytest.raises(AttributeError, match=msg): + df.assign(C=df.A, D=df.A + df.C) + + def test_assign_dependent(self): + df = DataFrame({"A": [1, 2], "B": [3, 4]}) + + result = df.assign(C=df.A, D=lambda x: x["A"] + x["C"]) + expected = DataFrame([[1, 3, 1, 2], [2, 4, 2, 4]], columns=list("ABCD")) + tm.assert_frame_equal(result, expected) + + result = df.assign(C=lambda df: df.A, D=lambda df: df["A"] + df["C"]) + expected = DataFrame([[1, 3, 1, 2], [2, 4, 2, 4]], columns=list("ABCD")) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_astype.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_astype.py new file mode 100644 index 0000000000000000000000000000000000000000..938f9cfcde3f834a3788dff186cd0dfff3c093d1 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_astype.py @@ -0,0 +1,924 @@ +import re + +import numpy as np +import pytest + +from pandas._config import using_string_dtype + +import pandas.util._test_decorators as td + +import pandas as pd +from pandas import ( + Categorical, + CategoricalDtype, + DataFrame, + DatetimeTZDtype, + Index, + Interval, + IntervalDtype, + NaT, + Series, + Timedelta, + Timestamp, + concat, + date_range, + option_context, +) +import pandas._testing as tm + + +def _check_cast(df, v): + """ + Check if all dtypes of df are equal to v + """ + assert all(s.dtype.name == v for _, s in df.items()) + + +class TestAstype: + def test_astype_float(self, float_frame): + casted = float_frame.astype(int) + expected = DataFrame( + float_frame.values.astype(int), + index=float_frame.index, + columns=float_frame.columns, + ) + tm.assert_frame_equal(casted, expected) + + casted = float_frame.astype(np.int32) + expected = DataFrame( + float_frame.values.astype(np.int32), + index=float_frame.index, + columns=float_frame.columns, + ) + tm.assert_frame_equal(casted, expected) + + float_frame["foo"] = "5" + casted = float_frame.astype(int) + expected = DataFrame( + float_frame.values.astype(int), + index=float_frame.index, + columns=float_frame.columns, + ) + tm.assert_frame_equal(casted, expected) + + def test_astype_mixed_float(self, mixed_float_frame): + # mixed casting + casted = mixed_float_frame.reindex(columns=["A", "B"]).astype("float32") + _check_cast(casted, "float32") + + casted = mixed_float_frame.reindex(columns=["A", "B"]).astype("float16") + _check_cast(casted, "float16") + + def test_astype_mixed_type(self): + # mixed casting + df = DataFrame( + { + "a": 1.0, + "b": 2, + "c": "foo", + "float32": np.array([1.0] * 10, dtype="float32"), + "int32": np.array([1] * 10, dtype="int32"), + }, + index=np.arange(10), + ) + mn = df._get_numeric_data().copy() + mn["little_float"] = np.array(12345.0, dtype="float16") + mn["big_float"] = np.array(123456789101112.0, dtype="float64") + + casted = mn.astype("float64") + _check_cast(casted, "float64") + + casted = mn.astype("int64") + _check_cast(casted, "int64") + + casted = mn.reindex(columns=["little_float"]).astype("float16") + _check_cast(casted, "float16") + + casted = mn.astype("float32") + _check_cast(casted, "float32") + + casted = mn.astype("int32") + _check_cast(casted, "int32") + + # to object + casted = mn.astype("O") + _check_cast(casted, "object") + + def test_astype_with_exclude_string(self, float_frame): + df = float_frame.copy() + expected = float_frame.astype(int) + df["string"] = "foo" + casted = df.astype(int, errors="ignore") + + expected["string"] = "foo" + tm.assert_frame_equal(casted, expected) + + df = float_frame.copy() + expected = float_frame.astype(np.int32) + df["string"] = "foo" + casted = df.astype(np.int32, errors="ignore") + + expected["string"] = "foo" + tm.assert_frame_equal(casted, expected) + + def test_astype_with_view_float(self, float_frame): + # this is the only real reason to do it this way + tf = np.round(float_frame).astype(np.int32) + tf.astype(np.float32, copy=False) + + # TODO(wesm): verification? + tf = float_frame.astype(np.float64) + tf.astype(np.int64, copy=False) + + def test_astype_with_view_mixed_float(self, mixed_float_frame): + tf = mixed_float_frame.reindex(columns=["A", "B", "C"]) + + tf.astype(np.int64) + tf.astype(np.float32) + + @pytest.mark.parametrize("dtype", [np.int32, np.int64]) + @pytest.mark.parametrize("val", [np.nan, np.inf]) + def test_astype_cast_nan_inf_int(self, val, dtype): + # see GH#14265 + # + # Check NaN and inf --> raise error when converting to int. + msg = "Cannot convert non-finite values \\(NA or inf\\) to integer" + df = DataFrame([val]) + + with pytest.raises(ValueError, match=msg): + df.astype(dtype) + + def test_astype_str(self): + # see GH#9757 + a = Series(date_range("2010-01-04", periods=5)) + b = Series(date_range("3/6/2012 00:00", periods=5, tz="US/Eastern")) + c = Series([Timedelta(x, unit="d") for x in range(5)]) + d = Series(range(5)) + e = Series([0.0, 0.2, 0.4, 0.6, 0.8]) + + df = DataFrame({"a": a, "b": b, "c": c, "d": d, "e": e}) + + # Datetime-like + result = df.astype(str) + + expected = DataFrame( + { + "a": list(map(str, (Timestamp(x)._date_repr for x in a._values))), + "b": list(map(str, map(Timestamp, b._values))), + "c": [Timedelta(x)._repr_base() for x in c._values], + "d": list(map(str, d._values)), + "e": list(map(str, e._values)), + }, + dtype="str", + ) + + tm.assert_frame_equal(result, expected) + + def test_astype_str_float(self, using_infer_string): + # see GH#11302 + result = DataFrame([np.nan]).astype(str) + expected = DataFrame([np.nan if using_infer_string else "nan"], dtype="str") + + tm.assert_frame_equal(result, expected) + result = DataFrame([1.12345678901234567890]).astype(str) + + val = "1.1234567890123457" + expected = DataFrame([val], dtype="str") + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("dtype_class", [dict, Series]) + def test_astype_dict_like(self, dtype_class): + # GH7271 & GH16717 + a = Series(date_range("2010-01-04", periods=5)) + b = Series(range(5)) + c = Series([0.0, 0.2, 0.4, 0.6, 0.8]) + d = Series(["1.0", "2", "3.14", "4", "5.4"]) + df = DataFrame({"a": a, "b": b, "c": c, "d": d}) + original = df.copy(deep=True) + + # change type of a subset of columns + dt1 = dtype_class({"b": "str", "d": "float32"}) + result = df.astype(dt1) + expected = DataFrame( + { + "a": a, + "b": Series(["0", "1", "2", "3", "4"], dtype="str"), + "c": c, + "d": Series([1.0, 2.0, 3.14, 4.0, 5.4], dtype="float32"), + } + ) + tm.assert_frame_equal(result, expected) + tm.assert_frame_equal(df, original) + + dt2 = dtype_class({"b": np.float32, "c": "float32", "d": np.float64}) + result = df.astype(dt2) + expected = DataFrame( + { + "a": a, + "b": Series([0.0, 1.0, 2.0, 3.0, 4.0], dtype="float32"), + "c": Series([0.0, 0.2, 0.4, 0.6, 0.8], dtype="float32"), + "d": Series([1.0, 2.0, 3.14, 4.0, 5.4], dtype="float64"), + } + ) + tm.assert_frame_equal(result, expected) + tm.assert_frame_equal(df, original) + + # change all columns + dt3 = dtype_class({"a": str, "b": str, "c": str, "d": str}) + tm.assert_frame_equal(df.astype(dt3), df.astype(str)) + tm.assert_frame_equal(df, original) + + # error should be raised when using something other than column labels + # in the keys of the dtype dict + dt4 = dtype_class({"b": str, 2: str}) + dt5 = dtype_class({"e": str}) + msg_frame = ( + "Only a column name can be used for the key in a dtype mappings argument. " + "'{}' not found in columns." + ) + with pytest.raises(KeyError, match=msg_frame.format(2)): + df.astype(dt4) + with pytest.raises(KeyError, match=msg_frame.format("e")): + df.astype(dt5) + tm.assert_frame_equal(df, original) + + # if the dtypes provided are the same as the original dtypes, the + # resulting DataFrame should be the same as the original DataFrame + dt6 = dtype_class({col: df[col].dtype for col in df.columns}) + equiv = df.astype(dt6) + tm.assert_frame_equal(df, equiv) + tm.assert_frame_equal(df, original) + + # GH#16717 + # if dtypes provided is empty, the resulting DataFrame + # should be the same as the original DataFrame + dt7 = dtype_class({}) if dtype_class is dict else dtype_class({}, dtype=object) + equiv = df.astype(dt7) + tm.assert_frame_equal(df, equiv) + tm.assert_frame_equal(df, original) + + def test_astype_duplicate_col(self): + a1 = Series([1, 2, 3, 4, 5], name="a") + b = Series([0.1, 0.2, 0.4, 0.6, 0.8], name="b") + a2 = Series([0, 1, 2, 3, 4], name="a") + df = concat([a1, b, a2], axis=1) + + result = df.astype("str") + a1_str = Series(["1", "2", "3", "4", "5"], dtype="str", name="a") + b_str = Series(["0.1", "0.2", "0.4", "0.6", "0.8"], dtype="str", name="b") + a2_str = Series(["0", "1", "2", "3", "4"], dtype="str", name="a") + expected = concat([a1_str, b_str, a2_str], axis=1) + tm.assert_frame_equal(result, expected) + + result = df.astype({"a": "str"}) + expected = concat([a1_str, b, a2_str], axis=1) + tm.assert_frame_equal(result, expected) + + def test_astype_duplicate_col_series_arg(self): + # GH#44417 + vals = np.random.default_rng(2).standard_normal((3, 4)) + df = DataFrame(vals, columns=["A", "B", "C", "A"]) + dtypes = df.dtypes + dtypes.iloc[0] = str + dtypes.iloc[2] = "Float64" + + result = df.astype(dtypes) + expected = DataFrame( + { + 0: Series(vals[:, 0].astype(str), dtype="str"), + 1: vals[:, 1], + 2: pd.array(vals[:, 2], dtype="Float64"), + 3: vals[:, 3], + } + ) + expected.columns = df.columns + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "dtype", + [ + "category", + CategoricalDtype(), + CategoricalDtype(ordered=True), + CategoricalDtype(ordered=False), + CategoricalDtype(categories=list("abcdef")), + CategoricalDtype(categories=list("edba"), ordered=False), + CategoricalDtype(categories=list("edcb"), ordered=True), + ], + ids=repr, + ) + def test_astype_categorical(self, dtype): + # GH#18099 + d = {"A": list("abbc"), "B": list("bccd"), "C": list("cdde")} + df = DataFrame(d) + result = df.astype(dtype) + expected = DataFrame({k: Categorical(v, dtype=dtype) for k, v in d.items()}) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("cls", [CategoricalDtype, DatetimeTZDtype, IntervalDtype]) + def test_astype_categoricaldtype_class_raises(self, cls): + df = DataFrame({"A": ["a", "a", "b", "c"]}) + xpr = f"Expected an instance of {cls.__name__}" + with pytest.raises(TypeError, match=xpr): + df.astype({"A": cls}) + + with pytest.raises(TypeError, match=xpr): + df["A"].astype(cls) + + @pytest.mark.parametrize("dtype", ["Int64", "Int32", "Int16"]) + def test_astype_extension_dtypes(self, dtype): + # GH#22578 + df = DataFrame([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], columns=["a", "b"]) + + expected1 = DataFrame( + { + "a": pd.array([1, 3, 5], dtype=dtype), + "b": pd.array([2, 4, 6], dtype=dtype), + } + ) + tm.assert_frame_equal(df.astype(dtype), expected1) + tm.assert_frame_equal(df.astype("int64").astype(dtype), expected1) + tm.assert_frame_equal(df.astype(dtype).astype("float64"), df) + + df = DataFrame([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], columns=["a", "b"]) + df["b"] = df["b"].astype(dtype) + expected2 = DataFrame( + {"a": [1.0, 3.0, 5.0], "b": pd.array([2, 4, 6], dtype=dtype)} + ) + tm.assert_frame_equal(df, expected2) + + tm.assert_frame_equal(df.astype(dtype), expected1) + tm.assert_frame_equal(df.astype("int64").astype(dtype), expected1) + + @pytest.mark.parametrize("dtype", ["Int64", "Int32", "Int16"]) + def test_astype_extension_dtypes_1d(self, dtype): + # GH#22578 + df = DataFrame({"a": [1.0, 2.0, 3.0]}) + + expected1 = DataFrame({"a": pd.array([1, 2, 3], dtype=dtype)}) + tm.assert_frame_equal(df.astype(dtype), expected1) + tm.assert_frame_equal(df.astype("int64").astype(dtype), expected1) + + df = DataFrame({"a": [1.0, 2.0, 3.0]}) + df["a"] = df["a"].astype(dtype) + expected2 = DataFrame({"a": pd.array([1, 2, 3], dtype=dtype)}) + tm.assert_frame_equal(df, expected2) + + tm.assert_frame_equal(df.astype(dtype), expected1) + tm.assert_frame_equal(df.astype("int64").astype(dtype), expected1) + + @pytest.mark.parametrize("dtype", ["category", "Int64"]) + def test_astype_extension_dtypes_duplicate_col(self, dtype): + # GH#24704 + a1 = Series([0, np.nan, 4], name="a") + a2 = Series([np.nan, 3, 5], name="a") + df = concat([a1, a2], axis=1) + + result = df.astype(dtype) + expected = concat([a1.astype(dtype), a2.astype(dtype)], axis=1) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "dtype", [{100: "float64", 200: "uint64"}, "category", "float64"] + ) + def test_astype_column_metadata(self, dtype): + # GH#19920 + columns = Index([100, 200, 300], dtype=np.uint64, name="foo") + df = DataFrame(np.arange(15).reshape(5, 3), columns=columns) + df = df.astype(dtype) + tm.assert_index_equal(df.columns, columns) + + @pytest.mark.parametrize("unit", ["Y", "M", "W", "D", "h", "m"]) + def test_astype_from_object_to_datetime_unit(self, unit): + vals = [ + ["2015-01-01", "2015-01-02", "2015-01-03"], + ["2017-01-01", "2017-01-02", "2017-02-03"], + ] + df = DataFrame(vals, dtype=object) + msg = ( + rf"Unexpected value for 'dtype': 'datetime64\[{unit}\]'. " + r"Must be 'datetime64\[s\]', 'datetime64\[ms\]', 'datetime64\[us\]', " + r"'datetime64\[ns\]' or DatetimeTZDtype" + ) + with pytest.raises(ValueError, match=msg): + df.astype(f"M8[{unit}]") + + @pytest.mark.parametrize("unit", ["Y", "M", "W", "D", "h", "m"]) + def test_astype_from_object_to_timedelta_unit(self, unit): + vals = [ + ["1 Day", "2 Days", "3 Days"], + ["4 Days", "5 Days", "6 Days"], + ] + df = DataFrame(vals, dtype=object) + msg = ( + r"Cannot convert from timedelta64\[ns\] to timedelta64\[.*\]. " + "Supported resolutions are 's', 'ms', 'us', 'ns'" + ) + with pytest.raises(ValueError, match=msg): + # TODO: this is ValueError while for DatetimeArray it is TypeError; + # get these consistent + df.astype(f"m8[{unit}]") + + @pytest.mark.parametrize("dtype", ["M8", "m8"]) + @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s", "h", "m", "D"]) + def test_astype_from_datetimelike_to_object(self, dtype, unit): + # tests astype to object dtype + # GH#19223 / GH#12425 + dtype = f"{dtype}[{unit}]" + arr = np.array([[1, 2, 3]], dtype=dtype) + df = DataFrame(arr) + result = df.astype(object) + assert (result.dtypes == object).all() + + if dtype.startswith("M8"): + assert result.iloc[0, 0] == Timestamp(1, unit=unit) + else: + assert result.iloc[0, 0] == Timedelta(1, unit=unit) + + @pytest.mark.parametrize("arr_dtype", [np.int64, np.float64]) + @pytest.mark.parametrize("dtype", ["M8", "m8"]) + @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s", "h", "m", "D"]) + def test_astype_to_datetimelike_unit(self, arr_dtype, dtype, unit): + # tests all units from numeric origination + # GH#19223 / GH#12425 + dtype = f"{dtype}[{unit}]" + arr = np.array([[1, 2, 3]], dtype=arr_dtype) + df = DataFrame(arr) + result = df.astype(dtype) + expected = DataFrame(arr.astype(dtype)) + + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s", "h", "m", "D"]) + def test_astype_to_datetime_unit(self, unit): + # tests all units from datetime origination + # GH#19223 + dtype = f"M8[{unit}]" + arr = np.array([[1, 2, 3]], dtype=dtype) + df = DataFrame(arr) + ser = df.iloc[:, 0] + idx = Index(ser) + dta = ser._values + + if unit in ["ns", "us", "ms", "s"]: + # GH#48928 + result = df.astype(dtype) + else: + # we use the nearest supported dtype (i.e. M8[s]) + msg = rf"Cannot cast DatetimeArray to dtype datetime64\[{unit}\]" + with pytest.raises(TypeError, match=msg): + df.astype(dtype) + + with pytest.raises(TypeError, match=msg): + ser.astype(dtype) + + with pytest.raises(TypeError, match=msg.replace("Array", "Index")): + idx.astype(dtype) + + with pytest.raises(TypeError, match=msg): + dta.astype(dtype) + + return + + exp_df = DataFrame(arr.astype(dtype)) + assert (exp_df.dtypes == dtype).all() + tm.assert_frame_equal(result, exp_df) + + res_ser = ser.astype(dtype) + exp_ser = exp_df.iloc[:, 0] + assert exp_ser.dtype == dtype + tm.assert_series_equal(res_ser, exp_ser) + + exp_dta = exp_ser._values + + res_index = idx.astype(dtype) + exp_index = Index(exp_ser) + assert exp_index.dtype == dtype + tm.assert_index_equal(res_index, exp_index) + + res_dta = dta.astype(dtype) + assert exp_dta.dtype == dtype + tm.assert_extension_array_equal(res_dta, exp_dta) + + @pytest.mark.parametrize("unit", ["ns"]) + def test_astype_to_timedelta_unit_ns(self, unit): + # preserver the timedelta conversion + # GH#19223 + dtype = f"m8[{unit}]" + arr = np.array([[1, 2, 3]], dtype=dtype) + df = DataFrame(arr) + result = df.astype(dtype) + expected = DataFrame(arr.astype(dtype)) + + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("unit", ["us", "ms", "s", "h", "m", "D"]) + def test_astype_to_timedelta_unit(self, unit): + # coerce to float + # GH#19223 until 2.0 used to coerce to float + dtype = f"m8[{unit}]" + arr = np.array([[1, 2, 3]], dtype=dtype) + df = DataFrame(arr) + ser = df.iloc[:, 0] + tdi = Index(ser) + tda = tdi._values + + if unit in ["us", "ms", "s"]: + assert (df.dtypes == dtype).all() + result = df.astype(dtype) + else: + # We get the nearest supported unit, i.e. "s" + assert (df.dtypes == "m8[s]").all() + + msg = ( + rf"Cannot convert from timedelta64\[s\] to timedelta64\[{unit}\]. " + "Supported resolutions are 's', 'ms', 'us', 'ns'" + ) + with pytest.raises(ValueError, match=msg): + df.astype(dtype) + with pytest.raises(ValueError, match=msg): + ser.astype(dtype) + with pytest.raises(ValueError, match=msg): + tdi.astype(dtype) + with pytest.raises(ValueError, match=msg): + tda.astype(dtype) + + return + + result = df.astype(dtype) + # The conversion is a no-op, so we just get a copy + expected = df + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s", "h", "m", "D"]) + def test_astype_to_incorrect_datetimelike(self, unit): + # trying to astype a m to a M, or vice-versa + # GH#19224 + dtype = f"M8[{unit}]" + other = f"m8[{unit}]" + + df = DataFrame(np.array([[1, 2, 3]], dtype=dtype)) + msg = "|".join( + [ + # BlockManager path + rf"Cannot cast DatetimeArray to dtype timedelta64\[{unit}\]", + # ArrayManager path + "cannot astype a datetimelike from " + rf"\[datetime64\[ns\]\] to \[timedelta64\[{unit}\]\]", + ] + ) + with pytest.raises(TypeError, match=msg): + df.astype(other) + + msg = "|".join( + [ + # BlockManager path + rf"Cannot cast TimedeltaArray to dtype datetime64\[{unit}\]", + # ArrayManager path + "cannot astype a timedelta from " + rf"\[timedelta64\[ns\]\] to \[datetime64\[{unit}\]\]", + ] + ) + df = DataFrame(np.array([[1, 2, 3]], dtype=other)) + with pytest.raises(TypeError, match=msg): + df.astype(dtype) + + def test_astype_arg_for_errors(self): + # GH#14878 + + df = DataFrame([1, 2, 3]) + + msg = ( + "Expected value of kwarg 'errors' to be one of " + "['raise', 'ignore']. Supplied value is 'True'" + ) + with pytest.raises(ValueError, match=re.escape(msg)): + df.astype(np.float64, errors=True) + + df.astype(np.int8, errors="ignore") + + def test_astype_invalid_conversion(self): + # GH#47571 + df = DataFrame({"a": [1, 2, "text"], "b": [1, 2, 3]}) + + msg = ( + "invalid literal for int() with base 10: 'text': " + "Error while type casting for column 'a'" + ) + + with pytest.raises(ValueError, match=re.escape(msg)): + df.astype({"a": int}) + + def test_astype_arg_for_errors_dictlist(self): + # GH#25905 + df = DataFrame( + [ + {"a": "1", "b": "16.5%", "c": "test"}, + {"a": "2.2", "b": "15.3", "c": "another_test"}, + ] + ) + expected = DataFrame( + [ + {"a": 1.0, "b": "16.5%", "c": "test"}, + {"a": 2.2, "b": "15.3", "c": "another_test"}, + ] + ) + expected["c"] = expected["c"].astype("object") + type_dict = {"a": "float64", "b": "float64", "c": "object"} + + result = df.astype(dtype=type_dict, errors="ignore") + + tm.assert_frame_equal(result, expected) + + def test_astype_dt64tz(self, timezone_frame): + # astype + expected = np.array( + [ + [ + Timestamp("2013-01-01 00:00:00"), + Timestamp("2013-01-02 00:00:00"), + Timestamp("2013-01-03 00:00:00"), + ], + [ + Timestamp("2013-01-01 00:00:00-0500", tz="US/Eastern"), + NaT, + Timestamp("2013-01-03 00:00:00-0500", tz="US/Eastern"), + ], + [ + Timestamp("2013-01-01 00:00:00+0100", tz="CET"), + NaT, + Timestamp("2013-01-03 00:00:00+0100", tz="CET"), + ], + ], + dtype=object, + ).T + expected = DataFrame( + expected, + index=timezone_frame.index, + columns=timezone_frame.columns, + dtype=object, + ) + result = timezone_frame.astype(object) + tm.assert_frame_equal(result, expected) + + msg = "Cannot use .astype to convert from timezone-aware dtype to timezone-" + with pytest.raises(TypeError, match=msg): + # dt64tz->dt64 deprecated + timezone_frame.astype("datetime64[ns]") + + def test_astype_dt64tz_to_str(self, timezone_frame, using_infer_string): + # str formatting + result = timezone_frame.astype(str) + na_value = np.nan if using_infer_string else "NaT" + expected = DataFrame( + [ + [ + "2013-01-01", + "2013-01-01 00:00:00-05:00", + "2013-01-01 00:00:00+01:00", + ], + ["2013-01-02", na_value, na_value], + [ + "2013-01-03", + "2013-01-03 00:00:00-05:00", + "2013-01-03 00:00:00+01:00", + ], + ], + columns=timezone_frame.columns, + dtype="str", + ) + tm.assert_frame_equal(result, expected) + + with option_context("display.max_columns", 20): + result = str(timezone_frame) + assert ( + "0 2013-01-01 2013-01-01 00:00:00-05:00 2013-01-01 00:00:00+01:00" + ) in result + assert ( + "1 2013-01-02 NaT NaT" + ) in result + assert ( + "2 2013-01-03 2013-01-03 00:00:00-05:00 2013-01-03 00:00:00+01:00" + ) in result + + def test_astype_empty_dtype_dict(self): + # issue mentioned further down in the following issue's thread + # https://github.com/pandas-dev/pandas/issues/33113 + df = DataFrame() + result = df.astype({}) + tm.assert_frame_equal(result, df) + assert result is not df + + @pytest.mark.parametrize( + "data, dtype", + [ + (["x", "y", "z"], "string[python]"), + pytest.param( + ["x", "y", "z"], + "string[pyarrow]", + marks=td.skip_if_no("pyarrow"), + ), + (["x", "y", "z"], "category"), + (3 * [Timestamp("2020-01-01", tz="UTC")], None), + (3 * [Interval(0, 1)], None), + ], + ) + @pytest.mark.parametrize("errors", ["raise", "ignore"]) + def test_astype_ignores_errors_for_extension_dtypes(self, data, dtype, errors): + # https://github.com/pandas-dev/pandas/issues/35471 + df = DataFrame(Series(data, dtype=dtype)) + if errors == "ignore": + expected = df + result = df.astype(float, errors=errors) + tm.assert_frame_equal(result, expected) + else: + msg = "(Cannot cast)|(could not convert)" + with pytest.raises((ValueError, TypeError), match=msg): + df.astype(float, errors=errors) + + def test_astype_tz_conversion(self): + # GH 35973 + val = {"tz": date_range("2020-08-30", freq="d", periods=2, tz="Europe/London")} + df = DataFrame(val) + result = df.astype({"tz": "datetime64[ns, Europe/Berlin]"}) + + expected = df + expected["tz"] = expected["tz"].dt.tz_convert("Europe/Berlin") + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("tz", ["UTC", "Europe/Berlin"]) + def test_astype_tz_object_conversion(self, tz): + # GH 35973 + val = {"tz": date_range("2020-08-30", freq="d", periods=2, tz="Europe/London")} + expected = DataFrame(val) + + # convert expected to object dtype from other tz str (independently tested) + result = expected.astype({"tz": f"datetime64[ns, {tz}]"}) + result = result.astype({"tz": "object"}) + + # do real test: object dtype to a specified tz, different from construction tz. + result = result.astype({"tz": "datetime64[ns, Europe/London]"}) + tm.assert_frame_equal(result, expected) + + @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string) GH#60639") + def test_astype_dt64_to_string( + self, frame_or_series, tz_naive_fixture, using_infer_string + ): + # GH#41409 + tz = tz_naive_fixture + + dti = date_range("2016-01-01", periods=3, tz=tz) + dta = dti._data + dta[0] = NaT + + obj = frame_or_series(dta) + result = obj.astype("string") + + # Check that Series/DataFrame.astype matches DatetimeArray.astype + expected = frame_or_series(dta.astype("string")) + tm.assert_equal(result, expected) + + item = result.iloc[0] + if frame_or_series is DataFrame: + item = item.iloc[0] + if using_infer_string: + assert item is np.nan + else: + assert item is pd.NA + + # For non-NA values, we should match what we get for non-EA str + alt = obj.astype(str) + assert np.all(alt.iloc[1:] == result.iloc[1:]) + + def test_astype_td64_to_string(self, frame_or_series): + # GH#41409 + tdi = pd.timedelta_range("1 Day", periods=3) + obj = frame_or_series(tdi) + + expected = frame_or_series(["1 days", "2 days", "3 days"], dtype="string") + result = obj.astype("string") + tm.assert_equal(result, expected) + + def test_astype_bytes(self): + # GH#39474 + result = DataFrame(["foo", "bar", "baz"]).astype(bytes) + assert result.dtypes[0] == np.dtype("S3") + + @pytest.mark.parametrize( + "index_slice", + [ + np.s_[:2, :2], + np.s_[:1, :2], + np.s_[:2, :1], + np.s_[::2, ::2], + np.s_[::1, ::2], + np.s_[::2, ::1], + ], + ) + def test_astype_noncontiguous(self, index_slice): + # GH#42396 + data = np.arange(16).reshape(4, 4) + df = DataFrame(data) + + result = df.iloc[index_slice].astype("int16") + expected = df.iloc[index_slice] + tm.assert_frame_equal(result, expected, check_dtype=False) + + def test_astype_retain_attrs(self, any_numpy_dtype): + # GH#44414 + df = DataFrame({"a": [0, 1, 2], "b": [3, 4, 5]}) + df.attrs["Location"] = "Michigan" + + result = df.astype({"a": any_numpy_dtype}).attrs + expected = df.attrs + + tm.assert_dict_equal(expected, result) + + +class TestAstypeCategorical: + def test_astype_from_categorical3(self): + df = DataFrame({"cats": [1, 2, 3, 4, 5, 6], "vals": [1, 2, 3, 4, 5, 6]}) + cats = Categorical([1, 2, 3, 4, 5, 6]) + exp_df = DataFrame({"cats": cats, "vals": [1, 2, 3, 4, 5, 6]}) + df["cats"] = df["cats"].astype("category") + tm.assert_frame_equal(exp_df, df) + + def test_astype_from_categorical4(self): + df = DataFrame( + {"cats": ["a", "b", "b", "a", "a", "d"], "vals": [1, 2, 3, 4, 5, 6]} + ) + cats = Categorical(["a", "b", "b", "a", "a", "d"]) + exp_df = DataFrame({"cats": cats, "vals": [1, 2, 3, 4, 5, 6]}) + df["cats"] = df["cats"].astype("category") + tm.assert_frame_equal(exp_df, df) + + def test_categorical_astype_to_int(self, any_int_dtype): + # GH#39402 + + df = DataFrame(data={"col1": pd.array([2.0, 1.0, 3.0])}) + df.col1 = df.col1.astype("category") + df.col1 = df.col1.astype(any_int_dtype) + expected = DataFrame({"col1": pd.array([2, 1, 3], dtype=any_int_dtype)}) + tm.assert_frame_equal(df, expected) + + def test_astype_categorical_to_string_missing(self): + # https://github.com/pandas-dev/pandas/issues/41797 + df = DataFrame(["a", "b", np.nan]) + expected = df.astype(str) + cat = df.astype("category") + result = cat.astype(str) + tm.assert_frame_equal(result, expected) + + +class IntegerArrayNoCopy(pd.core.arrays.IntegerArray): + # GH 42501 + + def copy(self): + assert False + + +class Int16DtypeNoCopy(pd.Int16Dtype): + # GH 42501 + + @classmethod + def construct_array_type(cls): + return IntegerArrayNoCopy + + +def test_frame_astype_no_copy(): + # GH 42501 + df = DataFrame({"a": [1, 4, None, 5], "b": [6, 7, 8, 9]}, dtype=object) + result = df.astype({"a": Int16DtypeNoCopy()}, copy=False) + + assert result.a.dtype == pd.Int16Dtype() + assert np.shares_memory(df.b.values, result.b.values) + + +@pytest.mark.parametrize("dtype", ["int64", "Int64"]) +def test_astype_copies(dtype): + # GH#50984 + pytest.importorskip("pyarrow") + df = DataFrame({"a": [1, 2, 3]}, dtype=dtype) + result = df.astype("int64[pyarrow]", copy=True) + df.iloc[0, 0] = 100 + expected = DataFrame({"a": [1, 2, 3]}, dtype="int64[pyarrow]") + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("val", [None, 1, 1.5, np.nan, NaT]) +def test_astype_to_string_not_modifying_input(string_storage, val): + # GH#51073 + df = DataFrame({"a": ["a", "b", val]}) + expected = df.copy() + with option_context("mode.string_storage", string_storage): + df.astype("string", copy=False) + tm.assert_frame_equal(df, expected) + + +@pytest.mark.parametrize("val", [None, 1, 1.5, np.nan, NaT]) +def test_astype_to_string_dtype_not_modifying_input(any_string_dtype, val): + # GH#51073 - variant of the above test with explicit dtype instances + df = DataFrame({"a": ["a", "b", val]}) + expected = df.copy() + df.astype(any_string_dtype) + tm.assert_frame_equal(df, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_at_time.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_at_time.py new file mode 100644 index 0000000000000000000000000000000000000000..4c1434bd66aff127e07c4ff3fce90a22721b2035 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_at_time.py @@ -0,0 +1,132 @@ +from datetime import time + +import numpy as np +import pytest +import pytz + +from pandas._libs.tslibs import timezones + +from pandas import ( + DataFrame, + date_range, +) +import pandas._testing as tm + + +class TestAtTime: + @pytest.mark.parametrize("tzstr", ["US/Eastern", "dateutil/US/Eastern"]) + def test_localized_at_time(self, tzstr, frame_or_series): + tz = timezones.maybe_get_tz(tzstr) + + rng = date_range("4/16/2012", "5/1/2012", freq="h") + ts = frame_or_series( + np.random.default_rng(2).standard_normal(len(rng)), index=rng + ) + + ts_local = ts.tz_localize(tzstr) + + result = ts_local.at_time(time(10, 0)) + expected = ts.at_time(time(10, 0)).tz_localize(tzstr) + tm.assert_equal(result, expected) + assert timezones.tz_compare(result.index.tz, tz) + + def test_at_time(self, frame_or_series): + rng = date_range("1/1/2000", "1/5/2000", freq="5min") + ts = DataFrame( + np.random.default_rng(2).standard_normal((len(rng), 2)), index=rng + ) + ts = tm.get_obj(ts, frame_or_series) + rs = ts.at_time(rng[1]) + assert (rs.index.hour == rng[1].hour).all() + assert (rs.index.minute == rng[1].minute).all() + assert (rs.index.second == rng[1].second).all() + + result = ts.at_time("9:30") + expected = ts.at_time(time(9, 30)) + tm.assert_equal(result, expected) + + def test_at_time_midnight(self, frame_or_series): + # midnight, everything + rng = date_range("1/1/2000", "1/31/2000") + ts = DataFrame( + np.random.default_rng(2).standard_normal((len(rng), 3)), index=rng + ) + ts = tm.get_obj(ts, frame_or_series) + + result = ts.at_time(time(0, 0)) + tm.assert_equal(result, ts) + + def test_at_time_nonexistent(self, frame_or_series): + # time doesn't exist + rng = date_range("1/1/2012", freq="23Min", periods=384) + ts = DataFrame(np.random.default_rng(2).standard_normal(len(rng)), rng) + ts = tm.get_obj(ts, frame_or_series) + rs = ts.at_time("16:00") + assert len(rs) == 0 + + @pytest.mark.parametrize( + "hour", ["1:00", "1:00AM", time(1), time(1, tzinfo=pytz.UTC)] + ) + def test_at_time_errors(self, hour): + # GH#24043 + dti = date_range("2018", periods=3, freq="h") + df = DataFrame(list(range(len(dti))), index=dti) + if getattr(hour, "tzinfo", None) is None: + result = df.at_time(hour) + expected = df.iloc[1:2] + tm.assert_frame_equal(result, expected) + else: + with pytest.raises(ValueError, match="Index must be timezone"): + df.at_time(hour) + + def test_at_time_tz(self): + # GH#24043 + dti = date_range("2018", periods=3, freq="h", tz="US/Pacific") + df = DataFrame(list(range(len(dti))), index=dti) + result = df.at_time(time(4, tzinfo=pytz.timezone("US/Eastern"))) + expected = df.iloc[1:2] + tm.assert_frame_equal(result, expected) + + def test_at_time_raises(self, frame_or_series): + # GH#20725 + obj = DataFrame([[1, 2, 3], [4, 5, 6]]) + obj = tm.get_obj(obj, frame_or_series) + msg = "Index must be DatetimeIndex" + with pytest.raises(TypeError, match=msg): # index is not a DatetimeIndex + obj.at_time("00:00") + + @pytest.mark.parametrize("axis", ["index", "columns", 0, 1]) + def test_at_time_axis(self, axis): + # issue 8839 + rng = date_range("1/1/2000", "1/5/2000", freq="5min") + ts = DataFrame(np.random.default_rng(2).standard_normal((len(rng), len(rng)))) + ts.index, ts.columns = rng, rng + + indices = rng[(rng.hour == 9) & (rng.minute == 30) & (rng.second == 0)] + + if axis in ["index", 0]: + expected = ts.loc[indices, :] + elif axis in ["columns", 1]: + expected = ts.loc[:, indices] + + result = ts.at_time("9:30", axis=axis) + + # Without clearing freq, result has freq 1440T and expected 5T + result.index = result.index._with_freq(None) + expected.index = expected.index._with_freq(None) + tm.assert_frame_equal(result, expected) + + def test_at_time_datetimeindex(self): + index = date_range("2012-01-01", "2012-01-05", freq="30min") + df = DataFrame( + np.random.default_rng(2).standard_normal((len(index), 5)), index=index + ) + akey = time(12, 0, 0) + ainds = [24, 72, 120, 168] + + result = df.at_time(akey) + expected = df.loc[akey] + expected2 = df.iloc[ainds] + tm.assert_frame_equal(result, expected) + tm.assert_frame_equal(result, expected2) + assert len(result) == 4 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_between_time.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_between_time.py new file mode 100644 index 0000000000000000000000000000000000000000..74d6291707e19d2b6536f4a5b758302ce3aa8e2b --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_between_time.py @@ -0,0 +1,227 @@ +from datetime import ( + datetime, + time, +) + +import numpy as np +import pytest + +from pandas._libs.tslibs import timezones +import pandas.util._test_decorators as td + +from pandas import ( + DataFrame, + Series, + date_range, +) +import pandas._testing as tm + + +class TestBetweenTime: + @td.skip_if_not_us_locale + def test_between_time_formats(self, frame_or_series): + # GH#11818 + rng = date_range("1/1/2000", "1/5/2000", freq="5min") + ts = DataFrame( + np.random.default_rng(2).standard_normal((len(rng), 2)), index=rng + ) + ts = tm.get_obj(ts, frame_or_series) + + strings = [ + ("2:00", "2:30"), + ("0200", "0230"), + ("2:00am", "2:30am"), + ("0200am", "0230am"), + ("2:00:00", "2:30:00"), + ("020000", "023000"), + ("2:00:00am", "2:30:00am"), + ("020000am", "023000am"), + ] + expected_length = 28 + + for time_string in strings: + assert len(ts.between_time(*time_string)) == expected_length + + @pytest.mark.parametrize("tzstr", ["US/Eastern", "dateutil/US/Eastern"]) + def test_localized_between_time(self, tzstr, frame_or_series): + tz = timezones.maybe_get_tz(tzstr) + + rng = date_range("4/16/2012", "5/1/2012", freq="h") + ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng) + if frame_or_series is DataFrame: + ts = ts.to_frame() + + ts_local = ts.tz_localize(tzstr) + + t1, t2 = time(10, 0), time(11, 0) + result = ts_local.between_time(t1, t2) + expected = ts.between_time(t1, t2).tz_localize(tzstr) + tm.assert_equal(result, expected) + assert timezones.tz_compare(result.index.tz, tz) + + def test_between_time_types(self, frame_or_series): + # GH11818 + rng = date_range("1/1/2000", "1/5/2000", freq="5min") + obj = DataFrame({"A": 0}, index=rng) + obj = tm.get_obj(obj, frame_or_series) + + msg = r"Cannot convert arg \[datetime\.datetime\(2010, 1, 2, 1, 0\)\] to a time" + with pytest.raises(ValueError, match=msg): + obj.between_time(datetime(2010, 1, 2, 1), datetime(2010, 1, 2, 5)) + + def test_between_time(self, inclusive_endpoints_fixture, frame_or_series): + rng = date_range("1/1/2000", "1/5/2000", freq="5min") + ts = DataFrame( + np.random.default_rng(2).standard_normal((len(rng), 2)), index=rng + ) + ts = tm.get_obj(ts, frame_or_series) + + stime = time(0, 0) + etime = time(1, 0) + inclusive = inclusive_endpoints_fixture + + filtered = ts.between_time(stime, etime, inclusive=inclusive) + exp_len = 13 * 4 + 1 + + if inclusive in ["right", "neither"]: + exp_len -= 5 + if inclusive in ["left", "neither"]: + exp_len -= 4 + + assert len(filtered) == exp_len + for rs in filtered.index: + t = rs.time() + if inclusive in ["left", "both"]: + assert t >= stime + else: + assert t > stime + + if inclusive in ["right", "both"]: + assert t <= etime + else: + assert t < etime + + result = ts.between_time("00:00", "01:00") + expected = ts.between_time(stime, etime) + tm.assert_equal(result, expected) + + # across midnight + rng = date_range("1/1/2000", "1/5/2000", freq="5min") + ts = DataFrame( + np.random.default_rng(2).standard_normal((len(rng), 2)), index=rng + ) + ts = tm.get_obj(ts, frame_or_series) + stime = time(22, 0) + etime = time(9, 0) + + filtered = ts.between_time(stime, etime, inclusive=inclusive) + exp_len = (12 * 11 + 1) * 4 + 1 + if inclusive in ["right", "neither"]: + exp_len -= 4 + if inclusive in ["left", "neither"]: + exp_len -= 4 + + assert len(filtered) == exp_len + for rs in filtered.index: + t = rs.time() + if inclusive in ["left", "both"]: + assert (t >= stime) or (t <= etime) + else: + assert (t > stime) or (t <= etime) + + if inclusive in ["right", "both"]: + assert (t <= etime) or (t >= stime) + else: + assert (t < etime) or (t >= stime) + + def test_between_time_raises(self, frame_or_series): + # GH#20725 + obj = DataFrame([[1, 2, 3], [4, 5, 6]]) + obj = tm.get_obj(obj, frame_or_series) + + msg = "Index must be DatetimeIndex" + with pytest.raises(TypeError, match=msg): # index is not a DatetimeIndex + obj.between_time(start_time="00:00", end_time="12:00") + + def test_between_time_axis(self, frame_or_series): + # GH#8839 + rng = date_range("1/1/2000", periods=100, freq="10min") + ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng) + if frame_or_series is DataFrame: + ts = ts.to_frame() + + stime, etime = ("08:00:00", "09:00:00") + expected_length = 7 + + assert len(ts.between_time(stime, etime)) == expected_length + assert len(ts.between_time(stime, etime, axis=0)) == expected_length + msg = f"No axis named {ts.ndim} for object type {type(ts).__name__}" + with pytest.raises(ValueError, match=msg): + ts.between_time(stime, etime, axis=ts.ndim) + + def test_between_time_axis_aliases(self, axis): + # GH#8839 + rng = date_range("1/1/2000", periods=100, freq="10min") + ts = DataFrame(np.random.default_rng(2).standard_normal((len(rng), len(rng)))) + stime, etime = ("08:00:00", "09:00:00") + exp_len = 7 + + if axis in ["index", 0]: + ts.index = rng + assert len(ts.between_time(stime, etime)) == exp_len + assert len(ts.between_time(stime, etime, axis=0)) == exp_len + + if axis in ["columns", 1]: + ts.columns = rng + selected = ts.between_time(stime, etime, axis=1).columns + assert len(selected) == exp_len + + def test_between_time_axis_raises(self, axis): + # issue 8839 + rng = date_range("1/1/2000", periods=100, freq="10min") + mask = np.arange(0, len(rng)) + rand_data = np.random.default_rng(2).standard_normal((len(rng), len(rng))) + ts = DataFrame(rand_data, index=rng, columns=rng) + stime, etime = ("08:00:00", "09:00:00") + + msg = "Index must be DatetimeIndex" + if axis in ["columns", 1]: + ts.index = mask + with pytest.raises(TypeError, match=msg): + ts.between_time(stime, etime) + with pytest.raises(TypeError, match=msg): + ts.between_time(stime, etime, axis=0) + + if axis in ["index", 0]: + ts.columns = mask + with pytest.raises(TypeError, match=msg): + ts.between_time(stime, etime, axis=1) + + def test_between_time_datetimeindex(self): + index = date_range("2012-01-01", "2012-01-05", freq="30min") + df = DataFrame( + np.random.default_rng(2).standard_normal((len(index), 5)), index=index + ) + bkey = slice(time(13, 0, 0), time(14, 0, 0)) + binds = [26, 27, 28, 74, 75, 76, 122, 123, 124, 170, 171, 172] + + result = df.between_time(bkey.start, bkey.stop) + expected = df.loc[bkey] + expected2 = df.iloc[binds] + tm.assert_frame_equal(result, expected) + tm.assert_frame_equal(result, expected2) + assert len(result) == 12 + + def test_between_time_incorrect_arg_inclusive(self): + # GH40245 + rng = date_range("1/1/2000", "1/5/2000", freq="5min") + ts = DataFrame( + np.random.default_rng(2).standard_normal((len(rng), 2)), index=rng + ) + + stime = time(0, 0) + etime = time(1, 0) + inclusive = "bad_string" + msg = "Inclusive has to be either 'both', 'neither', 'left' or 'right'" + with pytest.raises(ValueError, match=msg): + ts.between_time(stime, etime, inclusive=inclusive) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_clip.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_clip.py new file mode 100644 index 0000000000000000000000000000000000000000..f783a388d75179e9b68e373da65218dfffb04b55 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_clip.py @@ -0,0 +1,199 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Series, +) +import pandas._testing as tm + + +class TestDataFrameClip: + def test_clip(self, float_frame): + median = float_frame.median().median() + original = float_frame.copy() + + double = float_frame.clip(upper=median, lower=median) + assert not (double.values != median).any() + + # Verify that float_frame was not changed inplace + assert (float_frame.values == original.values).all() + + def test_inplace_clip(self, float_frame): + # GH#15388 + median = float_frame.median().median() + frame_copy = float_frame.copy() + + return_value = frame_copy.clip(upper=median, lower=median, inplace=True) + assert return_value is None + assert not (frame_copy.values != median).any() + + def test_dataframe_clip(self): + # GH#2747 + df = DataFrame(np.random.default_rng(2).standard_normal((1000, 2))) + + for lb, ub in [(-1, 1), (1, -1)]: + clipped_df = df.clip(lb, ub) + + lb, ub = min(lb, ub), max(ub, lb) + lb_mask = df.values <= lb + ub_mask = df.values >= ub + mask = ~lb_mask & ~ub_mask + assert (clipped_df.values[lb_mask] == lb).all() + assert (clipped_df.values[ub_mask] == ub).all() + assert (clipped_df.values[mask] == df.values[mask]).all() + + def test_clip_mixed_numeric(self): + # clip on mixed integer or floats + # GH#24162, clipping now preserves numeric types per column + df = DataFrame({"A": [1, 2, 3], "B": [1.0, np.nan, 3.0]}) + result = df.clip(1, 2) + expected = DataFrame({"A": [1, 2, 2], "B": [1.0, np.nan, 2.0]}) + tm.assert_frame_equal(result, expected) + + df = DataFrame([[1, 2, 3.4], [3, 4, 5.6]], columns=["foo", "bar", "baz"]) + expected = df.dtypes + result = df.clip(upper=3).dtypes + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("inplace", [True, False]) + def test_clip_against_series(self, inplace): + # GH#6966 + + df = DataFrame(np.random.default_rng(2).standard_normal((1000, 2))) + lb = Series(np.random.default_rng(2).standard_normal(1000)) + ub = lb + 1 + + original = df.copy() + clipped_df = df.clip(lb, ub, axis=0, inplace=inplace) + + if inplace: + clipped_df = df + + for i in range(2): + lb_mask = original.iloc[:, i] <= lb + ub_mask = original.iloc[:, i] >= ub + mask = ~lb_mask & ~ub_mask + + result = clipped_df.loc[lb_mask, i] + tm.assert_series_equal(result, lb[lb_mask], check_names=False) + assert result.name == i + + result = clipped_df.loc[ub_mask, i] + tm.assert_series_equal(result, ub[ub_mask], check_names=False) + assert result.name == i + + tm.assert_series_equal(clipped_df.loc[mask, i], df.loc[mask, i]) + + @pytest.mark.parametrize("inplace", [True, False]) + @pytest.mark.parametrize("lower", [[2, 3, 4], np.asarray([2, 3, 4])]) + @pytest.mark.parametrize( + "axis,res", + [ + (0, [[2.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 7.0, 7.0]]), + (1, [[2.0, 3.0, 4.0], [4.0, 5.0, 6.0], [5.0, 6.0, 7.0]]), + ], + ) + def test_clip_against_list_like(self, inplace, lower, axis, res): + # GH#15390 + arr = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]) + + original = DataFrame( + arr, columns=["one", "two", "three"], index=["a", "b", "c"] + ) + + result = original.clip(lower=lower, upper=[5, 6, 7], axis=axis, inplace=inplace) + + expected = DataFrame(res, columns=original.columns, index=original.index) + if inplace: + result = original + tm.assert_frame_equal(result, expected, check_exact=True) + + @pytest.mark.parametrize("axis", [0, 1, None]) + def test_clip_against_frame(self, axis): + df = DataFrame(np.random.default_rng(2).standard_normal((1000, 2))) + lb = DataFrame(np.random.default_rng(2).standard_normal((1000, 2))) + ub = lb + 1 + + clipped_df = df.clip(lb, ub, axis=axis) + + lb_mask = df <= lb + ub_mask = df >= ub + mask = ~lb_mask & ~ub_mask + + tm.assert_frame_equal(clipped_df[lb_mask], lb[lb_mask]) + tm.assert_frame_equal(clipped_df[ub_mask], ub[ub_mask]) + tm.assert_frame_equal(clipped_df[mask], df[mask]) + + def test_clip_against_unordered_columns(self): + # GH#20911 + df1 = DataFrame( + np.random.default_rng(2).standard_normal((1000, 4)), + columns=["A", "B", "C", "D"], + ) + df2 = DataFrame( + np.random.default_rng(2).standard_normal((1000, 4)), + columns=["D", "A", "B", "C"], + ) + df3 = DataFrame(df2.values - 1, columns=["B", "D", "C", "A"]) + result_upper = df1.clip(lower=0, upper=df2) + expected_upper = df1.clip(lower=0, upper=df2[df1.columns]) + result_lower = df1.clip(lower=df3, upper=3) + expected_lower = df1.clip(lower=df3[df1.columns], upper=3) + result_lower_upper = df1.clip(lower=df3, upper=df2) + expected_lower_upper = df1.clip(lower=df3[df1.columns], upper=df2[df1.columns]) + tm.assert_frame_equal(result_upper, expected_upper) + tm.assert_frame_equal(result_lower, expected_lower) + tm.assert_frame_equal(result_lower_upper, expected_lower_upper) + + def test_clip_with_na_args(self, float_frame): + """Should process np.nan argument as None""" + # GH#17276 + tm.assert_frame_equal(float_frame.clip(np.nan), float_frame) + tm.assert_frame_equal(float_frame.clip(upper=np.nan, lower=np.nan), float_frame) + + # GH#19992 and adjusted in GH#40420 + df = DataFrame({"col_0": [1, 2, 3], "col_1": [4, 5, 6], "col_2": [7, 8, 9]}) + + msg = "Downcasting behavior in Series and DataFrame methods 'where'" + # TODO: avoid this warning here? seems like we should never be upcasting + # in the first place? + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.clip(lower=[4, 5, np.nan], axis=0) + expected = DataFrame( + {"col_0": [4, 5, 3], "col_1": [4, 5, 6], "col_2": [7, 8, 9]} + ) + tm.assert_frame_equal(result, expected) + + result = df.clip(lower=[4, 5, np.nan], axis=1) + expected = DataFrame( + {"col_0": [4, 4, 4], "col_1": [5, 5, 6], "col_2": [7, 8, 9]} + ) + tm.assert_frame_equal(result, expected) + + # GH#40420 + data = {"col_0": [9, -3, 0, -1, 5], "col_1": [-2, -7, 6, 8, -5]} + df = DataFrame(data) + t = Series([2, -4, np.nan, 6, 3]) + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.clip(lower=t, axis=0) + expected = DataFrame({"col_0": [9, -3, 0, 6, 5], "col_1": [2, -4, 6, 8, 3]}) + tm.assert_frame_equal(result, expected) + + def test_clip_int_data_with_float_bound(self): + # GH51472 + df = DataFrame({"a": [1, 2, 3]}) + result = df.clip(lower=1.5) + expected = DataFrame({"a": [1.5, 2.0, 3.0]}) + tm.assert_frame_equal(result, expected) + + def test_clip_with_list_bound(self): + # GH#54817 + df = DataFrame([1, 5]) + expected = DataFrame([3, 5]) + result = df.clip([3]) + tm.assert_frame_equal(result, expected) + + expected = DataFrame([1, 3]) + result = df.clip(upper=[3]) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_combine.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_combine.py new file mode 100644 index 0000000000000000000000000000000000000000..bc6a67e4e1f320dbb71220d33ba03eaf788bcb4b --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_combine.py @@ -0,0 +1,47 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm + + +class TestCombine: + @pytest.mark.parametrize( + "data", + [ + pd.date_range("2000", periods=4), + pd.date_range("2000", periods=4, tz="US/Central"), + pd.period_range("2000", periods=4), + pd.timedelta_range(0, periods=4), + ], + ) + def test_combine_datetlike_udf(self, data): + # GH#23079 + df = pd.DataFrame({"A": data}) + other = df.copy() + df.iloc[1, 0] = None + + def combiner(a, b): + return b + + result = df.combine(other, combiner) + tm.assert_frame_equal(result, other) + + def test_combine_generic(self, float_frame): + df1 = float_frame + df2 = float_frame.loc[float_frame.index[:-5], ["A", "B", "C"]] + + combined = df1.combine(df2, np.add) + combined2 = df2.combine(df1, np.add) + assert combined["D"].isna().all() + assert combined2["D"].isna().all() + + chunk = combined.loc[combined.index[:-5], ["A", "B", "C"]] + chunk2 = combined2.loc[combined2.index[:-5], ["A", "B", "C"]] + + exp = ( + float_frame.loc[float_frame.index[:-5], ["A", "B", "C"]].reindex_like(chunk) + * 2 + ) + tm.assert_frame_equal(chunk, exp) + tm.assert_frame_equal(chunk2, exp) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_combine_first.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_combine_first.py new file mode 100644 index 0000000000000000000000000000000000000000..8aeab5dacd8b4aeb96ef5b91ea0de34c485eab2c --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_combine_first.py @@ -0,0 +1,556 @@ +from datetime import datetime + +import numpy as np +import pytest + +from pandas.core.dtypes.cast import find_common_type +from pandas.core.dtypes.common import is_dtype_equal + +import pandas as pd +from pandas import ( + DataFrame, + Index, + MultiIndex, + Series, +) +import pandas._testing as tm + + +class TestDataFrameCombineFirst: + def test_combine_first_mixed(self): + a = Series(["a", "b"], index=range(2)) + b = Series(range(2), index=range(2)) + f = DataFrame({"A": a, "B": b}) + + a = Series(["a", "b"], index=range(5, 7)) + b = Series(range(2), index=range(5, 7)) + g = DataFrame({"A": a, "B": b}) + + exp = DataFrame({"A": list("abab"), "B": [0, 1, 0, 1]}, index=[0, 1, 5, 6]) + combined = f.combine_first(g) + tm.assert_frame_equal(combined, exp) + + def test_combine_first(self, float_frame, using_infer_string): + # disjoint + head, tail = float_frame[:5], float_frame[5:] + + combined = head.combine_first(tail) + reordered_frame = float_frame.reindex(combined.index) + tm.assert_frame_equal(combined, reordered_frame) + tm.assert_index_equal(combined.columns, float_frame.columns) + tm.assert_series_equal(combined["A"], reordered_frame["A"]) + + # same index + fcopy = float_frame.copy() + fcopy["A"] = 1 + del fcopy["C"] + + fcopy2 = float_frame.copy() + fcopy2["B"] = 0 + del fcopy2["D"] + + combined = fcopy.combine_first(fcopy2) + + assert (combined["A"] == 1).all() + tm.assert_series_equal(combined["B"], fcopy["B"]) + tm.assert_series_equal(combined["C"], fcopy2["C"]) + tm.assert_series_equal(combined["D"], fcopy["D"]) + + # overlap + head, tail = reordered_frame[:10].copy(), reordered_frame + head["A"] = 1 + + combined = head.combine_first(tail) + assert (combined["A"][:10] == 1).all() + + # reverse overlap + tail.iloc[:10, tail.columns.get_loc("A")] = 0 + combined = tail.combine_first(head) + assert (combined["A"][:10] == 0).all() + + # no overlap + f = float_frame[:10] + g = float_frame[10:] + combined = f.combine_first(g) + tm.assert_series_equal(combined["A"].reindex(f.index), f["A"]) + tm.assert_series_equal(combined["A"].reindex(g.index), g["A"]) + + # corner cases + warning = FutureWarning if using_infer_string else None + with tm.assert_produces_warning(warning, match="empty entries"): + comb = float_frame.combine_first(DataFrame()) + tm.assert_frame_equal(comb, float_frame) + + comb = DataFrame().combine_first(float_frame) + tm.assert_frame_equal(comb, float_frame.sort_index()) + + comb = float_frame.combine_first(DataFrame(index=["faz", "boo"])) + assert "faz" in comb.index + + # #2525 + df = DataFrame({"a": [1]}, index=[datetime(2012, 1, 1)]) + df2 = DataFrame(columns=["b"]) + result = df.combine_first(df2) + assert "b" in result + + def test_combine_first_mixed_bug(self): + idx = Index(["a", "b", "c", "e"]) + ser1 = Series([5.0, -9.0, 4.0, 100.0], index=idx) + ser2 = Series(["a", "b", "c", "e"], index=idx) + ser3 = Series([12, 4, 5, 97], index=idx) + + frame1 = DataFrame({"col0": ser1, "col2": ser2, "col3": ser3}) + + idx = Index(["a", "b", "c", "f"]) + ser1 = Series([5.0, -9.0, 4.0, 100.0], index=idx) + ser2 = Series(["a", "b", "c", "f"], index=idx) + ser3 = Series([12, 4, 5, 97], index=idx) + + frame2 = DataFrame({"col1": ser1, "col2": ser2, "col5": ser3}) + + combined = frame1.combine_first(frame2) + assert len(combined.columns) == 5 + + def test_combine_first_same_as_in_update(self): + # gh 3016 (same as in update) + df = DataFrame( + [[1.0, 2.0, False, True], [4.0, 5.0, True, False]], + columns=["A", "B", "bool1", "bool2"], + ) + + other = DataFrame([[45, 45]], index=[0], columns=["A", "B"]) + result = df.combine_first(other) + tm.assert_frame_equal(result, df) + + df.loc[0, "A"] = np.nan + result = df.combine_first(other) + df.loc[0, "A"] = 45 + tm.assert_frame_equal(result, df) + + def test_combine_first_doc_example(self): + # doc example + df1 = DataFrame( + {"A": [1.0, np.nan, 3.0, 5.0, np.nan], "B": [np.nan, 2.0, 3.0, np.nan, 6.0]} + ) + + df2 = DataFrame( + { + "A": [5.0, 2.0, 4.0, np.nan, 3.0, 7.0], + "B": [np.nan, np.nan, 3.0, 4.0, 6.0, 8.0], + } + ) + + result = df1.combine_first(df2) + expected = DataFrame({"A": [1, 2, 3, 5, 3, 7.0], "B": [np.nan, 2, 3, 4, 6, 8]}) + tm.assert_frame_equal(result, expected) + + def test_combine_first_return_obj_type_with_bools(self): + # GH3552 + + df1 = DataFrame( + [[np.nan, 3.0, True], [-4.6, np.nan, True], [np.nan, 7.0, False]] + ) + df2 = DataFrame([[-42.6, np.nan, True], [-5.0, 1.6, False]], index=[1, 2]) + + expected = Series([True, True, False], name=2, dtype=bool) + + result_12 = df1.combine_first(df2)[2] + tm.assert_series_equal(result_12, expected) + + result_21 = df2.combine_first(df1)[2] + tm.assert_series_equal(result_21, expected) + + @pytest.mark.parametrize( + "data1, data2, data_expected", + ( + ( + [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)], + [pd.NaT, pd.NaT, pd.NaT], + [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)], + ), + ( + [pd.NaT, pd.NaT, pd.NaT], + [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)], + [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)], + ), + ( + [datetime(2000, 1, 2), pd.NaT, pd.NaT], + [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)], + [datetime(2000, 1, 2), datetime(2000, 1, 2), datetime(2000, 1, 3)], + ), + ( + [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)], + [datetime(2000, 1, 2), pd.NaT, pd.NaT], + [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)], + ), + ), + ) + def test_combine_first_convert_datatime_correctly( + self, data1, data2, data_expected + ): + # GH 3593 + + df1, df2 = DataFrame({"a": data1}), DataFrame({"a": data2}) + result = df1.combine_first(df2) + expected = DataFrame({"a": data_expected}) + tm.assert_frame_equal(result, expected) + + def test_combine_first_align_nan(self): + # GH 7509 (not fixed) + dfa = DataFrame([[pd.Timestamp("2011-01-01"), 2]], columns=["a", "b"]) + dfb = DataFrame([[4], [5]], columns=["b"]) + assert dfa["a"].dtype == "datetime64[ns]" + assert dfa["b"].dtype == "int64" + + res = dfa.combine_first(dfb) + exp = DataFrame( + {"a": [pd.Timestamp("2011-01-01"), pd.NaT], "b": [2, 5]}, + columns=["a", "b"], + ) + tm.assert_frame_equal(res, exp) + assert res["a"].dtype == "datetime64[ns]" + # TODO: this must be int64 + assert res["b"].dtype == "int64" + + res = dfa.iloc[:0].combine_first(dfb) + exp = DataFrame({"a": [np.nan, np.nan], "b": [4, 5]}, columns=["a", "b"]) + tm.assert_frame_equal(res, exp) + # TODO: this must be datetime64 + assert res["a"].dtype == "float64" + # TODO: this must be int64 + assert res["b"].dtype == "int64" + + def test_combine_first_timezone(self, unit): + # see gh-7630 + data1 = pd.to_datetime("20100101 01:01").tz_localize("UTC").as_unit(unit) + df1 = DataFrame( + columns=["UTCdatetime", "abc"], + data=data1, + index=pd.date_range("20140627", periods=1), + ) + data2 = pd.to_datetime("20121212 12:12").tz_localize("UTC").as_unit(unit) + df2 = DataFrame( + columns=["UTCdatetime", "xyz"], + data=data2, + index=pd.date_range("20140628", periods=1), + ) + res = df2[["UTCdatetime"]].combine_first(df1) + exp = DataFrame( + { + "UTCdatetime": [ + pd.Timestamp("2010-01-01 01:01", tz="UTC"), + pd.Timestamp("2012-12-12 12:12", tz="UTC"), + ], + "abc": [pd.Timestamp("2010-01-01 01:01:00", tz="UTC"), pd.NaT], + }, + columns=["UTCdatetime", "abc"], + index=pd.date_range("20140627", periods=2, freq="D"), + dtype=f"datetime64[{unit}, UTC]", + ) + assert res["UTCdatetime"].dtype == f"datetime64[{unit}, UTC]" + assert res["abc"].dtype == f"datetime64[{unit}, UTC]" + + tm.assert_frame_equal(res, exp) + + def test_combine_first_timezone2(self, unit): + # see gh-10567 + dts1 = pd.date_range("2015-01-01", "2015-01-05", tz="UTC", unit=unit) + df1 = DataFrame({"DATE": dts1}) + dts2 = pd.date_range("2015-01-03", "2015-01-05", tz="UTC", unit=unit) + df2 = DataFrame({"DATE": dts2}) + + res = df1.combine_first(df2) + tm.assert_frame_equal(res, df1) + assert res["DATE"].dtype == f"datetime64[{unit}, UTC]" + + def test_combine_first_timezone3(self, unit): + dts1 = pd.DatetimeIndex( + ["2011-01-01", "NaT", "2011-01-03", "2011-01-04"], tz="US/Eastern" + ).as_unit(unit) + df1 = DataFrame({"DATE": dts1}, index=[1, 3, 5, 7]) + dts2 = pd.DatetimeIndex( + ["2012-01-01", "2012-01-02", "2012-01-03"], tz="US/Eastern" + ).as_unit(unit) + df2 = DataFrame({"DATE": dts2}, index=[2, 4, 5]) + + res = df1.combine_first(df2) + exp_dts = pd.DatetimeIndex( + [ + "2011-01-01", + "2012-01-01", + "NaT", + "2012-01-02", + "2011-01-03", + "2011-01-04", + ], + tz="US/Eastern", + ).as_unit(unit) + exp = DataFrame({"DATE": exp_dts}, index=[1, 2, 3, 4, 5, 7]) + tm.assert_frame_equal(res, exp) + + # FIXME: parametrizing over unit breaks on non-nano + def test_combine_first_timezone4(self): + # different tz + dts1 = pd.date_range("2015-01-01", "2015-01-05", tz="US/Eastern") + df1 = DataFrame({"DATE": dts1}) + dts2 = pd.date_range("2015-01-03", "2015-01-05") + df2 = DataFrame({"DATE": dts2}) + + # if df1 doesn't have NaN, keep its dtype + res = df1.combine_first(df2) + tm.assert_frame_equal(res, df1) + assert res["DATE"].dtype == "datetime64[ns, US/Eastern]" + + def test_combine_first_timezone5(self, unit): + dts1 = pd.date_range("2015-01-01", "2015-01-02", tz="US/Eastern", unit=unit) + df1 = DataFrame({"DATE": dts1}) + dts2 = pd.date_range("2015-01-01", "2015-01-03", unit=unit) + df2 = DataFrame({"DATE": dts2}) + + res = df1.combine_first(df2) + exp_dts = [ + pd.Timestamp("2015-01-01", tz="US/Eastern"), + pd.Timestamp("2015-01-02", tz="US/Eastern"), + pd.Timestamp("2015-01-03"), + ] + exp = DataFrame({"DATE": exp_dts}) + tm.assert_frame_equal(res, exp) + assert res["DATE"].dtype == "object" + + def test_combine_first_timedelta(self): + data1 = pd.TimedeltaIndex(["1 day", "NaT", "3 day", "4day"]) + df1 = DataFrame({"TD": data1}, index=[1, 3, 5, 7]) + data2 = pd.TimedeltaIndex(["10 day", "11 day", "12 day"]) + df2 = DataFrame({"TD": data2}, index=[2, 4, 5]) + + res = df1.combine_first(df2) + exp_dts = pd.TimedeltaIndex( + ["1 day", "10 day", "NaT", "11 day", "3 day", "4 day"] + ) + exp = DataFrame({"TD": exp_dts}, index=[1, 2, 3, 4, 5, 7]) + tm.assert_frame_equal(res, exp) + assert res["TD"].dtype == "timedelta64[ns]" + + def test_combine_first_period(self): + data1 = pd.PeriodIndex(["2011-01", "NaT", "2011-03", "2011-04"], freq="M") + df1 = DataFrame({"P": data1}, index=[1, 3, 5, 7]) + data2 = pd.PeriodIndex(["2012-01-01", "2012-02", "2012-03"], freq="M") + df2 = DataFrame({"P": data2}, index=[2, 4, 5]) + + res = df1.combine_first(df2) + exp_dts = pd.PeriodIndex( + ["2011-01", "2012-01", "NaT", "2012-02", "2011-03", "2011-04"], freq="M" + ) + exp = DataFrame({"P": exp_dts}, index=[1, 2, 3, 4, 5, 7]) + tm.assert_frame_equal(res, exp) + assert res["P"].dtype == data1.dtype + + # different freq + dts2 = pd.PeriodIndex(["2012-01-01", "2012-01-02", "2012-01-03"], freq="D") + df2 = DataFrame({"P": dts2}, index=[2, 4, 5]) + + res = df1.combine_first(df2) + exp_dts = [ + pd.Period("2011-01", freq="M"), + pd.Period("2012-01-01", freq="D"), + pd.NaT, + pd.Period("2012-01-02", freq="D"), + pd.Period("2011-03", freq="M"), + pd.Period("2011-04", freq="M"), + ] + exp = DataFrame({"P": exp_dts}, index=[1, 2, 3, 4, 5, 7]) + tm.assert_frame_equal(res, exp) + assert res["P"].dtype == "object" + + def test_combine_first_int(self): + # GH14687 - integer series that do no align exactly + + df1 = DataFrame({"a": [0, 1, 3, 5]}, dtype="int64") + df2 = DataFrame({"a": [1, 4]}, dtype="int64") + + result_12 = df1.combine_first(df2) + expected_12 = DataFrame({"a": [0, 1, 3, 5]}) + tm.assert_frame_equal(result_12, expected_12) + + result_21 = df2.combine_first(df1) + expected_21 = DataFrame({"a": [1, 4, 3, 5]}) + tm.assert_frame_equal(result_21, expected_21) + + @pytest.mark.parametrize("val", [1, 1.0]) + def test_combine_first_with_asymmetric_other(self, val): + # see gh-20699 + df1 = DataFrame({"isNum": [val]}) + df2 = DataFrame({"isBool": [True]}) + + res = df1.combine_first(df2) + exp = DataFrame({"isBool": [True], "isNum": [val]}) + + tm.assert_frame_equal(res, exp) + + def test_combine_first_string_dtype_only_na(self, nullable_string_dtype): + # GH: 37519 + df = DataFrame( + {"a": ["962", "85"], "b": [pd.NA] * 2}, dtype=nullable_string_dtype + ) + df2 = DataFrame({"a": ["85"], "b": [pd.NA]}, dtype=nullable_string_dtype) + df.set_index(["a", "b"], inplace=True) + df2.set_index(["a", "b"], inplace=True) + result = df.combine_first(df2) + expected = DataFrame( + {"a": ["962", "85"], "b": [pd.NA] * 2}, dtype=nullable_string_dtype + ).set_index(["a", "b"]) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "scalar1, scalar2", + [ + (datetime(2020, 1, 1), datetime(2020, 1, 2)), + (pd.Period("2020-01-01", "D"), pd.Period("2020-01-02", "D")), + (pd.Timedelta("89 days"), pd.Timedelta("60 min")), + (pd.Interval(left=0, right=1), pd.Interval(left=2, right=3, closed="left")), + ], +) +def test_combine_first_timestamp_bug(scalar1, scalar2, nulls_fixture): + # GH28481 + na_value = nulls_fixture + + frame = DataFrame([[na_value, na_value]], columns=["a", "b"]) + other = DataFrame([[scalar1, scalar2]], columns=["b", "c"]) + + common_dtype = find_common_type([frame.dtypes["b"], other.dtypes["b"]]) + + if is_dtype_equal(common_dtype, "object") or frame.dtypes["b"] == other.dtypes["b"]: + val = scalar1 + else: + val = na_value + + result = frame.combine_first(other) + + expected = DataFrame([[na_value, val, scalar2]], columns=["a", "b", "c"]) + + expected["b"] = expected["b"].astype(common_dtype) + + tm.assert_frame_equal(result, expected) + + +def test_combine_first_timestamp_bug_NaT(): + # GH28481 + frame = DataFrame([[pd.NaT, pd.NaT]], columns=["a", "b"]) + other = DataFrame( + [[datetime(2020, 1, 1), datetime(2020, 1, 2)]], columns=["b", "c"] + ) + + result = frame.combine_first(other) + expected = DataFrame( + [[pd.NaT, datetime(2020, 1, 1), datetime(2020, 1, 2)]], columns=["a", "b", "c"] + ) + + tm.assert_frame_equal(result, expected) + + +def test_combine_first_with_nan_multiindex(): + # gh-36562 + + mi1 = MultiIndex.from_arrays( + [["b", "b", "c", "a", "b", np.nan], [1, 2, 3, 4, 5, 6]], names=["a", "b"] + ) + df = DataFrame({"c": [1, 1, 1, 1, 1, 1]}, index=mi1) + mi2 = MultiIndex.from_arrays( + [["a", "b", "c", "a", "b", "d"], [1, 1, 1, 1, 1, 1]], names=["a", "b"] + ) + s = Series([1, 2, 3, 4, 5, 6], index=mi2) + res = df.combine_first(DataFrame({"d": s})) + mi_expected = MultiIndex.from_arrays( + [ + ["a", "a", "a", "b", "b", "b", "b", "c", "c", "d", np.nan], + [1, 1, 4, 1, 1, 2, 5, 1, 3, 1, 6], + ], + names=["a", "b"], + ) + expected = DataFrame( + { + "c": [np.nan, np.nan, 1, 1, 1, 1, 1, np.nan, 1, np.nan, 1], + "d": [1.0, 4.0, np.nan, 2.0, 5.0, np.nan, np.nan, 3.0, np.nan, 6.0, np.nan], + }, + index=mi_expected, + ) + tm.assert_frame_equal(res, expected) + + +def test_combine_preserve_dtypes(): + # GH7509 + a_column = Series(["a", "b"], index=range(2)) + b_column = Series(range(2), index=range(2)) + df1 = DataFrame({"A": a_column, "B": b_column}) + + c_column = Series(["a", "b"], index=range(5, 7)) + b_column = Series(range(-1, 1), index=range(5, 7)) + df2 = DataFrame({"B": b_column, "C": c_column}) + + expected = DataFrame( + { + "A": ["a", "b", np.nan, np.nan], + "B": [0, 1, -1, 0], + "C": [np.nan, np.nan, "a", "b"], + }, + index=[0, 1, 5, 6], + ) + combined = df1.combine_first(df2) + tm.assert_frame_equal(combined, expected) + + +def test_combine_first_duplicates_rows_for_nan_index_values(): + # GH39881 + df1 = DataFrame( + {"x": [9, 10, 11]}, + index=MultiIndex.from_arrays([[1, 2, 3], [np.nan, 5, 6]], names=["a", "b"]), + ) + + df2 = DataFrame( + {"y": [12, 13, 14]}, + index=MultiIndex.from_arrays([[1, 2, 4], [np.nan, 5, 7]], names=["a", "b"]), + ) + + expected = DataFrame( + { + "x": [9.0, 10.0, 11.0, np.nan], + "y": [12.0, 13.0, np.nan, 14.0], + }, + index=MultiIndex.from_arrays( + [[1, 2, 3, 4], [np.nan, 5, 6, 7]], names=["a", "b"] + ), + ) + combined = df1.combine_first(df2) + tm.assert_frame_equal(combined, expected) + + +def test_combine_first_int64_not_cast_to_float64(): + # GH 28613 + df_1 = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) + df_2 = DataFrame({"A": [1, 20, 30], "B": [40, 50, 60], "C": [12, 34, 65]}) + result = df_1.combine_first(df_2) + expected = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [12, 34, 65]}) + tm.assert_frame_equal(result, expected) + + +def test_midx_losing_dtype(): + # GH#49830 + midx = MultiIndex.from_arrays([[0, 0], [np.nan, np.nan]]) + midx2 = MultiIndex.from_arrays([[1, 1], [np.nan, np.nan]]) + df1 = DataFrame({"a": [None, 4]}, index=midx) + df2 = DataFrame({"a": [3, 3]}, index=midx2) + result = df1.combine_first(df2) + expected_midx = MultiIndex.from_arrays( + [[0, 0, 1, 1], [np.nan, np.nan, np.nan, np.nan]] + ) + expected = DataFrame({"a": [np.nan, 4, 3, 3]}, index=expected_midx) + tm.assert_frame_equal(result, expected) + + +def test_combine_first_empty_columns(): + left = DataFrame(columns=["a", "b"]) + right = DataFrame(columns=["a", "c"]) + result = left.combine_first(right) + expected = DataFrame(columns=["a", "b", "c"]) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_compare.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_compare.py new file mode 100644 index 0000000000000000000000000000000000000000..a4d0a7068a3a650beb11529065d0b62ab702143b --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_compare.py @@ -0,0 +1,305 @@ +import numpy as np +import pytest + +from pandas.compat.numpy import np_version_gte1p25 + +import pandas as pd +import pandas._testing as tm + + +@pytest.mark.parametrize("align_axis", [0, 1, "index", "columns"]) +def test_compare_axis(align_axis): + # GH#30429 + df = pd.DataFrame( + {"col1": ["a", "b", "c"], "col2": [1.0, 2.0, np.nan], "col3": [1.0, 2.0, 3.0]}, + columns=["col1", "col2", "col3"], + ) + df2 = df.copy() + df2.loc[0, "col1"] = "c" + df2.loc[2, "col3"] = 4.0 + + result = df.compare(df2, align_axis=align_axis) + + if align_axis in (1, "columns"): + indices = pd.Index([0, 2]) + columns = pd.MultiIndex.from_product([["col1", "col3"], ["self", "other"]]) + expected = pd.DataFrame( + [["a", "c", np.nan, np.nan], [np.nan, np.nan, 3.0, 4.0]], + index=indices, + columns=columns, + ) + else: + indices = pd.MultiIndex.from_product([[0, 2], ["self", "other"]]) + columns = pd.Index(["col1", "col3"]) + expected = pd.DataFrame( + [["a", np.nan], ["c", np.nan], [np.nan, 3.0], [np.nan, 4.0]], + index=indices, + columns=columns, + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "keep_shape, keep_equal", + [ + (True, False), + (False, True), + (True, True), + # False, False case is already covered in test_compare_axis + ], +) +def test_compare_various_formats(keep_shape, keep_equal): + df = pd.DataFrame( + {"col1": ["a", "b", "c"], "col2": [1.0, 2.0, np.nan], "col3": [1.0, 2.0, 3.0]}, + columns=["col1", "col2", "col3"], + ) + df2 = df.copy() + df2.loc[0, "col1"] = "c" + df2.loc[2, "col3"] = 4.0 + + result = df.compare(df2, keep_shape=keep_shape, keep_equal=keep_equal) + + if keep_shape: + indices = pd.Index([0, 1, 2]) + columns = pd.MultiIndex.from_product( + [["col1", "col2", "col3"], ["self", "other"]] + ) + if keep_equal: + expected = pd.DataFrame( + [ + ["a", "c", 1.0, 1.0, 1.0, 1.0], + ["b", "b", 2.0, 2.0, 2.0, 2.0], + ["c", "c", np.nan, np.nan, 3.0, 4.0], + ], + index=indices, + columns=columns, + ) + else: + expected = pd.DataFrame( + [ + ["a", "c", np.nan, np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan, np.nan, 3.0, 4.0], + ], + index=indices, + columns=columns, + ) + else: + indices = pd.Index([0, 2]) + columns = pd.MultiIndex.from_product([["col1", "col3"], ["self", "other"]]) + expected = pd.DataFrame( + [["a", "c", 1.0, 1.0], ["c", "c", 3.0, 4.0]], index=indices, columns=columns + ) + tm.assert_frame_equal(result, expected) + + +def test_compare_with_equal_nulls(): + # We want to make sure two NaNs are considered the same + # and dropped where applicable + df = pd.DataFrame( + {"col1": ["a", "b", "c"], "col2": [1.0, 2.0, np.nan], "col3": [1.0, 2.0, 3.0]}, + columns=["col1", "col2", "col3"], + ) + df2 = df.copy() + df2.loc[0, "col1"] = "c" + + result = df.compare(df2) + indices = pd.Index([0]) + columns = pd.MultiIndex.from_product([["col1"], ["self", "other"]]) + expected = pd.DataFrame([["a", "c"]], index=indices, columns=columns) + tm.assert_frame_equal(result, expected) + + +def test_compare_with_non_equal_nulls(): + # We want to make sure the relevant NaNs do not get dropped + # even if the entire row or column are NaNs + df = pd.DataFrame( + {"col1": ["a", "b", "c"], "col2": [1.0, 2.0, np.nan], "col3": [1.0, 2.0, 3.0]}, + columns=["col1", "col2", "col3"], + ) + df2 = df.copy() + df2.loc[0, "col1"] = "c" + df2.loc[2, "col3"] = np.nan + + result = df.compare(df2) + + indices = pd.Index([0, 2]) + columns = pd.MultiIndex.from_product([["col1", "col3"], ["self", "other"]]) + expected = pd.DataFrame( + [["a", "c", np.nan, np.nan], [np.nan, np.nan, 3.0, np.nan]], + index=indices, + columns=columns, + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("align_axis", [0, 1]) +def test_compare_multi_index(align_axis): + df = pd.DataFrame( + {"col1": ["a", "b", "c"], "col2": [1.0, 2.0, np.nan], "col3": [1.0, 2.0, 3.0]} + ) + df.columns = pd.MultiIndex.from_arrays([["a", "a", "b"], ["col1", "col2", "col3"]]) + df.index = pd.MultiIndex.from_arrays([["x", "x", "y"], [0, 1, 2]]) + + df2 = df.copy() + df2.iloc[0, 0] = "c" + df2.iloc[2, 2] = 4.0 + + result = df.compare(df2, align_axis=align_axis) + + if align_axis == 0: + indices = pd.MultiIndex.from_arrays( + [["x", "x", "y", "y"], [0, 0, 2, 2], ["self", "other", "self", "other"]] + ) + columns = pd.MultiIndex.from_arrays([["a", "b"], ["col1", "col3"]]) + data = [["a", np.nan], ["c", np.nan], [np.nan, 3.0], [np.nan, 4.0]] + else: + indices = pd.MultiIndex.from_arrays([["x", "y"], [0, 2]]) + columns = pd.MultiIndex.from_arrays( + [ + ["a", "a", "b", "b"], + ["col1", "col1", "col3", "col3"], + ["self", "other", "self", "other"], + ] + ) + data = [["a", "c", np.nan, np.nan], [np.nan, np.nan, 3.0, 4.0]] + + expected = pd.DataFrame(data=data, index=indices, columns=columns) + tm.assert_frame_equal(result, expected) + + +def test_compare_unaligned_objects(): + # test DataFrames with different indices + msg = ( + r"Can only compare identically-labeled \(both index and columns\) DataFrame " + "objects" + ) + with pytest.raises(ValueError, match=msg): + df1 = pd.DataFrame([1, 2, 3], index=["a", "b", "c"]) + df2 = pd.DataFrame([1, 2, 3], index=["a", "b", "d"]) + df1.compare(df2) + + # test DataFrames with different shapes + msg = ( + r"Can only compare identically-labeled \(both index and columns\) DataFrame " + "objects" + ) + with pytest.raises(ValueError, match=msg): + df1 = pd.DataFrame(np.ones((3, 3))) + df2 = pd.DataFrame(np.zeros((2, 1))) + df1.compare(df2) + + +def test_compare_result_names(): + # GH 44354 + df1 = pd.DataFrame( + {"col1": ["a", "b", "c"], "col2": [1.0, 2.0, np.nan], "col3": [1.0, 2.0, 3.0]}, + ) + df2 = pd.DataFrame( + { + "col1": ["c", "b", "c"], + "col2": [1.0, 2.0, np.nan], + "col3": [1.0, 2.0, np.nan], + }, + ) + result = df1.compare(df2, result_names=("left", "right")) + expected = pd.DataFrame( + { + ("col1", "left"): {0: "a", 2: np.nan}, + ("col1", "right"): {0: "c", 2: np.nan}, + ("col3", "left"): {0: np.nan, 2: 3.0}, + ("col3", "right"): {0: np.nan, 2: np.nan}, + } + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "result_names", + [ + [1, 2], + "HK", + {"2": 2, "3": 3}, + 3, + 3.0, + ], +) +def test_invalid_input_result_names(result_names): + # GH 44354 + df1 = pd.DataFrame( + {"col1": ["a", "b", "c"], "col2": [1.0, 2.0, np.nan], "col3": [1.0, 2.0, 3.0]}, + ) + df2 = pd.DataFrame( + { + "col1": ["c", "b", "c"], + "col2": [1.0, 2.0, np.nan], + "col3": [1.0, 2.0, np.nan], + }, + ) + with pytest.raises( + TypeError, + match=( + f"Passing 'result_names' as a {type(result_names)} is not " + "supported. Provide 'result_names' as a tuple instead." + ), + ): + df1.compare(df2, result_names=result_names) + + +@pytest.mark.parametrize( + "val1,val2", + [(4, pd.NA), (pd.NA, pd.NA), (pd.NA, 4)], +) +def test_compare_ea_and_np_dtype(val1, val2): + # GH 48966 + arr = [4.0, val1] + ser = pd.Series([1, val2], dtype="Int64") + + df1 = pd.DataFrame({"a": arr, "b": [1.0, 2]}) + df2 = pd.DataFrame({"a": ser, "b": [1.0, 2]}) + expected = pd.DataFrame( + { + ("a", "self"): arr, + ("a", "other"): ser, + ("b", "self"): np.nan, + ("b", "other"): np.nan, + } + ) + if val1 is pd.NA and val2 is pd.NA: + # GH#18463 TODO: is this really the desired behavior? + expected.loc[1, ("a", "self")] = np.nan + + if val1 is pd.NA and np_version_gte1p25: + # can't compare with numpy array if it contains pd.NA + with pytest.raises(TypeError, match="boolean value of NA is ambiguous"): + result = df1.compare(df2, keep_shape=True) + else: + result = df1.compare(df2, keep_shape=True) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "df1_val,df2_val,diff_self,diff_other", + [ + (4, 3, 4, 3), + (4, 4, pd.NA, pd.NA), + (4, pd.NA, 4, pd.NA), + (pd.NA, pd.NA, pd.NA, pd.NA), + ], +) +def test_compare_nullable_int64_dtype(df1_val, df2_val, diff_self, diff_other): + # GH 48966 + df1 = pd.DataFrame({"a": pd.Series([df1_val, pd.NA], dtype="Int64"), "b": [1.0, 2]}) + df2 = df1.copy() + df2.loc[0, "a"] = df2_val + + expected = pd.DataFrame( + { + ("a", "self"): pd.Series([diff_self, pd.NA], dtype="Int64"), + ("a", "other"): pd.Series([diff_other, pd.NA], dtype="Int64"), + ("b", "self"): np.nan, + ("b", "other"): np.nan, + } + ) + result = df1.compare(df2, keep_shape=True) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_convert_dtypes.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_convert_dtypes.py new file mode 100644 index 0000000000000000000000000000000000000000..e7f6e5d625d3ece20131a5a719bf4f545b21a19b --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_convert_dtypes.py @@ -0,0 +1,198 @@ +import datetime + +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm + + +class TestConvertDtypes: + @pytest.mark.parametrize( + "convert_integer, expected", [(False, np.dtype("int32")), (True, "Int32")] + ) + def test_convert_dtypes(self, convert_integer, expected, string_storage): + # Specific types are tested in tests/series/test_dtypes.py + # Just check that it works for DataFrame here + df = pd.DataFrame( + { + "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")), + "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")), + } + ) + with pd.option_context("string_storage", string_storage): + result = df.convert_dtypes(True, True, convert_integer, False) + expected = pd.DataFrame( + { + "a": pd.Series([1, 2, 3], dtype=expected), + "b": pd.Series(["x", "y", "z"], dtype=f"string[{string_storage}]"), + } + ) + tm.assert_frame_equal(result, expected) + + def test_convert_empty(self): + # Empty DataFrame can pass convert_dtypes, see GH#40393 + empty_df = pd.DataFrame() + tm.assert_frame_equal(empty_df, empty_df.convert_dtypes()) + + def test_convert_dtypes_retain_column_names(self): + # GH#41435 + df = pd.DataFrame({"a": [1, 2], "b": [3, 4]}) + df.columns.name = "cols" + + result = df.convert_dtypes() + tm.assert_index_equal(result.columns, df.columns) + assert result.columns.name == "cols" + + def test_pyarrow_dtype_backend(self): + pa = pytest.importorskip("pyarrow") + df = pd.DataFrame( + { + "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")), + "b": pd.Series(["x", "y", None], dtype=np.dtype("O")), + "c": pd.Series([True, False, None], dtype=np.dtype("O")), + "d": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")), + "e": pd.Series(pd.date_range("2022", periods=3)), + "f": pd.Series(pd.date_range("2022", periods=3, tz="UTC").as_unit("s")), + "g": pd.Series(pd.timedelta_range("1D", periods=3)), + } + ) + result = df.convert_dtypes(dtype_backend="pyarrow") + expected = pd.DataFrame( + { + "a": pd.arrays.ArrowExtensionArray( + pa.array([1, 2, 3], type=pa.int32()) + ), + "b": pd.arrays.ArrowExtensionArray(pa.array(["x", "y", None])), + "c": pd.arrays.ArrowExtensionArray(pa.array([True, False, None])), + "d": pd.arrays.ArrowExtensionArray(pa.array([None, 100.5, 200.0])), + "e": pd.arrays.ArrowExtensionArray( + pa.array( + [ + datetime.datetime(2022, 1, 1), + datetime.datetime(2022, 1, 2), + datetime.datetime(2022, 1, 3), + ], + type=pa.timestamp(unit="ns"), + ) + ), + "f": pd.arrays.ArrowExtensionArray( + pa.array( + [ + datetime.datetime(2022, 1, 1), + datetime.datetime(2022, 1, 2), + datetime.datetime(2022, 1, 3), + ], + type=pa.timestamp(unit="s", tz="UTC"), + ) + ), + "g": pd.arrays.ArrowExtensionArray( + pa.array( + [ + datetime.timedelta(1), + datetime.timedelta(2), + datetime.timedelta(3), + ], + type=pa.duration("ns"), + ) + ), + } + ) + tm.assert_frame_equal(result, expected) + + def test_pyarrow_dtype_backend_already_pyarrow(self): + pytest.importorskip("pyarrow") + expected = pd.DataFrame([1, 2, 3], dtype="int64[pyarrow]") + result = expected.convert_dtypes(dtype_backend="pyarrow") + tm.assert_frame_equal(result, expected) + + def test_pyarrow_dtype_backend_from_pandas_nullable(self): + pa = pytest.importorskip("pyarrow") + df = pd.DataFrame( + { + "a": pd.Series([1, 2, None], dtype="Int32"), + "b": pd.Series(["x", "y", None], dtype="string[python]"), + "c": pd.Series([True, False, None], dtype="boolean"), + "d": pd.Series([None, 100.5, 200], dtype="Float64"), + } + ) + result = df.convert_dtypes(dtype_backend="pyarrow") + expected = pd.DataFrame( + { + "a": pd.arrays.ArrowExtensionArray( + pa.array([1, 2, None], type=pa.int32()) + ), + "b": pd.arrays.ArrowExtensionArray(pa.array(["x", "y", None])), + "c": pd.arrays.ArrowExtensionArray(pa.array([True, False, None])), + "d": pd.arrays.ArrowExtensionArray(pa.array([None, 100.5, 200.0])), + } + ) + tm.assert_frame_equal(result, expected) + + def test_pyarrow_dtype_empty_object(self): + # GH 50970 + pytest.importorskip("pyarrow") + expected = pd.DataFrame(columns=[0]) + result = expected.convert_dtypes(dtype_backend="pyarrow") + tm.assert_frame_equal(result, expected) + + def test_pyarrow_engine_lines_false(self): + # GH 48893 + df = pd.DataFrame({"a": [1, 2, 3]}) + msg = ( + "dtype_backend numpy is invalid, only 'numpy_nullable' and " + "'pyarrow' are allowed." + ) + with pytest.raises(ValueError, match=msg): + df.convert_dtypes(dtype_backend="numpy") + + def test_pyarrow_backend_no_conversion(self): + # GH#52872 + pytest.importorskip("pyarrow") + df = pd.DataFrame({"a": [1, 2], "b": 1.5, "c": True, "d": "x"}) + expected = df.copy() + result = df.convert_dtypes( + convert_floating=False, + convert_integer=False, + convert_boolean=False, + convert_string=False, + dtype_backend="pyarrow", + ) + tm.assert_frame_equal(result, expected) + + def test_convert_dtypes_pyarrow_to_np_nullable(self): + # GH 53648 + pytest.importorskip("pyarrow") + ser = pd.DataFrame(range(2), dtype="int32[pyarrow]") + result = ser.convert_dtypes(dtype_backend="numpy_nullable") + expected = pd.DataFrame(range(2), dtype="Int32") + tm.assert_frame_equal(result, expected) + + def test_convert_dtypes_pyarrow_timestamp(self): + # GH 54191 + pytest.importorskip("pyarrow") + ser = pd.Series(pd.date_range("2020-01-01", "2020-01-02", freq="1min")) + expected = ser.astype("timestamp[ms][pyarrow]") + result = expected.convert_dtypes(dtype_backend="pyarrow") + tm.assert_series_equal(result, expected) + + def test_convert_dtypes_avoid_block_splitting(self): + # GH#55341 + df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": "a"}) + result = df.convert_dtypes(convert_integer=False) + expected = pd.DataFrame( + { + "a": [1, 2, 3], + "b": [4, 5, 6], + "c": pd.Series(["a"] * 3, dtype="string[python]"), + } + ) + tm.assert_frame_equal(result, expected) + assert result._mgr.nblocks == 2 + + def test_convert_dtypes_from_arrow(self): + # GH#56581 + df = pd.DataFrame([["a", datetime.time(18, 12)]], columns=["a", "b"]) + result = df.convert_dtypes() + expected = df.astype({"a": "string[python]"}) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_copy.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_copy.py new file mode 100644 index 0000000000000000000000000000000000000000..e7901ed36310668dc21b96d44fed0686de368b1f --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_copy.py @@ -0,0 +1,64 @@ +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +from pandas import DataFrame +import pandas._testing as tm + + +class TestCopy: + @pytest.mark.parametrize("attr", ["index", "columns"]) + def test_copy_index_name_checking(self, float_frame, attr): + # don't want to be able to modify the index stored elsewhere after + # making a copy + ind = getattr(float_frame, attr) + ind.name = None + cp = float_frame.copy() + getattr(cp, attr).name = "foo" + assert getattr(float_frame, attr).name is None + + @td.skip_copy_on_write_invalid_test + def test_copy_cache(self): + # GH#31784 _item_cache not cleared on copy causes incorrect reads after updates + df = DataFrame({"a": [1]}) + + df["x"] = [0] + df["a"] + + df.copy() + + df["a"].values[0] = -1 + + tm.assert_frame_equal(df, DataFrame({"a": [-1], "x": [0]})) + + df["y"] = [0] + + assert df["a"].values[0] == -1 + tm.assert_frame_equal(df, DataFrame({"a": [-1], "x": [0], "y": [0]})) + + def test_copy(self, float_frame, float_string_frame): + cop = float_frame.copy() + cop["E"] = cop["A"] + assert "E" not in float_frame + + # copy objects + copy = float_string_frame.copy() + assert copy._mgr is not float_string_frame._mgr + + @td.skip_array_manager_invalid_test + def test_copy_consolidates(self): + # GH#42477 + df = DataFrame( + { + "a": np.random.default_rng(2).integers(0, 100, size=55), + "b": np.random.default_rng(2).integers(0, 100, size=55), + } + ) + + for i in range(10): + df.loc[:, f"n_{i}"] = np.random.default_rng(2).integers(0, 100, size=55) + + assert len(df._mgr.blocks) == 11 + result = df.copy() + assert len(result._mgr.blocks) == 1 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_count.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_count.py new file mode 100644 index 0000000000000000000000000000000000000000..1553a8a86305dd931c5378245daf272472d41b20 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_count.py @@ -0,0 +1,39 @@ +from pandas import ( + DataFrame, + Series, +) +import pandas._testing as tm + + +class TestDataFrameCount: + def test_count(self): + # corner case + frame = DataFrame() + ct1 = frame.count(1) + assert isinstance(ct1, Series) + + ct2 = frame.count(0) + assert isinstance(ct2, Series) + + # GH#423 + df = DataFrame(index=range(10)) + result = df.count(1) + expected = Series(0, index=df.index) + tm.assert_series_equal(result, expected) + + df = DataFrame(columns=range(10)) + result = df.count(0) + expected = Series(0, index=df.columns) + tm.assert_series_equal(result, expected) + + df = DataFrame() + result = df.count() + expected = Series(dtype="int64") + tm.assert_series_equal(result, expected) + + def test_count_objects(self, float_string_frame): + dm = DataFrame(float_string_frame._series) + df = DataFrame(float_string_frame._series) + + tm.assert_series_equal(dm.count(), df.count()) + tm.assert_series_equal(dm.count(1), df.count(1)) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_cov_corr.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_cov_corr.py new file mode 100644 index 0000000000000000000000000000000000000000..9abf1996c43e6bc262405a7d132986edc3219614 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_cov_corr.py @@ -0,0 +1,470 @@ +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +import pandas as pd +from pandas import ( + DataFrame, + Index, + Series, + date_range, + isna, +) +import pandas._testing as tm + + +class TestDataFrameCov: + def test_cov(self, float_frame, float_string_frame): + # min_periods no NAs (corner case) + expected = float_frame.cov() + result = float_frame.cov(min_periods=len(float_frame)) + + tm.assert_frame_equal(expected, result) + + result = float_frame.cov(min_periods=len(float_frame) + 1) + assert isna(result.values).all() + + # with NAs + frame = float_frame.copy() + frame.iloc[:5, frame.columns.get_loc("A")] = np.nan + frame.iloc[5:10, frame.columns.get_loc("B")] = np.nan + result = frame.cov(min_periods=len(frame) - 8) + expected = frame.cov() + expected.loc["A", "B"] = np.nan + expected.loc["B", "A"] = np.nan + tm.assert_frame_equal(result, expected) + + # regular + result = frame.cov() + expected = frame["A"].cov(frame["C"]) + tm.assert_almost_equal(result["A"]["C"], expected) + + # fails on non-numeric types + with pytest.raises(ValueError, match="could not convert string to float"): + float_string_frame.cov() + result = float_string_frame.cov(numeric_only=True) + expected = float_string_frame.loc[:, ["A", "B", "C", "D"]].cov() + tm.assert_frame_equal(result, expected) + + # Single column frame + df = DataFrame(np.linspace(0.0, 1.0, 10)) + result = df.cov() + expected = DataFrame( + np.cov(df.values.T).reshape((1, 1)), index=df.columns, columns=df.columns + ) + tm.assert_frame_equal(result, expected) + df.loc[0] = np.nan + result = df.cov() + expected = DataFrame( + np.cov(df.values[1:].T).reshape((1, 1)), + index=df.columns, + columns=df.columns, + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("test_ddof", [None, 0, 1, 2, 3]) + def test_cov_ddof(self, test_ddof): + # GH#34611 + np_array1 = np.random.default_rng(2).random(10) + np_array2 = np.random.default_rng(2).random(10) + df = DataFrame({0: np_array1, 1: np_array2}) + result = df.cov(ddof=test_ddof) + expected_np = np.cov(np_array1, np_array2, ddof=test_ddof) + expected = DataFrame(expected_np) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "other_column", [pd.array([1, 2, 3]), np.array([1.0, 2.0, 3.0])] + ) + def test_cov_nullable_integer(self, other_column): + # https://github.com/pandas-dev/pandas/issues/33803 + data = DataFrame({"a": pd.array([1, 2, None]), "b": other_column}) + result = data.cov() + arr = np.array([[0.5, 0.5], [0.5, 1.0]]) + expected = DataFrame(arr, columns=["a", "b"], index=["a", "b"]) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("numeric_only", [True, False]) + def test_cov_numeric_only(self, numeric_only): + # when dtypes of pandas series are different + # then ndarray will have dtype=object, + # so it need to be properly handled + df = DataFrame({"a": [1, 0], "c": ["x", "y"]}) + expected = DataFrame(0.5, index=["a"], columns=["a"]) + if numeric_only: + result = df.cov(numeric_only=numeric_only) + tm.assert_frame_equal(result, expected) + else: + with pytest.raises(ValueError, match="could not convert string to float"): + df.cov(numeric_only=numeric_only) + + +class TestDataFrameCorr: + # DataFrame.corr(), as opposed to DataFrame.corrwith + + @pytest.mark.parametrize("method", ["pearson", "kendall", "spearman"]) + def test_corr_scipy_method(self, float_frame, method): + pytest.importorskip("scipy") + float_frame.loc[float_frame.index[:5], "A"] = np.nan + float_frame.loc[float_frame.index[5:10], "B"] = np.nan + float_frame.loc[float_frame.index[:10], "A"] = float_frame["A"][10:20].copy() + + correls = float_frame.corr(method=method) + expected = float_frame["A"].corr(float_frame["C"], method=method) + tm.assert_almost_equal(correls["A"]["C"], expected) + + # --------------------------------------------------------------------- + + def test_corr_non_numeric(self, float_string_frame): + with pytest.raises(ValueError, match="could not convert string to float"): + float_string_frame.corr() + result = float_string_frame.corr(numeric_only=True) + expected = float_string_frame.loc[:, ["A", "B", "C", "D"]].corr() + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("meth", ["pearson", "kendall", "spearman"]) + def test_corr_nooverlap(self, meth): + # nothing in common + pytest.importorskip("scipy") + df = DataFrame( + { + "A": [1, 1.5, 1, np.nan, np.nan, np.nan], + "B": [np.nan, np.nan, np.nan, 1, 1.5, 1], + "C": [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan], + } + ) + rs = df.corr(meth) + assert isna(rs.loc["A", "B"]) + assert isna(rs.loc["B", "A"]) + assert rs.loc["A", "A"] == 1 + assert rs.loc["B", "B"] == 1 + assert isna(rs.loc["C", "C"]) + + @pytest.mark.parametrize("meth", ["pearson", "spearman"]) + def test_corr_constant(self, meth): + # constant --> all NA + df = DataFrame( + { + "A": [1, 1, 1, np.nan, np.nan, np.nan], + "B": [np.nan, np.nan, np.nan, 1, 1, 1], + } + ) + rs = df.corr(meth) + assert isna(rs.values).all() + + @pytest.mark.filterwarnings("ignore::RuntimeWarning") + @pytest.mark.parametrize("meth", ["pearson", "kendall", "spearman"]) + def test_corr_int_and_boolean(self, meth): + # when dtypes of pandas series are different + # then ndarray will have dtype=object, + # so it need to be properly handled + pytest.importorskip("scipy") + df = DataFrame({"a": [True, False], "b": [1, 0]}) + + expected = DataFrame(np.ones((2, 2)), index=["a", "b"], columns=["a", "b"]) + result = df.corr(meth) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("method", ["cov", "corr"]) + def test_corr_cov_independent_index_column(self, method): + # GH#14617 + df = DataFrame( + np.random.default_rng(2).standard_normal(4 * 10).reshape(10, 4), + columns=list("abcd"), + ) + result = getattr(df, method)() + assert result.index is not result.columns + assert result.index.equals(result.columns) + + def test_corr_invalid_method(self): + # GH#22298 + df = DataFrame(np.random.default_rng(2).normal(size=(10, 2))) + msg = "method must be either 'pearson', 'spearman', 'kendall', or a callable, " + with pytest.raises(ValueError, match=msg): + df.corr(method="____") + + def test_corr_int(self): + # dtypes other than float64 GH#1761 + df = DataFrame({"a": [1, 2, 3, 4], "b": [1, 2, 3, 4]}) + + df.cov() + df.corr() + + @pytest.mark.parametrize( + "nullable_column", [pd.array([1, 2, 3]), pd.array([1, 2, None])] + ) + @pytest.mark.parametrize( + "other_column", + [pd.array([1, 2, 3]), np.array([1.0, 2.0, 3.0]), np.array([1.0, 2.0, np.nan])], + ) + @pytest.mark.parametrize("method", ["pearson", "spearman", "kendall"]) + def test_corr_nullable_integer(self, nullable_column, other_column, method): + # https://github.com/pandas-dev/pandas/issues/33803 + pytest.importorskip("scipy") + data = DataFrame({"a": nullable_column, "b": other_column}) + result = data.corr(method=method) + expected = DataFrame(np.ones((2, 2)), columns=["a", "b"], index=["a", "b"]) + tm.assert_frame_equal(result, expected) + + def test_corr_item_cache(self, using_copy_on_write, warn_copy_on_write): + # Check that corr does not lead to incorrect entries in item_cache + + df = DataFrame({"A": range(10)}) + df["B"] = range(10)[::-1] + + ser = df["A"] # populate item_cache + assert len(df._mgr.arrays) == 2 # i.e. 2 blocks + + _ = df.corr(numeric_only=True) + + if using_copy_on_write: + ser.iloc[0] = 99 + assert df.loc[0, "A"] == 0 + else: + # Check that the corr didn't break link between ser and df + ser.values[0] = 99 + assert df.loc[0, "A"] == 99 + if not warn_copy_on_write: + assert df["A"] is ser + assert df.values[0, 0] == 99 + + @pytest.mark.parametrize("length", [2, 20, 200, 2000]) + def test_corr_for_constant_columns(self, length): + # GH: 37448 + df = DataFrame(length * [[0.4, 0.1]], columns=["A", "B"]) + result = df.corr() + expected = DataFrame( + {"A": [np.nan, np.nan], "B": [np.nan, np.nan]}, index=["A", "B"] + ) + tm.assert_frame_equal(result, expected) + + def test_calc_corr_small_numbers(self): + # GH: 37452 + df = DataFrame( + {"A": [1.0e-20, 2.0e-20, 3.0e-20], "B": [1.0e-20, 2.0e-20, 3.0e-20]} + ) + result = df.corr() + expected = DataFrame({"A": [1.0, 1.0], "B": [1.0, 1.0]}, index=["A", "B"]) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("method", ["pearson", "spearman", "kendall"]) + def test_corr_min_periods_greater_than_length(self, method): + pytest.importorskip("scipy") + df = DataFrame({"A": [1, 2], "B": [1, 2]}) + result = df.corr(method=method, min_periods=3) + expected = DataFrame( + {"A": [np.nan, np.nan], "B": [np.nan, np.nan]}, index=["A", "B"] + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("meth", ["pearson", "kendall", "spearman"]) + @pytest.mark.parametrize("numeric_only", [True, False]) + def test_corr_numeric_only(self, meth, numeric_only): + # when dtypes of pandas series are different + # then ndarray will have dtype=object, + # so it need to be properly handled + pytest.importorskip("scipy") + df = DataFrame({"a": [1, 0], "b": [1, 0], "c": ["x", "y"]}) + expected = DataFrame(np.ones((2, 2)), index=["a", "b"], columns=["a", "b"]) + if numeric_only: + result = df.corr(meth, numeric_only=numeric_only) + tm.assert_frame_equal(result, expected) + else: + with pytest.raises(ValueError, match="could not convert string to float"): + df.corr(meth, numeric_only=numeric_only) + + +class TestDataFrameCorrWith: + @pytest.mark.parametrize( + "dtype", + [ + "float64", + "Float64", + pytest.param("float64[pyarrow]", marks=td.skip_if_no("pyarrow")), + ], + ) + def test_corrwith(self, datetime_frame, dtype): + datetime_frame = datetime_frame.astype(dtype) + + a = datetime_frame + noise = Series(np.random.default_rng(2).standard_normal(len(a)), index=a.index) + + b = datetime_frame.add(noise, axis=0) + + # make sure order does not matter + b = b.reindex(columns=b.columns[::-1], index=b.index[::-1][10:]) + del b["B"] + + colcorr = a.corrwith(b, axis=0) + tm.assert_almost_equal(colcorr["A"], a["A"].corr(b["A"])) + + rowcorr = a.corrwith(b, axis=1) + tm.assert_series_equal(rowcorr, a.T.corrwith(b.T, axis=0)) + + dropped = a.corrwith(b, axis=0, drop=True) + tm.assert_almost_equal(dropped["A"], a["A"].corr(b["A"])) + assert "B" not in dropped + + dropped = a.corrwith(b, axis=1, drop=True) + assert a.index[-1] not in dropped.index + + # non time-series data + index = ["a", "b", "c", "d", "e"] + columns = ["one", "two", "three", "four"] + df1 = DataFrame( + np.random.default_rng(2).standard_normal((5, 4)), + index=index, + columns=columns, + ) + df2 = DataFrame( + np.random.default_rng(2).standard_normal((4, 4)), + index=index[:4], + columns=columns, + ) + correls = df1.corrwith(df2, axis=1) + for row in index[:4]: + tm.assert_almost_equal(correls[row], df1.loc[row].corr(df2.loc[row])) + + def test_corrwith_with_objects(self, using_infer_string): + df1 = DataFrame( + np.random.default_rng(2).standard_normal((10, 4)), + columns=Index(list("ABCD"), dtype=object), + index=date_range("2000-01-01", periods=10, freq="B"), + ) + df2 = df1.copy() + cols = ["A", "B", "C", "D"] + + df1["obj"] = "foo" + df2["obj"] = "bar" + + if using_infer_string: + msg = "Cannot perform reduction 'mean' with string dtype" + with pytest.raises(TypeError, match=msg): + df1.corrwith(df2) + else: + with pytest.raises(TypeError, match="Could not convert"): + df1.corrwith(df2) + result = df1.corrwith(df2, numeric_only=True) + expected = df1.loc[:, cols].corrwith(df2.loc[:, cols]) + tm.assert_series_equal(result, expected) + + with pytest.raises(TypeError, match="unsupported operand type"): + df1.corrwith(df2, axis=1) + result = df1.corrwith(df2, axis=1, numeric_only=True) + expected = df1.loc[:, cols].corrwith(df2.loc[:, cols], axis=1) + tm.assert_series_equal(result, expected) + + def test_corrwith_series(self, datetime_frame): + result = datetime_frame.corrwith(datetime_frame["A"]) + expected = datetime_frame.apply(datetime_frame["A"].corr) + + tm.assert_series_equal(result, expected) + + def test_corrwith_matches_corrcoef(self): + df1 = DataFrame(np.arange(10000), columns=["a"]) + df2 = DataFrame(np.arange(10000) ** 2, columns=["a"]) + c1 = df1.corrwith(df2)["a"] + c2 = np.corrcoef(df1["a"], df2["a"])[0][1] + + tm.assert_almost_equal(c1, c2) + assert c1 < 1 + + @pytest.mark.parametrize("numeric_only", [True, False]) + def test_corrwith_mixed_dtypes(self, numeric_only): + # GH#18570 + df = DataFrame( + {"a": [1, 4, 3, 2], "b": [4, 6, 7, 3], "c": ["a", "b", "c", "d"]} + ) + s = Series([0, 6, 7, 3]) + if numeric_only: + result = df.corrwith(s, numeric_only=numeric_only) + corrs = [df["a"].corr(s), df["b"].corr(s)] + expected = Series(data=corrs, index=["a", "b"]) + tm.assert_series_equal(result, expected) + else: + with pytest.raises( + ValueError, + match="could not convert string to float", + ): + df.corrwith(s, numeric_only=numeric_only) + + def test_corrwith_index_intersection(self): + df1 = DataFrame( + np.random.default_rng(2).random(size=(10, 2)), columns=["a", "b"] + ) + df2 = DataFrame( + np.random.default_rng(2).random(size=(10, 3)), columns=["a", "b", "c"] + ) + + result = df1.corrwith(df2, drop=True).index.sort_values() + expected = df1.columns.intersection(df2.columns).sort_values() + tm.assert_index_equal(result, expected) + + def test_corrwith_index_union(self): + df1 = DataFrame( + np.random.default_rng(2).random(size=(10, 2)), columns=["a", "b"] + ) + df2 = DataFrame( + np.random.default_rng(2).random(size=(10, 3)), columns=["a", "b", "c"] + ) + + result = df1.corrwith(df2, drop=False).index.sort_values() + expected = df1.columns.union(df2.columns).sort_values() + tm.assert_index_equal(result, expected) + + def test_corrwith_dup_cols(self): + # GH#21925 + df1 = DataFrame(np.vstack([np.arange(10)] * 3).T) + df2 = df1.copy() + df2 = pd.concat((df2, df2[0]), axis=1) + + result = df1.corrwith(df2) + expected = Series(np.ones(4), index=[0, 0, 1, 2]) + tm.assert_series_equal(result, expected) + + def test_corr_numerical_instabilities(self): + # GH#45640 + df = DataFrame([[0.2, 0.4], [0.4, 0.2]]) + result = df.corr() + expected = DataFrame({0: [1.0, -1.0], 1: [-1.0, 1.0]}) + tm.assert_frame_equal(result - 1, expected - 1, atol=1e-17) + + def test_corrwith_spearman(self): + # GH#21925 + pytest.importorskip("scipy") + df = DataFrame(np.random.default_rng(2).random(size=(100, 3))) + result = df.corrwith(df**2, method="spearman") + expected = Series(np.ones(len(result))) + tm.assert_series_equal(result, expected) + + def test_corrwith_kendall(self): + # GH#21925 + pytest.importorskip("scipy") + df = DataFrame(np.random.default_rng(2).random(size=(100, 3))) + result = df.corrwith(df**2, method="kendall") + expected = Series(np.ones(len(result))) + tm.assert_series_equal(result, expected) + + def test_corrwith_spearman_with_tied_data(self): + # GH#48826 + pytest.importorskip("scipy") + df1 = DataFrame( + { + "A": [1, np.nan, 7, 8], + "B": [False, True, True, False], + "C": [10, 4, 9, 3], + } + ) + df2 = df1[["B", "C"]] + result = (df1 + 1).corrwith(df2.B, method="spearman") + expected = Series([0.0, 1.0, 0.0], index=["A", "B", "C"]) + tm.assert_series_equal(result, expected) + + df_bool = DataFrame( + {"A": [True, True, False, False], "B": [True, False, False, True]} + ) + ser_bool = Series([True, True, False, True]) + result = df_bool.corrwith(ser_bool) + expected = Series([0.57735, 0.57735], index=["A", "B"]) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_describe.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_describe.py new file mode 100644 index 0000000000000000000000000000000000000000..5beb09940acf32a4a597819f5b130863d90261e5 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_describe.py @@ -0,0 +1,417 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Categorical, + DataFrame, + Series, + Timestamp, + date_range, +) +import pandas._testing as tm + + +class TestDataFrameDescribe: + def test_describe_bool_in_mixed_frame(self): + df = DataFrame( + { + "string_data": ["a", "b", "c", "d", "e"], + "bool_data": [True, True, False, False, False], + "int_data": [10, 20, 30, 40, 50], + } + ) + + # Integer data are included in .describe() output, + # Boolean and string data are not. + result = df.describe() + expected = DataFrame( + {"int_data": [5, 30, df.int_data.std(), 10, 20, 30, 40, 50]}, + index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"], + ) + tm.assert_frame_equal(result, expected) + + # Top value is a boolean value that is False + result = df.describe(include=["bool"]) + + expected = DataFrame( + {"bool_data": [5, 2, False, 3]}, index=["count", "unique", "top", "freq"] + ) + tm.assert_frame_equal(result, expected) + + def test_describe_empty_object(self): + # GH#27183 + df = DataFrame({"A": [None, None]}, dtype=object) + result = df.describe() + expected = DataFrame( + {"A": [0, 0, np.nan, np.nan]}, + dtype=object, + index=["count", "unique", "top", "freq"], + ) + tm.assert_frame_equal(result, expected) + + result = df.iloc[:0].describe() + tm.assert_frame_equal(result, expected) + + def test_describe_bool_frame(self): + # GH#13891 + df = DataFrame( + { + "bool_data_1": [False, False, True, True], + "bool_data_2": [False, True, True, True], + } + ) + result = df.describe() + expected = DataFrame( + {"bool_data_1": [4, 2, False, 2], "bool_data_2": [4, 2, True, 3]}, + index=["count", "unique", "top", "freq"], + ) + tm.assert_frame_equal(result, expected) + + df = DataFrame( + { + "bool_data": [False, False, True, True, False], + "int_data": [0, 1, 2, 3, 4], + } + ) + result = df.describe() + expected = DataFrame( + {"int_data": [5, 2, df.int_data.std(), 0, 1, 2, 3, 4]}, + index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"], + ) + tm.assert_frame_equal(result, expected) + + df = DataFrame( + {"bool_data": [False, False, True, True], "str_data": ["a", "b", "c", "a"]} + ) + result = df.describe() + expected = DataFrame( + {"bool_data": [4, 2, False, 2], "str_data": [4, 3, "a", 2]}, + index=["count", "unique", "top", "freq"], + ) + tm.assert_frame_equal(result, expected) + + def test_describe_categorical(self): + df = DataFrame({"value": np.random.default_rng(2).integers(0, 10000, 100)}) + labels = [f"{i} - {i + 499}" for i in range(0, 10000, 500)] + cat_labels = Categorical(labels, labels) + + df = df.sort_values(by=["value"], ascending=True) + df["value_group"] = pd.cut( + df.value, range(0, 10500, 500), right=False, labels=cat_labels + ) + cat = df + + # Categoricals should not show up together with numerical columns + result = cat.describe() + assert len(result.columns) == 1 + + # In a frame, describe() for the cat should be the same as for string + # arrays (count, unique, top, freq) + + cat = Categorical( + ["a", "b", "b", "b"], categories=["a", "b", "c"], ordered=True + ) + s = Series(cat) + result = s.describe() + expected = Series([4, 2, "b", 3], index=["count", "unique", "top", "freq"]) + tm.assert_series_equal(result, expected) + + cat = Series(Categorical(["a", "b", "c", "c"])) + df3 = DataFrame({"cat": cat, "s": ["a", "b", "c", "c"]}) + result = df3.describe() + tm.assert_numpy_array_equal(result["cat"].values, result["s"].values) + + def test_describe_empty_categorical_column(self): + # GH#26397 + # Ensure the index of an empty categorical DataFrame column + # also contains (count, unique, top, freq) + df = DataFrame({"empty_col": Categorical([])}) + result = df.describe() + expected = DataFrame( + {"empty_col": [0, 0, np.nan, np.nan]}, + index=["count", "unique", "top", "freq"], + dtype="object", + ) + tm.assert_frame_equal(result, expected) + # ensure NaN, not None + assert np.isnan(result.iloc[2, 0]) + assert np.isnan(result.iloc[3, 0]) + + def test_describe_categorical_columns(self): + # GH#11558 + columns = pd.CategoricalIndex(["int1", "int2", "obj"], ordered=True, name="XXX") + df = DataFrame( + { + "int1": [10, 20, 30, 40, 50], + "int2": [10, 20, 30, 40, 50], + "obj": ["A", 0, None, "X", 1], + }, + columns=columns, + ) + result = df.describe() + + exp_columns = pd.CategoricalIndex( + ["int1", "int2"], + categories=["int1", "int2", "obj"], + ordered=True, + name="XXX", + ) + expected = DataFrame( + { + "int1": [5, 30, df.int1.std(), 10, 20, 30, 40, 50], + "int2": [5, 30, df.int2.std(), 10, 20, 30, 40, 50], + }, + index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"], + columns=exp_columns, + ) + + tm.assert_frame_equal(result, expected) + tm.assert_categorical_equal(result.columns.values, expected.columns.values) + + def test_describe_datetime_columns(self): + columns = pd.DatetimeIndex( + ["2011-01-01", "2011-02-01", "2011-03-01"], + freq="MS", + tz="US/Eastern", + name="XXX", + ) + df = DataFrame( + { + 0: [10, 20, 30, 40, 50], + 1: [10, 20, 30, 40, 50], + 2: ["A", 0, None, "X", 1], + } + ) + df.columns = columns + result = df.describe() + + exp_columns = pd.DatetimeIndex( + ["2011-01-01", "2011-02-01"], freq="MS", tz="US/Eastern", name="XXX" + ) + expected = DataFrame( + { + 0: [5, 30, df.iloc[:, 0].std(), 10, 20, 30, 40, 50], + 1: [5, 30, df.iloc[:, 1].std(), 10, 20, 30, 40, 50], + }, + index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"], + ) + expected.columns = exp_columns + tm.assert_frame_equal(result, expected) + assert result.columns.freq == "MS" + assert result.columns.tz == expected.columns.tz + + def test_describe_timedelta_values(self): + # GH#6145 + t1 = pd.timedelta_range("1 days", freq="D", periods=5) + t2 = pd.timedelta_range("1 hours", freq="h", periods=5) + df = DataFrame({"t1": t1, "t2": t2}) + + expected = DataFrame( + { + "t1": [ + 5, + pd.Timedelta("3 days"), + df.iloc[:, 0].std(), + pd.Timedelta("1 days"), + pd.Timedelta("2 days"), + pd.Timedelta("3 days"), + pd.Timedelta("4 days"), + pd.Timedelta("5 days"), + ], + "t2": [ + 5, + pd.Timedelta("3 hours"), + df.iloc[:, 1].std(), + pd.Timedelta("1 hours"), + pd.Timedelta("2 hours"), + pd.Timedelta("3 hours"), + pd.Timedelta("4 hours"), + pd.Timedelta("5 hours"), + ], + }, + index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"], + ) + + result = df.describe() + tm.assert_frame_equal(result, expected) + + exp_repr = ( + " t1 t2\n" + "count 5 5\n" + "mean 3 days 00:00:00 0 days 03:00:00\n" + "std 1 days 13:56:50.394919273 0 days 01:34:52.099788303\n" + "min 1 days 00:00:00 0 days 01:00:00\n" + "25% 2 days 00:00:00 0 days 02:00:00\n" + "50% 3 days 00:00:00 0 days 03:00:00\n" + "75% 4 days 00:00:00 0 days 04:00:00\n" + "max 5 days 00:00:00 0 days 05:00:00" + ) + assert repr(result) == exp_repr + + def test_describe_tz_values(self, tz_naive_fixture): + # GH#21332 + tz = tz_naive_fixture + s1 = Series(range(5)) + start = Timestamp(2018, 1, 1) + end = Timestamp(2018, 1, 5) + s2 = Series(date_range(start, end, tz=tz)) + df = DataFrame({"s1": s1, "s2": s2}) + + expected = DataFrame( + { + "s1": [5, 2, 0, 1, 2, 3, 4, 1.581139], + "s2": [ + 5, + Timestamp(2018, 1, 3).tz_localize(tz), + start.tz_localize(tz), + s2[1], + s2[2], + s2[3], + end.tz_localize(tz), + np.nan, + ], + }, + index=["count", "mean", "min", "25%", "50%", "75%", "max", "std"], + ) + result = df.describe(include="all") + tm.assert_frame_equal(result, expected) + + def test_datetime_is_numeric_includes_datetime(self): + df = DataFrame({"a": date_range("2012", periods=3), "b": [1, 2, 3]}) + result = df.describe() + expected = DataFrame( + { + "a": [ + 3, + Timestamp("2012-01-02"), + Timestamp("2012-01-01"), + Timestamp("2012-01-01T12:00:00"), + Timestamp("2012-01-02"), + Timestamp("2012-01-02T12:00:00"), + Timestamp("2012-01-03"), + np.nan, + ], + "b": [3, 2, 1, 1.5, 2, 2.5, 3, 1], + }, + index=["count", "mean", "min", "25%", "50%", "75%", "max", "std"], + ) + tm.assert_frame_equal(result, expected) + + def test_describe_tz_values2(self): + tz = "CET" + s1 = Series(range(5)) + start = Timestamp(2018, 1, 1) + end = Timestamp(2018, 1, 5) + s2 = Series(date_range(start, end, tz=tz)) + df = DataFrame({"s1": s1, "s2": s2}) + + s1_ = s1.describe() + s2_ = s2.describe() + idx = [ + "count", + "mean", + "min", + "25%", + "50%", + "75%", + "max", + "std", + ] + expected = pd.concat([s1_, s2_], axis=1, keys=["s1", "s2"]).reindex( + idx, copy=False + ) + + result = df.describe(include="all") + tm.assert_frame_equal(result, expected) + + def test_describe_percentiles_integer_idx(self): + # GH#26660 + df = DataFrame({"x": [1]}) + pct = np.linspace(0, 1, 10 + 1) + result = df.describe(percentiles=pct) + + expected = DataFrame( + {"x": [1.0, 1.0, np.nan, 1.0, *(1.0 for _ in pct), 1.0]}, + index=[ + "count", + "mean", + "std", + "min", + "0%", + "10%", + "20%", + "30%", + "40%", + "50%", + "60%", + "70%", + "80%", + "90%", + "100%", + "max", + ], + ) + tm.assert_frame_equal(result, expected) + + def test_describe_does_not_raise_error_for_dictlike_elements(self): + # GH#32409 + df = DataFrame([{"test": {"a": "1"}}, {"test": {"a": "2"}}]) + expected = DataFrame( + {"test": [2, 2, {"a": "1"}, 1]}, index=["count", "unique", "top", "freq"] + ) + result = df.describe() + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("exclude", ["x", "y", ["x", "y"], ["x", "z"]]) + def test_describe_when_include_all_exclude_not_allowed(self, exclude): + """ + When include is 'all', then setting exclude != None is not allowed. + """ + df = DataFrame({"x": [1], "y": [2], "z": [3]}) + msg = "exclude must be None when include is 'all'" + with pytest.raises(ValueError, match=msg): + df.describe(include="all", exclude=exclude) + + def test_describe_with_duplicate_columns(self): + df = DataFrame( + [[1, 1, 1], [2, 2, 2], [3, 3, 3]], + columns=["bar", "a", "a"], + dtype="float64", + ) + result = df.describe() + ser = df.iloc[:, 0].describe() + expected = pd.concat([ser, ser, ser], keys=df.columns, axis=1) + tm.assert_frame_equal(result, expected) + + def test_ea_with_na(self, any_numeric_ea_dtype): + # GH#48778 + + df = DataFrame({"a": [1, pd.NA, pd.NA], "b": pd.NA}, dtype=any_numeric_ea_dtype) + result = df.describe() + expected = DataFrame( + {"a": [1.0, 1.0, pd.NA] + [1.0] * 5, "b": [0.0] + [pd.NA] * 7}, + index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"], + dtype="Float64", + ) + tm.assert_frame_equal(result, expected) + + def test_describe_exclude_pa_dtype(self): + # GH#52570 + pa = pytest.importorskip("pyarrow") + df = DataFrame( + { + "a": Series([1, 2, 3], dtype=pd.ArrowDtype(pa.int8())), + "b": Series([1, 2, 3], dtype=pd.ArrowDtype(pa.int16())), + "c": Series([1, 2, 3], dtype=pd.ArrowDtype(pa.int32())), + } + ) + result = df.describe( + include=pd.ArrowDtype(pa.int8()), exclude=pd.ArrowDtype(pa.int32()) + ) + expected = DataFrame( + {"a": [3, 2, 1, 1, 1.5, 2, 2.5, 3]}, + index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"], + dtype=pd.ArrowDtype(pa.float64()), + ) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_diff.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_diff.py new file mode 100644 index 0000000000000000000000000000000000000000..bef18dbaf8a8a914eae683c16f4e71cc90514c39 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_diff.py @@ -0,0 +1,308 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + Series, + Timestamp, + date_range, +) +import pandas._testing as tm + + +class TestDataFrameDiff: + def test_diff_requires_integer(self): + df = DataFrame(np.random.default_rng(2).standard_normal((2, 2))) + with pytest.raises(ValueError, match="periods must be an integer"): + df.diff(1.5) + + # GH#44572 np.int64 is accepted + @pytest.mark.parametrize("num", [1, np.int64(1)]) + def test_diff(self, datetime_frame, num): + df = datetime_frame + the_diff = df.diff(num) + + expected = df["A"] - df["A"].shift(num) + tm.assert_series_equal(the_diff["A"], expected) + + def test_diff_int_dtype(self): + # int dtype + a = 10_000_000_000_000_000 + b = a + 1 + ser = Series([a, b]) + + rs = DataFrame({"s": ser}).diff() + assert rs.s[1] == 1 + + def test_diff_mixed_numeric(self, datetime_frame): + # mixed numeric + tf = datetime_frame.astype("float32") + the_diff = tf.diff(1) + tm.assert_series_equal(the_diff["A"], tf["A"] - tf["A"].shift(1)) + + def test_diff_axis1_nonconsolidated(self): + # GH#10907 + df = DataFrame({"y": Series([2]), "z": Series([3])}) + df.insert(0, "x", 1) + result = df.diff(axis=1) + expected = DataFrame({"x": np.nan, "y": Series(1), "z": Series(1)}) + tm.assert_frame_equal(result, expected) + + def test_diff_timedelta64_with_nat(self): + # GH#32441 + arr = np.arange(6).reshape(3, 2).astype("timedelta64[ns]") + arr[:, 0] = np.timedelta64("NaT", "ns") + + df = DataFrame(arr) + result = df.diff(1, axis=0) + + expected = DataFrame({0: df[0], 1: [pd.NaT, pd.Timedelta(2), pd.Timedelta(2)]}) + tm.assert_equal(result, expected) + + result = df.diff(0) + expected = df - df + assert expected[0].isna().all() + tm.assert_equal(result, expected) + + result = df.diff(-1, axis=1) + expected = df * np.nan + tm.assert_equal(result, expected) + + @pytest.mark.parametrize("tz", [None, "UTC"]) + def test_diff_datetime_axis0_with_nat(self, tz, unit): + # GH#32441 + dti = pd.DatetimeIndex(["NaT", "2019-01-01", "2019-01-02"], tz=tz).as_unit(unit) + ser = Series(dti) + + df = ser.to_frame() + + result = df.diff() + ex_index = pd.TimedeltaIndex([pd.NaT, pd.NaT, pd.Timedelta(days=1)]).as_unit( + unit + ) + expected = Series(ex_index).to_frame() + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("tz", [None, "UTC"]) + def test_diff_datetime_with_nat_zero_periods(self, tz): + # diff on NaT values should give NaT, not timedelta64(0) + dti = date_range("2016-01-01", periods=4, tz=tz) + ser = Series(dti) + df = ser.to_frame().copy() + + df[1] = ser.copy() + + df.iloc[:, 0] = pd.NaT + + expected = df - df + assert expected[0].isna().all() + + result = df.diff(0, axis=0) + tm.assert_frame_equal(result, expected) + + result = df.diff(0, axis=1) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("tz", [None, "UTC"]) + def test_diff_datetime_axis0(self, tz): + # GH#18578 + df = DataFrame( + { + 0: date_range("2010", freq="D", periods=2, tz=tz), + 1: date_range("2010", freq="D", periods=2, tz=tz), + } + ) + + result = df.diff(axis=0) + expected = DataFrame( + { + 0: pd.TimedeltaIndex(["NaT", "1 days"]), + 1: pd.TimedeltaIndex(["NaT", "1 days"]), + } + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("tz", [None, "UTC"]) + def test_diff_datetime_axis1(self, tz): + # GH#18578 + df = DataFrame( + { + 0: date_range("2010", freq="D", periods=2, tz=tz), + 1: date_range("2010", freq="D", periods=2, tz=tz), + } + ) + + result = df.diff(axis=1) + expected = DataFrame( + { + 0: pd.TimedeltaIndex(["NaT", "NaT"]), + 1: pd.TimedeltaIndex(["0 days", "0 days"]), + } + ) + tm.assert_frame_equal(result, expected) + + def test_diff_timedelta(self, unit): + # GH#4533 + df = DataFrame( + { + "time": [Timestamp("20130101 9:01"), Timestamp("20130101 9:02")], + "value": [1.0, 2.0], + } + ) + df["time"] = df["time"].dt.as_unit(unit) + + res = df.diff() + exp = DataFrame( + [[pd.NaT, np.nan], [pd.Timedelta("00:01:00"), 1]], columns=["time", "value"] + ) + exp["time"] = exp["time"].dt.as_unit(unit) + tm.assert_frame_equal(res, exp) + + def test_diff_mixed_dtype(self): + df = DataFrame(np.random.default_rng(2).standard_normal((5, 3))) + df["A"] = np.array([1, 2, 3, 4, 5], dtype=object) + + result = df.diff() + assert result[0].dtype == np.float64 + + def test_diff_neg_n(self, datetime_frame): + rs = datetime_frame.diff(-1) + xp = datetime_frame - datetime_frame.shift(-1) + tm.assert_frame_equal(rs, xp) + + def test_diff_float_n(self, datetime_frame): + rs = datetime_frame.diff(1.0) + xp = datetime_frame.diff(1) + tm.assert_frame_equal(rs, xp) + + def test_diff_axis(self): + # GH#9727 + df = DataFrame([[1.0, 2.0], [3.0, 4.0]]) + tm.assert_frame_equal( + df.diff(axis=1), DataFrame([[np.nan, 1.0], [np.nan, 1.0]]) + ) + tm.assert_frame_equal( + df.diff(axis=0), DataFrame([[np.nan, np.nan], [2.0, 2.0]]) + ) + + def test_diff_period(self): + # GH#32995 Don't pass an incorrect axis + pi = date_range("2016-01-01", periods=3).to_period("D") + df = DataFrame({"A": pi}) + + result = df.diff(1, axis=1) + + expected = (df - pd.NaT).astype(object) + tm.assert_frame_equal(result, expected) + + def test_diff_axis1_mixed_dtypes(self): + # GH#32995 operate column-wise when we have mixed dtypes and axis=1 + df = DataFrame({"A": range(3), "B": 2 * np.arange(3, dtype=np.float64)}) + + expected = DataFrame({"A": [np.nan, np.nan, np.nan], "B": df["B"] / 2}) + + result = df.diff(axis=1) + tm.assert_frame_equal(result, expected) + + # GH#21437 mixed-float-dtypes + df = DataFrame( + {"a": np.arange(3, dtype="float32"), "b": np.arange(3, dtype="float64")} + ) + result = df.diff(axis=1) + expected = DataFrame({"a": df["a"] * np.nan, "b": df["b"] * 0}) + tm.assert_frame_equal(result, expected) + + def test_diff_axis1_mixed_dtypes_large_periods(self): + # GH#32995 operate column-wise when we have mixed dtypes and axis=1 + df = DataFrame({"A": range(3), "B": 2 * np.arange(3, dtype=np.float64)}) + + expected = df * np.nan + + result = df.diff(axis=1, periods=3) + tm.assert_frame_equal(result, expected) + + def test_diff_axis1_mixed_dtypes_negative_periods(self): + # GH#32995 operate column-wise when we have mixed dtypes and axis=1 + df = DataFrame({"A": range(3), "B": 2 * np.arange(3, dtype=np.float64)}) + + expected = DataFrame({"A": -1.0 * df["A"], "B": df["B"] * np.nan}) + + result = df.diff(axis=1, periods=-1) + tm.assert_frame_equal(result, expected) + + def test_diff_sparse(self): + # GH#28813 .diff() should work for sparse dataframes as well + sparse_df = DataFrame([[0, 1], [1, 0]], dtype="Sparse[int]") + + result = sparse_df.diff() + expected = DataFrame( + [[np.nan, np.nan], [1.0, -1.0]], dtype=pd.SparseDtype("float", 0.0) + ) + + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "axis,expected", + [ + ( + 0, + DataFrame( + { + "a": [np.nan, 0, 1, 0, np.nan, np.nan, np.nan, 0], + "b": [np.nan, 1, np.nan, np.nan, -2, 1, np.nan, np.nan], + "c": np.repeat(np.nan, 8), + "d": [np.nan, 3, 5, 7, 9, 11, 13, 15], + }, + dtype="Int64", + ), + ), + ( + 1, + DataFrame( + { + "a": np.repeat(np.nan, 8), + "b": [0, 1, np.nan, 1, np.nan, np.nan, np.nan, 0], + "c": np.repeat(np.nan, 8), + "d": np.repeat(np.nan, 8), + }, + dtype="Int64", + ), + ), + ], + ) + def test_diff_integer_na(self, axis, expected): + # GH#24171 IntegerNA Support for DataFrame.diff() + df = DataFrame( + { + "a": np.repeat([0, 1, np.nan, 2], 2), + "b": np.tile([0, 1, np.nan, 2], 2), + "c": np.repeat(np.nan, 8), + "d": np.arange(1, 9) ** 2, + }, + dtype="Int64", + ) + + # Test case for default behaviour of diff + result = df.diff(axis=axis) + tm.assert_frame_equal(result, expected) + + def test_diff_readonly(self): + # https://github.com/pandas-dev/pandas/issues/35559 + arr = np.random.default_rng(2).standard_normal((5, 2)) + arr.flags.writeable = False + df = DataFrame(arr) + result = df.diff() + expected = DataFrame(np.array(df)).diff() + tm.assert_frame_equal(result, expected) + + def test_diff_all_int_dtype(self, any_int_numpy_dtype): + # GH 14773 + df = DataFrame(range(5)) + df = df.astype(any_int_numpy_dtype) + result = df.diff() + expected_dtype = ( + "float32" if any_int_numpy_dtype in ("int8", "int16") else "float64" + ) + expected = DataFrame([np.nan, 1.0, 1.0, 1.0, 1.0], dtype=expected_dtype) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_dot.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_dot.py new file mode 100644 index 0000000000000000000000000000000000000000..3e01f67c8794bcf35d2b7be57f8bedcc06c2a137 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_dot.py @@ -0,0 +1,155 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Series, +) +import pandas._testing as tm + + +class DotSharedTests: + @pytest.fixture + def obj(self): + raise NotImplementedError + + @pytest.fixture + def other(self) -> DataFrame: + """ + other is a DataFrame that is indexed so that obj.dot(other) is valid + """ + raise NotImplementedError + + @pytest.fixture + def expected(self, obj, other) -> DataFrame: + """ + The expected result of obj.dot(other) + """ + raise NotImplementedError + + @classmethod + def reduced_dim_assert(cls, result, expected): + """ + Assertion about results with 1 fewer dimension that self.obj + """ + raise NotImplementedError + + def test_dot_equiv_values_dot(self, obj, other, expected): + # `expected` is constructed from obj.values.dot(other.values) + result = obj.dot(other) + tm.assert_equal(result, expected) + + def test_dot_2d_ndarray(self, obj, other, expected): + # Check ndarray argument; in this case we get matching values, + # but index/columns may not match + result = obj.dot(other.values) + assert np.all(result == expected.values) + + def test_dot_1d_ndarray(self, obj, expected): + # can pass correct-length array + row = obj.iloc[0] if obj.ndim == 2 else obj + + result = obj.dot(row.values) + expected = obj.dot(row) + self.reduced_dim_assert(result, expected) + + def test_dot_series(self, obj, other, expected): + # Check series argument + result = obj.dot(other["1"]) + self.reduced_dim_assert(result, expected["1"]) + + def test_dot_series_alignment(self, obj, other, expected): + result = obj.dot(other.iloc[::-1]["1"]) + self.reduced_dim_assert(result, expected["1"]) + + def test_dot_aligns(self, obj, other, expected): + # Check index alignment + other2 = other.iloc[::-1] + result = obj.dot(other2) + tm.assert_equal(result, expected) + + def test_dot_shape_mismatch(self, obj): + msg = "Dot product shape mismatch" + # exception raised is of type Exception + with pytest.raises(Exception, match=msg): + obj.dot(obj.values[:3]) + + def test_dot_misaligned(self, obj, other): + msg = "matrices are not aligned" + with pytest.raises(ValueError, match=msg): + obj.dot(other.T) + + +class TestSeriesDot(DotSharedTests): + @pytest.fixture + def obj(self): + return Series( + np.random.default_rng(2).standard_normal(4), index=["p", "q", "r", "s"] + ) + + @pytest.fixture + def other(self): + return DataFrame( + np.random.default_rng(2).standard_normal((3, 4)), + index=["1", "2", "3"], + columns=["p", "q", "r", "s"], + ).T + + @pytest.fixture + def expected(self, obj, other): + return Series(np.dot(obj.values, other.values), index=other.columns) + + @classmethod + def reduced_dim_assert(cls, result, expected): + """ + Assertion about results with 1 fewer dimension that self.obj + """ + tm.assert_almost_equal(result, expected) + + +class TestDataFrameDot(DotSharedTests): + @pytest.fixture + def obj(self): + return DataFrame( + np.random.default_rng(2).standard_normal((3, 4)), + index=["a", "b", "c"], + columns=["p", "q", "r", "s"], + ) + + @pytest.fixture + def other(self): + return DataFrame( + np.random.default_rng(2).standard_normal((4, 2)), + index=["p", "q", "r", "s"], + columns=["1", "2"], + ) + + @pytest.fixture + def expected(self, obj, other): + return DataFrame( + np.dot(obj.values, other.values), index=obj.index, columns=other.columns + ) + + @classmethod + def reduced_dim_assert(cls, result, expected): + """ + Assertion about results with 1 fewer dimension that self.obj + """ + tm.assert_series_equal(result, expected, check_names=False) + assert result.name is None + + +@pytest.mark.parametrize( + "dtype,exp_dtype", + [("Float32", "Float64"), ("Int16", "Int32"), ("float[pyarrow]", "double[pyarrow]")], +) +def test_arrow_dtype(dtype, exp_dtype): + pytest.importorskip("pyarrow") + + cols = ["a", "b"] + df_a = DataFrame([[1, 2], [3, 4], [5, 6]], columns=cols, dtype="int32") + df_b = DataFrame([[1, 0], [0, 1]], index=cols, dtype=dtype) + result = df_a.dot(df_b) + expected = DataFrame([[1, 2], [3, 4], [5, 6]], dtype=exp_dtype) + + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_drop.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_drop.py new file mode 100644 index 0000000000000000000000000000000000000000..06cd51b43a0aa038868d533d4e664db6681bc801 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_drop.py @@ -0,0 +1,546 @@ +import re + +import numpy as np +import pytest + +from pandas.errors import PerformanceWarning + +import pandas as pd +from pandas import ( + DataFrame, + DatetimeIndex, + Index, + MultiIndex, + Series, + Timestamp, +) +import pandas._testing as tm + + +@pytest.mark.parametrize( + "msg,labels,level", + [ + (r"labels \[4\] not found in level", 4, "a"), + (r"labels \[7\] not found in level", 7, "b"), + ], +) +def test_drop_raise_exception_if_labels_not_in_level(msg, labels, level): + # GH 8594 + mi = MultiIndex.from_arrays([[1, 2, 3], [4, 5, 6]], names=["a", "b"]) + s = Series([10, 20, 30], index=mi) + df = DataFrame([10, 20, 30], index=mi) + + with pytest.raises(KeyError, match=msg): + s.drop(labels, level=level) + with pytest.raises(KeyError, match=msg): + df.drop(labels, level=level) + + +@pytest.mark.parametrize("labels,level", [(4, "a"), (7, "b")]) +def test_drop_errors_ignore(labels, level): + # GH 8594 + mi = MultiIndex.from_arrays([[1, 2, 3], [4, 5, 6]], names=["a", "b"]) + s = Series([10, 20, 30], index=mi) + df = DataFrame([10, 20, 30], index=mi) + + expected_s = s.drop(labels, level=level, errors="ignore") + tm.assert_series_equal(s, expected_s) + + expected_df = df.drop(labels, level=level, errors="ignore") + tm.assert_frame_equal(df, expected_df) + + +def test_drop_with_non_unique_datetime_index_and_invalid_keys(): + # GH 30399 + + # define dataframe with unique datetime index + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 3)), + columns=["a", "b", "c"], + index=pd.date_range("2012", freq="h", periods=5), + ) + # create dataframe with non-unique datetime index + df = df.iloc[[0, 2, 2, 3]].copy() + + with pytest.raises(KeyError, match="not found in axis"): + df.drop(["a", "b"]) # Dropping with labels not exist in the index + + +class TestDataFrameDrop: + def test_drop_names(self): + df = DataFrame( + [[1, 2, 3], [3, 4, 5], [5, 6, 7]], + index=["a", "b", "c"], + columns=["d", "e", "f"], + ) + df.index.name, df.columns.name = "first", "second" + df_dropped_b = df.drop("b") + df_dropped_e = df.drop("e", axis=1) + df_inplace_b, df_inplace_e = df.copy(), df.copy() + return_value = df_inplace_b.drop("b", inplace=True) + assert return_value is None + return_value = df_inplace_e.drop("e", axis=1, inplace=True) + assert return_value is None + for obj in (df_dropped_b, df_dropped_e, df_inplace_b, df_inplace_e): + assert obj.index.name == "first" + assert obj.columns.name == "second" + assert list(df.columns) == ["d", "e", "f"] + + msg = r"\['g'\] not found in axis" + with pytest.raises(KeyError, match=msg): + df.drop(["g"]) + with pytest.raises(KeyError, match=msg): + df.drop(["g"], axis=1) + + # errors = 'ignore' + dropped = df.drop(["g"], errors="ignore") + expected = Index(["a", "b", "c"], name="first") + tm.assert_index_equal(dropped.index, expected) + + dropped = df.drop(["b", "g"], errors="ignore") + expected = Index(["a", "c"], name="first") + tm.assert_index_equal(dropped.index, expected) + + dropped = df.drop(["g"], axis=1, errors="ignore") + expected = Index(["d", "e", "f"], name="second") + tm.assert_index_equal(dropped.columns, expected) + + dropped = df.drop(["d", "g"], axis=1, errors="ignore") + expected = Index(["e", "f"], name="second") + tm.assert_index_equal(dropped.columns, expected) + + # GH 16398 + dropped = df.drop([], errors="ignore") + expected = Index(["a", "b", "c"], name="first") + tm.assert_index_equal(dropped.index, expected) + + def test_drop(self): + simple = DataFrame({"A": [1, 2, 3, 4], "B": [0, 1, 2, 3]}) + tm.assert_frame_equal(simple.drop("A", axis=1), simple[["B"]]) + tm.assert_frame_equal(simple.drop(["A", "B"], axis="columns"), simple[[]]) + tm.assert_frame_equal(simple.drop([0, 1, 3], axis=0), simple.loc[[2], :]) + tm.assert_frame_equal(simple.drop([0, 3], axis="index"), simple.loc[[1, 2], :]) + + with pytest.raises(KeyError, match=r"\[5\] not found in axis"): + simple.drop(5) + with pytest.raises(KeyError, match=r"\['C'\] not found in axis"): + simple.drop("C", axis=1) + with pytest.raises(KeyError, match=r"\[5\] not found in axis"): + simple.drop([1, 5]) + with pytest.raises(KeyError, match=r"\['C'\] not found in axis"): + simple.drop(["A", "C"], axis=1) + + # GH 42881 + with pytest.raises(KeyError, match=r"\['C', 'D', 'F'\] not found in axis"): + simple.drop(["C", "D", "F"], axis=1) + + # errors = 'ignore' + tm.assert_frame_equal(simple.drop(5, errors="ignore"), simple) + tm.assert_frame_equal( + simple.drop([0, 5], errors="ignore"), simple.loc[[1, 2, 3], :] + ) + tm.assert_frame_equal(simple.drop("C", axis=1, errors="ignore"), simple) + tm.assert_frame_equal( + simple.drop(["A", "C"], axis=1, errors="ignore"), simple[["B"]] + ) + + # non-unique - wheee! + nu_df = DataFrame( + list(zip(range(3), range(-3, 1), list("abc"))), columns=["a", "a", "b"] + ) + tm.assert_frame_equal(nu_df.drop("a", axis=1), nu_df[["b"]]) + tm.assert_frame_equal(nu_df.drop("b", axis="columns"), nu_df["a"]) + tm.assert_frame_equal(nu_df.drop([]), nu_df) # GH 16398 + + nu_df = nu_df.set_index(Index(["X", "Y", "X"])) + nu_df.columns = list("abc") + tm.assert_frame_equal(nu_df.drop("X", axis="rows"), nu_df.loc[["Y"], :]) + tm.assert_frame_equal(nu_df.drop(["X", "Y"], axis=0), nu_df.loc[[], :]) + + # inplace cache issue + # GH#5628 + df = DataFrame( + np.random.default_rng(2).standard_normal((10, 3)), columns=list("abc") + ) + expected = df[~(df.b > 0)] + return_value = df.drop(labels=df[df.b > 0].index, inplace=True) + assert return_value is None + tm.assert_frame_equal(df, expected) + + def test_drop_multiindex_not_lexsorted(self): + # GH#11640 + + # define the lexsorted version + lexsorted_mi = MultiIndex.from_tuples( + [("a", ""), ("b1", "c1"), ("b2", "c2")], names=["b", "c"] + ) + lexsorted_df = DataFrame([[1, 3, 4]], columns=lexsorted_mi) + assert lexsorted_df.columns._is_lexsorted() + + # define the non-lexsorted version + not_lexsorted_df = DataFrame( + columns=["a", "b", "c", "d"], data=[[1, "b1", "c1", 3], [1, "b2", "c2", 4]] + ) + not_lexsorted_df = not_lexsorted_df.pivot_table( + index="a", columns=["b", "c"], values="d" + ) + not_lexsorted_df = not_lexsorted_df.reset_index() + assert not not_lexsorted_df.columns._is_lexsorted() + + expected = lexsorted_df.drop("a", axis=1).astype(float) + with tm.assert_produces_warning(PerformanceWarning): + result = not_lexsorted_df.drop("a", axis=1) + + tm.assert_frame_equal(result, expected) + + def test_drop_api_equivalence(self): + # equivalence of the labels/axis and index/columns API's (GH#12392) + df = DataFrame( + [[1, 2, 3], [3, 4, 5], [5, 6, 7]], + index=["a", "b", "c"], + columns=["d", "e", "f"], + ) + + res1 = df.drop("a") + res2 = df.drop(index="a") + tm.assert_frame_equal(res1, res2) + + res1 = df.drop("d", axis=1) + res2 = df.drop(columns="d") + tm.assert_frame_equal(res1, res2) + + res1 = df.drop(labels="e", axis=1) + res2 = df.drop(columns="e") + tm.assert_frame_equal(res1, res2) + + res1 = df.drop(["a"], axis=0) + res2 = df.drop(index=["a"]) + tm.assert_frame_equal(res1, res2) + + res1 = df.drop(["a"], axis=0).drop(["d"], axis=1) + res2 = df.drop(index=["a"], columns=["d"]) + tm.assert_frame_equal(res1, res2) + + msg = "Cannot specify both 'labels' and 'index'/'columns'" + with pytest.raises(ValueError, match=msg): + df.drop(labels="a", index="b") + + with pytest.raises(ValueError, match=msg): + df.drop(labels="a", columns="b") + + msg = "Need to specify at least one of 'labels', 'index' or 'columns'" + with pytest.raises(ValueError, match=msg): + df.drop(axis=1) + + data = [[1, 2, 3], [1, 2, 3]] + + @pytest.mark.parametrize( + "actual", + [ + DataFrame(data=data, index=["a", "a"]), + DataFrame(data=data, index=["a", "b"]), + DataFrame(data=data, index=["a", "b"]).set_index([0, 1]), + DataFrame(data=data, index=["a", "a"]).set_index([0, 1]), + ], + ) + def test_raise_on_drop_duplicate_index(self, actual): + # GH#19186 + level = 0 if isinstance(actual.index, MultiIndex) else None + msg = re.escape("\"['c'] not found in axis\"") + with pytest.raises(KeyError, match=msg): + actual.drop("c", level=level, axis=0) + with pytest.raises(KeyError, match=msg): + actual.T.drop("c", level=level, axis=1) + expected_no_err = actual.drop("c", axis=0, level=level, errors="ignore") + tm.assert_frame_equal(expected_no_err, actual) + expected_no_err = actual.T.drop("c", axis=1, level=level, errors="ignore") + tm.assert_frame_equal(expected_no_err.T, actual) + + @pytest.mark.parametrize("index", [[1, 2, 3], [1, 1, 2]]) + @pytest.mark.parametrize("drop_labels", [[], [1], [2]]) + def test_drop_empty_list(self, index, drop_labels): + # GH#21494 + expected_index = [i for i in index if i not in drop_labels] + frame = DataFrame(index=index).drop(drop_labels) + tm.assert_frame_equal(frame, DataFrame(index=expected_index)) + + @pytest.mark.parametrize("index", [[1, 2, 3], [1, 2, 2]]) + @pytest.mark.parametrize("drop_labels", [[1, 4], [4, 5]]) + def test_drop_non_empty_list(self, index, drop_labels): + # GH# 21494 + with pytest.raises(KeyError, match="not found in axis"): + DataFrame(index=index).drop(drop_labels) + + @pytest.mark.parametrize( + "empty_listlike", + [ + [], + {}, + np.array([]), + Series([], dtype="datetime64[ns]"), + Index([]), + DatetimeIndex([]), + ], + ) + def test_drop_empty_listlike_non_unique_datetime_index(self, empty_listlike): + # GH#27994 + data = {"column_a": [5, 10], "column_b": ["one", "two"]} + index = [Timestamp("2021-01-01"), Timestamp("2021-01-01")] + df = DataFrame(data, index=index) + + # Passing empty list-like should return the same DataFrame. + expected = df.copy() + result = df.drop(empty_listlike) + tm.assert_frame_equal(result, expected) + + def test_mixed_depth_drop(self): + arrays = [ + ["a", "top", "top", "routine1", "routine1", "routine2"], + ["", "OD", "OD", "result1", "result2", "result1"], + ["", "wx", "wy", "", "", ""], + ] + + tuples = sorted(zip(*arrays)) + index = MultiIndex.from_tuples(tuples) + df = DataFrame(np.random.default_rng(2).standard_normal((4, 6)), columns=index) + + result = df.drop("a", axis=1) + expected = df.drop([("a", "", "")], axis=1) + tm.assert_frame_equal(expected, result) + + result = df.drop(["top"], axis=1) + expected = df.drop([("top", "OD", "wx")], axis=1) + expected = expected.drop([("top", "OD", "wy")], axis=1) + tm.assert_frame_equal(expected, result) + + result = df.drop(("top", "OD", "wx"), axis=1) + expected = df.drop([("top", "OD", "wx")], axis=1) + tm.assert_frame_equal(expected, result) + + expected = df.drop([("top", "OD", "wy")], axis=1) + expected = df.drop("top", axis=1) + + result = df.drop("result1", level=1, axis=1) + expected = df.drop( + [("routine1", "result1", ""), ("routine2", "result1", "")], axis=1 + ) + tm.assert_frame_equal(expected, result) + + def test_drop_multiindex_other_level_nan(self): + # GH#12754 + df = ( + DataFrame( + { + "A": ["one", "one", "two", "two"], + "B": [np.nan, 0.0, 1.0, 2.0], + "C": ["a", "b", "c", "c"], + "D": [1, 2, 3, 4], + } + ) + .set_index(["A", "B", "C"]) + .sort_index() + ) + result = df.drop("c", level="C") + expected = DataFrame( + [2, 1], + columns=["D"], + index=MultiIndex.from_tuples( + [("one", 0.0, "b"), ("one", np.nan, "a")], names=["A", "B", "C"] + ), + ) + tm.assert_frame_equal(result, expected) + + def test_drop_nonunique(self): + df = DataFrame( + [ + ["x-a", "x", "a", 1.5], + ["x-a", "x", "a", 1.2], + ["z-c", "z", "c", 3.1], + ["x-a", "x", "a", 4.1], + ["x-b", "x", "b", 5.1], + ["x-b", "x", "b", 4.1], + ["x-b", "x", "b", 2.2], + ["y-a", "y", "a", 1.2], + ["z-b", "z", "b", 2.1], + ], + columns=["var1", "var2", "var3", "var4"], + ) + + grp_size = df.groupby("var1").size() + drop_idx = grp_size.loc[grp_size == 1] + + idf = df.set_index(["var1", "var2", "var3"]) + + # it works! GH#2101 + result = idf.drop(drop_idx.index, level=0).reset_index() + expected = df[-df.var1.isin(drop_idx.index)] + + result.index = expected.index + + tm.assert_frame_equal(result, expected) + + def test_drop_level(self, multiindex_dataframe_random_data): + frame = multiindex_dataframe_random_data + + result = frame.drop(["bar", "qux"], level="first") + expected = frame.iloc[[0, 1, 2, 5, 6]] + tm.assert_frame_equal(result, expected) + + result = frame.drop(["two"], level="second") + expected = frame.iloc[[0, 2, 3, 6, 7, 9]] + tm.assert_frame_equal(result, expected) + + result = frame.T.drop(["bar", "qux"], axis=1, level="first") + expected = frame.iloc[[0, 1, 2, 5, 6]].T + tm.assert_frame_equal(result, expected) + + result = frame.T.drop(["two"], axis=1, level="second") + expected = frame.iloc[[0, 2, 3, 6, 7, 9]].T + tm.assert_frame_equal(result, expected) + + def test_drop_level_nonunique_datetime(self): + # GH#12701 + idx = Index([2, 3, 4, 4, 5], name="id") + idxdt = pd.to_datetime( + [ + "2016-03-23 14:00", + "2016-03-23 15:00", + "2016-03-23 16:00", + "2016-03-23 16:00", + "2016-03-23 17:00", + ] + ) + df = DataFrame(np.arange(10).reshape(5, 2), columns=list("ab"), index=idx) + df["tstamp"] = idxdt + df = df.set_index("tstamp", append=True) + ts = Timestamp("201603231600") + assert df.index.is_unique is False + + result = df.drop(ts, level="tstamp") + expected = df.loc[idx != 4] + tm.assert_frame_equal(result, expected) + + def test_drop_tz_aware_timestamp_across_dst(self, frame_or_series): + # GH#21761 + start = Timestamp("2017-10-29", tz="Europe/Berlin") + end = Timestamp("2017-10-29 04:00:00", tz="Europe/Berlin") + index = pd.date_range(start, end, freq="15min") + data = frame_or_series(data=[1] * len(index), index=index) + result = data.drop(start) + expected_start = Timestamp("2017-10-29 00:15:00", tz="Europe/Berlin") + expected_idx = pd.date_range(expected_start, end, freq="15min") + expected = frame_or_series(data=[1] * len(expected_idx), index=expected_idx) + tm.assert_equal(result, expected) + + def test_drop_preserve_names(self): + index = MultiIndex.from_arrays( + [[0, 0, 0, 1, 1, 1], [1, 2, 3, 1, 2, 3]], names=["one", "two"] + ) + + df = DataFrame(np.random.default_rng(2).standard_normal((6, 3)), index=index) + + result = df.drop([(0, 2)]) + assert result.index.names == ("one", "two") + + @pytest.mark.parametrize( + "operation", ["__iadd__", "__isub__", "__imul__", "__ipow__"] + ) + @pytest.mark.parametrize("inplace", [False, True]) + def test_inplace_drop_and_operation(self, operation, inplace): + # GH#30484 + df = DataFrame({"x": range(5)}) + expected = df.copy() + df["y"] = range(5) + y = df["y"] + + with tm.assert_produces_warning(None): + if inplace: + df.drop("y", axis=1, inplace=inplace) + else: + df = df.drop("y", axis=1, inplace=inplace) + + # Perform operation and check result + getattr(y, operation)(1) + tm.assert_frame_equal(df, expected) + + def test_drop_with_non_unique_multiindex(self): + # GH#36293 + mi = MultiIndex.from_arrays([["x", "y", "x"], ["i", "j", "i"]]) + df = DataFrame([1, 2, 3], index=mi) + result = df.drop(index="x") + expected = DataFrame([2], index=MultiIndex.from_arrays([["y"], ["j"]])) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("indexer", [("a", "a"), [("a", "a")]]) + def test_drop_tuple_with_non_unique_multiindex(self, indexer): + # GH#42771 + idx = MultiIndex.from_product([["a", "b"], ["a", "a"]]) + df = DataFrame({"x": range(len(idx))}, index=idx) + result = df.drop(index=[("a", "a")]) + expected = DataFrame( + {"x": [2, 3]}, index=MultiIndex.from_tuples([("b", "a"), ("b", "a")]) + ) + tm.assert_frame_equal(result, expected) + + def test_drop_with_duplicate_columns(self): + df = DataFrame( + [[1, 5, 7.0], [1, 5, 7.0], [1, 5, 7.0]], columns=["bar", "a", "a"] + ) + result = df.drop(["a"], axis=1) + expected = DataFrame([[1], [1], [1]], columns=["bar"]) + tm.assert_frame_equal(result, expected) + result = df.drop("a", axis=1) + tm.assert_frame_equal(result, expected) + + def test_drop_with_duplicate_columns2(self): + # drop buggy GH#6240 + df = DataFrame( + { + "A": np.random.default_rng(2).standard_normal(5), + "B": np.random.default_rng(2).standard_normal(5), + "C": np.random.default_rng(2).standard_normal(5), + "D": ["a", "b", "c", "d", "e"], + } + ) + + expected = df.take([0, 1, 1], axis=1) + df2 = df.take([2, 0, 1, 2, 1], axis=1) + result = df2.drop("C", axis=1) + tm.assert_frame_equal(result, expected) + + def test_drop_inplace_no_leftover_column_reference(self): + # GH 13934 + df = DataFrame({"a": [1, 2, 3]}, columns=Index(["a"], dtype="object")) + a = df.a + df.drop(["a"], axis=1, inplace=True) + tm.assert_index_equal(df.columns, Index([], dtype="object")) + a -= a.mean() + tm.assert_index_equal(df.columns, Index([], dtype="object")) + + def test_drop_level_missing_label_multiindex(self): + # GH 18561 + df = DataFrame(index=MultiIndex.from_product([range(3), range(3)])) + with pytest.raises(KeyError, match="labels \\[5\\] not found in level"): + df.drop(5, level=0) + + @pytest.mark.parametrize("idx, level", [(["a", "b"], 0), (["a"], None)]) + def test_drop_index_ea_dtype(self, any_numeric_ea_dtype, idx, level): + # GH#45860 + df = DataFrame( + {"a": [1, 2, 2, pd.NA], "b": 100}, dtype=any_numeric_ea_dtype + ).set_index(idx) + result = df.drop(Index([2, pd.NA]), level=level) + expected = DataFrame( + {"a": [1], "b": 100}, dtype=any_numeric_ea_dtype + ).set_index(idx) + tm.assert_frame_equal(result, expected) + + def test_drop_parse_strings_datetime_index(self): + # GH #5355 + df = DataFrame( + {"a": [1, 2], "b": [1, 2]}, + index=[Timestamp("2000-01-03"), Timestamp("2000-01-04")], + ) + result = df.drop("2000-01-03", axis=0) + expected = DataFrame({"a": [2], "b": [2]}, index=[Timestamp("2000-01-04")]) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_drop_duplicates.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_drop_duplicates.py new file mode 100644 index 0000000000000000000000000000000000000000..6bea97b2cf189d81b99996cc8cc78a3b92f7afc0 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_drop_duplicates.py @@ -0,0 +1,473 @@ +from datetime import datetime +import re + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + NaT, + concat, +) +import pandas._testing as tm + + +@pytest.mark.parametrize("subset", ["a", ["a"], ["a", "B"]]) +def test_drop_duplicates_with_misspelled_column_name(subset): + # GH 19730 + df = DataFrame({"A": [0, 0, 1], "B": [0, 0, 1], "C": [0, 0, 1]}) + msg = re.escape("Index(['a'], dtype=") + + with pytest.raises(KeyError, match=msg): + df.drop_duplicates(subset) + + +def test_drop_duplicates(): + df = DataFrame( + { + "AAA": ["foo", "bar", "foo", "bar", "foo", "bar", "bar", "foo"], + "B": ["one", "one", "two", "two", "two", "two", "one", "two"], + "C": [1, 1, 2, 2, 2, 2, 1, 2], + "D": range(8), + } + ) + # single column + result = df.drop_duplicates("AAA") + expected = df[:2] + tm.assert_frame_equal(result, expected) + + result = df.drop_duplicates("AAA", keep="last") + expected = df.loc[[6, 7]] + tm.assert_frame_equal(result, expected) + + result = df.drop_duplicates("AAA", keep=False) + expected = df.loc[[]] + tm.assert_frame_equal(result, expected) + assert len(result) == 0 + + # multi column + expected = df.loc[[0, 1, 2, 3]] + result = df.drop_duplicates(np.array(["AAA", "B"])) + tm.assert_frame_equal(result, expected) + result = df.drop_duplicates(["AAA", "B"]) + tm.assert_frame_equal(result, expected) + + result = df.drop_duplicates(("AAA", "B"), keep="last") + expected = df.loc[[0, 5, 6, 7]] + tm.assert_frame_equal(result, expected) + + result = df.drop_duplicates(("AAA", "B"), keep=False) + expected = df.loc[[0]] + tm.assert_frame_equal(result, expected) + + # consider everything + df2 = df.loc[:, ["AAA", "B", "C"]] + + result = df2.drop_duplicates() + # in this case only + expected = df2.drop_duplicates(["AAA", "B"]) + tm.assert_frame_equal(result, expected) + + result = df2.drop_duplicates(keep="last") + expected = df2.drop_duplicates(["AAA", "B"], keep="last") + tm.assert_frame_equal(result, expected) + + result = df2.drop_duplicates(keep=False) + expected = df2.drop_duplicates(["AAA", "B"], keep=False) + tm.assert_frame_equal(result, expected) + + # integers + result = df.drop_duplicates("C") + expected = df.iloc[[0, 2]] + tm.assert_frame_equal(result, expected) + result = df.drop_duplicates("C", keep="last") + expected = df.iloc[[-2, -1]] + tm.assert_frame_equal(result, expected) + + df["E"] = df["C"].astype("int8") + result = df.drop_duplicates("E") + expected = df.iloc[[0, 2]] + tm.assert_frame_equal(result, expected) + result = df.drop_duplicates("E", keep="last") + expected = df.iloc[[-2, -1]] + tm.assert_frame_equal(result, expected) + + # GH 11376 + df = DataFrame({"x": [7, 6, 3, 3, 4, 8, 0], "y": [0, 6, 5, 5, 9, 1, 2]}) + expected = df.loc[df.index != 3] + tm.assert_frame_equal(df.drop_duplicates(), expected) + + df = DataFrame([[1, 0], [0, 2]]) + tm.assert_frame_equal(df.drop_duplicates(), df) + + df = DataFrame([[-2, 0], [0, -4]]) + tm.assert_frame_equal(df.drop_duplicates(), df) + + x = np.iinfo(np.int64).max / 3 * 2 + df = DataFrame([[-x, x], [0, x + 4]]) + tm.assert_frame_equal(df.drop_duplicates(), df) + + df = DataFrame([[-x, x], [x, x + 4]]) + tm.assert_frame_equal(df.drop_duplicates(), df) + + # GH 11864 + df = DataFrame([i] * 9 for i in range(16)) + df = concat([df, DataFrame([[1] + [0] * 8])], ignore_index=True) + + for keep in ["first", "last", False]: + assert df.duplicated(keep=keep).sum() == 0 + + +def test_drop_duplicates_with_duplicate_column_names(): + # GH17836 + df = DataFrame([[1, 2, 5], [3, 4, 6], [3, 4, 7]], columns=["a", "a", "b"]) + + result0 = df.drop_duplicates() + tm.assert_frame_equal(result0, df) + + result1 = df.drop_duplicates("a") + expected1 = df[:2] + tm.assert_frame_equal(result1, expected1) + + +def test_drop_duplicates_for_take_all(): + df = DataFrame( + { + "AAA": ["foo", "bar", "baz", "bar", "foo", "bar", "qux", "foo"], + "B": ["one", "one", "two", "two", "two", "two", "one", "two"], + "C": [1, 1, 2, 2, 2, 2, 1, 2], + "D": range(8), + } + ) + # single column + result = df.drop_duplicates("AAA") + expected = df.iloc[[0, 1, 2, 6]] + tm.assert_frame_equal(result, expected) + + result = df.drop_duplicates("AAA", keep="last") + expected = df.iloc[[2, 5, 6, 7]] + tm.assert_frame_equal(result, expected) + + result = df.drop_duplicates("AAA", keep=False) + expected = df.iloc[[2, 6]] + tm.assert_frame_equal(result, expected) + + # multiple columns + result = df.drop_duplicates(["AAA", "B"]) + expected = df.iloc[[0, 1, 2, 3, 4, 6]] + tm.assert_frame_equal(result, expected) + + result = df.drop_duplicates(["AAA", "B"], keep="last") + expected = df.iloc[[0, 1, 2, 5, 6, 7]] + tm.assert_frame_equal(result, expected) + + result = df.drop_duplicates(["AAA", "B"], keep=False) + expected = df.iloc[[0, 1, 2, 6]] + tm.assert_frame_equal(result, expected) + + +def test_drop_duplicates_tuple(): + df = DataFrame( + { + ("AA", "AB"): ["foo", "bar", "foo", "bar", "foo", "bar", "bar", "foo"], + "B": ["one", "one", "two", "two", "two", "two", "one", "two"], + "C": [1, 1, 2, 2, 2, 2, 1, 2], + "D": range(8), + } + ) + # single column + result = df.drop_duplicates(("AA", "AB")) + expected = df[:2] + tm.assert_frame_equal(result, expected) + + result = df.drop_duplicates(("AA", "AB"), keep="last") + expected = df.loc[[6, 7]] + tm.assert_frame_equal(result, expected) + + result = df.drop_duplicates(("AA", "AB"), keep=False) + expected = df.loc[[]] # empty df + assert len(result) == 0 + tm.assert_frame_equal(result, expected) + + # multi column + expected = df.loc[[0, 1, 2, 3]] + result = df.drop_duplicates((("AA", "AB"), "B")) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "df", + [ + DataFrame(), + DataFrame(columns=[]), + DataFrame(columns=["A", "B", "C"]), + DataFrame(index=[]), + DataFrame(index=["A", "B", "C"]), + ], +) +def test_drop_duplicates_empty(df): + # GH 20516 + result = df.drop_duplicates() + tm.assert_frame_equal(result, df) + + result = df.copy() + result.drop_duplicates(inplace=True) + tm.assert_frame_equal(result, df) + + +def test_drop_duplicates_NA(): + # none + df = DataFrame( + { + "A": [None, None, "foo", "bar", "foo", "bar", "bar", "foo"], + "B": ["one", "one", "two", "two", "two", "two", "one", "two"], + "C": [1.0, np.nan, np.nan, np.nan, 1.0, 1.0, 1, 1.0], + "D": range(8), + } + ) + # single column + result = df.drop_duplicates("A") + expected = df.loc[[0, 2, 3]] + tm.assert_frame_equal(result, expected) + + result = df.drop_duplicates("A", keep="last") + expected = df.loc[[1, 6, 7]] + tm.assert_frame_equal(result, expected) + + result = df.drop_duplicates("A", keep=False) + expected = df.loc[[]] # empty df + tm.assert_frame_equal(result, expected) + assert len(result) == 0 + + # multi column + result = df.drop_duplicates(["A", "B"]) + expected = df.loc[[0, 2, 3, 6]] + tm.assert_frame_equal(result, expected) + + result = df.drop_duplicates(["A", "B"], keep="last") + expected = df.loc[[1, 5, 6, 7]] + tm.assert_frame_equal(result, expected) + + result = df.drop_duplicates(["A", "B"], keep=False) + expected = df.loc[[6]] + tm.assert_frame_equal(result, expected) + + # nan + df = DataFrame( + { + "A": ["foo", "bar", "foo", "bar", "foo", "bar", "bar", "foo"], + "B": ["one", "one", "two", "two", "two", "two", "one", "two"], + "C": [1.0, np.nan, np.nan, np.nan, 1.0, 1.0, 1, 1.0], + "D": range(8), + } + ) + # single column + result = df.drop_duplicates("C") + expected = df[:2] + tm.assert_frame_equal(result, expected) + + result = df.drop_duplicates("C", keep="last") + expected = df.loc[[3, 7]] + tm.assert_frame_equal(result, expected) + + result = df.drop_duplicates("C", keep=False) + expected = df.loc[[]] # empty df + tm.assert_frame_equal(result, expected) + assert len(result) == 0 + + # multi column + result = df.drop_duplicates(["C", "B"]) + expected = df.loc[[0, 1, 2, 4]] + tm.assert_frame_equal(result, expected) + + result = df.drop_duplicates(["C", "B"], keep="last") + expected = df.loc[[1, 3, 6, 7]] + tm.assert_frame_equal(result, expected) + + result = df.drop_duplicates(["C", "B"], keep=False) + expected = df.loc[[1]] + tm.assert_frame_equal(result, expected) + + +def test_drop_duplicates_NA_for_take_all(): + # none + df = DataFrame( + { + "A": [None, None, "foo", "bar", "foo", "baz", "bar", "qux"], + "C": [1.0, np.nan, np.nan, np.nan, 1.0, 2.0, 3, 1.0], + } + ) + + # single column + result = df.drop_duplicates("A") + expected = df.iloc[[0, 2, 3, 5, 7]] + tm.assert_frame_equal(result, expected) + + result = df.drop_duplicates("A", keep="last") + expected = df.iloc[[1, 4, 5, 6, 7]] + tm.assert_frame_equal(result, expected) + + result = df.drop_duplicates("A", keep=False) + expected = df.iloc[[5, 7]] + tm.assert_frame_equal(result, expected) + + # nan + + # single column + result = df.drop_duplicates("C") + expected = df.iloc[[0, 1, 5, 6]] + tm.assert_frame_equal(result, expected) + + result = df.drop_duplicates("C", keep="last") + expected = df.iloc[[3, 5, 6, 7]] + tm.assert_frame_equal(result, expected) + + result = df.drop_duplicates("C", keep=False) + expected = df.iloc[[5, 6]] + tm.assert_frame_equal(result, expected) + + +def test_drop_duplicates_inplace(): + orig = DataFrame( + { + "A": ["foo", "bar", "foo", "bar", "foo", "bar", "bar", "foo"], + "B": ["one", "one", "two", "two", "two", "two", "one", "two"], + "C": [1, 1, 2, 2, 2, 2, 1, 2], + "D": range(8), + } + ) + # single column + df = orig.copy() + return_value = df.drop_duplicates("A", inplace=True) + expected = orig[:2] + result = df + tm.assert_frame_equal(result, expected) + assert return_value is None + + df = orig.copy() + return_value = df.drop_duplicates("A", keep="last", inplace=True) + expected = orig.loc[[6, 7]] + result = df + tm.assert_frame_equal(result, expected) + assert return_value is None + + df = orig.copy() + return_value = df.drop_duplicates("A", keep=False, inplace=True) + expected = orig.loc[[]] + result = df + tm.assert_frame_equal(result, expected) + assert len(df) == 0 + assert return_value is None + + # multi column + df = orig.copy() + return_value = df.drop_duplicates(["A", "B"], inplace=True) + expected = orig.loc[[0, 1, 2, 3]] + result = df + tm.assert_frame_equal(result, expected) + assert return_value is None + + df = orig.copy() + return_value = df.drop_duplicates(["A", "B"], keep="last", inplace=True) + expected = orig.loc[[0, 5, 6, 7]] + result = df + tm.assert_frame_equal(result, expected) + assert return_value is None + + df = orig.copy() + return_value = df.drop_duplicates(["A", "B"], keep=False, inplace=True) + expected = orig.loc[[0]] + result = df + tm.assert_frame_equal(result, expected) + assert return_value is None + + # consider everything + orig2 = orig.loc[:, ["A", "B", "C"]].copy() + + df2 = orig2.copy() + return_value = df2.drop_duplicates(inplace=True) + # in this case only + expected = orig2.drop_duplicates(["A", "B"]) + result = df2 + tm.assert_frame_equal(result, expected) + assert return_value is None + + df2 = orig2.copy() + return_value = df2.drop_duplicates(keep="last", inplace=True) + expected = orig2.drop_duplicates(["A", "B"], keep="last") + result = df2 + tm.assert_frame_equal(result, expected) + assert return_value is None + + df2 = orig2.copy() + return_value = df2.drop_duplicates(keep=False, inplace=True) + expected = orig2.drop_duplicates(["A", "B"], keep=False) + result = df2 + tm.assert_frame_equal(result, expected) + assert return_value is None + + +@pytest.mark.parametrize("inplace", [True, False]) +@pytest.mark.parametrize( + "origin_dict, output_dict, ignore_index, output_index", + [ + ({"A": [2, 2, 3]}, {"A": [2, 3]}, True, [0, 1]), + ({"A": [2, 2, 3]}, {"A": [2, 3]}, False, [0, 2]), + ({"A": [2, 2, 3], "B": [2, 2, 4]}, {"A": [2, 3], "B": [2, 4]}, True, [0, 1]), + ({"A": [2, 2, 3], "B": [2, 2, 4]}, {"A": [2, 3], "B": [2, 4]}, False, [0, 2]), + ], +) +def test_drop_duplicates_ignore_index( + inplace, origin_dict, output_dict, ignore_index, output_index +): + # GH 30114 + df = DataFrame(origin_dict) + expected = DataFrame(output_dict, index=output_index) + + if inplace: + result_df = df.copy() + result_df.drop_duplicates(ignore_index=ignore_index, inplace=inplace) + else: + result_df = df.drop_duplicates(ignore_index=ignore_index, inplace=inplace) + + tm.assert_frame_equal(result_df, expected) + tm.assert_frame_equal(df, DataFrame(origin_dict)) + + +def test_drop_duplicates_null_in_object_column(nulls_fixture): + # https://github.com/pandas-dev/pandas/issues/32992 + df = DataFrame([[1, nulls_fixture], [2, "a"]], dtype=object) + result = df.drop_duplicates() + tm.assert_frame_equal(result, df) + + +def test_drop_duplicates_series_vs_dataframe(keep): + # GH#14192 + df = DataFrame( + { + "a": [1, 1, 1, "one", "one"], + "b": [2, 2, np.nan, np.nan, np.nan], + "c": [3, 3, np.nan, np.nan, "three"], + "d": [1, 2, 3, 4, 4], + "e": [ + datetime(2015, 1, 1), + datetime(2015, 1, 1), + datetime(2015, 2, 1), + NaT, + NaT, + ], + } + ) + for column in df.columns: + dropped_frame = df[[column]].drop_duplicates(keep=keep) + dropped_series = df[column].drop_duplicates(keep=keep) + tm.assert_frame_equal(dropped_frame, dropped_series.to_frame()) + + +@pytest.mark.parametrize("arg", [[1], 1, "True", [], 0]) +def test_drop_duplicates_non_boolean_ignore_index(arg): + # GH#38274 + df = DataFrame({"a": [1, 2, 1, 3]}) + msg = '^For argument "ignore_index" expected type bool, received type .*.$' + with pytest.raises(ValueError, match=msg): + df.drop_duplicates(ignore_index=arg) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_droplevel.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_droplevel.py new file mode 100644 index 0000000000000000000000000000000000000000..e1302d4b73f2b9c8e74b06c70ec29a92c1e48723 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_droplevel.py @@ -0,0 +1,36 @@ +import pytest + +from pandas import ( + DataFrame, + Index, + MultiIndex, +) +import pandas._testing as tm + + +class TestDropLevel: + def test_droplevel(self, frame_or_series): + # GH#20342 + cols = MultiIndex.from_tuples( + [("c", "e"), ("d", "f")], names=["level_1", "level_2"] + ) + mi = MultiIndex.from_tuples([(1, 2), (5, 6), (9, 10)], names=["a", "b"]) + df = DataFrame([[3, 4], [7, 8], [11, 12]], index=mi, columns=cols) + if frame_or_series is not DataFrame: + df = df.iloc[:, 0] + + # test that dropping of a level in index works + expected = df.reset_index("a", drop=True) + result = df.droplevel("a", axis="index") + tm.assert_equal(result, expected) + + if frame_or_series is DataFrame: + # test that dropping of a level in columns works + expected = df.copy() + expected.columns = Index(["c", "d"], name="level_1") + result = df.droplevel("level_2", axis="columns") + tm.assert_equal(result, expected) + else: + # test that droplevel raises ValueError on axis != 0 + with pytest.raises(ValueError, match="No axis named columns"): + df.droplevel(1, axis="columns") diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_dropna.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_dropna.py new file mode 100644 index 0000000000000000000000000000000000000000..7899b4aeac3fdef6548f3aadf76ff7718418f089 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_dropna.py @@ -0,0 +1,285 @@ +import datetime + +import dateutil +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + Series, +) +import pandas._testing as tm + + +class TestDataFrameMissingData: + def test_dropEmptyRows(self, float_frame): + N = len(float_frame.index) + mat = np.random.default_rng(2).standard_normal(N) + mat[:5] = np.nan + + frame = DataFrame({"foo": mat}, index=float_frame.index) + original = Series(mat, index=float_frame.index, name="foo") + expected = original.dropna() + inplace_frame1, inplace_frame2 = frame.copy(), frame.copy() + + smaller_frame = frame.dropna(how="all") + # check that original was preserved + tm.assert_series_equal(frame["foo"], original) + return_value = inplace_frame1.dropna(how="all", inplace=True) + tm.assert_series_equal(smaller_frame["foo"], expected) + tm.assert_series_equal(inplace_frame1["foo"], expected) + assert return_value is None + + smaller_frame = frame.dropna(how="all", subset=["foo"]) + return_value = inplace_frame2.dropna(how="all", subset=["foo"], inplace=True) + tm.assert_series_equal(smaller_frame["foo"], expected) + tm.assert_series_equal(inplace_frame2["foo"], expected) + assert return_value is None + + def test_dropIncompleteRows(self, float_frame): + N = len(float_frame.index) + mat = np.random.default_rng(2).standard_normal(N) + mat[:5] = np.nan + + frame = DataFrame({"foo": mat}, index=float_frame.index) + frame["bar"] = 5 + original = Series(mat, index=float_frame.index, name="foo") + inp_frame1, inp_frame2 = frame.copy(), frame.copy() + + smaller_frame = frame.dropna() + tm.assert_series_equal(frame["foo"], original) + return_value = inp_frame1.dropna(inplace=True) + + exp = Series(mat[5:], index=float_frame.index[5:], name="foo") + tm.assert_series_equal(smaller_frame["foo"], exp) + tm.assert_series_equal(inp_frame1["foo"], exp) + assert return_value is None + + samesize_frame = frame.dropna(subset=["bar"]) + tm.assert_series_equal(frame["foo"], original) + assert (frame["bar"] == 5).all() + return_value = inp_frame2.dropna(subset=["bar"], inplace=True) + tm.assert_index_equal(samesize_frame.index, float_frame.index) + tm.assert_index_equal(inp_frame2.index, float_frame.index) + assert return_value is None + + def test_dropna(self): + df = DataFrame(np.random.default_rng(2).standard_normal((6, 4))) + df.iloc[:2, 2] = np.nan + + dropped = df.dropna(axis=1) + expected = df.loc[:, [0, 1, 3]] + inp = df.copy() + return_value = inp.dropna(axis=1, inplace=True) + tm.assert_frame_equal(dropped, expected) + tm.assert_frame_equal(inp, expected) + assert return_value is None + + dropped = df.dropna(axis=0) + expected = df.loc[list(range(2, 6))] + inp = df.copy() + return_value = inp.dropna(axis=0, inplace=True) + tm.assert_frame_equal(dropped, expected) + tm.assert_frame_equal(inp, expected) + assert return_value is None + + # threshold + dropped = df.dropna(axis=1, thresh=5) + expected = df.loc[:, [0, 1, 3]] + inp = df.copy() + return_value = inp.dropna(axis=1, thresh=5, inplace=True) + tm.assert_frame_equal(dropped, expected) + tm.assert_frame_equal(inp, expected) + assert return_value is None + + dropped = df.dropna(axis=0, thresh=4) + expected = df.loc[range(2, 6)] + inp = df.copy() + return_value = inp.dropna(axis=0, thresh=4, inplace=True) + tm.assert_frame_equal(dropped, expected) + tm.assert_frame_equal(inp, expected) + assert return_value is None + + dropped = df.dropna(axis=1, thresh=4) + tm.assert_frame_equal(dropped, df) + + dropped = df.dropna(axis=1, thresh=3) + tm.assert_frame_equal(dropped, df) + + # subset + dropped = df.dropna(axis=0, subset=[0, 1, 3]) + inp = df.copy() + return_value = inp.dropna(axis=0, subset=[0, 1, 3], inplace=True) + tm.assert_frame_equal(dropped, df) + tm.assert_frame_equal(inp, df) + assert return_value is None + + # all + dropped = df.dropna(axis=1, how="all") + tm.assert_frame_equal(dropped, df) + + df[2] = np.nan + dropped = df.dropna(axis=1, how="all") + expected = df.loc[:, [0, 1, 3]] + tm.assert_frame_equal(dropped, expected) + + # bad input + msg = "No axis named 3 for object type DataFrame" + with pytest.raises(ValueError, match=msg): + df.dropna(axis=3) + + def test_drop_and_dropna_caching(self): + # tst that cacher updates + original = Series([1, 2, np.nan], name="A") + expected = Series([1, 2], dtype=original.dtype, name="A") + df = DataFrame({"A": original.values.copy()}) + df2 = df.copy() + df["A"].dropna() + tm.assert_series_equal(df["A"], original) + + ser = df["A"] + return_value = ser.dropna(inplace=True) + tm.assert_series_equal(ser, expected) + tm.assert_series_equal(df["A"], original) + assert return_value is None + + df2["A"].drop([1]) + tm.assert_series_equal(df2["A"], original) + + ser = df2["A"] + return_value = ser.drop([1], inplace=True) + tm.assert_series_equal(ser, original.drop([1])) + tm.assert_series_equal(df2["A"], original) + assert return_value is None + + def test_dropna_corner(self, float_frame): + # bad input + msg = "invalid how option: foo" + with pytest.raises(ValueError, match=msg): + float_frame.dropna(how="foo") + # non-existent column - 8303 + with pytest.raises(KeyError, match=r"^\['X'\]$"): + float_frame.dropna(subset=["A", "X"]) + + def test_dropna_multiple_axes(self): + df = DataFrame( + [ + [1, np.nan, 2, 3], + [4, np.nan, 5, 6], + [np.nan, np.nan, np.nan, np.nan], + [7, np.nan, 8, 9], + ] + ) + + # GH20987 + with pytest.raises(TypeError, match="supplying multiple axes"): + df.dropna(how="all", axis=[0, 1]) + with pytest.raises(TypeError, match="supplying multiple axes"): + df.dropna(how="all", axis=(0, 1)) + + inp = df.copy() + with pytest.raises(TypeError, match="supplying multiple axes"): + inp.dropna(how="all", axis=(0, 1), inplace=True) + + def test_dropna_tz_aware_datetime(self): + # GH13407 + df = DataFrame() + dt1 = datetime.datetime(2015, 1, 1, tzinfo=dateutil.tz.tzutc()) + dt2 = datetime.datetime(2015, 2, 2, tzinfo=dateutil.tz.tzutc()) + df["Time"] = [dt1] + result = df.dropna(axis=0) + expected = DataFrame({"Time": [dt1]}) + tm.assert_frame_equal(result, expected) + + # Ex2 + df = DataFrame({"Time": [dt1, None, np.nan, dt2]}) + result = df.dropna(axis=0) + expected = DataFrame([dt1, dt2], columns=["Time"], index=[0, 3]) + tm.assert_frame_equal(result, expected) + + def test_dropna_categorical_interval_index(self): + # GH 25087 + ii = pd.IntervalIndex.from_breaks([0, 2.78, 3.14, 6.28]) + ci = pd.CategoricalIndex(ii) + df = DataFrame({"A": list("abc")}, index=ci) + + expected = df + result = df.dropna() + tm.assert_frame_equal(result, expected) + + def test_dropna_with_duplicate_columns(self): + df = DataFrame( + { + "A": np.random.default_rng(2).standard_normal(5), + "B": np.random.default_rng(2).standard_normal(5), + "C": np.random.default_rng(2).standard_normal(5), + "D": ["a", "b", "c", "d", "e"], + } + ) + df.iloc[2, [0, 1, 2]] = np.nan + df.iloc[0, 0] = np.nan + df.iloc[1, 1] = np.nan + df.iloc[:, 3] = np.nan + expected = df.dropna(subset=["A", "B", "C"], how="all") + expected.columns = ["A", "A", "B", "C"] + + df.columns = ["A", "A", "B", "C"] + + result = df.dropna(subset=["A", "C"], how="all") + tm.assert_frame_equal(result, expected) + + def test_set_single_column_subset(self): + # GH 41021 + df = DataFrame({"A": [1, 2, 3], "B": list("abc"), "C": [4, np.nan, 5]}) + expected = DataFrame( + {"A": [1, 3], "B": list("ac"), "C": [4.0, 5.0]}, index=[0, 2] + ) + result = df.dropna(subset="C") + tm.assert_frame_equal(result, expected) + + def test_single_column_not_present_in_axis(self): + # GH 41021 + df = DataFrame({"A": [1, 2, 3]}) + + # Column not present + with pytest.raises(KeyError, match="['D']"): + df.dropna(subset="D", axis=0) + + def test_subset_is_nparray(self): + # GH 41021 + df = DataFrame({"A": [1, 2, np.nan], "B": list("abc"), "C": [4, np.nan, 5]}) + expected = DataFrame({"A": [1.0], "B": ["a"], "C": [4.0]}) + result = df.dropna(subset=np.array(["A", "C"])) + tm.assert_frame_equal(result, expected) + + def test_no_nans_in_frame(self, axis): + # GH#41965 + df = DataFrame([[1, 2], [3, 4]], columns=pd.RangeIndex(0, 2)) + expected = df.copy() + result = df.dropna(axis=axis) + tm.assert_frame_equal(result, expected, check_index_type=True) + + def test_how_thresh_param_incompatible(self): + # GH46575 + df = DataFrame([1, 2, pd.NA]) + msg = "You cannot set both the how and thresh arguments at the same time" + with pytest.raises(TypeError, match=msg): + df.dropna(how="all", thresh=2) + + with pytest.raises(TypeError, match=msg): + df.dropna(how="any", thresh=2) + + with pytest.raises(TypeError, match=msg): + df.dropna(how=None, thresh=None) + + @pytest.mark.parametrize("val", [1, 1.5]) + def test_dropna_ignore_index(self, val): + # GH#31725 + df = DataFrame({"a": [1, 2, val]}, index=[3, 2, 1]) + result = df.dropna(ignore_index=True) + expected = DataFrame({"a": [1, 2, val]}) + tm.assert_frame_equal(result, expected) + + df.dropna(ignore_index=True, inplace=True) + tm.assert_frame_equal(df, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_dtypes.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_dtypes.py new file mode 100644 index 0000000000000000000000000000000000000000..524a5587dce10b477f570efa01407f0c0b190bfd --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_dtypes.py @@ -0,0 +1,150 @@ +from datetime import timedelta + +import numpy as np +import pytest + +from pandas.core.dtypes.dtypes import DatetimeTZDtype + +import pandas as pd +from pandas import ( + DataFrame, + Series, + date_range, + option_context, +) +import pandas._testing as tm + + +class TestDataFrameDataTypes: + def test_empty_frame_dtypes(self): + empty_df = DataFrame() + tm.assert_series_equal(empty_df.dtypes, Series(dtype=object)) + + nocols_df = DataFrame(index=[1, 2, 3]) + tm.assert_series_equal(nocols_df.dtypes, Series(dtype=object)) + + norows_df = DataFrame(columns=list("abc")) + tm.assert_series_equal(norows_df.dtypes, Series(object, index=list("abc"))) + + norows_int_df = DataFrame(columns=list("abc")).astype(np.int32) + tm.assert_series_equal( + norows_int_df.dtypes, Series(np.dtype("int32"), index=list("abc")) + ) + + df = DataFrame({"a": 1, "b": True, "c": 1.0}, index=[1, 2, 3]) + ex_dtypes = Series({"a": np.int64, "b": np.bool_, "c": np.float64}) + tm.assert_series_equal(df.dtypes, ex_dtypes) + + # same but for empty slice of df + tm.assert_series_equal(df[:0].dtypes, ex_dtypes) + + def test_datetime_with_tz_dtypes(self): + tzframe = DataFrame( + { + "A": date_range("20130101", periods=3), + "B": date_range("20130101", periods=3, tz="US/Eastern"), + "C": date_range("20130101", periods=3, tz="CET"), + } + ) + tzframe.iloc[1, 1] = pd.NaT + tzframe.iloc[1, 2] = pd.NaT + result = tzframe.dtypes.sort_index() + expected = Series( + [ + np.dtype("datetime64[ns]"), + DatetimeTZDtype("ns", "US/Eastern"), + DatetimeTZDtype("ns", "CET"), + ], + ["A", "B", "C"], + ) + + tm.assert_series_equal(result, expected) + + def test_dtypes_are_correct_after_column_slice(self): + # GH6525 + df = DataFrame(index=range(5), columns=list("abc"), dtype=np.float64) + tm.assert_series_equal( + df.dtypes, + Series({"a": np.float64, "b": np.float64, "c": np.float64}), + ) + tm.assert_series_equal(df.iloc[:, 2:].dtypes, Series({"c": np.float64})) + tm.assert_series_equal( + df.dtypes, + Series({"a": np.float64, "b": np.float64, "c": np.float64}), + ) + + @pytest.mark.parametrize( + "data", + [pd.NA, True], + ) + def test_dtypes_are_correct_after_groupby_last(self, data): + # GH46409 + df = DataFrame( + {"id": [1, 2, 3, 4], "test": [True, pd.NA, data, False]} + ).convert_dtypes() + result = df.groupby("id").last().test + expected = df.set_index("id").test + assert result.dtype == pd.BooleanDtype() + tm.assert_series_equal(expected, result) + + def test_dtypes_gh8722(self, float_string_frame): + float_string_frame["bool"] = float_string_frame["A"] > 0 + result = float_string_frame.dtypes + expected = Series( + {k: v.dtype for k, v in float_string_frame.items()}, index=result.index + ) + tm.assert_series_equal(result, expected) + + # compat, GH 8722 + msg = "use_inf_as_na option is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + with option_context("use_inf_as_na", True): + df = DataFrame([[1]]) + result = df.dtypes + tm.assert_series_equal(result, Series({0: np.dtype("int64")})) + + def test_dtypes_timedeltas(self): + df = DataFrame( + { + "A": Series(date_range("2012-1-1", periods=3, freq="D")), + "B": Series([timedelta(days=i) for i in range(3)]), + } + ) + result = df.dtypes + expected = Series( + [np.dtype("datetime64[ns]"), np.dtype("timedelta64[ns]")], index=list("AB") + ) + tm.assert_series_equal(result, expected) + + df["C"] = df["A"] + df["B"] + result = df.dtypes + expected = Series( + [ + np.dtype("datetime64[ns]"), + np.dtype("timedelta64[ns]"), + np.dtype("datetime64[ns]"), + ], + index=list("ABC"), + ) + tm.assert_series_equal(result, expected) + + # mixed int types + df["D"] = 1 + result = df.dtypes + expected = Series( + [ + np.dtype("datetime64[ns]"), + np.dtype("timedelta64[ns]"), + np.dtype("datetime64[ns]"), + np.dtype("int64"), + ], + index=list("ABCD"), + ) + tm.assert_series_equal(result, expected) + + def test_frame_apply_np_array_return_type(self, using_infer_string): + # GH 35517 + df = DataFrame([["foo"]]) + result = df.apply(lambda col: np.array("bar")) + expected = Series(np.array("bar")) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_duplicated.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_duplicated.py new file mode 100644 index 0000000000000000000000000000000000000000..6052b61ea8db5b8c81c879250129a81634a33de0 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_duplicated.py @@ -0,0 +1,117 @@ +import re +import sys + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Series, + date_range, +) +import pandas._testing as tm + + +@pytest.mark.parametrize("subset", ["a", ["a"], ["a", "B"]]) +def test_duplicated_with_misspelled_column_name(subset): + # GH 19730 + df = DataFrame({"A": [0, 0, 1], "B": [0, 0, 1], "C": [0, 0, 1]}) + msg = re.escape("Index(['a'], dtype=") + + with pytest.raises(KeyError, match=msg): + df.duplicated(subset) + + +def test_duplicated_implemented_no_recursion(): + # gh-21524 + # Ensure duplicated isn't implemented using recursion that + # can fail on wide frames + df = DataFrame(np.random.default_rng(2).integers(0, 1000, (10, 1000))) + rec_limit = sys.getrecursionlimit() + try: + sys.setrecursionlimit(100) + result = df.duplicated() + finally: + sys.setrecursionlimit(rec_limit) + + # Then duplicates produce the bool Series as a result and don't fail during + # calculation. Actual values doesn't matter here, though usually it's all + # False in this case + assert isinstance(result, Series) + assert result.dtype == np.bool_ + + +@pytest.mark.parametrize( + "keep, expected", + [ + ("first", Series([False, False, True, False, True])), + ("last", Series([True, True, False, False, False])), + (False, Series([True, True, True, False, True])), + ], +) +def test_duplicated_keep(keep, expected): + df = DataFrame({"A": [0, 1, 1, 2, 0], "B": ["a", "b", "b", "c", "a"]}) + + result = df.duplicated(keep=keep) + tm.assert_series_equal(result, expected) + + +@pytest.mark.xfail(reason="GH#21720; nan/None falsely considered equal") +@pytest.mark.parametrize( + "keep, expected", + [ + ("first", Series([False, False, True, False, True])), + ("last", Series([True, True, False, False, False])), + (False, Series([True, True, True, False, True])), + ], +) +def test_duplicated_nan_none(keep, expected): + df = DataFrame({"C": [np.nan, 3, 3, None, np.nan], "x": 1}, dtype=object) + + result = df.duplicated(keep=keep) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("subset", [None, ["A", "B"], "A"]) +def test_duplicated_subset(subset, keep): + df = DataFrame( + { + "A": [0, 1, 1, 2, 0], + "B": ["a", "b", "b", "c", "a"], + "C": [np.nan, 3, 3, None, np.nan], + } + ) + + if subset is None: + subset = list(df.columns) + elif isinstance(subset, str): + # need to have a DataFrame, not a Series + # -> select columns with singleton list, not string + subset = [subset] + + expected = df[subset].duplicated(keep=keep) + result = df.duplicated(keep=keep, subset=subset) + tm.assert_series_equal(result, expected) + + +def test_duplicated_on_empty_frame(): + # GH 25184 + + df = DataFrame(columns=["a", "b"]) + dupes = df.duplicated("a") + + result = df[dupes] + expected = df.copy() + tm.assert_frame_equal(result, expected) + + +def test_frame_datetime64_duplicated(): + dates = date_range("2010-07-01", end="2010-08-05") + + tst = DataFrame({"symbol": "AAA", "date": dates}) + result = tst.duplicated(["date", "symbol"]) + assert (-result).all() + + tst = DataFrame({"date": dates}) + result = tst.date.duplicated() + assert (-result).all() diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_equals.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_equals.py new file mode 100644 index 0000000000000000000000000000000000000000..d0b9d96cafa0db15203cb3057517571a178b25db --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_equals.py @@ -0,0 +1,85 @@ +import numpy as np + +from pandas import ( + DataFrame, + date_range, +) +import pandas._testing as tm + + +class TestEquals: + def test_dataframe_not_equal(self): + # see GH#28839 + df1 = DataFrame({"a": [1, 2], "b": ["s", "d"]}) + df2 = DataFrame({"a": ["s", "d"], "b": [1, 2]}) + assert df1.equals(df2) is False + + def test_equals_different_blocks(self, using_array_manager, using_infer_string): + # GH#9330 + df0 = DataFrame({"A": ["x", "y"], "B": [1, 2], "C": ["w", "z"]}) + df1 = df0.reset_index()[["A", "B", "C"]] + if not using_array_manager and not using_infer_string: + # this assert verifies that the above operations have + # induced a block rearrangement + assert df0._mgr.blocks[0].dtype != df1._mgr.blocks[0].dtype + + # do the real tests + tm.assert_frame_equal(df0, df1) + assert df0.equals(df1) + assert df1.equals(df0) + + def test_equals(self): + # Add object dtype column with nans + index = np.random.default_rng(2).random(10) + df1 = DataFrame( + np.random.default_rng(2).random(10), index=index, columns=["floats"] + ) + df1["text"] = "the sky is so blue. we could use more chocolate.".split() + df1["start"] = date_range("2000-1-1", periods=10, freq="min") + df1["end"] = date_range("2000-1-1", periods=10, freq="D") + df1["diff"] = df1["end"] - df1["start"] + # Explicitly cast to object, to avoid implicit cast when setting np.nan + df1["bool"] = (np.arange(10) % 3 == 0).astype(object) + df1.loc[::2] = np.nan + df2 = df1.copy() + assert df1["text"].equals(df2["text"]) + assert df1["start"].equals(df2["start"]) + assert df1["end"].equals(df2["end"]) + assert df1["diff"].equals(df2["diff"]) + assert df1["bool"].equals(df2["bool"]) + assert df1.equals(df2) + assert not df1.equals(object) + + # different dtype + different = df1.copy() + different["floats"] = different["floats"].astype("float32") + assert not df1.equals(different) + + # different index + different_index = -index + different = df2.set_index(different_index) + assert not df1.equals(different) + + # different columns + different = df2.copy() + different.columns = df2.columns[::-1] + assert not df1.equals(different) + + # DatetimeIndex + index = date_range("2000-1-1", periods=10, freq="min") + df1 = df1.set_index(index) + df2 = df1.copy() + assert df1.equals(df2) + + # MultiIndex + df3 = df1.set_index(["text"], append=True) + df2 = df1.set_index(["text"], append=True) + assert df3.equals(df2) + + df2 = df1.set_index(["floats"], append=True) + assert not df3.equals(df2) + + # NaN in index + df3 = df1.set_index(["floats"], append=True) + df2 = df1.set_index(["floats"], append=True) + assert df3.equals(df2) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_explode.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_explode.py new file mode 100644 index 0000000000000000000000000000000000000000..bc3fdb56e649bdbc9d7ee21bd61f6b25da52c617 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_explode.py @@ -0,0 +1,311 @@ +import re + +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm + + +def test_error(): + df = pd.DataFrame( + {"A": pd.Series([[0, 1, 2], np.nan, [], (3, 4)], index=list("abcd")), "B": 1} + ) + with pytest.raises( + ValueError, match="column must be a scalar, tuple, or list thereof" + ): + df.explode([list("AA")]) + + with pytest.raises(ValueError, match="column must be unique"): + df.explode(list("AA")) + + df.columns = list("AA") + with pytest.raises( + ValueError, + match=re.escape("DataFrame columns must be unique. Duplicate columns: ['A']"), + ): + df.explode("A") + + +@pytest.mark.parametrize( + "input_subset, error_message", + [ + ( + list("AC"), + "columns must have matching element counts", + ), + ( + [], + "column must be nonempty", + ), + ( + list("AC"), + "columns must have matching element counts", + ), + ], +) +def test_error_multi_columns(input_subset, error_message): + # GH 39240 + df = pd.DataFrame( + { + "A": [[0, 1, 2], np.nan, [], (3, 4)], + "B": 1, + "C": [["a", "b", "c"], "foo", [], ["d", "e", "f"]], + }, + index=list("abcd"), + ) + with pytest.raises(ValueError, match=error_message): + df.explode(input_subset) + + +@pytest.mark.parametrize( + "scalar", + ["a", 0, 1.5, pd.Timedelta("1 days"), pd.Timestamp("2019-12-31")], +) +def test_basic(scalar): + df = pd.DataFrame( + {scalar: pd.Series([[0, 1, 2], np.nan, [], (3, 4)], index=list("abcd")), "B": 1} + ) + result = df.explode(scalar) + expected = pd.DataFrame( + { + scalar: pd.Series( + [0, 1, 2, np.nan, np.nan, 3, 4], index=list("aaabcdd"), dtype=object + ), + "B": 1, + } + ) + tm.assert_frame_equal(result, expected) + + +def test_multi_index_rows(): + df = pd.DataFrame( + {"A": np.array([[0, 1, 2], np.nan, [], (3, 4)], dtype=object), "B": 1}, + index=pd.MultiIndex.from_tuples([("a", 1), ("a", 2), ("b", 1), ("b", 2)]), + ) + + result = df.explode("A") + expected = pd.DataFrame( + { + "A": pd.Series( + [0, 1, 2, np.nan, np.nan, 3, 4], + index=pd.MultiIndex.from_tuples( + [ + ("a", 1), + ("a", 1), + ("a", 1), + ("a", 2), + ("b", 1), + ("b", 2), + ("b", 2), + ] + ), + dtype=object, + ), + "B": 1, + } + ) + tm.assert_frame_equal(result, expected) + + +def test_multi_index_columns(): + df = pd.DataFrame( + {("A", 1): np.array([[0, 1, 2], np.nan, [], (3, 4)], dtype=object), ("A", 2): 1} + ) + + result = df.explode(("A", 1)) + expected = pd.DataFrame( + { + ("A", 1): pd.Series( + [0, 1, 2, np.nan, np.nan, 3, 4], + index=pd.Index([0, 0, 0, 1, 2, 3, 3]), + dtype=object, + ), + ("A", 2): 1, + } + ) + tm.assert_frame_equal(result, expected) + + +def test_usecase(): + # explode a single column + # gh-10511 + df = pd.DataFrame( + [[11, range(5), 10], [22, range(3), 20]], columns=list("ABC") + ).set_index("C") + result = df.explode("B") + + expected = pd.DataFrame( + { + "A": [11, 11, 11, 11, 11, 22, 22, 22], + "B": np.array([0, 1, 2, 3, 4, 0, 1, 2], dtype=object), + "C": [10, 10, 10, 10, 10, 20, 20, 20], + }, + columns=list("ABC"), + ).set_index("C") + + tm.assert_frame_equal(result, expected) + + # gh-8517 + df = pd.DataFrame( + [["2014-01-01", "Alice", "A B"], ["2014-01-02", "Bob", "C D"]], + columns=["dt", "name", "text"], + ) + result = df.assign(text=df.text.str.split(" ")).explode("text") + expected = pd.DataFrame( + [ + ["2014-01-01", "Alice", "A"], + ["2014-01-01", "Alice", "B"], + ["2014-01-02", "Bob", "C"], + ["2014-01-02", "Bob", "D"], + ], + columns=["dt", "name", "text"], + index=[0, 0, 1, 1], + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "input_dict, input_index, expected_dict, expected_index", + [ + ( + {"col1": [[1, 2], [3, 4]], "col2": ["foo", "bar"]}, + [0, 0], + {"col1": [1, 2, 3, 4], "col2": ["foo", "foo", "bar", "bar"]}, + [0, 0, 0, 0], + ), + ( + {"col1": [[1, 2], [3, 4]], "col2": ["foo", "bar"]}, + pd.Index([0, 0], name="my_index"), + {"col1": [1, 2, 3, 4], "col2": ["foo", "foo", "bar", "bar"]}, + pd.Index([0, 0, 0, 0], name="my_index"), + ), + ( + {"col1": [[1, 2], [3, 4]], "col2": ["foo", "bar"]}, + pd.MultiIndex.from_arrays( + [[0, 0], [1, 1]], names=["my_first_index", "my_second_index"] + ), + {"col1": [1, 2, 3, 4], "col2": ["foo", "foo", "bar", "bar"]}, + pd.MultiIndex.from_arrays( + [[0, 0, 0, 0], [1, 1, 1, 1]], + names=["my_first_index", "my_second_index"], + ), + ), + ( + {"col1": [[1, 2], [3, 4]], "col2": ["foo", "bar"]}, + pd.MultiIndex.from_arrays([[0, 0], [1, 1]], names=["my_index", None]), + {"col1": [1, 2, 3, 4], "col2": ["foo", "foo", "bar", "bar"]}, + pd.MultiIndex.from_arrays( + [[0, 0, 0, 0], [1, 1, 1, 1]], names=["my_index", None] + ), + ), + ], +) +def test_duplicate_index(input_dict, input_index, expected_dict, expected_index): + # GH 28005 + df = pd.DataFrame(input_dict, index=input_index, dtype=object) + result = df.explode("col1") + expected = pd.DataFrame(expected_dict, index=expected_index, dtype=object) + tm.assert_frame_equal(result, expected) + + +def test_ignore_index(): + # GH 34932 + df = pd.DataFrame({"id": range(0, 20, 10), "values": [list("ab"), list("cd")]}) + result = df.explode("values", ignore_index=True) + expected = pd.DataFrame( + {"id": [0, 0, 10, 10], "values": list("abcd")}, index=[0, 1, 2, 3] + ) + tm.assert_frame_equal(result, expected) + + +def test_explode_sets(): + # https://github.com/pandas-dev/pandas/issues/35614 + df = pd.DataFrame({"a": [{"x", "y"}], "b": [1]}, index=[1]) + result = df.explode(column="a").sort_values(by="a") + expected = pd.DataFrame({"a": ["x", "y"], "b": [1, 1]}, index=[1, 1]) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "input_subset, expected_dict, expected_index", + [ + ( + list("AC"), + { + "A": pd.Series( + [0, 1, 2, np.nan, np.nan, 3, 4, np.nan], + index=list("aaabcdde"), + dtype=object, + ), + "B": 1, + "C": ["a", "b", "c", "foo", np.nan, "d", "e", np.nan], + }, + list("aaabcdde"), + ), + ( + list("A"), + { + "A": pd.Series( + [0, 1, 2, np.nan, np.nan, 3, 4, np.nan], + index=list("aaabcdde"), + dtype=object, + ), + "B": 1, + "C": [ + ["a", "b", "c"], + ["a", "b", "c"], + ["a", "b", "c"], + "foo", + [], + ["d", "e"], + ["d", "e"], + np.nan, + ], + }, + list("aaabcdde"), + ), + ], +) +def test_multi_columns(input_subset, expected_dict, expected_index): + # GH 39240 + df = pd.DataFrame( + { + "A": [[0, 1, 2], np.nan, [], (3, 4), np.nan], + "B": 1, + "C": [["a", "b", "c"], "foo", [], ["d", "e"], np.nan], + }, + index=list("abcde"), + ) + result = df.explode(input_subset) + expected = pd.DataFrame(expected_dict, expected_index) + tm.assert_frame_equal(result, expected) + + +def test_multi_columns_nan_empty(): + # GH 46084 + df = pd.DataFrame( + { + "A": [[0, 1], [5], [], [2, 3]], + "B": [9, 8, 7, 6], + "C": [[1, 2], np.nan, [], [3, 4]], + } + ) + result = df.explode(["A", "C"]) + expected = pd.DataFrame( + { + "A": np.array([0, 1, 5, np.nan, 2, 3], dtype=object), + "B": [9, 9, 8, 7, 6, 6], + "C": np.array([1, 2, np.nan, np.nan, 3, 4], dtype=object), + }, + index=[0, 0, 1, 2, 3, 3], + ) + tm.assert_frame_equal(result, expected) + + +def test_str_dtype(): + # https://github.com/pandas-dev/pandas/pull/61623 + df = pd.DataFrame({"a": ["x", "y"]}, dtype="str") + result = df.explode(column="a") + assert result is not df + tm.assert_frame_equal(result, df) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_fillna.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_fillna.py new file mode 100644 index 0000000000000000000000000000000000000000..b6f4680bff73ed43aa593eb6f430ca6da6cd884a --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_fillna.py @@ -0,0 +1,916 @@ +import numpy as np +import pytest + +from pandas.compat import WARNING_CHECK_DISABLED +import pandas.util._test_decorators as td + +from pandas import ( + Categorical, + DataFrame, + DatetimeIndex, + NaT, + PeriodIndex, + Series, + TimedeltaIndex, + Timestamp, + date_range, + to_datetime, +) +import pandas._testing as tm +from pandas.tests.frame.common import _check_mixed_float + + +class TestFillNA: + def test_fillna_dict_inplace_nonunique_columns( + self, using_copy_on_write, warn_copy_on_write + ): + df = DataFrame( + {"A": [np.nan] * 3, "B": [NaT, Timestamp(1), NaT], "C": [np.nan, "foo", 2]} + ) + df.columns = ["A", "A", "A"] + orig = df[:] + + # TODO(CoW-warn) better warning message + with tm.assert_cow_warning(warn_copy_on_write): + df.fillna({"A": 2}, inplace=True) + # The first and third columns can be set inplace, while the second cannot. + + expected = DataFrame( + {"A": [2.0] * 3, "B": [2, Timestamp(1), 2], "C": [2, "foo", 2]} + ) + expected.columns = ["A", "A", "A"] + tm.assert_frame_equal(df, expected) + + # TODO: what's the expected/desired behavior with CoW? + if not using_copy_on_write: + assert tm.shares_memory(df.iloc[:, 0], orig.iloc[:, 0]) + assert not tm.shares_memory(df.iloc[:, 1], orig.iloc[:, 1]) + if not using_copy_on_write: + assert tm.shares_memory(df.iloc[:, 2], orig.iloc[:, 2]) + + @td.skip_array_manager_not_yet_implemented + def test_fillna_on_column_view(self, using_copy_on_write): + # GH#46149 avoid unnecessary copies + arr = np.full((40, 50), np.nan) + df = DataFrame(arr, copy=False) + + if using_copy_on_write: + with tm.raises_chained_assignment_error(): + df[0].fillna(-1, inplace=True) + assert np.isnan(arr[:, 0]).all() + else: + with tm.assert_produces_warning( + FutureWarning if not WARNING_CHECK_DISABLED else None, + match="inplace method", + ): + df[0].fillna(-1, inplace=True) + assert (arr[:, 0] == -1).all() + + # i.e. we didn't create a new 49-column block + assert len(df._mgr.arrays) == 1 + assert np.shares_memory(df.values, arr) + + def test_fillna_datetime(self, datetime_frame): + tf = datetime_frame + tf.loc[tf.index[:5], "A"] = np.nan + tf.loc[tf.index[-5:], "A"] = np.nan + + zero_filled = datetime_frame.fillna(0) + assert (zero_filled.loc[zero_filled.index[:5], "A"] == 0).all() + + msg = "DataFrame.fillna with 'method' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + padded = datetime_frame.fillna(method="pad") + assert np.isnan(padded.loc[padded.index[:5], "A"]).all() + assert ( + padded.loc[padded.index[-5:], "A"] == padded.loc[padded.index[-5], "A"] + ).all() + + msg = "Must specify a fill 'value' or 'method'" + with pytest.raises(ValueError, match=msg): + datetime_frame.fillna() + msg = "Cannot specify both 'value' and 'method'" + with pytest.raises(ValueError, match=msg): + datetime_frame.fillna(5, method="ffill") + + def test_fillna_mixed_type(self, float_string_frame): + mf = float_string_frame + mf.loc[mf.index[5:20], "foo"] = np.nan + mf.loc[mf.index[-10:], "A"] = np.nan + # TODO: make stronger assertion here, GH 25640 + mf.fillna(value=0) + msg = "DataFrame.fillna with 'method' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + mf.fillna(method="pad") + + def test_fillna_mixed_float(self, mixed_float_frame): + # mixed numeric (but no float16) + mf = mixed_float_frame.reindex(columns=["A", "B", "D"]) + mf.loc[mf.index[-10:], "A"] = np.nan + result = mf.fillna(value=0) + _check_mixed_float(result, dtype={"C": None}) + + msg = "DataFrame.fillna with 'method' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = mf.fillna(method="pad") + _check_mixed_float(result, dtype={"C": None}) + + def test_fillna_empty(self, using_copy_on_write): + if using_copy_on_write: + pytest.skip("condition is unnecessary complex and is deprecated anyway") + # empty frame (GH#2778) + df = DataFrame(columns=["x"]) + for m in ["pad", "backfill"]: + msg = "Series.fillna with 'method' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + df.x.fillna(method=m, inplace=True) + df.x.fillna(method=m) + + def test_fillna_different_dtype(self): + # with different dtype (GH#3386) + df = DataFrame( + [["a", "a", np.nan, "a"], ["b", "b", np.nan, "b"], ["c", "c", np.nan, "c"]] + ) + + result = df.fillna({2: "foo"}) + expected = DataFrame( + [["a", "a", "foo", "a"], ["b", "b", "foo", "b"], ["c", "c", "foo", "c"]] + ) + # column is originally float (all-NaN) -> filling with string gives object dtype + expected[2] = expected[2].astype("object") + tm.assert_frame_equal(result, expected) + + return_value = df.fillna({2: "foo"}, inplace=True) + tm.assert_frame_equal(df, expected) + assert return_value is None + + def test_fillna_limit_and_value(self): + # limit and value + df = DataFrame(np.random.default_rng(2).standard_normal((10, 3))) + df.iloc[2:7, 0] = np.nan + df.iloc[3:5, 2] = np.nan + + expected = df.copy() + expected.iloc[2, 0] = 999 + expected.iloc[3, 2] = 999 + result = df.fillna(999, limit=1) + tm.assert_frame_equal(result, expected) + + def test_fillna_datelike(self): + # with datelike + # GH#6344 + df = DataFrame( + { + "Date": [NaT, Timestamp("2014-1-1")], + "Date2": [Timestamp("2013-1-1"), NaT], + } + ) + + expected = df.copy() + expected["Date"] = expected["Date"].fillna(df.loc[df.index[0], "Date2"]) + result = df.fillna(value={"Date": df["Date2"]}) + tm.assert_frame_equal(result, expected) + + def test_fillna_tzaware(self): + # with timezone + # GH#15855 + df = DataFrame({"A": [Timestamp("2012-11-11 00:00:00+01:00"), NaT]}) + exp = DataFrame( + { + "A": [ + Timestamp("2012-11-11 00:00:00+01:00"), + Timestamp("2012-11-11 00:00:00+01:00"), + ] + } + ) + msg = "DataFrame.fillna with 'method' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + res = df.fillna(method="pad") + tm.assert_frame_equal(res, exp) + + df = DataFrame({"A": [NaT, Timestamp("2012-11-11 00:00:00+01:00")]}) + exp = DataFrame( + { + "A": [ + Timestamp("2012-11-11 00:00:00+01:00"), + Timestamp("2012-11-11 00:00:00+01:00"), + ] + } + ) + msg = "DataFrame.fillna with 'method' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + res = df.fillna(method="bfill") + tm.assert_frame_equal(res, exp) + + def test_fillna_tzaware_different_column(self): + # with timezone in another column + # GH#15522 + df = DataFrame( + { + "A": date_range("20130101", periods=4, tz="US/Eastern"), + "B": [1, 2, np.nan, np.nan], + } + ) + msg = "DataFrame.fillna with 'method' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.fillna(method="pad") + expected = DataFrame( + { + "A": date_range("20130101", periods=4, tz="US/Eastern"), + "B": [1.0, 2.0, 2.0, 2.0], + } + ) + tm.assert_frame_equal(result, expected) + + def test_na_actions_categorical(self): + cat = Categorical([1, 2, 3, np.nan], categories=[1, 2, 3]) + vals = ["a", "b", np.nan, "d"] + df = DataFrame({"cats": cat, "vals": vals}) + cat2 = Categorical([1, 2, 3, 3], categories=[1, 2, 3]) + vals2 = ["a", "b", "b", "d"] + df_exp_fill = DataFrame({"cats": cat2, "vals": vals2}) + cat3 = Categorical([1, 2, 3], categories=[1, 2, 3]) + vals3 = ["a", "b", np.nan] + df_exp_drop_cats = DataFrame({"cats": cat3, "vals": vals3}) + cat4 = Categorical([1, 2], categories=[1, 2, 3]) + vals4 = ["a", "b"] + df_exp_drop_all = DataFrame({"cats": cat4, "vals": vals4}) + + # fillna + res = df.fillna(value={"cats": 3, "vals": "b"}) + tm.assert_frame_equal(res, df_exp_fill) + + msg = "Cannot setitem on a Categorical with a new category" + with pytest.raises(TypeError, match=msg): + df.fillna(value={"cats": 4, "vals": "c"}) + + msg = "DataFrame.fillna with 'method' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + res = df.fillna(method="pad") + tm.assert_frame_equal(res, df_exp_fill) + + # dropna + res = df.dropna(subset=["cats"]) + tm.assert_frame_equal(res, df_exp_drop_cats) + + res = df.dropna() + tm.assert_frame_equal(res, df_exp_drop_all) + + # make sure that fillna takes missing values into account + c = Categorical([np.nan, "b", np.nan], categories=["a", "b"]) + df = DataFrame({"cats": c, "vals": [1, 2, 3]}) + + cat_exp = Categorical(["a", "b", "a"], categories=["a", "b"]) + df_exp = DataFrame({"cats": cat_exp, "vals": [1, 2, 3]}) + + res = df.fillna("a") + tm.assert_frame_equal(res, df_exp) + + def test_fillna_categorical_nan(self): + # GH#14021 + # np.nan should always be a valid filler + cat = Categorical([np.nan, 2, np.nan]) + val = Categorical([np.nan, np.nan, np.nan]) + df = DataFrame({"cats": cat, "vals": val}) + + # GH#32950 df.median() is poorly behaved because there is no + # Categorical.median + median = Series({"cats": 2.0, "vals": np.nan}) + + res = df.fillna(median) + v_exp = [np.nan, np.nan, np.nan] + df_exp = DataFrame({"cats": [2, 2, 2], "vals": v_exp}, dtype="category") + tm.assert_frame_equal(res, df_exp) + + result = df.cats.fillna(np.nan) + tm.assert_series_equal(result, df.cats) + + result = df.vals.fillna(np.nan) + tm.assert_series_equal(result, df.vals) + + idx = DatetimeIndex( + ["2011-01-01 09:00", "2016-01-01 23:45", "2011-01-01 09:00", NaT, NaT] + ) + df = DataFrame({"a": Categorical(idx)}) + tm.assert_frame_equal(df.fillna(value=NaT), df) + + idx = PeriodIndex(["2011-01", "2011-01", "2011-01", NaT, NaT], freq="M") + df = DataFrame({"a": Categorical(idx)}) + tm.assert_frame_equal(df.fillna(value=NaT), df) + + idx = TimedeltaIndex(["1 days", "2 days", "1 days", NaT, NaT]) + df = DataFrame({"a": Categorical(idx)}) + tm.assert_frame_equal(df.fillna(value=NaT), df) + + def test_fillna_downcast(self): + # GH#15277 + # infer int64 from float64 + df = DataFrame({"a": [1.0, np.nan]}) + msg = "The 'downcast' keyword in fillna is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.fillna(0, downcast="infer") + expected = DataFrame({"a": [1, 0]}) + tm.assert_frame_equal(result, expected) + + # infer int64 from float64 when fillna value is a dict + df = DataFrame({"a": [1.0, np.nan]}) + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.fillna({"a": 0}, downcast="infer") + expected = DataFrame({"a": [1, 0]}) + tm.assert_frame_equal(result, expected) + + def test_fillna_downcast_false(self, frame_or_series): + # GH#45603 preserve object dtype with downcast=False + obj = frame_or_series([1, 2, 3], dtype="object") + msg = "The 'downcast' keyword in fillna" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = obj.fillna("", downcast=False) + tm.assert_equal(result, obj) + + def test_fillna_downcast_noop(self, frame_or_series): + # GH#45423 + # Two relevant paths: + # 1) not _can_hold_na (e.g. integer) + # 2) _can_hold_na + noop + not can_hold_element + + obj = frame_or_series([1, 2, 3], dtype=np.int64) + + msg = "The 'downcast' keyword in fillna" + with tm.assert_produces_warning(FutureWarning, match=msg): + # GH#40988 + res = obj.fillna("foo", downcast=np.dtype(np.int32)) + expected = obj.astype(np.int32) + tm.assert_equal(res, expected) + + obj2 = obj.astype(np.float64) + with tm.assert_produces_warning(FutureWarning, match=msg): + res2 = obj2.fillna("foo", downcast="infer") + expected2 = obj # get back int64 + tm.assert_equal(res2, expected2) + + with tm.assert_produces_warning(FutureWarning, match=msg): + # GH#40988 + res3 = obj2.fillna("foo", downcast=np.dtype(np.int32)) + tm.assert_equal(res3, expected) + + @pytest.mark.parametrize("columns", [["A", "A", "B"], ["A", "A"]]) + def test_fillna_dictlike_value_duplicate_colnames(self, columns): + # GH#43476 + df = DataFrame(np.nan, index=[0, 1], columns=columns) + with tm.assert_produces_warning(None): + result = df.fillna({"A": 0}) + + expected = df.copy() + expected["A"] = 0.0 + tm.assert_frame_equal(result, expected) + + def test_fillna_dtype_conversion(self, using_infer_string): + # make sure that fillna on an empty frame works + df = DataFrame(index=["A", "B", "C"], columns=[1, 2, 3, 4, 5]) + result = df.dtypes + expected = Series([np.dtype("object")] * 5, index=[1, 2, 3, 4, 5]) + tm.assert_series_equal(result, expected) + + msg = "Downcasting object dtype arrays" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.fillna(1) + expected = DataFrame(1, index=["A", "B", "C"], columns=[1, 2, 3, 4, 5]) + tm.assert_frame_equal(result, expected) + + # empty block + df = DataFrame(index=range(3), columns=["A", "B"], dtype="float64") + result = df.fillna("nan") + expected = DataFrame("nan", index=range(3), columns=["A", "B"], dtype=object) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("val", ["", 1, np.nan, 1.0]) + def test_fillna_dtype_conversion_equiv_replace(self, val): + df = DataFrame({"A": [1, np.nan], "B": [1.0, 2.0]}) + expected = df.replace(np.nan, val) + result = df.fillna(val) + tm.assert_frame_equal(result, expected) + + def test_fillna_datetime_columns(self): + # GH#7095 + df = DataFrame( + { + "A": [-1, -2, np.nan], + "B": date_range("20130101", periods=3), + "C": ["foo", "bar", None], + "D": ["foo2", "bar2", None], + }, + index=date_range("20130110", periods=3), + ) + result = df.fillna("?") + expected = DataFrame( + { + "A": [-1, -2, "?"], + "B": date_range("20130101", periods=3), + "C": ["foo", "bar", "?"], + "D": ["foo2", "bar2", "?"], + }, + index=date_range("20130110", periods=3), + ) + tm.assert_frame_equal(result, expected) + + df = DataFrame( + { + "A": [-1, -2, np.nan], + "B": [Timestamp("2013-01-01"), Timestamp("2013-01-02"), NaT], + "C": ["foo", "bar", None], + "D": ["foo2", "bar2", None], + }, + index=date_range("20130110", periods=3), + ) + result = df.fillna("?") + expected = DataFrame( + { + "A": [-1, -2, "?"], + "B": [Timestamp("2013-01-01"), Timestamp("2013-01-02"), "?"], + "C": ["foo", "bar", "?"], + "D": ["foo2", "bar2", "?"], + }, + index=date_range("20130110", periods=3), + ) + tm.assert_frame_equal(result, expected) + + def test_ffill(self, datetime_frame): + datetime_frame.loc[datetime_frame.index[:5], "A"] = np.nan + datetime_frame.loc[datetime_frame.index[-5:], "A"] = np.nan + + msg = "DataFrame.fillna with 'method' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + alt = datetime_frame.fillna(method="ffill") + tm.assert_frame_equal(datetime_frame.ffill(), alt) + + def test_bfill(self, datetime_frame): + datetime_frame.loc[datetime_frame.index[:5], "A"] = np.nan + datetime_frame.loc[datetime_frame.index[-5:], "A"] = np.nan + + msg = "DataFrame.fillna with 'method' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + alt = datetime_frame.fillna(method="bfill") + + tm.assert_frame_equal(datetime_frame.bfill(), alt) + + def test_frame_pad_backfill_limit(self): + index = np.arange(10) + df = DataFrame(np.random.default_rng(2).standard_normal((10, 4)), index=index) + + result = df[:2].reindex(index, method="pad", limit=5) + + msg = "DataFrame.fillna with 'method' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = df[:2].reindex(index).fillna(method="pad") + expected.iloc[-3:] = np.nan + tm.assert_frame_equal(result, expected) + + result = df[-2:].reindex(index, method="backfill", limit=5) + + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = df[-2:].reindex(index).fillna(method="backfill") + expected.iloc[:3] = np.nan + tm.assert_frame_equal(result, expected) + + def test_frame_fillna_limit(self): + index = np.arange(10) + df = DataFrame(np.random.default_rng(2).standard_normal((10, 4)), index=index) + + result = df[:2].reindex(index) + msg = "DataFrame.fillna with 'method' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = result.fillna(method="pad", limit=5) + + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = df[:2].reindex(index).fillna(method="pad") + expected.iloc[-3:] = np.nan + tm.assert_frame_equal(result, expected) + + result = df[-2:].reindex(index) + with tm.assert_produces_warning(FutureWarning, match=msg): + result = result.fillna(method="backfill", limit=5) + + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = df[-2:].reindex(index).fillna(method="backfill") + expected.iloc[:3] = np.nan + tm.assert_frame_equal(result, expected) + + def test_fillna_skip_certain_blocks(self): + # don't try to fill boolean, int blocks + + df = DataFrame(np.random.default_rng(2).standard_normal((10, 4)).astype(int)) + + # it works! + df.fillna(np.nan) + + @pytest.mark.parametrize("type", [int, float]) + def test_fillna_positive_limit(self, type): + df = DataFrame(np.random.default_rng(2).standard_normal((10, 4))).astype(type) + + msg = "Limit must be greater than 0" + with pytest.raises(ValueError, match=msg): + df.fillna(0, limit=-5) + + @pytest.mark.parametrize("type", [int, float]) + def test_fillna_integer_limit(self, type): + df = DataFrame(np.random.default_rng(2).standard_normal((10, 4))).astype(type) + + msg = "Limit must be an integer" + with pytest.raises(ValueError, match=msg): + df.fillna(0, limit=0.5) + + def test_fillna_inplace(self): + df = DataFrame(np.random.default_rng(2).standard_normal((10, 4))) + df.loc[:4, 1] = np.nan + df.loc[-4:, 3] = np.nan + + expected = df.fillna(value=0) + assert expected is not df + + df.fillna(value=0, inplace=True) + tm.assert_frame_equal(df, expected) + + expected = df.fillna(value={0: 0}, inplace=True) + assert expected is None + + df.loc[:4, 1] = np.nan + df.loc[-4:, 3] = np.nan + msg = "DataFrame.fillna with 'method' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = df.fillna(method="ffill") + assert expected is not df + + with tm.assert_produces_warning(FutureWarning, match=msg): + df.fillna(method="ffill", inplace=True) + tm.assert_frame_equal(df, expected) + + def test_fillna_dict_series(self): + df = DataFrame( + { + "a": [np.nan, 1, 2, np.nan, np.nan], + "b": [1, 2, 3, np.nan, np.nan], + "c": [np.nan, 1, 2, 3, 4], + } + ) + + result = df.fillna({"a": 0, "b": 5}) + + expected = df.copy() + expected["a"] = expected["a"].fillna(0) + expected["b"] = expected["b"].fillna(5) + tm.assert_frame_equal(result, expected) + + # it works + result = df.fillna({"a": 0, "b": 5, "d": 7}) + + # Series treated same as dict + result = df.fillna(df.max()) + expected = df.fillna(df.max().to_dict()) + tm.assert_frame_equal(result, expected) + + # disable this for now + with pytest.raises(NotImplementedError, match="column by column"): + df.fillna(df.max(1), axis=1) + + def test_fillna_dataframe(self): + # GH#8377 + df = DataFrame( + { + "a": [np.nan, 1, 2, np.nan, np.nan], + "b": [1, 2, 3, np.nan, np.nan], + "c": [np.nan, 1, 2, 3, 4], + }, + index=list("VWXYZ"), + ) + + # df2 may have different index and columns + df2 = DataFrame( + { + "a": [np.nan, 10, 20, 30, 40], + "b": [50, 60, 70, 80, 90], + "foo": ["bar"] * 5, + }, + index=list("VWXuZ"), + ) + + result = df.fillna(df2) + + # only those columns and indices which are shared get filled + expected = DataFrame( + { + "a": [np.nan, 1, 2, np.nan, 40], + "b": [1, 2, 3, np.nan, 90], + "c": [np.nan, 1, 2, 3, 4], + }, + index=list("VWXYZ"), + ) + + tm.assert_frame_equal(result, expected) + + def test_fillna_columns(self): + arr = np.random.default_rng(2).standard_normal((10, 10)) + arr[:, ::2] = np.nan + df = DataFrame(arr) + + msg = "DataFrame.fillna with 'method' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.fillna(method="ffill", axis=1) + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = df.T.fillna(method="pad").T + tm.assert_frame_equal(result, expected) + + df.insert(6, "foo", 5) + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.fillna(method="ffill", axis=1) + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = df.astype(float).fillna(method="ffill", axis=1) + tm.assert_frame_equal(result, expected) + + def test_fillna_invalid_method(self, float_frame): + with pytest.raises(ValueError, match="ffil"): + float_frame.fillna(method="ffil") + + def test_fillna_invalid_value(self, float_frame): + # list + msg = '"value" parameter must be a scalar or dict, but you passed a "{}"' + with pytest.raises(TypeError, match=msg.format("list")): + float_frame.fillna([1, 2]) + # tuple + with pytest.raises(TypeError, match=msg.format("tuple")): + float_frame.fillna((1, 2)) + # frame with series + msg = ( + '"value" parameter must be a scalar, dict or Series, but you ' + 'passed a "DataFrame"' + ) + with pytest.raises(TypeError, match=msg): + float_frame.iloc[:, 0].fillna(float_frame) + + def test_fillna_col_reordering(self): + cols = ["COL." + str(i) for i in range(5, 0, -1)] + data = np.random.default_rng(2).random((20, 5)) + df = DataFrame(index=range(20), columns=cols, data=data) + msg = "DataFrame.fillna with 'method' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + filled = df.fillna(method="ffill") + assert df.columns.tolist() == filled.columns.tolist() + + def test_fill_empty(self, float_frame): + df = float_frame.reindex(columns=[]) + result = df.fillna(value=0) + tm.assert_frame_equal(result, df) + + def test_fillna_downcast_dict(self): + # GH#40809 + df = DataFrame({"col1": [1, np.nan]}) + + msg = "The 'downcast' keyword in fillna" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.fillna({"col1": 2}, downcast={"col1": "int64"}) + expected = DataFrame({"col1": [1, 2]}) + tm.assert_frame_equal(result, expected) + + def test_fillna_with_columns_and_limit(self): + # GH40989 + df = DataFrame( + [ + [np.nan, 2, np.nan, 0], + [3, 4, np.nan, 1], + [np.nan, np.nan, np.nan, 5], + [np.nan, 3, np.nan, 4], + ], + columns=list("ABCD"), + ) + result = df.fillna(axis=1, value=100, limit=1) + result2 = df.fillna(axis=1, value=100, limit=2) + + expected = DataFrame( + { + "A": Series([100, 3, 100, 100], dtype="float64"), + "B": [2, 4, np.nan, 3], + "C": [np.nan, 100, np.nan, np.nan], + "D": Series([0, 1, 5, 4], dtype="float64"), + }, + index=[0, 1, 2, 3], + ) + expected2 = DataFrame( + { + "A": Series([100, 3, 100, 100], dtype="float64"), + "B": Series([2, 4, 100, 3], dtype="float64"), + "C": [100, 100, np.nan, 100], + "D": Series([0, 1, 5, 4], dtype="float64"), + }, + index=[0, 1, 2, 3], + ) + + tm.assert_frame_equal(result, expected) + tm.assert_frame_equal(result2, expected2) + + def test_fillna_datetime_inplace(self): + # GH#48863 + df = DataFrame( + { + "date1": to_datetime(["2018-05-30", None]), + "date2": to_datetime(["2018-09-30", None]), + } + ) + expected = df.copy() + df.fillna(np.nan, inplace=True) + tm.assert_frame_equal(df, expected) + + def test_fillna_inplace_with_columns_limit_and_value(self): + # GH40989 + df = DataFrame( + [ + [np.nan, 2, np.nan, 0], + [3, 4, np.nan, 1], + [np.nan, np.nan, np.nan, 5], + [np.nan, 3, np.nan, 4], + ], + columns=list("ABCD"), + ) + + expected = df.fillna(axis=1, value=100, limit=1) + assert expected is not df + + df.fillna(axis=1, value=100, limit=1, inplace=True) + tm.assert_frame_equal(df, expected) + + @td.skip_array_manager_invalid_test + @pytest.mark.parametrize("val", [-1, {"x": -1, "y": -1}]) + def test_inplace_dict_update_view( + self, val, using_copy_on_write, warn_copy_on_write + ): + # GH#47188 + df = DataFrame({"x": [np.nan, 2], "y": [np.nan, 2]}) + df_orig = df.copy() + result_view = df[:] + with tm.assert_cow_warning(warn_copy_on_write): + df.fillna(val, inplace=True) + expected = DataFrame({"x": [-1, 2.0], "y": [-1.0, 2]}) + tm.assert_frame_equal(df, expected) + if using_copy_on_write: + tm.assert_frame_equal(result_view, df_orig) + else: + tm.assert_frame_equal(result_view, expected) + + def test_single_block_df_with_horizontal_axis(self): + # GH 47713 + df = DataFrame( + { + "col1": [5, 0, np.nan, 10, np.nan], + "col2": [7, np.nan, np.nan, 5, 3], + "col3": [12, np.nan, 1, 2, 0], + "col4": [np.nan, 1, 1, np.nan, 18], + } + ) + result = df.fillna(50, limit=1, axis=1) + expected = DataFrame( + [ + [5.0, 7.0, 12.0, 50.0], + [0.0, 50.0, np.nan, 1.0], + [50.0, np.nan, 1.0, 1.0], + [10.0, 5.0, 2.0, 50.0], + [50.0, 3.0, 0.0, 18.0], + ], + columns=["col1", "col2", "col3", "col4"], + ) + tm.assert_frame_equal(result, expected) + + def test_fillna_with_multi_index_frame(self): + # GH 47649 + pdf = DataFrame( + { + ("x", "a"): [np.nan, 2.0, 3.0], + ("x", "b"): [1.0, 2.0, np.nan], + ("y", "c"): [1.0, 2.0, np.nan], + } + ) + expected = DataFrame( + { + ("x", "a"): [-1.0, 2.0, 3.0], + ("x", "b"): [1.0, 2.0, -1.0], + ("y", "c"): [1.0, 2.0, np.nan], + } + ) + tm.assert_frame_equal(pdf.fillna({"x": -1}), expected) + tm.assert_frame_equal(pdf.fillna({"x": -1, ("x", "b"): -2}), expected) + + expected = DataFrame( + { + ("x", "a"): [-1.0, 2.0, 3.0], + ("x", "b"): [1.0, 2.0, -2.0], + ("y", "c"): [1.0, 2.0, np.nan], + } + ) + tm.assert_frame_equal(pdf.fillna({("x", "b"): -2, "x": -1}), expected) + + +def test_fillna_nonconsolidated_frame(): + # https://github.com/pandas-dev/pandas/issues/36495 + df = DataFrame( + [ + [1, 1, 1, 1.0], + [2, 2, 2, 2.0], + [3, 3, 3, 3.0], + ], + columns=["i1", "i2", "i3", "f1"], + ) + df_nonconsol = df.pivot(index="i1", columns="i2") + result = df_nonconsol.fillna(0) + assert result.isna().sum().sum() == 0 + + +def test_fillna_nones_inplace(): + # GH 48480 + df = DataFrame( + [[None, None], [None, None]], + columns=["A", "B"], + ) + msg = "Downcasting object dtype arrays" + with tm.assert_produces_warning(FutureWarning, match=msg): + df.fillna(value={"A": 1, "B": 2}, inplace=True) + + expected = DataFrame([[1, 2], [1, 2]], columns=["A", "B"]) + tm.assert_frame_equal(df, expected) + + +@pytest.mark.parametrize("func", ["pad", "backfill"]) +def test_pad_backfill_deprecated(func): + # GH#33396 + df = DataFrame({"a": [1, 2, 3]}) + with tm.assert_produces_warning(FutureWarning): + getattr(df, func)() + + +@pytest.mark.parametrize( + "data, expected_data, method, kwargs", + ( + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [np.nan, np.nan, 3.0, 3.0, 3.0, 3.0, 7.0, np.nan, np.nan], + "ffill", + {"limit_area": "inside"}, + ), + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [np.nan, np.nan, 3.0, 3.0, np.nan, np.nan, 7.0, np.nan, np.nan], + "ffill", + {"limit_area": "inside", "limit": 1}, + ), + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, 7.0], + "ffill", + {"limit_area": "outside"}, + ), + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan], + "ffill", + {"limit_area": "outside", "limit": 1}, + ), + ( + [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan], + "ffill", + {"limit_area": "outside", "limit": 1}, + ), + ( + range(5), + range(5), + "ffill", + {"limit_area": "outside", "limit": 1}, + ), + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [np.nan, np.nan, 3.0, 7.0, 7.0, 7.0, 7.0, np.nan, np.nan], + "bfill", + {"limit_area": "inside"}, + ), + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [np.nan, np.nan, 3.0, np.nan, np.nan, 7.0, 7.0, np.nan, np.nan], + "bfill", + {"limit_area": "inside", "limit": 1}, + ), + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [3.0, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, np.nan, np.nan], + "bfill", + {"limit_area": "outside"}, + ), + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [np.nan, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, np.nan, np.nan], + "bfill", + {"limit_area": "outside", "limit": 1}, + ), + ), +) +def test_ffill_bfill_limit_area(data, expected_data, method, kwargs): + # GH#56492 + df = DataFrame(data) + expected = DataFrame(expected_data) + result = getattr(df, method)(**kwargs) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_filter.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_filter.py new file mode 100644 index 0000000000000000000000000000000000000000..9d5e6876bb08c2929e6a54c18b865e2720c1424d --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_filter.py @@ -0,0 +1,153 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import DataFrame +import pandas._testing as tm + + +class TestDataFrameFilter: + def test_filter(self, float_frame, float_string_frame): + # Items + filtered = float_frame.filter(["A", "B", "E"]) + assert len(filtered.columns) == 2 + assert "E" not in filtered + + filtered = float_frame.filter(["A", "B", "E"], axis="columns") + assert len(filtered.columns) == 2 + assert "E" not in filtered + + # Other axis + idx = float_frame.index[0:4] + filtered = float_frame.filter(idx, axis="index") + expected = float_frame.reindex(index=idx) + tm.assert_frame_equal(filtered, expected) + + # like + fcopy = float_frame.copy() + fcopy["AA"] = 1 + + filtered = fcopy.filter(like="A") + assert len(filtered.columns) == 2 + assert "AA" in filtered + + # like with ints in column names + df = DataFrame(0.0, index=[0, 1, 2], columns=[0, 1, "_A", "_B"]) + filtered = df.filter(like="_") + assert len(filtered.columns) == 2 + + # regex with ints in column names + # from PR #10384 + df = DataFrame(0.0, index=[0, 1, 2], columns=["A1", 1, "B", 2, "C"]) + expected = DataFrame( + 0.0, index=[0, 1, 2], columns=pd.Index([1, 2], dtype=object) + ) + filtered = df.filter(regex="^[0-9]+$") + tm.assert_frame_equal(filtered, expected) + + expected = DataFrame(0.0, index=[0, 1, 2], columns=[0, "0", 1, "1"]) + # shouldn't remove anything + filtered = expected.filter(regex="^[0-9]+$") + tm.assert_frame_equal(filtered, expected) + + # pass in None + with pytest.raises(TypeError, match="Must pass"): + float_frame.filter() + with pytest.raises(TypeError, match="Must pass"): + float_frame.filter(items=None) + with pytest.raises(TypeError, match="Must pass"): + float_frame.filter(axis=1) + + # test mutually exclusive arguments + with pytest.raises(TypeError, match="mutually exclusive"): + float_frame.filter(items=["one", "three"], regex="e$", like="bbi") + with pytest.raises(TypeError, match="mutually exclusive"): + float_frame.filter(items=["one", "three"], regex="e$", axis=1) + with pytest.raises(TypeError, match="mutually exclusive"): + float_frame.filter(items=["one", "three"], regex="e$") + with pytest.raises(TypeError, match="mutually exclusive"): + float_frame.filter(items=["one", "three"], like="bbi", axis=0) + with pytest.raises(TypeError, match="mutually exclusive"): + float_frame.filter(items=["one", "three"], like="bbi") + + # objects + filtered = float_string_frame.filter(like="foo") + assert "foo" in filtered + + # unicode columns, won't ascii-encode + df = float_frame.rename(columns={"B": "\u2202"}) + filtered = df.filter(like="C") + assert "C" in filtered + + def test_filter_regex_search(self, float_frame): + fcopy = float_frame.copy() + fcopy["AA"] = 1 + + # regex + filtered = fcopy.filter(regex="[A]+") + assert len(filtered.columns) == 2 + assert "AA" in filtered + + # doesn't have to be at beginning + df = DataFrame( + {"aBBa": [1, 2], "BBaBB": [1, 2], "aCCa": [1, 2], "aCCaBB": [1, 2]} + ) + + result = df.filter(regex="BB") + exp = df[[x for x in df.columns if "BB" in x]] + tm.assert_frame_equal(result, exp) + + @pytest.mark.parametrize( + "name,expected", + [ + ("a", DataFrame({"a": [1, 2]})), + ("a", DataFrame({"a": [1, 2]})), + ("あ", DataFrame({"あ": [3, 4]})), + ], + ) + def test_filter_unicode(self, name, expected): + # GH13101 + df = DataFrame({"a": [1, 2], "あ": [3, 4]}) + + tm.assert_frame_equal(df.filter(like=name), expected) + tm.assert_frame_equal(df.filter(regex=name), expected) + + @pytest.mark.parametrize("name", ["a", "a"]) + def test_filter_bytestring(self, name): + # GH13101 + df = DataFrame({b"a": [1, 2], b"b": [3, 4]}) + expected = DataFrame({b"a": [1, 2]}) + + tm.assert_frame_equal(df.filter(like=name), expected) + tm.assert_frame_equal(df.filter(regex=name), expected) + + def test_filter_corner(self): + empty = DataFrame() + + result = empty.filter([]) + tm.assert_frame_equal(result, empty) + + result = empty.filter(like="foo") + tm.assert_frame_equal(result, empty) + + def test_filter_regex_non_string(self): + # GH#5798 trying to filter on non-string columns should drop, + # not raise + df = DataFrame(np.random.default_rng(2).random((3, 2)), columns=["STRING", 123]) + result = df.filter(regex="STRING") + expected = df[["STRING"]] + tm.assert_frame_equal(result, expected) + + def test_filter_keep_order(self): + # GH#54980 + df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) + result = df.filter(items=["B", "A"]) + expected = df[["B", "A"]] + tm.assert_frame_equal(result, expected) + + def test_filter_different_dtype(self): + # GH#54980 + df = DataFrame({1: [1, 2, 3], 2: [4, 5, 6]}) + result = df.filter(items=["B", "A"]) + expected = df[[]] + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_first_and_last.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_first_and_last.py new file mode 100644 index 0000000000000000000000000000000000000000..212e56442ee07460d61c4ef0b790e7bb193f9b3e --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_first_and_last.py @@ -0,0 +1,143 @@ +""" +Note: includes tests for `last` +""" +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + Index, + bdate_range, + date_range, +) +import pandas._testing as tm + +deprecated_msg = "first is deprecated" +last_deprecated_msg = "last is deprecated" + + +class TestFirst: + def test_first_subset(self, frame_or_series): + ts = DataFrame( + np.random.default_rng(2).standard_normal((100, 4)), + columns=Index(list("ABCD"), dtype=object), + index=date_range("2000-01-01", periods=100, freq="12h"), + ) + ts = tm.get_obj(ts, frame_or_series) + with tm.assert_produces_warning(FutureWarning, match=deprecated_msg): + result = ts.first("10d") + assert len(result) == 20 + + ts = DataFrame( + np.random.default_rng(2).standard_normal((100, 4)), + columns=Index(list("ABCD"), dtype=object), + index=date_range("2000-01-01", periods=100, freq="D"), + ) + ts = tm.get_obj(ts, frame_or_series) + with tm.assert_produces_warning(FutureWarning, match=deprecated_msg): + result = ts.first("10d") + assert len(result) == 10 + + with tm.assert_produces_warning(FutureWarning, match=deprecated_msg): + result = ts.first("3ME") + expected = ts[:"3/31/2000"] + tm.assert_equal(result, expected) + + with tm.assert_produces_warning(FutureWarning, match=deprecated_msg): + result = ts.first("21D") + expected = ts[:21] + tm.assert_equal(result, expected) + + with tm.assert_produces_warning(FutureWarning, match=deprecated_msg): + result = ts[:0].first("3ME") + tm.assert_equal(result, ts[:0]) + + def test_first_last_raises(self, frame_or_series): + # GH#20725 + obj = DataFrame([[1, 2, 3], [4, 5, 6]]) + obj = tm.get_obj(obj, frame_or_series) + + msg = "'first' only supports a DatetimeIndex index" + with tm.assert_produces_warning( + FutureWarning, match=deprecated_msg + ), pytest.raises( + TypeError, match=msg + ): # index is not a DatetimeIndex + obj.first("1D") + + msg = "'last' only supports a DatetimeIndex index" + with tm.assert_produces_warning( + FutureWarning, match=last_deprecated_msg + ), pytest.raises( + TypeError, match=msg + ): # index is not a DatetimeIndex + obj.last("1D") + + def test_last_subset(self, frame_or_series): + ts = DataFrame( + np.random.default_rng(2).standard_normal((100, 4)), + columns=Index(list("ABCD"), dtype=object), + index=date_range("2000-01-01", periods=100, freq="12h"), + ) + ts = tm.get_obj(ts, frame_or_series) + with tm.assert_produces_warning(FutureWarning, match=last_deprecated_msg): + result = ts.last("10d") + assert len(result) == 20 + + ts = DataFrame( + np.random.default_rng(2).standard_normal((30, 4)), + columns=Index(list("ABCD"), dtype=object), + index=date_range("2000-01-01", periods=30, freq="D"), + ) + ts = tm.get_obj(ts, frame_or_series) + with tm.assert_produces_warning(FutureWarning, match=last_deprecated_msg): + result = ts.last("10d") + assert len(result) == 10 + + with tm.assert_produces_warning(FutureWarning, match=last_deprecated_msg): + result = ts.last("21D") + expected = ts["2000-01-10":] + tm.assert_equal(result, expected) + + with tm.assert_produces_warning(FutureWarning, match=last_deprecated_msg): + result = ts.last("21D") + expected = ts[-21:] + tm.assert_equal(result, expected) + + with tm.assert_produces_warning(FutureWarning, match=last_deprecated_msg): + result = ts[:0].last("3ME") + tm.assert_equal(result, ts[:0]) + + @pytest.mark.parametrize("start, periods", [("2010-03-31", 1), ("2010-03-30", 2)]) + def test_first_with_first_day_last_of_month(self, frame_or_series, start, periods): + # GH#29623 + x = frame_or_series([1] * 100, index=bdate_range(start, periods=100)) + with tm.assert_produces_warning(FutureWarning, match=deprecated_msg): + result = x.first("1ME") + expected = frame_or_series( + [1] * periods, index=bdate_range(start, periods=periods) + ) + tm.assert_equal(result, expected) + + def test_first_with_first_day_end_of_frq_n_greater_one(self, frame_or_series): + # GH#29623 + x = frame_or_series([1] * 100, index=bdate_range("2010-03-31", periods=100)) + with tm.assert_produces_warning(FutureWarning, match=deprecated_msg): + result = x.first("2ME") + expected = frame_or_series( + [1] * 23, index=bdate_range("2010-03-31", "2010-04-30") + ) + tm.assert_equal(result, expected) + + def test_empty_not_input(self): + # GH#51032 + df = DataFrame(index=pd.DatetimeIndex([])) + with tm.assert_produces_warning(FutureWarning, match=last_deprecated_msg): + result = df.last(offset=1) + + with tm.assert_produces_warning(FutureWarning, match=deprecated_msg): + result = df.first(offset=1) + + tm.assert_frame_equal(df, result) + assert df is not result diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_first_valid_index.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_first_valid_index.py new file mode 100644 index 0000000000000000000000000000000000000000..2e27f1aa7170058be9cf267984da6d3e3338dc85 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_first_valid_index.py @@ -0,0 +1,78 @@ +""" +Includes test for last_valid_index. +""" +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Index, + Series, + date_range, +) + + +class TestFirstValidIndex: + def test_first_valid_index_single_nan(self, frame_or_series): + # GH#9752 Series/DataFrame should both return None, not raise + obj = frame_or_series([np.nan]) + + assert obj.first_valid_index() is None + assert obj.iloc[:0].first_valid_index() is None + + @pytest.mark.parametrize( + "empty", [DataFrame(), Series(dtype=object), Series([], index=[], dtype=object)] + ) + def test_first_valid_index_empty(self, empty): + # GH#12800 + assert empty.last_valid_index() is None + assert empty.first_valid_index() is None + + @pytest.mark.parametrize( + "data,idx,expected_first,expected_last", + [ + ({"A": [1, 2, 3]}, [1, 1, 2], 1, 2), + ({"A": [1, 2, 3]}, [1, 2, 2], 1, 2), + ({"A": [1, 2, 3, 4]}, ["d", "d", "d", "d"], "d", "d"), + ({"A": [1, np.nan, 3]}, [1, 1, 2], 1, 2), + ({"A": [np.nan, np.nan, 3]}, [1, 1, 2], 2, 2), + ({"A": [1, np.nan, 3]}, [1, 2, 2], 1, 2), + ], + ) + def test_first_last_valid_frame(self, data, idx, expected_first, expected_last): + # GH#21441 + df = DataFrame(data, index=idx) + assert expected_first == df.first_valid_index() + assert expected_last == df.last_valid_index() + + @pytest.mark.parametrize( + "index", + [Index([str(i) for i in range(20)]), date_range("2020-01-01", periods=20)], + ) + def test_first_last_valid(self, index): + mat = np.random.default_rng(2).standard_normal(len(index)) + mat[:5] = np.nan + mat[-5:] = np.nan + + frame = DataFrame({"foo": mat}, index=index) + assert frame.first_valid_index() == frame.index[5] + assert frame.last_valid_index() == frame.index[-6] + + ser = frame["foo"] + assert ser.first_valid_index() == frame.index[5] + assert ser.last_valid_index() == frame.index[-6] + + @pytest.mark.parametrize( + "index", + [Index([str(i) for i in range(10)]), date_range("2020-01-01", periods=10)], + ) + def test_first_last_valid_all_nan(self, index): + # GH#17400: no valid entries + frame = DataFrame(np.nan, columns=["foo"], index=index) + + assert frame.last_valid_index() is None + assert frame.first_valid_index() is None + + ser = frame["foo"] + assert ser.first_valid_index() is None + assert ser.last_valid_index() is None diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_get_numeric_data.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_get_numeric_data.py new file mode 100644 index 0000000000000000000000000000000000000000..6d097e75f6703c277bd271dbd030293b459ec9ae --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_get_numeric_data.py @@ -0,0 +1,104 @@ +import numpy as np + +import pandas as pd +from pandas import ( + Categorical, + DataFrame, + Index, + Series, + Timestamp, +) +import pandas._testing as tm +from pandas.core.arrays import IntervalArray + + +class TestGetNumericData: + def test_get_numeric_data_preserve_dtype(self): + # get the numeric data + obj = DataFrame({"A": [1, "2", 3.0]}, columns=Index(["A"], dtype="object")) + result = obj._get_numeric_data() + expected = DataFrame(dtype=object, index=pd.RangeIndex(3), columns=[]) + tm.assert_frame_equal(result, expected) + + def test_get_numeric_data(self, using_infer_string): + datetime64name = np.dtype("M8[s]").name + objectname = np.dtype(np.object_).name + + df = DataFrame( + {"a": 1.0, "b": 2, "c": "foo", "f": Timestamp("20010102")}, + index=np.arange(10), + ) + result = df.dtypes + expected = Series( + [ + np.dtype("float64"), + np.dtype("int64"), + np.dtype(objectname) + if not using_infer_string + else pd.StringDtype(na_value=np.nan), + np.dtype(datetime64name), + ], + index=["a", "b", "c", "f"], + ) + tm.assert_series_equal(result, expected) + + df = DataFrame( + { + "a": 1.0, + "b": 2, + "c": "foo", + "d": np.array([1.0] * 10, dtype="float32"), + "e": np.array([1] * 10, dtype="int32"), + "f": np.array([1] * 10, dtype="int16"), + "g": Timestamp("20010102"), + }, + index=np.arange(10), + ) + + result = df._get_numeric_data() + expected = df.loc[:, ["a", "b", "d", "e", "f"]] + tm.assert_frame_equal(result, expected) + + only_obj = df.loc[:, ["c", "g"]] + result = only_obj._get_numeric_data() + expected = df.loc[:, []] + tm.assert_frame_equal(result, expected) + + df = DataFrame.from_dict({"a": [1, 2], "b": ["foo", "bar"], "c": [np.pi, np.e]}) + result = df._get_numeric_data() + expected = DataFrame.from_dict({"a": [1, 2], "c": [np.pi, np.e]}) + tm.assert_frame_equal(result, expected) + + df = result.copy() + result = df._get_numeric_data() + expected = df + tm.assert_frame_equal(result, expected) + + def test_get_numeric_data_mixed_dtype(self): + # numeric and object columns + + df = DataFrame( + { + "a": [1, 2, 3], + "b": [True, False, True], + "c": ["foo", "bar", "baz"], + "d": [None, None, None], + "e": [3.14, 0.577, 2.773], + } + ) + result = df._get_numeric_data() + tm.assert_index_equal(result.columns, Index(["a", "b", "e"])) + + def test_get_numeric_data_extension_dtype(self): + # GH#22290 + df = DataFrame( + { + "A": pd.array([-10, np.nan, 0, 10, 20, 30], dtype="Int64"), + "B": Categorical(list("abcabc")), + "C": pd.array([0, 1, 2, 3, np.nan, 5], dtype="UInt8"), + "D": IntervalArray.from_breaks(range(7)), + } + ) + result = df._get_numeric_data() + expected = df.loc[:, ["A", "C"]] + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_head_tail.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_head_tail.py new file mode 100644 index 0000000000000000000000000000000000000000..9363c4d79983f0530bc17666aec7ec8609fb93e4 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_head_tail.py @@ -0,0 +1,57 @@ +import numpy as np + +from pandas import DataFrame +import pandas._testing as tm + + +def test_head_tail_generic(index, frame_or_series): + # GH#5370 + + ndim = 2 if frame_or_series is DataFrame else 1 + shape = (len(index),) * ndim + vals = np.random.default_rng(2).standard_normal(shape) + obj = frame_or_series(vals, index=index) + + tm.assert_equal(obj.head(), obj.iloc[:5]) + tm.assert_equal(obj.tail(), obj.iloc[-5:]) + + # 0-len + tm.assert_equal(obj.head(0), obj.iloc[0:0]) + tm.assert_equal(obj.tail(0), obj.iloc[0:0]) + + # bounded + tm.assert_equal(obj.head(len(obj) + 1), obj) + tm.assert_equal(obj.tail(len(obj) + 1), obj) + + # neg index + tm.assert_equal(obj.head(-3), obj.head(len(index) - 3)) + tm.assert_equal(obj.tail(-3), obj.tail(len(index) - 3)) + + +def test_head_tail(float_frame): + tm.assert_frame_equal(float_frame.head(), float_frame[:5]) + tm.assert_frame_equal(float_frame.tail(), float_frame[-5:]) + + tm.assert_frame_equal(float_frame.head(0), float_frame[0:0]) + tm.assert_frame_equal(float_frame.tail(0), float_frame[0:0]) + + tm.assert_frame_equal(float_frame.head(-1), float_frame[:-1]) + tm.assert_frame_equal(float_frame.tail(-1), float_frame[1:]) + tm.assert_frame_equal(float_frame.head(1), float_frame[:1]) + tm.assert_frame_equal(float_frame.tail(1), float_frame[-1:]) + # with a float index + df = float_frame.copy() + df.index = np.arange(len(float_frame)) + 0.1 + tm.assert_frame_equal(df.head(), df.iloc[:5]) + tm.assert_frame_equal(df.tail(), df.iloc[-5:]) + tm.assert_frame_equal(df.head(0), df[0:0]) + tm.assert_frame_equal(df.tail(0), df[0:0]) + tm.assert_frame_equal(df.head(-1), df.iloc[:-1]) + tm.assert_frame_equal(df.tail(-1), df.iloc[1:]) + + +def test_head_tail_empty(): + # test empty dataframe + empty_df = DataFrame() + tm.assert_frame_equal(empty_df.tail(), empty_df) + tm.assert_frame_equal(empty_df.head(), empty_df) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_infer_objects.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_infer_objects.py new file mode 100644 index 0000000000000000000000000000000000000000..a824a615b5c297c13afeedeba600c1a0ba986695 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_infer_objects.py @@ -0,0 +1,42 @@ +from datetime import datetime + +from pandas import DataFrame +import pandas._testing as tm + + +class TestInferObjects: + def test_infer_objects(self): + # GH#11221 + df = DataFrame( + { + "a": ["a", 1, 2, 3], + "b": ["b", 2.0, 3.0, 4.1], + "c": [ + "c", + datetime(2016, 1, 1), + datetime(2016, 1, 2), + datetime(2016, 1, 3), + ], + "d": [1, 2, 3, "d"], + }, + columns=["a", "b", "c", "d"], + ) + df = df.iloc[1:].infer_objects() + + assert df["a"].dtype == "int64" + assert df["b"].dtype == "float64" + assert df["c"].dtype == "M8[ns]" + assert df["d"].dtype == "object" + + expected = DataFrame( + { + "a": [1, 2, 3], + "b": [2.0, 3.0, 4.1], + "c": [datetime(2016, 1, 1), datetime(2016, 1, 2), datetime(2016, 1, 3)], + "d": [2, 3, "d"], + }, + columns=["a", "b", "c", "d"], + ) + # reconstruct frame to verify inference is same + result = df.reset_index(drop=True) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_info.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_info.py new file mode 100644 index 0000000000000000000000000000000000000000..c2d15e5ae88e83b9e2306ba3c4bb4435a739944e --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_info.py @@ -0,0 +1,589 @@ +from io import StringIO +import re +from string import ascii_uppercase +import sys +import textwrap + +import numpy as np +import pytest + +from pandas._config import using_string_dtype + +from pandas.compat import ( + HAS_PYARROW, + IS64, + PYPY, + is_platform_arm, +) + +from pandas import ( + CategoricalIndex, + DataFrame, + Index, + MultiIndex, + Series, + date_range, + option_context, +) +import pandas._testing as tm +from pandas.util.version import Version + + +@pytest.fixture +def duplicate_columns_frame(): + """Dataframe with duplicate column names.""" + return DataFrame( + np.random.default_rng(2).standard_normal((1500, 4)), + columns=["a", "a", "b", "b"], + ) + + +def test_info_empty(): + # GH #45494 + df = DataFrame() + buf = StringIO() + df.info(buf=buf) + result = buf.getvalue() + expected = textwrap.dedent( + """\ + + RangeIndex: 0 entries + Empty DataFrame\n""" + ) + assert result == expected + + +def test_info_categorical_column_smoke_test(): + n = 2500 + df = DataFrame({"int64": np.random.default_rng(2).integers(100, size=n, dtype=int)}) + df["category"] = Series( + np.array(list("abcdefghij")).take( + np.random.default_rng(2).integers(0, 10, size=n, dtype=int) + ) + ).astype("category") + df.isna() + buf = StringIO() + df.info(buf=buf) + + df2 = df[df["category"] == "d"] + buf = StringIO() + df2.info(buf=buf) + + +@pytest.mark.parametrize( + "fixture_func_name", + [ + "int_frame", + "float_frame", + "datetime_frame", + "duplicate_columns_frame", + "float_string_frame", + ], +) +def test_info_smoke_test(fixture_func_name, request): + frame = request.getfixturevalue(fixture_func_name) + buf = StringIO() + frame.info(buf=buf) + result = buf.getvalue().splitlines() + assert len(result) > 10 + + buf = StringIO() + frame.info(buf=buf, verbose=False) + + +def test_info_smoke_test2(float_frame): + # pretty useless test, used to be mixed into the repr tests + buf = StringIO() + float_frame.reindex(columns=["A"]).info(verbose=False, buf=buf) + float_frame.reindex(columns=["A", "B"]).info(verbose=False, buf=buf) + + # no columns or index + DataFrame().info(buf=buf) + + +@pytest.mark.parametrize( + "num_columns, max_info_columns, verbose", + [ + (10, 100, True), + (10, 11, True), + (10, 10, True), + (10, 9, False), + (10, 1, False), + ], +) +def test_info_default_verbose_selection(num_columns, max_info_columns, verbose): + frame = DataFrame(np.random.default_rng(2).standard_normal((5, num_columns))) + with option_context("display.max_info_columns", max_info_columns): + io_default = StringIO() + frame.info(buf=io_default) + result = io_default.getvalue() + + io_explicit = StringIO() + frame.info(buf=io_explicit, verbose=verbose) + expected = io_explicit.getvalue() + + assert result == expected + + +def test_info_verbose_check_header_separator_body(): + buf = StringIO() + size = 1001 + start = 5 + frame = DataFrame(np.random.default_rng(2).standard_normal((3, size))) + frame.info(verbose=True, buf=buf) + + res = buf.getvalue() + header = " # Column Dtype \n--- ------ ----- " + assert header in res + + frame.info(verbose=True, buf=buf) + buf.seek(0) + lines = buf.readlines() + assert len(lines) > 0 + + for i, line in enumerate(lines): + if start <= i < start + size: + line_nr = f" {i - start} " + assert line.startswith(line_nr) + + +@pytest.mark.parametrize( + "size, header_exp, separator_exp, first_line_exp, last_line_exp", + [ + ( + 4, + " # Column Non-Null Count Dtype ", + "--- ------ -------------- ----- ", + " 0 0 3 non-null float64", + " 3 3 3 non-null float64", + ), + ( + 11, + " # Column Non-Null Count Dtype ", + "--- ------ -------------- ----- ", + " 0 0 3 non-null float64", + " 10 10 3 non-null float64", + ), + ( + 101, + " # Column Non-Null Count Dtype ", + "--- ------ -------------- ----- ", + " 0 0 3 non-null float64", + " 100 100 3 non-null float64", + ), + ( + 1001, + " # Column Non-Null Count Dtype ", + "--- ------ -------------- ----- ", + " 0 0 3 non-null float64", + " 1000 1000 3 non-null float64", + ), + ( + 10001, + " # Column Non-Null Count Dtype ", + "--- ------ -------------- ----- ", + " 0 0 3 non-null float64", + " 10000 10000 3 non-null float64", + ), + ], +) +def test_info_verbose_with_counts_spacing( + size, header_exp, separator_exp, first_line_exp, last_line_exp +): + """Test header column, spacer, first line and last line in verbose mode.""" + frame = DataFrame(np.random.default_rng(2).standard_normal((3, size))) + with StringIO() as buf: + frame.info(verbose=True, show_counts=True, buf=buf) + all_lines = buf.getvalue().splitlines() + # Here table would contain only header, separator and table lines + # dframe repr, index summary, memory usage and dtypes are excluded + table = all_lines[3:-2] + header, separator, first_line, *rest, last_line = table + assert header == header_exp + assert separator == separator_exp + assert first_line == first_line_exp + assert last_line == last_line_exp + + +def test_info_memory(): + # https://github.com/pandas-dev/pandas/issues/21056 + df = DataFrame({"a": Series([1, 2], dtype="i8")}) + buf = StringIO() + df.info(buf=buf) + result = buf.getvalue() + bytes = float(df.memory_usage().sum()) + expected = textwrap.dedent( + f"""\ + + RangeIndex: 2 entries, 0 to 1 + Data columns (total 1 columns): + # Column Non-Null Count Dtype + --- ------ -------------- ----- + 0 a 2 non-null int64 + dtypes: int64(1) + memory usage: {bytes} bytes + """ + ) + assert result == expected + + +def test_info_wide(): + io = StringIO() + df = DataFrame(np.random.default_rng(2).standard_normal((5, 101))) + df.info(buf=io) + + io = StringIO() + df.info(buf=io, max_cols=101) + result = io.getvalue() + assert len(result.splitlines()) > 100 + + expected = result + with option_context("display.max_info_columns", 101): + io = StringIO() + df.info(buf=io) + result = io.getvalue() + assert result == expected + + +def test_info_duplicate_columns_shows_correct_dtypes(): + # GH11761 + io = StringIO() + frame = DataFrame([[1, 2.0]], columns=["a", "a"]) + frame.info(buf=io) + lines = io.getvalue().splitlines(True) + assert " 0 a 1 non-null int64 \n" == lines[5] + assert " 1 a 1 non-null float64\n" == lines[6] + + +def test_info_shows_column_dtypes(): + dtypes = [ + "int64", + "float64", + "datetime64[ns]", + "timedelta64[ns]", + "complex128", + "object", + "bool", + ] + data = {} + n = 10 + for i, dtype in enumerate(dtypes): + data[i] = np.random.default_rng(2).integers(2, size=n).astype(dtype) + df = DataFrame(data) + buf = StringIO() + df.info(buf=buf) + res = buf.getvalue() + header = ( + " # Column Non-Null Count Dtype \n" + "--- ------ -------------- ----- " + ) + assert header in res + for i, dtype in enumerate(dtypes): + name = f" {i:d} {i:d} {n:d} non-null {dtype}" + assert name in res + + +def test_info_max_cols(): + df = DataFrame(np.random.default_rng(2).standard_normal((10, 5))) + for len_, verbose in [(5, None), (5, False), (12, True)]: + # For verbose always ^ setting ^ summarize ^ full output + with option_context("max_info_columns", 4): + buf = StringIO() + df.info(buf=buf, verbose=verbose) + res = buf.getvalue() + assert len(res.strip().split("\n")) == len_ + + for len_, verbose in [(12, None), (5, False), (12, True)]: + # max_cols not exceeded + with option_context("max_info_columns", 5): + buf = StringIO() + df.info(buf=buf, verbose=verbose) + res = buf.getvalue() + assert len(res.strip().split("\n")) == len_ + + for len_, max_cols in [(12, 5), (5, 4)]: + # setting truncates + with option_context("max_info_columns", 4): + buf = StringIO() + df.info(buf=buf, max_cols=max_cols) + res = buf.getvalue() + assert len(res.strip().split("\n")) == len_ + + # setting wouldn't truncate + with option_context("max_info_columns", 5): + buf = StringIO() + df.info(buf=buf, max_cols=max_cols) + res = buf.getvalue() + assert len(res.strip().split("\n")) == len_ + + +def test_info_memory_usage(): + # Ensure memory usage is displayed, when asserted, on the last line + dtypes = [ + "int64", + "float64", + "datetime64[ns]", + "timedelta64[ns]", + "complex128", + "object", + "bool", + ] + data = {} + n = 10 + for i, dtype in enumerate(dtypes): + data[i] = np.random.default_rng(2).integers(2, size=n).astype(dtype) + df = DataFrame(data) + buf = StringIO() + + # display memory usage case + df.info(buf=buf, memory_usage=True) + res = buf.getvalue().splitlines() + assert "memory usage: " in res[-1] + + # do not display memory usage case + df.info(buf=buf, memory_usage=False) + res = buf.getvalue().splitlines() + assert "memory usage: " not in res[-1] + + df.info(buf=buf, memory_usage=True) + res = buf.getvalue().splitlines() + + # memory usage is a lower bound, so print it as XYZ+ MB + assert re.match(r"memory usage: [^+]+\+", res[-1]) + + df.iloc[:, :5].info(buf=buf, memory_usage=True) + res = buf.getvalue().splitlines() + + # excluded column with object dtype, so estimate is accurate + assert not re.match(r"memory usage: [^+]+\+", res[-1]) + + # Test a DataFrame with duplicate columns + dtypes = ["int64", "int64", "int64", "float64"] + data = {} + n = 100 + for i, dtype in enumerate(dtypes): + data[i] = np.random.default_rng(2).integers(2, size=n).astype(dtype) + df = DataFrame(data) + df.columns = dtypes + + df_with_object_index = DataFrame({"a": [1]}, index=Index(["foo"], dtype=object)) + df_with_object_index.info(buf=buf, memory_usage=True) + res = buf.getvalue().splitlines() + assert re.match(r"memory usage: [^+]+\+", res[-1]) + + df_with_object_index.info(buf=buf, memory_usage="deep") + res = buf.getvalue().splitlines() + assert re.match(r"memory usage: [^+]+$", res[-1]) + + # Ensure df size is as expected + # (cols * rows * bytes) + index size + df_size = df.memory_usage().sum() + exp_size = len(dtypes) * n * 8 + df.index.nbytes + assert df_size == exp_size + + # Ensure number of cols in memory_usage is the same as df + size_df = np.size(df.columns.values) + 1 # index=True; default + assert size_df == np.size(df.memory_usage()) + + # assert deep works only on object + assert df.memory_usage().sum() == df.memory_usage(deep=True).sum() + + # test for validity + DataFrame(1, index=["a"], columns=["A"]).memory_usage(index=True) + DataFrame(1, index=["a"], columns=["A"]).index.nbytes + df = DataFrame( + data=1, index=MultiIndex.from_product([["a"], range(1000)]), columns=["A"] + ) + df.index.nbytes + df.memory_usage(index=True) + df.index.values.nbytes + + mem = df.memory_usage(deep=True).sum() + assert mem > 0 + + +@pytest.mark.skipif(PYPY, reason="on PyPy deep=True doesn't change result") +def test_info_memory_usage_deep_not_pypy(): + df_with_object_index = DataFrame({"a": [1]}, index=Index(["foo"], dtype=object)) + assert ( + df_with_object_index.memory_usage(index=True, deep=True).sum() + > df_with_object_index.memory_usage(index=True).sum() + ) + + df_object = DataFrame({"a": Series(["a"], dtype=object)}) + assert df_object.memory_usage(deep=True).sum() > df_object.memory_usage().sum() + + +@pytest.mark.xfail(not PYPY, reason="on PyPy deep=True does not change result") +def test_info_memory_usage_deep_pypy(): + df_with_object_index = DataFrame({"a": [1]}, index=Index(["foo"], dtype=object)) + assert ( + df_with_object_index.memory_usage(index=True, deep=True).sum() + == df_with_object_index.memory_usage(index=True).sum() + ) + + df_object = DataFrame({"a": Series(["a"], dtype=object)}) + assert df_object.memory_usage(deep=True).sum() == df_object.memory_usage().sum() + + +@pytest.mark.skipif(PYPY, reason="PyPy getsizeof() fails by design") +def test_usage_via_getsizeof(): + df = DataFrame( + data=1, index=MultiIndex.from_product([["a"], range(1000)]), columns=["A"] + ) + mem = df.memory_usage(deep=True).sum() + # sys.getsizeof will call the .memory_usage with + # deep=True, and add on some GC overhead + diff = mem - sys.getsizeof(df) + assert abs(diff) < 100 + + +def test_info_memory_usage_qualified(using_infer_string): + buf = StringIO() + df = DataFrame(1, columns=list("ab"), index=[1, 2, 3]) + df.info(buf=buf) + assert "+" not in buf.getvalue() + + buf = StringIO() + df = DataFrame(1, columns=list("ab"), index=Index(list("ABC"), dtype=object)) + df.info(buf=buf) + assert "+" in buf.getvalue() + + buf = StringIO() + df = DataFrame(1, columns=list("ab"), index=Index(list("ABC"), dtype="str")) + df.info(buf=buf) + if using_infer_string and HAS_PYARROW: + assert "+" not in buf.getvalue() + else: + assert "+" in buf.getvalue() + + buf = StringIO() + df = DataFrame( + 1, columns=list("ab"), index=MultiIndex.from_product([range(3), range(3)]) + ) + df.info(buf=buf) + assert "+" not in buf.getvalue() + + buf = StringIO() + df = DataFrame( + 1, columns=list("ab"), index=MultiIndex.from_product([range(3), ["foo", "bar"]]) + ) + df.info(buf=buf) + if using_infer_string and HAS_PYARROW: + assert "+" not in buf.getvalue() + else: + assert "+" in buf.getvalue() + + +def test_info_memory_usage_bug_on_multiindex(): + # GH 14308 + # memory usage introspection should not materialize .values + + def memory_usage(f): + return f.memory_usage(deep=True).sum() + + N = 100 + M = len(ascii_uppercase) + index = MultiIndex.from_product( + [list(ascii_uppercase), date_range("20160101", periods=N)], + names=["id", "date"], + ) + df = DataFrame( + {"value": np.random.default_rng(2).standard_normal(N * M)}, index=index + ) + + unstacked = df.unstack("id") + assert df.values.nbytes == unstacked.values.nbytes + assert memory_usage(df) > memory_usage(unstacked) + + # high upper bound + assert memory_usage(unstacked) - memory_usage(df) < 2000 + + +def test_info_categorical(): + # GH14298 + idx = CategoricalIndex(["a", "b"]) + df = DataFrame(np.zeros((2, 2)), index=idx, columns=idx) + + buf = StringIO() + df.info(buf=buf) + + +@pytest.mark.xfail(not IS64, reason="GH 36579: fail on 32-bit system") +def test_info_int_columns(using_infer_string): + # GH#37245 + df = DataFrame({1: [1, 2], 2: [2, 3]}, index=["A", "B"]) + buf = StringIO() + df.info(show_counts=True, buf=buf) + result = buf.getvalue() + expected = textwrap.dedent( + f"""\ + + Index: 2 entries, A to B + Data columns (total 2 columns): + # Column Non-Null Count Dtype + --- ------ -------------- ----- + 0 1 2 non-null int64 + 1 2 2 non-null int64 + dtypes: int64(2) + memory usage: {'50.0' if using_infer_string and HAS_PYARROW else '48.0+'} bytes + """ + ) + assert result == expected + + +@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)") +def test_memory_usage_empty_no_warning(using_infer_string): + # GH#50066 + df = DataFrame(index=["a", "b"]) + with tm.assert_produces_warning(None): + result = df.memory_usage() + if using_infer_string and HAS_PYARROW: + value = 18 + else: + value = 16 if IS64 else 8 + expected = Series(value, index=["Index"]) + tm.assert_series_equal(result, expected) + + +@pytest.mark.single_cpu +def test_info_compute_numba(): + # GH#51922 + numba = pytest.importorskip("numba") + if Version(numba.__version__) == Version("0.61") and is_platform_arm(): + pytest.skip(f"Segfaults on ARM platforms with numba {numba.__version__}") + df = DataFrame([[1, 2], [3, 4]]) + + with option_context("compute.use_numba", True): + buf = StringIO() + df.info(buf=buf) + result = buf.getvalue() + + buf = StringIO() + df.info(buf=buf) + expected = buf.getvalue() + assert result == expected + + +@pytest.mark.parametrize( + "row, columns, show_counts, result", + [ + [20, 20, None, True], + [20, 20, True, True], + [20, 20, False, False], + [5, 5, None, False], + [5, 5, True, False], + [5, 5, False, False], + ], +) +def test_info_show_counts(row, columns, show_counts, result): + # Explicit cast to float to avoid implicit cast when setting nan + df = DataFrame(1, columns=range(10), index=range(10)).astype({1: "float"}) + df.iloc[1, 1] = np.nan + + with option_context( + "display.max_info_rows", row, "display.max_info_columns", columns + ): + with StringIO() as buf: + df.info(buf=buf, show_counts=show_counts) + assert ("non-null" in buf.getvalue()) is result diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_interpolate.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_interpolate.py new file mode 100644 index 0000000000000000000000000000000000000000..214c7cb229f56cad27680a26264a562223e9660c --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_interpolate.py @@ -0,0 +1,556 @@ +import numpy as np +import pytest + +from pandas._config import using_string_dtype + +from pandas.compat import WARNING_CHECK_DISABLED +from pandas.errors import ChainedAssignmentError +import pandas.util._test_decorators as td + +from pandas import ( + DataFrame, + NaT, + Series, + date_range, +) +import pandas._testing as tm + + +class TestDataFrameInterpolate: + def test_interpolate_complex(self): + # GH#53635 + ser = Series([complex("1+1j"), float("nan"), complex("2+2j")]) + assert ser.dtype.kind == "c" + + res = ser.interpolate() + expected = Series([ser[0], ser[0] * 1.5, ser[2]]) + tm.assert_series_equal(res, expected) + + df = ser.to_frame() + res = df.interpolate() + expected = expected.to_frame() + tm.assert_frame_equal(res, expected) + + def test_interpolate_datetimelike_values(self, frame_or_series): + # GH#11312, GH#51005 + orig = Series(date_range("2012-01-01", periods=5)) + ser = orig.copy() + ser[2] = NaT + + res = frame_or_series(ser).interpolate() + expected = frame_or_series(orig) + tm.assert_equal(res, expected) + + # datetime64tz cast + ser_tz = ser.dt.tz_localize("US/Pacific") + res_tz = frame_or_series(ser_tz).interpolate() + expected_tz = frame_or_series(orig.dt.tz_localize("US/Pacific")) + tm.assert_equal(res_tz, expected_tz) + + # timedelta64 cast + ser_td = ser - ser[0] + res_td = frame_or_series(ser_td).interpolate() + expected_td = frame_or_series(orig - orig[0]) + tm.assert_equal(res_td, expected_td) + + def test_interpolate_inplace(self, frame_or_series, using_array_manager, request): + # GH#44749 + if using_array_manager and frame_or_series is DataFrame: + mark = pytest.mark.xfail(reason=".values-based in-place check is invalid") + request.applymarker(mark) + + obj = frame_or_series([1, np.nan, 2]) + orig = obj.values + + obj.interpolate(inplace=True) + expected = frame_or_series([1, 1.5, 2]) + tm.assert_equal(obj, expected) + + # check we operated *actually* inplace + assert np.shares_memory(orig, obj.values) + assert orig.squeeze()[1] == 1.5 + + def test_interp_basic(self, using_copy_on_write, using_infer_string): + df = DataFrame( + { + "A": [1, 2, np.nan, 4], + "B": [1, 4, 9, np.nan], + "C": [1, 2, 3, 5], + "D": list("abcd"), + } + ) + expected = DataFrame( + { + "A": [1.0, 2.0, 3.0, 4.0], + "B": [1.0, 4.0, 9.0, 9.0], + "C": [1, 2, 3, 5], + "D": list("abcd"), + } + ) + if using_infer_string: + dtype = "str" if using_infer_string else "object" + msg = f"[Cc]annot interpolate with {dtype} dtype" + with pytest.raises(TypeError, match=msg): + df.interpolate() + return + + msg = "DataFrame.interpolate with object dtype" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.interpolate() + tm.assert_frame_equal(result, expected) + + # check we didn't operate inplace GH#45791 + cvalues = df["C"]._values + dvalues = df["D"].values + if using_copy_on_write: + assert np.shares_memory(cvalues, result["C"]._values) + assert np.shares_memory(dvalues, result["D"]._values) + else: + assert not np.shares_memory(cvalues, result["C"]._values) + assert not np.shares_memory(dvalues, result["D"]._values) + + with tm.assert_produces_warning(FutureWarning, match=msg): + res = df.interpolate(inplace=True) + assert res is None + tm.assert_frame_equal(df, expected) + + # check we DID operate inplace + assert tm.shares_memory(df["C"]._values, cvalues) + assert tm.shares_memory(df["D"]._values, dvalues) + + @pytest.mark.xfail( + using_string_dtype(), reason="interpolate doesn't work for string" + ) + def test_interp_basic_with_non_range_index(self, using_infer_string): + df = DataFrame( + { + "A": [1, 2, np.nan, 4], + "B": [1, 4, 9, np.nan], + "C": [1, 2, 3, 5], + "D": list("abcd"), + } + ) + + msg = "DataFrame.interpolate with object dtype" + warning = FutureWarning if not using_infer_string else None + with tm.assert_produces_warning(warning, match=msg): + result = df.set_index("C").interpolate() + expected = df.set_index("C") + expected.loc[3, "A"] = 3 + expected.loc[5, "B"] = 9 + tm.assert_frame_equal(result, expected) + + def test_interp_empty(self): + # https://github.com/pandas-dev/pandas/issues/35598 + df = DataFrame() + result = df.interpolate() + assert result is not df + expected = df + tm.assert_frame_equal(result, expected) + + def test_interp_bad_method(self): + df = DataFrame( + { + "A": [1, 2, np.nan, 4], + "B": [1, 4, 9, np.nan], + "C": [1, 2, 3, 5], + } + ) + msg = ( + r"method must be one of \['linear', 'time', 'index', 'values', " + r"'nearest', 'zero', 'slinear', 'quadratic', 'cubic', " + r"'barycentric', 'krogh', 'spline', 'polynomial', " + r"'from_derivatives', 'piecewise_polynomial', 'pchip', 'akima', " + r"'cubicspline'\]. Got 'not_a_method' instead." + ) + with pytest.raises(ValueError, match=msg): + df.interpolate(method="not_a_method") + + def test_interp_combo(self): + df = DataFrame( + { + "A": [1.0, 2.0, np.nan, 4.0], + "B": [1, 4, 9, np.nan], + "C": [1, 2, 3, 5], + "D": list("abcd"), + } + ) + + result = df["A"].interpolate() + expected = Series([1.0, 2.0, 3.0, 4.0], name="A") + tm.assert_series_equal(result, expected) + + msg = "The 'downcast' keyword in Series.interpolate is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df["A"].interpolate(downcast="infer") + expected = Series([1, 2, 3, 4], name="A") + tm.assert_series_equal(result, expected) + + def test_inerpolate_invalid_downcast(self): + # GH#53103 + df = DataFrame( + { + "A": [1.0, 2.0, np.nan, 4.0], + "B": [1, 4, 9, np.nan], + "C": [1, 2, 3, 5], + "D": list("abcd"), + } + ) + + msg = "downcast must be either None or 'infer'" + msg2 = "The 'downcast' keyword in DataFrame.interpolate is deprecated" + msg3 = "The 'downcast' keyword in Series.interpolate is deprecated" + with pytest.raises(ValueError, match=msg): + with tm.assert_produces_warning(FutureWarning, match=msg2): + df.interpolate(downcast="int64") + with pytest.raises(ValueError, match=msg): + with tm.assert_produces_warning(FutureWarning, match=msg3): + df["A"].interpolate(downcast="int64") + + def test_interp_nan_idx(self): + df = DataFrame({"A": [1, 2, np.nan, 4], "B": [np.nan, 2, 3, 4]}) + df = df.set_index("A") + msg = ( + "Interpolation with NaNs in the index has not been implemented. " + "Try filling those NaNs before interpolating." + ) + with pytest.raises(NotImplementedError, match=msg): + df.interpolate(method="values") + + def test_interp_various(self): + pytest.importorskip("scipy") + df = DataFrame( + {"A": [1, 2, np.nan, 4, 5, np.nan, 7], "C": [1, 2, 3, 5, 8, 13, 21]} + ) + df = df.set_index("C") + expected = df.copy() + result = df.interpolate(method="polynomial", order=1) + + expected.loc[3, "A"] = 2.66666667 + expected.loc[13, "A"] = 5.76923076 + tm.assert_frame_equal(result, expected) + + result = df.interpolate(method="cubic") + # GH #15662. + expected.loc[3, "A"] = 2.81547781 + expected.loc[13, "A"] = 5.52964175 + tm.assert_frame_equal(result, expected) + + result = df.interpolate(method="nearest") + expected.loc[3, "A"] = 2 + expected.loc[13, "A"] = 5 + tm.assert_frame_equal(result, expected, check_dtype=False) + + result = df.interpolate(method="quadratic") + expected.loc[3, "A"] = 2.82150771 + expected.loc[13, "A"] = 6.12648668 + tm.assert_frame_equal(result, expected) + + result = df.interpolate(method="slinear") + expected.loc[3, "A"] = 2.66666667 + expected.loc[13, "A"] = 5.76923077 + tm.assert_frame_equal(result, expected) + + result = df.interpolate(method="zero") + expected.loc[3, "A"] = 2.0 + expected.loc[13, "A"] = 5 + tm.assert_frame_equal(result, expected, check_dtype=False) + + def test_interp_alt_scipy(self): + pytest.importorskip("scipy") + df = DataFrame( + {"A": [1, 2, np.nan, 4, 5, np.nan, 7], "C": [1, 2, 3, 5, 8, 13, 21]} + ) + result = df.interpolate(method="barycentric") + expected = df.copy() + expected.loc[2, "A"] = 3 + expected.loc[5, "A"] = 6 + tm.assert_frame_equal(result, expected) + + msg = "The 'downcast' keyword in DataFrame.interpolate is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.interpolate(method="barycentric", downcast="infer") + tm.assert_frame_equal(result, expected.astype(np.int64)) + + result = df.interpolate(method="krogh") + expectedk = df.copy() + expectedk["A"] = expected["A"] + tm.assert_frame_equal(result, expectedk) + + result = df.interpolate(method="pchip") + expected.loc[2, "A"] = 3 + expected.loc[5, "A"] = 6.0 + + tm.assert_frame_equal(result, expected) + + def test_interp_rowwise(self): + df = DataFrame( + { + 0: [1, 2, np.nan, 4], + 1: [2, 3, 4, np.nan], + 2: [np.nan, 4, 5, 6], + 3: [4, np.nan, 6, 7], + 4: [1, 2, 3, 4], + } + ) + result = df.interpolate(axis=1) + expected = df.copy() + expected.loc[3, 1] = 5 + expected.loc[0, 2] = 3 + expected.loc[1, 3] = 3 + expected[4] = expected[4].astype(np.float64) + tm.assert_frame_equal(result, expected) + + result = df.interpolate(axis=1, method="values") + tm.assert_frame_equal(result, expected) + + result = df.interpolate(axis=0) + expected = df.interpolate() + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "axis_name, axis_number", + [ + pytest.param("rows", 0, id="rows_0"), + pytest.param("index", 0, id="index_0"), + pytest.param("columns", 1, id="columns_1"), + ], + ) + def test_interp_axis_names(self, axis_name, axis_number): + # GH 29132: test axis names + data = {0: [0, np.nan, 6], 1: [1, np.nan, 7], 2: [2, 5, 8]} + + df = DataFrame(data, dtype=np.float64) + result = df.interpolate(axis=axis_name, method="linear") + expected = df.interpolate(axis=axis_number, method="linear") + tm.assert_frame_equal(result, expected) + + def test_rowwise_alt(self): + df = DataFrame( + { + 0: [0, 0.5, 1.0, np.nan, 4, 8, np.nan, np.nan, 64], + 1: [1, 2, 3, 4, 3, 2, 1, 0, -1], + } + ) + df.interpolate(axis=0) + # TODO: assert something? + + @pytest.mark.parametrize( + "check_scipy", [False, pytest.param(True, marks=td.skip_if_no("scipy"))] + ) + def test_interp_leading_nans(self, check_scipy): + df = DataFrame( + {"A": [np.nan, np.nan, 0.5, 0.25, 0], "B": [np.nan, -3, -3.5, np.nan, -4]} + ) + result = df.interpolate() + expected = df.copy() + expected.loc[3, "B"] = -3.75 + tm.assert_frame_equal(result, expected) + + if check_scipy: + result = df.interpolate(method="polynomial", order=1) + tm.assert_frame_equal(result, expected) + + def test_interp_raise_on_only_mixed(self, axis): + df = DataFrame( + { + "A": [1, 2, np.nan, 4], + "B": ["a", "b", "c", "d"], + "C": [np.nan, 2, 5, 7], + "D": [np.nan, np.nan, 9, 9], + "E": [1, 2, 3, 4], + } + ) + msg = ( + "Cannot interpolate with all object-dtype columns " + "in the DataFrame. Try setting at least one " + "column to a numeric dtype." + ) + with pytest.raises(TypeError, match=msg): + df.astype("object").interpolate(axis=axis) + + def test_interp_raise_on_all_object_dtype(self): + # GH 22985 + df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}, dtype="object") + msg = ( + "Cannot interpolate with all object-dtype columns " + "in the DataFrame. Try setting at least one " + "column to a numeric dtype." + ) + with pytest.raises(TypeError, match=msg): + df.interpolate() + + def test_interp_inplace(self, using_copy_on_write): + df = DataFrame({"a": [1.0, 2.0, np.nan, 4.0]}) + expected = DataFrame({"a": [1.0, 2.0, 3.0, 4.0]}) + expected_cow = df.copy() + result = df.copy() + + if using_copy_on_write: + with tm.raises_chained_assignment_error(): + return_value = result["a"].interpolate(inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected_cow) + else: + with tm.assert_produces_warning( + FutureWarning if not WARNING_CHECK_DISABLED else None, + match="inplace method", + ): + return_value = result["a"].interpolate(inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + + result = df.copy() + msg = "The 'downcast' keyword in Series.interpolate is deprecated" + + if using_copy_on_write: + with tm.assert_produces_warning( + (FutureWarning, ChainedAssignmentError), match=msg + ): + return_value = result["a"].interpolate(inplace=True, downcast="infer") + assert return_value is None + tm.assert_frame_equal(result, expected_cow) + else: + with tm.assert_produces_warning(FutureWarning, match=msg): + return_value = result["a"].interpolate(inplace=True, downcast="infer") + assert return_value is None + tm.assert_frame_equal(result, expected.astype("int64")) + + def test_interp_inplace_row(self): + # GH 10395 + result = DataFrame( + {"a": [1.0, 2.0, 3.0, 4.0], "b": [np.nan, 2.0, 3.0, 4.0], "c": [3, 2, 2, 2]} + ) + expected = result.interpolate(method="linear", axis=1, inplace=False) + return_value = result.interpolate(method="linear", axis=1, inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + + def test_interp_ignore_all_good(self): + # GH + df = DataFrame( + { + "A": [1, 2, np.nan, 4], + "B": [1, 2, 3, 4], + "C": [1.0, 2.0, np.nan, 4.0], + "D": [1.0, 2.0, 3.0, 4.0], + } + ) + expected = DataFrame( + { + "A": np.array([1, 2, 3, 4], dtype="float64"), + "B": np.array([1, 2, 3, 4], dtype="int64"), + "C": np.array([1.0, 2.0, 3, 4.0], dtype="float64"), + "D": np.array([1.0, 2.0, 3.0, 4.0], dtype="float64"), + } + ) + + msg = "The 'downcast' keyword in DataFrame.interpolate is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.interpolate(downcast=None) + tm.assert_frame_equal(result, expected) + + # all good + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df[["B", "D"]].interpolate(downcast=None) + tm.assert_frame_equal(result, df[["B", "D"]]) + + def test_interp_time_inplace_axis(self): + # GH 9687 + periods = 5 + idx = date_range(start="2014-01-01", periods=periods) + data = np.random.default_rng(2).random((periods, periods)) + data[data < 0.5] = np.nan + expected = DataFrame(index=idx, columns=idx, data=data) + + result = expected.interpolate(axis=0, method="time") + return_value = expected.interpolate(axis=0, method="time", inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("axis_name, axis_number", [("index", 0), ("columns", 1)]) + def test_interp_string_axis(self, axis_name, axis_number): + # https://github.com/pandas-dev/pandas/issues/25190 + x = np.linspace(0, 100, 1000) + y = np.sin(x) + df = DataFrame( + data=np.tile(y, (10, 1)), index=np.arange(10), columns=x + ).reindex(columns=x * 1.005) + result = df.interpolate(method="linear", axis=axis_name) + expected = df.interpolate(method="linear", axis=axis_number) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("multiblock", [True, False]) + @pytest.mark.parametrize("method", ["ffill", "bfill", "pad"]) + def test_interp_fillna_methods( + self, request, axis, multiblock, method, using_array_manager + ): + # GH 12918 + if using_array_manager and axis in (1, "columns"): + # TODO(ArrayManager) support axis=1 + td.mark_array_manager_not_yet_implemented(request) + + df = DataFrame( + { + "A": [1.0, 2.0, 3.0, 4.0, np.nan, 5.0], + "B": [2.0, 4.0, 6.0, np.nan, 8.0, 10.0], + "C": [3.0, 6.0, 9.0, np.nan, np.nan, 30.0], + } + ) + if multiblock: + df["D"] = np.nan + df["E"] = 1.0 + + method2 = method if method != "pad" else "ffill" + expected = getattr(df, method2)(axis=axis) + msg = f"DataFrame.interpolate with method={method} is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.interpolate(method=method, axis=axis) + tm.assert_frame_equal(result, expected) + + def test_interpolate_empty_df(self): + # GH#53199 + df = DataFrame() + expected = df.copy() + result = df.interpolate(inplace=True) + assert result is None + tm.assert_frame_equal(df, expected) + + def test_interpolate_ea(self, any_int_ea_dtype): + # GH#55347 + df = DataFrame({"a": [1, None, None, None, 3]}, dtype=any_int_ea_dtype) + orig = df.copy() + result = df.interpolate(limit=2) + expected = DataFrame({"a": [1, 1.5, 2.0, None, 3]}, dtype="Float64") + tm.assert_frame_equal(result, expected) + tm.assert_frame_equal(df, orig) + + @pytest.mark.parametrize( + "dtype", + [ + "Float64", + "Float32", + pytest.param("float32[pyarrow]", marks=td.skip_if_no("pyarrow")), + pytest.param("float64[pyarrow]", marks=td.skip_if_no("pyarrow")), + ], + ) + def test_interpolate_ea_float(self, dtype): + # GH#55347 + df = DataFrame({"a": [1, None, None, None, 3]}, dtype=dtype) + orig = df.copy() + result = df.interpolate(limit=2) + expected = DataFrame({"a": [1, 1.5, 2.0, None, 3]}, dtype=dtype) + tm.assert_frame_equal(result, expected) + tm.assert_frame_equal(df, orig) + + @pytest.mark.parametrize( + "dtype", + ["int64", "uint64", "int32", "int16", "int8", "uint32", "uint16", "uint8"], + ) + def test_interpolate_arrow(self, dtype): + # GH#55347 + pytest.importorskip("pyarrow") + df = DataFrame({"a": [1, None, None, None, 3]}, dtype=dtype + "[pyarrow]") + result = df.interpolate(limit=2) + expected = DataFrame({"a": [1, 1.5, 2.0, None, 3]}, dtype="float64[pyarrow]") + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_is_homogeneous_dtype.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_is_homogeneous_dtype.py new file mode 100644 index 0000000000000000000000000000000000000000..1fe28cb8eb8562d116ed3306a7576a06c9c50450 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_is_homogeneous_dtype.py @@ -0,0 +1,58 @@ +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +from pandas import ( + Categorical, + DataFrame, +) + +# _is_homogeneous_type always returns True for ArrayManager +pytestmark = td.skip_array_manager_invalid_test + + +@pytest.mark.parametrize( + "data, expected", + [ + # empty + (DataFrame(), True), + # multi-same + (DataFrame({"A": [1, 2], "B": [1, 2]}), True), + # multi-object + ( + DataFrame( + { + "A": np.array([1, 2], dtype=object), + "B": np.array(["a", "b"], dtype=object), + }, + dtype="object", + ), + True, + ), + # multi-extension + ( + DataFrame({"A": Categorical(["a", "b"]), "B": Categorical(["a", "b"])}), + True, + ), + # differ types + (DataFrame({"A": [1, 2], "B": [1.0, 2.0]}), False), + # differ sizes + ( + DataFrame( + { + "A": np.array([1, 2], dtype=np.int32), + "B": np.array([1, 2], dtype=np.int64), + } + ), + False, + ), + # multi-extension differ + ( + DataFrame({"A": Categorical(["a", "b"]), "B": Categorical(["b", "c"])}), + False, + ), + ], +) +def test_is_homogeneous_type(data, expected): + assert data._is_homogeneous_type is expected diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_isetitem.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_isetitem.py new file mode 100644 index 0000000000000000000000000000000000000000..69f394afb65191fe4cc52519fbc52959d2e1dd76 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_isetitem.py @@ -0,0 +1,50 @@ +import pytest + +from pandas import ( + DataFrame, + Series, +) +import pandas._testing as tm + + +class TestDataFrameSetItem: + def test_isetitem_ea_df(self): + # GH#49922 + df = DataFrame([[1, 2, 3], [4, 5, 6]]) + rhs = DataFrame([[11, 12], [13, 14]], dtype="Int64") + + df.isetitem([0, 1], rhs) + expected = DataFrame( + { + 0: Series([11, 13], dtype="Int64"), + 1: Series([12, 14], dtype="Int64"), + 2: [3, 6], + } + ) + tm.assert_frame_equal(df, expected) + + def test_isetitem_ea_df_scalar_indexer(self): + # GH#49922 + df = DataFrame([[1, 2, 3], [4, 5, 6]]) + rhs = DataFrame([[11], [13]], dtype="Int64") + + df.isetitem(2, rhs) + expected = DataFrame( + { + 0: [1, 4], + 1: [2, 5], + 2: Series([11, 13], dtype="Int64"), + } + ) + tm.assert_frame_equal(df, expected) + + def test_isetitem_dimension_mismatch(self): + # GH#51701 + df = DataFrame({"a": [1, 2], "b": [3, 4], "c": [5, 6]}) + value = df.copy() + with pytest.raises(ValueError, match="Got 2 positions but value has 3 columns"): + df.isetitem([1, 2], value) + + value = df.copy() + with pytest.raises(ValueError, match="Got 2 positions but value has 1 columns"): + df.isetitem([1, 2], value[["a"]]) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_isin.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_isin.py new file mode 100644 index 0000000000000000000000000000000000000000..b4511aad27a93bd2d9411ac5cdb427196dbf9dda --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_isin.py @@ -0,0 +1,227 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + MultiIndex, + Series, +) +import pandas._testing as tm + + +class TestDataFrameIsIn: + def test_isin(self): + # GH#4211 + df = DataFrame( + { + "vals": [1, 2, 3, 4], + "ids": ["a", "b", "f", "n"], + "ids2": ["a", "n", "c", "n"], + }, + index=["foo", "bar", "baz", "qux"], + ) + other = ["a", "b", "c"] + + result = df.isin(other) + expected = DataFrame([df.loc[s].isin(other) for s in df.index]) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("empty", [[], Series(dtype=object), np.array([])]) + def test_isin_empty(self, empty): + # GH#16991 + df = DataFrame({"A": ["a", "b", "c"], "B": ["a", "e", "f"]}) + expected = DataFrame(False, df.index, df.columns) + + result = df.isin(empty) + tm.assert_frame_equal(result, expected) + + def test_isin_dict(self): + df = DataFrame({"A": ["a", "b", "c"], "B": ["a", "e", "f"]}) + d = {"A": ["a"]} + + expected = DataFrame(False, df.index, df.columns) + expected.loc[0, "A"] = True + + result = df.isin(d) + tm.assert_frame_equal(result, expected) + + # non unique columns + df = DataFrame({"A": ["a", "b", "c"], "B": ["a", "e", "f"]}) + df.columns = ["A", "A"] + expected = DataFrame(False, df.index, df.columns) + expected.loc[0, "A"] = True + result = df.isin(d) + tm.assert_frame_equal(result, expected) + + def test_isin_with_string_scalar(self): + # GH#4763 + df = DataFrame( + { + "vals": [1, 2, 3, 4], + "ids": ["a", "b", "f", "n"], + "ids2": ["a", "n", "c", "n"], + }, + index=["foo", "bar", "baz", "qux"], + ) + msg = ( + r"only list-like or dict-like objects are allowed " + r"to be passed to DataFrame.isin\(\), you passed a 'str'" + ) + with pytest.raises(TypeError, match=msg): + df.isin("a") + + with pytest.raises(TypeError, match=msg): + df.isin("aaa") + + def test_isin_df(self): + df1 = DataFrame({"A": [1, 2, 3, 4], "B": [2, np.nan, 4, 4]}) + df2 = DataFrame({"A": [0, 2, 12, 4], "B": [2, np.nan, 4, 5]}) + expected = DataFrame(False, df1.index, df1.columns) + result = df1.isin(df2) + expected.loc[[1, 3], "A"] = True + expected.loc[[0, 2], "B"] = True + tm.assert_frame_equal(result, expected) + + # partial overlapping columns + df2.columns = ["A", "C"] + result = df1.isin(df2) + expected["B"] = False + tm.assert_frame_equal(result, expected) + + def test_isin_tuples(self): + # GH#16394 + df = DataFrame({"A": [1, 2, 3], "B": ["a", "b", "f"]}) + df["C"] = list(zip(df["A"], df["B"])) + result = df["C"].isin([(1, "a")]) + tm.assert_series_equal(result, Series([True, False, False], name="C")) + + def test_isin_df_dupe_values(self): + df1 = DataFrame({"A": [1, 2, 3, 4], "B": [2, np.nan, 4, 4]}) + # just cols duped + df2 = DataFrame([[0, 2], [12, 4], [2, np.nan], [4, 5]], columns=["B", "B"]) + msg = r"cannot compute isin with a duplicate axis\." + with pytest.raises(ValueError, match=msg): + df1.isin(df2) + + # just index duped + df2 = DataFrame( + [[0, 2], [12, 4], [2, np.nan], [4, 5]], + columns=["A", "B"], + index=[0, 0, 1, 1], + ) + with pytest.raises(ValueError, match=msg): + df1.isin(df2) + + # cols and index: + df2.columns = ["B", "B"] + with pytest.raises(ValueError, match=msg): + df1.isin(df2) + + def test_isin_dupe_self(self): + other = DataFrame({"A": [1, 0, 1, 0], "B": [1, 1, 0, 0]}) + df = DataFrame([[1, 1], [1, 0], [0, 0]], columns=["A", "A"]) + result = df.isin(other) + expected = DataFrame(False, index=df.index, columns=df.columns) + expected.loc[0] = True + expected.iloc[1, 1] = True + tm.assert_frame_equal(result, expected) + + def test_isin_against_series(self): + df = DataFrame( + {"A": [1, 2, 3, 4], "B": [2, np.nan, 4, 4]}, index=["a", "b", "c", "d"] + ) + s = Series([1, 3, 11, 4], index=["a", "b", "c", "d"]) + expected = DataFrame(False, index=df.index, columns=df.columns) + expected.loc["a", "A"] = True + expected.loc["d"] = True + result = df.isin(s) + tm.assert_frame_equal(result, expected) + + def test_isin_multiIndex(self): + idx = MultiIndex.from_tuples( + [ + (0, "a", "foo"), + (0, "a", "bar"), + (0, "b", "bar"), + (0, "b", "baz"), + (2, "a", "foo"), + (2, "a", "bar"), + (2, "c", "bar"), + (2, "c", "baz"), + (1, "b", "foo"), + (1, "b", "bar"), + (1, "c", "bar"), + (1, "c", "baz"), + ] + ) + df1 = DataFrame({"A": np.ones(12), "B": np.zeros(12)}, index=idx) + df2 = DataFrame( + { + "A": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], + "B": [1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1], + } + ) + # against regular index + expected = DataFrame(False, index=df1.index, columns=df1.columns) + result = df1.isin(df2) + tm.assert_frame_equal(result, expected) + + df2.index = idx + expected = df2.values.astype(bool) + expected[:, 1] = ~expected[:, 1] + expected = DataFrame(expected, columns=["A", "B"], index=idx) + + result = df1.isin(df2) + tm.assert_frame_equal(result, expected) + + def test_isin_empty_datetimelike(self): + # GH#15473 + df1_ts = DataFrame({"date": pd.to_datetime(["2014-01-01", "2014-01-02"])}) + df1_td = DataFrame({"date": [pd.Timedelta(1, "s"), pd.Timedelta(2, "s")]}) + df2 = DataFrame({"date": []}) + df3 = DataFrame() + + expected = DataFrame({"date": [False, False]}) + + result = df1_ts.isin(df2) + tm.assert_frame_equal(result, expected) + result = df1_ts.isin(df3) + tm.assert_frame_equal(result, expected) + + result = df1_td.isin(df2) + tm.assert_frame_equal(result, expected) + result = df1_td.isin(df3) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "values", + [ + DataFrame({"a": [1, 2, 3]}, dtype="category"), + Series([1, 2, 3], dtype="category"), + ], + ) + def test_isin_category_frame(self, values): + # GH#34256 + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + expected = DataFrame({"a": [True, True, True], "b": [False, False, False]}) + + result = df.isin(values) + tm.assert_frame_equal(result, expected) + + def test_isin_read_only(self): + # https://github.com/pandas-dev/pandas/issues/37174 + arr = np.array([1, 2, 3]) + arr.setflags(write=False) + df = DataFrame([1, 2, 3]) + result = df.isin(arr) + expected = DataFrame([True, True, True]) + tm.assert_frame_equal(result, expected) + + def test_isin_not_lossy(self): + # GH 53514 + val = 1666880195890293744 + df = DataFrame({"a": [val], "b": [1.0]}) + result = df.isin([val]) + expected = DataFrame({"a": [True], "b": [False]}) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_iterrows.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_iterrows.py new file mode 100644 index 0000000000000000000000000000000000000000..0bd0bed76dc9dea5df4d0afb76ebaf0760a23ecc --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_iterrows.py @@ -0,0 +1,16 @@ +from pandas import ( + DataFrame, + Timedelta, +) + + +def test_no_overflow_of_freq_and_time_in_dataframe(): + # GH 35665 + df = DataFrame( + { + "some_string": ["2222Y3"], + "time": [Timedelta("0 days 00:00:00.990000")], + } + ) + for _, row in df.iterrows(): + assert row.dtype == "object" diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_join.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_join.py new file mode 100644 index 0000000000000000000000000000000000000000..735f6c50ab739dba04ebc49fec73cfa3147fc661 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_join.py @@ -0,0 +1,576 @@ +from datetime import datetime + +import numpy as np +import pytest + +from pandas.errors import MergeError + +import pandas as pd +from pandas import ( + DataFrame, + Index, + MultiIndex, + date_range, + period_range, +) +import pandas._testing as tm +from pandas.core.reshape.concat import concat + + +@pytest.fixture +def frame_with_period_index(): + return DataFrame( + data=np.arange(20).reshape(4, 5), + columns=list("abcde"), + index=period_range(start="2000", freq="Y", periods=4), + ) + + +@pytest.fixture +def left(): + return DataFrame({"a": [20, 10, 0]}, index=[2, 1, 0]) + + +@pytest.fixture +def right(): + return DataFrame({"b": [300, 100, 200]}, index=[3, 1, 2]) + + +@pytest.fixture +def left_no_dup(): + return DataFrame( + {"a": ["a", "b", "c", "d"], "b": ["cat", "dog", "weasel", "horse"]}, + index=range(4), + ) + + +@pytest.fixture +def right_no_dup(): + return DataFrame( + { + "a": ["a", "b", "c", "d", "e"], + "c": ["meow", "bark", "um... weasel noise?", "nay", "chirp"], + }, + index=range(5), + ).set_index("a") + + +@pytest.fixture +def left_w_dups(left_no_dup): + return concat( + [left_no_dup, DataFrame({"a": ["a"], "b": ["cow"]}, index=[3])], sort=True + ) + + +@pytest.fixture +def right_w_dups(right_no_dup): + return concat( + [right_no_dup, DataFrame({"a": ["e"], "c": ["moo"]}, index=[3])] + ).set_index("a") + + +@pytest.mark.parametrize( + "how, sort, expected", + [ + ("inner", False, DataFrame({"a": [20, 10], "b": [200, 100]}, index=[2, 1])), + ("inner", True, DataFrame({"a": [10, 20], "b": [100, 200]}, index=[1, 2])), + ( + "left", + False, + DataFrame({"a": [20, 10, 0], "b": [200, 100, np.nan]}, index=[2, 1, 0]), + ), + ( + "left", + True, + DataFrame({"a": [0, 10, 20], "b": [np.nan, 100, 200]}, index=[0, 1, 2]), + ), + ( + "right", + False, + DataFrame({"a": [np.nan, 10, 20], "b": [300, 100, 200]}, index=[3, 1, 2]), + ), + ( + "right", + True, + DataFrame({"a": [10, 20, np.nan], "b": [100, 200, 300]}, index=[1, 2, 3]), + ), + ( + "outer", + False, + DataFrame( + {"a": [0, 10, 20, np.nan], "b": [np.nan, 100, 200, 300]}, + index=[0, 1, 2, 3], + ), + ), + ( + "outer", + True, + DataFrame( + {"a": [0, 10, 20, np.nan], "b": [np.nan, 100, 200, 300]}, + index=[0, 1, 2, 3], + ), + ), + ], +) +def test_join(left, right, how, sort, expected): + result = left.join(right, how=how, sort=sort, validate="1:1") + tm.assert_frame_equal(result, expected) + + +def test_suffix_on_list_join(): + first = DataFrame({"key": [1, 2, 3, 4, 5]}) + second = DataFrame({"key": [1, 8, 3, 2, 5], "v1": [1, 2, 3, 4, 5]}) + third = DataFrame({"keys": [5, 2, 3, 4, 1], "v2": [1, 2, 3, 4, 5]}) + + # check proper errors are raised + msg = "Suffixes not supported when joining multiple DataFrames" + with pytest.raises(ValueError, match=msg): + first.join([second], lsuffix="y") + with pytest.raises(ValueError, match=msg): + first.join([second, third], rsuffix="x") + with pytest.raises(ValueError, match=msg): + first.join([second, third], lsuffix="y", rsuffix="x") + with pytest.raises(ValueError, match="Indexes have overlapping values"): + first.join([second, third]) + + # no errors should be raised + arr_joined = first.join([third]) + norm_joined = first.join(third) + tm.assert_frame_equal(arr_joined, norm_joined) + + +def test_join_invalid_validate(left_no_dup, right_no_dup): + # GH 46622 + # Check invalid arguments + msg = ( + '"invalid" is not a valid argument. ' + "Valid arguments are:\n" + '- "1:1"\n' + '- "1:m"\n' + '- "m:1"\n' + '- "m:m"\n' + '- "one_to_one"\n' + '- "one_to_many"\n' + '- "many_to_one"\n' + '- "many_to_many"' + ) + with pytest.raises(ValueError, match=msg): + left_no_dup.merge(right_no_dup, on="a", validate="invalid") + + +@pytest.mark.parametrize("dtype", ["object", "string[pyarrow]"]) +def test_join_on_single_col_dup_on_right(left_no_dup, right_w_dups, dtype): + # GH 46622 + # Dups on right allowed by one_to_many constraint + if dtype == "string[pyarrow]": + pytest.importorskip("pyarrow") + left_no_dup = left_no_dup.astype(dtype) + right_w_dups.index = right_w_dups.index.astype(dtype) + left_no_dup.join( + right_w_dups, + on="a", + validate="one_to_many", + ) + + # Dups on right not allowed by one_to_one constraint + msg = "Merge keys are not unique in right dataset; not a one-to-one merge" + with pytest.raises(MergeError, match=msg): + left_no_dup.join( + right_w_dups, + on="a", + validate="one_to_one", + ) + + +def test_join_on_single_col_dup_on_left(left_w_dups, right_no_dup): + # GH 46622 + # Dups on left allowed by many_to_one constraint + left_w_dups.join( + right_no_dup, + on="a", + validate="many_to_one", + ) + + # Dups on left not allowed by one_to_one constraint + msg = "Merge keys are not unique in left dataset; not a one-to-one merge" + with pytest.raises(MergeError, match=msg): + left_w_dups.join( + right_no_dup, + on="a", + validate="one_to_one", + ) + + +def test_join_on_single_col_dup_on_both(left_w_dups, right_w_dups): + # GH 46622 + # Dups on both allowed by many_to_many constraint + left_w_dups.join(right_w_dups, on="a", validate="many_to_many") + + # Dups on both not allowed by many_to_one constraint + msg = "Merge keys are not unique in right dataset; not a many-to-one merge" + with pytest.raises(MergeError, match=msg): + left_w_dups.join( + right_w_dups, + on="a", + validate="many_to_one", + ) + + # Dups on both not allowed by one_to_many constraint + msg = "Merge keys are not unique in left dataset; not a one-to-many merge" + with pytest.raises(MergeError, match=msg): + left_w_dups.join( + right_w_dups, + on="a", + validate="one_to_many", + ) + + +def test_join_on_multi_col_check_dup(): + # GH 46622 + # Two column join, dups in both, but jointly no dups + left = DataFrame( + { + "a": ["a", "a", "b", "b"], + "b": [0, 1, 0, 1], + "c": ["cat", "dog", "weasel", "horse"], + }, + index=range(4), + ).set_index(["a", "b"]) + + right = DataFrame( + { + "a": ["a", "a", "b"], + "b": [0, 1, 0], + "d": ["meow", "bark", "um... weasel noise?"], + }, + index=range(3), + ).set_index(["a", "b"]) + + expected_multi = DataFrame( + { + "a": ["a", "a", "b"], + "b": [0, 1, 0], + "c": ["cat", "dog", "weasel"], + "d": ["meow", "bark", "um... weasel noise?"], + }, + index=range(3), + ).set_index(["a", "b"]) + + # Jointly no dups allowed by one_to_one constraint + result = left.join(right, how="inner", validate="1:1") + tm.assert_frame_equal(result, expected_multi) + + +def test_join_index(float_frame): + # left / right + + f = float_frame.loc[float_frame.index[:10], ["A", "B"]] + f2 = float_frame.loc[float_frame.index[5:], ["C", "D"]].iloc[::-1] + + joined = f.join(f2) + tm.assert_index_equal(f.index, joined.index) + expected_columns = Index(["A", "B", "C", "D"]) + tm.assert_index_equal(joined.columns, expected_columns) + + joined = f.join(f2, how="left") + tm.assert_index_equal(joined.index, f.index) + tm.assert_index_equal(joined.columns, expected_columns) + + joined = f.join(f2, how="right") + tm.assert_index_equal(joined.index, f2.index) + tm.assert_index_equal(joined.columns, expected_columns) + + # inner + + joined = f.join(f2, how="inner") + tm.assert_index_equal(joined.index, f.index[5:10]) + tm.assert_index_equal(joined.columns, expected_columns) + + # outer + + joined = f.join(f2, how="outer") + tm.assert_index_equal(joined.index, float_frame.index.sort_values()) + tm.assert_index_equal(joined.columns, expected_columns) + + with pytest.raises(ValueError, match="join method"): + f.join(f2, how="foo") + + # corner case - overlapping columns + msg = "columns overlap but no suffix" + for how in ("outer", "left", "inner"): + with pytest.raises(ValueError, match=msg): + float_frame.join(float_frame, how=how) + + +def test_join_index_more(float_frame): + af = float_frame.loc[:, ["A", "B"]] + bf = float_frame.loc[::2, ["C", "D"]] + + expected = af.copy() + expected["C"] = float_frame["C"][::2] + expected["D"] = float_frame["D"][::2] + + result = af.join(bf) + tm.assert_frame_equal(result, expected) + + result = af.join(bf, how="right") + tm.assert_frame_equal(result, expected[::2]) + + result = bf.join(af, how="right") + tm.assert_frame_equal(result, expected.loc[:, result.columns]) + + +def test_join_index_series(float_frame): + df = float_frame.copy() + ser = df.pop(float_frame.columns[-1]) + joined = df.join(ser) + + tm.assert_frame_equal(joined, float_frame) + + ser.name = None + with pytest.raises(ValueError, match="must have a name"): + df.join(ser) + + +def test_join_overlap(float_frame): + df1 = float_frame.loc[:, ["A", "B", "C"]] + df2 = float_frame.loc[:, ["B", "C", "D"]] + + joined = df1.join(df2, lsuffix="_df1", rsuffix="_df2") + df1_suf = df1.loc[:, ["B", "C"]].add_suffix("_df1") + df2_suf = df2.loc[:, ["B", "C"]].add_suffix("_df2") + + no_overlap = float_frame.loc[:, ["A", "D"]] + expected = df1_suf.join(df2_suf).join(no_overlap) + + # column order not necessarily sorted + tm.assert_frame_equal(joined, expected.loc[:, joined.columns]) + + +def test_join_period_index(frame_with_period_index): + other = frame_with_period_index.rename(columns=lambda key: f"{key}{key}") + + joined_values = np.concatenate([frame_with_period_index.values] * 2, axis=1) + + joined_cols = frame_with_period_index.columns.append(other.columns) + + joined = frame_with_period_index.join(other) + expected = DataFrame( + data=joined_values, columns=joined_cols, index=frame_with_period_index.index + ) + + tm.assert_frame_equal(joined, expected) + + +def test_join_left_sequence_non_unique_index(): + # https://github.com/pandas-dev/pandas/issues/19607 + df1 = DataFrame({"a": [0, 10, 20]}, index=[1, 2, 3]) + df2 = DataFrame({"b": [100, 200, 300]}, index=[4, 3, 2]) + df3 = DataFrame({"c": [400, 500, 600]}, index=[2, 2, 4]) + + joined = df1.join([df2, df3], how="left") + + expected = DataFrame( + { + "a": [0, 10, 10, 20], + "b": [np.nan, 300, 300, 200], + "c": [np.nan, 400, 500, np.nan], + }, + index=[1, 2, 2, 3], + ) + + tm.assert_frame_equal(joined, expected) + + +def test_join_list_series(float_frame): + # GH#46850 + # Join a DataFrame with a list containing both a Series and a DataFrame + left = float_frame.A.to_frame() + right = [float_frame.B, float_frame[["C", "D"]]] + result = left.join(right) + tm.assert_frame_equal(result, float_frame) + + +@pytest.mark.parametrize("sort_kw", [True, False]) +def test_suppress_future_warning_with_sort_kw(sort_kw): + a = DataFrame({"col1": [1, 2]}, index=["c", "a"]) + + b = DataFrame({"col2": [4, 5]}, index=["b", "a"]) + + c = DataFrame({"col3": [7, 8]}, index=["a", "b"]) + + expected = DataFrame( + { + "col1": {"a": 2.0, "b": float("nan"), "c": 1.0}, + "col2": {"a": 5.0, "b": 4.0, "c": float("nan")}, + "col3": {"a": 7.0, "b": 8.0, "c": float("nan")}, + } + ) + if sort_kw is False: + expected = expected.reindex(index=["c", "a", "b"]) + + with tm.assert_produces_warning(None): + result = a.join([b, c], how="outer", sort=sort_kw) + tm.assert_frame_equal(result, expected) + + +class TestDataFrameJoin: + def test_join(self, multiindex_dataframe_random_data): + frame = multiindex_dataframe_random_data + + a = frame.loc[frame.index[:5], ["A"]] + b = frame.loc[frame.index[2:], ["B", "C"]] + + joined = a.join(b, how="outer").reindex(frame.index) + expected = frame.copy().values.copy() + expected[np.isnan(joined.values)] = np.nan + expected = DataFrame(expected, index=frame.index, columns=frame.columns) + + assert not np.isnan(joined.values).all() + + tm.assert_frame_equal(joined, expected) + + def test_join_segfault(self): + # GH#1532 + df1 = DataFrame({"a": [1, 1], "b": [1, 2], "x": [1, 2]}) + df2 = DataFrame({"a": [2, 2], "b": [1, 2], "y": [1, 2]}) + df1 = df1.set_index(["a", "b"]) + df2 = df2.set_index(["a", "b"]) + # it works! + for how in ["left", "right", "outer"]: + df1.join(df2, how=how) + + def test_join_str_datetime(self): + str_dates = ["20120209", "20120222"] + dt_dates = [datetime(2012, 2, 9), datetime(2012, 2, 22)] + + A = DataFrame(str_dates, index=range(2), columns=["aa"]) + C = DataFrame([[1, 2], [3, 4]], index=str_dates, columns=dt_dates) + + tst = A.join(C, on="aa") + + assert len(tst.columns) == 3 + + def test_join_multiindex_leftright(self): + # GH 10741 + df1 = DataFrame( + [ + ["a", "x", 0.471780], + ["a", "y", 0.774908], + ["a", "z", 0.563634], + ["b", "x", -0.353756], + ["b", "y", 0.368062], + ["b", "z", -1.721840], + ["c", "x", 1], + ["c", "y", 2], + ["c", "z", 3], + ], + columns=["first", "second", "value1"], + ).set_index(["first", "second"]) + + df2 = DataFrame([["a", 10], ["b", 20]], columns=["first", "value2"]).set_index( + ["first"] + ) + + exp = DataFrame( + [ + [0.471780, 10], + [0.774908, 10], + [0.563634, 10], + [-0.353756, 20], + [0.368062, 20], + [-1.721840, 20], + [1.000000, np.nan], + [2.000000, np.nan], + [3.000000, np.nan], + ], + index=df1.index, + columns=["value1", "value2"], + ) + + # these must be the same results (but columns are flipped) + tm.assert_frame_equal(df1.join(df2, how="left"), exp) + tm.assert_frame_equal(df2.join(df1, how="right"), exp[["value2", "value1"]]) + + exp_idx = MultiIndex.from_product( + [["a", "b"], ["x", "y", "z"]], names=["first", "second"] + ) + exp = DataFrame( + [ + [0.471780, 10], + [0.774908, 10], + [0.563634, 10], + [-0.353756, 20], + [0.368062, 20], + [-1.721840, 20], + ], + index=exp_idx, + columns=["value1", "value2"], + ) + + tm.assert_frame_equal(df1.join(df2, how="right"), exp) + tm.assert_frame_equal(df2.join(df1, how="left"), exp[["value2", "value1"]]) + + def test_join_multiindex_dates(self): + # GH 33692 + date = pd.Timestamp(2000, 1, 1).date() + + df1_index = MultiIndex.from_tuples([(0, date)], names=["index_0", "date"]) + df1 = DataFrame({"col1": [0]}, index=df1_index) + df2_index = MultiIndex.from_tuples([(0, date)], names=["index_0", "date"]) + df2 = DataFrame({"col2": [0]}, index=df2_index) + df3_index = MultiIndex.from_tuples([(0, date)], names=["index_0", "date"]) + df3 = DataFrame({"col3": [0]}, index=df3_index) + + result = df1.join([df2, df3]) + + expected_index = MultiIndex.from_tuples([(0, date)], names=["index_0", "date"]) + expected = DataFrame( + {"col1": [0], "col2": [0], "col3": [0]}, index=expected_index + ) + + tm.assert_equal(result, expected) + + def test_merge_join_different_levels_raises(self): + # GH#9455 + # GH 40993: For raising, enforced in 2.0 + + # first dataframe + df1 = DataFrame(columns=["a", "b"], data=[[1, 11], [0, 22]]) + + # second dataframe + columns = MultiIndex.from_tuples([("a", ""), ("c", "c1")]) + df2 = DataFrame(columns=columns, data=[[1, 33], [0, 44]]) + + # merge + with pytest.raises( + MergeError, match="Not allowed to merge between different levels" + ): + pd.merge(df1, df2, on="a") + + # join, see discussion in GH#12219 + with pytest.raises( + MergeError, match="Not allowed to merge between different levels" + ): + df1.join(df2, on="a") + + def test_frame_join_tzaware(self): + test1 = DataFrame( + np.zeros((6, 3)), + index=date_range( + "2012-11-15 00:00:00", periods=6, freq="100ms", tz="US/Central" + ), + ) + test2 = DataFrame( + np.zeros((3, 3)), + index=date_range( + "2012-11-15 00:00:00", periods=3, freq="250ms", tz="US/Central" + ), + columns=range(3, 6), + ) + + result = test1.join(test2, how="outer") + expected = test1.index.union(test2.index) + + tm.assert_index_equal(result.index, expected) + assert result.index.tz.zone == "US/Central" diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_map.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_map.py new file mode 100644 index 0000000000000000000000000000000000000000..03681c3df844e058e147a026e45c226469f38f9d --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_map.py @@ -0,0 +1,216 @@ +from datetime import datetime + +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + Series, + Timestamp, + date_range, +) +import pandas._testing as tm + +from pandas.tseries.offsets import BDay + + +def test_map(float_frame): + result = float_frame.map(lambda x: x * 2) + tm.assert_frame_equal(result, float_frame * 2) + float_frame.map(type) + + # GH 465: function returning tuples + result = float_frame.map(lambda x: (x, x))["A"].iloc[0] + assert isinstance(result, tuple) + + +@pytest.mark.parametrize("val", [1, 1.0]) +def test_map_float_object_conversion(val): + # GH 2909: object conversion to float in constructor? + df = DataFrame(data=[val, "a"]) + result = df.map(lambda x: x).dtypes[0] + assert result == object + + +@pytest.mark.parametrize("na_action", [None, "ignore"]) +def test_map_keeps_dtype(na_action): + # GH52219 + arr = Series(["a", np.nan, "b"]) + sparse_arr = arr.astype(pd.SparseDtype(object)) + df = DataFrame(data={"a": arr, "b": sparse_arr}) + + def func(x): + return str.upper(x) if not pd.isna(x) else x + + result = df.map(func, na_action=na_action) + + expected_sparse = pd.array(["A", np.nan, "B"], dtype=pd.SparseDtype(object)) + expected_arr = expected_sparse.astype(object) + expected = DataFrame({"a": expected_arr, "b": expected_sparse}) + + tm.assert_frame_equal(result, expected) + + result_empty = df.iloc[:0, :].map(func, na_action=na_action) + expected_empty = expected.iloc[:0, :] + tm.assert_frame_equal(result_empty, expected_empty) + + +def test_map_str(): + # GH 2786 + df = DataFrame(np.random.default_rng(2).random((3, 4))) + df2 = df.copy() + cols = ["a", "a", "a", "a"] + df.columns = cols + + expected = df2.map(str) + expected.columns = cols + result = df.map(str) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "col, val", + [["datetime", Timestamp("20130101")], ["timedelta", pd.Timedelta("1 min")]], +) +def test_map_datetimelike(col, val): + # datetime/timedelta + df = DataFrame(np.random.default_rng(2).random((3, 4))) + df[col] = val + result = df.map(str) + assert result.loc[0, col] == str(df.loc[0, col]) + + +@pytest.mark.parametrize( + "expected", + [ + DataFrame(), + DataFrame(columns=list("ABC")), + DataFrame(index=list("ABC")), + DataFrame({"A": [], "B": [], "C": []}), + ], +) +@pytest.mark.parametrize("func", [round, lambda x: x]) +def test_map_empty(expected, func): + # GH 8222 + result = expected.map(func) + tm.assert_frame_equal(result, expected) + + +def test_map_kwargs(): + # GH 40652 + result = DataFrame([[1, 2], [3, 4]]).map(lambda x, y: x + y, y=2) + expected = DataFrame([[3, 4], [5, 6]]) + tm.assert_frame_equal(result, expected) + + +def test_map_na_ignore(float_frame): + # GH 23803 + strlen_frame = float_frame.map(lambda x: len(str(x))) + float_frame_with_na = float_frame.copy() + mask = np.random.default_rng(2).integers(0, 2, size=float_frame.shape, dtype=bool) + float_frame_with_na[mask] = pd.NA + strlen_frame_na_ignore = float_frame_with_na.map( + lambda x: len(str(x)), na_action="ignore" + ) + # Set float64 type to avoid upcast when setting NA below + strlen_frame_with_na = strlen_frame.copy().astype("float64") + strlen_frame_with_na[mask] = pd.NA + tm.assert_frame_equal(strlen_frame_na_ignore, strlen_frame_with_na) + + +def test_map_box_timestamps(): + # GH 2689, GH 2627 + ser = Series(date_range("1/1/2000", periods=10)) + + def func(x): + return (x.hour, x.day, x.month) + + # it works! + DataFrame(ser).map(func) + + +def test_map_box(): + # ufunc will not be boxed. Same test cases as the test_map_box + df = DataFrame( + { + "a": [Timestamp("2011-01-01"), Timestamp("2011-01-02")], + "b": [ + Timestamp("2011-01-01", tz="US/Eastern"), + Timestamp("2011-01-02", tz="US/Eastern"), + ], + "c": [pd.Timedelta("1 days"), pd.Timedelta("2 days")], + "d": [ + pd.Period("2011-01-01", freq="M"), + pd.Period("2011-01-02", freq="M"), + ], + } + ) + + result = df.map(lambda x: type(x).__name__) + expected = DataFrame( + { + "a": ["Timestamp", "Timestamp"], + "b": ["Timestamp", "Timestamp"], + "c": ["Timedelta", "Timedelta"], + "d": ["Period", "Period"], + } + ) + tm.assert_frame_equal(result, expected) + + +def test_frame_map_dont_convert_datetime64(): + df = DataFrame({"x1": [datetime(1996, 1, 1)]}) + + df = df.map(lambda x: x + BDay()) + df = df.map(lambda x: x + BDay()) + + result = df.x1.dtype + assert result == "M8[ns]" + + +def test_map_function_runs_once(): + df = DataFrame({"a": [1, 2, 3]}) + values = [] # Save values function is applied to + + def reducing_function(val): + values.append(val) + + def non_reducing_function(val): + values.append(val) + return val + + for func in [reducing_function, non_reducing_function]: + del values[:] + + df.map(func) + assert values == df.a.to_list() + + +def test_map_type(): + # GH 46719 + df = DataFrame( + {"col1": [3, "string", float], "col2": [0.25, datetime(2020, 1, 1), np.nan]}, + index=["a", "b", "c"], + ) + + result = df.map(type) + expected = DataFrame( + {"col1": [int, str, type], "col2": [float, datetime, float]}, + index=["a", "b", "c"], + ) + tm.assert_frame_equal(result, expected) + + +def test_map_invalid_na_action(float_frame): + # GH 23803 + with pytest.raises(ValueError, match="na_action must be .*Got 'abc'"): + float_frame.map(lambda x: len(str(x)), na_action="abc") + + +def test_applymap_deprecated(): + # GH52353 + df = DataFrame({"a": [1, 2, 3]}) + msg = "DataFrame.applymap has been deprecated. Use DataFrame.map instead." + with tm.assert_produces_warning(FutureWarning, match=msg): + df.applymap(lambda x: x) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_matmul.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_matmul.py new file mode 100644 index 0000000000000000000000000000000000000000..be9462b64fa1b919b13772e9d07727258931b952 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_matmul.py @@ -0,0 +1,98 @@ +import operator + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Index, + Series, +) +import pandas._testing as tm + + +class TestMatMul: + def test_matmul(self): + # matmul test is for GH#10259 + a = DataFrame( + np.random.default_rng(2).standard_normal((3, 4)), + index=["a", "b", "c"], + columns=["p", "q", "r", "s"], + ) + b = DataFrame( + np.random.default_rng(2).standard_normal((4, 2)), + index=["p", "q", "r", "s"], + columns=["one", "two"], + ) + + # DataFrame @ DataFrame + result = operator.matmul(a, b) + expected = DataFrame( + np.dot(a.values, b.values), index=["a", "b", "c"], columns=["one", "two"] + ) + tm.assert_frame_equal(result, expected) + + # DataFrame @ Series + result = operator.matmul(a, b.one) + expected = Series(np.dot(a.values, b.one.values), index=["a", "b", "c"]) + tm.assert_series_equal(result, expected) + + # np.array @ DataFrame + result = operator.matmul(a.values, b) + assert isinstance(result, DataFrame) + assert result.columns.equals(b.columns) + assert result.index.equals(Index(range(3))) + expected = np.dot(a.values, b.values) + tm.assert_almost_equal(result.values, expected) + + # nested list @ DataFrame (__rmatmul__) + result = operator.matmul(a.values.tolist(), b) + expected = DataFrame( + np.dot(a.values, b.values), index=["a", "b", "c"], columns=["one", "two"] + ) + tm.assert_almost_equal(result.values, expected.values) + + # mixed dtype DataFrame @ DataFrame + a["q"] = a.q.round().astype(int) + result = operator.matmul(a, b) + expected = DataFrame( + np.dot(a.values, b.values), index=["a", "b", "c"], columns=["one", "two"] + ) + tm.assert_frame_equal(result, expected) + + # different dtypes DataFrame @ DataFrame + a = a.astype(int) + result = operator.matmul(a, b) + expected = DataFrame( + np.dot(a.values, b.values), index=["a", "b", "c"], columns=["one", "two"] + ) + tm.assert_frame_equal(result, expected) + + # unaligned + df = DataFrame( + np.random.default_rng(2).standard_normal((3, 4)), + index=[1, 2, 3], + columns=range(4), + ) + df2 = DataFrame( + np.random.default_rng(2).standard_normal((5, 3)), + index=range(5), + columns=[1, 2, 3], + ) + + with pytest.raises(ValueError, match="aligned"): + operator.matmul(df, df2) + + def test_matmul_message_shapes(self): + # GH#21581 exception message should reflect original shapes, + # not transposed shapes + a = np.random.default_rng(2).random((10, 4)) + b = np.random.default_rng(2).random((5, 3)) + + df = DataFrame(b) + + msg = r"shapes \(10, 4\) and \(5, 3\) not aligned" + with pytest.raises(ValueError, match=msg): + a @ df + with pytest.raises(ValueError, match=msg): + a.tolist() @ df diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_nlargest.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_nlargest.py new file mode 100644 index 0000000000000000000000000000000000000000..54f2e45488b7886eebf6635a812c5eb2a21b0400 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_nlargest.py @@ -0,0 +1,250 @@ +""" +Note: for naming purposes, most tests are title with as e.g. "test_nlargest_foo" +but are implicitly also testing nsmallest_foo. +""" +from string import ascii_lowercase + +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm +from pandas.util.version import Version + + +@pytest.fixture +def df_duplicates(): + return pd.DataFrame( + {"a": [1, 2, 3, 4, 4], "b": [1, 1, 1, 1, 1], "c": [0, 1, 2, 5, 4]}, + index=[0, 0, 1, 1, 1], + ) + + +@pytest.fixture +def df_strings(): + return pd.DataFrame( + { + "a": np.random.default_rng(2).permutation(10), + "b": list(ascii_lowercase[:10]), + "c": np.random.default_rng(2).permutation(10).astype("float64"), + } + ) + + +@pytest.fixture +def df_main_dtypes(): + return pd.DataFrame( + { + "group": [1, 1, 2], + "int": [1, 2, 3], + "float": [4.0, 5.0, 6.0], + "string": list("abc"), + "category_string": pd.Series(list("abc")).astype("category"), + "category_int": [7, 8, 9], + "datetime": pd.date_range("20130101", periods=3), + "datetimetz": pd.date_range("20130101", periods=3, tz="US/Eastern"), + "timedelta": pd.timedelta_range("1 s", periods=3, freq="s"), + }, + columns=[ + "group", + "int", + "float", + "string", + "category_string", + "category_int", + "datetime", + "datetimetz", + "timedelta", + ], + ) + + +class TestNLargestNSmallest: + # ---------------------------------------------------------------------- + # Top / bottom + @pytest.mark.parametrize( + "order", + [ + ["a"], + ["c"], + ["a", "b"], + ["a", "c"], + ["b", "a"], + ["b", "c"], + ["a", "b", "c"], + ["c", "a", "b"], + ["c", "b", "a"], + ["b", "c", "a"], + ["b", "a", "c"], + # dups! + ["b", "c", "c"], + ], + ) + @pytest.mark.parametrize("n", range(1, 11)) + def test_nlargest_n(self, df_strings, nselect_method, n, order): + # GH#10393 + df = df_strings + if "b" in order: + error_msg = ( + f"Column 'b' has dtype (object|str), " + f"cannot use method '{nselect_method}' with this dtype" + ) + with pytest.raises(TypeError, match=error_msg): + getattr(df, nselect_method)(n, order) + else: + ascending = nselect_method == "nsmallest" + result = getattr(df, nselect_method)(n, order) + expected = df.sort_values(order, ascending=ascending).head(n) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "columns", [["group", "category_string"], ["group", "string"]] + ) + def test_nlargest_error(self, df_main_dtypes, nselect_method, columns): + df = df_main_dtypes + col = columns[1] + error_msg = ( + f"Column '{col}' has dtype {df[col].dtype}, " + f"cannot use method '{nselect_method}' with this dtype" + ) + # escape some characters that may be in the repr + error_msg = ( + error_msg.replace("(", "\\(") + .replace(")", "\\)") + .replace("[", "\\[") + .replace("]", "\\]") + ) + with pytest.raises(TypeError, match=error_msg): + getattr(df, nselect_method)(2, columns) + + def test_nlargest_all_dtypes(self, df_main_dtypes): + df = df_main_dtypes + df.nsmallest(2, list(set(df) - {"category_string", "string"})) + df.nlargest(2, list(set(df) - {"category_string", "string"})) + + def test_nlargest_duplicates_on_starter_columns(self): + # regression test for GH#22752 + + df = pd.DataFrame({"a": [2, 2, 2, 1, 1, 1], "b": [1, 2, 3, 3, 2, 1]}) + + result = df.nlargest(4, columns=["a", "b"]) + expected = pd.DataFrame( + {"a": [2, 2, 2, 1], "b": [3, 2, 1, 3]}, index=[2, 1, 0, 3] + ) + tm.assert_frame_equal(result, expected) + + result = df.nsmallest(4, columns=["a", "b"]) + expected = pd.DataFrame( + {"a": [1, 1, 1, 2], "b": [1, 2, 3, 1]}, index=[5, 4, 3, 0] + ) + tm.assert_frame_equal(result, expected) + + def test_nlargest_n_identical_values(self): + # GH#15297 + df = pd.DataFrame({"a": [1] * 5, "b": [1, 2, 3, 4, 5]}) + + result = df.nlargest(3, "a") + expected = pd.DataFrame({"a": [1] * 3, "b": [1, 2, 3]}, index=[0, 1, 2]) + tm.assert_frame_equal(result, expected) + + result = df.nsmallest(3, "a") + expected = pd.DataFrame({"a": [1] * 3, "b": [1, 2, 3]}) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "order", + [["a", "b", "c"], ["c", "b", "a"], ["a"], ["b"], ["a", "b"], ["c", "b"]], + ) + @pytest.mark.parametrize("n", range(1, 6)) + def test_nlargest_n_duplicate_index(self, df_duplicates, n, order, request): + # GH#13412 + + df = df_duplicates + result = df.nsmallest(n, order) + expected = df.sort_values(order).head(n) + tm.assert_frame_equal(result, expected) + + result = df.nlargest(n, order) + expected = df.sort_values(order, ascending=False).head(n) + if Version(np.__version__) >= Version("1.25") and ( + (order == ["a"] and n in (1, 2, 3, 4)) or (order == ["a", "b"]) and n == 5 + ): + request.applymarker( + pytest.mark.xfail( + reason=( + "pandas default unstable sorting of duplicates" + "issue with numpy>=1.25 with AVX instructions" + ), + strict=False, + ) + ) + tm.assert_frame_equal(result, expected) + + def test_nlargest_duplicate_keep_all_ties(self): + # GH#16818 + df = pd.DataFrame( + {"a": [5, 4, 4, 2, 3, 3, 3, 3], "b": [10, 9, 8, 7, 5, 50, 10, 20]} + ) + result = df.nlargest(4, "a", keep="all") + expected = pd.DataFrame( + { + "a": {0: 5, 1: 4, 2: 4, 4: 3, 5: 3, 6: 3, 7: 3}, + "b": {0: 10, 1: 9, 2: 8, 4: 5, 5: 50, 6: 10, 7: 20}, + } + ) + tm.assert_frame_equal(result, expected) + + result = df.nsmallest(2, "a", keep="all") + expected = pd.DataFrame( + { + "a": {3: 2, 4: 3, 5: 3, 6: 3, 7: 3}, + "b": {3: 7, 4: 5, 5: 50, 6: 10, 7: 20}, + } + ) + tm.assert_frame_equal(result, expected) + + def test_nlargest_multiindex_column_lookup(self): + # Check whether tuples are correctly treated as multi-level lookups. + # GH#23033 + df = pd.DataFrame( + columns=pd.MultiIndex.from_product([["x"], ["a", "b"]]), + data=[[0.33, 0.13], [0.86, 0.25], [0.25, 0.70], [0.85, 0.91]], + ) + + # nsmallest + result = df.nsmallest(3, ("x", "a")) + expected = df.iloc[[2, 0, 3]] + tm.assert_frame_equal(result, expected) + + # nlargest + result = df.nlargest(3, ("x", "b")) + expected = df.iloc[[3, 2, 1]] + tm.assert_frame_equal(result, expected) + + def test_nlargest_nan(self): + # GH#43060 + df = pd.DataFrame([np.nan, np.nan, 0, 1, 2, 3]) + result = df.nlargest(5, 0) + expected = df.sort_values(0, ascending=False).head(5) + tm.assert_frame_equal(result, expected) + + def test_nsmallest_nan_after_n_element(self): + # GH#46589 + df = pd.DataFrame( + { + "a": [1, 2, 3, 4, 5, None, 7], + "b": [7, 6, 5, 4, 3, 2, 1], + "c": [1, 1, 2, 2, 3, 3, 3], + }, + index=range(7), + ) + result = df.nsmallest(5, columns=["a", "b"]) + expected = pd.DataFrame( + { + "a": [1, 2, 3, 4, 5], + "b": [7, 6, 5, 4, 3], + "c": [1, 1, 2, 2, 3], + }, + index=range(5), + ).astype({"a": "float"}) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_pct_change.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_pct_change.py new file mode 100644 index 0000000000000000000000000000000000000000..92b66e12d4356ca33d6351b092e3655541c9e8bb --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_pct_change.py @@ -0,0 +1,180 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Series, +) +import pandas._testing as tm + + +class TestDataFramePctChange: + @pytest.mark.parametrize( + "periods, fill_method, limit, exp", + [ + (1, "ffill", None, [np.nan, np.nan, np.nan, 1, 1, 1.5, 0, 0]), + (1, "ffill", 1, [np.nan, np.nan, np.nan, 1, 1, 1.5, 0, np.nan]), + (1, "bfill", None, [np.nan, 0, 0, 1, 1, 1.5, np.nan, np.nan]), + (1, "bfill", 1, [np.nan, np.nan, 0, 1, 1, 1.5, np.nan, np.nan]), + (-1, "ffill", None, [np.nan, np.nan, -0.5, -0.5, -0.6, 0, 0, np.nan]), + (-1, "ffill", 1, [np.nan, np.nan, -0.5, -0.5, -0.6, 0, np.nan, np.nan]), + (-1, "bfill", None, [0, 0, -0.5, -0.5, -0.6, np.nan, np.nan, np.nan]), + (-1, "bfill", 1, [np.nan, 0, -0.5, -0.5, -0.6, np.nan, np.nan, np.nan]), + ], + ) + def test_pct_change_with_nas( + self, periods, fill_method, limit, exp, frame_or_series + ): + vals = [np.nan, np.nan, 1, 2, 4, 10, np.nan, np.nan] + obj = frame_or_series(vals) + + msg = ( + "The 'fill_method' keyword being not None and the 'limit' keyword in " + f"{type(obj).__name__}.pct_change are deprecated" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + res = obj.pct_change(periods=periods, fill_method=fill_method, limit=limit) + tm.assert_equal(res, frame_or_series(exp)) + + def test_pct_change_numeric(self): + # GH#11150 + pnl = DataFrame( + [np.arange(0, 40, 10), np.arange(0, 40, 10), np.arange(0, 40, 10)] + ).astype(np.float64) + pnl.iat[1, 0] = np.nan + pnl.iat[1, 1] = np.nan + pnl.iat[2, 3] = 60 + + msg = ( + "The 'fill_method' keyword being not None and the 'limit' keyword in " + "DataFrame.pct_change are deprecated" + ) + + for axis in range(2): + expected = pnl.ffill(axis=axis) / pnl.ffill(axis=axis).shift(axis=axis) - 1 + + with tm.assert_produces_warning(FutureWarning, match=msg): + result = pnl.pct_change(axis=axis, fill_method="pad") + tm.assert_frame_equal(result, expected) + + def test_pct_change(self, datetime_frame): + msg = ( + "The 'fill_method' keyword being not None and the 'limit' keyword in " + "DataFrame.pct_change are deprecated" + ) + + rs = datetime_frame.pct_change(fill_method=None) + tm.assert_frame_equal(rs, datetime_frame / datetime_frame.shift(1) - 1) + + rs = datetime_frame.pct_change(2) + filled = datetime_frame.ffill() + tm.assert_frame_equal(rs, filled / filled.shift(2) - 1) + + with tm.assert_produces_warning(FutureWarning, match=msg): + rs = datetime_frame.pct_change(fill_method="bfill", limit=1) + filled = datetime_frame.bfill(limit=1) + tm.assert_frame_equal(rs, filled / filled.shift(1) - 1) + + rs = datetime_frame.pct_change(freq="5D") + filled = datetime_frame.ffill() + tm.assert_frame_equal( + rs, (filled / filled.shift(freq="5D") - 1).reindex_like(filled) + ) + + def test_pct_change_shift_over_nas(self): + s = Series([1.0, 1.5, np.nan, 2.5, 3.0]) + + df = DataFrame({"a": s, "b": s}) + + msg = "The default fill_method='pad' in DataFrame.pct_change is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + chg = df.pct_change() + + expected = Series([np.nan, 0.5, 0.0, 2.5 / 1.5 - 1, 0.2]) + edf = DataFrame({"a": expected, "b": expected}) + tm.assert_frame_equal(chg, edf) + + @pytest.mark.parametrize( + "freq, periods, fill_method, limit", + [ + ("5B", 5, None, None), + ("3B", 3, None, None), + ("3B", 3, "bfill", None), + ("7B", 7, "pad", 1), + ("7B", 7, "bfill", 3), + ("14B", 14, None, None), + ], + ) + def test_pct_change_periods_freq( + self, datetime_frame, freq, periods, fill_method, limit + ): + msg = ( + "The 'fill_method' keyword being not None and the 'limit' keyword in " + "DataFrame.pct_change are deprecated" + ) + + # GH#7292 + with tm.assert_produces_warning(FutureWarning, match=msg): + rs_freq = datetime_frame.pct_change( + freq=freq, fill_method=fill_method, limit=limit + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + rs_periods = datetime_frame.pct_change( + periods, fill_method=fill_method, limit=limit + ) + tm.assert_frame_equal(rs_freq, rs_periods) + + empty_ts = DataFrame(index=datetime_frame.index, columns=datetime_frame.columns) + with tm.assert_produces_warning(FutureWarning, match=msg): + rs_freq = empty_ts.pct_change( + freq=freq, fill_method=fill_method, limit=limit + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + rs_periods = empty_ts.pct_change( + periods, fill_method=fill_method, limit=limit + ) + tm.assert_frame_equal(rs_freq, rs_periods) + + +@pytest.mark.parametrize("fill_method", ["pad", "ffill", None]) +def test_pct_change_with_duplicated_indices(fill_method): + # GH30463 + data = DataFrame( + {0: [np.nan, 1, 2, 3, 9, 18], 1: [0, 1, np.nan, 3, 9, 18]}, index=["a", "b"] * 3 + ) + + warn = None if fill_method is None else FutureWarning + msg = ( + "The 'fill_method' keyword being not None and the 'limit' keyword in " + "DataFrame.pct_change are deprecated" + ) + with tm.assert_produces_warning(warn, match=msg): + result = data.pct_change(fill_method=fill_method) + + if fill_method is None: + second_column = [np.nan, np.inf, np.nan, np.nan, 2.0, 1.0] + else: + second_column = [np.nan, np.inf, 0.0, 2.0, 2.0, 1.0] + expected = DataFrame( + {0: [np.nan, np.nan, 1.0, 0.5, 2.0, 1.0], 1: second_column}, + index=["a", "b"] * 3, + ) + tm.assert_frame_equal(result, expected) + + +def test_pct_change_none_beginning_no_warning(): + # GH#54481 + df = DataFrame( + [ + [1, None], + [2, 1], + [3, 2], + [4, 3], + [5, 4], + ] + ) + result = df.pct_change() + expected = DataFrame( + {0: [np.nan, 1, 0.5, 1 / 3, 0.25], 1: [np.nan, np.nan, 1, 0.5, 1 / 3]} + ) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_pipe.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_pipe.py new file mode 100644 index 0000000000000000000000000000000000000000..5bcc4360487f38491e2ae9f4c79d837e72ed0f6d --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_pipe.py @@ -0,0 +1,39 @@ +import pytest + +from pandas import ( + DataFrame, + Series, +) +import pandas._testing as tm + + +class TestPipe: + def test_pipe(self, frame_or_series): + obj = DataFrame({"A": [1, 2, 3]}) + expected = DataFrame({"A": [1, 4, 9]}) + if frame_or_series is Series: + obj = obj["A"] + expected = expected["A"] + + f = lambda x, y: x**y + result = obj.pipe(f, 2) + tm.assert_equal(result, expected) + + def test_pipe_tuple(self, frame_or_series): + obj = DataFrame({"A": [1, 2, 3]}) + obj = tm.get_obj(obj, frame_or_series) + + f = lambda x, y: y + result = obj.pipe((f, "y"), 0) + tm.assert_equal(result, obj) + + def test_pipe_tuple_error(self, frame_or_series): + obj = DataFrame({"A": [1, 2, 3]}) + obj = tm.get_obj(obj, frame_or_series) + + f = lambda x, y: y + + msg = "y is both the pipe target and a keyword argument" + + with pytest.raises(ValueError, match=msg): + obj.pipe((f, "y"), x=1, y=0) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_pop.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_pop.py new file mode 100644 index 0000000000000000000000000000000000000000..3eb058015cd3da081e3c34954c0bd3229337de31 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_pop.py @@ -0,0 +1,72 @@ +import numpy as np + +from pandas import ( + DataFrame, + MultiIndex, + Series, +) +import pandas._testing as tm + + +class TestDataFramePop: + def test_pop(self, float_frame, warn_copy_on_write): + float_frame.columns.name = "baz" + + float_frame.pop("A") + assert "A" not in float_frame + + float_frame["foo"] = "bar" + float_frame.pop("foo") + assert "foo" not in float_frame + assert float_frame.columns.name == "baz" + + # gh-10912: inplace ops cause caching issue + a = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["A", "B", "C"], index=["X", "Y"]) + b = a.pop("B") + with tm.assert_cow_warning(warn_copy_on_write): + b += 1 + + # original frame + expected = DataFrame([[1, 3], [4, 6]], columns=["A", "C"], index=["X", "Y"]) + tm.assert_frame_equal(a, expected) + + # result + expected = Series([2, 5], index=["X", "Y"], name="B") + 1 + tm.assert_series_equal(b, expected) + + def test_pop_non_unique_cols(self): + df = DataFrame({0: [0, 1], 1: [0, 1], 2: [4, 5]}) + df.columns = ["a", "b", "a"] + + res = df.pop("a") + assert type(res) == DataFrame + assert len(res) == 2 + assert len(df.columns) == 1 + assert "b" in df.columns + assert "a" not in df.columns + assert len(df.index) == 2 + + def test_mixed_depth_pop(self): + arrays = [ + ["a", "top", "top", "routine1", "routine1", "routine2"], + ["", "OD", "OD", "result1", "result2", "result1"], + ["", "wx", "wy", "", "", ""], + ] + + tuples = sorted(zip(*arrays)) + index = MultiIndex.from_tuples(tuples) + df = DataFrame(np.random.default_rng(2).standard_normal((4, 6)), columns=index) + + df1 = df.copy() + df2 = df.copy() + result = df1.pop("a") + expected = df2.pop(("a", "", "")) + tm.assert_series_equal(expected, result, check_names=False) + tm.assert_frame_equal(df1, df2) + assert result.name == "a" + + expected = df1["top"] + df1 = df1.drop(["top"], axis=1) + result = df2.pop("top") + tm.assert_frame_equal(expected, result) + tm.assert_frame_equal(df1, df2) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_quantile.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_quantile.py new file mode 100644 index 0000000000000000000000000000000000000000..15af2a14a042e82dabd4a11dd9002859befba87d --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_quantile.py @@ -0,0 +1,977 @@ +import numpy as np +import pytest + +from pandas._config import using_string_dtype + +import pandas as pd +from pandas import ( + DataFrame, + Index, + Series, + Timestamp, +) +import pandas._testing as tm + + +@pytest.fixture( + params=[["linear", "single"], ["nearest", "table"]], ids=lambda x: "-".join(x) +) +def interp_method(request): + """(interpolation, method) arguments for quantile""" + return request.param + + +class TestDataFrameQuantile: + @pytest.mark.parametrize( + "df,expected", + [ + [ + DataFrame( + { + 0: Series(pd.arrays.SparseArray([1, 2])), + 1: Series(pd.arrays.SparseArray([3, 4])), + } + ), + Series([1.5, 3.5], name=0.5), + ], + [ + DataFrame(Series([0.0, None, 1.0, 2.0], dtype="Sparse[float]")), + Series([1.0], name=0.5), + ], + ], + ) + def test_quantile_sparse(self, df, expected): + # GH#17198 + # GH#24600 + result = df.quantile() + expected = expected.astype("Sparse[float]") + tm.assert_series_equal(result, expected) + + def test_quantile( + self, datetime_frame, interp_method, using_array_manager, request + ): + interpolation, method = interp_method + df = datetime_frame + result = df.quantile( + 0.1, axis=0, numeric_only=True, interpolation=interpolation, method=method + ) + expected = Series( + [np.percentile(df[col], 10) for col in df.columns], + index=df.columns, + name=0.1, + ) + if interpolation == "linear": + # np.percentile values only comparable to linear interpolation + tm.assert_series_equal(result, expected) + else: + tm.assert_index_equal(result.index, expected.index) + request.applymarker( + pytest.mark.xfail( + using_array_manager, reason="Name set incorrectly for arraymanager" + ) + ) + assert result.name == expected.name + + result = df.quantile( + 0.9, axis=1, numeric_only=True, interpolation=interpolation, method=method + ) + expected = Series( + [np.percentile(df.loc[date], 90) for date in df.index], + index=df.index, + name=0.9, + ) + if interpolation == "linear": + # np.percentile values only comparable to linear interpolation + tm.assert_series_equal(result, expected) + else: + tm.assert_index_equal(result.index, expected.index) + request.applymarker( + pytest.mark.xfail( + using_array_manager, reason="Name set incorrectly for arraymanager" + ) + ) + assert result.name == expected.name + + def test_empty(self, interp_method): + interpolation, method = interp_method + q = DataFrame({"x": [], "y": []}).quantile( + 0.1, axis=0, numeric_only=True, interpolation=interpolation, method=method + ) + assert np.isnan(q["x"]) and np.isnan(q["y"]) + + def test_non_numeric_exclusion(self, interp_method, request, using_array_manager): + interpolation, method = interp_method + df = DataFrame({"col1": ["A", "A", "B", "B"], "col2": [1, 2, 3, 4]}) + rs = df.quantile( + 0.5, numeric_only=True, interpolation=interpolation, method=method + ) + xp = df.median(numeric_only=True).rename(0.5) + if interpolation == "nearest": + xp = (xp + 0.5).astype(np.int64) + if method == "table" and using_array_manager: + request.applymarker(pytest.mark.xfail(reason="Axis name incorrectly set.")) + tm.assert_series_equal(rs, xp) + + def test_axis(self, interp_method, request, using_array_manager): + # axis + interpolation, method = interp_method + df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3]) + result = df.quantile(0.5, axis=1, interpolation=interpolation, method=method) + expected = Series([1.5, 2.5, 3.5], index=[1, 2, 3], name=0.5) + if interpolation == "nearest": + expected = expected.astype(np.int64) + if method == "table" and using_array_manager: + request.applymarker(pytest.mark.xfail(reason="Axis name incorrectly set.")) + tm.assert_series_equal(result, expected) + + result = df.quantile( + [0.5, 0.75], axis=1, interpolation=interpolation, method=method + ) + expected = DataFrame( + {1: [1.5, 1.75], 2: [2.5, 2.75], 3: [3.5, 3.75]}, index=[0.5, 0.75] + ) + if interpolation == "nearest": + expected.iloc[0, :] -= 0.5 + expected.iloc[1, :] += 0.25 + expected = expected.astype(np.int64) + tm.assert_frame_equal(result, expected, check_index_type=True) + + def test_axis_numeric_only_true(self, interp_method, request, using_array_manager): + # We may want to break API in the future to change this + # so that we exclude non-numeric along the same axis + # See GH #7312 + interpolation, method = interp_method + df = DataFrame([[1, 2, 3], ["a", "b", 4]]) + result = df.quantile( + 0.5, axis=1, numeric_only=True, interpolation=interpolation, method=method + ) + expected = Series([3.0, 4.0], index=[0, 1], name=0.5) + if interpolation == "nearest": + expected = expected.astype(np.int64) + if method == "table" and using_array_manager: + request.applymarker(pytest.mark.xfail(reason="Axis name incorrectly set.")) + tm.assert_series_equal(result, expected) + + def test_quantile_date_range(self, interp_method, request, using_array_manager): + # GH 2460 + interpolation, method = interp_method + dti = pd.date_range("2016-01-01", periods=3, tz="US/Pacific") + ser = Series(dti) + df = DataFrame(ser) + + result = df.quantile( + numeric_only=False, interpolation=interpolation, method=method + ) + expected = Series( + ["2016-01-02 00:00:00"], name=0.5, dtype="datetime64[ns, US/Pacific]" + ) + if method == "table" and using_array_manager: + request.applymarker(pytest.mark.xfail(reason="Axis name incorrectly set.")) + + tm.assert_series_equal(result, expected) + + def test_quantile_axis_mixed(self, interp_method, request, using_array_manager): + # mixed on axis=1 + interpolation, method = interp_method + df = DataFrame( + { + "A": [1, 2, 3], + "B": [2.0, 3.0, 4.0], + "C": pd.date_range("20130101", periods=3), + "D": ["foo", "bar", "baz"], + } + ) + result = df.quantile( + 0.5, axis=1, numeric_only=True, interpolation=interpolation, method=method + ) + expected = Series([1.5, 2.5, 3.5], name=0.5) + if interpolation == "nearest": + expected -= 0.5 + if method == "table" and using_array_manager: + request.applymarker(pytest.mark.xfail(reason="Axis name incorrectly set.")) + tm.assert_series_equal(result, expected) + + # must raise + msg = "'<' not supported between instances of 'Timestamp' and 'float'" + with pytest.raises(TypeError, match=msg): + df.quantile(0.5, axis=1, numeric_only=False) + + def test_quantile_axis_parameter(self, interp_method, request, using_array_manager): + # GH 9543/9544 + interpolation, method = interp_method + if method == "table" and using_array_manager: + request.applymarker(pytest.mark.xfail(reason="Axis name incorrectly set.")) + df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3]) + + result = df.quantile(0.5, axis=0, interpolation=interpolation, method=method) + + expected = Series([2.0, 3.0], index=["A", "B"], name=0.5) + if interpolation == "nearest": + expected = expected.astype(np.int64) + tm.assert_series_equal(result, expected) + + expected = df.quantile( + 0.5, axis="index", interpolation=interpolation, method=method + ) + if interpolation == "nearest": + expected = expected.astype(np.int64) + tm.assert_series_equal(result, expected) + + result = df.quantile(0.5, axis=1, interpolation=interpolation, method=method) + + expected = Series([1.5, 2.5, 3.5], index=[1, 2, 3], name=0.5) + if interpolation == "nearest": + expected = expected.astype(np.int64) + tm.assert_series_equal(result, expected) + + result = df.quantile( + 0.5, axis="columns", interpolation=interpolation, method=method + ) + tm.assert_series_equal(result, expected) + + msg = "No axis named -1 for object type DataFrame" + with pytest.raises(ValueError, match=msg): + df.quantile(0.1, axis=-1, interpolation=interpolation, method=method) + msg = "No axis named column for object type DataFrame" + with pytest.raises(ValueError, match=msg): + df.quantile(0.1, axis="column") + + def test_quantile_interpolation(self): + # see gh-10174 + + # interpolation method other than default linear + df = DataFrame({"A": [1, 2, 3], "B": [2, 3, 4]}, index=[1, 2, 3]) + result = df.quantile(0.5, axis=1, interpolation="nearest") + expected = Series([1, 2, 3], index=[1, 2, 3], name=0.5) + tm.assert_series_equal(result, expected) + + # cross-check interpolation=nearest results in original dtype + exp = np.percentile( + np.array([[1, 2, 3], [2, 3, 4]]), + 0.5, + axis=0, + method="nearest", + ) + expected = Series(exp, index=[1, 2, 3], name=0.5, dtype="int64") + tm.assert_series_equal(result, expected) + + # float + df = DataFrame({"A": [1.0, 2.0, 3.0], "B": [2.0, 3.0, 4.0]}, index=[1, 2, 3]) + result = df.quantile(0.5, axis=1, interpolation="nearest") + expected = Series([1.0, 2.0, 3.0], index=[1, 2, 3], name=0.5) + tm.assert_series_equal(result, expected) + exp = np.percentile( + np.array([[1.0, 2.0, 3.0], [2.0, 3.0, 4.0]]), + 0.5, + axis=0, + method="nearest", + ) + expected = Series(exp, index=[1, 2, 3], name=0.5, dtype="float64") + tm.assert_series_equal(result, expected) + + # axis + result = df.quantile([0.5, 0.75], axis=1, interpolation="lower") + expected = DataFrame( + {1: [1.0, 1.0], 2: [2.0, 2.0], 3: [3.0, 3.0]}, index=[0.5, 0.75] + ) + tm.assert_frame_equal(result, expected) + + # test degenerate case + df = DataFrame({"x": [], "y": []}) + q = df.quantile(0.1, axis=0, interpolation="higher") + assert np.isnan(q["x"]) and np.isnan(q["y"]) + + # multi + df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]], columns=["a", "b", "c"]) + result = df.quantile([0.25, 0.5], interpolation="midpoint") + + # https://github.com/numpy/numpy/issues/7163 + expected = DataFrame( + [[1.5, 1.5, 1.5], [2.0, 2.0, 2.0]], + index=[0.25, 0.5], + columns=["a", "b", "c"], + ) + tm.assert_frame_equal(result, expected) + + def test_quantile_interpolation_datetime(self, datetime_frame): + # see gh-10174 + + # interpolation = linear (default case) + df = datetime_frame + q = df.quantile(0.1, axis=0, numeric_only=True, interpolation="linear") + assert q["A"] == np.percentile(df["A"], 10) + + def test_quantile_interpolation_int(self, int_frame): + # see gh-10174 + + df = int_frame + # interpolation = linear (default case) + q = df.quantile(0.1) + assert q["A"] == np.percentile(df["A"], 10) + + # test with and without interpolation keyword + q1 = df.quantile(0.1, axis=0, interpolation="linear") + assert q1["A"] == np.percentile(df["A"], 10) + tm.assert_series_equal(q, q1) + + def test_quantile_multi(self, interp_method, request, using_array_manager): + interpolation, method = interp_method + df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]], columns=["a", "b", "c"]) + result = df.quantile([0.25, 0.5], interpolation=interpolation, method=method) + expected = DataFrame( + [[1.5, 1.5, 1.5], [2.0, 2.0, 2.0]], + index=[0.25, 0.5], + columns=["a", "b", "c"], + ) + if interpolation == "nearest": + expected = expected.astype(np.int64) + if method == "table" and using_array_manager: + request.applymarker(pytest.mark.xfail(reason="Axis name incorrectly set.")) + tm.assert_frame_equal(result, expected) + + def test_quantile_multi_axis_1(self, interp_method, request, using_array_manager): + interpolation, method = interp_method + df = DataFrame([[1, 1, 1], [2, 2, 2], [3, 3, 3]], columns=["a", "b", "c"]) + result = df.quantile( + [0.25, 0.5], axis=1, interpolation=interpolation, method=method + ) + expected = DataFrame( + [[1.0, 2.0, 3.0]] * 2, index=[0.25, 0.5], columns=[0, 1, 2] + ) + if interpolation == "nearest": + expected = expected.astype(np.int64) + if method == "table" and using_array_manager: + request.applymarker(pytest.mark.xfail(reason="Axis name incorrectly set.")) + tm.assert_frame_equal(result, expected) + + def test_quantile_multi_empty(self, interp_method): + interpolation, method = interp_method + result = DataFrame({"x": [], "y": []}).quantile( + [0.1, 0.9], axis=0, interpolation=interpolation, method=method + ) + expected = DataFrame( + {"x": [np.nan, np.nan], "y": [np.nan, np.nan]}, index=[0.1, 0.9] + ) + tm.assert_frame_equal(result, expected) + + def test_quantile_datetime(self, unit): + dti = pd.to_datetime(["2010", "2011"]).as_unit(unit) + df = DataFrame({"a": dti, "b": [0, 5]}) + + # exclude datetime + result = df.quantile(0.5, numeric_only=True) + expected = Series([2.5], index=["b"], name=0.5) + tm.assert_series_equal(result, expected) + + # datetime + result = df.quantile(0.5, numeric_only=False) + expected = Series( + [Timestamp("2010-07-02 12:00:00"), 2.5], index=["a", "b"], name=0.5 + ) + tm.assert_series_equal(result, expected) + + # datetime w/ multi + result = df.quantile([0.5], numeric_only=False) + expected = DataFrame( + {"a": Timestamp("2010-07-02 12:00:00").as_unit(unit), "b": 2.5}, + index=[0.5], + ) + tm.assert_frame_equal(result, expected) + + # axis = 1 + df["c"] = pd.to_datetime(["2011", "2012"]).as_unit(unit) + result = df[["a", "c"]].quantile(0.5, axis=1, numeric_only=False) + expected = Series( + [Timestamp("2010-07-02 12:00:00"), Timestamp("2011-07-02 12:00:00")], + index=[0, 1], + name=0.5, + dtype=f"M8[{unit}]", + ) + tm.assert_series_equal(result, expected) + + result = df[["a", "c"]].quantile([0.5], axis=1, numeric_only=False) + expected = DataFrame( + [[Timestamp("2010-07-02 12:00:00"), Timestamp("2011-07-02 12:00:00")]], + index=[0.5], + columns=[0, 1], + dtype=f"M8[{unit}]", + ) + tm.assert_frame_equal(result, expected) + + # empty when numeric_only=True + result = df[["a", "c"]].quantile(0.5, numeric_only=True) + expected = Series([], index=[], dtype=np.float64, name=0.5) + tm.assert_series_equal(result, expected) + + result = df[["a", "c"]].quantile([0.5], numeric_only=True) + expected = DataFrame(index=[0.5], columns=[]) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "dtype", + [ + "datetime64[ns]", + "datetime64[ns, US/Pacific]", + "timedelta64[ns]", + "Period[D]", + ], + ) + def test_quantile_dt64_empty(self, dtype, interp_method): + # GH#41544 + interpolation, method = interp_method + df = DataFrame(columns=["a", "b"], dtype=dtype) + + res = df.quantile( + 0.5, axis=1, numeric_only=False, interpolation=interpolation, method=method + ) + expected = Series([], index=[], name=0.5, dtype=dtype) + tm.assert_series_equal(res, expected) + + # no columns in result, so no dtype preservation + res = df.quantile( + [0.5], + axis=1, + numeric_only=False, + interpolation=interpolation, + method=method, + ) + expected = DataFrame(index=[0.5], columns=[]) + tm.assert_frame_equal(res, expected) + + @pytest.mark.parametrize("invalid", [-1, 2, [0.5, -1], [0.5, 2]]) + def test_quantile_invalid(self, invalid, datetime_frame, interp_method): + msg = "percentiles should all be in the interval \\[0, 1\\]" + interpolation, method = interp_method + with pytest.raises(ValueError, match=msg): + datetime_frame.quantile(invalid, interpolation=interpolation, method=method) + + def test_quantile_box(self, interp_method, request, using_array_manager): + interpolation, method = interp_method + if method == "table" and using_array_manager: + request.applymarker(pytest.mark.xfail(reason="Axis name incorrectly set.")) + df = DataFrame( + { + "A": [ + Timestamp("2011-01-01"), + Timestamp("2011-01-02"), + Timestamp("2011-01-03"), + ], + "B": [ + Timestamp("2011-01-01", tz="US/Eastern"), + Timestamp("2011-01-02", tz="US/Eastern"), + Timestamp("2011-01-03", tz="US/Eastern"), + ], + "C": [ + pd.Timedelta("1 days"), + pd.Timedelta("2 days"), + pd.Timedelta("3 days"), + ], + } + ) + + res = df.quantile( + 0.5, numeric_only=False, interpolation=interpolation, method=method + ) + + exp = Series( + [ + Timestamp("2011-01-02"), + Timestamp("2011-01-02", tz="US/Eastern"), + pd.Timedelta("2 days"), + ], + name=0.5, + index=["A", "B", "C"], + ) + tm.assert_series_equal(res, exp) + + res = df.quantile( + [0.5], numeric_only=False, interpolation=interpolation, method=method + ) + exp = DataFrame( + [ + [ + Timestamp("2011-01-02"), + Timestamp("2011-01-02", tz="US/Eastern"), + pd.Timedelta("2 days"), + ] + ], + index=[0.5], + columns=["A", "B", "C"], + ) + tm.assert_frame_equal(res, exp) + + def test_quantile_box_nat(self): + # DatetimeLikeBlock may be consolidated and contain NaT in different loc + df = DataFrame( + { + "A": [ + Timestamp("2011-01-01"), + pd.NaT, + Timestamp("2011-01-02"), + Timestamp("2011-01-03"), + ], + "a": [ + Timestamp("2011-01-01"), + Timestamp("2011-01-02"), + pd.NaT, + Timestamp("2011-01-03"), + ], + "B": [ + Timestamp("2011-01-01", tz="US/Eastern"), + pd.NaT, + Timestamp("2011-01-02", tz="US/Eastern"), + Timestamp("2011-01-03", tz="US/Eastern"), + ], + "b": [ + Timestamp("2011-01-01", tz="US/Eastern"), + Timestamp("2011-01-02", tz="US/Eastern"), + pd.NaT, + Timestamp("2011-01-03", tz="US/Eastern"), + ], + "C": [ + pd.Timedelta("1 days"), + pd.Timedelta("2 days"), + pd.Timedelta("3 days"), + pd.NaT, + ], + "c": [ + pd.NaT, + pd.Timedelta("1 days"), + pd.Timedelta("2 days"), + pd.Timedelta("3 days"), + ], + }, + columns=list("AaBbCc"), + ) + + res = df.quantile(0.5, numeric_only=False) + exp = Series( + [ + Timestamp("2011-01-02"), + Timestamp("2011-01-02"), + Timestamp("2011-01-02", tz="US/Eastern"), + Timestamp("2011-01-02", tz="US/Eastern"), + pd.Timedelta("2 days"), + pd.Timedelta("2 days"), + ], + name=0.5, + index=list("AaBbCc"), + ) + tm.assert_series_equal(res, exp) + + res = df.quantile([0.5], numeric_only=False) + exp = DataFrame( + [ + [ + Timestamp("2011-01-02"), + Timestamp("2011-01-02"), + Timestamp("2011-01-02", tz="US/Eastern"), + Timestamp("2011-01-02", tz="US/Eastern"), + pd.Timedelta("2 days"), + pd.Timedelta("2 days"), + ] + ], + index=[0.5], + columns=list("AaBbCc"), + ) + tm.assert_frame_equal(res, exp) + + def test_quantile_nan(self, interp_method, request, using_array_manager): + interpolation, method = interp_method + if method == "table" and using_array_manager: + request.applymarker(pytest.mark.xfail(reason="Axis name incorrectly set.")) + # GH 14357 - float block where some cols have missing values + df = DataFrame({"a": np.arange(1, 6.0), "b": np.arange(1, 6.0)}) + df.iloc[-1, 1] = np.nan + + res = df.quantile(0.5, interpolation=interpolation, method=method) + exp = Series( + [3.0, 2.5 if interpolation == "linear" else 3.0], index=["a", "b"], name=0.5 + ) + tm.assert_series_equal(res, exp) + + res = df.quantile([0.5, 0.75], interpolation=interpolation, method=method) + exp = DataFrame( + { + "a": [3.0, 4.0], + "b": [2.5, 3.25] if interpolation == "linear" else [3.0, 4.0], + }, + index=[0.5, 0.75], + ) + tm.assert_frame_equal(res, exp) + + res = df.quantile(0.5, axis=1, interpolation=interpolation, method=method) + exp = Series(np.arange(1.0, 6.0), name=0.5) + tm.assert_series_equal(res, exp) + + res = df.quantile( + [0.5, 0.75], axis=1, interpolation=interpolation, method=method + ) + exp = DataFrame([np.arange(1.0, 6.0)] * 2, index=[0.5, 0.75]) + if interpolation == "nearest": + exp.iloc[1, -1] = np.nan + tm.assert_frame_equal(res, exp) + + # full-nan column + df["b"] = np.nan + + res = df.quantile(0.5, interpolation=interpolation, method=method) + exp = Series([3.0, np.nan], index=["a", "b"], name=0.5) + tm.assert_series_equal(res, exp) + + res = df.quantile([0.5, 0.75], interpolation=interpolation, method=method) + exp = DataFrame({"a": [3.0, 4.0], "b": [np.nan, np.nan]}, index=[0.5, 0.75]) + tm.assert_frame_equal(res, exp) + + def test_quantile_nat(self, interp_method, request, using_array_manager, unit): + interpolation, method = interp_method + if method == "table" and using_array_manager: + request.applymarker(pytest.mark.xfail(reason="Axis name incorrectly set.")) + # full NaT column + df = DataFrame({"a": [pd.NaT, pd.NaT, pd.NaT]}, dtype=f"M8[{unit}]") + + res = df.quantile( + 0.5, numeric_only=False, interpolation=interpolation, method=method + ) + exp = Series([pd.NaT], index=["a"], name=0.5, dtype=f"M8[{unit}]") + tm.assert_series_equal(res, exp) + + res = df.quantile( + [0.5], numeric_only=False, interpolation=interpolation, method=method + ) + exp = DataFrame({"a": [pd.NaT]}, index=[0.5], dtype=f"M8[{unit}]") + tm.assert_frame_equal(res, exp) + + # mixed non-null / full null column + df = DataFrame( + { + "a": [ + Timestamp("2012-01-01"), + Timestamp("2012-01-02"), + Timestamp("2012-01-03"), + ], + "b": [pd.NaT, pd.NaT, pd.NaT], + }, + dtype=f"M8[{unit}]", + ) + + res = df.quantile( + 0.5, numeric_only=False, interpolation=interpolation, method=method + ) + exp = Series( + [Timestamp("2012-01-02"), pd.NaT], + index=["a", "b"], + name=0.5, + dtype=f"M8[{unit}]", + ) + tm.assert_series_equal(res, exp) + + res = df.quantile( + [0.5], numeric_only=False, interpolation=interpolation, method=method + ) + exp = DataFrame( + [[Timestamp("2012-01-02"), pd.NaT]], + index=[0.5], + columns=["a", "b"], + dtype=f"M8[{unit}]", + ) + tm.assert_frame_equal(res, exp) + + def test_quantile_empty_no_rows_floats(self, interp_method): + interpolation, method = interp_method + + df = DataFrame(columns=["a", "b"], dtype="float64") + + res = df.quantile(0.5, interpolation=interpolation, method=method) + exp = Series([np.nan, np.nan], index=["a", "b"], name=0.5) + tm.assert_series_equal(res, exp) + + res = df.quantile([0.5], interpolation=interpolation, method=method) + exp = DataFrame([[np.nan, np.nan]], columns=["a", "b"], index=[0.5]) + tm.assert_frame_equal(res, exp) + + res = df.quantile(0.5, axis=1, interpolation=interpolation, method=method) + exp = Series([], index=[], dtype="float64", name=0.5) + tm.assert_series_equal(res, exp) + + res = df.quantile([0.5], axis=1, interpolation=interpolation, method=method) + exp = DataFrame(columns=[], index=[0.5]) + tm.assert_frame_equal(res, exp) + + def test_quantile_empty_no_rows_ints(self, interp_method): + interpolation, method = interp_method + df = DataFrame(columns=["a", "b"], dtype="int64") + + res = df.quantile(0.5, interpolation=interpolation, method=method) + exp = Series([np.nan, np.nan], index=["a", "b"], name=0.5) + tm.assert_series_equal(res, exp) + + def test_quantile_empty_no_rows_dt64(self, interp_method): + interpolation, method = interp_method + # datetimes + df = DataFrame(columns=["a", "b"], dtype="datetime64[ns]") + + res = df.quantile( + 0.5, numeric_only=False, interpolation=interpolation, method=method + ) + exp = Series( + [pd.NaT, pd.NaT], index=["a", "b"], dtype="datetime64[ns]", name=0.5 + ) + tm.assert_series_equal(res, exp) + + # Mixed dt64/dt64tz + df["a"] = df["a"].dt.tz_localize("US/Central") + res = df.quantile( + 0.5, numeric_only=False, interpolation=interpolation, method=method + ) + exp = exp.astype(object) + if interpolation == "nearest": + # GH#18463 TODO: would we prefer NaTs here? + msg = "The 'downcast' keyword in fillna is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + exp = exp.fillna(np.nan, downcast=False) + tm.assert_series_equal(res, exp) + + # both dt64tz + df["b"] = df["b"].dt.tz_localize("US/Central") + res = df.quantile( + 0.5, numeric_only=False, interpolation=interpolation, method=method + ) + exp = exp.astype(df["b"].dtype) + tm.assert_series_equal(res, exp) + + def test_quantile_empty_no_columns(self, interp_method): + # GH#23925 _get_numeric_data may drop all columns + interpolation, method = interp_method + df = DataFrame(pd.date_range("1/1/18", periods=5)) + df.columns.name = "captain tightpants" + result = df.quantile( + 0.5, numeric_only=True, interpolation=interpolation, method=method + ) + expected = Series([], index=[], name=0.5, dtype=np.float64) + expected.index.name = "captain tightpants" + tm.assert_series_equal(result, expected) + + result = df.quantile( + [0.5], numeric_only=True, interpolation=interpolation, method=method + ) + expected = DataFrame([], index=[0.5], columns=[]) + expected.columns.name = "captain tightpants" + tm.assert_frame_equal(result, expected) + + def test_quantile_item_cache( + self, using_array_manager, interp_method, using_copy_on_write + ): + # previous behavior incorrect retained an invalid _item_cache entry + interpolation, method = interp_method + df = DataFrame( + np.random.default_rng(2).standard_normal((4, 3)), columns=["A", "B", "C"] + ) + df["D"] = df["A"] * 2 + ser = df["A"] + if not using_array_manager: + assert len(df._mgr.blocks) == 2 + + df.quantile(numeric_only=False, interpolation=interpolation, method=method) + + if using_copy_on_write: + ser.iloc[0] = 99 + assert df.iloc[0, 0] == df["A"][0] + assert df.iloc[0, 0] != 99 + else: + ser.values[0] = 99 + assert df.iloc[0, 0] == df["A"][0] + assert df.iloc[0, 0] == 99 + + def test_invalid_method(self): + with pytest.raises(ValueError, match="Invalid method: foo"): + DataFrame(range(1)).quantile(0.5, method="foo") + + def test_table_invalid_interpolation(self): + with pytest.raises(ValueError, match="Invalid interpolation: foo"): + DataFrame(range(1)).quantile(0.5, method="table", interpolation="foo") + + +class TestQuantileExtensionDtype: + # TODO: tests for axis=1? + # TODO: empty case? + + @pytest.fixture( + params=[ + pytest.param( + pd.IntervalIndex.from_breaks(range(10)), + marks=pytest.mark.xfail(reason="raises when trying to add Intervals"), + ), + pd.period_range("2016-01-01", periods=9, freq="D"), + pd.date_range("2016-01-01", periods=9, tz="US/Pacific"), + pd.timedelta_range("1 Day", periods=9), + pd.array(np.arange(9), dtype="Int64"), + pd.array(np.arange(9), dtype="Float64"), + ], + ids=lambda x: str(x.dtype), + ) + def index(self, request): + # NB: not actually an Index object + idx = request.param + idx.name = "A" + return idx + + @pytest.fixture + def obj(self, index, frame_or_series): + # bc index is not always an Index (yet), we need to re-patch .name + obj = frame_or_series(index).copy() + + if frame_or_series is Series: + obj.name = "A" + else: + obj.columns = ["A"] + return obj + + def compute_quantile(self, obj, qs): + if isinstance(obj, Series): + result = obj.quantile(qs) + else: + result = obj.quantile(qs, numeric_only=False) + return result + + def test_quantile_ea(self, request, obj, index): + # result should be invariant to shuffling + indexer = np.arange(len(index), dtype=np.intp) + np.random.default_rng(2).shuffle(indexer) + obj = obj.iloc[indexer] + + qs = [0.5, 0, 1] + result = self.compute_quantile(obj, qs) + + exp_dtype = index.dtype + if index.dtype == "Int64": + # match non-nullable casting behavior + exp_dtype = "Float64" + + # expected here assumes len(index) == 9 + expected = Series( + [index[4], index[0], index[-1]], dtype=exp_dtype, index=qs, name="A" + ) + expected = type(obj)(expected) + + tm.assert_equal(result, expected) + + def test_quantile_ea_with_na(self, obj, index): + obj.iloc[0] = index._na_value + obj.iloc[-1] = index._na_value + + # result should be invariant to shuffling + indexer = np.arange(len(index), dtype=np.intp) + np.random.default_rng(2).shuffle(indexer) + obj = obj.iloc[indexer] + + qs = [0.5, 0, 1] + result = self.compute_quantile(obj, qs) + + # expected here assumes len(index) == 9 + expected = Series( + [index[4], index[1], index[-2]], dtype=index.dtype, index=qs, name="A" + ) + expected = type(obj)(expected) + tm.assert_equal(result, expected) + + def test_quantile_ea_all_na(self, request, obj, index): + obj.iloc[:] = index._na_value + # Check dtypes were preserved; this was once a problem see GH#39763 + assert np.all(obj.dtypes == index.dtype) + + # result should be invariant to shuffling + indexer = np.arange(len(index), dtype=np.intp) + np.random.default_rng(2).shuffle(indexer) + obj = obj.iloc[indexer] + + qs = [0.5, 0, 1] + result = self.compute_quantile(obj, qs) + + expected = index.take([-1, -1, -1], allow_fill=True, fill_value=index._na_value) + expected = Series(expected, index=qs, name="A") + expected = type(obj)(expected) + tm.assert_equal(result, expected) + + def test_quantile_ea_scalar(self, request, obj, index): + # scalar qs + + # result should be invariant to shuffling + indexer = np.arange(len(index), dtype=np.intp) + np.random.default_rng(2).shuffle(indexer) + obj = obj.iloc[indexer] + + qs = 0.5 + result = self.compute_quantile(obj, qs) + + exp_dtype = index.dtype + if index.dtype == "Int64": + exp_dtype = "Float64" + + expected = Series({"A": index[4]}, dtype=exp_dtype, name=0.5) + if isinstance(obj, Series): + expected = expected["A"] + assert result == expected + else: + tm.assert_series_equal(result, expected) + + @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False) + @pytest.mark.parametrize( + "dtype, expected_data, expected_index, axis", + [ + ["float64", [], [], 1], + ["int64", [], [], 1], + ["float64", [np.nan, np.nan], ["a", "b"], 0], + ["int64", [np.nan, np.nan], ["a", "b"], 0], + ], + ) + def test_empty_numeric(self, dtype, expected_data, expected_index, axis): + # GH 14564 + df = DataFrame(columns=["a", "b"], dtype=dtype) + result = df.quantile(0.5, axis=axis) + expected = Series( + expected_data, name=0.5, index=Index(expected_index), dtype="float64" + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False) + @pytest.mark.parametrize( + "dtype, expected_data, expected_index, axis, expected_dtype", + [ + ["datetime64[ns]", [], [], 1, "datetime64[ns]"], + ["datetime64[ns]", [pd.NaT, pd.NaT], ["a", "b"], 0, "datetime64[ns]"], + ], + ) + def test_empty_datelike( + self, dtype, expected_data, expected_index, axis, expected_dtype + ): + # GH 14564 + df = DataFrame(columns=["a", "b"], dtype=dtype) + result = df.quantile(0.5, axis=axis, numeric_only=False) + expected = Series( + expected_data, name=0.5, index=Index(expected_index), dtype=expected_dtype + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False) + @pytest.mark.parametrize( + "expected_data, expected_index, axis", + [ + [[np.nan, np.nan], range(2), 1], + [[], [], 0], + ], + ) + def test_datelike_numeric_only(self, expected_data, expected_index, axis): + # GH 14564 + df = DataFrame( + { + "a": pd.to_datetime(["2010", "2011"]), + "b": [0, 5], + "c": pd.to_datetime(["2011", "2012"]), + } + ) + result = df[["a", "c"]].quantile(0.5, axis=axis, numeric_only=True) + expected = Series( + expected_data, name=0.5, index=Index(expected_index), dtype=np.float64 + ) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_rank.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_rank.py new file mode 100644 index 0000000000000000000000000000000000000000..37bed2da0574305977dece25ce02771c4364d1de --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_rank.py @@ -0,0 +1,507 @@ +from datetime import ( + datetime, + timedelta, +) + +import numpy as np +import pytest + +from pandas._libs.algos import ( + Infinity, + NegInfinity, +) + +from pandas import ( + DataFrame, + Index, + Series, +) +import pandas._testing as tm + + +class TestRank: + s = Series([1, 3, 4, 2, np.nan, 2, 1, 5, np.nan, 3]) + df = DataFrame({"A": s, "B": s}) + + results = { + "average": np.array([1.5, 5.5, 7.0, 3.5, np.nan, 3.5, 1.5, 8.0, np.nan, 5.5]), + "min": np.array([1, 5, 7, 3, np.nan, 3, 1, 8, np.nan, 5]), + "max": np.array([2, 6, 7, 4, np.nan, 4, 2, 8, np.nan, 6]), + "first": np.array([1, 5, 7, 3, np.nan, 4, 2, 8, np.nan, 6]), + "dense": np.array([1, 3, 4, 2, np.nan, 2, 1, 5, np.nan, 3]), + } + + @pytest.fixture(params=["average", "min", "max", "first", "dense"]) + def method(self, request): + """ + Fixture for trying all rank methods + """ + return request.param + + def test_rank(self, float_frame): + sp_stats = pytest.importorskip("scipy.stats") + + float_frame.loc[::2, "A"] = np.nan + float_frame.loc[::3, "B"] = np.nan + float_frame.loc[::4, "C"] = np.nan + float_frame.loc[::5, "D"] = np.nan + + ranks0 = float_frame.rank() + ranks1 = float_frame.rank(1) + mask = np.isnan(float_frame.values) + + fvals = float_frame.fillna(np.inf).values + + exp0 = np.apply_along_axis(sp_stats.rankdata, 0, fvals) + exp0[mask] = np.nan + + exp1 = np.apply_along_axis(sp_stats.rankdata, 1, fvals) + exp1[mask] = np.nan + + tm.assert_almost_equal(ranks0.values, exp0) + tm.assert_almost_equal(ranks1.values, exp1) + + # integers + df = DataFrame( + np.random.default_rng(2).integers(0, 5, size=40).reshape((10, 4)) + ) + + result = df.rank() + exp = df.astype(float).rank() + tm.assert_frame_equal(result, exp) + + result = df.rank(1) + exp = df.astype(float).rank(1) + tm.assert_frame_equal(result, exp) + + def test_rank2(self): + df = DataFrame([[1, 3, 2], [1, 2, 3]]) + expected = DataFrame([[1.0, 3.0, 2.0], [1, 2, 3]]) / 3.0 + result = df.rank(1, pct=True) + tm.assert_frame_equal(result, expected) + + df = DataFrame([[1, 3, 2], [1, 2, 3]]) + expected = df.rank(0) / 2.0 + result = df.rank(0, pct=True) + tm.assert_frame_equal(result, expected) + + df = DataFrame([["b", "c", "a"], ["a", "c", "b"]]) + expected = DataFrame([[2.0, 3.0, 1.0], [1, 3, 2]]) + result = df.rank(1, numeric_only=False) + tm.assert_frame_equal(result, expected) + + expected = DataFrame([[2.0, 1.5, 1.0], [1, 1.5, 2]]) + result = df.rank(0, numeric_only=False) + tm.assert_frame_equal(result, expected) + + df = DataFrame([["b", np.nan, "a"], ["a", "c", "b"]]) + expected = DataFrame([[2.0, np.nan, 1.0], [1.0, 3.0, 2.0]]) + result = df.rank(1, numeric_only=False) + tm.assert_frame_equal(result, expected) + + expected = DataFrame([[2.0, np.nan, 1.0], [1.0, 1.0, 2.0]]) + result = df.rank(0, numeric_only=False) + tm.assert_frame_equal(result, expected) + + # f7u12, this does not work without extensive workaround + data = [ + [datetime(2001, 1, 5), np.nan, datetime(2001, 1, 2)], + [datetime(2000, 1, 2), datetime(2000, 1, 3), datetime(2000, 1, 1)], + ] + df = DataFrame(data) + + # check the rank + expected = DataFrame([[2.0, np.nan, 1.0], [2.0, 3.0, 1.0]]) + result = df.rank(1, numeric_only=False, ascending=True) + tm.assert_frame_equal(result, expected) + + expected = DataFrame([[1.0, np.nan, 2.0], [2.0, 1.0, 3.0]]) + result = df.rank(1, numeric_only=False, ascending=False) + tm.assert_frame_equal(result, expected) + + df = DataFrame({"a": [1e-20, -5, 1e-20 + 1e-40, 10, 1e60, 1e80, 1e-30]}) + exp = DataFrame({"a": [3.5, 1.0, 3.5, 5.0, 6.0, 7.0, 2.0]}) + tm.assert_frame_equal(df.rank(), exp) + + def test_rank_does_not_mutate(self): + # GH#18521 + # Check rank does not mutate DataFrame + df = DataFrame( + np.random.default_rng(2).standard_normal((10, 3)), dtype="float64" + ) + expected = df.copy() + df.rank() + result = df + tm.assert_frame_equal(result, expected) + + def test_rank_mixed_frame(self, float_string_frame): + float_string_frame["datetime"] = datetime.now() + float_string_frame["timedelta"] = timedelta(days=1, seconds=1) + + float_string_frame.rank(numeric_only=False) + with pytest.raises(TypeError, match="not supported between instances of"): + float_string_frame.rank(axis=1) + + def test_rank_na_option(self, float_frame): + sp_stats = pytest.importorskip("scipy.stats") + + float_frame.loc[::2, "A"] = np.nan + float_frame.loc[::3, "B"] = np.nan + float_frame.loc[::4, "C"] = np.nan + float_frame.loc[::5, "D"] = np.nan + + # bottom + ranks0 = float_frame.rank(na_option="bottom") + ranks1 = float_frame.rank(1, na_option="bottom") + + fvals = float_frame.fillna(np.inf).values + + exp0 = np.apply_along_axis(sp_stats.rankdata, 0, fvals) + exp1 = np.apply_along_axis(sp_stats.rankdata, 1, fvals) + + tm.assert_almost_equal(ranks0.values, exp0) + tm.assert_almost_equal(ranks1.values, exp1) + + # top + ranks0 = float_frame.rank(na_option="top") + ranks1 = float_frame.rank(1, na_option="top") + + fval0 = float_frame.fillna((float_frame.min() - 1).to_dict()).values + fval1 = float_frame.T + fval1 = fval1.fillna((fval1.min() - 1).to_dict()).T + fval1 = fval1.fillna(np.inf).values + + exp0 = np.apply_along_axis(sp_stats.rankdata, 0, fval0) + exp1 = np.apply_along_axis(sp_stats.rankdata, 1, fval1) + + tm.assert_almost_equal(ranks0.values, exp0) + tm.assert_almost_equal(ranks1.values, exp1) + + # descending + + # bottom + ranks0 = float_frame.rank(na_option="top", ascending=False) + ranks1 = float_frame.rank(1, na_option="top", ascending=False) + + fvals = float_frame.fillna(np.inf).values + + exp0 = np.apply_along_axis(sp_stats.rankdata, 0, -fvals) + exp1 = np.apply_along_axis(sp_stats.rankdata, 1, -fvals) + + tm.assert_almost_equal(ranks0.values, exp0) + tm.assert_almost_equal(ranks1.values, exp1) + + # descending + + # top + ranks0 = float_frame.rank(na_option="bottom", ascending=False) + ranks1 = float_frame.rank(1, na_option="bottom", ascending=False) + + fval0 = float_frame.fillna((float_frame.min() - 1).to_dict()).values + fval1 = float_frame.T + fval1 = fval1.fillna((fval1.min() - 1).to_dict()).T + fval1 = fval1.fillna(np.inf).values + + exp0 = np.apply_along_axis(sp_stats.rankdata, 0, -fval0) + exp1 = np.apply_along_axis(sp_stats.rankdata, 1, -fval1) + + tm.assert_numpy_array_equal(ranks0.values, exp0) + tm.assert_numpy_array_equal(ranks1.values, exp1) + + # bad values throw error + msg = "na_option must be one of 'keep', 'top', or 'bottom'" + + with pytest.raises(ValueError, match=msg): + float_frame.rank(na_option="bad", ascending=False) + + # invalid type + with pytest.raises(ValueError, match=msg): + float_frame.rank(na_option=True, ascending=False) + + def test_rank_axis(self): + # check if using axes' names gives the same result + df = DataFrame([[2, 1], [4, 3]]) + tm.assert_frame_equal(df.rank(axis=0), df.rank(axis="index")) + tm.assert_frame_equal(df.rank(axis=1), df.rank(axis="columns")) + + @pytest.mark.parametrize("ax", [0, 1]) + @pytest.mark.parametrize("m", ["average", "min", "max", "first", "dense"]) + def test_rank_methods_frame(self, ax, m): + sp_stats = pytest.importorskip("scipy.stats") + + xs = np.random.default_rng(2).integers(0, 21, (100, 26)) + xs = (xs - 10.0) / 10.0 + cols = [chr(ord("z") - i) for i in range(xs.shape[1])] + + for vals in [xs, xs + 1e6, xs * 1e-6]: + df = DataFrame(vals, columns=cols) + + result = df.rank(axis=ax, method=m) + sprank = np.apply_along_axis( + sp_stats.rankdata, ax, vals, m if m != "first" else "ordinal" + ) + sprank = sprank.astype(np.float64) + expected = DataFrame(sprank, columns=cols).astype("float64") + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("dtype", ["O", "f8", "i8"]) + def test_rank_descending(self, method, dtype): + if "i" in dtype: + df = self.df.dropna().astype(dtype) + else: + df = self.df.astype(dtype) + + res = df.rank(ascending=False) + expected = (df.max() - df).rank() + tm.assert_frame_equal(res, expected) + + expected = (df.max() - df).rank(method=method) + + if dtype != "O": + res2 = df.rank(method=method, ascending=False, numeric_only=True) + tm.assert_frame_equal(res2, expected) + + res3 = df.rank(method=method, ascending=False, numeric_only=False) + tm.assert_frame_equal(res3, expected) + + @pytest.mark.parametrize("axis", [0, 1]) + @pytest.mark.parametrize("dtype", [None, object]) + def test_rank_2d_tie_methods(self, method, axis, dtype): + df = self.df + + def _check2d(df, expected, method="average", axis=0): + exp_df = DataFrame({"A": expected, "B": expected}) + + if axis == 1: + df = df.T + exp_df = exp_df.T + + result = df.rank(method=method, axis=axis) + tm.assert_frame_equal(result, exp_df) + + frame = df if dtype is None else df.astype(dtype) + _check2d(frame, self.results[method], method=method, axis=axis) + + @pytest.mark.parametrize( + "method,exp", + [ + ("dense", [[1.0, 1.0, 1.0], [1.0, 0.5, 2.0 / 3], [1.0, 0.5, 1.0 / 3]]), + ( + "min", + [ + [1.0 / 3, 1.0, 1.0], + [1.0 / 3, 1.0 / 3, 2.0 / 3], + [1.0 / 3, 1.0 / 3, 1.0 / 3], + ], + ), + ( + "max", + [[1.0, 1.0, 1.0], [1.0, 2.0 / 3, 2.0 / 3], [1.0, 2.0 / 3, 1.0 / 3]], + ), + ( + "average", + [[2.0 / 3, 1.0, 1.0], [2.0 / 3, 0.5, 2.0 / 3], [2.0 / 3, 0.5, 1.0 / 3]], + ), + ( + "first", + [ + [1.0 / 3, 1.0, 1.0], + [2.0 / 3, 1.0 / 3, 2.0 / 3], + [3.0 / 3, 2.0 / 3, 1.0 / 3], + ], + ), + ], + ) + def test_rank_pct_true(self, method, exp): + # see gh-15630. + + df = DataFrame([[2012, 66, 3], [2012, 65, 2], [2012, 65, 1]]) + result = df.rank(method=method, pct=True) + + expected = DataFrame(exp) + tm.assert_frame_equal(result, expected) + + @pytest.mark.single_cpu + def test_pct_max_many_rows(self): + # GH 18271 + df = DataFrame( + {"A": np.arange(2**24 + 1), "B": np.arange(2**24 + 1, 0, -1)} + ) + result = df.rank(pct=True).max() + assert (result == 1).all() + + @pytest.mark.parametrize( + "contents,dtype", + [ + ( + [ + -np.inf, + -50, + -1, + -1e-20, + -1e-25, + -1e-50, + 0, + 1e-40, + 1e-20, + 1e-10, + 2, + 40, + np.inf, + ], + "float64", + ), + ( + [ + -np.inf, + -50, + -1, + -1e-20, + -1e-25, + -1e-45, + 0, + 1e-40, + 1e-20, + 1e-10, + 2, + 40, + np.inf, + ], + "float32", + ), + ([np.iinfo(np.uint8).min, 1, 2, 100, np.iinfo(np.uint8).max], "uint8"), + ( + [ + np.iinfo(np.int64).min, + -100, + 0, + 1, + 9999, + 100000, + 1e10, + np.iinfo(np.int64).max, + ], + "int64", + ), + ([NegInfinity(), "1", "A", "BA", "Ba", "C", Infinity()], "object"), + ( + [datetime(2001, 1, 1), datetime(2001, 1, 2), datetime(2001, 1, 5)], + "datetime64", + ), + ], + ) + def test_rank_inf_and_nan(self, contents, dtype, frame_or_series): + dtype_na_map = { + "float64": np.nan, + "float32": np.nan, + "object": None, + "datetime64": np.datetime64("nat"), + } + # Insert nans at random positions if underlying dtype has missing + # value. Then adjust the expected order by adding nans accordingly + # This is for testing whether rank calculation is affected + # when values are interwined with nan values. + values = np.array(contents, dtype=dtype) + exp_order = np.array(range(len(values)), dtype="float64") + 1.0 + if dtype in dtype_na_map: + na_value = dtype_na_map[dtype] + nan_indices = np.random.default_rng(2).choice(range(len(values)), 5) + values = np.insert(values, nan_indices, na_value) + exp_order = np.insert(exp_order, nan_indices, np.nan) + + # Shuffle the testing array and expected results in the same way + random_order = np.random.default_rng(2).permutation(len(values)) + obj = frame_or_series(values[random_order]) + expected = frame_or_series(exp_order[random_order], dtype="float64") + result = obj.rank() + tm.assert_equal(result, expected) + + def test_df_series_inf_nan_consistency(self): + # GH#32593 + index = [5, 4, 3, 2, 1, 6, 7, 8, 9, 10] + col1 = [5, 4, 3, 5, 8, 5, 2, 1, 6, 6] + col2 = [5, 4, np.nan, 5, 8, 5, np.inf, np.nan, 6, -np.inf] + df = DataFrame( + data={ + "col1": col1, + "col2": col2, + }, + index=index, + dtype="f8", + ) + df_result = df.rank() + + series_result = df.copy() + series_result["col1"] = df["col1"].rank() + series_result["col2"] = df["col2"].rank() + + tm.assert_frame_equal(df_result, series_result) + + def test_rank_both_inf(self): + # GH#32593 + df = DataFrame({"a": [-np.inf, 0, np.inf]}) + expected = DataFrame({"a": [1.0, 2.0, 3.0]}) + result = df.rank() + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "na_option,ascending,expected", + [ + ("top", True, [3.0, 1.0, 2.0]), + ("top", False, [2.0, 1.0, 3.0]), + ("bottom", True, [2.0, 3.0, 1.0]), + ("bottom", False, [1.0, 3.0, 2.0]), + ], + ) + def test_rank_inf_nans_na_option( + self, frame_or_series, method, na_option, ascending, expected + ): + obj = frame_or_series([np.inf, np.nan, -np.inf]) + result = obj.rank(method=method, na_option=na_option, ascending=ascending) + expected = frame_or_series(expected) + tm.assert_equal(result, expected) + + @pytest.mark.parametrize( + "na_option,ascending,expected", + [ + ("bottom", True, [1.0, 2.0, 4.0, 3.0]), + ("bottom", False, [1.0, 2.0, 4.0, 3.0]), + ("top", True, [2.0, 3.0, 1.0, 4.0]), + ("top", False, [2.0, 3.0, 1.0, 4.0]), + ], + ) + def test_rank_object_first(self, frame_or_series, na_option, ascending, expected): + obj = frame_or_series(["foo", "foo", None, "foo"]) + result = obj.rank(method="first", na_option=na_option, ascending=ascending) + expected = frame_or_series(expected) + tm.assert_equal(result, expected) + + @pytest.mark.parametrize( + "data,expected", + [ + ( + {"a": [1, 2, "a"], "b": [4, 5, 6]}, + DataFrame({"b": [1.0, 2.0, 3.0]}, columns=Index(["b"], dtype=object)), + ), + ({"a": [1, 2, "a"]}, DataFrame(index=range(3), columns=[])), + ], + ) + def test_rank_mixed_axis_zero(self, data, expected): + df = DataFrame(data, columns=Index(list(data.keys()), dtype=object)) + with pytest.raises(TypeError, match="'<' not supported between instances of"): + df.rank() + result = df.rank(numeric_only=True) + tm.assert_frame_equal(result, expected) + + def test_rank_string_dtype(self, string_dtype_no_object): + # GH#55362 + obj = Series(["foo", "foo", None, "foo"], dtype=string_dtype_no_object) + result = obj.rank(method="first") + exp_dtype = ( + "Float64" if string_dtype_no_object == "string[pyarrow]" else "float64" + ) + if string_dtype_no_object.storage == "python": + # TODO nullable string[python] should also return nullable Int64 + exp_dtype = "float64" + expected = Series([1, 2, None, 3], dtype=exp_dtype) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_reindex.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_reindex.py new file mode 100644 index 0000000000000000000000000000000000000000..d862e14ce86cbc2bc0e74e5e9bd768c2f2eb285c --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_reindex.py @@ -0,0 +1,1327 @@ +from datetime import ( + datetime, + timedelta, +) +import inspect + +import numpy as np +import pytest + +from pandas._libs.tslibs.timezones import dateutil_gettz as gettz +from pandas.compat import ( + IS64, + is_platform_windows, +) +from pandas.compat.numpy import np_version_gt2 +import pandas.util._test_decorators as td + +import pandas as pd +from pandas import ( + Categorical, + CategoricalIndex, + DataFrame, + Index, + MultiIndex, + Series, + date_range, + isna, +) +import pandas._testing as tm +from pandas.api.types import CategoricalDtype + + +class TestReindexSetIndex: + # Tests that check both reindex and set_index + + def test_dti_set_index_reindex_datetimeindex(self): + # GH#6631 + df = DataFrame(np.random.default_rng(2).random(6)) + idx1 = date_range("2011/01/01", periods=6, freq="ME", tz="US/Eastern") + idx2 = date_range("2013", periods=6, freq="YE", tz="Asia/Tokyo") + + df = df.set_index(idx1) + tm.assert_index_equal(df.index, idx1) + df = df.reindex(idx2) + tm.assert_index_equal(df.index, idx2) + + def test_dti_set_index_reindex_freq_with_tz(self): + # GH#11314 with tz + index = date_range( + datetime(2015, 10, 1), datetime(2015, 10, 1, 23), freq="h", tz="US/Eastern" + ) + df = DataFrame( + np.random.default_rng(2).standard_normal((24, 1)), + columns=["a"], + index=index, + ) + new_index = date_range( + datetime(2015, 10, 2), datetime(2015, 10, 2, 23), freq="h", tz="US/Eastern" + ) + + result = df.set_index(new_index) + assert result.index.freq == index.freq + + def test_set_reset_index_intervalindex(self): + df = DataFrame({"A": range(10)}) + ser = pd.cut(df.A, 5) + df["B"] = ser + df = df.set_index("B") + + df = df.reset_index() + + def test_setitem_reset_index_dtypes(self): + # GH 22060 + df = DataFrame(columns=["a", "b", "c"]).astype( + {"a": "datetime64[ns]", "b": np.int64, "c": np.float64} + ) + df1 = df.set_index(["a"]) + df1["d"] = [] + result = df1.reset_index() + expected = DataFrame(columns=["a", "b", "c", "d"], index=range(0)).astype( + {"a": "datetime64[ns]", "b": np.int64, "c": np.float64, "d": np.float64} + ) + tm.assert_frame_equal(result, expected) + + df2 = df.set_index(["a", "b"]) + df2["d"] = [] + result = df2.reset_index() + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "timezone, year, month, day, hour", + [["America/Chicago", 2013, 11, 3, 1], ["America/Santiago", 2021, 4, 3, 23]], + ) + def test_reindex_timestamp_with_fold(self, timezone, year, month, day, hour): + # see gh-40817 + test_timezone = gettz(timezone) + transition_1 = pd.Timestamp( + year=year, + month=month, + day=day, + hour=hour, + minute=0, + fold=0, + tzinfo=test_timezone, + ) + transition_2 = pd.Timestamp( + year=year, + month=month, + day=day, + hour=hour, + minute=0, + fold=1, + tzinfo=test_timezone, + ) + df = ( + DataFrame({"index": [transition_1, transition_2], "vals": ["a", "b"]}) + .set_index("index") + .reindex(["1", "2"]) + ) + exp = DataFrame({"index": ["1", "2"], "vals": [np.nan, np.nan]}).set_index( + "index" + ) + exp = exp.astype(df.vals.dtype) + tm.assert_frame_equal( + df, + exp, + ) + + +class TestDataFrameSelectReindex: + # These are specific reindex-based tests; other indexing tests should go in + # test_indexing + + @pytest.mark.xfail( + not IS64 or (is_platform_windows() and not np_version_gt2), + reason="Passes int32 values to DatetimeArray in make_na_array on " + "windows, 32bit linux builds", + ) + @td.skip_array_manager_not_yet_implemented + def test_reindex_tzaware_fill_value(self): + # GH#52586 + df = DataFrame([[1]]) + + ts = pd.Timestamp("2023-04-10 17:32", tz="US/Pacific") + res = df.reindex([0, 1], axis=1, fill_value=ts) + assert res.dtypes[1] == pd.DatetimeTZDtype(unit="s", tz="US/Pacific") + expected = DataFrame({0: [1], 1: [ts]}) + expected[1] = expected[1].astype(res.dtypes[1]) + tm.assert_frame_equal(res, expected) + + per = ts.tz_localize(None).to_period("s") + res = df.reindex([0, 1], axis=1, fill_value=per) + assert res.dtypes[1] == pd.PeriodDtype("s") + expected = DataFrame({0: [1], 1: [per]}) + tm.assert_frame_equal(res, expected) + + interval = pd.Interval(ts, ts + pd.Timedelta(seconds=1)) + res = df.reindex([0, 1], axis=1, fill_value=interval) + assert res.dtypes[1] == pd.IntervalDtype("datetime64[s, US/Pacific]", "right") + expected = DataFrame({0: [1], 1: [interval]}) + expected[1] = expected[1].astype(res.dtypes[1]) + tm.assert_frame_equal(res, expected) + + def test_reindex_copies(self): + # based on asv time_reindex_axis1 + N = 10 + df = DataFrame(np.random.default_rng(2).standard_normal((N * 10, N))) + cols = np.arange(N) + np.random.default_rng(2).shuffle(cols) + + result = df.reindex(columns=cols, copy=True) + assert not np.shares_memory(result[0]._values, df[0]._values) + + # pass both columns and index + result2 = df.reindex(columns=cols, index=df.index, copy=True) + assert not np.shares_memory(result2[0]._values, df[0]._values) + + def test_reindex_copies_ea(self, using_copy_on_write): + # https://github.com/pandas-dev/pandas/pull/51197 + # also ensure to honor copy keyword for ExtensionDtypes + N = 10 + df = DataFrame( + np.random.default_rng(2).standard_normal((N * 10, N)), dtype="Float64" + ) + cols = np.arange(N) + np.random.default_rng(2).shuffle(cols) + + result = df.reindex(columns=cols, copy=True) + if using_copy_on_write: + assert np.shares_memory(result[0].array._data, df[0].array._data) + else: + assert not np.shares_memory(result[0].array._data, df[0].array._data) + + # pass both columns and index + result2 = df.reindex(columns=cols, index=df.index, copy=True) + if using_copy_on_write: + assert np.shares_memory(result2[0].array._data, df[0].array._data) + else: + assert not np.shares_memory(result2[0].array._data, df[0].array._data) + + @td.skip_array_manager_not_yet_implemented + def test_reindex_date_fill_value(self): + # passing date to dt64 is deprecated; enforced in 2.0 to cast to object + arr = date_range("2016-01-01", periods=6).values.reshape(3, 2) + df = DataFrame(arr, columns=["A", "B"], index=range(3)) + + ts = df.iloc[0, 0] + fv = ts.date() + + res = df.reindex(index=range(4), columns=["A", "B", "C"], fill_value=fv) + + expected = DataFrame( + {"A": df["A"].tolist() + [fv], "B": df["B"].tolist() + [fv], "C": [fv] * 4}, + dtype=object, + ) + tm.assert_frame_equal(res, expected) + + # only reindexing rows + res = df.reindex(index=range(4), fill_value=fv) + tm.assert_frame_equal(res, expected[["A", "B"]]) + + # same with a datetime-castable str + res = df.reindex( + index=range(4), columns=["A", "B", "C"], fill_value="2016-01-01" + ) + expected = DataFrame( + {"A": df["A"].tolist() + [ts], "B": df["B"].tolist() + [ts], "C": [ts] * 4}, + ) + tm.assert_frame_equal(res, expected) + + def test_reindex_with_multi_index(self): + # https://github.com/pandas-dev/pandas/issues/29896 + # tests for reindexing a multi-indexed DataFrame with a new MultiIndex + # + # confirms that we can reindex a multi-indexed DataFrame with a new + # MultiIndex object correctly when using no filling, backfilling, and + # padding + # + # The DataFrame, `df`, used in this test is: + # c + # a b + # -1 0 A + # 1 B + # 2 C + # 3 D + # 4 E + # 5 F + # 6 G + # 0 0 A + # 1 B + # 2 C + # 3 D + # 4 E + # 5 F + # 6 G + # 1 0 A + # 1 B + # 2 C + # 3 D + # 4 E + # 5 F + # 6 G + # + # and the other MultiIndex, `new_multi_index`, is: + # 0: 0 0.5 + # 1: 2.0 + # 2: 5.0 + # 3: 5.8 + df = DataFrame( + { + "a": [-1] * 7 + [0] * 7 + [1] * 7, + "b": list(range(7)) * 3, + "c": ["A", "B", "C", "D", "E", "F", "G"] * 3, + } + ).set_index(["a", "b"]) + new_index = [0.5, 2.0, 5.0, 5.8] + new_multi_index = MultiIndex.from_product([[0], new_index], names=["a", "b"]) + + # reindexing w/o a `method` value + reindexed = df.reindex(new_multi_index) + expected = DataFrame( + {"a": [0] * 4, "b": new_index, "c": [np.nan, "C", "F", np.nan]} + ).set_index(["a", "b"]) + tm.assert_frame_equal(expected, reindexed) + + # reindexing with backfilling + expected = DataFrame( + {"a": [0] * 4, "b": new_index, "c": ["B", "C", "F", "G"]} + ).set_index(["a", "b"]) + reindexed_with_backfilling = df.reindex(new_multi_index, method="bfill") + tm.assert_frame_equal(expected, reindexed_with_backfilling) + + reindexed_with_backfilling = df.reindex(new_multi_index, method="backfill") + tm.assert_frame_equal(expected, reindexed_with_backfilling) + + # reindexing with padding + expected = DataFrame( + {"a": [0] * 4, "b": new_index, "c": ["A", "C", "F", "F"]} + ).set_index(["a", "b"]) + reindexed_with_padding = df.reindex(new_multi_index, method="pad") + tm.assert_frame_equal(expected, reindexed_with_padding) + + reindexed_with_padding = df.reindex(new_multi_index, method="ffill") + tm.assert_frame_equal(expected, reindexed_with_padding) + + @pytest.mark.parametrize( + "method,expected_values", + [ + ("nearest", [0, 1, 1, 2]), + ("pad", [np.nan, 0, 1, 1]), + ("backfill", [0, 1, 2, 2]), + ], + ) + def test_reindex_methods(self, method, expected_values): + df = DataFrame({"x": list(range(5))}) + target = np.array([-0.1, 0.9, 1.1, 1.5]) + + expected = DataFrame({"x": expected_values}, index=target) + actual = df.reindex(target, method=method) + tm.assert_frame_equal(expected, actual) + + actual = df.reindex(target, method=method, tolerance=1) + tm.assert_frame_equal(expected, actual) + actual = df.reindex(target, method=method, tolerance=[1, 1, 1, 1]) + tm.assert_frame_equal(expected, actual) + + e2 = expected[::-1] + actual = df.reindex(target[::-1], method=method) + tm.assert_frame_equal(e2, actual) + + new_order = [3, 0, 2, 1] + e2 = expected.iloc[new_order] + actual = df.reindex(target[new_order], method=method) + tm.assert_frame_equal(e2, actual) + + switched_method = ( + "pad" if method == "backfill" else "backfill" if method == "pad" else method + ) + actual = df[::-1].reindex(target, method=switched_method) + tm.assert_frame_equal(expected, actual) + + def test_reindex_methods_nearest_special(self): + df = DataFrame({"x": list(range(5))}) + target = np.array([-0.1, 0.9, 1.1, 1.5]) + + expected = DataFrame({"x": [0, 1, 1, np.nan]}, index=target) + actual = df.reindex(target, method="nearest", tolerance=0.2) + tm.assert_frame_equal(expected, actual) + + expected = DataFrame({"x": [0, np.nan, 1, np.nan]}, index=target) + actual = df.reindex(target, method="nearest", tolerance=[0.5, 0.01, 0.4, 0.1]) + tm.assert_frame_equal(expected, actual) + + def test_reindex_nearest_tz(self, tz_aware_fixture): + # GH26683 + tz = tz_aware_fixture + idx = date_range("2019-01-01", periods=5, tz=tz) + df = DataFrame({"x": list(range(5))}, index=idx) + + expected = df.head(3) + actual = df.reindex(idx[:3], method="nearest") + tm.assert_frame_equal(expected, actual) + + def test_reindex_nearest_tz_empty_frame(self): + # https://github.com/pandas-dev/pandas/issues/31964 + dti = pd.DatetimeIndex(["2016-06-26 14:27:26+00:00"]) + df = DataFrame(index=pd.DatetimeIndex(["2016-07-04 14:00:59+00:00"])) + expected = DataFrame(index=dti) + result = df.reindex(dti, method="nearest") + tm.assert_frame_equal(result, expected) + + def test_reindex_frame_add_nat(self): + rng = date_range("1/1/2000 00:00:00", periods=10, freq="10s") + df = DataFrame( + {"A": np.random.default_rng(2).standard_normal(len(rng)), "B": rng} + ) + + result = df.reindex(range(15)) + assert np.issubdtype(result["B"].dtype, np.dtype("M8[ns]")) + + mask = isna(result)["B"] + assert mask[-5:].all() + assert not mask[:-5].any() + + @pytest.mark.parametrize( + "method, exp_values", + [("ffill", [0, 1, 2, 3]), ("bfill", [1.0, 2.0, 3.0, np.nan])], + ) + def test_reindex_frame_tz_ffill_bfill(self, frame_or_series, method, exp_values): + # GH#38566 + obj = frame_or_series( + [0, 1, 2, 3], + index=date_range("2020-01-01 00:00:00", periods=4, freq="h", tz="UTC"), + ) + new_index = date_range("2020-01-01 00:01:00", periods=4, freq="h", tz="UTC") + result = obj.reindex(new_index, method=method, tolerance=pd.Timedelta("1 hour")) + expected = frame_or_series(exp_values, index=new_index) + tm.assert_equal(result, expected) + + def test_reindex_limit(self): + # GH 28631 + data = [["A", "A", "A"], ["B", "B", "B"], ["C", "C", "C"], ["D", "D", "D"]] + exp_data = [ + ["A", "A", "A"], + ["B", "B", "B"], + ["C", "C", "C"], + ["D", "D", "D"], + ["D", "D", "D"], + [np.nan, np.nan, np.nan], + ] + df = DataFrame(data) + result = df.reindex([0, 1, 2, 3, 4, 5], method="ffill", limit=1) + expected = DataFrame(exp_data) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "idx, check_index_type", + [ + [["C", "B", "A"], True], + [["F", "C", "A", "D"], True], + [["A"], True], + [["A", "B", "C"], True], + [["C", "A", "B"], True], + [["C", "B"], True], + [["C", "A"], True], + [["A", "B"], True], + [["B", "A", "C"], True], + # reindex by these causes different MultiIndex levels + [["D", "F"], False], + [["A", "C", "B"], False], + ], + ) + def test_reindex_level_verify_first_level(self, idx, check_index_type): + df = DataFrame( + { + "jim": list("B" * 4 + "A" * 2 + "C" * 3), + "joe": list("abcdeabcd")[::-1], + "jolie": [10, 20, 30] * 3, + "joline": np.random.default_rng(2).integers(0, 1000, 9), + } + ) + icol = ["jim", "joe", "jolie"] + + def f(val): + return np.nonzero((df["jim"] == val).to_numpy())[0] + + i = np.concatenate(list(map(f, idx))) + left = df.set_index(icol).reindex(idx, level="jim") + right = df.iloc[i].set_index(icol) + tm.assert_frame_equal(left, right, check_index_type=check_index_type) + + @pytest.mark.parametrize( + "idx", + [ + ("mid",), + ("mid", "btm"), + ("mid", "btm", "top"), + ("mid",), + ("mid", "top"), + ("mid", "top", "btm"), + ("btm",), + ("btm", "mid"), + ("btm", "mid", "top"), + ("btm",), + ("btm", "top"), + ("btm", "top", "mid"), + ("top",), + ("top", "mid"), + ("top", "mid", "btm"), + ("top",), + ("top", "btm"), + ("top", "btm", "mid"), + ], + ) + def test_reindex_level_verify_first_level_repeats(self, idx): + df = DataFrame( + { + "jim": ["mid"] * 5 + ["btm"] * 8 + ["top"] * 7, + "joe": ["3rd"] * 2 + + ["1st"] * 3 + + ["2nd"] * 3 + + ["1st"] * 2 + + ["3rd"] * 3 + + ["1st"] * 2 + + ["3rd"] * 3 + + ["2nd"] * 2, + # this needs to be jointly unique with jim and joe or + # reindexing will fail ~1.5% of the time, this works + # out to needing unique groups of same size as joe + "jolie": np.concatenate( + [ + np.random.default_rng(2).choice(1000, x, replace=False) + for x in [2, 3, 3, 2, 3, 2, 3, 2] + ] + ), + "joline": np.random.default_rng(2).standard_normal(20).round(3) * 10, + } + ) + icol = ["jim", "joe", "jolie"] + + def f(val): + return np.nonzero((df["jim"] == val).to_numpy())[0] + + i = np.concatenate(list(map(f, idx))) + left = df.set_index(icol).reindex(idx, level="jim") + right = df.iloc[i].set_index(icol) + tm.assert_frame_equal(left, right) + + @pytest.mark.parametrize( + "idx, indexer", + [ + [ + ["1st", "2nd", "3rd"], + [2, 3, 4, 0, 1, 8, 9, 5, 6, 7, 10, 11, 12, 13, 14, 18, 19, 15, 16, 17], + ], + [ + ["3rd", "2nd", "1st"], + [0, 1, 2, 3, 4, 10, 11, 12, 5, 6, 7, 8, 9, 15, 16, 17, 18, 19, 13, 14], + ], + [["2nd", "3rd"], [0, 1, 5, 6, 7, 10, 11, 12, 18, 19, 15, 16, 17]], + [["3rd", "1st"], [0, 1, 2, 3, 4, 10, 11, 12, 8, 9, 15, 16, 17, 13, 14]], + ], + ) + def test_reindex_level_verify_repeats(self, idx, indexer): + df = DataFrame( + { + "jim": ["mid"] * 5 + ["btm"] * 8 + ["top"] * 7, + "joe": ["3rd"] * 2 + + ["1st"] * 3 + + ["2nd"] * 3 + + ["1st"] * 2 + + ["3rd"] * 3 + + ["1st"] * 2 + + ["3rd"] * 3 + + ["2nd"] * 2, + # this needs to be jointly unique with jim and joe or + # reindexing will fail ~1.5% of the time, this works + # out to needing unique groups of same size as joe + "jolie": np.concatenate( + [ + np.random.default_rng(2).choice(1000, x, replace=False) + for x in [2, 3, 3, 2, 3, 2, 3, 2] + ] + ), + "joline": np.random.default_rng(2).standard_normal(20).round(3) * 10, + } + ) + icol = ["jim", "joe", "jolie"] + left = df.set_index(icol).reindex(idx, level="joe") + right = df.iloc[indexer].set_index(icol) + tm.assert_frame_equal(left, right) + + @pytest.mark.parametrize( + "idx, indexer, check_index_type", + [ + [list("abcde"), [3, 2, 1, 0, 5, 4, 8, 7, 6], True], + [list("abcd"), [3, 2, 1, 0, 5, 8, 7, 6], True], + [list("abc"), [3, 2, 1, 8, 7, 6], True], + [list("eca"), [1, 3, 4, 6, 8], True], + [list("edc"), [0, 1, 4, 5, 6], True], + [list("eadbc"), [3, 0, 2, 1, 4, 5, 8, 7, 6], True], + [list("edwq"), [0, 4, 5], True], + [list("wq"), [], False], + ], + ) + def test_reindex_level_verify(self, idx, indexer, check_index_type): + df = DataFrame( + { + "jim": list("B" * 4 + "A" * 2 + "C" * 3), + "joe": list("abcdeabcd")[::-1], + "jolie": [10, 20, 30] * 3, + "joline": np.random.default_rng(2).integers(0, 1000, 9), + } + ) + icol = ["jim", "joe", "jolie"] + left = df.set_index(icol).reindex(idx, level="joe") + right = df.iloc[indexer].set_index(icol) + tm.assert_frame_equal(left, right, check_index_type=check_index_type) + + def test_non_monotonic_reindex_methods(self): + dr = date_range("2013-08-01", periods=6, freq="B") + data = np.random.default_rng(2).standard_normal((6, 1)) + df = DataFrame(data, index=dr, columns=list("A")) + df_rev = DataFrame(data, index=dr[[3, 4, 5] + [0, 1, 2]], columns=list("A")) + # index is not monotonic increasing or decreasing + msg = "index must be monotonic increasing or decreasing" + with pytest.raises(ValueError, match=msg): + df_rev.reindex(df.index, method="pad") + with pytest.raises(ValueError, match=msg): + df_rev.reindex(df.index, method="ffill") + with pytest.raises(ValueError, match=msg): + df_rev.reindex(df.index, method="bfill") + with pytest.raises(ValueError, match=msg): + df_rev.reindex(df.index, method="nearest") + + def test_reindex_sparse(self): + # https://github.com/pandas-dev/pandas/issues/35286 + df = DataFrame( + {"A": [0, 1], "B": pd.array([0, 1], dtype=pd.SparseDtype("int64", 0))} + ) + result = df.reindex([0, 2]) + expected = DataFrame( + { + "A": [0.0, np.nan], + "B": pd.array([0.0, np.nan], dtype=pd.SparseDtype("float64", 0.0)), + }, + index=[0, 2], + ) + tm.assert_frame_equal(result, expected) + + def test_reindex(self, float_frame, using_copy_on_write): + datetime_series = Series( + np.arange(30, dtype=np.float64), index=date_range("2020-01-01", periods=30) + ) + + newFrame = float_frame.reindex(datetime_series.index) + + for col in newFrame.columns: + for idx, val in newFrame[col].items(): + if idx in float_frame.index: + if np.isnan(val): + assert np.isnan(float_frame[col][idx]) + else: + assert val == float_frame[col][idx] + else: + assert np.isnan(val) + + for col, series in newFrame.items(): + tm.assert_index_equal(series.index, newFrame.index) + emptyFrame = float_frame.reindex(Index([])) + assert len(emptyFrame.index) == 0 + + # Cython code should be unit-tested directly + nonContigFrame = float_frame.reindex(datetime_series.index[::2]) + + for col in nonContigFrame.columns: + for idx, val in nonContigFrame[col].items(): + if idx in float_frame.index: + if np.isnan(val): + assert np.isnan(float_frame[col][idx]) + else: + assert val == float_frame[col][idx] + else: + assert np.isnan(val) + + for col, series in nonContigFrame.items(): + tm.assert_index_equal(series.index, nonContigFrame.index) + + # corner cases + + # Same index, copies values but not index if copy=False + newFrame = float_frame.reindex(float_frame.index, copy=False) + if using_copy_on_write: + assert newFrame.index.is_(float_frame.index) + else: + assert newFrame.index is float_frame.index + + # length zero + newFrame = float_frame.reindex([]) + assert newFrame.empty + assert len(newFrame.columns) == len(float_frame.columns) + + # length zero with columns reindexed with non-empty index + newFrame = float_frame.reindex([]) + newFrame = newFrame.reindex(float_frame.index) + assert len(newFrame.index) == len(float_frame.index) + assert len(newFrame.columns) == len(float_frame.columns) + + # pass non-Index + newFrame = float_frame.reindex(list(datetime_series.index)) + expected = datetime_series.index._with_freq(None) + tm.assert_index_equal(newFrame.index, expected) + + # copy with no axes + result = float_frame.reindex() + tm.assert_frame_equal(result, float_frame) + assert result is not float_frame + + def test_reindex_nan(self): + df = DataFrame( + [[1, 2], [3, 5], [7, 11], [9, 23]], + index=[2, np.nan, 1, 5], + columns=["joe", "jim"], + ) + + i, j = [np.nan, 5, 5, np.nan, 1, 2, np.nan], [1, 3, 3, 1, 2, 0, 1] + tm.assert_frame_equal(df.reindex(i), df.iloc[j]) + + df.index = df.index.astype("object") + tm.assert_frame_equal(df.reindex(i), df.iloc[j], check_index_type=False) + + # GH10388 + df = DataFrame( + { + "other": ["a", "b", np.nan, "c"], + "date": ["2015-03-22", np.nan, "2012-01-08", np.nan], + "amount": [2, 3, 4, 5], + } + ) + + df["date"] = pd.to_datetime(df.date) + df["delta"] = (pd.to_datetime("2015-06-18") - df["date"]).shift(1) + + left = df.set_index(["delta", "other", "date"]).reset_index() + right = df.reindex(columns=["delta", "other", "date", "amount"]) + tm.assert_frame_equal(left, right) + + def test_reindex_name_remains(self): + s = Series(np.random.default_rng(2).random(10)) + df = DataFrame(s, index=np.arange(len(s))) + i = Series(np.arange(10), name="iname") + + df = df.reindex(i) + assert df.index.name == "iname" + + df = df.reindex(Index(np.arange(10), name="tmpname")) + assert df.index.name == "tmpname" + + s = Series(np.random.default_rng(2).random(10)) + df = DataFrame(s.T, index=np.arange(len(s))) + i = Series(np.arange(10), name="iname") + df = df.reindex(columns=i) + assert df.columns.name == "iname" + + def test_reindex_int(self, int_frame): + smaller = int_frame.reindex(int_frame.index[::2]) + + assert smaller["A"].dtype == np.int64 + + bigger = smaller.reindex(int_frame.index) + assert bigger["A"].dtype == np.float64 + + smaller = int_frame.reindex(columns=["A", "B"]) + assert smaller["A"].dtype == np.int64 + + def test_reindex_columns(self, float_frame): + new_frame = float_frame.reindex(columns=["A", "B", "E"]) + + tm.assert_series_equal(new_frame["B"], float_frame["B"]) + assert np.isnan(new_frame["E"]).all() + assert "C" not in new_frame + + # Length zero + new_frame = float_frame.reindex(columns=[]) + assert new_frame.empty + + def test_reindex_columns_method(self): + # GH 14992, reindexing over columns ignored method + df = DataFrame( + data=[[11, 12, 13], [21, 22, 23], [31, 32, 33]], + index=[1, 2, 4], + columns=[1, 2, 4], + dtype=float, + ) + + # default method + result = df.reindex(columns=range(6)) + expected = DataFrame( + data=[ + [np.nan, 11, 12, np.nan, 13, np.nan], + [np.nan, 21, 22, np.nan, 23, np.nan], + [np.nan, 31, 32, np.nan, 33, np.nan], + ], + index=[1, 2, 4], + columns=range(6), + dtype=float, + ) + tm.assert_frame_equal(result, expected) + + # method='ffill' + result = df.reindex(columns=range(6), method="ffill") + expected = DataFrame( + data=[ + [np.nan, 11, 12, 12, 13, 13], + [np.nan, 21, 22, 22, 23, 23], + [np.nan, 31, 32, 32, 33, 33], + ], + index=[1, 2, 4], + columns=range(6), + dtype=float, + ) + tm.assert_frame_equal(result, expected) + + # method='bfill' + result = df.reindex(columns=range(6), method="bfill") + expected = DataFrame( + data=[ + [11, 11, 12, 13, 13, np.nan], + [21, 21, 22, 23, 23, np.nan], + [31, 31, 32, 33, 33, np.nan], + ], + index=[1, 2, 4], + columns=range(6), + dtype=float, + ) + tm.assert_frame_equal(result, expected) + + def test_reindex_axes(self): + # GH 3317, reindexing by both axes loses freq of the index + df = DataFrame( + np.ones((3, 3)), + index=[datetime(2012, 1, 1), datetime(2012, 1, 2), datetime(2012, 1, 3)], + columns=["a", "b", "c"], + ) + time_freq = date_range("2012-01-01", "2012-01-03", freq="d") + some_cols = ["a", "b"] + + index_freq = df.reindex(index=time_freq).index.freq + both_freq = df.reindex(index=time_freq, columns=some_cols).index.freq + seq_freq = df.reindex(index=time_freq).reindex(columns=some_cols).index.freq + assert index_freq == both_freq + assert index_freq == seq_freq + + def test_reindex_fill_value(self): + df = DataFrame(np.random.default_rng(2).standard_normal((10, 4))) + + # axis=0 + result = df.reindex(list(range(15))) + assert np.isnan(result.values[-5:]).all() + + result = df.reindex(range(15), fill_value=0) + expected = df.reindex(range(15)).fillna(0) + tm.assert_frame_equal(result, expected) + + # axis=1 + result = df.reindex(columns=range(5), fill_value=0.0) + expected = df.copy() + expected[4] = 0.0 + tm.assert_frame_equal(result, expected) + + result = df.reindex(columns=range(5), fill_value=0) + expected = df.copy() + expected[4] = 0 + tm.assert_frame_equal(result, expected) + + result = df.reindex(columns=range(5), fill_value="foo") + expected = df.copy() + expected[4] = "foo" + tm.assert_frame_equal(result, expected) + + # other dtypes + df["foo"] = "foo" + result = df.reindex(range(15), fill_value="0") + expected = df.reindex(range(15)).fillna("0") + tm.assert_frame_equal(result, expected) + + def test_reindex_uint_dtypes_fill_value(self, any_unsigned_int_numpy_dtype): + # GH#48184 + df = DataFrame({"a": [1, 2], "b": [1, 2]}, dtype=any_unsigned_int_numpy_dtype) + result = df.reindex(columns=list("abcd"), index=[0, 1, 2, 3], fill_value=10) + expected = DataFrame( + {"a": [1, 2, 10, 10], "b": [1, 2, 10, 10], "c": 10, "d": 10}, + dtype=any_unsigned_int_numpy_dtype, + ) + tm.assert_frame_equal(result, expected) + + def test_reindex_single_column_ea_index_and_columns(self, any_numeric_ea_dtype): + # GH#48190 + df = DataFrame({"a": [1, 2]}, dtype=any_numeric_ea_dtype) + result = df.reindex(columns=list("ab"), index=[0, 1, 2], fill_value=10) + expected = DataFrame( + {"a": Series([1, 2, 10], dtype=any_numeric_ea_dtype), "b": 10} + ) + tm.assert_frame_equal(result, expected) + + def test_reindex_dups(self): + # GH4746, reindex on duplicate index error messages + arr = np.random.default_rng(2).standard_normal(10) + df = DataFrame(arr, index=[1, 2, 3, 4, 5, 1, 2, 3, 4, 5]) + + # set index is ok + result = df.copy() + result.index = list(range(len(df))) + expected = DataFrame(arr, index=list(range(len(df)))) + tm.assert_frame_equal(result, expected) + + # reindex fails + msg = "cannot reindex on an axis with duplicate labels" + with pytest.raises(ValueError, match=msg): + df.reindex(index=list(range(len(df)))) + + def test_reindex_with_duplicate_columns(self): + # reindex is invalid! + df = DataFrame( + [[1, 5, 7.0], [1, 5, 7.0], [1, 5, 7.0]], columns=["bar", "a", "a"] + ) + msg = "cannot reindex on an axis with duplicate labels" + with pytest.raises(ValueError, match=msg): + df.reindex(columns=["bar"]) + with pytest.raises(ValueError, match=msg): + df.reindex(columns=["bar", "foo"]) + + def test_reindex_axis_style(self): + # https://github.com/pandas-dev/pandas/issues/12392 + df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) + expected = DataFrame( + {"A": [1, 2, np.nan], "B": [4, 5, np.nan]}, index=[0, 1, 3] + ) + result = df.reindex([0, 1, 3]) + tm.assert_frame_equal(result, expected) + + result = df.reindex([0, 1, 3], axis=0) + tm.assert_frame_equal(result, expected) + + result = df.reindex([0, 1, 3], axis="index") + tm.assert_frame_equal(result, expected) + + def test_reindex_positional_raises(self): + # https://github.com/pandas-dev/pandas/issues/12392 + # Enforced in 2.0 + df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) + msg = r"reindex\(\) takes from 1 to 2 positional arguments but 3 were given" + with pytest.raises(TypeError, match=msg): + df.reindex([0, 1], ["A", "B", "C"]) + + def test_reindex_axis_style_raises(self): + # https://github.com/pandas-dev/pandas/issues/12392 + df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) + with pytest.raises(TypeError, match="Cannot specify both 'axis'"): + df.reindex([0, 1], columns=["A"], axis=1) + + with pytest.raises(TypeError, match="Cannot specify both 'axis'"): + df.reindex([0, 1], columns=["A"], axis="index") + + with pytest.raises(TypeError, match="Cannot specify both 'axis'"): + df.reindex(index=[0, 1], axis="index") + + with pytest.raises(TypeError, match="Cannot specify both 'axis'"): + df.reindex(index=[0, 1], axis="columns") + + with pytest.raises(TypeError, match="Cannot specify both 'axis'"): + df.reindex(columns=[0, 1], axis="columns") + + with pytest.raises(TypeError, match="Cannot specify both 'axis'"): + df.reindex(index=[0, 1], columns=[0, 1], axis="columns") + + with pytest.raises(TypeError, match="Cannot specify all"): + df.reindex(labels=[0, 1], index=[0], columns=["A"]) + + # Mixing styles + with pytest.raises(TypeError, match="Cannot specify both 'axis'"): + df.reindex(index=[0, 1], axis="index") + + with pytest.raises(TypeError, match="Cannot specify both 'axis'"): + df.reindex(index=[0, 1], axis="columns") + + # Duplicates + with pytest.raises(TypeError, match="multiple values"): + df.reindex([0, 1], labels=[0, 1]) + + def test_reindex_single_named_indexer(self): + # https://github.com/pandas-dev/pandas/issues/12392 + df = DataFrame({"A": [1, 2, 3], "B": [1, 2, 3]}) + result = df.reindex([0, 1], columns=["A"]) + expected = DataFrame({"A": [1, 2]}) + tm.assert_frame_equal(result, expected) + + def test_reindex_api_equivalence(self): + # https://github.com/pandas-dev/pandas/issues/12392 + # equivalence of the labels/axis and index/columns API's + df = DataFrame( + [[1, 2, 3], [3, 4, 5], [5, 6, 7]], + index=["a", "b", "c"], + columns=["d", "e", "f"], + ) + + res1 = df.reindex(["b", "a"]) + res2 = df.reindex(index=["b", "a"]) + res3 = df.reindex(labels=["b", "a"]) + res4 = df.reindex(labels=["b", "a"], axis=0) + res5 = df.reindex(["b", "a"], axis=0) + for res in [res2, res3, res4, res5]: + tm.assert_frame_equal(res1, res) + + res1 = df.reindex(columns=["e", "d"]) + res2 = df.reindex(["e", "d"], axis=1) + res3 = df.reindex(labels=["e", "d"], axis=1) + for res in [res2, res3]: + tm.assert_frame_equal(res1, res) + + res1 = df.reindex(index=["b", "a"], columns=["e", "d"]) + res2 = df.reindex(columns=["e", "d"], index=["b", "a"]) + res3 = df.reindex(labels=["b", "a"], axis=0).reindex(labels=["e", "d"], axis=1) + for res in [res2, res3]: + tm.assert_frame_equal(res1, res) + + def test_reindex_boolean(self): + frame = DataFrame( + np.ones((10, 2), dtype=bool), index=np.arange(0, 20, 2), columns=[0, 2] + ) + + reindexed = frame.reindex(np.arange(10)) + assert reindexed.values.dtype == np.object_ + assert isna(reindexed[0][1]) + + reindexed = frame.reindex(columns=range(3)) + assert reindexed.values.dtype == np.object_ + assert isna(reindexed[1]).all() + + def test_reindex_objects(self, float_string_frame): + reindexed = float_string_frame.reindex(columns=["foo", "A", "B"]) + assert "foo" in reindexed + + reindexed = float_string_frame.reindex(columns=["A", "B"]) + assert "foo" not in reindexed + + def test_reindex_corner(self, int_frame): + index = Index(["a", "b", "c"]) + dm = DataFrame({}).reindex(index=[1, 2, 3]) + reindexed = dm.reindex(columns=index) + tm.assert_index_equal(reindexed.columns, index) + + # ints are weird + smaller = int_frame.reindex(columns=["A", "B", "E"]) + assert smaller["E"].dtype == np.float64 + + def test_reindex_with_nans(self): + df = DataFrame( + [[1, 2], [3, 4], [np.nan, np.nan], [7, 8], [9, 10]], + columns=["a", "b"], + index=[100.0, 101.0, np.nan, 102.0, 103.0], + ) + + result = df.reindex(index=[101.0, 102.0, 103.0]) + expected = df.iloc[[1, 3, 4]] + tm.assert_frame_equal(result, expected) + + result = df.reindex(index=[103.0]) + expected = df.iloc[[4]] + tm.assert_frame_equal(result, expected) + + result = df.reindex(index=[101.0]) + expected = df.iloc[[1]] + tm.assert_frame_equal(result, expected) + + def test_reindex_multi(self): + df = DataFrame(np.random.default_rng(2).standard_normal((3, 3))) + + result = df.reindex(index=range(4), columns=range(4)) + expected = df.reindex(list(range(4))).reindex(columns=range(4)) + + tm.assert_frame_equal(result, expected) + + df = DataFrame(np.random.default_rng(2).integers(0, 10, (3, 3))) + + result = df.reindex(index=range(4), columns=range(4)) + expected = df.reindex(list(range(4))).reindex(columns=range(4)) + + tm.assert_frame_equal(result, expected) + + df = DataFrame(np.random.default_rng(2).integers(0, 10, (3, 3))) + + result = df.reindex(index=range(2), columns=range(2)) + expected = df.reindex(range(2)).reindex(columns=range(2)) + + tm.assert_frame_equal(result, expected) + + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 3)) + 1j, + columns=["a", "b", "c"], + ) + + result = df.reindex(index=[0, 1], columns=["a", "b"]) + expected = df.reindex([0, 1]).reindex(columns=["a", "b"]) + + tm.assert_frame_equal(result, expected) + + def test_reindex_multi_categorical_time(self): + # https://github.com/pandas-dev/pandas/issues/21390 + midx = MultiIndex.from_product( + [ + Categorical(["a", "b", "c"]), + Categorical(date_range("2012-01-01", periods=3, freq="h")), + ] + ) + df = DataFrame({"a": range(len(midx))}, index=midx) + df2 = df.iloc[[0, 1, 2, 3, 4, 5, 6, 8]] + + result = df2.reindex(midx) + expected = DataFrame({"a": [0, 1, 2, 3, 4, 5, 6, np.nan, 8]}, index=midx) + tm.assert_frame_equal(result, expected) + + def test_reindex_with_categoricalindex(self): + df = DataFrame( + { + "A": np.arange(3, dtype="int64"), + }, + index=CategoricalIndex( + list("abc"), dtype=CategoricalDtype(list("cabe")), name="B" + ), + ) + + # reindexing + # convert to a regular index + result = df.reindex(["a", "b", "e"]) + expected = DataFrame({"A": [0, 1, np.nan], "B": Series(list("abe"))}).set_index( + "B" + ) + tm.assert_frame_equal(result, expected, check_index_type=True) + + result = df.reindex(["a", "b"]) + expected = DataFrame({"A": [0, 1], "B": Series(list("ab"))}).set_index("B") + tm.assert_frame_equal(result, expected, check_index_type=True) + + result = df.reindex(["e"]) + expected = DataFrame({"A": [np.nan], "B": Series(["e"])}).set_index("B") + tm.assert_frame_equal(result, expected, check_index_type=True) + + result = df.reindex(["d"]) + expected = DataFrame({"A": [np.nan], "B": Series(["d"])}).set_index("B") + tm.assert_frame_equal(result, expected, check_index_type=True) + + # since we are actually reindexing with a Categorical + # then return a Categorical + cats = list("cabe") + + result = df.reindex(Categorical(["a", "e"], categories=cats)) + expected = DataFrame( + {"A": [0, np.nan], "B": Series(list("ae")).astype(CategoricalDtype(cats))} + ).set_index("B") + tm.assert_frame_equal(result, expected, check_index_type=True) + + result = df.reindex(Categorical(["a"], categories=cats)) + expected = DataFrame( + {"A": [0], "B": Series(list("a")).astype(CategoricalDtype(cats))} + ).set_index("B") + tm.assert_frame_equal(result, expected, check_index_type=True) + + result = df.reindex(["a", "b", "e"]) + expected = DataFrame({"A": [0, 1, np.nan], "B": Series(list("abe"))}).set_index( + "B" + ) + tm.assert_frame_equal(result, expected, check_index_type=True) + + result = df.reindex(["a", "b"]) + expected = DataFrame({"A": [0, 1], "B": Series(list("ab"))}).set_index("B") + tm.assert_frame_equal(result, expected, check_index_type=True) + + result = df.reindex(["e"]) + expected = DataFrame({"A": [np.nan], "B": Series(["e"])}).set_index("B") + tm.assert_frame_equal(result, expected, check_index_type=True) + + # give back the type of categorical that we received + result = df.reindex(Categorical(["a", "e"], categories=cats, ordered=True)) + expected = DataFrame( + { + "A": [0, np.nan], + "B": Series(list("ae")).astype(CategoricalDtype(cats, ordered=True)), + } + ).set_index("B") + tm.assert_frame_equal(result, expected, check_index_type=True) + + result = df.reindex(Categorical(["a", "d"], categories=["a", "d"])) + expected = DataFrame( + { + "A": [0, np.nan], + "B": Series(list("ad")).astype(CategoricalDtype(["a", "d"])), + } + ).set_index("B") + tm.assert_frame_equal(result, expected, check_index_type=True) + + df2 = DataFrame( + { + "A": np.arange(6, dtype="int64"), + }, + index=CategoricalIndex( + list("aabbca"), dtype=CategoricalDtype(list("cabe")), name="B" + ), + ) + # passed duplicate indexers are not allowed + msg = "cannot reindex on an axis with duplicate labels" + with pytest.raises(ValueError, match=msg): + df2.reindex(["a", "b"]) + + # args NotImplemented ATM + msg = r"argument {} is not implemented for CategoricalIndex\.reindex" + with pytest.raises(NotImplementedError, match=msg.format("method")): + df.reindex(["a"], method="ffill") + with pytest.raises(NotImplementedError, match=msg.format("level")): + df.reindex(["a"], level=1) + with pytest.raises(NotImplementedError, match=msg.format("limit")): + df.reindex(["a"], limit=2) + + def test_reindex_signature(self): + sig = inspect.signature(DataFrame.reindex) + parameters = set(sig.parameters) + assert parameters == { + "self", + "labels", + "index", + "columns", + "axis", + "limit", + "copy", + "level", + "method", + "fill_value", + "tolerance", + } + + def test_reindex_multiindex_ffill_added_rows(self): + # GH#23693 + # reindex added rows with nan values even when fill method was specified + mi = MultiIndex.from_tuples([("a", "b"), ("d", "e")]) + df = DataFrame([[0, 7], [3, 4]], index=mi, columns=["x", "y"]) + mi2 = MultiIndex.from_tuples([("a", "b"), ("d", "e"), ("h", "i")]) + result = df.reindex(mi2, axis=0, method="ffill") + expected = DataFrame([[0, 7], [3, 4], [3, 4]], index=mi2, columns=["x", "y"]) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "kwargs", + [ + {"method": "pad", "tolerance": timedelta(seconds=9)}, + {"method": "backfill", "tolerance": timedelta(seconds=9)}, + {"method": "nearest"}, + {"method": None}, + ], + ) + def test_reindex_empty_frame(self, kwargs): + # GH#27315 + idx = date_range(start="2020", freq="30s", periods=3) + df = DataFrame([], index=Index([], name="time"), columns=["a"]) + result = df.reindex(idx, **kwargs) + expected = DataFrame({"a": [np.nan] * 3}, index=idx, dtype=object) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "src_idx", + [ + Index([]), + CategoricalIndex([]), + ], + ) + @pytest.mark.parametrize( + "cat_idx", + [ + # No duplicates + Index([]), + CategoricalIndex([]), + Index(["A", "B"]), + CategoricalIndex(["A", "B"]), + # Duplicates: GH#38906 + Index(["A", "A"]), + CategoricalIndex(["A", "A"]), + ], + ) + def test_reindex_empty(self, src_idx, cat_idx): + df = DataFrame(columns=src_idx, index=["K"], dtype="f8") + + result = df.reindex(columns=cat_idx) + expected = DataFrame(index=["K"], columns=cat_idx, dtype="f8") + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("dtype", ["m8[ns]", "M8[ns]"]) + def test_reindex_datetimelike_to_object(self, dtype): + # GH#39755 dont cast dt64/td64 to ints + mi = MultiIndex.from_product([list("ABCDE"), range(2)]) + + dti = date_range("2016-01-01", periods=10) + fv = np.timedelta64("NaT", "ns") + if dtype == "m8[ns]": + dti = dti - dti[0] + fv = np.datetime64("NaT", "ns") + + ser = Series(dti, index=mi) + ser[::3] = pd.NaT + + df = ser.unstack() + + index = df.index.append(Index([1])) + columns = df.columns.append(Index(["foo"])) + + res = df.reindex(index=index, columns=columns, fill_value=fv) + + expected = DataFrame( + { + 0: df[0].tolist() + [fv], + 1: df[1].tolist() + [fv], + "foo": np.array(["NaT"] * 6, dtype=fv.dtype), + }, + index=index, + ) + assert (res.dtypes[[0, 1]] == object).all() + assert res.iloc[0, 0] is pd.NaT + assert res.iloc[-1, 0] is fv + assert res.iloc[-1, 1] is fv + tm.assert_frame_equal(res, expected) + + @pytest.mark.parametrize( + "index_df,index_res,index_exp", + [ + ( + CategoricalIndex([], categories=["A"]), + Index(["A"]), + Index(["A"]), + ), + ( + CategoricalIndex([], categories=["A"]), + Index(["B"]), + Index(["B"]), + ), + ( + CategoricalIndex([], categories=["A"]), + CategoricalIndex(["A"]), + CategoricalIndex(["A"]), + ), + ( + CategoricalIndex([], categories=["A"]), + CategoricalIndex(["B"]), + CategoricalIndex(["B"]), + ), + ], + ) + def test_reindex_not_category(self, index_df, index_res, index_exp): + # GH#28690 + df = DataFrame(index=index_df) + result = df.reindex(index=index_res) + expected = DataFrame(index=index_exp) + tm.assert_frame_equal(result, expected) + + def test_invalid_method(self): + df = DataFrame({"A": [1, np.nan, 2]}) + + msg = "Invalid fill method" + with pytest.raises(ValueError, match=msg): + df.reindex([1, 0, 2], method="asfreq") diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_reindex_like.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_reindex_like.py new file mode 100644 index 0000000000000000000000000000000000000000..ce68ec28eec3dd85461fcecfe506524040f64542 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_reindex_like.py @@ -0,0 +1,39 @@ +import numpy as np +import pytest + +from pandas import DataFrame +import pandas._testing as tm + + +class TestDataFrameReindexLike: + def test_reindex_like(self, float_frame): + other = float_frame.reindex(index=float_frame.index[:10], columns=["C", "B"]) + + tm.assert_frame_equal(other, float_frame.reindex_like(other)) + + @pytest.mark.parametrize( + "method,expected_values", + [ + ("nearest", [0, 1, 1, 2]), + ("pad", [np.nan, 0, 1, 1]), + ("backfill", [0, 1, 2, 2]), + ], + ) + def test_reindex_like_methods(self, method, expected_values): + df = DataFrame({"x": list(range(5))}) + + result = df.reindex_like(df, method=method, tolerance=0) + tm.assert_frame_equal(df, result) + result = df.reindex_like(df, method=method, tolerance=[0, 0, 0, 0]) + tm.assert_frame_equal(df, result) + + def test_reindex_like_subclass(self): + # https://github.com/pandas-dev/pandas/issues/31925 + class MyDataFrame(DataFrame): + pass + + expected = DataFrame() + df = MyDataFrame() + result = df.reindex_like(expected) + + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_rename.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_rename.py new file mode 100644 index 0000000000000000000000000000000000000000..c3bc96b44c80745d2b96a4c57f936778372affb8 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_rename.py @@ -0,0 +1,415 @@ +from collections import ChainMap +import inspect + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Index, + MultiIndex, + merge, +) +import pandas._testing as tm + + +class TestRename: + def test_rename_signature(self): + sig = inspect.signature(DataFrame.rename) + parameters = set(sig.parameters) + assert parameters == { + "self", + "mapper", + "index", + "columns", + "axis", + "inplace", + "copy", + "level", + "errors", + } + + def test_rename_mi(self, frame_or_series): + obj = frame_or_series( + [11, 21, 31], + index=MultiIndex.from_tuples([("A", x) for x in ["a", "B", "c"]]), + ) + obj.rename(str.lower) + + def test_rename(self, float_frame): + mapping = {"A": "a", "B": "b", "C": "c", "D": "d"} + + renamed = float_frame.rename(columns=mapping) + renamed2 = float_frame.rename(columns=str.lower) + + tm.assert_frame_equal(renamed, renamed2) + tm.assert_frame_equal( + renamed2.rename(columns=str.upper), float_frame, check_names=False + ) + + # index + data = {"A": {"foo": 0, "bar": 1}} + + df = DataFrame(data) + renamed = df.rename(index={"foo": "bar", "bar": "foo"}) + tm.assert_index_equal(renamed.index, Index(["bar", "foo"])) + + renamed = df.rename(index=str.upper) + tm.assert_index_equal(renamed.index, Index(["FOO", "BAR"])) + + # have to pass something + with pytest.raises(TypeError, match="must pass an index to rename"): + float_frame.rename() + + # partial columns + renamed = float_frame.rename(columns={"C": "foo", "D": "bar"}) + tm.assert_index_equal(renamed.columns, Index(["A", "B", "foo", "bar"])) + + # other axis + renamed = float_frame.T.rename(index={"C": "foo", "D": "bar"}) + tm.assert_index_equal(renamed.index, Index(["A", "B", "foo", "bar"])) + + # index with name + index = Index(["foo", "bar"], name="name") + renamer = DataFrame(data, index=index) + renamed = renamer.rename(index={"foo": "bar", "bar": "foo"}) + tm.assert_index_equal(renamed.index, Index(["bar", "foo"], name="name")) + assert renamed.index.name == renamer.index.name + + @pytest.mark.parametrize( + "args,kwargs", + [ + ((ChainMap({"A": "a"}, {"B": "b"}),), {"axis": "columns"}), + ((), {"columns": ChainMap({"A": "a"}, {"B": "b"})}), + ], + ) + def test_rename_chainmap(self, args, kwargs): + # see gh-23859 + colAData = range(1, 11) + colBdata = np.random.default_rng(2).standard_normal(10) + + df = DataFrame({"A": colAData, "B": colBdata}) + result = df.rename(*args, **kwargs) + + expected = DataFrame({"a": colAData, "b": colBdata}) + tm.assert_frame_equal(result, expected) + + def test_rename_multiindex(self): + tuples_index = [("foo1", "bar1"), ("foo2", "bar2")] + tuples_columns = [("fizz1", "buzz1"), ("fizz2", "buzz2")] + index = MultiIndex.from_tuples(tuples_index, names=["foo", "bar"]) + columns = MultiIndex.from_tuples(tuples_columns, names=["fizz", "buzz"]) + df = DataFrame([(0, 0), (1, 1)], index=index, columns=columns) + + # + # without specifying level -> across all levels + + renamed = df.rename( + index={"foo1": "foo3", "bar2": "bar3"}, + columns={"fizz1": "fizz3", "buzz2": "buzz3"}, + ) + new_index = MultiIndex.from_tuples( + [("foo3", "bar1"), ("foo2", "bar3")], names=["foo", "bar"] + ) + new_columns = MultiIndex.from_tuples( + [("fizz3", "buzz1"), ("fizz2", "buzz3")], names=["fizz", "buzz"] + ) + tm.assert_index_equal(renamed.index, new_index) + tm.assert_index_equal(renamed.columns, new_columns) + assert renamed.index.names == df.index.names + assert renamed.columns.names == df.columns.names + + # + # with specifying a level (GH13766) + + # dict + new_columns = MultiIndex.from_tuples( + [("fizz3", "buzz1"), ("fizz2", "buzz2")], names=["fizz", "buzz"] + ) + renamed = df.rename(columns={"fizz1": "fizz3", "buzz2": "buzz3"}, level=0) + tm.assert_index_equal(renamed.columns, new_columns) + renamed = df.rename(columns={"fizz1": "fizz3", "buzz2": "buzz3"}, level="fizz") + tm.assert_index_equal(renamed.columns, new_columns) + + new_columns = MultiIndex.from_tuples( + [("fizz1", "buzz1"), ("fizz2", "buzz3")], names=["fizz", "buzz"] + ) + renamed = df.rename(columns={"fizz1": "fizz3", "buzz2": "buzz3"}, level=1) + tm.assert_index_equal(renamed.columns, new_columns) + renamed = df.rename(columns={"fizz1": "fizz3", "buzz2": "buzz3"}, level="buzz") + tm.assert_index_equal(renamed.columns, new_columns) + + # function + func = str.upper + new_columns = MultiIndex.from_tuples( + [("FIZZ1", "buzz1"), ("FIZZ2", "buzz2")], names=["fizz", "buzz"] + ) + renamed = df.rename(columns=func, level=0) + tm.assert_index_equal(renamed.columns, new_columns) + renamed = df.rename(columns=func, level="fizz") + tm.assert_index_equal(renamed.columns, new_columns) + + new_columns = MultiIndex.from_tuples( + [("fizz1", "BUZZ1"), ("fizz2", "BUZZ2")], names=["fizz", "buzz"] + ) + renamed = df.rename(columns=func, level=1) + tm.assert_index_equal(renamed.columns, new_columns) + renamed = df.rename(columns=func, level="buzz") + tm.assert_index_equal(renamed.columns, new_columns) + + # index + new_index = MultiIndex.from_tuples( + [("foo3", "bar1"), ("foo2", "bar2")], names=["foo", "bar"] + ) + renamed = df.rename(index={"foo1": "foo3", "bar2": "bar3"}, level=0) + tm.assert_index_equal(renamed.index, new_index) + + def test_rename_nocopy(self, float_frame, using_copy_on_write, warn_copy_on_write): + renamed = float_frame.rename(columns={"C": "foo"}, copy=False) + + assert np.shares_memory(renamed["foo"]._values, float_frame["C"]._values) + + with tm.assert_cow_warning(warn_copy_on_write): + renamed.loc[:, "foo"] = 1.0 + if using_copy_on_write: + assert not (float_frame["C"] == 1.0).all() + else: + assert (float_frame["C"] == 1.0).all() + + def test_rename_inplace(self, float_frame): + float_frame.rename(columns={"C": "foo"}) + assert "C" in float_frame + assert "foo" not in float_frame + + c_values = float_frame["C"] + float_frame = float_frame.copy() + return_value = float_frame.rename(columns={"C": "foo"}, inplace=True) + assert return_value is None + + assert "C" not in float_frame + assert "foo" in float_frame + # GH 44153 + # Used to be id(float_frame["foo"]) != c_id, but flaky in the CI + assert float_frame["foo"] is not c_values + + def test_rename_bug(self): + # GH 5344 + # rename set ref_locs, and set_index was not resetting + df = DataFrame({0: ["foo", "bar"], 1: ["bah", "bas"], 2: [1, 2]}) + df = df.rename(columns={0: "a"}) + df = df.rename(columns={1: "b"}) + df = df.set_index(["a", "b"]) + df.columns = ["2001-01-01"] + expected = DataFrame( + [[1], [2]], + index=MultiIndex.from_tuples( + [("foo", "bah"), ("bar", "bas")], names=["a", "b"] + ), + columns=["2001-01-01"], + ) + tm.assert_frame_equal(df, expected) + + def test_rename_bug2(self): + # GH 19497 + # rename was changing Index to MultiIndex if Index contained tuples + + df = DataFrame(data=np.arange(3), index=[(0, 0), (1, 1), (2, 2)], columns=["a"]) + df = df.rename({(1, 1): (5, 4)}, axis="index") + expected = DataFrame( + data=np.arange(3), index=[(0, 0), (5, 4), (2, 2)], columns=["a"] + ) + tm.assert_frame_equal(df, expected) + + def test_rename_errors_raises(self): + df = DataFrame(columns=["A", "B", "C", "D"]) + with pytest.raises(KeyError, match="'E'] not found in axis"): + df.rename(columns={"A": "a", "E": "e"}, errors="raise") + + @pytest.mark.parametrize( + "mapper, errors, expected_columns", + [ + ({"A": "a", "E": "e"}, "ignore", ["a", "B", "C", "D"]), + ({"A": "a"}, "raise", ["a", "B", "C", "D"]), + (str.lower, "raise", ["a", "b", "c", "d"]), + ], + ) + def test_rename_errors(self, mapper, errors, expected_columns): + # GH 13473 + # rename now works with errors parameter + df = DataFrame(columns=["A", "B", "C", "D"]) + result = df.rename(columns=mapper, errors=errors) + expected = DataFrame(columns=expected_columns) + tm.assert_frame_equal(result, expected) + + def test_rename_objects(self, float_string_frame): + renamed = float_string_frame.rename(columns=str.upper) + + assert "FOO" in renamed + assert "foo" not in renamed + + def test_rename_axis_style(self): + # https://github.com/pandas-dev/pandas/issues/12392 + df = DataFrame({"A": [1, 2], "B": [1, 2]}, index=["X", "Y"]) + expected = DataFrame({"a": [1, 2], "b": [1, 2]}, index=["X", "Y"]) + + result = df.rename(str.lower, axis=1) + tm.assert_frame_equal(result, expected) + + result = df.rename(str.lower, axis="columns") + tm.assert_frame_equal(result, expected) + + result = df.rename({"A": "a", "B": "b"}, axis=1) + tm.assert_frame_equal(result, expected) + + result = df.rename({"A": "a", "B": "b"}, axis="columns") + tm.assert_frame_equal(result, expected) + + # Index + expected = DataFrame({"A": [1, 2], "B": [1, 2]}, index=["x", "y"]) + result = df.rename(str.lower, axis=0) + tm.assert_frame_equal(result, expected) + + result = df.rename(str.lower, axis="index") + tm.assert_frame_equal(result, expected) + + result = df.rename({"X": "x", "Y": "y"}, axis=0) + tm.assert_frame_equal(result, expected) + + result = df.rename({"X": "x", "Y": "y"}, axis="index") + tm.assert_frame_equal(result, expected) + + result = df.rename(mapper=str.lower, axis="index") + tm.assert_frame_equal(result, expected) + + def test_rename_mapper_multi(self): + df = DataFrame({"A": ["a", "b"], "B": ["c", "d"], "C": [1, 2]}).set_index( + ["A", "B"] + ) + result = df.rename(str.upper) + expected = df.rename(index=str.upper) + tm.assert_frame_equal(result, expected) + + def test_rename_positional_named(self): + # https://github.com/pandas-dev/pandas/issues/12392 + df = DataFrame({"a": [1, 2], "b": [1, 2]}, index=["X", "Y"]) + result = df.rename(index=str.lower, columns=str.upper) + expected = DataFrame({"A": [1, 2], "B": [1, 2]}, index=["x", "y"]) + tm.assert_frame_equal(result, expected) + + def test_rename_axis_style_raises(self): + # see gh-12392 + df = DataFrame({"A": [1, 2], "B": [1, 2]}, index=["0", "1"]) + + # Named target and axis + over_spec_msg = "Cannot specify both 'axis' and any of 'index' or 'columns'" + with pytest.raises(TypeError, match=over_spec_msg): + df.rename(index=str.lower, axis=1) + + with pytest.raises(TypeError, match=over_spec_msg): + df.rename(index=str.lower, axis="columns") + + with pytest.raises(TypeError, match=over_spec_msg): + df.rename(columns=str.lower, axis="columns") + + with pytest.raises(TypeError, match=over_spec_msg): + df.rename(index=str.lower, axis=0) + + # Multiple targets and axis + with pytest.raises(TypeError, match=over_spec_msg): + df.rename(str.lower, index=str.lower, axis="columns") + + # Too many targets + over_spec_msg = "Cannot specify both 'mapper' and any of 'index' or 'columns'" + with pytest.raises(TypeError, match=over_spec_msg): + df.rename(str.lower, index=str.lower, columns=str.lower) + + # Duplicates + with pytest.raises(TypeError, match="multiple values"): + df.rename(id, mapper=id) + + def test_rename_positional_raises(self): + # GH 29136 + df = DataFrame(columns=["A", "B"]) + msg = r"rename\(\) takes from 1 to 2 positional arguments" + + with pytest.raises(TypeError, match=msg): + df.rename(None, str.lower) + + def test_rename_no_mappings_raises(self): + # GH 29136 + df = DataFrame([[1]]) + msg = "must pass an index to rename" + with pytest.raises(TypeError, match=msg): + df.rename() + + with pytest.raises(TypeError, match=msg): + df.rename(None, index=None) + + with pytest.raises(TypeError, match=msg): + df.rename(None, columns=None) + + with pytest.raises(TypeError, match=msg): + df.rename(None, columns=None, index=None) + + def test_rename_mapper_and_positional_arguments_raises(self): + # GH 29136 + df = DataFrame([[1]]) + msg = "Cannot specify both 'mapper' and any of 'index' or 'columns'" + with pytest.raises(TypeError, match=msg): + df.rename({}, index={}) + + with pytest.raises(TypeError, match=msg): + df.rename({}, columns={}) + + with pytest.raises(TypeError, match=msg): + df.rename({}, columns={}, index={}) + + def test_rename_with_duplicate_columns(self): + # GH#4403 + df4 = DataFrame( + {"RT": [0.0454], "TClose": [22.02], "TExg": [0.0422]}, + index=MultiIndex.from_tuples( + [(600809, 20130331)], names=["STK_ID", "RPT_Date"] + ), + ) + + df5 = DataFrame( + { + "RPT_Date": [20120930, 20121231, 20130331], + "STK_ID": [600809] * 3, + "STK_Name": ["饡驦", "饡驦", "饡驦"], + "TClose": [38.05, 41.66, 30.01], + }, + index=MultiIndex.from_tuples( + [(600809, 20120930), (600809, 20121231), (600809, 20130331)], + names=["STK_ID", "RPT_Date"], + ), + ) + # TODO: can we construct this without merge? + k = merge(df4, df5, how="inner", left_index=True, right_index=True) + result = k.rename(columns={"TClose_x": "TClose", "TClose_y": "QT_Close"}) + + expected = DataFrame( + [[0.0454, 22.02, 0.0422, 20130331, 600809, "饡驦", 30.01]], + columns=[ + "RT", + "TClose", + "TExg", + "RPT_Date", + "STK_ID", + "STK_Name", + "QT_Close", + ], + ).set_index(["STK_ID", "RPT_Date"], drop=False) + tm.assert_frame_equal(result, expected) + + def test_rename_boolean_index(self): + df = DataFrame(np.arange(15).reshape(3, 5), columns=[False, True, 2, 3, 4]) + mapper = {0: "foo", 1: "bar", 2: "bah"} + res = df.rename(index=mapper) + exp = DataFrame( + np.arange(15).reshape(3, 5), + columns=[False, True, 2, 3, 4], + index=["foo", "bar", "bah"], + ) + tm.assert_frame_equal(res, exp) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_rename_axis.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_rename_axis.py new file mode 100644 index 0000000000000000000000000000000000000000..dd4a77c6509b8de7eb767bb44238004399c159a4 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_rename_axis.py @@ -0,0 +1,111 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Index, + MultiIndex, +) +import pandas._testing as tm + + +class TestDataFrameRenameAxis: + def test_rename_axis_inplace(self, float_frame): + # GH#15704 + expected = float_frame.rename_axis("foo") + result = float_frame.copy() + return_value = no_return = result.rename_axis("foo", inplace=True) + assert return_value is None + + assert no_return is None + tm.assert_frame_equal(result, expected) + + expected = float_frame.rename_axis("bar", axis=1) + result = float_frame.copy() + return_value = no_return = result.rename_axis("bar", axis=1, inplace=True) + assert return_value is None + + assert no_return is None + tm.assert_frame_equal(result, expected) + + def test_rename_axis_raises(self): + # GH#17833 + df = DataFrame({"A": [1, 2], "B": [1, 2]}) + with pytest.raises(ValueError, match="Use `.rename`"): + df.rename_axis(id, axis=0) + + with pytest.raises(ValueError, match="Use `.rename`"): + df.rename_axis({0: 10, 1: 20}, axis=0) + + with pytest.raises(ValueError, match="Use `.rename`"): + df.rename_axis(id, axis=1) + + with pytest.raises(ValueError, match="Use `.rename`"): + df["A"].rename_axis(id) + + def test_rename_axis_mapper(self): + # GH#19978 + mi = MultiIndex.from_product([["a", "b", "c"], [1, 2]], names=["ll", "nn"]) + df = DataFrame( + {"x": list(range(len(mi))), "y": [i * 10 for i in range(len(mi))]}, index=mi + ) + + # Test for rename of the Index object of columns + result = df.rename_axis("cols", axis=1) + tm.assert_index_equal(result.columns, Index(["x", "y"], name="cols")) + + # Test for rename of the Index object of columns using dict + result = result.rename_axis(columns={"cols": "new"}, axis=1) + tm.assert_index_equal(result.columns, Index(["x", "y"], name="new")) + + # Test for renaming index using dict + result = df.rename_axis(index={"ll": "foo"}) + assert result.index.names == ["foo", "nn"] + + # Test for renaming index using a function + result = df.rename_axis(index=str.upper, axis=0) + assert result.index.names == ["LL", "NN"] + + # Test for renaming index providing complete list + result = df.rename_axis(index=["foo", "goo"]) + assert result.index.names == ["foo", "goo"] + + # Test for changing index and columns at same time + sdf = df.reset_index().set_index("nn").drop(columns=["ll", "y"]) + result = sdf.rename_axis(index="foo", columns="meh") + assert result.index.name == "foo" + assert result.columns.name == "meh" + + # Test different error cases + with pytest.raises(TypeError, match="Must pass"): + df.rename_axis(index="wrong") + + with pytest.raises(ValueError, match="Length of names"): + df.rename_axis(index=["wrong"]) + + with pytest.raises(TypeError, match="bogus"): + df.rename_axis(bogus=None) + + @pytest.mark.parametrize( + "kwargs, rename_index, rename_columns", + [ + ({"mapper": None, "axis": 0}, True, False), + ({"mapper": None, "axis": 1}, False, True), + ({"index": None}, True, False), + ({"columns": None}, False, True), + ({"index": None, "columns": None}, True, True), + ({}, False, False), + ], + ) + def test_rename_axis_none(self, kwargs, rename_index, rename_columns): + # GH 25034 + index = Index(list("abc"), name="foo") + columns = Index(["col1", "col2"], name="bar") + data = np.arange(6).reshape(3, 2) + df = DataFrame(data, index, columns) + + result = df.rename_axis(**kwargs) + expected_index = index.rename(None) if rename_index else index + expected_columns = columns.rename(None) if rename_columns else columns + expected = DataFrame(data, expected_index, expected_columns) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_reorder_levels.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_reorder_levels.py new file mode 100644 index 0000000000000000000000000000000000000000..5d6b65daae4d513b3d3333856a57a2199cb79ed0 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_reorder_levels.py @@ -0,0 +1,74 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + MultiIndex, +) +import pandas._testing as tm + + +class TestReorderLevels: + def test_reorder_levels(self, frame_or_series): + index = MultiIndex( + levels=[["bar"], ["one", "two", "three"], [0, 1]], + codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]], + names=["L0", "L1", "L2"], + ) + df = DataFrame({"A": np.arange(6), "B": np.arange(6)}, index=index) + obj = tm.get_obj(df, frame_or_series) + + # no change, position + result = obj.reorder_levels([0, 1, 2]) + tm.assert_equal(obj, result) + + # no change, labels + result = obj.reorder_levels(["L0", "L1", "L2"]) + tm.assert_equal(obj, result) + + # rotate, position + result = obj.reorder_levels([1, 2, 0]) + e_idx = MultiIndex( + levels=[["one", "two", "three"], [0, 1], ["bar"]], + codes=[[0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1], [0, 0, 0, 0, 0, 0]], + names=["L1", "L2", "L0"], + ) + expected = DataFrame({"A": np.arange(6), "B": np.arange(6)}, index=e_idx) + expected = tm.get_obj(expected, frame_or_series) + tm.assert_equal(result, expected) + + result = obj.reorder_levels([0, 0, 0]) + e_idx = MultiIndex( + levels=[["bar"], ["bar"], ["bar"]], + codes=[[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]], + names=["L0", "L0", "L0"], + ) + expected = DataFrame({"A": np.arange(6), "B": np.arange(6)}, index=e_idx) + expected = tm.get_obj(expected, frame_or_series) + tm.assert_equal(result, expected) + + result = obj.reorder_levels(["L0", "L0", "L0"]) + tm.assert_equal(result, expected) + + def test_reorder_levels_swaplevel_equivalence( + self, multiindex_year_month_day_dataframe_random_data + ): + ymd = multiindex_year_month_day_dataframe_random_data + + result = ymd.reorder_levels(["month", "day", "year"]) + expected = ymd.swaplevel(0, 1).swaplevel(1, 2) + tm.assert_frame_equal(result, expected) + + result = ymd["A"].reorder_levels(["month", "day", "year"]) + expected = ymd["A"].swaplevel(0, 1).swaplevel(1, 2) + tm.assert_series_equal(result, expected) + + result = ymd.T.reorder_levels(["month", "day", "year"], axis=1) + expected = ymd.T.swaplevel(0, 1, axis=1).swaplevel(1, 2, axis=1) + tm.assert_frame_equal(result, expected) + + with pytest.raises(TypeError, match="hierarchical axis"): + ymd.reorder_levels([1, 2], axis=1) + + with pytest.raises(IndexError, match="Too many levels"): + ymd.index.reorder_levels([1, 2, 3]) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_replace.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_replace.py new file mode 100644 index 0000000000000000000000000000000000000000..0971fb7e604c0da7bf517936d68c938eb10748a1 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_replace.py @@ -0,0 +1,1665 @@ +from __future__ import annotations + +from datetime import datetime +import re + +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + Index, + Series, + Timestamp, + date_range, +) +import pandas._testing as tm + + +@pytest.fixture +def mix_ab() -> dict[str, list[int | str]]: + return {"a": list(range(4)), "b": list("ab..")} + + +@pytest.fixture +def mix_abc() -> dict[str, list[float | str]]: + return {"a": list(range(4)), "b": list("ab.."), "c": ["a", "b", np.nan, "d"]} + + +class TestDataFrameReplace: + def test_replace_inplace(self, datetime_frame, float_string_frame): + datetime_frame.loc[datetime_frame.index[:5], "A"] = np.nan + datetime_frame.loc[datetime_frame.index[-5:], "A"] = np.nan + + tsframe = datetime_frame.copy() + return_value = tsframe.replace(np.nan, 0, inplace=True) + assert return_value is None + tm.assert_frame_equal(tsframe, datetime_frame.fillna(0)) + + # mixed type + mf = float_string_frame + mf.iloc[5:20, mf.columns.get_loc("foo")] = np.nan + mf.iloc[-10:, mf.columns.get_loc("A")] = np.nan + + result = float_string_frame.replace(np.nan, 0) + expected = float_string_frame.copy() + expected["foo"] = expected["foo"].astype(object) + expected = expected.fillna(value=0) + tm.assert_frame_equal(result, expected) + + tsframe = datetime_frame.copy() + return_value = tsframe.replace([np.nan], [0], inplace=True) + assert return_value is None + tm.assert_frame_equal(tsframe, datetime_frame.fillna(0)) + + @pytest.mark.parametrize( + "to_replace,values,expected", + [ + # lists of regexes and values + # list of [re1, re2, ..., reN] -> [v1, v2, ..., vN] + ( + [r"\s*\.\s*", r"e|f|g"], + [np.nan, "crap"], + { + "a": ["a", "b", np.nan, np.nan], + "b": ["crap"] * 3 + ["h"], + "c": ["h", "crap", "l", "o"], + }, + ), + # list of [re1, re2, ..., reN] -> [re1, re2, .., reN] + ( + [r"\s*(\.)\s*", r"(e|f|g)"], + [r"\1\1", r"\1_crap"], + { + "a": ["a", "b", "..", ".."], + "b": ["e_crap", "f_crap", "g_crap", "h"], + "c": ["h", "e_crap", "l", "o"], + }, + ), + # list of [re1, re2, ..., reN] -> [(re1 or v1), (re2 or v2), ..., (reN + # or vN)] + ( + [r"\s*(\.)\s*", r"e"], + [r"\1\1", r"crap"], + { + "a": ["a", "b", "..", ".."], + "b": ["crap", "f", "g", "h"], + "c": ["h", "crap", "l", "o"], + }, + ), + ], + ) + @pytest.mark.parametrize("inplace", [True, False]) + @pytest.mark.parametrize("use_value_regex_args", [True, False]) + def test_regex_replace_list_obj( + self, to_replace, values, expected, inplace, use_value_regex_args + ): + df = DataFrame({"a": list("ab.."), "b": list("efgh"), "c": list("helo")}) + + if use_value_regex_args: + result = df.replace(value=values, regex=to_replace, inplace=inplace) + else: + result = df.replace(to_replace, values, regex=True, inplace=inplace) + + if inplace: + assert result is None + result = df + + expected = DataFrame(expected) + tm.assert_frame_equal(result, expected) + + def test_regex_replace_list_mixed(self, mix_ab): + # mixed frame to make sure this doesn't break things + dfmix = DataFrame(mix_ab) + + # lists of regexes and values + # list of [re1, re2, ..., reN] -> [v1, v2, ..., vN] + to_replace_res = [r"\s*\.\s*", r"a"] + values = [np.nan, "crap"] + mix2 = {"a": list(range(4)), "b": list("ab.."), "c": list("halo")} + dfmix2 = DataFrame(mix2) + res = dfmix2.replace(to_replace_res, values, regex=True) + expec = DataFrame( + { + "a": mix2["a"], + "b": ["crap", "b", np.nan, np.nan], + "c": ["h", "crap", "l", "o"], + } + ) + tm.assert_frame_equal(res, expec) + + # list of [re1, re2, ..., reN] -> [re1, re2, .., reN] + to_replace_res = [r"\s*(\.)\s*", r"(a|b)"] + values = [r"\1\1", r"\1_crap"] + res = dfmix.replace(to_replace_res, values, regex=True) + expec = DataFrame({"a": mix_ab["a"], "b": ["a_crap", "b_crap", "..", ".."]}) + tm.assert_frame_equal(res, expec) + + # list of [re1, re2, ..., reN] -> [(re1 or v1), (re2 or v2), ..., (reN + # or vN)] + to_replace_res = [r"\s*(\.)\s*", r"a", r"(b)"] + values = [r"\1\1", r"crap", r"\1_crap"] + res = dfmix.replace(to_replace_res, values, regex=True) + expec = DataFrame({"a": mix_ab["a"], "b": ["crap", "b_crap", "..", ".."]}) + tm.assert_frame_equal(res, expec) + + to_replace_res = [r"\s*(\.)\s*", r"a", r"(b)"] + values = [r"\1\1", r"crap", r"\1_crap"] + res = dfmix.replace(regex=to_replace_res, value=values) + expec = DataFrame({"a": mix_ab["a"], "b": ["crap", "b_crap", "..", ".."]}) + tm.assert_frame_equal(res, expec) + + def test_regex_replace_list_mixed_inplace(self, mix_ab): + dfmix = DataFrame(mix_ab) + # the same inplace + # lists of regexes and values + # list of [re1, re2, ..., reN] -> [v1, v2, ..., vN] + to_replace_res = [r"\s*\.\s*", r"a"] + values = [np.nan, "crap"] + res = dfmix.copy() + return_value = res.replace(to_replace_res, values, inplace=True, regex=True) + assert return_value is None + expec = DataFrame({"a": mix_ab["a"], "b": ["crap", "b", np.nan, np.nan]}) + tm.assert_frame_equal(res, expec) + + # list of [re1, re2, ..., reN] -> [re1, re2, .., reN] + to_replace_res = [r"\s*(\.)\s*", r"(a|b)"] + values = [r"\1\1", r"\1_crap"] + res = dfmix.copy() + return_value = res.replace(to_replace_res, values, inplace=True, regex=True) + assert return_value is None + expec = DataFrame({"a": mix_ab["a"], "b": ["a_crap", "b_crap", "..", ".."]}) + tm.assert_frame_equal(res, expec) + + # list of [re1, re2, ..., reN] -> [(re1 or v1), (re2 or v2), ..., (reN + # or vN)] + to_replace_res = [r"\s*(\.)\s*", r"a", r"(b)"] + values = [r"\1\1", r"crap", r"\1_crap"] + res = dfmix.copy() + return_value = res.replace(to_replace_res, values, inplace=True, regex=True) + assert return_value is None + expec = DataFrame({"a": mix_ab["a"], "b": ["crap", "b_crap", "..", ".."]}) + tm.assert_frame_equal(res, expec) + + to_replace_res = [r"\s*(\.)\s*", r"a", r"(b)"] + values = [r"\1\1", r"crap", r"\1_crap"] + res = dfmix.copy() + return_value = res.replace(regex=to_replace_res, value=values, inplace=True) + assert return_value is None + expec = DataFrame({"a": mix_ab["a"], "b": ["crap", "b_crap", "..", ".."]}) + tm.assert_frame_equal(res, expec) + + def test_regex_replace_dict_mixed(self, mix_abc): + dfmix = DataFrame(mix_abc) + + # dicts + # single dict {re1: v1}, search the whole frame + # need test for this... + + # list of dicts {re1: v1, re2: v2, ..., re3: v3}, search the whole + # frame + res = dfmix.replace({"b": r"\s*\.\s*"}, {"b": np.nan}, regex=True) + res2 = dfmix.copy() + return_value = res2.replace( + {"b": r"\s*\.\s*"}, {"b": np.nan}, inplace=True, regex=True + ) + assert return_value is None + expec = DataFrame( + {"a": mix_abc["a"], "b": ["a", "b", np.nan, np.nan], "c": mix_abc["c"]} + ) + tm.assert_frame_equal(res, expec) + tm.assert_frame_equal(res2, expec) + + # list of dicts {re1: re11, re2: re12, ..., reN: re1N}, search the + # whole frame + res = dfmix.replace({"b": r"\s*(\.)\s*"}, {"b": r"\1ty"}, regex=True) + res2 = dfmix.copy() + return_value = res2.replace( + {"b": r"\s*(\.)\s*"}, {"b": r"\1ty"}, inplace=True, regex=True + ) + assert return_value is None + expec = DataFrame( + {"a": mix_abc["a"], "b": ["a", "b", ".ty", ".ty"], "c": mix_abc["c"]} + ) + tm.assert_frame_equal(res, expec) + tm.assert_frame_equal(res2, expec) + + res = dfmix.replace(regex={"b": r"\s*(\.)\s*"}, value={"b": r"\1ty"}) + res2 = dfmix.copy() + return_value = res2.replace( + regex={"b": r"\s*(\.)\s*"}, value={"b": r"\1ty"}, inplace=True + ) + assert return_value is None + expec = DataFrame( + {"a": mix_abc["a"], "b": ["a", "b", ".ty", ".ty"], "c": mix_abc["c"]} + ) + tm.assert_frame_equal(res, expec) + tm.assert_frame_equal(res2, expec) + + # scalar -> dict + # to_replace regex, {value: value} + expec = DataFrame( + {"a": mix_abc["a"], "b": [np.nan, "b", ".", "."], "c": mix_abc["c"]} + ) + res = dfmix.replace("a", {"b": np.nan}, regex=True) + res2 = dfmix.copy() + return_value = res2.replace("a", {"b": np.nan}, regex=True, inplace=True) + assert return_value is None + tm.assert_frame_equal(res, expec) + tm.assert_frame_equal(res2, expec) + + res = dfmix.replace("a", {"b": np.nan}, regex=True) + res2 = dfmix.copy() + return_value = res2.replace(regex="a", value={"b": np.nan}, inplace=True) + assert return_value is None + expec = DataFrame( + {"a": mix_abc["a"], "b": [np.nan, "b", ".", "."], "c": mix_abc["c"]} + ) + tm.assert_frame_equal(res, expec) + tm.assert_frame_equal(res2, expec) + + def test_regex_replace_dict_nested(self, mix_abc): + # nested dicts will not work until this is implemented for Series + dfmix = DataFrame(mix_abc) + res = dfmix.replace({"b": {r"\s*\.\s*": np.nan}}, regex=True) + res2 = dfmix.copy() + res4 = dfmix.copy() + return_value = res2.replace( + {"b": {r"\s*\.\s*": np.nan}}, inplace=True, regex=True + ) + assert return_value is None + res3 = dfmix.replace(regex={"b": {r"\s*\.\s*": np.nan}}) + return_value = res4.replace(regex={"b": {r"\s*\.\s*": np.nan}}, inplace=True) + assert return_value is None + expec = DataFrame( + {"a": mix_abc["a"], "b": ["a", "b", np.nan, np.nan], "c": mix_abc["c"]} + ) + tm.assert_frame_equal(res, expec) + tm.assert_frame_equal(res2, expec) + tm.assert_frame_equal(res3, expec) + tm.assert_frame_equal(res4, expec) + + def test_regex_replace_dict_nested_non_first_character(self, any_string_dtype): + # GH 25259 + dtype = any_string_dtype + df = DataFrame({"first": ["abc", "bca", "cab"]}, dtype=dtype) + result = df.replace({"a": "."}, regex=True) + expected = DataFrame({"first": [".bc", "bc.", "c.b"]}, dtype=dtype) + tm.assert_frame_equal(result, expected) + + def test_regex_replace_dict_nested_gh4115(self): + df = DataFrame( + {"Type": Series(["Q", "T", "Q", "Q", "T"], dtype=object), "tmp": 2} + ) + expected = DataFrame({"Type": [0, 1, 0, 0, 1], "tmp": 2}) + msg = "Downcasting behavior in `replace`" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.replace({"Type": {"Q": 0, "T": 1}}) + + tm.assert_frame_equal(result, expected) + + def test_regex_replace_list_to_scalar(self, mix_abc, using_infer_string): + df = DataFrame(mix_abc) + expec = DataFrame( + { + "a": mix_abc["a"], + "b": [np.nan] * 4, + "c": [np.nan, np.nan, np.nan, "d"], + } + ) + if using_infer_string: + expec["b"] = expec["b"].astype("str") + msg = "Downcasting behavior in `replace`" + warn = None if using_infer_string else FutureWarning + with tm.assert_produces_warning(warn, match=msg): + res = df.replace([r"\s*\.\s*", "a|b"], np.nan, regex=True) + res2 = df.copy() + res3 = df.copy() + with tm.assert_produces_warning(warn, match=msg): + return_value = res2.replace( + [r"\s*\.\s*", "a|b"], np.nan, regex=True, inplace=True + ) + assert return_value is None + with tm.assert_produces_warning(warn, match=msg): + return_value = res3.replace( + regex=[r"\s*\.\s*", "a|b"], value=np.nan, inplace=True + ) + assert return_value is None + tm.assert_frame_equal(res, expec) + tm.assert_frame_equal(res2, expec) + tm.assert_frame_equal(res3, expec) + + def test_regex_replace_str_to_numeric(self, mix_abc): + # what happens when you try to replace a numeric value with a regex? + df = DataFrame(mix_abc) + res = df.replace(r"\s*\.\s*", 0, regex=True) + res2 = df.copy() + return_value = res2.replace(r"\s*\.\s*", 0, inplace=True, regex=True) + assert return_value is None + res3 = df.copy() + return_value = res3.replace(regex=r"\s*\.\s*", value=0, inplace=True) + assert return_value is None + expec = DataFrame({"a": mix_abc["a"], "b": ["a", "b", 0, 0], "c": mix_abc["c"]}) + tm.assert_frame_equal(res, expec) + tm.assert_frame_equal(res2, expec) + tm.assert_frame_equal(res3, expec) + + def test_regex_replace_regex_list_to_numeric(self, mix_abc): + df = DataFrame(mix_abc) + res = df.replace([r"\s*\.\s*", "b"], 0, regex=True) + res2 = df.copy() + return_value = res2.replace([r"\s*\.\s*", "b"], 0, regex=True, inplace=True) + assert return_value is None + res3 = df.copy() + return_value = res3.replace(regex=[r"\s*\.\s*", "b"], value=0, inplace=True) + assert return_value is None + expec = DataFrame( + {"a": mix_abc["a"], "b": ["a", 0, 0, 0], "c": ["a", 0, np.nan, "d"]} + ) + tm.assert_frame_equal(res, expec) + tm.assert_frame_equal(res2, expec) + tm.assert_frame_equal(res3, expec) + + def test_regex_replace_series_of_regexes(self, mix_abc): + df = DataFrame(mix_abc) + s1 = Series({"b": r"\s*\.\s*"}) + s2 = Series({"b": np.nan}) + res = df.replace(s1, s2, regex=True) + res2 = df.copy() + return_value = res2.replace(s1, s2, inplace=True, regex=True) + assert return_value is None + res3 = df.copy() + return_value = res3.replace(regex=s1, value=s2, inplace=True) + assert return_value is None + expec = DataFrame( + {"a": mix_abc["a"], "b": ["a", "b", np.nan, np.nan], "c": mix_abc["c"]} + ) + tm.assert_frame_equal(res, expec) + tm.assert_frame_equal(res2, expec) + tm.assert_frame_equal(res3, expec) + + def test_regex_replace_numeric_to_object_conversion(self, mix_abc): + df = DataFrame(mix_abc) + expec = DataFrame({"a": ["a", 1, 2, 3], "b": mix_abc["b"], "c": mix_abc["c"]}) + res = df.replace(0, "a") + tm.assert_frame_equal(res, expec) + assert res.a.dtype == np.object_ + + @pytest.mark.parametrize( + "to_replace", [{"": np.nan, ",": ""}, {",": "", "": np.nan}] + ) + def test_joint_simple_replace_and_regex_replace(self, to_replace): + # GH-39338 + df = DataFrame( + { + "col1": ["1,000", "a", "3"], + "col2": ["a", "", "b"], + "col3": ["a", "b", "c"], + } + ) + result = df.replace(regex=to_replace) + expected = DataFrame( + { + "col1": ["1000", "a", "3"], + "col2": ["a", np.nan, "b"], + "col3": ["a", "b", "c"], + } + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("metachar", ["[]", "()", r"\d", r"\w", r"\s"]) + def test_replace_regex_metachar(self, metachar): + df = DataFrame({"a": [metachar, "else"]}) + result = df.replace({"a": {metachar: "paren"}}) + expected = DataFrame({"a": ["paren", "else"]}) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "data,to_replace,expected", + [ + (["xax", "xbx"], {"a": "c", "b": "d"}, ["xcx", "xdx"]), + (["d", "", ""], {r"^\s*$": pd.NA}, ["d", pd.NA, pd.NA]), + ], + ) + def test_regex_replace_string_types( + self, data, to_replace, expected, frame_or_series, any_string_dtype + ): + # GH-41333, GH-35977 + dtype = any_string_dtype + obj = frame_or_series(data, dtype=dtype) + result = obj.replace(to_replace, regex=True) + expected = frame_or_series(expected, dtype=dtype) + + tm.assert_equal(result, expected) + + def test_replace(self, datetime_frame): + datetime_frame.loc[datetime_frame.index[:5], "A"] = np.nan + datetime_frame.loc[datetime_frame.index[-5:], "A"] = np.nan + + zero_filled = datetime_frame.replace(np.nan, -1e8) + tm.assert_frame_equal(zero_filled, datetime_frame.fillna(-1e8)) + tm.assert_frame_equal(zero_filled.replace(-1e8, np.nan), datetime_frame) + + datetime_frame.loc[datetime_frame.index[:5], "A"] = np.nan + datetime_frame.loc[datetime_frame.index[-5:], "A"] = np.nan + datetime_frame.loc[datetime_frame.index[:5], "B"] = -1e8 + + # empty + df = DataFrame(index=["a", "b"]) + tm.assert_frame_equal(df, df.replace(5, 7)) + + # GH 11698 + # test for mixed data types. + df = DataFrame( + [("-", pd.to_datetime("20150101")), ("a", pd.to_datetime("20150102"))] + ) + df1 = df.replace("-", np.nan) + expected_df = DataFrame( + [(np.nan, pd.to_datetime("20150101")), ("a", pd.to_datetime("20150102"))] + ) + tm.assert_frame_equal(df1, expected_df) + + def test_replace_list(self): + obj = {"a": list("ab.."), "b": list("efgh"), "c": list("helo")} + dfobj = DataFrame(obj) + + # lists of regexes and values + # list of [v1, v2, ..., vN] -> [v1, v2, ..., vN] + to_replace_res = [r".", r"e"] + values = [np.nan, "crap"] + res = dfobj.replace(to_replace_res, values) + expec = DataFrame( + { + "a": ["a", "b", np.nan, np.nan], + "b": ["crap", "f", "g", "h"], + "c": ["h", "crap", "l", "o"], + } + ) + tm.assert_frame_equal(res, expec) + + # list of [v1, v2, ..., vN] -> [v1, v2, .., vN] + to_replace_res = [r".", r"f"] + values = [r"..", r"crap"] + res = dfobj.replace(to_replace_res, values) + expec = DataFrame( + { + "a": ["a", "b", "..", ".."], + "b": ["e", "crap", "g", "h"], + "c": ["h", "e", "l", "o"], + } + ) + tm.assert_frame_equal(res, expec) + + def test_replace_with_empty_list(self, frame_or_series): + # GH 21977 + ser = Series([["a", "b"], [], np.nan, [1]]) + obj = DataFrame({"col": ser}) + obj = tm.get_obj(obj, frame_or_series) + expected = obj + result = obj.replace([], np.nan) + tm.assert_equal(result, expected) + + # GH 19266 + msg = ( + "NumPy boolean array indexing assignment cannot assign {size} " + "input values to the 1 output values where the mask is true" + ) + with pytest.raises(ValueError, match=msg.format(size=0)): + obj.replace({np.nan: []}) + with pytest.raises(ValueError, match=msg.format(size=2)): + obj.replace({np.nan: ["dummy", "alt"]}) + + def test_replace_series_dict(self): + # from GH 3064 + df = DataFrame({"zero": {"a": 0.0, "b": 1}, "one": {"a": 2.0, "b": 0}}) + result = df.replace(0, {"zero": 0.5, "one": 1.0}) + expected = DataFrame({"zero": {"a": 0.5, "b": 1}, "one": {"a": 2.0, "b": 1.0}}) + tm.assert_frame_equal(result, expected) + + result = df.replace(0, df.mean()) + tm.assert_frame_equal(result, expected) + + # series to series/dict + df = DataFrame({"zero": {"a": 0.0, "b": 1}, "one": {"a": 2.0, "b": 0}}) + s = Series({"zero": 0.0, "one": 2.0}) + result = df.replace(s, {"zero": 0.5, "one": 1.0}) + expected = DataFrame({"zero": {"a": 0.5, "b": 1}, "one": {"a": 1.0, "b": 0.0}}) + tm.assert_frame_equal(result, expected) + + result = df.replace(s, df.mean()) + tm.assert_frame_equal(result, expected) + + def test_replace_convert(self): + # gh 3907 + df = DataFrame([["foo", "bar", "bah"], ["bar", "foo", "bah"]]) + m = {"foo": 1, "bar": 2, "bah": 3} + msg = "Downcasting behavior in `replace` " + with tm.assert_produces_warning(FutureWarning, match=msg): + rep = df.replace(m) + expec = Series([np.int64] * 3) + res = rep.dtypes + tm.assert_series_equal(expec, res) + + def test_replace_mixed(self, float_string_frame): + mf = float_string_frame + mf.iloc[5:20, mf.columns.get_loc("foo")] = np.nan + mf.iloc[-10:, mf.columns.get_loc("A")] = np.nan + + result = float_string_frame.replace(np.nan, -18) + expected = float_string_frame.copy() + expected["foo"] = expected["foo"].astype(object) + expected = expected.fillna(value=-18) + tm.assert_frame_equal(result, expected) + expected2 = float_string_frame.copy() + expected2["foo"] = expected2["foo"].astype(object) + tm.assert_frame_equal(result.replace(-18, np.nan), expected2) + + result = float_string_frame.replace(np.nan, -1e8) + expected = float_string_frame.copy() + expected["foo"] = expected["foo"].astype(object) + expected = expected.fillna(value=-1e8) + tm.assert_frame_equal(result, expected) + expected2 = float_string_frame.copy() + expected2["foo"] = expected2["foo"].astype(object) + tm.assert_frame_equal(result.replace(-1e8, np.nan), expected2) + + def test_replace_mixed_int_block_upcasting(self): + # int block upcasting + df = DataFrame( + { + "A": Series([1.0, 2.0], dtype="float64"), + "B": Series([0, 1], dtype="int64"), + } + ) + expected = DataFrame( + { + "A": Series([1.0, 2.0], dtype="float64"), + "B": Series([0.5, 1], dtype="float64"), + } + ) + result = df.replace(0, 0.5) + tm.assert_frame_equal(result, expected) + + return_value = df.replace(0, 0.5, inplace=True) + assert return_value is None + tm.assert_frame_equal(df, expected) + + def test_replace_mixed_int_block_splitting(self): + # int block splitting + df = DataFrame( + { + "A": Series([1.0, 2.0], dtype="float64"), + "B": Series([0, 1], dtype="int64"), + "C": Series([1, 2], dtype="int64"), + } + ) + expected = DataFrame( + { + "A": Series([1.0, 2.0], dtype="float64"), + "B": Series([0.5, 1], dtype="float64"), + "C": Series([1, 2], dtype="int64"), + } + ) + result = df.replace(0, 0.5) + tm.assert_frame_equal(result, expected) + + def test_replace_mixed2(self, using_infer_string): + # to object block upcasting + df = DataFrame( + { + "A": Series([1.0, 2.0], dtype="float64"), + "B": Series([0, 1], dtype="int64"), + } + ) + expected = DataFrame( + { + "A": Series([1, "foo"], dtype="object"), + "B": Series([0, 1], dtype="int64"), + } + ) + result = df.replace(2, "foo") + tm.assert_frame_equal(result, expected) + + expected = DataFrame( + { + "A": Series(["foo", "bar"], dtype="object"), + "B": Series([0, "foo"], dtype="object"), + } + ) + result = df.replace([1, 2], ["foo", "bar"]) + tm.assert_frame_equal(result, expected) + + def test_replace_mixed3(self): + # test case from + df = DataFrame( + {"A": Series([3, 0], dtype="int64"), "B": Series([0, 3], dtype="int64")} + ) + result = df.replace(3, df.mean().to_dict()) + expected = df.copy().astype("float64") + m = df.mean() + expected.iloc[0, 0] = m.iloc[0] + expected.iloc[1, 1] = m.iloc[1] + tm.assert_frame_equal(result, expected) + + def test_replace_nullable_int_with_string_doesnt_cast(self): + # GH#25438 don't cast df['a'] to float64 + df = DataFrame({"a": [1, 2, 3, np.nan], "b": ["some", "strings", "here", "he"]}) + df["a"] = df["a"].astype("Int64") + + res = df.replace("", np.nan) + tm.assert_series_equal(res["a"], df["a"]) + + @pytest.mark.parametrize("dtype", ["boolean", "Int64", "Float64"]) + def test_replace_with_nullable_column(self, dtype): + # GH-44499 + nullable_ser = Series([1, 0, 1], dtype=dtype) + df = DataFrame({"A": ["A", "B", "x"], "B": nullable_ser}) + result = df.replace("x", "X") + expected = DataFrame({"A": ["A", "B", "X"], "B": nullable_ser}) + tm.assert_frame_equal(result, expected) + + def test_replace_simple_nested_dict(self): + df = DataFrame({"col": range(1, 5)}) + expected = DataFrame({"col": ["a", 2, 3, "b"]}) + + result = df.replace({"col": {1: "a", 4: "b"}}) + tm.assert_frame_equal(expected, result) + + # in this case, should be the same as the not nested version + result = df.replace({1: "a", 4: "b"}) + tm.assert_frame_equal(expected, result) + + def test_replace_simple_nested_dict_with_nonexistent_value(self): + df = DataFrame({"col": range(1, 5)}) + expected = DataFrame({"col": ["a", 2, 3, "b"]}) + + result = df.replace({-1: "-", 1: "a", 4: "b"}) + tm.assert_frame_equal(expected, result) + + result = df.replace({"col": {-1: "-", 1: "a", 4: "b"}}) + tm.assert_frame_equal(expected, result) + + def test_replace_NA_with_None(self): + # gh-45601 + df = DataFrame({"value": [42, None]}).astype({"value": "Int64"}) + result = df.replace({pd.NA: None}) + expected = DataFrame({"value": [42, None]}, dtype=object) + tm.assert_frame_equal(result, expected) + + def test_replace_NAT_with_None(self): + # gh-45836 + df = DataFrame([pd.NaT, pd.NaT]) + result = df.replace({pd.NaT: None, np.nan: None}) + expected = DataFrame([None, None]) + tm.assert_frame_equal(result, expected) + + def test_replace_with_None_keeps_categorical(self): + # gh-46634 + cat_series = Series(["b", "b", "b", "d"], dtype="category") + df = DataFrame( + { + "id": Series([5, 4, 3, 2], dtype="float64"), + "col": cat_series, + } + ) + result = df.replace({3: None}) + + expected = DataFrame( + { + "id": Series([5.0, 4.0, None, 2.0], dtype="object"), + "col": cat_series, + } + ) + tm.assert_frame_equal(result, expected) + + def test_replace_value_is_none(self, datetime_frame): + orig_value = datetime_frame.iloc[0, 0] + orig2 = datetime_frame.iloc[1, 0] + + datetime_frame.iloc[0, 0] = np.nan + datetime_frame.iloc[1, 0] = 1 + + result = datetime_frame.replace(to_replace={np.nan: 0}) + expected = datetime_frame.T.replace(to_replace={np.nan: 0}).T + tm.assert_frame_equal(result, expected) + + result = datetime_frame.replace(to_replace={np.nan: 0, 1: -1e8}) + tsframe = datetime_frame.copy() + tsframe.iloc[0, 0] = 0 + tsframe.iloc[1, 0] = -1e8 + expected = tsframe + tm.assert_frame_equal(expected, result) + datetime_frame.iloc[0, 0] = orig_value + datetime_frame.iloc[1, 0] = orig2 + + def test_replace_for_new_dtypes(self, datetime_frame): + # dtypes + tsframe = datetime_frame.copy().astype(np.float32) + tsframe.loc[tsframe.index[:5], "A"] = np.nan + tsframe.loc[tsframe.index[-5:], "A"] = np.nan + + zero_filled = tsframe.replace(np.nan, -1e8) + tm.assert_frame_equal(zero_filled, tsframe.fillna(-1e8)) + tm.assert_frame_equal(zero_filled.replace(-1e8, np.nan), tsframe) + + tsframe.loc[tsframe.index[:5], "A"] = np.nan + tsframe.loc[tsframe.index[-5:], "A"] = np.nan + tsframe.loc[tsframe.index[:5], "B"] = np.nan + msg = "DataFrame.fillna with 'method' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + # TODO: what is this even testing? + result = tsframe.fillna(method="bfill") + tm.assert_frame_equal(result, tsframe.fillna(method="bfill")) + + @pytest.mark.parametrize( + "frame, to_replace, value, expected", + [ + (DataFrame({"ints": [1, 2, 3]}), 1, 0, DataFrame({"ints": [0, 2, 3]})), + ( + DataFrame({"ints": [1, 2, 3]}, dtype=np.int32), + 1, + 0, + DataFrame({"ints": [0, 2, 3]}, dtype=np.int32), + ), + ( + DataFrame({"ints": [1, 2, 3]}, dtype=np.int16), + 1, + 0, + DataFrame({"ints": [0, 2, 3]}, dtype=np.int16), + ), + ( + DataFrame({"bools": [True, False, True]}), + False, + True, + DataFrame({"bools": [True, True, True]}), + ), + ( + DataFrame({"complex": [1j, 2j, 3j]}), + 1j, + 0, + DataFrame({"complex": [0j, 2j, 3j]}), + ), + ( + DataFrame( + { + "datetime64": Index( + [ + datetime(2018, 5, 28), + datetime(2018, 7, 28), + datetime(2018, 5, 28), + ] + ) + } + ), + datetime(2018, 5, 28), + datetime(2018, 7, 28), + DataFrame({"datetime64": Index([datetime(2018, 7, 28)] * 3)}), + ), + # GH 20380 + ( + DataFrame({"dt": [datetime(3017, 12, 20)], "str": ["foo"]}), + "foo", + "bar", + DataFrame({"dt": [datetime(3017, 12, 20)], "str": ["bar"]}), + ), + # GH 36782 + ( + DataFrame({"dt": [datetime(2920, 10, 1)]}), + datetime(2920, 10, 1), + datetime(2020, 10, 1), + DataFrame({"dt": [datetime(2020, 10, 1)]}), + ), + ( + DataFrame( + { + "A": date_range("20130101", periods=3, tz="US/Eastern"), + "B": [0, np.nan, 2], + } + ), + Timestamp("20130102", tz="US/Eastern"), + Timestamp("20130104", tz="US/Eastern"), + DataFrame( + { + "A": pd.DatetimeIndex( + [ + Timestamp("20130101", tz="US/Eastern"), + Timestamp("20130104", tz="US/Eastern"), + Timestamp("20130103", tz="US/Eastern"), + ] + ).as_unit("ns"), + "B": [0, np.nan, 2], + } + ), + ), + # GH 35376 + ( + DataFrame([[1, 1.0], [2, 2.0]]), + 1.0, + 5, + DataFrame([[5, 5.0], [2, 2.0]]), + ), + ( + DataFrame([[1, 1.0], [2, 2.0]]), + 1, + 5, + DataFrame([[5, 5.0], [2, 2.0]]), + ), + ( + DataFrame([[1, 1.0], [2, 2.0]]), + 1.0, + 5.0, + DataFrame([[5, 5.0], [2, 2.0]]), + ), + ( + DataFrame([[1, 1.0], [2, 2.0]]), + 1, + 5.0, + DataFrame([[5, 5.0], [2, 2.0]]), + ), + ], + ) + def test_replace_dtypes(self, frame, to_replace, value, expected): + warn = None + if isinstance(to_replace, datetime) and to_replace.year == 2920: + warn = FutureWarning + msg = "Downcasting behavior in `replace` " + with tm.assert_produces_warning(warn, match=msg): + result = frame.replace(to_replace, value) + tm.assert_frame_equal(result, expected) + + def test_replace_input_formats_listlike(self): + # both dicts + to_rep = {"A": np.nan, "B": 0, "C": ""} + values = {"A": 0, "B": -1, "C": "missing"} + df = DataFrame( + {"A": [np.nan, 0, np.inf], "B": [0, 2, 5], "C": ["", "asdf", "fd"]} + ) + filled = df.replace(to_rep, values) + expected = {k: v.replace(to_rep[k], values[k]) for k, v in df.items()} + tm.assert_frame_equal(filled, DataFrame(expected)) + + result = df.replace([0, 2, 5], [5, 2, 0]) + expected = DataFrame( + {"A": [np.nan, 5, np.inf], "B": [5, 2, 0], "C": ["", "asdf", "fd"]} + ) + tm.assert_frame_equal(result, expected) + + # scalar to dict + values = {"A": 0, "B": -1, "C": "missing"} + df = DataFrame( + {"A": [np.nan, 0, np.nan], "B": [0, 2, 5], "C": ["", "asdf", "fd"]} + ) + filled = df.replace(np.nan, values) + expected = {k: v.replace(np.nan, values[k]) for k, v in df.items()} + tm.assert_frame_equal(filled, DataFrame(expected)) + + # list to list + to_rep = [np.nan, 0, ""] + values = [-2, -1, "missing"] + result = df.replace(to_rep, values) + expected = df.copy() + for rep, value in zip(to_rep, values): + return_value = expected.replace(rep, value, inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + + msg = r"Replacement lists must match in length\. Expecting 3 got 2" + with pytest.raises(ValueError, match=msg): + df.replace(to_rep, values[1:]) + + def test_replace_input_formats_scalar(self): + df = DataFrame( + {"A": [np.nan, 0, np.inf], "B": [0, 2, 5], "C": ["", "asdf", "fd"]} + ) + + # dict to scalar + to_rep = {"A": np.nan, "B": 0, "C": ""} + filled = df.replace(to_rep, 0) + expected = {k: v.replace(to_rep[k], 0) for k, v in df.items()} + tm.assert_frame_equal(filled, DataFrame(expected)) + + msg = "value argument must be scalar, dict, or Series" + with pytest.raises(TypeError, match=msg): + df.replace(to_rep, [np.nan, 0, ""]) + + # list to scalar + to_rep = [np.nan, 0, ""] + result = df.replace(to_rep, -1) + expected = df.copy() + for rep in to_rep: + return_value = expected.replace(rep, -1, inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + + def test_replace_limit(self): + # TODO + pass + + def test_replace_dict_no_regex(self, any_string_dtype): + answer = Series( + { + 0: "Strongly Agree", + 1: "Agree", + 2: "Neutral", + 3: "Disagree", + 4: "Strongly Disagree", + }, + dtype=any_string_dtype, + ) + weights = { + "Agree": 4, + "Disagree": 2, + "Neutral": 3, + "Strongly Agree": 5, + "Strongly Disagree": 1, + } + expected = Series({0: 5, 1: 4, 2: 3, 3: 2, 4: 1}) + msg = "Downcasting behavior in `replace` " + with tm.assert_produces_warning(FutureWarning, match=msg): + result = answer.replace(weights) + tm.assert_series_equal(result, expected) + + def test_replace_series_no_regex(self, any_string_dtype): + answer = Series( + { + 0: "Strongly Agree", + 1: "Agree", + 2: "Neutral", + 3: "Disagree", + 4: "Strongly Disagree", + }, + dtype=any_string_dtype, + ) + weights = Series( + { + "Agree": 4, + "Disagree": 2, + "Neutral": 3, + "Strongly Agree": 5, + "Strongly Disagree": 1, + } + ) + expected = Series({0: 5, 1: 4, 2: 3, 3: 2, 4: 1}) + msg = "Downcasting behavior in `replace` " + with tm.assert_produces_warning(FutureWarning, match=msg): + result = answer.replace(weights) + tm.assert_series_equal(result, expected) + + def test_replace_dict_tuple_list_ordering_remains_the_same(self): + df = DataFrame({"A": [np.nan, 1]}) + res1 = df.replace(to_replace={np.nan: 0, 1: -1e8}) + res2 = df.replace(to_replace=(1, np.nan), value=[-1e8, 0]) + res3 = df.replace(to_replace=[1, np.nan], value=[-1e8, 0]) + + expected = DataFrame({"A": [0, -1e8]}) + tm.assert_frame_equal(res1, res2) + tm.assert_frame_equal(res2, res3) + tm.assert_frame_equal(res3, expected) + + def test_replace_doesnt_replace_without_regex(self): + df = DataFrame( + { + "fol": [1, 2, 2, 3], + "T_opp": ["0", "vr", "0", "0"], + "T_Dir": ["0", "0", "0", "bt"], + "T_Enh": ["vo", "0", "0", "0"], + } + ) + res = df.replace({r"\D": 1}) + tm.assert_frame_equal(df, res) + + def test_replace_bool_with_string(self): + df = DataFrame({"a": [True, False], "b": list("ab")}) + result = df.replace(True, "a") + expected = DataFrame({"a": ["a", False], "b": df.b}) + tm.assert_frame_equal(result, expected) + + def test_replace_pure_bool_with_string_no_op(self): + df = DataFrame(np.random.default_rng(2).random((2, 2)) > 0.5) + result = df.replace("asdf", "fdsa") + tm.assert_frame_equal(df, result) + + def test_replace_bool_with_bool(self): + df = DataFrame(np.random.default_rng(2).random((2, 2)) > 0.5) + result = df.replace(False, True) + expected = DataFrame(np.ones((2, 2), dtype=bool)) + tm.assert_frame_equal(result, expected) + + def test_replace_with_dict_with_bool_keys(self): + df = DataFrame({0: [True, False], 1: [False, True]}) + result = df.replace({"asdf": "asdb", True: "yes"}) + expected = DataFrame({0: ["yes", False], 1: [False, "yes"]}) + tm.assert_frame_equal(result, expected) + + def test_replace_dict_strings_vs_ints(self): + # GH#34789 + df = DataFrame({"Y0": [1, 2], "Y1": [3, 4]}) + result = df.replace({"replace_string": "test"}) + + tm.assert_frame_equal(result, df) + + result = df["Y0"].replace({"replace_string": "test"}) + tm.assert_series_equal(result, df["Y0"]) + + def test_replace_truthy(self): + df = DataFrame({"a": [True, True]}) + r = df.replace([np.inf, -np.inf], np.nan) + e = df + tm.assert_frame_equal(r, e) + + def test_nested_dict_overlapping_keys_replace_int(self): + # GH 27660 keep behaviour consistent for simple dictionary and + # nested dictionary replacement + df = DataFrame({"a": list(range(1, 5))}) + + result = df.replace({"a": dict(zip(range(1, 5), range(2, 6)))}) + expected = df.replace(dict(zip(range(1, 5), range(2, 6)))) + tm.assert_frame_equal(result, expected) + + def test_nested_dict_overlapping_keys_replace_str(self): + # GH 27660 + a = np.arange(1, 5) + astr = a.astype(str) + bstr = np.arange(2, 6).astype(str) + df = DataFrame({"a": astr}) + result = df.replace(dict(zip(astr, bstr))) + expected = df.replace({"a": dict(zip(astr, bstr))}) + tm.assert_frame_equal(result, expected) + + def test_replace_swapping_bug(self): + df = DataFrame({"a": [True, False, True]}) + res = df.replace({"a": {True: "Y", False: "N"}}) + expect = DataFrame({"a": ["Y", "N", "Y"]}, dtype=object) + tm.assert_frame_equal(res, expect) + + df = DataFrame({"a": [0, 1, 0]}) + res = df.replace({"a": {0: "Y", 1: "N"}}) + expect = DataFrame({"a": ["Y", "N", "Y"]}, dtype=object) + tm.assert_frame_equal(res, expect) + + def test_replace_period(self): + d = { + "fname": { + "out_augmented_AUG_2011.json": pd.Period(year=2011, month=8, freq="M"), + "out_augmented_JAN_2011.json": pd.Period(year=2011, month=1, freq="M"), + "out_augmented_MAY_2012.json": pd.Period(year=2012, month=5, freq="M"), + "out_augmented_SUBSIDY_WEEK.json": pd.Period( + year=2011, month=4, freq="M" + ), + "out_augmented_AUG_2012.json": pd.Period(year=2012, month=8, freq="M"), + "out_augmented_MAY_2011.json": pd.Period(year=2011, month=5, freq="M"), + "out_augmented_SEP_2013.json": pd.Period(year=2013, month=9, freq="M"), + } + } + + df = DataFrame( + [ + "out_augmented_AUG_2012.json", + "out_augmented_SEP_2013.json", + "out_augmented_SUBSIDY_WEEK.json", + "out_augmented_MAY_2012.json", + "out_augmented_MAY_2011.json", + "out_augmented_AUG_2011.json", + "out_augmented_JAN_2011.json", + ], + columns=["fname"], + ) + assert set(df.fname.values) == set(d["fname"].keys()) + + expected = DataFrame({"fname": [d["fname"][k] for k in df.fname.values]}) + assert expected.dtypes.iloc[0] == "Period[M]" + msg = "Downcasting behavior in `replace` " + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.replace(d) + tm.assert_frame_equal(result, expected) + + def test_replace_datetime(self): + d = { + "fname": { + "out_augmented_AUG_2011.json": Timestamp("2011-08"), + "out_augmented_JAN_2011.json": Timestamp("2011-01"), + "out_augmented_MAY_2012.json": Timestamp("2012-05"), + "out_augmented_SUBSIDY_WEEK.json": Timestamp("2011-04"), + "out_augmented_AUG_2012.json": Timestamp("2012-08"), + "out_augmented_MAY_2011.json": Timestamp("2011-05"), + "out_augmented_SEP_2013.json": Timestamp("2013-09"), + } + } + + df = DataFrame( + [ + "out_augmented_AUG_2012.json", + "out_augmented_SEP_2013.json", + "out_augmented_SUBSIDY_WEEK.json", + "out_augmented_MAY_2012.json", + "out_augmented_MAY_2011.json", + "out_augmented_AUG_2011.json", + "out_augmented_JAN_2011.json", + ], + columns=["fname"], + ) + assert set(df.fname.values) == set(d["fname"].keys()) + expected = DataFrame({"fname": [d["fname"][k] for k in df.fname.values]}) + msg = "Downcasting behavior in `replace` " + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.replace(d) + tm.assert_frame_equal(result, expected) + + def test_replace_datetimetz(self): + # GH 11326 + # behaving poorly when presented with a datetime64[ns, tz] + df = DataFrame( + { + "A": date_range("20130101", periods=3, tz="US/Eastern"), + "B": [0, np.nan, 2], + } + ) + result = df.replace(np.nan, 1) + expected = DataFrame( + { + "A": date_range("20130101", periods=3, tz="US/Eastern"), + "B": Series([0, 1, 2], dtype="float64"), + } + ) + tm.assert_frame_equal(result, expected) + + result = df.fillna(1) + tm.assert_frame_equal(result, expected) + + result = df.replace(0, np.nan) + expected = DataFrame( + { + "A": date_range("20130101", periods=3, tz="US/Eastern"), + "B": [np.nan, np.nan, 2], + } + ) + tm.assert_frame_equal(result, expected) + + result = df.replace( + Timestamp("20130102", tz="US/Eastern"), + Timestamp("20130104", tz="US/Eastern"), + ) + expected = DataFrame( + { + "A": [ + Timestamp("20130101", tz="US/Eastern"), + Timestamp("20130104", tz="US/Eastern"), + Timestamp("20130103", tz="US/Eastern"), + ], + "B": [0, np.nan, 2], + } + ) + expected["A"] = expected["A"].dt.as_unit("ns") + tm.assert_frame_equal(result, expected) + + result = df.copy() + result.iloc[1, 0] = np.nan + result = result.replace({"A": pd.NaT}, Timestamp("20130104", tz="US/Eastern")) + tm.assert_frame_equal(result, expected) + + # pre-2.0 this would coerce to object with mismatched tzs + result = df.copy() + result.iloc[1, 0] = np.nan + result = result.replace({"A": pd.NaT}, Timestamp("20130104", tz="US/Pacific")) + expected = DataFrame( + { + "A": [ + Timestamp("20130101", tz="US/Eastern"), + Timestamp("20130104", tz="US/Pacific").tz_convert("US/Eastern"), + Timestamp("20130103", tz="US/Eastern"), + ], + "B": [0, np.nan, 2], + } + ) + expected["A"] = expected["A"].dt.as_unit("ns") + tm.assert_frame_equal(result, expected) + + result = df.copy() + result.iloc[1, 0] = np.nan + result = result.replace({"A": np.nan}, Timestamp("20130104")) + expected = DataFrame( + { + "A": [ + Timestamp("20130101", tz="US/Eastern"), + Timestamp("20130104"), + Timestamp("20130103", tz="US/Eastern"), + ], + "B": [0, np.nan, 2], + } + ) + tm.assert_frame_equal(result, expected) + + def test_replace_with_empty_dictlike(self, mix_abc): + # GH 15289 + df = DataFrame(mix_abc) + tm.assert_frame_equal(df, df.replace({})) + tm.assert_frame_equal(df, df.replace(Series([], dtype=object))) + + tm.assert_frame_equal(df, df.replace({"b": {}})) + tm.assert_frame_equal(df, df.replace(Series({"b": {}}))) + + @pytest.mark.parametrize( + "to_replace, method, expected", + [ + (0, "bfill", {"A": [1, 1, 2], "B": [5, np.nan, 7], "C": ["a", "b", "c"]}), + ( + np.nan, + "bfill", + {"A": [0, 1, 2], "B": [5.0, 7.0, 7.0], "C": ["a", "b", "c"]}, + ), + ("d", "ffill", {"A": [0, 1, 2], "B": [5, np.nan, 7], "C": ["a", "b", "c"]}), + ( + [0, 2], + "bfill", + {"A": [1, 1, 2], "B": [5, np.nan, 7], "C": ["a", "b", "c"]}, + ), + ( + [1, 2], + "pad", + {"A": [0, 0, 0], "B": [5, np.nan, 7], "C": ["a", "b", "c"]}, + ), + ( + (1, 2), + "bfill", + {"A": [0, 2, 2], "B": [5, np.nan, 7], "C": ["a", "b", "c"]}, + ), + ( + ["b", "c"], + "ffill", + {"A": [0, 1, 2], "B": [5, np.nan, 7], "C": ["a", "a", "a"]}, + ), + ], + ) + def test_replace_method(self, to_replace, method, expected): + # GH 19632 + df = DataFrame({"A": [0, 1, 2], "B": [5, np.nan, 7], "C": ["a", "b", "c"]}) + + msg = "The 'method' keyword in DataFrame.replace is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.replace(to_replace=to_replace, value=None, method=method) + expected = DataFrame(expected) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "replace_dict, final_data", + [({"a": 1, "b": 1}, [[3, 3], [2, 2]]), ({"a": 1, "b": 2}, [[3, 1], [2, 3]])], + ) + def test_categorical_replace_with_dict(self, replace_dict, final_data): + # GH 26988 + df = DataFrame([[1, 1], [2, 2]], columns=["a", "b"], dtype="category") + + final_data = np.array(final_data) + + a = pd.Categorical(final_data[:, 0], categories=[3, 2]) + + ex_cat = [3, 2] if replace_dict["b"] == 1 else [1, 3] + b = pd.Categorical(final_data[:, 1], categories=ex_cat) + + expected = DataFrame({"a": a, "b": b}) + msg2 = "with CategoricalDtype is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg2): + result = df.replace(replace_dict, 3) + tm.assert_frame_equal(result, expected) + msg = ( + r"Attributes of DataFrame.iloc\[:, 0\] \(column name=\"a\"\) are " + "different" + ) + with pytest.raises(AssertionError, match=msg): + # ensure non-inplace call does not affect original + tm.assert_frame_equal(df, expected) + with tm.assert_produces_warning(FutureWarning, match=msg2): + return_value = df.replace(replace_dict, 3, inplace=True) + assert return_value is None + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize( + "df, to_replace, exp", + [ + ( + {"col1": [1, 2, 3], "col2": [4, 5, 6]}, + {4: 5, 5: 6, 6: 7}, + {"col1": [1, 2, 3], "col2": [5, 6, 7]}, + ), + ( + {"col1": [1, 2, 3], "col2": ["4", "5", "6"]}, + {"4": "5", "5": "6", "6": "7"}, + {"col1": [1, 2, 3], "col2": ["5", "6", "7"]}, + ), + ], + ) + def test_replace_commutative(self, df, to_replace, exp): + # GH 16051 + # DataFrame.replace() overwrites when values are non-numeric + # also added to data frame whilst issue was for series + + df = DataFrame(df) + + expected = DataFrame(exp) + result = df.replace(to_replace) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "replacer", + [ + Timestamp("20170827"), + np.int8(1), + np.int16(1), + np.float32(1), + np.float64(1), + ], + ) + def test_replace_replacer_dtype(self, replacer): + # GH26632 + df = DataFrame(["a"], dtype=object) + msg = "Downcasting behavior in `replace` " + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.replace({"a": replacer, "b": replacer}) + expected = DataFrame([replacer]) + tm.assert_frame_equal(result, expected) + + def test_replace_after_convert_dtypes(self): + # GH31517 + df = DataFrame({"grp": [1, 2, 3, 4, 5]}, dtype="Int64") + result = df.replace(1, 10) + expected = DataFrame({"grp": [10, 2, 3, 4, 5]}, dtype="Int64") + tm.assert_frame_equal(result, expected) + + def test_replace_invalid_to_replace(self): + # GH 18634 + # API: replace() should raise an exception if invalid argument is given + df = DataFrame({"one": ["a", "b ", "c"], "two": ["d ", "e ", "f "]}) + msg = ( + r"Expecting 'to_replace' to be either a scalar, array-like, " + r"dict or None, got invalid type.*" + ) + msg2 = ( + "DataFrame.replace without 'value' and with non-dict-like " + "'to_replace' is deprecated" + ) + with pytest.raises(TypeError, match=msg): + with tm.assert_produces_warning(FutureWarning, match=msg2): + df.replace(lambda x: x.strip()) + + @pytest.mark.parametrize("dtype", ["float", "float64", "int64", "Int64", "boolean"]) + @pytest.mark.parametrize("value", [np.nan, pd.NA]) + def test_replace_no_replacement_dtypes(self, dtype, value): + # https://github.com/pandas-dev/pandas/issues/32988 + df = DataFrame(np.eye(2), dtype=dtype) + result = df.replace(to_replace=[None, -np.inf, np.inf], value=value) + tm.assert_frame_equal(result, df) + + @pytest.mark.parametrize("replacement", [np.nan, 5]) + def test_replace_with_duplicate_columns(self, replacement): + # GH 24798 + result = DataFrame({"A": [1, 2, 3], "A1": [4, 5, 6], "B": [7, 8, 9]}) + result.columns = list("AAB") + + expected = DataFrame( + {"A": [1, 2, 3], "A1": [4, 5, 6], "B": [replacement, 8, 9]} + ) + expected.columns = list("AAB") + + result["B"] = result["B"].replace(7, replacement) + + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("value", [pd.Period("2020-01"), pd.Interval(0, 5)]) + def test_replace_ea_ignore_float(self, frame_or_series, value): + # GH#34871 + obj = DataFrame({"Per": [value] * 3}) + obj = tm.get_obj(obj, frame_or_series) + + expected = obj.copy() + result = obj.replace(1.0, 0.0) + tm.assert_equal(expected, result) + + def test_replace_value_category_type(self): + """ + Test for #23305: to ensure category dtypes are maintained + after replace with direct values + """ + + # create input data + input_dict = { + "col1": [1, 2, 3, 4], + "col2": ["a", "b", "c", "d"], + "col3": [1.5, 2.5, 3.5, 4.5], + "col4": ["cat1", "cat2", "cat3", "cat4"], + "col5": ["obj1", "obj2", "obj3", "obj4"], + } + # explicitly cast columns as category and order them + input_df = DataFrame(data=input_dict).astype( + {"col2": "category", "col4": "category"} + ) + input_df["col2"] = input_df["col2"].cat.reorder_categories( + ["a", "b", "c", "d"], ordered=True + ) + input_df["col4"] = input_df["col4"].cat.reorder_categories( + ["cat1", "cat2", "cat3", "cat4"], ordered=True + ) + + # create expected dataframe + expected_dict = { + "col1": [1, 2, 3, 4], + "col2": ["a", "b", "c", "z"], + "col3": [1.5, 2.5, 3.5, 4.5], + "col4": ["cat1", "catX", "cat3", "cat4"], + "col5": ["obj9", "obj2", "obj3", "obj4"], + } + # explicitly cast columns as category and order them + expected = DataFrame(data=expected_dict).astype( + {"col2": "category", "col4": "category"} + ) + expected["col2"] = expected["col2"].cat.reorder_categories( + ["a", "b", "c", "z"], ordered=True + ) + expected["col4"] = expected["col4"].cat.reorder_categories( + ["cat1", "catX", "cat3", "cat4"], ordered=True + ) + + # replace values in input dataframe + msg = ( + r"The behavior of Series\.replace \(and DataFrame.replace\) " + "with CategoricalDtype" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + input_df = input_df.replace("d", "z") + input_df = input_df.replace("obj1", "obj9") + result = input_df.replace("cat2", "catX") + + result = result.astype({"col1": "int64", "col3": "float64", "col5": "str"}) + tm.assert_frame_equal(result, expected) + + def test_replace_dict_category_type(self): + """ + Test to ensure category dtypes are maintained + after replace with dict values + """ + # GH#35268, GH#44940 + + # create input dataframe + input_dict = {"col1": ["a"], "col2": ["obj1"], "col3": ["cat1"]} + # explicitly cast columns as category + input_df = DataFrame(data=input_dict).astype( + {"col1": "category", "col2": "category", "col3": "category"} + ) + + # create expected dataframe + expected_dict = {"col1": ["z"], "col2": ["obj9"], "col3": ["catX"]} + # explicitly cast columns as category + expected = DataFrame(data=expected_dict).astype( + {"col1": "category", "col2": "category", "col3": "category"} + ) + + # replace values in input dataframe using a dict + msg = ( + r"The behavior of Series\.replace \(and DataFrame.replace\) " + "with CategoricalDtype" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + result = input_df.replace({"a": "z", "obj1": "obj9", "cat1": "catX"}) + + tm.assert_frame_equal(result, expected) + + def test_replace_with_compiled_regex(self): + # https://github.com/pandas-dev/pandas/issues/35680 + df = DataFrame(["a", "b", "c"]) + regex = re.compile("^a$") + result = df.replace({regex: "z"}, regex=True) + expected = DataFrame(["z", "b", "c"]) + tm.assert_frame_equal(result, expected) + + def test_replace_intervals(self): + # https://github.com/pandas-dev/pandas/issues/35931 + df = DataFrame({"a": [pd.Interval(0, 1), pd.Interval(0, 1)]}) + result = df.replace({"a": {pd.Interval(0, 1): "x"}}) + expected = DataFrame({"a": ["x", "x"]}, dtype=object) + tm.assert_frame_equal(result, expected) + + def test_replace_unicode(self): + # GH: 16784 + columns_values_map = {"positive": {"正面": 1, "中立": 1, "负面": 0}} + df1 = DataFrame({"positive": np.ones(3)}) + result = df1.replace(columns_values_map) + expected = DataFrame({"positive": np.ones(3)}) + tm.assert_frame_equal(result, expected) + + def test_replace_bytes(self, frame_or_series): + # GH#38900 + obj = frame_or_series(["o"]).astype("|S") + expected = obj.copy() + obj = obj.replace({None: np.nan}) + tm.assert_equal(obj, expected) + + @pytest.mark.parametrize( + "data, to_replace, value, expected", + [ + ([1], [1.0], [0], [0]), + ([1], [1], [0], [0]), + ([1.0], [1.0], [0], [0.0]), + ([1.0], [1], [0], [0.0]), + ], + ) + @pytest.mark.parametrize("box", [list, tuple, np.array]) + def test_replace_list_with_mixed_type( + self, data, to_replace, value, expected, box, frame_or_series + ): + # GH#40371 + obj = frame_or_series(data) + expected = frame_or_series(expected) + result = obj.replace(box(to_replace), value) + tm.assert_equal(result, expected) + + @pytest.mark.parametrize("val", [2, np.nan, 2.0]) + def test_replace_value_none_dtype_numeric(self, val): + # GH#48231 + df = DataFrame({"a": [1, val]}) + result = df.replace(val, None) + expected = DataFrame({"a": [1, None]}, dtype=object) + tm.assert_frame_equal(result, expected) + + df = DataFrame({"a": [1, val]}) + result = df.replace({val: None}) + tm.assert_frame_equal(result, expected) + + def test_replace_with_nil_na(self): + # GH 32075 + ser = DataFrame({"a": ["nil", pd.NA]}) + expected = DataFrame({"a": ["anything else", pd.NA]}, index=[0, 1]) + result = ser.replace("nil", "anything else") + tm.assert_frame_equal(expected, result) + + +class TestDataFrameReplaceRegex: + @pytest.mark.parametrize( + "data", + [ + {"a": list("ab.."), "b": list("efgh")}, + {"a": list("ab.."), "b": list(range(4))}, + ], + ) + @pytest.mark.parametrize( + "to_replace,value", [(r"\s*\.\s*", np.nan), (r"\s*(\.)\s*", r"\1\1\1")] + ) + @pytest.mark.parametrize("compile_regex", [True, False]) + @pytest.mark.parametrize("regex_kwarg", [True, False]) + @pytest.mark.parametrize("inplace", [True, False]) + def test_regex_replace_scalar( + self, data, to_replace, value, compile_regex, regex_kwarg, inplace + ): + df = DataFrame(data) + expected = df.copy() + + if compile_regex: + to_replace = re.compile(to_replace) + + if regex_kwarg: + regex = to_replace + to_replace = None + else: + regex = True + + result = df.replace(to_replace, value, inplace=inplace, regex=regex) + + if inplace: + assert result is None + result = df + + if value is np.nan: + expected_replace_val = np.nan + else: + expected_replace_val = "..." + + expected.loc[expected["a"] == ".", "a"] = expected_replace_val + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("regex", [False, True]) + def test_replace_regex_dtype_frame(self, regex): + # GH-48644 + df1 = DataFrame({"A": ["0"], "B": ["0"]}) + expected_df1 = DataFrame({"A": [1], "B": [1]}) + msg = "Downcasting behavior in `replace`" + with tm.assert_produces_warning(FutureWarning, match=msg): + result_df1 = df1.replace(to_replace="0", value=1, regex=regex) + tm.assert_frame_equal(result_df1, expected_df1) + + df2 = DataFrame({"A": ["0"], "B": ["1"]}) + expected_df2 = DataFrame({"A": [1], "B": ["1"]}) + with tm.assert_produces_warning(FutureWarning, match=msg): + result_df2 = df2.replace(to_replace="0", value=1, regex=regex) + tm.assert_frame_equal(result_df2, expected_df2) + + def test_replace_with_value_also_being_replaced(self): + # GH46306 + df = DataFrame({"A": [0, 1, 2], "B": [1, 0, 2]}) + result = df.replace({0: 1, 1: np.nan}) + expected = DataFrame({"A": [1, np.nan, 2], "B": [np.nan, 1, 2]}) + tm.assert_frame_equal(result, expected) + + def test_replace_categorical_no_replacement(self): + # GH#46672 + df = DataFrame( + { + "a": ["one", "two", None, "three"], + "b": ["one", None, "two", "three"], + }, + dtype="category", + ) + expected = df.copy() + + result = df.replace(to_replace=[".", "def"], value=["_", None]) + tm.assert_frame_equal(result, expected) + + def test_replace_object_splitting(self, using_infer_string): + # GH#53977 + df = DataFrame({"a": ["a"], "b": "b"}) + if using_infer_string: + assert len(df._mgr.blocks) == 2 + else: + assert len(df._mgr.blocks) == 1 + df.replace(to_replace=r"^\s*$", value="", inplace=True, regex=True) + if using_infer_string: + assert len(df._mgr.blocks) == 2 + else: + assert len(df._mgr.blocks) == 1 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_reset_index.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_reset_index.py new file mode 100644 index 0000000000000000000000000000000000000000..e762c8ebdcd6072f90512c80447a5081e891b1ae --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_reset_index.py @@ -0,0 +1,813 @@ +from datetime import datetime +from itertools import product + +import numpy as np +import pytest + +from pandas.core.dtypes.common import ( + is_float_dtype, + is_integer_dtype, +) + +import pandas as pd +from pandas import ( + Categorical, + CategoricalIndex, + DataFrame, + Index, + Interval, + IntervalIndex, + MultiIndex, + RangeIndex, + Series, + Timestamp, + cut, + date_range, +) +import pandas._testing as tm + + +@pytest.fixture() +def multiindex_df(): + levels = [["A", ""], ["B", "b"]] + return DataFrame([[0, 2], [1, 3]], columns=MultiIndex.from_tuples(levels)) + + +class TestResetIndex: + def test_reset_index_empty_rangeindex(self): + # GH#45230 + df = DataFrame( + columns=["brand"], dtype=np.int64, index=RangeIndex(0, 0, 1, name="foo") + ) + + df2 = df.set_index([df.index, "brand"]) + + result = df2.reset_index([1], drop=True) + tm.assert_frame_equal(result, df[[]], check_index_type=True) + + def test_set_reset(self): + idx = Index([2**63, 2**63 + 5, 2**63 + 10], name="foo") + + # set/reset + df = DataFrame({"A": [0, 1, 2]}, index=idx) + result = df.reset_index() + assert result["foo"].dtype == np.dtype("uint64") + + df = result.set_index("foo") + tm.assert_index_equal(df.index, idx) + + def test_set_index_reset_index_dt64tz(self): + idx = Index(date_range("20130101", periods=3, tz="US/Eastern"), name="foo") + + # set/reset + df = DataFrame({"A": [0, 1, 2]}, index=idx) + result = df.reset_index() + assert result["foo"].dtype == "datetime64[ns, US/Eastern]" + + df = result.set_index("foo") + tm.assert_index_equal(df.index, idx) + + def test_reset_index_tz(self, tz_aware_fixture): + # GH 3950 + # reset_index with single level + tz = tz_aware_fixture + idx = date_range("1/1/2011", periods=5, freq="D", tz=tz, name="idx") + df = DataFrame({"a": range(5), "b": ["A", "B", "C", "D", "E"]}, index=idx) + + expected = DataFrame( + { + "idx": idx, + "a": range(5), + "b": ["A", "B", "C", "D", "E"], + }, + columns=["idx", "a", "b"], + ) + result = df.reset_index() + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("tz", ["US/Eastern", "dateutil/US/Eastern"]) + def test_frame_reset_index_tzaware_index(self, tz): + dr = date_range("2012-06-02", periods=10, tz=tz) + df = DataFrame(np.random.default_rng(2).standard_normal(len(dr)), dr) + roundtripped = df.reset_index().set_index("index") + xp = df.index.tz + rs = roundtripped.index.tz + assert xp == rs + + def test_reset_index_with_intervals(self): + idx = IntervalIndex.from_breaks(np.arange(11), name="x") + original = DataFrame({"x": idx, "y": np.arange(10)})[["x", "y"]] + + result = original.set_index("x") + expected = DataFrame({"y": np.arange(10)}, index=idx) + tm.assert_frame_equal(result, expected) + + result2 = result.reset_index() + tm.assert_frame_equal(result2, original) + + def test_reset_index(self, float_frame): + stacked = float_frame.stack(future_stack=True)[::2] + stacked = DataFrame({"foo": stacked, "bar": stacked}) + + names = ["first", "second"] + stacked.index.names = names + deleveled = stacked.reset_index() + for i, (lev, level_codes) in enumerate( + zip(stacked.index.levels, stacked.index.codes) + ): + values = lev.take(level_codes) + name = names[i] + tm.assert_index_equal(values, Index(deleveled[name])) + + stacked.index.names = [None, None] + deleveled2 = stacked.reset_index() + tm.assert_series_equal( + deleveled["first"], deleveled2["level_0"], check_names=False + ) + tm.assert_series_equal( + deleveled["second"], deleveled2["level_1"], check_names=False + ) + + # default name assigned + rdf = float_frame.reset_index() + exp = Series(float_frame.index.values, name="index") + tm.assert_series_equal(rdf["index"], exp) + + # default name assigned, corner case + df = float_frame.copy() + df["index"] = "foo" + rdf = df.reset_index() + exp = Series(float_frame.index.values, name="level_0") + tm.assert_series_equal(rdf["level_0"], exp) + + # but this is ok + float_frame.index.name = "index" + deleveled = float_frame.reset_index() + tm.assert_series_equal(deleveled["index"], Series(float_frame.index)) + tm.assert_index_equal(deleveled.index, Index(range(len(deleveled))), exact=True) + + # preserve column names + float_frame.columns.name = "columns" + reset = float_frame.reset_index() + assert reset.columns.name == "columns" + + # only remove certain columns + df = float_frame.reset_index().set_index(["index", "A", "B"]) + rs = df.reset_index(["A", "B"]) + + tm.assert_frame_equal(rs, float_frame) + + rs = df.reset_index(["index", "A", "B"]) + tm.assert_frame_equal(rs, float_frame.reset_index()) + + rs = df.reset_index(["index", "A", "B"]) + tm.assert_frame_equal(rs, float_frame.reset_index()) + + rs = df.reset_index("A") + xp = float_frame.reset_index().set_index(["index", "B"]) + tm.assert_frame_equal(rs, xp) + + # test resetting in place + df = float_frame.copy() + reset = float_frame.reset_index() + return_value = df.reset_index(inplace=True) + assert return_value is None + tm.assert_frame_equal(df, reset) + + df = float_frame.reset_index().set_index(["index", "A", "B"]) + rs = df.reset_index("A", drop=True) + xp = float_frame.copy() + del xp["A"] + xp = xp.set_index(["B"], append=True) + tm.assert_frame_equal(rs, xp) + + def test_reset_index_name(self): + df = DataFrame( + [[1, 2, 3, 4], [5, 6, 7, 8]], + columns=["A", "B", "C", "D"], + index=Index(range(2), name="x"), + ) + assert df.reset_index().index.name is None + assert df.reset_index(drop=True).index.name is None + return_value = df.reset_index(inplace=True) + assert return_value is None + assert df.index.name is None + + @pytest.mark.parametrize("levels", [["A", "B"], [0, 1]]) + def test_reset_index_level(self, levels): + df = DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]], columns=["A", "B", "C", "D"]) + + # With MultiIndex + result = df.set_index(["A", "B"]).reset_index(level=levels[0]) + tm.assert_frame_equal(result, df.set_index("B")) + + result = df.set_index(["A", "B"]).reset_index(level=levels[:1]) + tm.assert_frame_equal(result, df.set_index("B")) + + result = df.set_index(["A", "B"]).reset_index(level=levels) + tm.assert_frame_equal(result, df) + + result = df.set_index(["A", "B"]).reset_index(level=levels, drop=True) + tm.assert_frame_equal(result, df[["C", "D"]]) + + # With single-level Index (GH 16263) + result = df.set_index("A").reset_index(level=levels[0]) + tm.assert_frame_equal(result, df) + + result = df.set_index("A").reset_index(level=levels[:1]) + tm.assert_frame_equal(result, df) + + result = df.set_index(["A"]).reset_index(level=levels[0], drop=True) + tm.assert_frame_equal(result, df[["B", "C", "D"]]) + + @pytest.mark.parametrize("idx_lev", [["A", "B"], ["A"]]) + def test_reset_index_level_missing(self, idx_lev): + # Missing levels - for both MultiIndex and single-level Index: + df = DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]], columns=["A", "B", "C", "D"]) + + with pytest.raises(KeyError, match=r"(L|l)evel \(?E\)?"): + df.set_index(idx_lev).reset_index(level=["A", "E"]) + with pytest.raises(IndexError, match="Too many levels"): + df.set_index(idx_lev).reset_index(level=[0, 1, 2]) + + def test_reset_index_right_dtype(self): + time = np.arange(0.0, 10, np.sqrt(2) / 2) + s1 = Series( + (9.81 * time**2) / 2, index=Index(time, name="time"), name="speed" + ) + df = DataFrame(s1) + + reset = s1.reset_index() + assert reset["time"].dtype == np.float64 + + reset = df.reset_index() + assert reset["time"].dtype == np.float64 + + def test_reset_index_multiindex_col(self): + vals = np.random.default_rng(2).standard_normal((3, 3)).astype(object) + idx = ["x", "y", "z"] + full = np.hstack(([[x] for x in idx], vals)) + df = DataFrame( + vals, + Index(idx, name="a"), + columns=[["b", "b", "c"], ["mean", "median", "mean"]], + ) + rs = df.reset_index() + xp = DataFrame( + full, columns=[["a", "b", "b", "c"], ["", "mean", "median", "mean"]] + ) + tm.assert_frame_equal(rs, xp) + + rs = df.reset_index(col_fill=None) + xp = DataFrame( + full, columns=[["a", "b", "b", "c"], ["a", "mean", "median", "mean"]] + ) + tm.assert_frame_equal(rs, xp) + + rs = df.reset_index(col_level=1, col_fill="blah") + xp = DataFrame( + full, columns=[["blah", "b", "b", "c"], ["a", "mean", "median", "mean"]] + ) + tm.assert_frame_equal(rs, xp) + + df = DataFrame( + vals, + MultiIndex.from_arrays([[0, 1, 2], ["x", "y", "z"]], names=["d", "a"]), + columns=[["b", "b", "c"], ["mean", "median", "mean"]], + ) + rs = df.reset_index("a") + xp = DataFrame( + full, + Index([0, 1, 2], name="d"), + columns=[["a", "b", "b", "c"], ["", "mean", "median", "mean"]], + ) + tm.assert_frame_equal(rs, xp) + + rs = df.reset_index("a", col_fill=None) + xp = DataFrame( + full, + Index(range(3), name="d"), + columns=[["a", "b", "b", "c"], ["a", "mean", "median", "mean"]], + ) + tm.assert_frame_equal(rs, xp) + + rs = df.reset_index("a", col_fill="blah", col_level=1) + xp = DataFrame( + full, + Index(range(3), name="d"), + columns=[["blah", "b", "b", "c"], ["a", "mean", "median", "mean"]], + ) + tm.assert_frame_equal(rs, xp) + + def test_reset_index_multiindex_nan(self): + # GH#6322, testing reset_index on MultiIndexes + # when we have a nan or all nan + df = DataFrame( + { + "A": ["a", "b", "c"], + "B": [0, 1, np.nan], + "C": np.random.default_rng(2).random(3), + } + ) + rs = df.set_index(["A", "B"]).reset_index() + tm.assert_frame_equal(rs, df) + + df = DataFrame( + { + "A": [np.nan, "b", "c"], + "B": [0, 1, 2], + "C": np.random.default_rng(2).random(3), + } + ) + rs = df.set_index(["A", "B"]).reset_index() + tm.assert_frame_equal(rs, df) + + df = DataFrame({"A": ["a", "b", "c"], "B": [0, 1, 2], "C": [np.nan, 1.1, 2.2]}) + rs = df.set_index(["A", "B"]).reset_index() + tm.assert_frame_equal(rs, df) + + df = DataFrame( + { + "A": ["a", "b", "c"], + "B": [np.nan, np.nan, np.nan], + "C": np.random.default_rng(2).random(3), + } + ) + rs = df.set_index(["A", "B"]).reset_index() + tm.assert_frame_equal(rs, df) + + @pytest.mark.parametrize( + "name", + [ + None, + "foo", + 2, + 3.0, + pd.Timedelta(6), + Timestamp("2012-12-30", tz="UTC"), + "2012-12-31", + ], + ) + def test_reset_index_with_datetimeindex_cols(self, name): + # GH#5818 + df = DataFrame( + [[1, 2], [3, 4]], + columns=date_range("1/1/2013", "1/2/2013"), + index=["A", "B"], + ) + df.index.name = name + + result = df.reset_index() + + item = name if name is not None else "index" + columns = Index([item, datetime(2013, 1, 1), datetime(2013, 1, 2)]) + if isinstance(item, str) and item == "2012-12-31": + columns = columns.astype("datetime64[ns]") + else: + assert columns.dtype == object + + expected = DataFrame( + [["A", 1, 2], ["B", 3, 4]], + columns=columns, + ) + tm.assert_frame_equal(result, expected) + + def test_reset_index_range(self): + # GH#12071 + df = DataFrame([[0, 0], [1, 1]], columns=["A", "B"], index=RangeIndex(stop=2)) + result = df.reset_index() + assert isinstance(result.index, RangeIndex) + expected = DataFrame( + [[0, 0, 0], [1, 1, 1]], + columns=["index", "A", "B"], + index=RangeIndex(stop=2), + ) + tm.assert_frame_equal(result, expected) + + def test_reset_index_multiindex_columns(self, multiindex_df): + result = multiindex_df[["B"]].rename_axis("A").reset_index() + tm.assert_frame_equal(result, multiindex_df) + + # GH#16120: already existing column + msg = r"cannot insert \('A', ''\), already exists" + with pytest.raises(ValueError, match=msg): + multiindex_df.rename_axis("A").reset_index() + + # GH#16164: multiindex (tuple) full key + result = multiindex_df.set_index([("A", "")]).reset_index() + tm.assert_frame_equal(result, multiindex_df) + + # with additional (unnamed) index level + idx_col = DataFrame( + [[0], [1]], columns=MultiIndex.from_tuples([("level_0", "")]) + ) + expected = pd.concat([idx_col, multiindex_df[[("B", "b"), ("A", "")]]], axis=1) + result = multiindex_df.set_index([("B", "b")], append=True).reset_index() + tm.assert_frame_equal(result, expected) + + # with index name which is a too long tuple... + msg = "Item must have length equal to number of levels." + with pytest.raises(ValueError, match=msg): + multiindex_df.rename_axis([("C", "c", "i")]).reset_index() + + # or too short... + levels = [["A", "a", ""], ["B", "b", "i"]] + df2 = DataFrame([[0, 2], [1, 3]], columns=MultiIndex.from_tuples(levels)) + idx_col = DataFrame( + [[0], [1]], columns=MultiIndex.from_tuples([("C", "c", "ii")]) + ) + expected = pd.concat([idx_col, df2], axis=1) + result = df2.rename_axis([("C", "c")]).reset_index(col_fill="ii") + tm.assert_frame_equal(result, expected) + + # ... which is incompatible with col_fill=None + with pytest.raises( + ValueError, + match=( + "col_fill=None is incompatible with " + r"incomplete column name \('C', 'c'\)" + ), + ): + df2.rename_axis([("C", "c")]).reset_index(col_fill=None) + + # with col_level != 0 + result = df2.rename_axis([("c", "ii")]).reset_index(col_level=1, col_fill="C") + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("flag", [False, True]) + @pytest.mark.parametrize("allow_duplicates", [False, True]) + def test_reset_index_duplicate_columns_allow( + self, multiindex_df, flag, allow_duplicates + ): + # GH#44755 reset_index with duplicate column labels + df = multiindex_df.rename_axis("A") + df = df.set_flags(allows_duplicate_labels=flag) + + if flag and allow_duplicates: + result = df.reset_index(allow_duplicates=allow_duplicates) + levels = [["A", ""], ["A", ""], ["B", "b"]] + expected = DataFrame( + [[0, 0, 2], [1, 1, 3]], columns=MultiIndex.from_tuples(levels) + ) + tm.assert_frame_equal(result, expected) + else: + if not flag and allow_duplicates: + msg = ( + "Cannot specify 'allow_duplicates=True' when " + "'self.flags.allows_duplicate_labels' is False" + ) + else: + msg = r"cannot insert \('A', ''\), already exists" + with pytest.raises(ValueError, match=msg): + df.reset_index(allow_duplicates=allow_duplicates) + + @pytest.mark.parametrize("flag", [False, True]) + def test_reset_index_duplicate_columns_default(self, multiindex_df, flag): + df = multiindex_df.rename_axis("A") + df = df.set_flags(allows_duplicate_labels=flag) + + msg = r"cannot insert \('A', ''\), already exists" + with pytest.raises(ValueError, match=msg): + df.reset_index() + + @pytest.mark.parametrize("allow_duplicates", ["bad value"]) + def test_reset_index_allow_duplicates_check(self, multiindex_df, allow_duplicates): + with pytest.raises(ValueError, match="expected type bool"): + multiindex_df.reset_index(allow_duplicates=allow_duplicates) + + def test_reset_index_datetime(self, tz_naive_fixture): + # GH#3950 + tz = tz_naive_fixture + idx1 = date_range("1/1/2011", periods=5, freq="D", tz=tz, name="idx1") + idx2 = Index(range(5), name="idx2", dtype="int64") + idx = MultiIndex.from_arrays([idx1, idx2]) + df = DataFrame( + {"a": np.arange(5, dtype="int64"), "b": ["A", "B", "C", "D", "E"]}, + index=idx, + ) + + expected = DataFrame( + { + "idx1": idx1, + "idx2": np.arange(5, dtype="int64"), + "a": np.arange(5, dtype="int64"), + "b": ["A", "B", "C", "D", "E"], + }, + columns=["idx1", "idx2", "a", "b"], + ) + + tm.assert_frame_equal(df.reset_index(), expected) + + def test_reset_index_datetime2(self, tz_naive_fixture): + tz = tz_naive_fixture + idx1 = date_range("1/1/2011", periods=5, freq="D", tz=tz, name="idx1") + idx2 = Index(range(5), name="idx2", dtype="int64") + idx3 = date_range( + "1/1/2012", periods=5, freq="MS", tz="Europe/Paris", name="idx3" + ) + idx = MultiIndex.from_arrays([idx1, idx2, idx3]) + df = DataFrame( + {"a": np.arange(5, dtype="int64"), "b": ["A", "B", "C", "D", "E"]}, + index=idx, + ) + + expected = DataFrame( + { + "idx1": idx1, + "idx2": np.arange(5, dtype="int64"), + "idx3": idx3, + "a": np.arange(5, dtype="int64"), + "b": ["A", "B", "C", "D", "E"], + }, + columns=["idx1", "idx2", "idx3", "a", "b"], + ) + result = df.reset_index() + tm.assert_frame_equal(result, expected) + + def test_reset_index_datetime3(self, tz_naive_fixture): + # GH#7793 + tz = tz_naive_fixture + dti = date_range("20130101", periods=3, tz=tz) + idx = MultiIndex.from_product([["a", "b"], dti]) + df = DataFrame( + np.arange(6, dtype="int64").reshape(6, 1), columns=["a"], index=idx + ) + + expected = DataFrame( + { + "level_0": "a a a b b b".split(), + "level_1": dti.append(dti), + "a": np.arange(6, dtype="int64"), + }, + columns=["level_0", "level_1", "a"], + ) + result = df.reset_index() + tm.assert_frame_equal(result, expected) + + def test_reset_index_period(self): + # GH#7746 + idx = MultiIndex.from_product( + [pd.period_range("20130101", periods=3, freq="M"), list("abc")], + names=["month", "feature"], + ) + + df = DataFrame( + np.arange(9, dtype="int64").reshape(-1, 1), index=idx, columns=["a"] + ) + expected = DataFrame( + { + "month": ( + [pd.Period("2013-01", freq="M")] * 3 + + [pd.Period("2013-02", freq="M")] * 3 + + [pd.Period("2013-03", freq="M")] * 3 + ), + "feature": ["a", "b", "c"] * 3, + "a": np.arange(9, dtype="int64"), + }, + columns=["month", "feature", "a"], + ) + result = df.reset_index() + tm.assert_frame_equal(result, expected) + + def test_reset_index_delevel_infer_dtype(self): + tuples = list(product(["foo", "bar"], [10, 20], [1.0, 1.1])) + index = MultiIndex.from_tuples(tuples, names=["prm0", "prm1", "prm2"]) + df = DataFrame( + np.random.default_rng(2).standard_normal((8, 3)), + columns=["A", "B", "C"], + index=index, + ) + deleveled = df.reset_index() + assert is_integer_dtype(deleveled["prm1"]) + assert is_float_dtype(deleveled["prm2"]) + + def test_reset_index_with_drop( + self, multiindex_year_month_day_dataframe_random_data + ): + ymd = multiindex_year_month_day_dataframe_random_data + + deleveled = ymd.reset_index(drop=True) + assert len(deleveled.columns) == len(ymd.columns) + assert deleveled.index.name == ymd.index.name + + @pytest.mark.parametrize( + "ix_data, exp_data", + [ + ( + [(pd.NaT, 1), (pd.NaT, 2)], + {"a": [pd.NaT, pd.NaT], "b": [1, 2], "x": [11, 12]}, + ), + ( + [(pd.NaT, 1), (Timestamp("2020-01-01"), 2)], + {"a": [pd.NaT, Timestamp("2020-01-01")], "b": [1, 2], "x": [11, 12]}, + ), + ( + [(pd.NaT, 1), (pd.Timedelta(123, "d"), 2)], + {"a": [pd.NaT, pd.Timedelta(123, "d")], "b": [1, 2], "x": [11, 12]}, + ), + ], + ) + def test_reset_index_nat_multiindex(self, ix_data, exp_data): + # GH#36541: that reset_index() does not raise ValueError + ix = MultiIndex.from_tuples(ix_data, names=["a", "b"]) + result = DataFrame({"x": [11, 12]}, index=ix) + result = result.reset_index() + + expected = DataFrame(exp_data) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "codes", ([[0, 0, 1, 1], [0, 1, 0, 1]], [[0, 0, -1, 1], [0, 1, 0, 1]]) + ) + def test_rest_index_multiindex_categorical_with_missing_values(self, codes): + # GH#24206 + + index = MultiIndex( + [CategoricalIndex(["A", "B"]), CategoricalIndex(["a", "b"])], codes + ) + data = {"col": range(len(index))} + df = DataFrame(data=data, index=index) + + expected = DataFrame( + { + "level_0": Categorical.from_codes(codes[0], categories=["A", "B"]), + "level_1": Categorical.from_codes(codes[1], categories=["a", "b"]), + "col": range(4), + } + ) + + res = df.reset_index() + tm.assert_frame_equal(res, expected) + + # roundtrip + res = expected.set_index(["level_0", "level_1"]).reset_index() + tm.assert_frame_equal(res, expected) + + +@pytest.mark.parametrize( + "array, dtype", + [ + (["a", "b"], object), + ( + pd.period_range("12-1-2000", periods=2, freq="Q-DEC"), + pd.PeriodDtype(freq="Q-DEC"), + ), + ], +) +def test_reset_index_dtypes_on_empty_frame_with_multiindex( + array, dtype, using_infer_string +): + # GH 19602 - Preserve dtype on empty DataFrame with MultiIndex + idx = MultiIndex.from_product([[0, 1], [0.5, 1.0], array]) + result = DataFrame(index=idx)[:0].reset_index().dtypes + if using_infer_string and dtype == object: + dtype = pd.StringDtype(na_value=np.nan) + expected = Series({"level_0": np.int64, "level_1": np.float64, "level_2": dtype}) + tm.assert_series_equal(result, expected) + + +def test_reset_index_empty_frame_with_datetime64_multiindex(): + # https://github.com/pandas-dev/pandas/issues/35606 + dti = pd.DatetimeIndex(["2020-07-20 00:00:00"], dtype="M8[ns]") + idx = MultiIndex.from_product([dti, [3, 4]], names=["a", "b"])[:0] + df = DataFrame(index=idx, columns=["c", "d"]) + result = df.reset_index() + expected = DataFrame( + columns=list("abcd"), index=RangeIndex(start=0, stop=0, step=1) + ) + expected["a"] = expected["a"].astype("datetime64[ns]") + expected["b"] = expected["b"].astype("int64") + tm.assert_frame_equal(result, expected) + + +def test_reset_index_empty_frame_with_datetime64_multiindex_from_groupby( + using_infer_string, +): + # https://github.com/pandas-dev/pandas/issues/35657 + dti = pd.DatetimeIndex(["2020-01-01"], dtype="M8[ns]") + df = DataFrame({"c1": [10.0], "c2": ["a"], "c3": dti}) + df = df.head(0).groupby(["c2", "c3"])[["c1"]].sum() + result = df.reset_index() + expected = DataFrame( + columns=["c2", "c3", "c1"], index=RangeIndex(start=0, stop=0, step=1) + ) + expected["c3"] = expected["c3"].astype("datetime64[ns]") + expected["c1"] = expected["c1"].astype("float64") + if using_infer_string: + expected["c2"] = expected["c2"].astype("str") + tm.assert_frame_equal(result, expected) + + +def test_reset_index_multiindex_nat(): + # GH 11479 + idx = range(3) + tstamp = date_range("2015-07-01", freq="D", periods=3) + df = DataFrame({"id": idx, "tstamp": tstamp, "a": list("abc")}) + df.loc[2, "tstamp"] = pd.NaT + result = df.set_index(["id", "tstamp"]).reset_index("id") + exp_dti = pd.DatetimeIndex( + ["2015-07-01", "2015-07-02", "NaT"], dtype="M8[ns]", name="tstamp" + ) + expected = DataFrame( + {"id": range(3), "a": list("abc")}, + index=exp_dti, + ) + tm.assert_frame_equal(result, expected) + + +def test_reset_index_interval_columns_object_cast(): + # GH 19136 + df = DataFrame( + np.eye(2), index=Index([1, 2], name="Year"), columns=cut([1, 2], [0, 1, 2]) + ) + result = df.reset_index() + expected = DataFrame( + [[1, 1.0, 0.0], [2, 0.0, 1.0]], + columns=Index(["Year", Interval(0, 1), Interval(1, 2)]), + ) + tm.assert_frame_equal(result, expected) + + +def test_reset_index_rename(float_frame): + # GH 6878 + result = float_frame.reset_index(names="new_name") + expected = Series(float_frame.index.values, name="new_name") + tm.assert_series_equal(result["new_name"], expected) + + result = float_frame.reset_index(names=123) + expected = Series(float_frame.index.values, name=123) + tm.assert_series_equal(result[123], expected) + + +def test_reset_index_rename_multiindex(float_frame): + # GH 6878 + stacked_df = float_frame.stack(future_stack=True)[::2] + stacked_df = DataFrame({"foo": stacked_df, "bar": stacked_df}) + + names = ["first", "second"] + stacked_df.index.names = names + + result = stacked_df.reset_index() + expected = stacked_df.reset_index(names=["new_first", "new_second"]) + tm.assert_series_equal(result["first"], expected["new_first"], check_names=False) + tm.assert_series_equal(result["second"], expected["new_second"], check_names=False) + + +def test_errorreset_index_rename(float_frame): + # GH 6878 + stacked_df = float_frame.stack(future_stack=True)[::2] + stacked_df = DataFrame({"first": stacked_df, "second": stacked_df}) + + with pytest.raises( + ValueError, match="Index names must be str or 1-dimensional list" + ): + stacked_df.reset_index(names={"first": "new_first", "second": "new_second"}) + + with pytest.raises(IndexError, match="list index out of range"): + stacked_df.reset_index(names=["new_first"]) + + +def test_reset_index_false_index_name(): + result_series = Series(data=range(5, 10), index=range(5)) + result_series.index.name = False + result_series.reset_index() + expected_series = Series(range(5, 10), RangeIndex(range(5), name=False)) + tm.assert_series_equal(result_series, expected_series) + + # GH 38147 + result_frame = DataFrame(data=range(5, 10), index=range(5)) + result_frame.index.name = False + result_frame.reset_index() + expected_frame = DataFrame(range(5, 10), RangeIndex(range(5), name=False)) + tm.assert_frame_equal(result_frame, expected_frame) + + +@pytest.mark.parametrize("columns", [None, Index([])]) +def test_reset_index_with_empty_frame(columns): + # Currently empty DataFrame has RangeIndex or object dtype Index, but when + # resetting the index we still want to end up with the default string dtype + # https://github.com/pandas-dev/pandas/issues/60338 + + index = Index([], name="foo") + df = DataFrame(index=index, columns=columns) + result = df.reset_index() + expected = DataFrame(columns=["foo"]) + tm.assert_frame_equal(result, expected) + + index = Index([1, 2, 3], name="foo") + df = DataFrame(index=index, columns=columns) + result = df.reset_index() + expected = DataFrame({"foo": [1, 2, 3]}) + tm.assert_frame_equal(result, expected) + + index = MultiIndex.from_tuples([], names=["foo", "bar"]) + df = DataFrame(index=index, columns=columns) + result = df.reset_index() + expected = DataFrame(columns=["foo", "bar"]) + tm.assert_frame_equal(result, expected) + + index = MultiIndex.from_tuples([(1, 2), (2, 3)], names=["foo", "bar"]) + df = DataFrame(index=index, columns=columns) + result = df.reset_index() + expected = DataFrame({"foo": [1, 2], "bar": [2, 3]}) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_round.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_round.py new file mode 100644 index 0000000000000000000000000000000000000000..a96df27b48d7d8dc0b2f26cc25f0dca16ea3b462 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_round.py @@ -0,0 +1,225 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + Series, + date_range, +) +import pandas._testing as tm + + +class TestDataFrameRound: + def test_round(self): + # GH#2665 + + # Test that rounding an empty DataFrame does nothing + df = DataFrame() + tm.assert_frame_equal(df, df.round()) + + # Here's the test frame we'll be working with + df = DataFrame({"col1": [1.123, 2.123, 3.123], "col2": [1.234, 2.234, 3.234]}) + + # Default round to integer (i.e. decimals=0) + expected_rounded = DataFrame({"col1": [1.0, 2.0, 3.0], "col2": [1.0, 2.0, 3.0]}) + tm.assert_frame_equal(df.round(), expected_rounded) + + # Round with an integer + decimals = 2 + expected_rounded = DataFrame( + {"col1": [1.12, 2.12, 3.12], "col2": [1.23, 2.23, 3.23]} + ) + tm.assert_frame_equal(df.round(decimals), expected_rounded) + + # This should also work with np.round (since np.round dispatches to + # df.round) + tm.assert_frame_equal(np.round(df, decimals), expected_rounded) + + # Round with a list + round_list = [1, 2] + msg = "decimals must be an integer, a dict-like or a Series" + with pytest.raises(TypeError, match=msg): + df.round(round_list) + + # Round with a dictionary + expected_rounded = DataFrame( + {"col1": [1.1, 2.1, 3.1], "col2": [1.23, 2.23, 3.23]} + ) + round_dict = {"col1": 1, "col2": 2} + tm.assert_frame_equal(df.round(round_dict), expected_rounded) + + # Incomplete dict + expected_partially_rounded = DataFrame( + {"col1": [1.123, 2.123, 3.123], "col2": [1.2, 2.2, 3.2]} + ) + partial_round_dict = {"col2": 1} + tm.assert_frame_equal(df.round(partial_round_dict), expected_partially_rounded) + + # Dict with unknown elements + wrong_round_dict = {"col3": 2, "col2": 1} + tm.assert_frame_equal(df.round(wrong_round_dict), expected_partially_rounded) + + # float input to `decimals` + non_int_round_dict = {"col1": 1, "col2": 0.5} + msg = "Values in decimals must be integers" + with pytest.raises(TypeError, match=msg): + df.round(non_int_round_dict) + + # String input + non_int_round_dict = {"col1": 1, "col2": "foo"} + with pytest.raises(TypeError, match=msg): + df.round(non_int_round_dict) + + non_int_round_Series = Series(non_int_round_dict) + with pytest.raises(TypeError, match=msg): + df.round(non_int_round_Series) + + # List input + non_int_round_dict = {"col1": 1, "col2": [1, 2]} + with pytest.raises(TypeError, match=msg): + df.round(non_int_round_dict) + + non_int_round_Series = Series(non_int_round_dict) + with pytest.raises(TypeError, match=msg): + df.round(non_int_round_Series) + + # Non integer Series inputs + non_int_round_Series = Series(non_int_round_dict) + with pytest.raises(TypeError, match=msg): + df.round(non_int_round_Series) + + non_int_round_Series = Series(non_int_round_dict) + with pytest.raises(TypeError, match=msg): + df.round(non_int_round_Series) + + # Negative numbers + negative_round_dict = {"col1": -1, "col2": -2} + big_df = df * 100 + expected_neg_rounded = DataFrame( + {"col1": [110.0, 210, 310], "col2": [100.0, 200, 300]} + ) + tm.assert_frame_equal(big_df.round(negative_round_dict), expected_neg_rounded) + + # nan in Series round + nan_round_Series = Series({"col1": np.nan, "col2": 1}) + + with pytest.raises(TypeError, match=msg): + df.round(nan_round_Series) + + # Make sure this doesn't break existing Series.round + tm.assert_series_equal(df["col1"].round(1), expected_rounded["col1"]) + + # named columns + # GH#11986 + decimals = 2 + expected_rounded = DataFrame( + {"col1": [1.12, 2.12, 3.12], "col2": [1.23, 2.23, 3.23]} + ) + df.columns.name = "cols" + expected_rounded.columns.name = "cols" + tm.assert_frame_equal(df.round(decimals), expected_rounded) + + # interaction of named columns & series + tm.assert_series_equal(df["col1"].round(decimals), expected_rounded["col1"]) + tm.assert_series_equal(df.round(decimals)["col1"], expected_rounded["col1"]) + + def test_round_numpy(self): + # GH#12600 + df = DataFrame([[1.53, 1.36], [0.06, 7.01]]) + out = np.round(df, decimals=0) + expected = DataFrame([[2.0, 1.0], [0.0, 7.0]]) + tm.assert_frame_equal(out, expected) + + msg = "the 'out' parameter is not supported" + with pytest.raises(ValueError, match=msg): + np.round(df, decimals=0, out=df) + + def test_round_numpy_with_nan(self): + # See GH#14197 + df = Series([1.53, np.nan, 0.06]).to_frame() + with tm.assert_produces_warning(None): + result = df.round() + expected = Series([2.0, np.nan, 0.0]).to_frame() + tm.assert_frame_equal(result, expected) + + def test_round_mixed_type(self): + # GH#11885 + df = DataFrame( + { + "col1": [1.1, 2.2, 3.3, 4.4], + "col2": ["1", "a", "c", "f"], + "col3": date_range("20111111", periods=4), + } + ) + round_0 = DataFrame( + { + "col1": [1.0, 2.0, 3.0, 4.0], + "col2": ["1", "a", "c", "f"], + "col3": date_range("20111111", periods=4), + } + ) + tm.assert_frame_equal(df.round(), round_0) + tm.assert_frame_equal(df.round(1), df) + tm.assert_frame_equal(df.round({"col1": 1}), df) + tm.assert_frame_equal(df.round({"col1": 0}), round_0) + tm.assert_frame_equal(df.round({"col1": 0, "col2": 1}), round_0) + tm.assert_frame_equal(df.round({"col3": 1}), df) + + def test_round_with_duplicate_columns(self): + # GH#11611 + + df = DataFrame( + np.random.default_rng(2).random([3, 3]), + columns=["A", "B", "C"], + index=["first", "second", "third"], + ) + + dfs = pd.concat((df, df), axis=1) + rounded = dfs.round() + tm.assert_index_equal(rounded.index, dfs.index) + + decimals = Series([1, 0, 2], index=["A", "B", "A"]) + msg = "Index of decimals must be unique" + with pytest.raises(ValueError, match=msg): + df.round(decimals) + + def test_round_builtin(self): + # GH#11763 + # Here's the test frame we'll be working with + df = DataFrame({"col1": [1.123, 2.123, 3.123], "col2": [1.234, 2.234, 3.234]}) + + # Default round to integer (i.e. decimals=0) + expected_rounded = DataFrame({"col1": [1.0, 2.0, 3.0], "col2": [1.0, 2.0, 3.0]}) + tm.assert_frame_equal(round(df), expected_rounded) + + def test_round_nonunique_categorical(self): + # See GH#21809 + idx = pd.CategoricalIndex(["low"] * 3 + ["hi"] * 3) + df = DataFrame(np.random.default_rng(2).random((6, 3)), columns=list("abc")) + + expected = df.round(3) + expected.index = idx + + df_categorical = df.copy().set_index(idx) + assert df_categorical.shape == (6, 3) + result = df_categorical.round(3) + assert result.shape == (6, 3) + + tm.assert_frame_equal(result, expected) + + def test_round_interval_category_columns(self): + # GH#30063 + columns = pd.CategoricalIndex(pd.interval_range(0, 2)) + df = DataFrame([[0.66, 1.1], [0.3, 0.25]], columns=columns) + + result = df.round() + expected = DataFrame([[1.0, 1.0], [0.0, 0.0]], columns=columns) + tm.assert_frame_equal(result, expected) + + def test_round_empty_not_input(self): + # GH#51032 + df = DataFrame() + result = df.round() + tm.assert_frame_equal(df, result) + assert df is not result diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_sample.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..6b3459fbdc0359c4d234dd9d1f3c2bbfbcb5c260 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_sample.py @@ -0,0 +1,372 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Index, + Series, +) +import pandas._testing as tm +import pandas.core.common as com + + +class TestSample: + @pytest.fixture + def obj(self, frame_or_series): + if frame_or_series is Series: + arr = np.random.default_rng(2).standard_normal(10) + else: + arr = np.random.default_rng(2).standard_normal((10, 10)) + return frame_or_series(arr, dtype=None) + + @pytest.mark.parametrize("test", list(range(10))) + def test_sample(self, test, obj): + # Fixes issue: 2419 + # Check behavior of random_state argument + # Check for stability when receives seed or random state -- run 10 + # times. + + seed = np.random.default_rng(2).integers(0, 100) + tm.assert_equal( + obj.sample(n=4, random_state=seed), obj.sample(n=4, random_state=seed) + ) + + tm.assert_equal( + obj.sample(frac=0.7, random_state=seed), + obj.sample(frac=0.7, random_state=seed), + ) + + tm.assert_equal( + obj.sample(n=4, random_state=np.random.default_rng(test)), + obj.sample(n=4, random_state=np.random.default_rng(test)), + ) + + tm.assert_equal( + obj.sample(frac=0.7, random_state=np.random.default_rng(test)), + obj.sample(frac=0.7, random_state=np.random.default_rng(test)), + ) + + tm.assert_equal( + obj.sample( + frac=2, + replace=True, + random_state=np.random.default_rng(test), + ), + obj.sample( + frac=2, + replace=True, + random_state=np.random.default_rng(test), + ), + ) + + os1, os2 = [], [] + for _ in range(2): + os1.append(obj.sample(n=4, random_state=test)) + os2.append(obj.sample(frac=0.7, random_state=test)) + tm.assert_equal(*os1) + tm.assert_equal(*os2) + + def test_sample_lengths(self, obj): + # Check lengths are right + assert len(obj.sample(n=4) == 4) + assert len(obj.sample(frac=0.34) == 3) + assert len(obj.sample(frac=0.36) == 4) + + def test_sample_invalid_random_state(self, obj): + # Check for error when random_state argument invalid. + msg = ( + "random_state must be an integer, array-like, a BitGenerator, Generator, " + "a numpy RandomState, or None" + ) + with pytest.raises(ValueError, match=msg): + obj.sample(random_state="a_string") + + def test_sample_wont_accept_n_and_frac(self, obj): + # Giving both frac and N throws error + msg = "Please enter a value for `frac` OR `n`, not both" + with pytest.raises(ValueError, match=msg): + obj.sample(n=3, frac=0.3) + + def test_sample_requires_positive_n_frac(self, obj): + with pytest.raises( + ValueError, + match="A negative number of rows requested. Please provide `n` >= 0", + ): + obj.sample(n=-3) + with pytest.raises( + ValueError, + match="A negative number of rows requested. Please provide `frac` >= 0", + ): + obj.sample(frac=-0.3) + + def test_sample_requires_integer_n(self, obj): + # Make sure float values of `n` give error + with pytest.raises(ValueError, match="Only integers accepted as `n` values"): + obj.sample(n=3.2) + + def test_sample_invalid_weight_lengths(self, obj): + # Weight length must be right + msg = "Weights and axis to be sampled must be of same length" + with pytest.raises(ValueError, match=msg): + obj.sample(n=3, weights=[0, 1]) + + with pytest.raises(ValueError, match=msg): + bad_weights = [0.5] * 11 + obj.sample(n=3, weights=bad_weights) + + with pytest.raises(ValueError, match="Fewer non-zero entries in p than size"): + bad_weight_series = Series([0, 0, 0.2]) + obj.sample(n=4, weights=bad_weight_series) + + def test_sample_negative_weights(self, obj): + # Check won't accept negative weights + bad_weights = [-0.1] * 10 + msg = "weight vector many not include negative values" + with pytest.raises(ValueError, match=msg): + obj.sample(n=3, weights=bad_weights) + + def test_sample_inf_weights(self, obj): + # Check inf and -inf throw errors: + + weights_with_inf = [0.1] * 10 + weights_with_inf[0] = np.inf + msg = "weight vector may not include `inf` values" + with pytest.raises(ValueError, match=msg): + obj.sample(n=3, weights=weights_with_inf) + + weights_with_ninf = [0.1] * 10 + weights_with_ninf[0] = -np.inf + with pytest.raises(ValueError, match=msg): + obj.sample(n=3, weights=weights_with_ninf) + + def test_sample_zero_weights(self, obj): + # All zeros raises errors + + zero_weights = [0] * 10 + with pytest.raises(ValueError, match="Invalid weights: weights sum to zero"): + obj.sample(n=3, weights=zero_weights) + + def test_sample_missing_weights(self, obj): + # All missing weights + + nan_weights = [np.nan] * 10 + with pytest.raises(ValueError, match="Invalid weights: weights sum to zero"): + obj.sample(n=3, weights=nan_weights) + + def test_sample_none_weights(self, obj): + # Check None are also replaced by zeros. + weights_with_None = [None] * 10 + weights_with_None[5] = 0.5 + tm.assert_equal( + obj.sample(n=1, axis=0, weights=weights_with_None), obj.iloc[5:6] + ) + + @pytest.mark.parametrize( + "func_str,arg", + [ + ("np.array", [2, 3, 1, 0]), + ("np.random.MT19937", 3), + ("np.random.PCG64", 11), + ], + ) + def test_sample_random_state(self, func_str, arg, frame_or_series): + # GH#32503 + obj = DataFrame({"col1": range(10, 20), "col2": range(20, 30)}) + obj = tm.get_obj(obj, frame_or_series) + result = obj.sample(n=3, random_state=eval(func_str)(arg)) + expected = obj.sample(n=3, random_state=com.random_state(eval(func_str)(arg))) + tm.assert_equal(result, expected) + + def test_sample_generator(self, frame_or_series): + # GH#38100 + obj = frame_or_series(np.arange(100)) + rng = np.random.default_rng(2) + + # Consecutive calls should advance the seed + result1 = obj.sample(n=50, random_state=rng) + result2 = obj.sample(n=50, random_state=rng) + assert not (result1.index.values == result2.index.values).all() + + # Matching generator initialization must give same result + # Consecutive calls should advance the seed + result1 = obj.sample(n=50, random_state=np.random.default_rng(11)) + result2 = obj.sample(n=50, random_state=np.random.default_rng(11)) + tm.assert_equal(result1, result2) + + def test_sample_upsampling_without_replacement(self, frame_or_series): + # GH#27451 + + obj = DataFrame({"A": list("abc")}) + obj = tm.get_obj(obj, frame_or_series) + + msg = ( + "Replace has to be set to `True` when " + "upsampling the population `frac` > 1." + ) + with pytest.raises(ValueError, match=msg): + obj.sample(frac=2, replace=False) + + +class TestSampleDataFrame: + # Tests which are relevant only for DataFrame, so these are + # as fully parametrized as they can get. + + def test_sample(self): + # GH#2419 + # additional specific object based tests + + # A few dataframe test with degenerate weights. + easy_weight_list = [0] * 10 + easy_weight_list[5] = 1 + + df = DataFrame( + { + "col1": range(10, 20), + "col2": range(20, 30), + "colString": ["a"] * 10, + "easyweights": easy_weight_list, + } + ) + sample1 = df.sample(n=1, weights="easyweights") + tm.assert_frame_equal(sample1, df.iloc[5:6]) + + # Ensure proper error if string given as weight for Series or + # DataFrame with axis = 1. + ser = Series(range(10)) + msg = "Strings cannot be passed as weights when sampling from a Series." + with pytest.raises(ValueError, match=msg): + ser.sample(n=3, weights="weight_column") + + msg = ( + "Strings can only be passed to weights when sampling from rows on a " + "DataFrame" + ) + with pytest.raises(ValueError, match=msg): + df.sample(n=1, weights="weight_column", axis=1) + + # Check weighting key error + with pytest.raises( + KeyError, match="'String passed to weights not a valid column'" + ): + df.sample(n=3, weights="not_a_real_column_name") + + # Check that re-normalizes weights that don't sum to one. + weights_less_than_1 = [0] * 10 + weights_less_than_1[0] = 0.5 + tm.assert_frame_equal(df.sample(n=1, weights=weights_less_than_1), df.iloc[:1]) + + ### + # Test axis argument + ### + + # Test axis argument + df = DataFrame({"col1": range(10), "col2": ["a"] * 10}) + second_column_weight = [0, 1] + tm.assert_frame_equal( + df.sample(n=1, axis=1, weights=second_column_weight), df[["col2"]] + ) + + # Different axis arg types + tm.assert_frame_equal( + df.sample(n=1, axis="columns", weights=second_column_weight), df[["col2"]] + ) + + weight = [0] * 10 + weight[5] = 0.5 + tm.assert_frame_equal(df.sample(n=1, axis="rows", weights=weight), df.iloc[5:6]) + tm.assert_frame_equal( + df.sample(n=1, axis="index", weights=weight), df.iloc[5:6] + ) + + # Check out of range axis values + msg = "No axis named 2 for object type DataFrame" + with pytest.raises(ValueError, match=msg): + df.sample(n=1, axis=2) + + msg = "No axis named not_a_name for object type DataFrame" + with pytest.raises(ValueError, match=msg): + df.sample(n=1, axis="not_a_name") + + ser = Series(range(10)) + with pytest.raises(ValueError, match="No axis named 1 for object type Series"): + ser.sample(n=1, axis=1) + + # Test weight length compared to correct axis + msg = "Weights and axis to be sampled must be of same length" + with pytest.raises(ValueError, match=msg): + df.sample(n=1, axis=1, weights=[0.5] * 10) + + def test_sample_axis1(self): + # Check weights with axis = 1 + easy_weight_list = [0] * 3 + easy_weight_list[2] = 1 + + df = DataFrame( + {"col1": range(10, 20), "col2": range(20, 30), "colString": ["a"] * 10} + ) + sample1 = df.sample(n=1, axis=1, weights=easy_weight_list) + tm.assert_frame_equal(sample1, df[["colString"]]) + + # Test default axes + tm.assert_frame_equal( + df.sample(n=3, random_state=42), df.sample(n=3, axis=0, random_state=42) + ) + + def test_sample_aligns_weights_with_frame(self): + # Test that function aligns weights with frame + df = DataFrame({"col1": [5, 6, 7], "col2": ["a", "b", "c"]}, index=[9, 5, 3]) + ser = Series([1, 0, 0], index=[3, 5, 9]) + tm.assert_frame_equal(df.loc[[3]], df.sample(1, weights=ser)) + + # Weights have index values to be dropped because not in + # sampled DataFrame + ser2 = Series([0.001, 0, 10000], index=[3, 5, 10]) + tm.assert_frame_equal(df.loc[[3]], df.sample(1, weights=ser2)) + + # Weights have empty values to be filed with zeros + ser3 = Series([0.01, 0], index=[3, 5]) + tm.assert_frame_equal(df.loc[[3]], df.sample(1, weights=ser3)) + + # No overlap in weight and sampled DataFrame indices + ser4 = Series([1, 0], index=[1, 2]) + + with pytest.raises(ValueError, match="Invalid weights: weights sum to zero"): + df.sample(1, weights=ser4) + + def test_sample_is_copy(self): + # GH#27357, GH#30784: ensure the result of sample is an actual copy and + # doesn't track the parent dataframe / doesn't give SettingWithCopy warnings + df = DataFrame( + np.random.default_rng(2).standard_normal((10, 3)), columns=["a", "b", "c"] + ) + df2 = df.sample(3) + + with tm.assert_produces_warning(None): + df2["d"] = 1 + + def test_sample_does_not_modify_weights(self): + # GH-42843 + result = np.array([np.nan, 1, np.nan]) + expected = result.copy() + ser = Series([1, 2, 3]) + + # Test numpy array weights won't be modified in place + ser.sample(weights=result) + tm.assert_numpy_array_equal(result, expected) + + # Test DataFrame column won't be modified in place + df = DataFrame({"values": [1, 1, 1], "weights": [1, np.nan, np.nan]}) + expected = df["weights"].copy() + + df.sample(frac=1.0, replace=True, weights="weights") + result = df["weights"] + tm.assert_series_equal(result, expected) + + def test_sample_ignore_index(self): + # GH 38581 + df = DataFrame( + {"col1": range(10, 20), "col2": range(20, 30), "colString": ["a"] * 10} + ) + result = df.sample(3, ignore_index=True) + expected_index = Index(range(3)) + tm.assert_index_equal(result.index, expected_index, exact=True) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_select_dtypes.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_select_dtypes.py new file mode 100644 index 0000000000000000000000000000000000000000..1ba6b9c437726ffbd10391ebe7585482afa6f00c --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_select_dtypes.py @@ -0,0 +1,510 @@ +import numpy as np +import pytest + +from pandas.core.dtypes.dtypes import ExtensionDtype + +import pandas as pd +from pandas import ( + DataFrame, + Timestamp, +) +import pandas._testing as tm +from pandas.core.arrays import ExtensionArray + + +class DummyDtype(ExtensionDtype): + type = int + + def __init__(self, numeric) -> None: + self._numeric = numeric + + @property + def name(self): + return "Dummy" + + @property + def _is_numeric(self): + return self._numeric + + +class DummyArray(ExtensionArray): + def __init__(self, data, dtype) -> None: + self.data = data + self._dtype = dtype + + def __array__(self, dtype=None, copy=None): + return self.data + + @property + def dtype(self): + return self._dtype + + def __len__(self) -> int: + return len(self.data) + + def __getitem__(self, item): + pass + + def copy(self): + return self + + +class TestSelectDtypes: + def test_select_dtypes_include_using_list_like(self, using_infer_string): + df = DataFrame( + { + "a": list("abc"), + "b": list(range(1, 4)), + "c": np.arange(3, 6).astype("u1"), + "d": np.arange(4.0, 7.0, dtype="float64"), + "e": [True, False, True], + "f": pd.Categorical(list("abc")), + "g": pd.date_range("20130101", periods=3), + "h": pd.date_range("20130101", periods=3, tz="US/Eastern"), + "i": pd.date_range("20130101", periods=3, tz="CET"), + "j": pd.period_range("2013-01", periods=3, freq="M"), + "k": pd.timedelta_range("1 day", periods=3), + } + ) + + ri = df.select_dtypes(include=[np.number]) + ei = df[["b", "c", "d", "k"]] + tm.assert_frame_equal(ri, ei) + + ri = df.select_dtypes(include=[np.number], exclude=["timedelta"]) + ei = df[["b", "c", "d"]] + tm.assert_frame_equal(ri, ei) + + ri = df.select_dtypes(include=[np.number, "category"], exclude=["timedelta"]) + ei = df[["b", "c", "d", "f"]] + tm.assert_frame_equal(ri, ei) + + ri = df.select_dtypes(include=["datetime"]) + ei = df[["g"]] + tm.assert_frame_equal(ri, ei) + + ri = df.select_dtypes(include=["datetime64"]) + ei = df[["g"]] + tm.assert_frame_equal(ri, ei) + + ri = df.select_dtypes(include=["datetimetz"]) + ei = df[["h", "i"]] + tm.assert_frame_equal(ri, ei) + + with pytest.raises(NotImplementedError, match=r"^$"): + df.select_dtypes(include=["period"]) + + if using_infer_string: + ri = df.select_dtypes(include=["str"]) + ei = df[["a"]] + tm.assert_frame_equal(ri, ei) + + ri = df.select_dtypes(include=[str]) + tm.assert_frame_equal(ri, ei) + + ri = df.select_dtypes(include=["object"]) + ei = df[["a"]] + tm.assert_frame_equal(ri, ei) + + def test_select_dtypes_exclude_using_list_like(self): + df = DataFrame( + { + "a": list("abc"), + "b": list(range(1, 4)), + "c": np.arange(3, 6).astype("u1"), + "d": np.arange(4.0, 7.0, dtype="float64"), + "e": [True, False, True], + } + ) + re = df.select_dtypes(exclude=[np.number]) + ee = df[["a", "e"]] + tm.assert_frame_equal(re, ee) + + def test_select_dtypes_exclude_include_using_list_like(self): + df = DataFrame( + { + "a": list("abc"), + "b": list(range(1, 4)), + "c": np.arange(3, 6, dtype="u1"), + "d": np.arange(4.0, 7.0, dtype="float64"), + "e": [True, False, True], + "f": pd.date_range("now", periods=3).values, + } + ) + exclude = (np.datetime64,) + include = np.bool_, "integer" + r = df.select_dtypes(include=include, exclude=exclude) + e = df[["b", "c", "e"]] + tm.assert_frame_equal(r, e) + + exclude = ("datetime",) + include = "bool", "int64", "int32" + r = df.select_dtypes(include=include, exclude=exclude) + e = df[["b", "e"]] + tm.assert_frame_equal(r, e) + + @pytest.mark.parametrize( + "include", [(np.bool_, "int"), (np.bool_, "integer"), ("bool", int)] + ) + def test_select_dtypes_exclude_include_int(self, include): + # Fix select_dtypes(include='int') for Windows, FYI #36596 + df = DataFrame( + { + "a": list("abc"), + "b": list(range(1, 4)), + "c": np.arange(3, 6, dtype="int32"), + "d": np.arange(4.0, 7.0, dtype="float64"), + "e": [True, False, True], + "f": pd.date_range("now", periods=3).values, + } + ) + exclude = (np.datetime64,) + result = df.select_dtypes(include=include, exclude=exclude) + expected = df[["b", "c", "e"]] + tm.assert_frame_equal(result, expected) + + def test_select_dtypes_include_using_scalars(self, using_infer_string): + df = DataFrame( + { + "a": list("abc"), + "b": list(range(1, 4)), + "c": np.arange(3, 6).astype("u1"), + "d": np.arange(4.0, 7.0, dtype="float64"), + "e": [True, False, True], + "f": pd.Categorical(list("abc")), + "g": pd.date_range("20130101", periods=3), + "h": pd.date_range("20130101", periods=3, tz="US/Eastern"), + "i": pd.date_range("20130101", periods=3, tz="CET"), + "j": pd.period_range("2013-01", periods=3, freq="M"), + "k": pd.timedelta_range("1 day", periods=3), + } + ) + + ri = df.select_dtypes(include=np.number) + ei = df[["b", "c", "d", "k"]] + tm.assert_frame_equal(ri, ei) + + ri = df.select_dtypes(include="datetime") + ei = df[["g"]] + tm.assert_frame_equal(ri, ei) + + ri = df.select_dtypes(include="datetime64") + ei = df[["g"]] + tm.assert_frame_equal(ri, ei) + + ri = df.select_dtypes(include="category") + ei = df[["f"]] + tm.assert_frame_equal(ri, ei) + + with pytest.raises(NotImplementedError, match=r"^$"): + df.select_dtypes(include="period") + + if using_infer_string: + ri = df.select_dtypes(include="str") + ei = df[["a"]] + tm.assert_frame_equal(ri, ei) + + def test_select_dtypes_exclude_using_scalars(self): + df = DataFrame( + { + "a": list("abc"), + "b": list(range(1, 4)), + "c": np.arange(3, 6).astype("u1"), + "d": np.arange(4.0, 7.0, dtype="float64"), + "e": [True, False, True], + "f": pd.Categorical(list("abc")), + "g": pd.date_range("20130101", periods=3), + "h": pd.date_range("20130101", periods=3, tz="US/Eastern"), + "i": pd.date_range("20130101", periods=3, tz="CET"), + "j": pd.period_range("2013-01", periods=3, freq="M"), + "k": pd.timedelta_range("1 day", periods=3), + } + ) + + ri = df.select_dtypes(exclude=np.number) + ei = df[["a", "e", "f", "g", "h", "i", "j"]] + tm.assert_frame_equal(ri, ei) + + ri = df.select_dtypes(exclude="category") + ei = df[["a", "b", "c", "d", "e", "g", "h", "i", "j", "k"]] + tm.assert_frame_equal(ri, ei) + + with pytest.raises(NotImplementedError, match=r"^$"): + df.select_dtypes(exclude="period") + + def test_select_dtypes_include_exclude_using_scalars(self): + df = DataFrame( + { + "a": list("abc"), + "b": list(range(1, 4)), + "c": np.arange(3, 6).astype("u1"), + "d": np.arange(4.0, 7.0, dtype="float64"), + "e": [True, False, True], + "f": pd.Categorical(list("abc")), + "g": pd.date_range("20130101", periods=3), + "h": pd.date_range("20130101", periods=3, tz="US/Eastern"), + "i": pd.date_range("20130101", periods=3, tz="CET"), + "j": pd.period_range("2013-01", periods=3, freq="M"), + "k": pd.timedelta_range("1 day", periods=3), + } + ) + + ri = df.select_dtypes(include=np.number, exclude="floating") + ei = df[["b", "c", "k"]] + tm.assert_frame_equal(ri, ei) + + def test_select_dtypes_include_exclude_mixed_scalars_lists(self): + df = DataFrame( + { + "a": list("abc"), + "b": list(range(1, 4)), + "c": np.arange(3, 6).astype("u1"), + "d": np.arange(4.0, 7.0, dtype="float64"), + "e": [True, False, True], + "f": pd.Categorical(list("abc")), + "g": pd.date_range("20130101", periods=3), + "h": pd.date_range("20130101", periods=3, tz="US/Eastern"), + "i": pd.date_range("20130101", periods=3, tz="CET"), + "j": pd.period_range("2013-01", periods=3, freq="M"), + "k": pd.timedelta_range("1 day", periods=3), + } + ) + + ri = df.select_dtypes(include=np.number, exclude=["floating", "timedelta"]) + ei = df[["b", "c"]] + tm.assert_frame_equal(ri, ei) + + ri = df.select_dtypes(include=[np.number, "category"], exclude="floating") + ei = df[["b", "c", "f", "k"]] + tm.assert_frame_equal(ri, ei) + + def test_select_dtypes_duplicate_columns(self): + # GH20839 + df = DataFrame( + { + "a": ["a", "b", "c"], + "b": [1, 2, 3], + "c": np.arange(3, 6).astype("u1"), + "d": np.arange(4.0, 7.0, dtype="float64"), + "e": [True, False, True], + "f": pd.date_range("now", periods=3).values, + } + ) + df.columns = ["a", "a", "b", "b", "b", "c"] + + expected = DataFrame( + {"a": list(range(1, 4)), "b": np.arange(3, 6).astype("u1")} + ) + + result = df.select_dtypes(include=[np.number], exclude=["floating"]) + tm.assert_frame_equal(result, expected) + + def test_select_dtypes_not_an_attr_but_still_valid_dtype(self, using_infer_string): + df = DataFrame( + { + "a": list("abc"), + "b": list(range(1, 4)), + "c": np.arange(3, 6).astype("u1"), + "d": np.arange(4.0, 7.0, dtype="float64"), + "e": [True, False, True], + "f": pd.date_range("now", periods=3).values, + } + ) + df["g"] = df.f.diff() + assert not hasattr(np, "u8") + r = df.select_dtypes(include=["i8", "O"], exclude=["timedelta"]) + # if using_infer_string: + # TODO warn + e = df[["a", "b"]] + tm.assert_frame_equal(r, e) + + r = df.select_dtypes(include=["i8", "O", "timedelta64[ns]"]) + # if using_infer_string: + # TODO warn + e = df[["a", "b", "g"]] + tm.assert_frame_equal(r, e) + + def test_select_dtypes_empty(self): + df = DataFrame({"a": list("abc"), "b": list(range(1, 4))}) + msg = "at least one of include or exclude must be nonempty" + with pytest.raises(ValueError, match=msg): + df.select_dtypes() + + def test_select_dtypes_bad_datetime64(self): + df = DataFrame( + { + "a": list("abc"), + "b": list(range(1, 4)), + "c": np.arange(3, 6).astype("u1"), + "d": np.arange(4.0, 7.0, dtype="float64"), + "e": [True, False, True], + "f": pd.date_range("now", periods=3).values, + } + ) + with pytest.raises(ValueError, match=".+ is too specific"): + df.select_dtypes(include=["datetime64[D]"]) + + with pytest.raises(ValueError, match=".+ is too specific"): + df.select_dtypes(exclude=["datetime64[as]"]) + + def test_select_dtypes_datetime_with_tz(self): + df2 = DataFrame( + { + "A": Timestamp("20130102", tz="US/Eastern"), + "B": Timestamp("20130603", tz="CET"), + }, + index=range(5), + ) + df3 = pd.concat([df2.A.to_frame(), df2.B.to_frame()], axis=1) + result = df3.select_dtypes(include=["datetime64[ns]"]) + expected = df3.reindex(columns=[]) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("dtype", [str, "str", np.bytes_, "S1", np.str_, "U1"]) + @pytest.mark.parametrize("arg", ["include", "exclude"]) + def test_select_dtypes_str_raises(self, dtype, arg, using_infer_string): + if using_infer_string and (dtype == "str" or dtype is str): + # this is tested below + pytest.skip("Selecting string columns works with future strings") + df = DataFrame( + { + "a": list("abc"), + "g": list("abc"), + "b": list(range(1, 4)), + "c": np.arange(3, 6).astype("u1"), + "d": np.arange(4.0, 7.0, dtype="float64"), + "e": [True, False, True], + "f": pd.date_range("now", periods=3).values, + } + ) + msg = "string dtypes are not allowed" + kwargs = {arg: [dtype]} + + with pytest.raises(TypeError, match=msg): + df.select_dtypes(**kwargs) + + def test_select_dtypes_bad_arg_raises(self): + df = DataFrame( + { + "a": list("abc"), + "g": list("abc"), + "b": list(range(1, 4)), + "c": np.arange(3, 6).astype("u1"), + "d": np.arange(4.0, 7.0, dtype="float64"), + "e": [True, False, True], + "f": pd.date_range("now", periods=3).values, + } + ) + + msg = "data type.*not understood" + with pytest.raises(TypeError, match=msg): + df.select_dtypes(["blargy, blarg, blarg"]) + + def test_select_dtypes_typecodes(self): + # GH 11990 + df = DataFrame(np.random.default_rng(2).random((5, 3))) + FLOAT_TYPES = list(np.typecodes["AllFloat"]) + tm.assert_frame_equal(df.select_dtypes(FLOAT_TYPES), df) + + @pytest.mark.parametrize( + "arr,expected", + ( + (np.array([1, 2], dtype=np.int32), True), + (pd.array([1, 2], dtype="Int32"), True), + (DummyArray([1, 2], dtype=DummyDtype(numeric=True)), True), + (DummyArray([1, 2], dtype=DummyDtype(numeric=False)), False), + ), + ) + def test_select_dtypes_numeric(self, arr, expected): + # GH 35340 + + df = DataFrame(arr) + is_selected = df.select_dtypes(np.number).shape == df.shape + assert is_selected == expected + + def test_select_dtypes_numeric_nullable_string(self, nullable_string_dtype): + arr = pd.array(["a", "b"], dtype=nullable_string_dtype) + df = DataFrame(arr) + is_selected = df.select_dtypes(np.number).shape == df.shape + assert not is_selected + + @pytest.mark.parametrize( + "expected, float_dtypes", + [ + [ + DataFrame( + {"A": range(3), "B": range(5, 8), "C": range(10, 7, -1)} + ).astype(dtype={"A": float, "B": np.float64, "C": np.float32}), + float, + ], + [ + DataFrame( + {"A": range(3), "B": range(5, 8), "C": range(10, 7, -1)} + ).astype(dtype={"A": float, "B": np.float64, "C": np.float32}), + "float", + ], + [DataFrame({"C": range(10, 7, -1)}, dtype=np.float32), np.float32], + [ + DataFrame({"A": range(3), "B": range(5, 8)}).astype( + dtype={"A": float, "B": np.float64} + ), + np.float64, + ], + ], + ) + def test_select_dtypes_float_dtype(self, expected, float_dtypes): + # GH#42452 + dtype_dict = {"A": float, "B": np.float64, "C": np.float32} + df = DataFrame( + {"A": range(3), "B": range(5, 8), "C": range(10, 7, -1)}, + ) + df = df.astype(dtype_dict) + result = df.select_dtypes(include=float_dtypes) + tm.assert_frame_equal(result, expected) + + def test_np_bool_ea_boolean_include_number(self): + # GH 46870 + df = DataFrame( + { + "a": [1, 2, 3], + "b": pd.Series([True, False, True], dtype="boolean"), + "c": np.array([True, False, True]), + "d": pd.Categorical([True, False, True]), + "e": pd.arrays.SparseArray([True, False, True]), + } + ) + result = df.select_dtypes(include="number") + expected = DataFrame({"a": [1, 2, 3]}) + tm.assert_frame_equal(result, expected) + + def test_select_dtypes_no_view(self): + # https://github.com/pandas-dev/pandas/issues/48090 + # result of this method is not a view on the original dataframe + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + df_orig = df.copy() + result = df.select_dtypes(include=["number"]) + result.iloc[0, 0] = 0 + tm.assert_frame_equal(df, df_orig) + + def test_select_dtype_object_and_str(self, using_infer_string): + # https://github.com/pandas-dev/pandas/issues/61916 + df = DataFrame( + { + "a": ["a", "b", "c"], + "b": [1, 2, 3], + "c": pd.array(["a", "b", "c"], dtype="string"), + } + ) + + # with "object" -> only select the object or default str dtype column + result = df.select_dtypes(include=["object"]) + expected = df[["a"]] + tm.assert_frame_equal(result, expected) + + # with "string" -> select both the default 'str' and the nullable 'string' + result = df.select_dtypes(include=["string"]) + if using_infer_string: + expected = df[["a", "c"]] + else: + expected = df[["c"]] + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_set_axis.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_set_axis.py new file mode 100644 index 0000000000000000000000000000000000000000..8d249bc7b7fa471db401ed44e50fdb514cf85a51 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_set_axis.py @@ -0,0 +1,143 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Series, +) +import pandas._testing as tm + + +class SharedSetAxisTests: + @pytest.fixture + def obj(self): + raise NotImplementedError("Implemented by subclasses") + + def test_set_axis(self, obj): + # GH14636; this tests setting index for both Series and DataFrame + new_index = list("abcd")[: len(obj)] + expected = obj.copy() + expected.index = new_index + result = obj.set_axis(new_index, axis=0) + tm.assert_equal(expected, result) + + def test_set_axis_copy(self, obj, using_copy_on_write): + # Test copy keyword GH#47932 + new_index = list("abcd")[: len(obj)] + + orig = obj.iloc[:] + expected = obj.copy() + expected.index = new_index + + result = obj.set_axis(new_index, axis=0, copy=True) + tm.assert_equal(expected, result) + assert result is not obj + # check we DID make a copy + if not using_copy_on_write: + if obj.ndim == 1: + assert not tm.shares_memory(result, obj) + else: + assert not any( + tm.shares_memory(result.iloc[:, i], obj.iloc[:, i]) + for i in range(obj.shape[1]) + ) + + result = obj.set_axis(new_index, axis=0, copy=False) + tm.assert_equal(expected, result) + assert result is not obj + # check we did NOT make a copy + if obj.ndim == 1: + assert tm.shares_memory(result, obj) + else: + assert all( + tm.shares_memory(result.iloc[:, i], obj.iloc[:, i]) + for i in range(obj.shape[1]) + ) + + # copy defaults to True + result = obj.set_axis(new_index, axis=0) + tm.assert_equal(expected, result) + assert result is not obj + if using_copy_on_write: + # check we DID NOT make a copy + if obj.ndim == 1: + assert tm.shares_memory(result, obj) + else: + assert any( + tm.shares_memory(result.iloc[:, i], obj.iloc[:, i]) + for i in range(obj.shape[1]) + ) + # check we DID make a copy + elif obj.ndim == 1: + assert not tm.shares_memory(result, obj) + else: + assert not any( + tm.shares_memory(result.iloc[:, i], obj.iloc[:, i]) + for i in range(obj.shape[1]) + ) + + res = obj.set_axis(new_index, copy=False) + tm.assert_equal(expected, res) + # check we did NOT make a copy + if res.ndim == 1: + assert tm.shares_memory(res, orig) + else: + assert all( + tm.shares_memory(res.iloc[:, i], orig.iloc[:, i]) + for i in range(res.shape[1]) + ) + + def test_set_axis_unnamed_kwarg_warns(self, obj): + # omitting the "axis" parameter + new_index = list("abcd")[: len(obj)] + + expected = obj.copy() + expected.index = new_index + + result = obj.set_axis(new_index) + tm.assert_equal(result, expected) + + @pytest.mark.parametrize("axis", [3, "foo"]) + def test_set_axis_invalid_axis_name(self, axis, obj): + # wrong values for the "axis" parameter + with pytest.raises(ValueError, match="No axis named"): + obj.set_axis(list("abc"), axis=axis) + + def test_set_axis_setattr_index_not_collection(self, obj): + # wrong type + msg = ( + r"Index\(\.\.\.\) must be called with a collection of some " + r"kind, None was passed" + ) + with pytest.raises(TypeError, match=msg): + obj.index = None + + def test_set_axis_setattr_index_wrong_length(self, obj): + # wrong length + msg = ( + f"Length mismatch: Expected axis has {len(obj)} elements, " + f"new values have {len(obj)-1} elements" + ) + with pytest.raises(ValueError, match=msg): + obj.index = np.arange(len(obj) - 1) + + if obj.ndim == 2: + with pytest.raises(ValueError, match="Length mismatch"): + obj.columns = obj.columns[::2] + + +class TestDataFrameSetAxis(SharedSetAxisTests): + @pytest.fixture + def obj(self): + df = DataFrame( + {"A": [1.1, 2.2, 3.3], "B": [5.0, 6.1, 7.2], "C": [4.4, 5.5, 6.6]}, + index=[2010, 2011, 2012], + ) + return df + + +class TestSeriesSetAxis(SharedSetAxisTests): + @pytest.fixture + def obj(self): + ser = Series(np.arange(4), index=[1, 3, 5, 7], dtype="int64") + return ser diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_set_index.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_set_index.py new file mode 100644 index 0000000000000000000000000000000000000000..1c8d365f0d6c056c8c023e5173c9b3671cbfbd48 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_set_index.py @@ -0,0 +1,734 @@ +""" +See also: test_reindex.py:TestReindexSetIndex +""" + +from datetime import ( + datetime, + timedelta, +) + +import numpy as np +import pytest + +from pandas import ( + Categorical, + CategoricalIndex, + DataFrame, + DatetimeIndex, + Index, + MultiIndex, + Series, + date_range, + period_range, + to_datetime, +) +import pandas._testing as tm + + +@pytest.fixture +def frame_of_index_cols(): + """ + Fixture for DataFrame of columns that can be used for indexing + + Columns are ['A', 'B', 'C', 'D', 'E', ('tuple', 'as', 'label')]; + 'A' & 'B' contain duplicates (but are jointly unique), the rest are unique. + + A B C D E (tuple, as, label) + 0 foo one a 0.608477 -0.012500 -1.664297 + 1 foo two b -0.633460 0.249614 -0.364411 + 2 foo three c 0.615256 2.154968 -0.834666 + 3 bar one d 0.234246 1.085675 0.718445 + 4 bar two e 0.533841 -0.005702 -3.533912 + """ + df = DataFrame( + { + "A": ["foo", "foo", "foo", "bar", "bar"], + "B": ["one", "two", "three", "one", "two"], + "C": ["a", "b", "c", "d", "e"], + "D": np.random.default_rng(2).standard_normal(5), + "E": np.random.default_rng(2).standard_normal(5), + ("tuple", "as", "label"): np.random.default_rng(2).standard_normal(5), + } + ) + return df + + +class TestSetIndex: + def test_set_index_multiindex(self): + # segfault in GH#3308 + d = {"t1": [2, 2.5, 3], "t2": [4, 5, 6]} + df = DataFrame(d) + tuples = [(0, 1), (0, 2), (1, 2)] + df["tuples"] = tuples + + index = MultiIndex.from_tuples(df["tuples"]) + # it works! + df.set_index(index) + + def test_set_index_empty_column(self): + # GH#1971 + df = DataFrame( + [ + {"a": 1, "p": 0}, + {"a": 2, "m": 10}, + {"a": 3, "m": 11, "p": 20}, + {"a": 4, "m": 12, "p": 21}, + ], + columns=["a", "m", "p", "x"], + ) + + result = df.set_index(["a", "x"]) + + expected = df[["m", "p"]] + expected.index = MultiIndex.from_arrays([df["a"], df["x"]], names=["a", "x"]) + tm.assert_frame_equal(result, expected) + + def test_set_index_empty_dataframe(self): + # GH#38419 + df1 = DataFrame( + {"a": Series(dtype="datetime64[ns]"), "b": Series(dtype="int64"), "c": []} + ) + + df2 = df1.set_index(["a", "b"]) + result = df2.index.to_frame().dtypes + expected = df1[["a", "b"]].dtypes + tm.assert_series_equal(result, expected) + + def test_set_index_multiindexcolumns(self): + columns = MultiIndex.from_tuples([("foo", 1), ("foo", 2), ("bar", 1)]) + df = DataFrame( + np.random.default_rng(2).standard_normal((3, 3)), columns=columns + ) + + result = df.set_index(df.columns[0]) + + expected = df.iloc[:, 1:] + expected.index = df.iloc[:, 0].values + expected.index.names = [df.columns[0]] + tm.assert_frame_equal(result, expected) + + def test_set_index_timezone(self): + # GH#12358 + # tz-aware Series should retain the tz + idx = DatetimeIndex(["2014-01-01 10:10:10"], tz="UTC").tz_convert("Europe/Rome") + df = DataFrame({"A": idx}) + assert df.set_index(idx).index[0].hour == 11 + assert DatetimeIndex(Series(df.A))[0].hour == 11 + assert df.set_index(df.A).index[0].hour == 11 + + def test_set_index_cast_datetimeindex(self): + df = DataFrame( + { + "A": [datetime(2000, 1, 1) + timedelta(i) for i in range(1000)], + "B": np.random.default_rng(2).standard_normal(1000), + } + ) + + idf = df.set_index("A") + assert isinstance(idf.index, DatetimeIndex) + + def test_set_index_dst(self): + di = date_range("2006-10-29 00:00:00", periods=3, freq="h", tz="US/Pacific") + + df = DataFrame(data={"a": [0, 1, 2], "b": [3, 4, 5]}, index=di).reset_index() + # single level + res = df.set_index("index") + exp = DataFrame( + data={"a": [0, 1, 2], "b": [3, 4, 5]}, + index=Index(di, name="index"), + ) + exp.index = exp.index._with_freq(None) + tm.assert_frame_equal(res, exp) + + # GH#12920 + res = df.set_index(["index", "a"]) + exp_index = MultiIndex.from_arrays([di, [0, 1, 2]], names=["index", "a"]) + exp = DataFrame({"b": [3, 4, 5]}, index=exp_index) + tm.assert_frame_equal(res, exp) + + def test_set_index(self, float_string_frame): + df = float_string_frame + idx = Index(np.arange(len(df))[::-1]) + + df = df.set_index(idx) + tm.assert_index_equal(df.index, idx) + with pytest.raises(ValueError, match="Length mismatch"): + df.set_index(idx[::2]) + + def test_set_index_names(self): + df = DataFrame( + np.ones((10, 4)), + columns=Index(list("ABCD")), + index=Index([f"i-{i}" for i in range(10)]), + ) + df.index.name = "name" + + assert df.set_index(df.index).index.names == ["name"] + + mi = MultiIndex.from_arrays(df[["A", "B"]].T.values, names=["A", "B"]) + mi2 = MultiIndex.from_arrays( + df[["A", "B", "A", "B"]].T.values, names=["A", "B", "C", "D"] + ) + + df = df.set_index(["A", "B"]) + + assert df.set_index(df.index).index.names == ["A", "B"] + + # Check that set_index isn't converting a MultiIndex into an Index + assert isinstance(df.set_index(df.index).index, MultiIndex) + + # Check actual equality + tm.assert_index_equal(df.set_index(df.index).index, mi) + + idx2 = df.index.rename(["C", "D"]) + + # Check that [MultiIndex, MultiIndex] yields a MultiIndex rather + # than a pair of tuples + assert isinstance(df.set_index([df.index, idx2]).index, MultiIndex) + + # Check equality + tm.assert_index_equal(df.set_index([df.index, idx2]).index, mi2) + + # A has duplicate values, C does not + @pytest.mark.parametrize("keys", ["A", "C", ["A", "B"], ("tuple", "as", "label")]) + @pytest.mark.parametrize("inplace", [True, False]) + @pytest.mark.parametrize("drop", [True, False]) + def test_set_index_drop_inplace(self, frame_of_index_cols, drop, inplace, keys): + df = frame_of_index_cols + + if isinstance(keys, list): + idx = MultiIndex.from_arrays([df[x] for x in keys], names=keys) + else: + idx = Index(df[keys], name=keys) + expected = df.drop(keys, axis=1) if drop else df + expected.index = idx + + if inplace: + result = df.copy() + return_value = result.set_index(keys, drop=drop, inplace=True) + assert return_value is None + else: + result = df.set_index(keys, drop=drop) + + tm.assert_frame_equal(result, expected) + + # A has duplicate values, C does not + @pytest.mark.parametrize("keys", ["A", "C", ["A", "B"], ("tuple", "as", "label")]) + @pytest.mark.parametrize("drop", [True, False]) + def test_set_index_append(self, frame_of_index_cols, drop, keys): + df = frame_of_index_cols + + keys = keys if isinstance(keys, list) else [keys] + idx = MultiIndex.from_arrays( + [df.index] + [df[x] for x in keys], names=[None] + keys + ) + expected = df.drop(keys, axis=1) if drop else df.copy() + expected.index = idx + + result = df.set_index(keys, drop=drop, append=True) + + tm.assert_frame_equal(result, expected) + + # A has duplicate values, C does not + @pytest.mark.parametrize("keys", ["A", "C", ["A", "B"], ("tuple", "as", "label")]) + @pytest.mark.parametrize("drop", [True, False]) + def test_set_index_append_to_multiindex(self, frame_of_index_cols, drop, keys): + # append to existing multiindex + df = frame_of_index_cols.set_index(["D"], drop=drop, append=True) + + keys = keys if isinstance(keys, list) else [keys] + expected = frame_of_index_cols.set_index(["D"] + keys, drop=drop, append=True) + + result = df.set_index(keys, drop=drop, append=True) + + tm.assert_frame_equal(result, expected) + + def test_set_index_after_mutation(self): + # GH#1590 + df = DataFrame({"val": [0, 1, 2], "key": ["a", "b", "c"]}) + expected = DataFrame({"val": [1, 2]}, Index(["b", "c"], name="key")) + + df2 = df.loc[df.index.map(lambda indx: indx >= 1)] + result = df2.set_index("key") + tm.assert_frame_equal(result, expected) + + # MultiIndex constructor does not work directly on Series -> lambda + # Add list-of-list constructor because list is ambiguous -> lambda + # also test index name if append=True (name is duplicate here for B) + @pytest.mark.parametrize( + "box", + [ + Series, + Index, + np.array, + list, + lambda x: [list(x)], + lambda x: MultiIndex.from_arrays([x]), + ], + ) + @pytest.mark.parametrize( + "append, index_name", [(True, None), (True, "B"), (True, "test"), (False, None)] + ) + @pytest.mark.parametrize("drop", [True, False]) + def test_set_index_pass_single_array( + self, frame_of_index_cols, drop, append, index_name, box + ): + df = frame_of_index_cols + df.index.name = index_name + + key = box(df["B"]) + if box == list: + # list of strings gets interpreted as list of keys + msg = "['one', 'two', 'three', 'one', 'two']" + with pytest.raises(KeyError, match=msg): + df.set_index(key, drop=drop, append=append) + else: + # np.array/list-of-list "forget" the name of B + name_mi = getattr(key, "names", None) + name = [getattr(key, "name", None)] if name_mi is None else name_mi + + result = df.set_index(key, drop=drop, append=append) + + # only valid column keys are dropped + # since B is always passed as array above, nothing is dropped + expected = df.set_index(["B"], drop=False, append=append) + expected.index.names = [index_name] + name if append else name + + tm.assert_frame_equal(result, expected) + + # MultiIndex constructor does not work directly on Series -> lambda + # also test index name if append=True (name is duplicate here for A & B) + @pytest.mark.parametrize( + "box", [Series, Index, np.array, list, lambda x: MultiIndex.from_arrays([x])] + ) + @pytest.mark.parametrize( + "append, index_name", + [(True, None), (True, "A"), (True, "B"), (True, "test"), (False, None)], + ) + @pytest.mark.parametrize("drop", [True, False]) + def test_set_index_pass_arrays( + self, frame_of_index_cols, drop, append, index_name, box + ): + df = frame_of_index_cols + df.index.name = index_name + + keys = ["A", box(df["B"])] + # np.array/list "forget" the name of B + names = ["A", None if box in [np.array, list, tuple, iter] else "B"] + + result = df.set_index(keys, drop=drop, append=append) + + # only valid column keys are dropped + # since B is always passed as array above, only A is dropped, if at all + expected = df.set_index(["A", "B"], drop=False, append=append) + expected = expected.drop("A", axis=1) if drop else expected + expected.index.names = [index_name] + names if append else names + + tm.assert_frame_equal(result, expected) + + # MultiIndex constructor does not work directly on Series -> lambda + # We also emulate a "constructor" for the label -> lambda + # also test index name if append=True (name is duplicate here for A) + @pytest.mark.parametrize( + "box2", + [ + Series, + Index, + np.array, + list, + iter, + lambda x: MultiIndex.from_arrays([x]), + lambda x: x.name, + ], + ) + @pytest.mark.parametrize( + "box1", + [ + Series, + Index, + np.array, + list, + iter, + lambda x: MultiIndex.from_arrays([x]), + lambda x: x.name, + ], + ) + @pytest.mark.parametrize( + "append, index_name", [(True, None), (True, "A"), (True, "test"), (False, None)] + ) + @pytest.mark.parametrize("drop", [True, False]) + def test_set_index_pass_arrays_duplicate( + self, frame_of_index_cols, drop, append, index_name, box1, box2 + ): + df = frame_of_index_cols + df.index.name = index_name + + keys = [box1(df["A"]), box2(df["A"])] + result = df.set_index(keys, drop=drop, append=append) + + # if either box is iter, it has been consumed; re-read + keys = [box1(df["A"]), box2(df["A"])] + + # need to adapt first drop for case that both keys are 'A' -- + # cannot drop the same column twice; + # plain == would give ambiguous Boolean error for containers + first_drop = ( + False + if ( + isinstance(keys[0], str) + and keys[0] == "A" + and isinstance(keys[1], str) + and keys[1] == "A" + ) + else drop + ) + # to test against already-tested behaviour, we add sequentially, + # hence second append always True; must wrap keys in list, otherwise + # box = list would be interpreted as keys + expected = df.set_index([keys[0]], drop=first_drop, append=append) + expected = expected.set_index([keys[1]], drop=drop, append=True) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("append", [True, False]) + @pytest.mark.parametrize("drop", [True, False]) + def test_set_index_pass_multiindex(self, frame_of_index_cols, drop, append): + df = frame_of_index_cols + keys = MultiIndex.from_arrays([df["A"], df["B"]], names=["A", "B"]) + + result = df.set_index(keys, drop=drop, append=append) + + # setting with a MultiIndex will never drop columns + expected = df.set_index(["A", "B"], drop=False, append=append) + + tm.assert_frame_equal(result, expected) + + def test_construction_with_categorical_index(self): + ci = CategoricalIndex(list("ab") * 5, name="B") + + # with Categorical + df = DataFrame( + {"A": np.random.default_rng(2).standard_normal(10), "B": ci.values} + ) + idf = df.set_index("B") + tm.assert_index_equal(idf.index, ci) + + # from a CategoricalIndex + df = DataFrame({"A": np.random.default_rng(2).standard_normal(10), "B": ci}) + idf = df.set_index("B") + tm.assert_index_equal(idf.index, ci) + + # round-trip + idf = idf.reset_index().set_index("B") + tm.assert_index_equal(idf.index, ci) + + def test_set_index_preserve_categorical_dtype(self): + # GH#13743, GH#13854 + df = DataFrame( + { + "A": [1, 2, 1, 1, 2], + "B": [10, 16, 22, 28, 34], + "C1": Categorical(list("abaab"), categories=list("bac"), ordered=False), + "C2": Categorical(list("abaab"), categories=list("bac"), ordered=True), + } + ) + for cols in ["C1", "C2", ["A", "C1"], ["A", "C2"], ["C1", "C2"]]: + result = df.set_index(cols).reset_index() + result = result.reindex(columns=df.columns) + tm.assert_frame_equal(result, df) + + def test_set_index_datetime(self): + # GH#3950 + df = DataFrame( + { + "label": ["a", "a", "a", "b", "b", "b"], + "datetime": [ + "2011-07-19 07:00:00", + "2011-07-19 08:00:00", + "2011-07-19 09:00:00", + "2011-07-19 07:00:00", + "2011-07-19 08:00:00", + "2011-07-19 09:00:00", + ], + "value": range(6), + } + ) + df.index = to_datetime(df.pop("datetime"), utc=True) + df.index = df.index.tz_convert("US/Pacific") + + expected = DatetimeIndex( + ["2011-07-19 07:00:00", "2011-07-19 08:00:00", "2011-07-19 09:00:00"], + name="datetime", + ) + expected = expected.tz_localize("UTC").tz_convert("US/Pacific") + + df = df.set_index("label", append=True) + tm.assert_index_equal(df.index.levels[0], expected) + tm.assert_index_equal(df.index.levels[1], Index(["a", "b"], name="label")) + assert df.index.names == ["datetime", "label"] + + df = df.swaplevel(0, 1) + tm.assert_index_equal(df.index.levels[0], Index(["a", "b"], name="label")) + tm.assert_index_equal(df.index.levels[1], expected) + assert df.index.names == ["label", "datetime"] + + df = DataFrame(np.random.default_rng(2).random(6)) + idx1 = DatetimeIndex( + [ + "2011-07-19 07:00:00", + "2011-07-19 08:00:00", + "2011-07-19 09:00:00", + "2011-07-19 07:00:00", + "2011-07-19 08:00:00", + "2011-07-19 09:00:00", + ], + tz="US/Eastern", + ) + idx2 = DatetimeIndex( + [ + "2012-04-01 09:00", + "2012-04-01 09:00", + "2012-04-01 09:00", + "2012-04-02 09:00", + "2012-04-02 09:00", + "2012-04-02 09:00", + ], + tz="US/Eastern", + ) + idx3 = date_range("2011-01-01 09:00", periods=6, tz="Asia/Tokyo") + idx3 = idx3._with_freq(None) + + df = df.set_index(idx1) + df = df.set_index(idx2, append=True) + df = df.set_index(idx3, append=True) + + expected1 = DatetimeIndex( + ["2011-07-19 07:00:00", "2011-07-19 08:00:00", "2011-07-19 09:00:00"], + tz="US/Eastern", + ) + expected2 = DatetimeIndex( + ["2012-04-01 09:00", "2012-04-02 09:00"], tz="US/Eastern" + ) + + tm.assert_index_equal(df.index.levels[0], expected1) + tm.assert_index_equal(df.index.levels[1], expected2) + tm.assert_index_equal(df.index.levels[2], idx3) + + # GH#7092 + tm.assert_index_equal(df.index.get_level_values(0), idx1) + tm.assert_index_equal(df.index.get_level_values(1), idx2) + tm.assert_index_equal(df.index.get_level_values(2), idx3) + + def test_set_index_period(self): + # GH#6631 + df = DataFrame(np.random.default_rng(2).random(6)) + idx1 = period_range("2011-01-01", periods=3, freq="M") + idx1 = idx1.append(idx1) + idx2 = period_range("2013-01-01 09:00", periods=2, freq="h") + idx2 = idx2.append(idx2).append(idx2) + idx3 = period_range("2005", periods=6, freq="Y") + + df = df.set_index(idx1) + df = df.set_index(idx2, append=True) + df = df.set_index(idx3, append=True) + + expected1 = period_range("2011-01-01", periods=3, freq="M") + expected2 = period_range("2013-01-01 09:00", periods=2, freq="h") + + tm.assert_index_equal(df.index.levels[0], expected1) + tm.assert_index_equal(df.index.levels[1], expected2) + tm.assert_index_equal(df.index.levels[2], idx3) + + tm.assert_index_equal(df.index.get_level_values(0), idx1) + tm.assert_index_equal(df.index.get_level_values(1), idx2) + tm.assert_index_equal(df.index.get_level_values(2), idx3) + + +class TestSetIndexInvalid: + def test_set_index_verify_integrity(self, frame_of_index_cols): + df = frame_of_index_cols + + with pytest.raises(ValueError, match="Index has duplicate keys"): + df.set_index("A", verify_integrity=True) + # with MultiIndex + with pytest.raises(ValueError, match="Index has duplicate keys"): + df.set_index([df["A"], df["A"]], verify_integrity=True) + + @pytest.mark.parametrize("append", [True, False]) + @pytest.mark.parametrize("drop", [True, False]) + def test_set_index_raise_keys(self, frame_of_index_cols, drop, append): + df = frame_of_index_cols + + with pytest.raises(KeyError, match="['foo', 'bar', 'baz']"): + # column names are A-E, as well as one tuple + df.set_index(["foo", "bar", "baz"], drop=drop, append=append) + + # non-existent key in list with arrays + with pytest.raises(KeyError, match="X"): + df.set_index([df["A"], df["B"], "X"], drop=drop, append=append) + + msg = "[('foo', 'foo', 'foo', 'bar', 'bar')]" + # tuples always raise KeyError + with pytest.raises(KeyError, match=msg): + df.set_index(tuple(df["A"]), drop=drop, append=append) + + # also within a list + with pytest.raises(KeyError, match=msg): + df.set_index(["A", df["A"], tuple(df["A"])], drop=drop, append=append) + + @pytest.mark.parametrize("append", [True, False]) + @pytest.mark.parametrize("drop", [True, False]) + @pytest.mark.parametrize("box", [set], ids=["set"]) + def test_set_index_raise_on_type(self, frame_of_index_cols, box, drop, append): + df = frame_of_index_cols + + msg = 'The parameter "keys" may be a column key, .*' + # forbidden type, e.g. set + with pytest.raises(TypeError, match=msg): + df.set_index(box(df["A"]), drop=drop, append=append) + + # forbidden type in list, e.g. set + with pytest.raises(TypeError, match=msg): + df.set_index(["A", df["A"], box(df["A"])], drop=drop, append=append) + + # MultiIndex constructor does not work directly on Series -> lambda + @pytest.mark.parametrize( + "box", + [Series, Index, np.array, iter, lambda x: MultiIndex.from_arrays([x])], + ids=["Series", "Index", "np.array", "iter", "MultiIndex"], + ) + @pytest.mark.parametrize("length", [4, 6], ids=["too_short", "too_long"]) + @pytest.mark.parametrize("append", [True, False]) + @pytest.mark.parametrize("drop", [True, False]) + def test_set_index_raise_on_len( + self, frame_of_index_cols, box, length, drop, append + ): + # GH 24984 + df = frame_of_index_cols # has length 5 + + values = np.random.default_rng(2).integers(0, 10, (length,)) + + msg = "Length mismatch: Expected 5 rows, received array of length.*" + + # wrong length directly + with pytest.raises(ValueError, match=msg): + df.set_index(box(values), drop=drop, append=append) + + # wrong length in list + with pytest.raises(ValueError, match=msg): + df.set_index(["A", df.A, box(values)], drop=drop, append=append) + + +class TestSetIndexCustomLabelType: + def test_set_index_custom_label_type(self): + # GH#24969 + + class Thing: + def __init__(self, name, color) -> None: + self.name = name + self.color = color + + def __str__(self) -> str: + return f"" + + # necessary for pretty KeyError + __repr__ = __str__ + + thing1 = Thing("One", "red") + thing2 = Thing("Two", "blue") + df = DataFrame({thing1: [0, 1], thing2: [2, 3]}) + expected = DataFrame({thing1: [0, 1]}, index=Index([2, 3], name=thing2)) + + # use custom label directly + result = df.set_index(thing2) + tm.assert_frame_equal(result, expected) + + # custom label wrapped in list + result = df.set_index([thing2]) + tm.assert_frame_equal(result, expected) + + # missing key + thing3 = Thing("Three", "pink") + msg = "" + with pytest.raises(KeyError, match=msg): + # missing label directly + df.set_index(thing3) + + with pytest.raises(KeyError, match=msg): + # missing label in list + df.set_index([thing3]) + + def test_set_index_custom_label_hashable_iterable(self): + # GH#24969 + + # actual example discussed in GH 24984 was e.g. for shapely.geometry + # objects (e.g. a collection of Points) that can be both hashable and + # iterable; using frozenset as a stand-in for testing here + + class Thing(frozenset): + # need to stabilize repr for KeyError (due to random order in sets) + def __repr__(self) -> str: + tmp = sorted(self) + joined_reprs = ", ".join(map(repr, tmp)) + # double curly brace prints one brace in format string + return f"frozenset({{{joined_reprs}}})" + + thing1 = Thing(["One", "red"]) + thing2 = Thing(["Two", "blue"]) + df = DataFrame({thing1: [0, 1], thing2: [2, 3]}) + expected = DataFrame({thing1: [0, 1]}, index=Index([2, 3], name=thing2)) + + # use custom label directly + result = df.set_index(thing2) + tm.assert_frame_equal(result, expected) + + # custom label wrapped in list + result = df.set_index([thing2]) + tm.assert_frame_equal(result, expected) + + # missing key + thing3 = Thing(["Three", "pink"]) + msg = r"frozenset\(\{'Three', 'pink'\}\)" + with pytest.raises(KeyError, match=msg): + # missing label directly + df.set_index(thing3) + + with pytest.raises(KeyError, match=msg): + # missing label in list + df.set_index([thing3]) + + def test_set_index_custom_label_type_raises(self): + # GH#24969 + + # purposefully inherit from something unhashable + class Thing(set): + def __init__(self, name, color) -> None: + self.name = name + self.color = color + + def __str__(self) -> str: + return f"" + + thing1 = Thing("One", "red") + thing2 = Thing("Two", "blue") + df = DataFrame([[0, 2], [1, 3]], columns=[thing1, thing2]) + + msg = 'The parameter "keys" may be a column key, .*' + + with pytest.raises(TypeError, match=msg): + # use custom label directly + df.set_index(thing2) + + with pytest.raises(TypeError, match=msg): + # custom label wrapped in list + df.set_index([thing2]) + + def test_set_index_periodindex(self): + # GH#6631 + df = DataFrame(np.random.default_rng(2).random(6)) + idx1 = period_range("2011/01/01", periods=6, freq="M") + idx2 = period_range("2013", periods=6, freq="Y") + + df = df.set_index(idx1) + tm.assert_index_equal(df.index, idx1) + df = df.set_index(idx2) + tm.assert_index_equal(df.index, idx2) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_shift.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_shift.py new file mode 100644 index 0000000000000000000000000000000000000000..abb30595fdcb8466f1873642c2c355c92a61cd49 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_shift.py @@ -0,0 +1,764 @@ +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +import pandas as pd +from pandas import ( + CategoricalIndex, + DataFrame, + Index, + NaT, + Series, + date_range, + offsets, +) +import pandas._testing as tm + + +class TestDataFrameShift: + def test_shift_axis1_with_valid_fill_value_one_array(self): + # Case with axis=1 that does not go through the "len(arrays)>1" path + # in DataFrame.shift + data = np.random.default_rng(2).standard_normal((5, 3)) + df = DataFrame(data) + res = df.shift(axis=1, periods=1, fill_value=12345) + expected = df.T.shift(periods=1, fill_value=12345).T + tm.assert_frame_equal(res, expected) + + # same but with an 1D ExtensionArray backing it + df2 = df[[0]].astype("Float64") + res2 = df2.shift(axis=1, periods=1, fill_value=12345) + expected2 = DataFrame([12345] * 5, dtype="Float64") + tm.assert_frame_equal(res2, expected2) + + def test_shift_deprecate_freq_and_fill_value(self, frame_or_series): + # Can't pass both! + obj = frame_or_series( + np.random.default_rng(2).standard_normal(5), + index=date_range("1/1/2000", periods=5, freq="h"), + ) + + msg = ( + "Passing a 'freq' together with a 'fill_value' silently ignores the " + "fill_value" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + obj.shift(1, fill_value=1, freq="h") + + if frame_or_series is DataFrame: + obj.columns = date_range("1/1/2000", periods=1, freq="h") + with tm.assert_produces_warning(FutureWarning, match=msg): + obj.shift(1, axis=1, fill_value=1, freq="h") + + @pytest.mark.parametrize( + "input_data, output_data", + [(np.empty(shape=(0,)), []), (np.ones(shape=(2,)), [np.nan, 1.0])], + ) + def test_shift_non_writable_array(self, input_data, output_data, frame_or_series): + # GH21049 Verify whether non writable numpy array is shiftable + input_data.setflags(write=False) + + result = frame_or_series(input_data).shift(1) + if frame_or_series is not Series: + # need to explicitly specify columns in the empty case + expected = frame_or_series( + output_data, + index=range(len(output_data)), + columns=range(1), + dtype="float64", + ) + else: + expected = frame_or_series(output_data, dtype="float64") + + tm.assert_equal(result, expected) + + def test_shift_mismatched_freq(self, frame_or_series): + ts = frame_or_series( + np.random.default_rng(2).standard_normal(5), + index=date_range("1/1/2000", periods=5, freq="h"), + ) + + result = ts.shift(1, freq="5min") + exp_index = ts.index.shift(1, freq="5min") + tm.assert_index_equal(result.index, exp_index) + + # GH#1063, multiple of same base + result = ts.shift(1, freq="4h") + exp_index = ts.index + offsets.Hour(4) + tm.assert_index_equal(result.index, exp_index) + + @pytest.mark.parametrize( + "obj", + [ + Series([np.arange(5)]), + date_range("1/1/2011", periods=24, freq="h"), + Series(range(5), index=date_range("2017", periods=5)), + ], + ) + @pytest.mark.parametrize("shift_size", [0, 1, 2]) + def test_shift_always_copy(self, obj, shift_size, frame_or_series): + # GH#22397 + if frame_or_series is not Series: + obj = obj.to_frame() + assert obj.shift(shift_size) is not obj + + def test_shift_object_non_scalar_fill(self): + # shift requires scalar fill_value except for object dtype + ser = Series(range(3)) + with pytest.raises(ValueError, match="fill_value must be a scalar"): + ser.shift(1, fill_value=[]) + + df = ser.to_frame() + with pytest.raises(ValueError, match="fill_value must be a scalar"): + df.shift(1, fill_value=np.arange(3)) + + obj_ser = ser.astype(object) + result = obj_ser.shift(1, fill_value={}) + assert result[0] == {} + + obj_df = obj_ser.to_frame() + result = obj_df.shift(1, fill_value={}) + assert result.iloc[0, 0] == {} + + def test_shift_int(self, datetime_frame, frame_or_series): + ts = tm.get_obj(datetime_frame, frame_or_series).astype(int) + shifted = ts.shift(1) + expected = ts.astype(float).shift(1) + tm.assert_equal(shifted, expected) + + @pytest.mark.parametrize("dtype", ["int32", "int64"]) + def test_shift_32bit_take(self, frame_or_series, dtype): + # 32-bit taking + # GH#8129 + index = date_range("2000-01-01", periods=5) + arr = np.arange(5, dtype=dtype) + s1 = frame_or_series(arr, index=index) + p = arr[1] + result = s1.shift(periods=p) + expected = frame_or_series([np.nan, 0, 1, 2, 3], index=index) + tm.assert_equal(result, expected) + + @pytest.mark.parametrize("periods", [1, 2, 3, 4]) + def test_shift_preserve_freqstr(self, periods, frame_or_series): + # GH#21275 + obj = frame_or_series( + range(periods), + index=date_range("2016-1-1 00:00:00", periods=periods, freq="h"), + ) + + result = obj.shift(1, "2h") + + expected = frame_or_series( + range(periods), + index=date_range("2016-1-1 02:00:00", periods=periods, freq="h"), + ) + tm.assert_equal(result, expected) + + def test_shift_dst(self, frame_or_series): + # GH#13926 + dates = date_range("2016-11-06", freq="h", periods=10, tz="US/Eastern") + obj = frame_or_series(dates) + + res = obj.shift(0) + tm.assert_equal(res, obj) + assert tm.get_dtype(res) == "datetime64[ns, US/Eastern]" + + res = obj.shift(1) + exp_vals = [NaT] + dates.astype(object).values.tolist()[:9] + exp = frame_or_series(exp_vals) + tm.assert_equal(res, exp) + assert tm.get_dtype(res) == "datetime64[ns, US/Eastern]" + + res = obj.shift(-2) + exp_vals = dates.astype(object).values.tolist()[2:] + [NaT, NaT] + exp = frame_or_series(exp_vals) + tm.assert_equal(res, exp) + assert tm.get_dtype(res) == "datetime64[ns, US/Eastern]" + + @pytest.mark.parametrize("ex", [10, -10, 20, -20]) + def test_shift_dst_beyond(self, frame_or_series, ex): + # GH#13926 + dates = date_range("2016-11-06", freq="h", periods=10, tz="US/Eastern") + obj = frame_or_series(dates) + res = obj.shift(ex) + exp = frame_or_series([NaT] * 10, dtype="datetime64[ns, US/Eastern]") + tm.assert_equal(res, exp) + assert tm.get_dtype(res) == "datetime64[ns, US/Eastern]" + + def test_shift_by_zero(self, datetime_frame, frame_or_series): + # shift by 0 + obj = tm.get_obj(datetime_frame, frame_or_series) + unshifted = obj.shift(0) + tm.assert_equal(unshifted, obj) + + def test_shift(self, datetime_frame): + # naive shift + ser = datetime_frame["A"] + + shifted = datetime_frame.shift(5) + tm.assert_index_equal(shifted.index, datetime_frame.index) + + shifted_ser = ser.shift(5) + tm.assert_series_equal(shifted["A"], shifted_ser) + + shifted = datetime_frame.shift(-5) + tm.assert_index_equal(shifted.index, datetime_frame.index) + + shifted_ser = ser.shift(-5) + tm.assert_series_equal(shifted["A"], shifted_ser) + + unshifted = datetime_frame.shift(5).shift(-5) + tm.assert_numpy_array_equal( + unshifted.dropna().values, datetime_frame.values[:-5] + ) + + unshifted_ser = ser.shift(5).shift(-5) + tm.assert_numpy_array_equal(unshifted_ser.dropna().values, ser.values[:-5]) + + def test_shift_by_offset(self, datetime_frame, frame_or_series): + # shift by DateOffset + obj = tm.get_obj(datetime_frame, frame_or_series) + offset = offsets.BDay() + + shifted = obj.shift(5, freq=offset) + assert len(shifted) == len(obj) + unshifted = shifted.shift(-5, freq=offset) + tm.assert_equal(unshifted, obj) + + shifted2 = obj.shift(5, freq="B") + tm.assert_equal(shifted, shifted2) + + unshifted = obj.shift(0, freq=offset) + tm.assert_equal(unshifted, obj) + + d = obj.index[0] + shifted_d = d + offset * 5 + if frame_or_series is DataFrame: + tm.assert_series_equal(obj.xs(d), shifted.xs(shifted_d), check_names=False) + else: + tm.assert_almost_equal(obj.at[d], shifted.at[shifted_d]) + + def test_shift_with_periodindex(self, frame_or_series): + # Shifting with PeriodIndex + ps = DataFrame( + np.arange(4, dtype=float), index=pd.period_range("2020-01-01", periods=4) + ) + ps = tm.get_obj(ps, frame_or_series) + + shifted = ps.shift(1) + unshifted = shifted.shift(-1) + tm.assert_index_equal(shifted.index, ps.index) + tm.assert_index_equal(unshifted.index, ps.index) + if frame_or_series is DataFrame: + tm.assert_numpy_array_equal( + unshifted.iloc[:, 0].dropna().values, ps.iloc[:-1, 0].values + ) + else: + tm.assert_numpy_array_equal(unshifted.dropna().values, ps.values[:-1]) + + shifted2 = ps.shift(1, "D") + shifted3 = ps.shift(1, offsets.Day()) + tm.assert_equal(shifted2, shifted3) + tm.assert_equal(ps, shifted2.shift(-1, "D")) + + msg = "does not match PeriodIndex freq" + with pytest.raises(ValueError, match=msg): + ps.shift(freq="W") + + # legacy support + shifted4 = ps.shift(1, freq="D") + tm.assert_equal(shifted2, shifted4) + + shifted5 = ps.shift(1, freq=offsets.Day()) + tm.assert_equal(shifted5, shifted4) + + def test_shift_other_axis(self): + # shift other axis + # GH#6371 + df = DataFrame(np.random.default_rng(2).random((10, 5))) + expected = pd.concat( + [DataFrame(np.nan, index=df.index, columns=[0]), df.iloc[:, 0:-1]], + ignore_index=True, + axis=1, + ) + result = df.shift(1, axis=1) + tm.assert_frame_equal(result, expected) + + def test_shift_named_axis(self): + # shift named axis + df = DataFrame(np.random.default_rng(2).random((10, 5))) + expected = pd.concat( + [DataFrame(np.nan, index=df.index, columns=[0]), df.iloc[:, 0:-1]], + ignore_index=True, + axis=1, + ) + result = df.shift(1, axis="columns") + tm.assert_frame_equal(result, expected) + + def test_shift_other_axis_with_freq(self, datetime_frame): + obj = datetime_frame.T + offset = offsets.BDay() + + # GH#47039 + shifted = obj.shift(5, freq=offset, axis=1) + assert len(shifted) == len(obj) + unshifted = shifted.shift(-5, freq=offset, axis=1) + tm.assert_equal(unshifted, obj) + + def test_shift_bool(self): + df = DataFrame({"high": [True, False], "low": [False, False]}) + rs = df.shift(1) + xp = DataFrame( + np.array([[np.nan, np.nan], [True, False]], dtype=object), + columns=["high", "low"], + ) + tm.assert_frame_equal(rs, xp) + + def test_shift_categorical1(self, frame_or_series): + # GH#9416 + obj = frame_or_series(["a", "b", "c", "d"], dtype="category") + + rt = obj.shift(1).shift(-1) + tm.assert_equal(obj.iloc[:-1], rt.dropna()) + + def get_cat_values(ndframe): + # For Series we could just do ._values; for DataFrame + # we may be able to do this if we ever have 2D Categoricals + return ndframe._mgr.arrays[0] + + cat = get_cat_values(obj) + + sp1 = obj.shift(1) + tm.assert_index_equal(obj.index, sp1.index) + assert np.all(get_cat_values(sp1).codes[:1] == -1) + assert np.all(cat.codes[:-1] == get_cat_values(sp1).codes[1:]) + + sn2 = obj.shift(-2) + tm.assert_index_equal(obj.index, sn2.index) + assert np.all(get_cat_values(sn2).codes[-2:] == -1) + assert np.all(cat.codes[2:] == get_cat_values(sn2).codes[:-2]) + + tm.assert_index_equal(cat.categories, get_cat_values(sp1).categories) + tm.assert_index_equal(cat.categories, get_cat_values(sn2).categories) + + def test_shift_categorical(self): + # GH#9416 + s1 = Series(["a", "b", "c"], dtype="category") + s2 = Series(["A", "B", "C"], dtype="category") + df = DataFrame({"one": s1, "two": s2}) + rs = df.shift(1) + xp = DataFrame({"one": s1.shift(1), "two": s2.shift(1)}) + tm.assert_frame_equal(rs, xp) + + def test_shift_categorical_fill_value(self, frame_or_series): + ts = frame_or_series(["a", "b", "c", "d"], dtype="category") + res = ts.shift(1, fill_value="a") + expected = frame_or_series( + pd.Categorical( + ["a", "a", "b", "c"], categories=["a", "b", "c", "d"], ordered=False + ) + ) + tm.assert_equal(res, expected) + + # check for incorrect fill_value + msg = r"Cannot setitem on a Categorical with a new category \(f\)" + with pytest.raises(TypeError, match=msg): + ts.shift(1, fill_value="f") + + def test_shift_fill_value(self, frame_or_series): + # GH#24128 + dti = date_range("1/1/2000", periods=5, freq="h") + + ts = frame_or_series([1.0, 2.0, 3.0, 4.0, 5.0], index=dti) + exp = frame_or_series([0.0, 1.0, 2.0, 3.0, 4.0], index=dti) + # check that fill value works + result = ts.shift(1, fill_value=0.0) + tm.assert_equal(result, exp) + + exp = frame_or_series([0.0, 0.0, 1.0, 2.0, 3.0], index=dti) + result = ts.shift(2, fill_value=0.0) + tm.assert_equal(result, exp) + + ts = frame_or_series([1, 2, 3]) + res = ts.shift(2, fill_value=0) + assert tm.get_dtype(res) == tm.get_dtype(ts) + + # retain integer dtype + obj = frame_or_series([1, 2, 3, 4, 5], index=dti) + exp = frame_or_series([0, 1, 2, 3, 4], index=dti) + result = obj.shift(1, fill_value=0) + tm.assert_equal(result, exp) + + exp = frame_or_series([0, 0, 1, 2, 3], index=dti) + result = obj.shift(2, fill_value=0) + tm.assert_equal(result, exp) + + def test_shift_empty(self): + # Regression test for GH#8019 + df = DataFrame({"foo": []}) + rs = df.shift(-1) + + tm.assert_frame_equal(df, rs) + + def test_shift_duplicate_columns(self): + # GH#9092; verify that position-based shifting works + # in the presence of duplicate columns + column_lists = [list(range(5)), [1] * 5, [1, 1, 2, 2, 1]] + data = np.random.default_rng(2).standard_normal((20, 5)) + + shifted = [] + for columns in column_lists: + df = DataFrame(data.copy(), columns=columns) + for s in range(5): + df.iloc[:, s] = df.iloc[:, s].shift(s + 1) + df.columns = range(5) + shifted.append(df) + + # sanity check the base case + nulls = shifted[0].isna().sum() + tm.assert_series_equal(nulls, Series(range(1, 6), dtype="int64")) + + # check all answers are the same + tm.assert_frame_equal(shifted[0], shifted[1]) + tm.assert_frame_equal(shifted[0], shifted[2]) + + def test_shift_axis1_multiple_blocks(self, using_array_manager): + # GH#35488 + df1 = DataFrame(np.random.default_rng(2).integers(1000, size=(5, 3))) + df2 = DataFrame(np.random.default_rng(2).integers(1000, size=(5, 2))) + df3 = pd.concat([df1, df2], axis=1) + if not using_array_manager: + assert len(df3._mgr.blocks) == 2 + + result = df3.shift(2, axis=1) + + expected = df3.take([-1, -1, 0, 1, 2], axis=1) + # Explicit cast to float to avoid implicit cast when setting nan. + # Column names aren't unique, so directly calling `expected.astype` won't work. + expected = expected.pipe( + lambda df: df.set_axis(range(df.shape[1]), axis=1) + .astype({0: "float", 1: "float"}) + .set_axis(df.columns, axis=1) + ) + expected.iloc[:, :2] = np.nan + expected.columns = df3.columns + + tm.assert_frame_equal(result, expected) + + # Case with periods < 0 + # rebuild df3 because `take` call above consolidated + df3 = pd.concat([df1, df2], axis=1) + if not using_array_manager: + assert len(df3._mgr.blocks) == 2 + result = df3.shift(-2, axis=1) + + expected = df3.take([2, 3, 4, -1, -1], axis=1) + # Explicit cast to float to avoid implicit cast when setting nan. + # Column names aren't unique, so directly calling `expected.astype` won't work. + expected = expected.pipe( + lambda df: df.set_axis(range(df.shape[1]), axis=1) + .astype({3: "float", 4: "float"}) + .set_axis(df.columns, axis=1) + ) + expected.iloc[:, -2:] = np.nan + expected.columns = df3.columns + + tm.assert_frame_equal(result, expected) + + @td.skip_array_manager_not_yet_implemented # TODO(ArrayManager) axis=1 support + def test_shift_axis1_multiple_blocks_with_int_fill(self): + # GH#42719 + rng = np.random.default_rng(2) + df1 = DataFrame(rng.integers(1000, size=(5, 3), dtype=int)) + df2 = DataFrame(rng.integers(1000, size=(5, 2), dtype=int)) + df3 = pd.concat([df1.iloc[:4, 1:3], df2.iloc[:4, :]], axis=1) + result = df3.shift(2, axis=1, fill_value=np.int_(0)) + assert len(df3._mgr.blocks) == 2 + + expected = df3.take([-1, -1, 0, 1], axis=1) + expected.iloc[:, :2] = np.int_(0) + expected.columns = df3.columns + + tm.assert_frame_equal(result, expected) + + # Case with periods < 0 + df3 = pd.concat([df1.iloc[:4, 1:3], df2.iloc[:4, :]], axis=1) + result = df3.shift(-2, axis=1, fill_value=np.int_(0)) + assert len(df3._mgr.blocks) == 2 + + expected = df3.take([2, 3, -1, -1], axis=1) + expected.iloc[:, -2:] = np.int_(0) + expected.columns = df3.columns + + tm.assert_frame_equal(result, expected) + + def test_period_index_frame_shift_with_freq(self, frame_or_series): + ps = DataFrame(range(4), index=pd.period_range("2020-01-01", periods=4)) + ps = tm.get_obj(ps, frame_or_series) + + shifted = ps.shift(1, freq="infer") + unshifted = shifted.shift(-1, freq="infer") + tm.assert_equal(unshifted, ps) + + shifted2 = ps.shift(freq="D") + tm.assert_equal(shifted, shifted2) + + shifted3 = ps.shift(freq=offsets.Day()) + tm.assert_equal(shifted, shifted3) + + def test_datetime_frame_shift_with_freq(self, datetime_frame, frame_or_series): + dtobj = tm.get_obj(datetime_frame, frame_or_series) + shifted = dtobj.shift(1, freq="infer") + unshifted = shifted.shift(-1, freq="infer") + tm.assert_equal(dtobj, unshifted) + + shifted2 = dtobj.shift(freq=dtobj.index.freq) + tm.assert_equal(shifted, shifted2) + + inferred_ts = DataFrame( + datetime_frame.values, + Index(np.asarray(datetime_frame.index)), + columns=datetime_frame.columns, + ) + inferred_ts = tm.get_obj(inferred_ts, frame_or_series) + shifted = inferred_ts.shift(1, freq="infer") + expected = dtobj.shift(1, freq="infer") + expected.index = expected.index._with_freq(None) + tm.assert_equal(shifted, expected) + + unshifted = shifted.shift(-1, freq="infer") + tm.assert_equal(unshifted, inferred_ts) + + def test_period_index_frame_shift_with_freq_error(self, frame_or_series): + ps = DataFrame(range(4), index=pd.period_range("2020-01-01", periods=4)) + ps = tm.get_obj(ps, frame_or_series) + msg = "Given freq M does not match PeriodIndex freq D" + with pytest.raises(ValueError, match=msg): + ps.shift(freq="M") + + def test_datetime_frame_shift_with_freq_error( + self, datetime_frame, frame_or_series + ): + dtobj = tm.get_obj(datetime_frame, frame_or_series) + no_freq = dtobj.iloc[[0, 5, 7]] + msg = "Freq was not set in the index hence cannot be inferred" + with pytest.raises(ValueError, match=msg): + no_freq.shift(freq="infer") + + def test_shift_dt64values_int_fill_deprecated(self): + # GH#31971 + ser = Series([pd.Timestamp("2020-01-01"), pd.Timestamp("2020-01-02")]) + + with pytest.raises(TypeError, match="value should be a"): + ser.shift(1, fill_value=0) + + df = ser.to_frame() + with pytest.raises(TypeError, match="value should be a"): + df.shift(1, fill_value=0) + + # axis = 1 + df2 = DataFrame({"A": ser, "B": ser}) + df2._consolidate_inplace() + + result = df2.shift(1, axis=1, fill_value=0) + expected = DataFrame({"A": [0, 0], "B": df2["A"]}) + tm.assert_frame_equal(result, expected) + + # same thing but not consolidated; pre-2.0 we got different behavior + df3 = DataFrame({"A": ser}) + df3["B"] = ser + assert len(df3._mgr.arrays) == 2 + result = df3.shift(1, axis=1, fill_value=0) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "as_cat", + [ + pytest.param( + True, + marks=pytest.mark.xfail( + reason="_can_hold_element incorrectly always returns True" + ), + ), + False, + ], + ) + @pytest.mark.parametrize( + "vals", + [ + date_range("2020-01-01", periods=2), + date_range("2020-01-01", periods=2, tz="US/Pacific"), + pd.period_range("2020-01-01", periods=2, freq="D"), + pd.timedelta_range("2020 Days", periods=2, freq="D"), + pd.interval_range(0, 3, periods=2), + pytest.param( + pd.array([1, 2], dtype="Int64"), + marks=pytest.mark.xfail( + reason="_can_hold_element incorrectly always returns True" + ), + ), + pytest.param( + pd.array([1, 2], dtype="Float32"), + marks=pytest.mark.xfail( + reason="_can_hold_element incorrectly always returns True" + ), + ), + ], + ids=lambda x: str(x.dtype), + ) + def test_shift_dt64values_axis1_invalid_fill(self, vals, as_cat): + # GH#44564 + ser = Series(vals) + if as_cat: + ser = ser.astype("category") + + df = DataFrame({"A": ser}) + result = df.shift(-1, axis=1, fill_value="foo") + expected = DataFrame({"A": ["foo", "foo"]}) + tm.assert_frame_equal(result, expected) + + # same thing but multiple blocks + df2 = DataFrame({"A": ser, "B": ser}) + df2._consolidate_inplace() + + result = df2.shift(-1, axis=1, fill_value="foo") + expected = DataFrame({"A": df2["B"], "B": ["foo", "foo"]}) + tm.assert_frame_equal(result, expected) + + # same thing but not consolidated + df3 = DataFrame({"A": ser}) + df3["B"] = ser + assert len(df3._mgr.arrays) == 2 + result = df3.shift(-1, axis=1, fill_value="foo") + tm.assert_frame_equal(result, expected) + + def test_shift_axis1_categorical_columns(self): + # GH#38434 + ci = CategoricalIndex(["a", "b", "c"]) + df = DataFrame( + {"a": [1, 3], "b": [2, 4], "c": [5, 6]}, index=ci[:-1], columns=ci + ) + result = df.shift(axis=1) + + expected = DataFrame( + {"a": [np.nan, np.nan], "b": [1, 3], "c": [2, 4]}, index=ci[:-1], columns=ci + ) + tm.assert_frame_equal(result, expected) + + # periods != 1 + result = df.shift(2, axis=1) + expected = DataFrame( + {"a": [np.nan, np.nan], "b": [np.nan, np.nan], "c": [1, 3]}, + index=ci[:-1], + columns=ci, + ) + tm.assert_frame_equal(result, expected) + + def test_shift_axis1_many_periods(self): + # GH#44978 periods > len(columns) + df = DataFrame(np.random.default_rng(2).random((5, 3))) + shifted = df.shift(6, axis=1, fill_value=None) + + expected = df * np.nan + tm.assert_frame_equal(shifted, expected) + + shifted2 = df.shift(-6, axis=1, fill_value=None) + tm.assert_frame_equal(shifted2, expected) + + def test_shift_with_offsets_freq(self): + df = DataFrame({"x": [1, 2, 3]}, index=date_range("2000", periods=3)) + shifted = df.shift(freq="1MS") + expected = DataFrame( + {"x": [1, 2, 3]}, + index=date_range(start="02/01/2000", end="02/01/2000", periods=3), + ) + tm.assert_frame_equal(shifted, expected) + + def test_shift_with_iterable_basic_functionality(self): + # GH#44424 + data = {"a": [1, 2, 3], "b": [4, 5, 6]} + shifts = [0, 1, 2] + + df = DataFrame(data) + shifted = df.shift(shifts) + + expected = DataFrame( + { + "a_0": [1, 2, 3], + "b_0": [4, 5, 6], + "a_1": [np.nan, 1.0, 2.0], + "b_1": [np.nan, 4.0, 5.0], + "a_2": [np.nan, np.nan, 1.0], + "b_2": [np.nan, np.nan, 4.0], + } + ) + tm.assert_frame_equal(expected, shifted) + + def test_shift_with_iterable_series(self): + # GH#44424 + data = {"a": [1, 2, 3]} + shifts = [0, 1, 2] + + df = DataFrame(data) + s = df["a"] + tm.assert_frame_equal(s.shift(shifts), df.shift(shifts)) + + def test_shift_with_iterable_freq_and_fill_value(self): + # GH#44424 + df = DataFrame( + np.random.default_rng(2).standard_normal(5), + index=date_range("1/1/2000", periods=5, freq="h"), + ) + + tm.assert_frame_equal( + # rename because shift with an iterable leads to str column names + df.shift([1], fill_value=1).rename(columns=lambda x: int(x[0])), + df.shift(1, fill_value=1), + ) + + tm.assert_frame_equal( + df.shift([1], freq="h").rename(columns=lambda x: int(x[0])), + df.shift(1, freq="h"), + ) + + msg = ( + "Passing a 'freq' together with a 'fill_value' silently ignores the " + "fill_value" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + df.shift([1, 2], fill_value=1, freq="h") + + def test_shift_with_iterable_check_other_arguments(self): + # GH#44424 + data = {"a": [1, 2], "b": [4, 5]} + shifts = [0, 1] + df = DataFrame(data) + + # test suffix + shifted = df[["a"]].shift(shifts, suffix="_suffix") + expected = DataFrame({"a_suffix_0": [1, 2], "a_suffix_1": [np.nan, 1.0]}) + tm.assert_frame_equal(shifted, expected) + + # check bad inputs when doing multiple shifts + msg = "If `periods` contains multiple shifts, `axis` cannot be 1." + with pytest.raises(ValueError, match=msg): + df.shift(shifts, axis=1) + + msg = "Periods must be integer, but s is ." + with pytest.raises(TypeError, match=msg): + df.shift(["s"]) + + msg = "If `periods` is an iterable, it cannot be empty." + with pytest.raises(ValueError, match=msg): + df.shift([]) + + msg = "Cannot specify `suffix` if `periods` is an int." + with pytest.raises(ValueError, match=msg): + df.shift(1, suffix="fails") + + def test_shift_axis_one_empty(self): + # GH#57301 + df = DataFrame() + result = df.shift(1, axis=1) + tm.assert_frame_equal(result, df) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_size.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_size.py new file mode 100644 index 0000000000000000000000000000000000000000..0c8b6473c85ea8e4a9749e79c8b4459afe6637d8 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_size.py @@ -0,0 +1,21 @@ +import numpy as np +import pytest + +from pandas import DataFrame + + +@pytest.mark.parametrize( + "data, index, expected", + [ + ({"col1": [1], "col2": [3]}, None, 2), + ({}, None, 0), + ({"col1": [1, np.nan], "col2": [3, 4]}, None, 4), + ({"col1": [1, 2], "col2": [3, 4]}, [["a", "b"], [1, 2]], 4), + ({"col1": [1, 2, 3, 4], "col2": [3, 4, 5, 6]}, ["x", "y", "a", "b"], 8), + ], +) +def test_size(data, index, expected): + # GH#52897 + df = DataFrame(data, index=index) + assert df.size == expected + assert isinstance(df.size, int) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_sort_index.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_sort_index.py new file mode 100644 index 0000000000000000000000000000000000000000..830561a1349ee73b68f1f95c31b0e3b8dcccb48b --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_sort_index.py @@ -0,0 +1,1028 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + CategoricalDtype, + CategoricalIndex, + DataFrame, + IntervalIndex, + MultiIndex, + RangeIndex, + Series, + Timestamp, +) +import pandas._testing as tm + + +class TestDataFrameSortIndex: + def test_sort_index_and_reconstruction_doc_example(self): + # doc example + df = DataFrame( + {"value": [1, 2, 3, 4]}, + index=MultiIndex( + levels=[["a", "b"], ["bb", "aa"]], codes=[[0, 0, 1, 1], [0, 1, 0, 1]] + ), + ) + assert df.index._is_lexsorted() + assert not df.index.is_monotonic_increasing + + # sort it + expected = DataFrame( + {"value": [2, 1, 4, 3]}, + index=MultiIndex( + levels=[["a", "b"], ["aa", "bb"]], codes=[[0, 0, 1, 1], [0, 1, 0, 1]] + ), + ) + result = df.sort_index() + assert result.index.is_monotonic_increasing + tm.assert_frame_equal(result, expected) + + # reconstruct + result = df.sort_index().copy() + result.index = result.index._sort_levels_monotonic() + assert result.index.is_monotonic_increasing + tm.assert_frame_equal(result, expected) + + def test_sort_index_non_existent_label_multiindex(self): + # GH#12261 + df = DataFrame(0, columns=[], index=MultiIndex.from_product([[], []])) + with tm.assert_produces_warning(None): + df.loc["b", "2"] = 1 + df.loc["a", "3"] = 1 + result = df.sort_index().index.is_monotonic_increasing + assert result is True + + def test_sort_index_reorder_on_ops(self): + # GH#15687 + df = DataFrame( + np.random.default_rng(2).standard_normal((8, 2)), + index=MultiIndex.from_product( + [["a", "b"], ["big", "small"], ["red", "blu"]], + names=["letter", "size", "color"], + ), + columns=["near", "far"], + ) + df = df.sort_index() + + def my_func(group): + group.index = ["newz", "newa"] + return group + + result = df.groupby(level=["letter", "size"]).apply(my_func).sort_index() + expected = MultiIndex.from_product( + [["a", "b"], ["big", "small"], ["newa", "newz"]], + names=["letter", "size", None], + ) + + tm.assert_index_equal(result.index, expected) + + def test_sort_index_nan_multiindex(self): + # GH#14784 + # incorrect sorting w.r.t. nans + tuples = [[12, 13], [np.nan, np.nan], [np.nan, 3], [1, 2]] + mi = MultiIndex.from_tuples(tuples) + + df = DataFrame(np.arange(16).reshape(4, 4), index=mi, columns=list("ABCD")) + s = Series(np.arange(4), index=mi) + + df2 = DataFrame( + { + "date": pd.DatetimeIndex( + [ + "20121002", + "20121007", + "20130130", + "20130202", + "20130305", + "20121002", + "20121207", + "20130130", + "20130202", + "20130305", + "20130202", + "20130305", + ] + ), + "user_id": [1, 1, 1, 1, 1, 3, 3, 3, 5, 5, 5, 5], + "whole_cost": [ + 1790, + np.nan, + 280, + 259, + np.nan, + 623, + 90, + 312, + np.nan, + 301, + 359, + 801, + ], + "cost": [12, 15, 10, 24, 39, 1, 0, np.nan, 45, 34, 1, 12], + } + ).set_index(["date", "user_id"]) + + # sorting frame, default nan position is last + result = df.sort_index() + expected = df.iloc[[3, 0, 2, 1], :] + tm.assert_frame_equal(result, expected) + + # sorting frame, nan position last + result = df.sort_index(na_position="last") + expected = df.iloc[[3, 0, 2, 1], :] + tm.assert_frame_equal(result, expected) + + # sorting frame, nan position first + result = df.sort_index(na_position="first") + expected = df.iloc[[1, 2, 3, 0], :] + tm.assert_frame_equal(result, expected) + + # sorting frame with removed rows + result = df2.dropna().sort_index() + expected = df2.sort_index().dropna() + tm.assert_frame_equal(result, expected) + + # sorting series, default nan position is last + result = s.sort_index() + expected = s.iloc[[3, 0, 2, 1]] + tm.assert_series_equal(result, expected) + + # sorting series, nan position last + result = s.sort_index(na_position="last") + expected = s.iloc[[3, 0, 2, 1]] + tm.assert_series_equal(result, expected) + + # sorting series, nan position first + result = s.sort_index(na_position="first") + expected = s.iloc[[1, 2, 3, 0]] + tm.assert_series_equal(result, expected) + + def test_sort_index_nan(self): + # GH#3917 + + # Test DataFrame with nan label + df = DataFrame( + {"A": [1, 2, np.nan, 1, 6, 8, 4], "B": [9, np.nan, 5, 2, 5, 4, 5]}, + index=[1, 2, 3, 4, 5, 6, np.nan], + ) + + # NaN label, ascending=True, na_position='last' + sorted_df = df.sort_index(kind="quicksort", ascending=True, na_position="last") + expected = DataFrame( + {"A": [1, 2, np.nan, 1, 6, 8, 4], "B": [9, np.nan, 5, 2, 5, 4, 5]}, + index=[1, 2, 3, 4, 5, 6, np.nan], + ) + tm.assert_frame_equal(sorted_df, expected) + + # NaN label, ascending=True, na_position='first' + sorted_df = df.sort_index(na_position="first") + expected = DataFrame( + {"A": [4, 1, 2, np.nan, 1, 6, 8], "B": [5, 9, np.nan, 5, 2, 5, 4]}, + index=[np.nan, 1, 2, 3, 4, 5, 6], + ) + tm.assert_frame_equal(sorted_df, expected) + + # NaN label, ascending=False, na_position='last' + sorted_df = df.sort_index(kind="quicksort", ascending=False) + expected = DataFrame( + {"A": [8, 6, 1, np.nan, 2, 1, 4], "B": [4, 5, 2, 5, np.nan, 9, 5]}, + index=[6, 5, 4, 3, 2, 1, np.nan], + ) + tm.assert_frame_equal(sorted_df, expected) + + # NaN label, ascending=False, na_position='first' + sorted_df = df.sort_index( + kind="quicksort", ascending=False, na_position="first" + ) + expected = DataFrame( + {"A": [4, 8, 6, 1, np.nan, 2, 1], "B": [5, 4, 5, 2, 5, np.nan, 9]}, + index=[np.nan, 6, 5, 4, 3, 2, 1], + ) + tm.assert_frame_equal(sorted_df, expected) + + def test_sort_index_multi_index(self): + # GH#25775, testing that sorting by index works with a multi-index. + df = DataFrame( + {"a": [3, 1, 2], "b": [0, 0, 0], "c": [0, 1, 2], "d": list("abc")} + ) + result = df.set_index(list("abc")).sort_index(level=list("ba")) + + expected = DataFrame( + {"a": [1, 2, 3], "b": [0, 0, 0], "c": [1, 2, 0], "d": list("bca")} + ) + expected = expected.set_index(list("abc")) + + tm.assert_frame_equal(result, expected) + + def test_sort_index_inplace(self): + frame = DataFrame( + np.random.default_rng(2).standard_normal((4, 4)), + index=[1, 2, 3, 4], + columns=["A", "B", "C", "D"], + ) + + # axis=0 + unordered = frame.loc[[3, 2, 4, 1]] + a_values = unordered["A"] + df = unordered.copy() + return_value = df.sort_index(inplace=True) + assert return_value is None + expected = frame + tm.assert_frame_equal(df, expected) + # GH 44153 related + # Used to be a_id != id(df["A"]), but flaky in the CI + assert a_values is not df["A"] + + df = unordered.copy() + return_value = df.sort_index(ascending=False, inplace=True) + assert return_value is None + expected = frame[::-1] + tm.assert_frame_equal(df, expected) + + # axis=1 + unordered = frame.loc[:, ["D", "B", "C", "A"]] + df = unordered.copy() + return_value = df.sort_index(axis=1, inplace=True) + assert return_value is None + expected = frame + tm.assert_frame_equal(df, expected) + + df = unordered.copy() + return_value = df.sort_index(axis=1, ascending=False, inplace=True) + assert return_value is None + expected = frame.iloc[:, ::-1] + tm.assert_frame_equal(df, expected) + + def test_sort_index_different_sortorder(self): + A = np.arange(20).repeat(5) + B = np.tile(np.arange(5), 20) + + indexer = np.random.default_rng(2).permutation(100) + A = A.take(indexer) + B = B.take(indexer) + + df = DataFrame( + {"A": A, "B": B, "C": np.random.default_rng(2).standard_normal(100)} + ) + + ex_indexer = np.lexsort((df.B.max() - df.B, df.A)) + expected = df.take(ex_indexer) + + # test with multiindex, too + idf = df.set_index(["A", "B"]) + + result = idf.sort_index(ascending=[1, 0]) + expected = idf.take(ex_indexer) + tm.assert_frame_equal(result, expected) + + # also, Series! + result = idf["C"].sort_index(ascending=[1, 0]) + tm.assert_series_equal(result, expected["C"]) + + def test_sort_index_level(self): + mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC")) + df = DataFrame([[1, 2], [3, 4]], mi) + + result = df.sort_index(level="A", sort_remaining=False) + expected = df + tm.assert_frame_equal(result, expected) + + result = df.sort_index(level=["A", "B"], sort_remaining=False) + expected = df + tm.assert_frame_equal(result, expected) + + # Error thrown by sort_index when + # first index is sorted last (GH#26053) + result = df.sort_index(level=["C", "B", "A"]) + expected = df.iloc[[1, 0]] + tm.assert_frame_equal(result, expected) + + result = df.sort_index(level=["B", "C", "A"]) + expected = df.iloc[[1, 0]] + tm.assert_frame_equal(result, expected) + + result = df.sort_index(level=["C", "A"]) + expected = df.iloc[[1, 0]] + tm.assert_frame_equal(result, expected) + + def test_sort_index_categorical_index(self): + df = DataFrame( + { + "A": np.arange(6, dtype="int64"), + "B": Series(list("aabbca")).astype(CategoricalDtype(list("cab"))), + } + ).set_index("B") + + result = df.sort_index() + expected = df.iloc[[4, 0, 1, 5, 2, 3]] + tm.assert_frame_equal(result, expected) + + result = df.sort_index(ascending=False) + expected = df.iloc[[2, 3, 0, 1, 5, 4]] + tm.assert_frame_equal(result, expected) + + def test_sort_index(self): + # GH#13496 + + frame = DataFrame( + np.arange(16).reshape(4, 4), + index=[1, 2, 3, 4], + columns=["A", "B", "C", "D"], + ) + + # axis=0 : sort rows by index labels + unordered = frame.loc[[3, 2, 4, 1]] + result = unordered.sort_index(axis=0) + expected = frame + tm.assert_frame_equal(result, expected) + + result = unordered.sort_index(ascending=False) + expected = frame[::-1] + tm.assert_frame_equal(result, expected) + + # axis=1 : sort columns by column names + unordered = frame.iloc[:, [2, 1, 3, 0]] + result = unordered.sort_index(axis=1) + tm.assert_frame_equal(result, frame) + + result = unordered.sort_index(axis=1, ascending=False) + expected = frame.iloc[:, ::-1] + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("level", ["A", 0]) # GH#21052 + def test_sort_index_multiindex(self, level): + # GH#13496 + + # sort rows by specified level of multi-index + mi = MultiIndex.from_tuples( + [[2, 1, 3], [2, 1, 2], [1, 1, 1]], names=list("ABC") + ) + df = DataFrame([[1, 2], [3, 4], [5, 6]], index=mi) + + expected_mi = MultiIndex.from_tuples( + [[1, 1, 1], [2, 1, 2], [2, 1, 3]], names=list("ABC") + ) + expected = DataFrame([[5, 6], [3, 4], [1, 2]], index=expected_mi) + result = df.sort_index(level=level) + tm.assert_frame_equal(result, expected) + + # sort_remaining=False + expected_mi = MultiIndex.from_tuples( + [[1, 1, 1], [2, 1, 3], [2, 1, 2]], names=list("ABC") + ) + expected = DataFrame([[5, 6], [1, 2], [3, 4]], index=expected_mi) + result = df.sort_index(level=level, sort_remaining=False) + tm.assert_frame_equal(result, expected) + + def test_sort_index_intervalindex(self): + # this is a de-facto sort via unstack + # confirming that we sort in the order of the bins + y = Series(np.random.default_rng(2).standard_normal(100)) + x1 = Series(np.sign(np.random.default_rng(2).standard_normal(100))) + x2 = pd.cut( + Series(np.random.default_rng(2).standard_normal(100)), + bins=[-3, -0.5, 0, 0.5, 3], + ) + model = pd.concat([y, x1, x2], axis=1, keys=["Y", "X1", "X2"]) + + result = model.groupby(["X1", "X2"], observed=True).mean().unstack() + expected = IntervalIndex.from_tuples( + [(-3.0, -0.5), (-0.5, 0.0), (0.0, 0.5), (0.5, 3.0)], closed="right" + ) + result = result.columns.levels[1].categories + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize("inplace", [True, False]) + @pytest.mark.parametrize( + "original_dict, sorted_dict, ascending, ignore_index, output_index", + [ + ({"A": [1, 2, 3]}, {"A": [2, 3, 1]}, False, True, [0, 1, 2]), + ({"A": [1, 2, 3]}, {"A": [1, 3, 2]}, True, True, [0, 1, 2]), + ({"A": [1, 2, 3]}, {"A": [2, 3, 1]}, False, False, [5, 3, 2]), + ({"A": [1, 2, 3]}, {"A": [1, 3, 2]}, True, False, [2, 3, 5]), + ], + ) + def test_sort_index_ignore_index( + self, inplace, original_dict, sorted_dict, ascending, ignore_index, output_index + ): + # GH 30114 + original_index = [2, 5, 3] + df = DataFrame(original_dict, index=original_index) + expected_df = DataFrame(sorted_dict, index=output_index) + kwargs = { + "ascending": ascending, + "ignore_index": ignore_index, + "inplace": inplace, + } + + if inplace: + result_df = df.copy() + result_df.sort_index(**kwargs) + else: + result_df = df.sort_index(**kwargs) + + tm.assert_frame_equal(result_df, expected_df) + tm.assert_frame_equal(df, DataFrame(original_dict, index=original_index)) + + @pytest.mark.parametrize("inplace", [True, False]) + @pytest.mark.parametrize("ignore_index", [True, False]) + def test_respect_ignore_index(self, inplace, ignore_index): + # GH 43591 + df = DataFrame({"a": [1, 2, 3]}, index=RangeIndex(4, -1, -2)) + result = df.sort_index( + ascending=False, ignore_index=ignore_index, inplace=inplace + ) + + if inplace: + result = df + if ignore_index: + expected = DataFrame({"a": [1, 2, 3]}) + else: + expected = DataFrame({"a": [1, 2, 3]}, index=RangeIndex(4, -1, -2)) + + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("inplace", [True, False]) + @pytest.mark.parametrize( + "original_dict, sorted_dict, ascending, ignore_index, output_index", + [ + ( + {"M1": [1, 2], "M2": [3, 4]}, + {"M1": [1, 2], "M2": [3, 4]}, + True, + True, + [0, 1], + ), + ( + {"M1": [1, 2], "M2": [3, 4]}, + {"M1": [2, 1], "M2": [4, 3]}, + False, + True, + [0, 1], + ), + ( + {"M1": [1, 2], "M2": [3, 4]}, + {"M1": [1, 2], "M2": [3, 4]}, + True, + False, + MultiIndex.from_tuples([(2, 1), (3, 4)], names=list("AB")), + ), + ( + {"M1": [1, 2], "M2": [3, 4]}, + {"M1": [2, 1], "M2": [4, 3]}, + False, + False, + MultiIndex.from_tuples([(3, 4), (2, 1)], names=list("AB")), + ), + ], + ) + def test_sort_index_ignore_index_multi_index( + self, inplace, original_dict, sorted_dict, ascending, ignore_index, output_index + ): + # GH 30114, this is to test ignore_index on MultiIndex of index + mi = MultiIndex.from_tuples([(2, 1), (3, 4)], names=list("AB")) + df = DataFrame(original_dict, index=mi) + expected_df = DataFrame(sorted_dict, index=output_index) + + kwargs = { + "ascending": ascending, + "ignore_index": ignore_index, + "inplace": inplace, + } + + if inplace: + result_df = df.copy() + result_df.sort_index(**kwargs) + else: + result_df = df.sort_index(**kwargs) + + tm.assert_frame_equal(result_df, expected_df) + tm.assert_frame_equal(df, DataFrame(original_dict, index=mi)) + + def test_sort_index_categorical_multiindex(self): + # GH#15058 + df = DataFrame( + { + "a": range(6), + "l1": pd.Categorical( + ["a", "a", "b", "b", "c", "c"], + categories=["c", "a", "b"], + ordered=True, + ), + "l2": [0, 1, 0, 1, 0, 1], + } + ) + result = df.set_index(["l1", "l2"]).sort_index() + expected = DataFrame( + [4, 5, 0, 1, 2, 3], + columns=["a"], + index=MultiIndex( + levels=[ + CategoricalIndex( + ["c", "a", "b"], + categories=["c", "a", "b"], + ordered=True, + name="l1", + dtype="category", + ), + [0, 1], + ], + codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]], + names=["l1", "l2"], + ), + ) + tm.assert_frame_equal(result, expected) + + def test_sort_index_and_reconstruction(self): + # GH#15622 + # lexsortedness should be identical + # across MultiIndex construction methods + + df = DataFrame([[1, 1], [2, 2]], index=list("ab")) + expected = DataFrame( + [[1, 1], [2, 2], [1, 1], [2, 2]], + index=MultiIndex.from_tuples( + [(0.5, "a"), (0.5, "b"), (0.8, "a"), (0.8, "b")] + ), + ) + assert expected.index._is_lexsorted() + + result = DataFrame( + [[1, 1], [2, 2], [1, 1], [2, 2]], + index=MultiIndex.from_product([[0.5, 0.8], list("ab")]), + ) + result = result.sort_index() + assert result.index.is_monotonic_increasing + + tm.assert_frame_equal(result, expected) + + result = DataFrame( + [[1, 1], [2, 2], [1, 1], [2, 2]], + index=MultiIndex( + levels=[[0.5, 0.8], ["a", "b"]], codes=[[0, 0, 1, 1], [0, 1, 0, 1]] + ), + ) + result = result.sort_index() + assert result.index._is_lexsorted() + + tm.assert_frame_equal(result, expected) + + concatted = pd.concat([df, df], keys=[0.8, 0.5]) + result = concatted.sort_index() + + assert result.index.is_monotonic_increasing + + tm.assert_frame_equal(result, expected) + + # GH#14015 + df = DataFrame( + [[1, 2], [6, 7]], + columns=MultiIndex.from_tuples( + [(0, "20160811 12:00:00"), (0, "20160809 12:00:00")], + names=["l1", "Date"], + ), + ) + + df.columns = df.columns.set_levels( + pd.to_datetime(df.columns.levels[1]), level=1 + ) + assert not df.columns.is_monotonic_increasing + result = df.sort_index(axis=1) + assert result.columns.is_monotonic_increasing + result = df.sort_index(axis=1, level=1) + assert result.columns.is_monotonic_increasing + + # TODO: better name, de-duplicate with test_sort_index_level above + def test_sort_index_level2(self, multiindex_dataframe_random_data): + frame = multiindex_dataframe_random_data + + df = frame.copy() + df.index = np.arange(len(df)) + + # axis=1 + + # series + a_sorted = frame["A"].sort_index(level=0) + + # preserve names + assert a_sorted.index.names == frame.index.names + + # inplace + rs = frame.copy() + return_value = rs.sort_index(level=0, inplace=True) + assert return_value is None + tm.assert_frame_equal(rs, frame.sort_index(level=0)) + + def test_sort_index_level_large_cardinality(self): + # GH#2684 (int64) + index = MultiIndex.from_arrays([np.arange(4000)] * 3) + df = DataFrame( + np.random.default_rng(2).standard_normal(4000).astype("int64"), index=index + ) + + # it works! + result = df.sort_index(level=0) + assert result.index._lexsort_depth == 3 + + # GH#2684 (int32) + index = MultiIndex.from_arrays([np.arange(4000)] * 3) + df = DataFrame( + np.random.default_rng(2).standard_normal(4000).astype("int32"), index=index + ) + + # it works! + result = df.sort_index(level=0) + assert (result.dtypes.values == df.dtypes.values).all() + assert result.index._lexsort_depth == 3 + + def test_sort_index_level_by_name(self, multiindex_dataframe_random_data): + frame = multiindex_dataframe_random_data + + frame.index.names = ["first", "second"] + result = frame.sort_index(level="second") + expected = frame.sort_index(level=1) + tm.assert_frame_equal(result, expected) + + def test_sort_index_level_mixed(self, multiindex_dataframe_random_data): + frame = multiindex_dataframe_random_data + + sorted_before = frame.sort_index(level=1) + + df = frame.copy() + df["foo"] = "bar" + sorted_after = df.sort_index(level=1) + tm.assert_frame_equal(sorted_before, sorted_after.drop(["foo"], axis=1)) + + dft = frame.T + sorted_before = dft.sort_index(level=1, axis=1) + dft["foo", "three"] = "bar" + + sorted_after = dft.sort_index(level=1, axis=1) + tm.assert_frame_equal( + sorted_before.drop([("foo", "three")], axis=1), + sorted_after.drop([("foo", "three")], axis=1), + ) + + def test_sort_index_preserve_levels(self, multiindex_dataframe_random_data): + frame = multiindex_dataframe_random_data + + result = frame.sort_index() + assert result.index.names == frame.index.names + + @pytest.mark.parametrize( + "gen,extra", + [ + ([1.0, 3.0, 2.0, 5.0], 4.0), + ([1, 3, 2, 5], 4), + ( + [ + Timestamp("20130101"), + Timestamp("20130103"), + Timestamp("20130102"), + Timestamp("20130105"), + ], + Timestamp("20130104"), + ), + (["1one", "3one", "2one", "5one"], "4one"), + ], + ) + def test_sort_index_multilevel_repr_8017(self, gen, extra): + data = np.random.default_rng(2).standard_normal((3, 4)) + + columns = MultiIndex.from_tuples([("red", i) for i in gen]) + df = DataFrame(data, index=list("def"), columns=columns) + df2 = pd.concat( + [ + df, + DataFrame( + "world", + index=list("def"), + columns=MultiIndex.from_tuples([("red", extra)]), + ), + ], + axis=1, + ) + + # check that the repr is good + # make sure that we have a correct sparsified repr + # e.g. only 1 header of read + assert str(df2).splitlines()[0].split() == ["red"] + + # GH 8017 + # sorting fails after columns added + + # construct single-dtype then sort + result = df.copy().sort_index(axis=1) + expected = df.iloc[:, [0, 2, 1, 3]] + tm.assert_frame_equal(result, expected) + + result = df2.sort_index(axis=1) + expected = df2.iloc[:, [0, 2, 1, 4, 3]] + tm.assert_frame_equal(result, expected) + + # setitem then sort + result = df.copy() + result[("red", extra)] = "world" + + result = result.sort_index(axis=1) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "categories", + [ + pytest.param(["a", "b", "c"], id="str"), + pytest.param( + [pd.Interval(0, 1), pd.Interval(1, 2), pd.Interval(2, 3)], + id="pd.Interval", + ), + ], + ) + def test_sort_index_with_categories(self, categories): + # GH#23452 + df = DataFrame( + {"foo": range(len(categories))}, + index=CategoricalIndex( + data=categories, categories=categories, ordered=True + ), + ) + df.index = df.index.reorder_categories(df.index.categories[::-1]) + result = df.sort_index() + expected = DataFrame( + {"foo": reversed(range(len(categories)))}, + index=CategoricalIndex( + data=categories[::-1], categories=categories[::-1], ordered=True + ), + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "ascending", + [ + None, + [True, None], + [False, "True"], + ], + ) + def test_sort_index_ascending_bad_value_raises(self, ascending): + # GH 39434 + df = DataFrame(np.arange(64)) + length = len(df.index) + df.index = [(i - length / 2) % length for i in range(length)] + match = 'For argument "ascending" expected type bool' + with pytest.raises(ValueError, match=match): + df.sort_index(axis=0, ascending=ascending, na_position="first") + + def test_sort_index_use_inf_as_na(self): + # GH 29687 + expected = DataFrame( + {"col1": [1, 2, 3], "col2": [3, 4, 5]}, + index=pd.date_range("2020", periods=3), + ) + msg = "use_inf_as_na option is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + with pd.option_context("mode.use_inf_as_na", True): + result = expected.sort_index() + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "ascending", + [(True, False), [True, False]], + ) + def test_sort_index_ascending_tuple(self, ascending): + df = DataFrame( + { + "legs": [4, 2, 4, 2, 2], + }, + index=MultiIndex.from_tuples( + [ + ("mammal", "dog"), + ("bird", "duck"), + ("mammal", "horse"), + ("bird", "penguin"), + ("mammal", "kangaroo"), + ], + names=["class", "animal"], + ), + ) + + # parameter `ascending`` is a tuple + result = df.sort_index(level=(0, 1), ascending=ascending) + + expected = DataFrame( + { + "legs": [2, 2, 2, 4, 4], + }, + index=MultiIndex.from_tuples( + [ + ("bird", "penguin"), + ("bird", "duck"), + ("mammal", "kangaroo"), + ("mammal", "horse"), + ("mammal", "dog"), + ], + names=["class", "animal"], + ), + ) + + tm.assert_frame_equal(result, expected) + + +class TestDataFrameSortIndexKey: + def test_sort_multi_index_key(self): + # GH 25775, testing that sorting by index works with a multi-index. + df = DataFrame( + {"a": [3, 1, 2], "b": [0, 0, 0], "c": [0, 1, 2], "d": list("abc")} + ).set_index(list("abc")) + + result = df.sort_index(level=list("ac"), key=lambda x: x) + + expected = DataFrame( + {"a": [1, 2, 3], "b": [0, 0, 0], "c": [1, 2, 0], "d": list("bca")} + ).set_index(list("abc")) + tm.assert_frame_equal(result, expected) + + result = df.sort_index(level=list("ac"), key=lambda x: -x) + expected = DataFrame( + {"a": [3, 2, 1], "b": [0, 0, 0], "c": [0, 2, 1], "d": list("acb")} + ).set_index(list("abc")) + + tm.assert_frame_equal(result, expected) + + def test_sort_index_key(self): # issue 27237 + df = DataFrame(np.arange(6, dtype="int64"), index=list("aaBBca")) + + result = df.sort_index() + expected = df.iloc[[2, 3, 0, 1, 5, 4]] + tm.assert_frame_equal(result, expected) + + result = df.sort_index(key=lambda x: x.str.lower()) + expected = df.iloc[[0, 1, 5, 2, 3, 4]] + tm.assert_frame_equal(result, expected) + + result = df.sort_index(key=lambda x: x.str.lower(), ascending=False) + expected = df.iloc[[4, 2, 3, 0, 1, 5]] + tm.assert_frame_equal(result, expected) + + def test_sort_index_key_int(self): + df = DataFrame(np.arange(6, dtype="int64"), index=np.arange(6, dtype="int64")) + + result = df.sort_index() + tm.assert_frame_equal(result, df) + + result = df.sort_index(key=lambda x: -x) + expected = df.sort_index(ascending=False) + tm.assert_frame_equal(result, expected) + + result = df.sort_index(key=lambda x: 2 * x) + tm.assert_frame_equal(result, df) + + def test_sort_multi_index_key_str(self): + # GH 25775, testing that sorting by index works with a multi-index. + df = DataFrame( + {"a": ["B", "a", "C"], "b": [0, 1, 0], "c": list("abc"), "d": [0, 1, 2]} + ).set_index(list("abc")) + + result = df.sort_index(level="a", key=lambda x: x.str.lower()) + + expected = DataFrame( + {"a": ["a", "B", "C"], "b": [1, 0, 0], "c": list("bac"), "d": [1, 0, 2]} + ).set_index(list("abc")) + tm.assert_frame_equal(result, expected) + + result = df.sort_index( + level=list("abc"), # can refer to names + key=lambda x: x.str.lower() if x.name in ["a", "c"] else -x, + ) + + expected = DataFrame( + {"a": ["a", "B", "C"], "b": [1, 0, 0], "c": list("bac"), "d": [1, 0, 2]} + ).set_index(list("abc")) + tm.assert_frame_equal(result, expected) + + def test_changes_length_raises(self): + df = DataFrame({"A": [1, 2, 3]}) + with pytest.raises(ValueError, match="change the shape"): + df.sort_index(key=lambda x: x[:1]) + + def test_sort_index_multiindex_sparse_column(self): + # GH 29735, testing that sort_index on a multiindexed frame with sparse + # columns fills with 0. + expected = DataFrame( + { + i: pd.array([0.0, 0.0, 0.0, 0.0], dtype=pd.SparseDtype("float64", 0.0)) + for i in range(4) + }, + index=MultiIndex.from_product([[1, 2], [1, 2]]), + ) + + result = expected.sort_index(level=0) + + tm.assert_frame_equal(result, expected) + + def test_sort_index_na_position(self): + # GH#51612 + df = DataFrame([1, 2], index=MultiIndex.from_tuples([(1, 1), (1, pd.NA)])) + expected = df.copy() + result = df.sort_index(level=[0, 1], na_position="last") + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("ascending", [True, False]) + def test_sort_index_multiindex_sort_remaining(self, ascending): + # GH #24247 + df = DataFrame( + {"A": [1, 2, 3, 4, 5], "B": [10, 20, 30, 40, 50]}, + index=MultiIndex.from_tuples( + [("a", "x"), ("a", "y"), ("b", "x"), ("b", "y"), ("c", "x")] + ), + ) + + result = df.sort_index(level=1, sort_remaining=False, ascending=ascending) + + if ascending: + expected = DataFrame( + {"A": [1, 3, 5, 2, 4], "B": [10, 30, 50, 20, 40]}, + index=MultiIndex.from_tuples( + [("a", "x"), ("b", "x"), ("c", "x"), ("a", "y"), ("b", "y")] + ), + ) + else: + expected = DataFrame( + {"A": [2, 4, 1, 3, 5], "B": [20, 40, 10, 30, 50]}, + index=MultiIndex.from_tuples( + [("a", "y"), ("b", "y"), ("a", "x"), ("b", "x"), ("c", "x")] + ), + ) + + tm.assert_frame_equal(result, expected) + + +def test_sort_index_with_sliced_multiindex(): + # GH 55379 + mi = MultiIndex.from_tuples( + [ + ("a", "10"), + ("a", "18"), + ("a", "25"), + ("b", "16"), + ("b", "26"), + ("a", "45"), + ("b", "28"), + ("a", "5"), + ("a", "50"), + ("a", "51"), + ("b", "4"), + ], + names=["group", "str"], + ) + + df = DataFrame({"x": range(len(mi))}, index=mi) + result = df.iloc[0:6].sort_index() + + expected = DataFrame( + {"x": [0, 1, 2, 5, 3, 4]}, + index=MultiIndex.from_tuples( + [ + ("a", "10"), + ("a", "18"), + ("a", "25"), + ("a", "45"), + ("b", "16"), + ("b", "26"), + ], + names=["group", "str"], + ), + ) + tm.assert_frame_equal(result, expected) + + +def test_axis_columns_ignore_index(): + # GH 56478 + df = DataFrame([[1, 2]], columns=["d", "c"]) + result = df.sort_index(axis="columns", ignore_index=True) + expected = DataFrame([[2, 1]]) + tm.assert_frame_equal(result, expected) + + +def test_sort_index_stable_sort(): + # GH 57151 + df = DataFrame( + data=[ + (Timestamp("2024-01-30 13:00:00"), 13.0), + (Timestamp("2024-01-30 13:00:00"), 13.1), + (Timestamp("2024-01-30 12:00:00"), 12.0), + (Timestamp("2024-01-30 12:00:00"), 12.1), + ], + columns=["dt", "value"], + ).set_index(["dt"]) + result = df.sort_index(level="dt", kind="stable") + expected = DataFrame( + data=[ + (Timestamp("2024-01-30 12:00:00"), 12.0), + (Timestamp("2024-01-30 12:00:00"), 12.1), + (Timestamp("2024-01-30 13:00:00"), 13.0), + (Timestamp("2024-01-30 13:00:00"), 13.1), + ], + columns=["dt", "value"], + ).set_index(["dt"]) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_sort_values.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_sort_values.py new file mode 100644 index 0000000000000000000000000000000000000000..f2f02058a534e782a1fe1bd302512897218c1a1d --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_sort_values.py @@ -0,0 +1,940 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Categorical, + DataFrame, + NaT, + Timestamp, + date_range, +) +import pandas._testing as tm +from pandas.util.version import Version + + +class TestDataFrameSortValues: + @pytest.mark.parametrize("dtype", [np.uint8, bool]) + def test_sort_values_sparse_no_warning(self, dtype): + # GH#45618 + ser = pd.Series(Categorical(["a", "b", "a"], categories=["a", "b", "c"])) + df = pd.get_dummies(ser, dtype=dtype, sparse=True) + + with tm.assert_produces_warning(None): + # No warnings about constructing Index from SparseArray + df.sort_values(by=df.columns.tolist()) + + def test_sort_values(self): + frame = DataFrame( + [[1, 1, 2], [3, 1, 0], [4, 5, 6]], index=[1, 2, 3], columns=list("ABC") + ) + + # by column (axis=0) + sorted_df = frame.sort_values(by="A") + indexer = frame["A"].argsort().values + expected = frame.loc[frame.index[indexer]] + tm.assert_frame_equal(sorted_df, expected) + + sorted_df = frame.sort_values(by="A", ascending=False) + indexer = indexer[::-1] + expected = frame.loc[frame.index[indexer]] + tm.assert_frame_equal(sorted_df, expected) + + sorted_df = frame.sort_values(by="A", ascending=False) + tm.assert_frame_equal(sorted_df, expected) + + # GH4839 + sorted_df = frame.sort_values(by=["A"], ascending=[False]) + tm.assert_frame_equal(sorted_df, expected) + + # multiple bys + sorted_df = frame.sort_values(by=["B", "C"]) + expected = frame.loc[[2, 1, 3]] + tm.assert_frame_equal(sorted_df, expected) + + sorted_df = frame.sort_values(by=["B", "C"], ascending=False) + tm.assert_frame_equal(sorted_df, expected[::-1]) + + sorted_df = frame.sort_values(by=["B", "A"], ascending=[True, False]) + tm.assert_frame_equal(sorted_df, expected) + + msg = "No axis named 2 for object type DataFrame" + with pytest.raises(ValueError, match=msg): + frame.sort_values(by=["A", "B"], axis=2, inplace=True) + + # by row (axis=1): GH#10806 + sorted_df = frame.sort_values(by=3, axis=1) + expected = frame + tm.assert_frame_equal(sorted_df, expected) + + sorted_df = frame.sort_values(by=3, axis=1, ascending=False) + expected = frame.reindex(columns=["C", "B", "A"]) + tm.assert_frame_equal(sorted_df, expected) + + sorted_df = frame.sort_values(by=[1, 2], axis="columns") + expected = frame.reindex(columns=["B", "A", "C"]) + tm.assert_frame_equal(sorted_df, expected) + + sorted_df = frame.sort_values(by=[1, 3], axis=1, ascending=[True, False]) + tm.assert_frame_equal(sorted_df, expected) + + sorted_df = frame.sort_values(by=[1, 3], axis=1, ascending=False) + expected = frame.reindex(columns=["C", "B", "A"]) + tm.assert_frame_equal(sorted_df, expected) + + msg = r"Length of ascending \(5\) != length of by \(2\)" + with pytest.raises(ValueError, match=msg): + frame.sort_values(by=["A", "B"], axis=0, ascending=[True] * 5) + + def test_sort_values_by_empty_list(self): + # https://github.com/pandas-dev/pandas/issues/40258 + expected = DataFrame({"a": [1, 4, 2, 5, 3, 6]}) + result = expected.sort_values(by=[]) + tm.assert_frame_equal(result, expected) + assert result is not expected + + def test_sort_values_inplace(self): + frame = DataFrame( + np.random.default_rng(2).standard_normal((4, 4)), + index=[1, 2, 3, 4], + columns=["A", "B", "C", "D"], + ) + + sorted_df = frame.copy() + return_value = sorted_df.sort_values(by="A", inplace=True) + assert return_value is None + expected = frame.sort_values(by="A") + tm.assert_frame_equal(sorted_df, expected) + + sorted_df = frame.copy() + return_value = sorted_df.sort_values(by=1, axis=1, inplace=True) + assert return_value is None + expected = frame.sort_values(by=1, axis=1) + tm.assert_frame_equal(sorted_df, expected) + + sorted_df = frame.copy() + return_value = sorted_df.sort_values(by="A", ascending=False, inplace=True) + assert return_value is None + expected = frame.sort_values(by="A", ascending=False) + tm.assert_frame_equal(sorted_df, expected) + + sorted_df = frame.copy() + return_value = sorted_df.sort_values( + by=["A", "B"], ascending=False, inplace=True + ) + assert return_value is None + expected = frame.sort_values(by=["A", "B"], ascending=False) + tm.assert_frame_equal(sorted_df, expected) + + def test_sort_values_multicolumn(self): + A = np.arange(5).repeat(20) + B = np.tile(np.arange(5), 20) + np.random.default_rng(2).shuffle(A) + np.random.default_rng(2).shuffle(B) + frame = DataFrame( + {"A": A, "B": B, "C": np.random.default_rng(2).standard_normal(100)} + ) + + result = frame.sort_values(by=["A", "B"]) + indexer = np.lexsort((frame["B"], frame["A"])) + expected = frame.take(indexer) + tm.assert_frame_equal(result, expected) + + result = frame.sort_values(by=["A", "B"], ascending=False) + indexer = np.lexsort( + (frame["B"].rank(ascending=False), frame["A"].rank(ascending=False)) + ) + expected = frame.take(indexer) + tm.assert_frame_equal(result, expected) + + result = frame.sort_values(by=["B", "A"]) + indexer = np.lexsort((frame["A"], frame["B"])) + expected = frame.take(indexer) + tm.assert_frame_equal(result, expected) + + def test_sort_values_multicolumn_uint64(self): + # GH#9918 + # uint64 multicolumn sort + + df = DataFrame( + { + "a": pd.Series([18446637057563306014, 1162265347240853609]), + "b": pd.Series([1, 2]), + } + ) + df["a"] = df["a"].astype(np.uint64) + result = df.sort_values(["a", "b"]) + + expected = DataFrame( + { + "a": pd.Series([18446637057563306014, 1162265347240853609]), + "b": pd.Series([1, 2]), + }, + index=pd.Index([1, 0]), + ) + + tm.assert_frame_equal(result, expected) + + def test_sort_values_nan(self): + # GH#3917 + df = DataFrame( + {"A": [1, 2, np.nan, 1, 6, 8, 4], "B": [9, np.nan, 5, 2, 5, 4, 5]} + ) + + # sort one column only + expected = DataFrame( + {"A": [np.nan, 1, 1, 2, 4, 6, 8], "B": [5, 9, 2, np.nan, 5, 5, 4]}, + index=[2, 0, 3, 1, 6, 4, 5], + ) + sorted_df = df.sort_values(["A"], na_position="first") + tm.assert_frame_equal(sorted_df, expected) + + expected = DataFrame( + {"A": [np.nan, 8, 6, 4, 2, 1, 1], "B": [5, 4, 5, 5, np.nan, 9, 2]}, + index=[2, 5, 4, 6, 1, 0, 3], + ) + sorted_df = df.sort_values(["A"], na_position="first", ascending=False) + tm.assert_frame_equal(sorted_df, expected) + + expected = df.reindex(columns=["B", "A"]) + sorted_df = df.sort_values(by=1, axis=1, na_position="first") + tm.assert_frame_equal(sorted_df, expected) + + # na_position='last', order + expected = DataFrame( + {"A": [1, 1, 2, 4, 6, 8, np.nan], "B": [2, 9, np.nan, 5, 5, 4, 5]}, + index=[3, 0, 1, 6, 4, 5, 2], + ) + sorted_df = df.sort_values(["A", "B"]) + tm.assert_frame_equal(sorted_df, expected) + + # na_position='first', order + expected = DataFrame( + {"A": [np.nan, 1, 1, 2, 4, 6, 8], "B": [5, 2, 9, np.nan, 5, 5, 4]}, + index=[2, 3, 0, 1, 6, 4, 5], + ) + sorted_df = df.sort_values(["A", "B"], na_position="first") + tm.assert_frame_equal(sorted_df, expected) + + # na_position='first', not order + expected = DataFrame( + {"A": [np.nan, 1, 1, 2, 4, 6, 8], "B": [5, 9, 2, np.nan, 5, 5, 4]}, + index=[2, 0, 3, 1, 6, 4, 5], + ) + sorted_df = df.sort_values(["A", "B"], ascending=[1, 0], na_position="first") + tm.assert_frame_equal(sorted_df, expected) + + # na_position='last', not order + expected = DataFrame( + {"A": [8, 6, 4, 2, 1, 1, np.nan], "B": [4, 5, 5, np.nan, 2, 9, 5]}, + index=[5, 4, 6, 1, 3, 0, 2], + ) + sorted_df = df.sort_values(["A", "B"], ascending=[0, 1], na_position="last") + tm.assert_frame_equal(sorted_df, expected) + + def test_sort_values_stable_descending_sort(self): + # GH#6399 + df = DataFrame( + [[2, "first"], [2, "second"], [1, "a"], [1, "b"]], + columns=["sort_col", "order"], + ) + sorted_df = df.sort_values(by="sort_col", kind="mergesort", ascending=False) + tm.assert_frame_equal(df, sorted_df) + + @pytest.mark.parametrize( + "expected_idx_non_na, ascending", + [ + [ + [3, 4, 5, 0, 1, 8, 6, 9, 7, 10, 13, 14], + [True, True], + ], + [ + [0, 3, 4, 5, 1, 8, 6, 7, 10, 13, 14, 9], + [True, False], + ], + [ + [9, 7, 10, 13, 14, 6, 8, 1, 3, 4, 5, 0], + [False, True], + ], + [ + [7, 10, 13, 14, 9, 6, 8, 1, 0, 3, 4, 5], + [False, False], + ], + ], + ) + @pytest.mark.parametrize("na_position", ["first", "last"]) + def test_sort_values_stable_multicolumn_sort( + self, expected_idx_non_na, ascending, na_position + ): + # GH#38426 Clarify sort_values with mult. columns / labels is stable + df = DataFrame( + { + "A": [1, 2, np.nan, 1, 1, 1, 6, 8, 4, 8, 8, np.nan, np.nan, 8, 8], + "B": [9, np.nan, 5, 2, 2, 2, 5, 4, 5, 3, 4, np.nan, np.nan, 4, 4], + } + ) + # All rows with NaN in col "B" only have unique values in "A", therefore, + # only the rows with NaNs in "A" have to be treated individually: + expected_idx = ( + [11, 12, 2] + expected_idx_non_na + if na_position == "first" + else expected_idx_non_na + [2, 11, 12] + ) + expected = df.take(expected_idx) + sorted_df = df.sort_values( + ["A", "B"], ascending=ascending, na_position=na_position + ) + tm.assert_frame_equal(sorted_df, expected) + + def test_sort_values_stable_categorial(self): + # GH#16793 + df = DataFrame({"x": Categorical(np.repeat([1, 2, 3, 4], 5), ordered=True)}) + expected = df.copy() + sorted_df = df.sort_values("x", kind="mergesort") + tm.assert_frame_equal(sorted_df, expected) + + def test_sort_values_datetimes(self): + # GH#3461, argsort / lexsort differences for a datetime column + df = DataFrame( + ["a", "a", "a", "b", "c", "d", "e", "f", "g"], + columns=["A"], + index=date_range("20130101", periods=9), + ) + dts = [ + Timestamp(x) + for x in [ + "2004-02-11", + "2004-01-21", + "2004-01-26", + "2005-09-20", + "2010-10-04", + "2009-05-12", + "2008-11-12", + "2010-09-28", + "2010-09-28", + ] + ] + df["B"] = dts[::2] + dts[1::2] + df["C"] = 2.0 + df["A1"] = 3.0 + + df1 = df.sort_values(by="A") + df2 = df.sort_values(by=["A"]) + tm.assert_frame_equal(df1, df2) + + df1 = df.sort_values(by="B") + df2 = df.sort_values(by=["B"]) + tm.assert_frame_equal(df1, df2) + + df1 = df.sort_values(by="B") + + df2 = df.sort_values(by=["C", "B"]) + tm.assert_frame_equal(df1, df2) + + def test_sort_values_frame_column_inplace_sort_exception( + self, float_frame, using_copy_on_write + ): + s = float_frame["A"] + float_frame_orig = float_frame.copy() + if using_copy_on_write: + # INFO(CoW) Series is a new object, so can be changed inplace + # without modifying original datafame + s.sort_values(inplace=True) + tm.assert_series_equal(s, float_frame_orig["A"].sort_values()) + # column in dataframe is not changed + tm.assert_frame_equal(float_frame, float_frame_orig) + else: + with pytest.raises(ValueError, match="This Series is a view"): + s.sort_values(inplace=True) + + cp = s.copy() + cp.sort_values() # it works! + + def test_sort_values_nat_values_in_int_column(self): + # GH#14922: "sorting with large float and multiple columns incorrect" + + # cause was that the int64 value NaT was considered as "na". Which is + # only correct for datetime64 columns. + + int_values = (2, int(NaT._value)) + float_values = (2.0, -1.797693e308) + + df = DataFrame( + {"int": int_values, "float": float_values}, columns=["int", "float"] + ) + + df_reversed = DataFrame( + {"int": int_values[::-1], "float": float_values[::-1]}, + columns=["int", "float"], + index=[1, 0], + ) + + # NaT is not a "na" for int64 columns, so na_position must not + # influence the result: + df_sorted = df.sort_values(["int", "float"], na_position="last") + tm.assert_frame_equal(df_sorted, df_reversed) + + df_sorted = df.sort_values(["int", "float"], na_position="first") + tm.assert_frame_equal(df_sorted, df_reversed) + + # reverse sorting order + df_sorted = df.sort_values(["int", "float"], ascending=False) + tm.assert_frame_equal(df_sorted, df) + + # and now check if NaT is still considered as "na" for datetime64 + # columns: + df = DataFrame( + {"datetime": [Timestamp("2016-01-01"), NaT], "float": float_values}, + columns=["datetime", "float"], + ) + + df_reversed = DataFrame( + {"datetime": [NaT, Timestamp("2016-01-01")], "float": float_values[::-1]}, + columns=["datetime", "float"], + index=[1, 0], + ) + + df_sorted = df.sort_values(["datetime", "float"], na_position="first") + tm.assert_frame_equal(df_sorted, df_reversed) + + df_sorted = df.sort_values(["datetime", "float"], na_position="last") + tm.assert_frame_equal(df_sorted, df) + + # Ascending should not affect the results. + df_sorted = df.sort_values(["datetime", "float"], ascending=False) + tm.assert_frame_equal(df_sorted, df) + + def test_sort_nat(self): + # GH 16836 + + d1 = [Timestamp(x) for x in ["2016-01-01", "2015-01-01", np.nan, "2016-01-01"]] + d2 = [ + Timestamp(x) + for x in ["2017-01-01", "2014-01-01", "2016-01-01", "2015-01-01"] + ] + df = DataFrame({"a": d1, "b": d2}, index=[0, 1, 2, 3]) + + d3 = [Timestamp(x) for x in ["2015-01-01", "2016-01-01", "2016-01-01", np.nan]] + d4 = [ + Timestamp(x) + for x in ["2014-01-01", "2015-01-01", "2017-01-01", "2016-01-01"] + ] + expected = DataFrame({"a": d3, "b": d4}, index=[1, 3, 0, 2]) + sorted_df = df.sort_values(by=["a", "b"]) + tm.assert_frame_equal(sorted_df, expected) + + def test_sort_values_na_position_with_categories(self): + # GH#22556 + # Positioning missing value properly when column is Categorical. + categories = ["A", "B", "C"] + category_indices = [0, 2, 4] + list_of_nans = [np.nan, np.nan] + na_indices = [1, 3] + na_position_first = "first" + na_position_last = "last" + column_name = "c" + + reversed_categories = sorted(categories, reverse=True) + reversed_category_indices = sorted(category_indices, reverse=True) + reversed_na_indices = sorted(na_indices) + + df = DataFrame( + { + column_name: Categorical( + ["A", np.nan, "B", np.nan, "C"], categories=categories, ordered=True + ) + } + ) + # sort ascending with na first + result = df.sort_values( + by=column_name, ascending=True, na_position=na_position_first + ) + expected = DataFrame( + { + column_name: Categorical( + list_of_nans + categories, categories=categories, ordered=True + ) + }, + index=na_indices + category_indices, + ) + + tm.assert_frame_equal(result, expected) + + # sort ascending with na last + result = df.sort_values( + by=column_name, ascending=True, na_position=na_position_last + ) + expected = DataFrame( + { + column_name: Categorical( + categories + list_of_nans, categories=categories, ordered=True + ) + }, + index=category_indices + na_indices, + ) + + tm.assert_frame_equal(result, expected) + + # sort descending with na first + result = df.sort_values( + by=column_name, ascending=False, na_position=na_position_first + ) + expected = DataFrame( + { + column_name: Categorical( + list_of_nans + reversed_categories, + categories=categories, + ordered=True, + ) + }, + index=reversed_na_indices + reversed_category_indices, + ) + + tm.assert_frame_equal(result, expected) + + # sort descending with na last + result = df.sort_values( + by=column_name, ascending=False, na_position=na_position_last + ) + expected = DataFrame( + { + column_name: Categorical( + reversed_categories + list_of_nans, + categories=categories, + ordered=True, + ) + }, + index=reversed_category_indices + reversed_na_indices, + ) + + tm.assert_frame_equal(result, expected) + + def test_sort_values_nat(self): + # GH#16836 + + d1 = [Timestamp(x) for x in ["2016-01-01", "2015-01-01", np.nan, "2016-01-01"]] + d2 = [ + Timestamp(x) + for x in ["2017-01-01", "2014-01-01", "2016-01-01", "2015-01-01"] + ] + df = DataFrame({"a": d1, "b": d2}, index=[0, 1, 2, 3]) + + d3 = [Timestamp(x) for x in ["2015-01-01", "2016-01-01", "2016-01-01", np.nan]] + d4 = [ + Timestamp(x) + for x in ["2014-01-01", "2015-01-01", "2017-01-01", "2016-01-01"] + ] + expected = DataFrame({"a": d3, "b": d4}, index=[1, 3, 0, 2]) + sorted_df = df.sort_values(by=["a", "b"]) + tm.assert_frame_equal(sorted_df, expected) + + def test_sort_values_na_position_with_categories_raises(self): + df = DataFrame( + { + "c": Categorical( + ["A", np.nan, "B", np.nan, "C"], + categories=["A", "B", "C"], + ordered=True, + ) + } + ) + + with pytest.raises(ValueError, match="invalid na_position: bad_position"): + df.sort_values(by="c", ascending=False, na_position="bad_position") + + @pytest.mark.parametrize("inplace", [True, False]) + @pytest.mark.parametrize( + "original_dict, sorted_dict, ignore_index, output_index", + [ + ({"A": [1, 2, 3]}, {"A": [3, 2, 1]}, True, [0, 1, 2]), + ({"A": [1, 2, 3]}, {"A": [3, 2, 1]}, False, [2, 1, 0]), + ( + {"A": [1, 2, 3], "B": [2, 3, 4]}, + {"A": [3, 2, 1], "B": [4, 3, 2]}, + True, + [0, 1, 2], + ), + ( + {"A": [1, 2, 3], "B": [2, 3, 4]}, + {"A": [3, 2, 1], "B": [4, 3, 2]}, + False, + [2, 1, 0], + ), + ], + ) + def test_sort_values_ignore_index( + self, inplace, original_dict, sorted_dict, ignore_index, output_index + ): + # GH 30114 + df = DataFrame(original_dict) + expected = DataFrame(sorted_dict, index=output_index) + kwargs = {"ignore_index": ignore_index, "inplace": inplace} + + if inplace: + result_df = df.copy() + result_df.sort_values("A", ascending=False, **kwargs) + else: + result_df = df.sort_values("A", ascending=False, **kwargs) + + tm.assert_frame_equal(result_df, expected) + tm.assert_frame_equal(df, DataFrame(original_dict)) + + def test_sort_values_nat_na_position_default(self): + # GH 13230 + expected = DataFrame( + { + "A": [1, 2, 3, 4, 4], + "date": pd.DatetimeIndex( + [ + "2010-01-01 09:00:00", + "2010-01-01 09:00:01", + "2010-01-01 09:00:02", + "2010-01-01 09:00:03", + "NaT", + ] + ), + } + ) + result = expected.sort_values(["A", "date"]) + tm.assert_frame_equal(result, expected) + + def test_sort_values_item_cache(self, using_array_manager, using_copy_on_write): + # previous behavior incorrect retained an invalid _item_cache entry + df = DataFrame( + np.random.default_rng(2).standard_normal((4, 3)), columns=["A", "B", "C"] + ) + df["D"] = df["A"] * 2 + ser = df["A"] + if not using_array_manager: + assert len(df._mgr.blocks) == 2 + + df.sort_values(by="A") + + if using_copy_on_write: + ser.iloc[0] = 99 + assert df.iloc[0, 0] == df["A"][0] + assert df.iloc[0, 0] != 99 + else: + ser.values[0] = 99 + assert df.iloc[0, 0] == df["A"][0] + assert df.iloc[0, 0] == 99 + + def test_sort_values_reshaping(self): + # GH 39426 + values = list(range(21)) + expected = DataFrame([values], columns=values) + df = expected.sort_values(expected.index[0], axis=1, ignore_index=True) + + tm.assert_frame_equal(df, expected) + + def test_sort_values_no_by_inplace(self): + # GH#50643 + df = DataFrame({"a": [1, 2, 3]}) + expected = df.copy() + result = df.sort_values(by=[], inplace=True) + tm.assert_frame_equal(df, expected) + assert result is None + + def test_sort_values_no_op_reset_index(self): + # GH#52553 + df = DataFrame({"A": [10, 20], "B": [1, 5]}, index=[2, 3]) + result = df.sort_values(by="A", ignore_index=True) + expected = DataFrame({"A": [10, 20], "B": [1, 5]}) + tm.assert_frame_equal(result, expected) + + +class TestDataFrameSortKey: # test key sorting (issue 27237) + def test_sort_values_inplace_key(self, sort_by_key): + frame = DataFrame( + np.random.default_rng(2).standard_normal((4, 4)), + index=[1, 2, 3, 4], + columns=["A", "B", "C", "D"], + ) + + sorted_df = frame.copy() + return_value = sorted_df.sort_values(by="A", inplace=True, key=sort_by_key) + assert return_value is None + expected = frame.sort_values(by="A", key=sort_by_key) + tm.assert_frame_equal(sorted_df, expected) + + sorted_df = frame.copy() + return_value = sorted_df.sort_values( + by=1, axis=1, inplace=True, key=sort_by_key + ) + assert return_value is None + expected = frame.sort_values(by=1, axis=1, key=sort_by_key) + tm.assert_frame_equal(sorted_df, expected) + + sorted_df = frame.copy() + return_value = sorted_df.sort_values( + by="A", ascending=False, inplace=True, key=sort_by_key + ) + assert return_value is None + expected = frame.sort_values(by="A", ascending=False, key=sort_by_key) + tm.assert_frame_equal(sorted_df, expected) + + sorted_df = frame.copy() + sorted_df.sort_values( + by=["A", "B"], ascending=False, inplace=True, key=sort_by_key + ) + expected = frame.sort_values(by=["A", "B"], ascending=False, key=sort_by_key) + tm.assert_frame_equal(sorted_df, expected) + + def test_sort_values_key(self): + df = DataFrame(np.array([0, 5, np.nan, 3, 2, np.nan])) + + result = df.sort_values(0) + expected = df.iloc[[0, 4, 3, 1, 2, 5]] + tm.assert_frame_equal(result, expected) + + result = df.sort_values(0, key=lambda x: x + 5) + expected = df.iloc[[0, 4, 3, 1, 2, 5]] + tm.assert_frame_equal(result, expected) + + result = df.sort_values(0, key=lambda x: -x, ascending=False) + expected = df.iloc[[0, 4, 3, 1, 2, 5]] + tm.assert_frame_equal(result, expected) + + def test_sort_values_by_key(self): + df = DataFrame( + { + "a": np.array([0, 3, np.nan, 3, 2, np.nan]), + "b": np.array([0, 2, np.nan, 5, 2, np.nan]), + } + ) + + result = df.sort_values("a", key=lambda x: -x) + expected = df.iloc[[1, 3, 4, 0, 2, 5]] + tm.assert_frame_equal(result, expected) + + result = df.sort_values(by=["a", "b"], key=lambda x: -x) + expected = df.iloc[[3, 1, 4, 0, 2, 5]] + tm.assert_frame_equal(result, expected) + + result = df.sort_values(by=["a", "b"], key=lambda x: -x, ascending=False) + expected = df.iloc[[0, 4, 1, 3, 2, 5]] + tm.assert_frame_equal(result, expected) + + def test_sort_values_by_key_by_name(self): + df = DataFrame( + { + "a": np.array([0, 3, np.nan, 3, 2, np.nan]), + "b": np.array([0, 2, np.nan, 5, 2, np.nan]), + } + ) + + def key(col): + if col.name == "a": + return -col + else: + return col + + result = df.sort_values(by="a", key=key) + expected = df.iloc[[1, 3, 4, 0, 2, 5]] + tm.assert_frame_equal(result, expected) + + result = df.sort_values(by=["a"], key=key) + expected = df.iloc[[1, 3, 4, 0, 2, 5]] + tm.assert_frame_equal(result, expected) + + result = df.sort_values(by="b", key=key) + expected = df.iloc[[0, 1, 4, 3, 2, 5]] + tm.assert_frame_equal(result, expected) + + result = df.sort_values(by=["a", "b"], key=key) + expected = df.iloc[[1, 3, 4, 0, 2, 5]] + tm.assert_frame_equal(result, expected) + + def test_sort_values_key_string(self): + df = DataFrame(np.array([["hello", "goodbye"], ["hello", "Hello"]])) + + result = df.sort_values(1) + expected = df[::-1] + tm.assert_frame_equal(result, expected) + + result = df.sort_values([0, 1], key=lambda col: col.str.lower()) + tm.assert_frame_equal(result, df) + + result = df.sort_values( + [0, 1], key=lambda col: col.str.lower(), ascending=False + ) + expected = df.sort_values(1, key=lambda col: col.str.lower(), ascending=False) + tm.assert_frame_equal(result, expected) + + def test_sort_values_key_empty(self, sort_by_key): + df = DataFrame(np.array([])) + + df.sort_values(0, key=sort_by_key) + df.sort_index(key=sort_by_key) + + def test_changes_length_raises(self): + df = DataFrame({"A": [1, 2, 3]}) + with pytest.raises(ValueError, match="change the shape"): + df.sort_values("A", key=lambda x: x[:1]) + + def test_sort_values_key_axes(self): + df = DataFrame({0: ["Hello", "goodbye"], 1: [0, 1]}) + + result = df.sort_values(0, key=lambda col: col.str.lower()) + expected = df[::-1] + tm.assert_frame_equal(result, expected) + + result = df.sort_values(1, key=lambda col: -col) + expected = df[::-1] + tm.assert_frame_equal(result, expected) + + def test_sort_values_key_dict_axis(self): + df = DataFrame({0: ["Hello", 0], 1: ["goodbye", 1]}) + + result = df.sort_values(0, key=lambda col: col.str.lower(), axis=1) + expected = df.loc[:, ::-1] + tm.assert_frame_equal(result, expected) + + result = df.sort_values(1, key=lambda col: -col, axis=1) + expected = df.loc[:, ::-1] + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("ordered", [True, False]) + def test_sort_values_key_casts_to_categorical(self, ordered): + # https://github.com/pandas-dev/pandas/issues/36383 + categories = ["c", "b", "a"] + df = DataFrame({"x": [1, 1, 1], "y": ["a", "b", "c"]}) + + def sorter(key): + if key.name == "y": + return pd.Series( + Categorical(key, categories=categories, ordered=ordered) + ) + return key + + result = df.sort_values(by=["x", "y"], key=sorter) + expected = DataFrame( + {"x": [1, 1, 1], "y": ["c", "b", "a"]}, index=pd.Index([2, 1, 0]) + ) + + tm.assert_frame_equal(result, expected) + + +@pytest.fixture +def df_none(): + return DataFrame( + { + "outer": ["a", "a", "a", "b", "b", "b"], + "inner": [1, 2, 2, 2, 1, 1], + "A": np.arange(6, 0, -1), + ("B", 5): ["one", "one", "two", "two", "one", "one"], + } + ) + + +@pytest.fixture(params=[["outer"], ["outer", "inner"]]) +def df_idx(request, df_none): + levels = request.param + return df_none.set_index(levels) + + +@pytest.fixture( + params=[ + "inner", # index level + ["outer"], # list of index level + "A", # column + [("B", 5)], # list of column + ["inner", "outer"], # two index levels + [("B", 5), "outer"], # index level and column + ["A", ("B", 5)], # Two columns + ["inner", "outer"], # two index levels and column + ] +) +def sort_names(request): + return request.param + + +@pytest.fixture(params=[True, False]) +def ascending(request): + return request.param + + +class TestSortValuesLevelAsStr: + def test_sort_index_level_and_column_label( + self, df_none, df_idx, sort_names, ascending, request + ): + # GH#14353 + if ( + Version(np.__version__) >= Version("1.25") + and request.node.callspec.id == "df_idx0-inner-True" + ): + request.applymarker( + pytest.mark.xfail( + reason=( + "pandas default unstable sorting of duplicates" + "issue with numpy>=1.25 with AVX instructions" + ), + strict=False, + ) + ) + + # Get index levels from df_idx + levels = df_idx.index.names + + # Compute expected by sorting on columns and the setting index + expected = df_none.sort_values( + by=sort_names, ascending=ascending, axis=0 + ).set_index(levels) + + # Compute result sorting on mix on columns and index levels + result = df_idx.sort_values(by=sort_names, ascending=ascending, axis=0) + + tm.assert_frame_equal(result, expected) + + def test_sort_column_level_and_index_label( + self, df_none, df_idx, sort_names, ascending, request + ): + # GH#14353 + + # Get levels from df_idx + levels = df_idx.index.names + + # Compute expected by sorting on axis=0, setting index levels, and then + # transposing. For some cases this will result in a frame with + # multiple column levels + expected = ( + df_none.sort_values(by=sort_names, ascending=ascending, axis=0) + .set_index(levels) + .T + ) + + # Compute result by transposing and sorting on axis=1. + result = df_idx.T.sort_values(by=sort_names, ascending=ascending, axis=1) + + if Version(np.__version__) >= Version("1.25"): + request.applymarker( + pytest.mark.xfail( + reason=( + "pandas default unstable sorting of duplicates" + "issue with numpy>=1.25 with AVX instructions" + ), + strict=False, + ) + ) + + tm.assert_frame_equal(result, expected) + + def test_sort_values_validate_ascending_for_value_error(self): + # GH41634 + df = DataFrame({"D": [23, 7, 21]}) + + msg = 'For argument "ascending" expected type bool, received type str.' + with pytest.raises(ValueError, match=msg): + df.sort_values(by="D", ascending="False") + + @pytest.mark.parametrize("ascending", [False, 0, 1, True]) + def test_sort_values_validate_ascending_functional(self, ascending): + df = DataFrame({"D": [23, 7, 21]}) + indexer = df["D"].argsort().values + + if not ascending: + indexer = indexer[::-1] + + expected = df.loc[df.index[indexer]] + result = df.sort_values(by="D", ascending=ascending) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_swapaxes.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_swapaxes.py new file mode 100644 index 0000000000000000000000000000000000000000..53a4691d48b1c7027e6e05c2050f4aa0eca4b3b4 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_swapaxes.py @@ -0,0 +1,37 @@ +import numpy as np +import pytest + +from pandas import DataFrame +import pandas._testing as tm + + +class TestSwapAxes: + def test_swapaxes(self): + df = DataFrame(np.random.default_rng(2).standard_normal((10, 5))) + msg = "'DataFrame.swapaxes' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + tm.assert_frame_equal(df.T, df.swapaxes(0, 1)) + tm.assert_frame_equal(df.T, df.swapaxes(1, 0)) + + def test_swapaxes_noop(self): + df = DataFrame(np.random.default_rng(2).standard_normal((10, 5))) + msg = "'DataFrame.swapaxes' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + tm.assert_frame_equal(df, df.swapaxes(0, 0)) + + def test_swapaxes_invalid_axis(self): + df = DataFrame(np.random.default_rng(2).standard_normal((10, 5))) + msg = "'DataFrame.swapaxes' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + msg = "No axis named 2 for object type DataFrame" + with pytest.raises(ValueError, match=msg): + df.swapaxes(2, 5) + + def test_round_empty_not_input(self): + # GH#51032 + df = DataFrame({"a": [1, 2]}) + msg = "'DataFrame.swapaxes' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.swapaxes("index", "index") + tm.assert_frame_equal(df, result) + assert df is not result diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_swaplevel.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_swaplevel.py new file mode 100644 index 0000000000000000000000000000000000000000..5511ac7d6b1b209ba00a7414671aa7e61d403898 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_swaplevel.py @@ -0,0 +1,36 @@ +import pytest + +from pandas import DataFrame +import pandas._testing as tm + + +class TestSwaplevel: + def test_swaplevel(self, multiindex_dataframe_random_data): + frame = multiindex_dataframe_random_data + + swapped = frame["A"].swaplevel() + swapped2 = frame["A"].swaplevel(0) + swapped3 = frame["A"].swaplevel(0, 1) + swapped4 = frame["A"].swaplevel("first", "second") + assert not swapped.index.equals(frame.index) + tm.assert_series_equal(swapped, swapped2) + tm.assert_series_equal(swapped, swapped3) + tm.assert_series_equal(swapped, swapped4) + + back = swapped.swaplevel() + back2 = swapped.swaplevel(0) + back3 = swapped.swaplevel(0, 1) + back4 = swapped.swaplevel("second", "first") + assert back.index.equals(frame.index) + tm.assert_series_equal(back, back2) + tm.assert_series_equal(back, back3) + tm.assert_series_equal(back, back4) + + ft = frame.T + swapped = ft.swaplevel("first", "second", axis=1) + exp = frame.swaplevel("first", "second").T + tm.assert_frame_equal(swapped, exp) + + msg = "Can only swap levels on a hierarchical axis." + with pytest.raises(TypeError, match=msg): + DataFrame(range(3)).swaplevel() diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_to_csv.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_to_csv.py new file mode 100644 index 0000000000000000000000000000000000000000..3b6a54698b5b6ee6de55193eeacb974312362125 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_to_csv.py @@ -0,0 +1,1403 @@ +import csv +from io import StringIO +import os + +import numpy as np +import pytest + +from pandas.errors import ParserError + +import pandas as pd +from pandas import ( + DataFrame, + Index, + MultiIndex, + NaT, + Series, + Timestamp, + date_range, + period_range, + read_csv, + to_datetime, +) +import pandas._testing as tm +import pandas.core.common as com + +from pandas.io.common import get_handle + + +class TestDataFrameToCSV: + def read_csv(self, path, **kwargs): + params = {"index_col": 0} + params.update(**kwargs) + + return read_csv(path, **params) + + def test_to_csv_from_csv1(self, float_frame, datetime_frame): + with tm.ensure_clean("__tmp_to_csv_from_csv1__") as path: + float_frame.iloc[:5, float_frame.columns.get_loc("A")] = np.nan + + float_frame.to_csv(path) + float_frame.to_csv(path, columns=["A", "B"]) + float_frame.to_csv(path, header=False) + float_frame.to_csv(path, index=False) + + # test roundtrip + # freq does not roundtrip + datetime_frame.index = datetime_frame.index._with_freq(None) + datetime_frame.to_csv(path) + recons = self.read_csv(path, parse_dates=True) + tm.assert_frame_equal(datetime_frame, recons) + + datetime_frame.to_csv(path, index_label="index") + recons = self.read_csv(path, index_col=None, parse_dates=True) + + assert len(recons.columns) == len(datetime_frame.columns) + 1 + + # no index + datetime_frame.to_csv(path, index=False) + recons = self.read_csv(path, index_col=None, parse_dates=True) + tm.assert_almost_equal(datetime_frame.values, recons.values) + + # corner case + dm = DataFrame( + { + "s1": Series(range(3), index=np.arange(3, dtype=np.int64)), + "s2": Series(range(2), index=np.arange(2, dtype=np.int64)), + } + ) + dm.to_csv(path) + + recons = self.read_csv(path) + tm.assert_frame_equal(dm, recons) + + def test_to_csv_from_csv2(self, float_frame): + with tm.ensure_clean("__tmp_to_csv_from_csv2__") as path: + # duplicate index + df = DataFrame( + np.random.default_rng(2).standard_normal((3, 3)), + index=["a", "a", "b"], + columns=["x", "y", "z"], + ) + df.to_csv(path) + result = self.read_csv(path) + tm.assert_frame_equal(result, df) + + midx = MultiIndex.from_tuples([("A", 1, 2), ("A", 1, 2), ("B", 1, 2)]) + df = DataFrame( + np.random.default_rng(2).standard_normal((3, 3)), + index=midx, + columns=["x", "y", "z"], + ) + + df.to_csv(path) + result = self.read_csv(path, index_col=[0, 1, 2], parse_dates=False) + tm.assert_frame_equal(result, df, check_names=False) + + # column aliases + col_aliases = Index(["AA", "X", "Y", "Z"]) + float_frame.to_csv(path, header=col_aliases) + + rs = self.read_csv(path) + xp = float_frame.copy() + xp.columns = col_aliases + tm.assert_frame_equal(xp, rs) + + msg = "Writing 4 cols but got 2 aliases" + with pytest.raises(ValueError, match=msg): + float_frame.to_csv(path, header=["AA", "X"]) + + def test_to_csv_from_csv3(self): + with tm.ensure_clean("__tmp_to_csv_from_csv3__") as path: + df1 = DataFrame(np.random.default_rng(2).standard_normal((3, 1))) + df2 = DataFrame(np.random.default_rng(2).standard_normal((3, 1))) + + df1.to_csv(path) + df2.to_csv(path, mode="a", header=False) + xp = pd.concat([df1, df2]) + rs = read_csv(path, index_col=0) + rs.columns = [int(label) for label in rs.columns] + xp.columns = [int(label) for label in xp.columns] + tm.assert_frame_equal(xp, rs) + + def test_to_csv_from_csv4(self): + with tm.ensure_clean("__tmp_to_csv_from_csv4__") as path: + # GH 10833 (TimedeltaIndex formatting) + dt = pd.Timedelta(seconds=1) + df = DataFrame( + {"dt_data": [i * dt for i in range(3)]}, + index=Index([i * dt for i in range(3)], name="dt_index"), + ) + df.to_csv(path) + + result = read_csv(path, index_col="dt_index") + result.index = pd.to_timedelta(result.index) + result["dt_data"] = pd.to_timedelta(result["dt_data"]) + + tm.assert_frame_equal(df, result, check_index_type=True) + + def test_to_csv_from_csv5(self, timezone_frame): + # tz, 8260 + with tm.ensure_clean("__tmp_to_csv_from_csv5__") as path: + timezone_frame.to_csv(path) + result = read_csv(path, index_col=0, parse_dates=["A"]) + + converter = ( + lambda c: to_datetime(result[c]) + .dt.tz_convert("UTC") + .dt.tz_convert(timezone_frame[c].dt.tz) + ) + result["B"] = converter("B") + result["C"] = converter("C") + tm.assert_frame_equal(result, timezone_frame) + + def test_to_csv_cols_reordering(self): + # GH3454 + chunksize = 5 + N = int(chunksize * 2.5) + + df = DataFrame( + np.ones((N, 3)), + index=Index([f"i-{i}" for i in range(N)], name="a"), + columns=Index([f"i-{i}" for i in range(3)], name="a"), + ) + cs = df.columns + cols = [cs[2], cs[0]] + + with tm.ensure_clean() as path: + df.to_csv(path, columns=cols, chunksize=chunksize) + rs_c = read_csv(path, index_col=0) + + tm.assert_frame_equal(df[cols], rs_c, check_names=False) + + @pytest.mark.parametrize("cols", [None, ["b", "a"]]) + def test_to_csv_new_dupe_cols(self, cols): + chunksize = 5 + N = int(chunksize * 2.5) + + # dupe cols + df = DataFrame( + np.ones((N, 3)), + index=Index([f"i-{i}" for i in range(N)], name="a"), + columns=["a", "a", "b"], + ) + with tm.ensure_clean() as path: + df.to_csv(path, columns=cols, chunksize=chunksize) + rs_c = read_csv(path, index_col=0) + + # we wrote them in a different order + # so compare them in that order + if cols is not None: + if df.columns.is_unique: + rs_c.columns = cols + else: + indexer, missing = df.columns.get_indexer_non_unique(cols) + rs_c.columns = df.columns.take(indexer) + + for c in cols: + obj_df = df[c] + obj_rs = rs_c[c] + if isinstance(obj_df, Series): + tm.assert_series_equal(obj_df, obj_rs) + else: + tm.assert_frame_equal(obj_df, obj_rs, check_names=False) + + # wrote in the same order + else: + rs_c.columns = df.columns + tm.assert_frame_equal(df, rs_c, check_names=False) + + @pytest.mark.slow + def test_to_csv_dtnat(self): + # GH3437 + def make_dtnat_arr(n, nnat=None): + if nnat is None: + nnat = int(n * 0.1) # 10% + s = list(date_range("2000", freq="5min", periods=n)) + if nnat: + for i in np.random.default_rng(2).integers(0, len(s), nnat): + s[i] = NaT + i = np.random.default_rng(2).integers(100) + s[-i] = NaT + s[i] = NaT + return s + + chunksize = 1000 + s1 = make_dtnat_arr(chunksize + 5) + s2 = make_dtnat_arr(chunksize + 5, 0) + + with tm.ensure_clean("1.csv") as pth: + df = DataFrame({"a": s1, "b": s2}) + df.to_csv(pth, chunksize=chunksize) + + recons = self.read_csv(pth).apply(to_datetime) + tm.assert_frame_equal(df, recons, check_names=False) + + def _return_result_expected( + self, + df, + chunksize, + r_dtype=None, + c_dtype=None, + rnlvl=None, + cnlvl=None, + dupe_col=False, + ): + kwargs = {"parse_dates": False} + if cnlvl: + if rnlvl is not None: + kwargs["index_col"] = list(range(rnlvl)) + kwargs["header"] = list(range(cnlvl)) + + with tm.ensure_clean("__tmp_to_csv_moar__") as path: + df.to_csv(path, encoding="utf8", chunksize=chunksize) + recons = self.read_csv(path, **kwargs) + else: + kwargs["header"] = 0 + + with tm.ensure_clean("__tmp_to_csv_moar__") as path: + df.to_csv(path, encoding="utf8", chunksize=chunksize) + recons = self.read_csv(path, **kwargs) + + def _to_uni(x): + if not isinstance(x, str): + return x.decode("utf8") + return x + + if dupe_col: + # read_Csv disambiguates the columns by + # labeling them dupe.1,dupe.2, etc'. monkey patch columns + recons.columns = df.columns + if rnlvl and not cnlvl: + delta_lvl = [recons.iloc[:, i].values for i in range(rnlvl - 1)] + ix = MultiIndex.from_arrays([list(recons.index)] + delta_lvl) + recons.index = ix + recons = recons.iloc[:, rnlvl - 1 :] + + type_map = {"i": "i", "f": "f", "s": "O", "u": "O", "dt": "O", "p": "O"} + if r_dtype: + if r_dtype == "u": # unicode + r_dtype = "O" + recons.index = np.array( + [_to_uni(label) for label in recons.index], dtype=r_dtype + ) + df.index = np.array( + [_to_uni(label) for label in df.index], dtype=r_dtype + ) + elif r_dtype == "dt": # unicode + r_dtype = "O" + recons.index = np.array( + [Timestamp(label) for label in recons.index], dtype=r_dtype + ) + df.index = np.array( + [Timestamp(label) for label in df.index], dtype=r_dtype + ) + elif r_dtype == "p": + r_dtype = "O" + idx_list = to_datetime(recons.index) + recons.index = np.array( + [Timestamp(label) for label in idx_list], dtype=r_dtype + ) + df.index = np.array( + list(map(Timestamp, df.index.to_timestamp())), dtype=r_dtype + ) + else: + r_dtype = type_map.get(r_dtype) + recons.index = np.array(recons.index, dtype=r_dtype) + df.index = np.array(df.index, dtype=r_dtype) + if c_dtype: + if c_dtype == "u": + c_dtype = "O" + recons.columns = np.array( + [_to_uni(label) for label in recons.columns], dtype=c_dtype + ) + df.columns = np.array( + [_to_uni(label) for label in df.columns], dtype=c_dtype + ) + elif c_dtype == "dt": + c_dtype = "O" + recons.columns = np.array( + [Timestamp(label) for label in recons.columns], dtype=c_dtype + ) + df.columns = np.array( + [Timestamp(label) for label in df.columns], dtype=c_dtype + ) + elif c_dtype == "p": + c_dtype = "O" + col_list = to_datetime(recons.columns) + recons.columns = np.array( + [Timestamp(label) for label in col_list], dtype=c_dtype + ) + col_list = df.columns.to_timestamp() + df.columns = np.array( + [Timestamp(label) for label in col_list], dtype=c_dtype + ) + else: + c_dtype = type_map.get(c_dtype) + recons.columns = np.array(recons.columns, dtype=c_dtype) + df.columns = np.array(df.columns, dtype=c_dtype) + return df, recons + + @pytest.mark.slow + @pytest.mark.parametrize( + "nrows", [2, 10, 99, 100, 101, 102, 198, 199, 200, 201, 202, 249, 250, 251] + ) + def test_to_csv_nrows(self, nrows): + df = DataFrame( + np.ones((nrows, 4)), + index=date_range("2020-01-01", periods=nrows), + columns=Index(list("abcd"), dtype=object), + ) + result, expected = self._return_result_expected(df, 1000, "dt", "s") + tm.assert_frame_equal(result, expected, check_names=False) + + @pytest.mark.slow + @pytest.mark.parametrize( + "nrows", [2, 10, 99, 100, 101, 102, 198, 199, 200, 201, 202, 249, 250, 251] + ) + @pytest.mark.parametrize( + "r_idx_type, c_idx_type", [("i", "i"), ("s", "s"), ("s", "dt"), ("p", "p")] + ) + @pytest.mark.parametrize("ncols", [1, 2, 3, 4]) + @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning") + def test_to_csv_idx_types(self, nrows, r_idx_type, c_idx_type, ncols): + axes = { + "i": lambda n: Index(np.arange(n), dtype=np.int64), + "s": lambda n: Index([f"{i}_{chr(i)}" for i in range(97, 97 + n)]), + "dt": lambda n: date_range("2020-01-01", periods=n), + "p": lambda n: period_range("2020-01-01", periods=n, freq="D"), + } + df = DataFrame( + np.ones((nrows, ncols)), + index=axes[r_idx_type](nrows), + columns=axes[c_idx_type](ncols), + ) + result, expected = self._return_result_expected( + df, + 1000, + r_idx_type, + c_idx_type, + ) + tm.assert_frame_equal(result, expected, check_names=False) + + @pytest.mark.slow + @pytest.mark.parametrize( + "nrows", [10, 98, 99, 100, 101, 102, 198, 199, 200, 201, 202, 249, 250, 251] + ) + @pytest.mark.parametrize("ncols", [1, 2, 3, 4]) + def test_to_csv_idx_ncols(self, nrows, ncols): + df = DataFrame( + np.ones((nrows, ncols)), + index=Index([f"i-{i}" for i in range(nrows)], name="a"), + columns=Index([f"i-{i}" for i in range(ncols)], name="a"), + ) + result, expected = self._return_result_expected(df, 1000) + tm.assert_frame_equal(result, expected, check_names=False) + + @pytest.mark.slow + @pytest.mark.parametrize("nrows", [10, 98, 99, 100, 101, 102]) + def test_to_csv_dup_cols(self, nrows): + df = DataFrame( + np.ones((nrows, 3)), + index=Index([f"i-{i}" for i in range(nrows)], name="a"), + columns=Index([f"i-{i}" for i in range(3)], name="a"), + ) + + cols = list(df.columns) + cols[:2] = ["dupe", "dupe"] + cols[-2:] = ["dupe", "dupe"] + ix = list(df.index) + ix[:2] = ["rdupe", "rdupe"] + ix[-2:] = ["rdupe", "rdupe"] + df.index = ix + df.columns = cols + result, expected = self._return_result_expected(df, 1000, dupe_col=True) + tm.assert_frame_equal(result, expected, check_names=False) + + @pytest.mark.slow + def test_to_csv_empty(self): + df = DataFrame(index=np.arange(10, dtype=np.int64)) + result, expected = self._return_result_expected(df, 1000) + tm.assert_frame_equal(result, expected, check_column_type=False) + + @pytest.mark.slow + def test_to_csv_chunksize(self): + chunksize = 1000 + rows = chunksize // 2 + 1 + df = DataFrame( + np.ones((rows, 2)), + columns=Index(list("ab")), + index=MultiIndex.from_arrays([range(rows) for _ in range(2)]), + ) + result, expected = self._return_result_expected(df, chunksize, rnlvl=2) + tm.assert_frame_equal(result, expected, check_names=False) + + @pytest.mark.slow + @pytest.mark.parametrize( + "nrows", [2, 10, 99, 100, 101, 102, 198, 199, 200, 201, 202, 249, 250, 251] + ) + @pytest.mark.parametrize("ncols", [2, 3, 4]) + @pytest.mark.parametrize( + "df_params, func_params", + [ + [{"r_idx_nlevels": 2}, {"rnlvl": 2}], + [{"c_idx_nlevels": 2}, {"cnlvl": 2}], + [{"r_idx_nlevels": 2, "c_idx_nlevels": 2}, {"rnlvl": 2, "cnlvl": 2}], + ], + ) + def test_to_csv_params(self, nrows, df_params, func_params, ncols): + if df_params.get("r_idx_nlevels"): + index = MultiIndex.from_arrays( + [f"i-{i}" for i in range(nrows)] + for _ in range(df_params["r_idx_nlevels"]) + ) + else: + index = None + + if df_params.get("c_idx_nlevels"): + columns = MultiIndex.from_arrays( + [f"i-{i}" for i in range(ncols)] + for _ in range(df_params["c_idx_nlevels"]) + ) + else: + columns = Index([f"i-{i}" for i in range(ncols)]) + df = DataFrame(np.ones((nrows, ncols)), index=index, columns=columns) + result, expected = self._return_result_expected(df, 1000, **func_params) + tm.assert_frame_equal(result, expected, check_names=False) + + def test_to_csv_from_csv_w_some_infs(self, float_frame): + # test roundtrip with inf, -inf, nan, as full columns and mix + float_frame["G"] = np.nan + f = lambda x: [np.inf, np.nan][np.random.default_rng(2).random() < 0.5] + float_frame["h"] = float_frame.index.map(f) + + with tm.ensure_clean() as path: + float_frame.to_csv(path) + recons = self.read_csv(path) + + tm.assert_frame_equal(float_frame, recons) + tm.assert_frame_equal(np.isinf(float_frame), np.isinf(recons)) + + def test_to_csv_from_csv_w_all_infs(self, float_frame): + # test roundtrip with inf, -inf, nan, as full columns and mix + float_frame["E"] = np.inf + float_frame["F"] = -np.inf + + with tm.ensure_clean() as path: + float_frame.to_csv(path) + recons = self.read_csv(path) + + tm.assert_frame_equal(float_frame, recons) + tm.assert_frame_equal(np.isinf(float_frame), np.isinf(recons)) + + def test_to_csv_no_index(self): + # GH 3624, after appending columns, to_csv fails + with tm.ensure_clean("__tmp_to_csv_no_index__") as path: + df = DataFrame({"c1": [1, 2, 3], "c2": [4, 5, 6]}) + df.to_csv(path, index=False) + result = read_csv(path) + tm.assert_frame_equal(df, result) + df["c3"] = Series([7, 8, 9], dtype="int64") + df.to_csv(path, index=False) + result = read_csv(path) + tm.assert_frame_equal(df, result) + + def test_to_csv_with_mix_columns(self): + # gh-11637: incorrect output when a mix of integer and string column + # names passed as columns parameter in to_csv + + df = DataFrame({0: ["a", "b", "c"], 1: ["aa", "bb", "cc"]}) + df["test"] = "txt" + assert df.to_csv() == df.to_csv(columns=[0, 1, "test"]) + + def test_to_csv_headers(self): + # GH6186, the presence or absence of `index` incorrectly + # causes to_csv to have different header semantics. + from_df = DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) + to_df = DataFrame([[1, 2], [3, 4]], columns=["X", "Y"]) + with tm.ensure_clean("__tmp_to_csv_headers__") as path: + from_df.to_csv(path, header=["X", "Y"]) + recons = self.read_csv(path) + + tm.assert_frame_equal(to_df, recons) + + from_df.to_csv(path, index=False, header=["X", "Y"]) + recons = self.read_csv(path) + + return_value = recons.reset_index(inplace=True) + assert return_value is None + tm.assert_frame_equal(to_df, recons) + + def test_to_csv_multiindex(self, float_frame, datetime_frame): + frame = float_frame + old_index = frame.index + arrays = np.arange(len(old_index) * 2, dtype=np.int64).reshape(2, -1) + new_index = MultiIndex.from_arrays(arrays, names=["first", "second"]) + frame.index = new_index + + with tm.ensure_clean("__tmp_to_csv_multiindex__") as path: + frame.to_csv(path, header=False) + frame.to_csv(path, columns=["A", "B"]) + + # round trip + frame.to_csv(path) + + df = self.read_csv(path, index_col=[0, 1], parse_dates=False) + + # TODO to_csv drops column name + tm.assert_frame_equal(frame, df, check_names=False) + assert frame.index.names == df.index.names + + # needed if setUp becomes a class method + float_frame.index = old_index + + # try multiindex with dates + tsframe = datetime_frame + old_index = tsframe.index + new_index = [old_index, np.arange(len(old_index), dtype=np.int64)] + tsframe.index = MultiIndex.from_arrays(new_index) + + tsframe.to_csv(path, index_label=["time", "foo"]) + with tm.assert_produces_warning( + UserWarning, match="Could not infer format" + ): + recons = self.read_csv(path, index_col=[0, 1], parse_dates=True) + + # TODO to_csv drops column name + tm.assert_frame_equal(tsframe, recons, check_names=False) + + # do not load index + tsframe.to_csv(path) + recons = self.read_csv(path, index_col=None) + assert len(recons.columns) == len(tsframe.columns) + 2 + + # no index + tsframe.to_csv(path, index=False) + recons = self.read_csv(path, index_col=None) + tm.assert_almost_equal(recons.values, datetime_frame.values) + + # needed if setUp becomes class method + datetime_frame.index = old_index + + with tm.ensure_clean("__tmp_to_csv_multiindex__") as path: + # GH3571, GH1651, GH3141 + + def _make_frame(names=None): + if names is True: + names = ["first", "second"] + return DataFrame( + np.random.default_rng(2).integers(0, 10, size=(3, 3)), + columns=MultiIndex.from_tuples( + [("bah", "foo"), ("bah", "bar"), ("ban", "baz")], names=names + ), + dtype="int64", + ) + + # column & index are multi-index + df = DataFrame( + np.ones((5, 3)), + columns=MultiIndex.from_arrays( + [[f"i-{i}" for i in range(3)] for _ in range(4)], names=list("abcd") + ), + index=MultiIndex.from_arrays( + [[f"i-{i}" for i in range(5)] for _ in range(2)], names=list("ab") + ), + ) + df.to_csv(path) + result = read_csv(path, header=[0, 1, 2, 3], index_col=[0, 1]) + tm.assert_frame_equal(df, result) + + # column is mi + df = DataFrame( + np.ones((5, 3)), + columns=MultiIndex.from_arrays( + [[f"i-{i}" for i in range(3)] for _ in range(4)], names=list("abcd") + ), + ) + df.to_csv(path) + result = read_csv(path, header=[0, 1, 2, 3], index_col=0) + tm.assert_frame_equal(df, result) + + # dup column names? + df = DataFrame( + np.ones((5, 3)), + columns=MultiIndex.from_arrays( + [[f"i-{i}" for i in range(3)] for _ in range(4)], names=list("abcd") + ), + index=MultiIndex.from_arrays( + [[f"i-{i}" for i in range(5)] for _ in range(3)], names=list("abc") + ), + ) + df.to_csv(path) + result = read_csv(path, header=[0, 1, 2, 3], index_col=[0, 1, 2]) + tm.assert_frame_equal(df, result) + + # writing with no index + df = _make_frame() + df.to_csv(path, index=False) + result = read_csv(path, header=[0, 1]) + tm.assert_frame_equal(df, result) + + # we lose the names here + df = _make_frame(True) + df.to_csv(path, index=False) + result = read_csv(path, header=[0, 1]) + assert com.all_none(*result.columns.names) + result.columns.names = df.columns.names + tm.assert_frame_equal(df, result) + + # whatsnew example + df = _make_frame() + df.to_csv(path) + result = read_csv(path, header=[0, 1], index_col=[0]) + tm.assert_frame_equal(df, result) + + df = _make_frame(True) + df.to_csv(path) + result = read_csv(path, header=[0, 1], index_col=[0]) + tm.assert_frame_equal(df, result) + + # invalid options + df = _make_frame(True) + df.to_csv(path) + + for i in [6, 7]: + msg = f"len of {i}, but only 5 lines in file" + with pytest.raises(ParserError, match=msg): + read_csv(path, header=list(range(i)), index_col=0) + + # write with cols + msg = "cannot specify cols with a MultiIndex" + with pytest.raises(TypeError, match=msg): + df.to_csv(path, columns=["foo", "bar"]) + + with tm.ensure_clean("__tmp_to_csv_multiindex__") as path: + # empty + tsframe[:0].to_csv(path) + recons = self.read_csv(path) + + exp = tsframe[:0] + exp.index = [] + + tm.assert_index_equal(recons.columns, exp.columns) + assert len(recons) == 0 + + def test_to_csv_interval_index(self, using_infer_string): + # GH 28210 + df = DataFrame({"A": list("abc"), "B": range(3)}, index=pd.interval_range(0, 3)) + + with tm.ensure_clean("__tmp_to_csv_interval_index__.csv") as path: + df.to_csv(path) + result = self.read_csv(path, index_col=0) + + # can't roundtrip intervalindex via read_csv so check string repr (GH 23595) + expected = df.copy() + expected.index = expected.index.astype("str") + + tm.assert_frame_equal(result, expected) + + def test_to_csv_float32_nanrep(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((1, 4)).astype(np.float32) + ) + df[1] = np.nan + + with tm.ensure_clean("__tmp_to_csv_float32_nanrep__.csv") as path: + df.to_csv(path, na_rep=999) + + with open(path, encoding="utf-8") as f: + lines = f.readlines() + assert lines[1].split(",")[2] == "999" + + def test_to_csv_withcommas(self): + # Commas inside fields should be correctly escaped when saving as CSV. + df = DataFrame({"A": [1, 2, 3], "B": ["5,6", "7,8", "9,0"]}) + + with tm.ensure_clean("__tmp_to_csv_withcommas__.csv") as path: + df.to_csv(path) + df2 = self.read_csv(path) + tm.assert_frame_equal(df2, df) + + def test_to_csv_mixed(self): + def create_cols(name): + return [f"{name}{i:03d}" for i in range(5)] + + df_float = DataFrame( + np.random.default_rng(2).standard_normal((100, 5)), + dtype="float64", + columns=create_cols("float"), + ) + df_int = DataFrame( + np.random.default_rng(2).standard_normal((100, 5)).astype("int64"), + dtype="int64", + columns=create_cols("int"), + ) + df_bool = DataFrame(True, index=df_float.index, columns=create_cols("bool")) + df_object = DataFrame( + "foo", index=df_float.index, columns=create_cols("object"), dtype="object" + ) + df_dt = DataFrame( + Timestamp("20010101").as_unit("ns"), + index=df_float.index, + columns=create_cols("date"), + ) + + # add in some nans + df_float.iloc[30:50, 1:3] = np.nan + df_dt.iloc[30:50, 1:3] = np.nan + + df = pd.concat([df_float, df_int, df_bool, df_object, df_dt], axis=1) + + # dtype + dtypes = {} + for n, dtype in [ + ("float", np.float64), + ("int", np.int64), + ("bool", np.bool_), + ("object", object), + ]: + for c in create_cols(n): + dtypes[c] = dtype + + with tm.ensure_clean() as filename: + df.to_csv(filename) + rs = read_csv( + filename, index_col=0, dtype=dtypes, parse_dates=create_cols("date") + ) + tm.assert_frame_equal(rs, df) + + def test_to_csv_dups_cols(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((1000, 30)), + columns=list(range(15)) + list(range(15)), + dtype="float64", + ) + + with tm.ensure_clean() as filename: + df.to_csv(filename) # single dtype, fine + result = read_csv(filename, index_col=0) + result.columns = df.columns + tm.assert_frame_equal(result, df) + + df_float = DataFrame( + np.random.default_rng(2).standard_normal((1000, 3)), dtype="float64" + ) + df_int = DataFrame(np.random.default_rng(2).standard_normal((1000, 3))).astype( + "int64" + ) + df_bool = DataFrame(True, index=df_float.index, columns=range(3)) + df_object = DataFrame("foo", index=df_float.index, columns=range(3)) + df_dt = DataFrame( + Timestamp("20010101").as_unit("ns"), index=df_float.index, columns=range(3) + ) + df = pd.concat( + [df_float, df_int, df_bool, df_object, df_dt], axis=1, ignore_index=True + ) + + df.columns = [0, 1, 2] * 5 + + with tm.ensure_clean() as filename: + df.to_csv(filename) + result = read_csv(filename, index_col=0) + + # date cols + for i in ["0.4", "1.4", "2.4"]: + result[i] = to_datetime(result[i]) + + result.columns = df.columns + tm.assert_frame_equal(result, df) + + def test_to_csv_dups_cols2(self): + # GH3457 + df = DataFrame( + np.ones((5, 3)), + index=Index([f"i-{i}" for i in range(5)], name="foo"), + columns=Index(["a", "a", "b"]), + ) + + with tm.ensure_clean() as filename: + df.to_csv(filename) + + # read_csv will rename the dups columns + result = read_csv(filename, index_col=0) + result = result.rename(columns={"a.1": "a"}) + tm.assert_frame_equal(result, df) + + @pytest.mark.parametrize("chunksize", [10000, 50000, 100000]) + def test_to_csv_chunking(self, chunksize): + aa = DataFrame({"A": range(100000)}) + aa["B"] = aa.A + 1.0 + aa["C"] = aa.A + 2.0 + aa["D"] = aa.A + 3.0 + + with tm.ensure_clean() as filename: + aa.to_csv(filename, chunksize=chunksize) + rs = read_csv(filename, index_col=0) + tm.assert_frame_equal(rs, aa) + + @pytest.mark.slow + def test_to_csv_wide_frame_formatting(self, monkeypatch): + # Issue #8621 + chunksize = 100 + df = DataFrame( + np.random.default_rng(2).standard_normal((1, chunksize + 10)), + columns=None, + index=None, + ) + with tm.ensure_clean() as filename: + with monkeypatch.context() as m: + m.setattr("pandas.io.formats.csvs._DEFAULT_CHUNKSIZE_CELLS", chunksize) + df.to_csv(filename, header=False, index=False) + rs = read_csv(filename, header=None) + tm.assert_frame_equal(rs, df) + + def test_to_csv_bug(self): + f1 = StringIO("a,1.0\nb,2.0") + df = self.read_csv(f1, header=None) + newdf = DataFrame({"t": df[df.columns[0]]}) + + with tm.ensure_clean() as path: + newdf.to_csv(path) + + recons = read_csv(path, index_col=0) + # don't check_names as t != 1 + tm.assert_frame_equal(recons, newdf, check_names=False) + + def test_to_csv_unicode(self): + df = DataFrame({"c/\u03c3": [1, 2, 3]}) + with tm.ensure_clean() as path: + df.to_csv(path, encoding="UTF-8") + df2 = read_csv(path, index_col=0, encoding="UTF-8") + tm.assert_frame_equal(df, df2) + + df.to_csv(path, encoding="UTF-8", index=False) + df2 = read_csv(path, index_col=None, encoding="UTF-8") + tm.assert_frame_equal(df, df2) + + def test_to_csv_unicode_index_col(self): + buf = StringIO("") + df = DataFrame( + [["\u05d0", "d2", "d3", "d4"], ["a1", "a2", "a3", "a4"]], + columns=["\u05d0", "\u05d1", "\u05d2", "\u05d3"], + index=["\u05d0", "\u05d1"], + ) + + df.to_csv(buf, encoding="UTF-8") + buf.seek(0) + + df2 = read_csv(buf, index_col=0, encoding="UTF-8") + tm.assert_frame_equal(df, df2) + + def test_to_csv_stringio(self, float_frame): + buf = StringIO() + float_frame.to_csv(buf) + buf.seek(0) + recons = read_csv(buf, index_col=0) + tm.assert_frame_equal(recons, float_frame) + + def test_to_csv_float_format(self): + df = DataFrame( + [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]], + index=["A", "B"], + columns=["X", "Y", "Z"], + ) + + with tm.ensure_clean() as filename: + df.to_csv(filename, float_format="%.2f") + + rs = read_csv(filename, index_col=0) + xp = DataFrame( + [[0.12, 0.23, 0.57], [12.32, 123123.20, 321321.20]], + index=["A", "B"], + columns=["X", "Y", "Z"], + ) + tm.assert_frame_equal(rs, xp) + + def test_to_csv_float_format_over_decimal(self): + # GH#47436 + df = DataFrame({"a": [0.5, 1.0]}) + result = df.to_csv( + decimal=",", + float_format=lambda x: np.format_float_positional(x, trim="-"), + index=False, + ) + expected_rows = ["a", "0.5", "1"] + expected = tm.convert_rows_list_to_csv_str(expected_rows) + assert result == expected + + def test_to_csv_unicodewriter_quoting(self): + df = DataFrame({"A": [1, 2, 3], "B": ["foo", "bar", "baz"]}) + + buf = StringIO() + df.to_csv(buf, index=False, quoting=csv.QUOTE_NONNUMERIC, encoding="utf-8") + + result = buf.getvalue() + expected_rows = ['"A","B"', '1,"foo"', '2,"bar"', '3,"baz"'] + expected = tm.convert_rows_list_to_csv_str(expected_rows) + assert result == expected + + @pytest.mark.parametrize("encoding", [None, "utf-8"]) + def test_to_csv_quote_none(self, encoding): + # GH4328 + df = DataFrame({"A": ["hello", '{"hello"}']}) + buf = StringIO() + df.to_csv(buf, quoting=csv.QUOTE_NONE, encoding=encoding, index=False) + + result = buf.getvalue() + expected_rows = ["A", "hello", '{"hello"}'] + expected = tm.convert_rows_list_to_csv_str(expected_rows) + assert result == expected + + def test_to_csv_index_no_leading_comma(self): + df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}, index=["one", "two", "three"]) + + buf = StringIO() + df.to_csv(buf, index_label=False) + + expected_rows = ["A,B", "one,1,4", "two,2,5", "three,3,6"] + expected = tm.convert_rows_list_to_csv_str(expected_rows) + assert buf.getvalue() == expected + + def test_to_csv_lineterminators(self): + # see gh-20353 + df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}, index=["one", "two", "three"]) + + with tm.ensure_clean() as path: + # case 1: CRLF as line terminator + df.to_csv(path, lineterminator="\r\n") + expected = b",A,B\r\none,1,4\r\ntwo,2,5\r\nthree,3,6\r\n" + + with open(path, mode="rb") as f: + assert f.read() == expected + + with tm.ensure_clean() as path: + # case 2: LF as line terminator + df.to_csv(path, lineterminator="\n") + expected = b",A,B\none,1,4\ntwo,2,5\nthree,3,6\n" + + with open(path, mode="rb") as f: + assert f.read() == expected + + with tm.ensure_clean() as path: + # case 3: The default line terminator(=os.linesep)(gh-21406) + df.to_csv(path) + os_linesep = os.linesep.encode("utf-8") + expected = ( + b",A,B" + + os_linesep + + b"one,1,4" + + os_linesep + + b"two,2,5" + + os_linesep + + b"three,3,6" + + os_linesep + ) + + with open(path, mode="rb") as f: + assert f.read() == expected + + def test_to_csv_from_csv_categorical(self): + # CSV with categoricals should result in the same output + # as when one would add a "normal" Series/DataFrame. + s = Series(pd.Categorical(["a", "b", "b", "a", "a", "c", "c", "c"])) + s2 = Series(["a", "b", "b", "a", "a", "c", "c", "c"]) + res = StringIO() + + s.to_csv(res, header=False) + exp = StringIO() + + s2.to_csv(exp, header=False) + assert res.getvalue() == exp.getvalue() + + df = DataFrame({"s": s}) + df2 = DataFrame({"s": s2}) + + res = StringIO() + df.to_csv(res) + + exp = StringIO() + df2.to_csv(exp) + + assert res.getvalue() == exp.getvalue() + + def test_to_csv_path_is_none(self, float_frame): + # GH 8215 + # Make sure we return string for consistency with + # Series.to_csv() + csv_str = float_frame.to_csv(path_or_buf=None) + assert isinstance(csv_str, str) + recons = read_csv(StringIO(csv_str), index_col=0) + tm.assert_frame_equal(float_frame, recons) + + @pytest.mark.parametrize( + "df,encoding", + [ + ( + DataFrame( + [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]], + index=["A", "B"], + columns=["X", "Y", "Z"], + ), + None, + ), + # GH 21241, 21118 + (DataFrame([["abc", "def", "ghi"]], columns=["X", "Y", "Z"]), "ascii"), + (DataFrame(5 * [[123, "你好", "世界"]], columns=["X", "Y", "Z"]), "gb2312"), + ( + DataFrame( + 5 * [[123, "Γειά σου", "Κόσμε"]], # noqa: RUF001 + columns=["X", "Y", "Z"], + ), + "cp737", + ), + ], + ) + def test_to_csv_compression(self, df, encoding, compression): + with tm.ensure_clean() as filename: + df.to_csv(filename, compression=compression, encoding=encoding) + # test the round trip - to_csv -> read_csv + result = read_csv( + filename, compression=compression, index_col=0, encoding=encoding + ) + tm.assert_frame_equal(df, result) + + # test the round trip using file handle - to_csv -> read_csv + with get_handle( + filename, "w", compression=compression, encoding=encoding + ) as handles: + df.to_csv(handles.handle, encoding=encoding) + assert not handles.handle.closed + + result = read_csv( + filename, + compression=compression, + encoding=encoding, + index_col=0, + ).squeeze("columns") + tm.assert_frame_equal(df, result) + + # explicitly make sure file is compressed + with tm.decompress_file(filename, compression) as fh: + text = fh.read().decode(encoding or "utf8") + for col in df.columns: + assert col in text + + with tm.decompress_file(filename, compression) as fh: + tm.assert_frame_equal(df, read_csv(fh, index_col=0, encoding=encoding)) + + def test_to_csv_date_format(self, datetime_frame): + with tm.ensure_clean("__tmp_to_csv_date_format__") as path: + dt_index = datetime_frame.index + datetime_frame = DataFrame( + {"A": dt_index, "B": dt_index.shift(1)}, index=dt_index + ) + datetime_frame.to_csv(path, date_format="%Y%m%d") + + # Check that the data was put in the specified format + test = read_csv(path, index_col=0) + + datetime_frame_int = datetime_frame.map(lambda x: int(x.strftime("%Y%m%d"))) + datetime_frame_int.index = datetime_frame_int.index.map( + lambda x: int(x.strftime("%Y%m%d")) + ) + + tm.assert_frame_equal(test, datetime_frame_int) + + datetime_frame.to_csv(path, date_format="%Y-%m-%d") + + # Check that the data was put in the specified format + test = read_csv(path, index_col=0) + datetime_frame_str = datetime_frame.map(lambda x: x.strftime("%Y-%m-%d")) + datetime_frame_str.index = datetime_frame_str.index.map( + lambda x: x.strftime("%Y-%m-%d") + ) + + tm.assert_frame_equal(test, datetime_frame_str) + + # Check that columns get converted + datetime_frame_columns = datetime_frame.T + datetime_frame_columns.to_csv(path, date_format="%Y%m%d") + + test = read_csv(path, index_col=0) + + datetime_frame_columns = datetime_frame_columns.map( + lambda x: int(x.strftime("%Y%m%d")) + ) + # Columns don't get converted to ints by read_csv + datetime_frame_columns.columns = datetime_frame_columns.columns.map( + lambda x: x.strftime("%Y%m%d") + ) + + tm.assert_frame_equal(test, datetime_frame_columns) + + # test NaTs + nat_index = to_datetime( + ["NaT"] * 10 + ["2000-01-01", "2000-01-01", "2000-01-01"] + ) + nat_frame = DataFrame({"A": nat_index}, index=nat_index) + nat_frame.to_csv(path, date_format="%Y-%m-%d") + + test = read_csv(path, parse_dates=[0, 1], index_col=0) + + tm.assert_frame_equal(test, nat_frame) + + @pytest.mark.parametrize("td", [pd.Timedelta(0), pd.Timedelta("10s")]) + def test_to_csv_with_dst_transitions(self, td): + with tm.ensure_clean("csv_date_format_with_dst") as path: + # make sure we are not failing on transitions + times = date_range( + "2013-10-26 23:00", + "2013-10-27 01:00", + tz="Europe/London", + freq="h", + ambiguous="infer", + ) + i = times + td + i = i._with_freq(None) # freq is not preserved by read_csv + time_range = np.array(range(len(i)), dtype="int64") + df = DataFrame({"A": time_range}, index=i) + df.to_csv(path, index=True) + # we have to reconvert the index as we + # don't parse the tz's + result = read_csv(path, index_col=0) + result.index = to_datetime(result.index, utc=True).tz_convert( + "Europe/London" + ) + tm.assert_frame_equal(result, df) + + def test_to_csv_with_dst_transitions_with_pickle(self): + # GH11619 + idx = date_range("2015-01-01", "2015-12-31", freq="h", tz="Europe/Paris") + idx = idx._with_freq(None) # freq does not round-trip + idx._data._freq = None # otherwise there is trouble on unpickle + df = DataFrame({"values": 1, "idx": idx}, index=idx) + with tm.ensure_clean("csv_date_format_with_dst") as path: + df.to_csv(path, index=True) + result = read_csv(path, index_col=0) + result.index = to_datetime(result.index, utc=True).tz_convert( + "Europe/Paris" + ) + result["idx"] = to_datetime(result["idx"], utc=True).astype( + "datetime64[ns, Europe/Paris]" + ) + tm.assert_frame_equal(result, df) + + # assert working + df.astype(str) + + with tm.ensure_clean("csv_date_format_with_dst") as path: + df.to_pickle(path) + result = pd.read_pickle(path) + tm.assert_frame_equal(result, df) + + def test_to_csv_quoting(self): + df = DataFrame( + { + "c_bool": [True, False], + "c_float": [1.0, 3.2], + "c_int": [42, np.nan], + "c_string": ["a", "b,c"], + } + ) + + expected_rows = [ + ",c_bool,c_float,c_int,c_string", + "0,True,1.0,42.0,a", + '1,False,3.2,,"b,c"', + ] + expected = tm.convert_rows_list_to_csv_str(expected_rows) + + result = df.to_csv() + assert result == expected + + result = df.to_csv(quoting=None) + assert result == expected + + expected_rows = [ + ",c_bool,c_float,c_int,c_string", + "0,True,1.0,42.0,a", + '1,False,3.2,,"b,c"', + ] + expected = tm.convert_rows_list_to_csv_str(expected_rows) + + result = df.to_csv(quoting=csv.QUOTE_MINIMAL) + assert result == expected + + expected_rows = [ + '"","c_bool","c_float","c_int","c_string"', + '"0","True","1.0","42.0","a"', + '"1","False","3.2","","b,c"', + ] + expected = tm.convert_rows_list_to_csv_str(expected_rows) + + result = df.to_csv(quoting=csv.QUOTE_ALL) + assert result == expected + + # see gh-12922, gh-13259: make sure changes to + # the formatters do not break this behaviour + expected_rows = [ + '"","c_bool","c_float","c_int","c_string"', + '0,True,1.0,42.0,"a"', + '1,False,3.2,"","b,c"', + ] + expected = tm.convert_rows_list_to_csv_str(expected_rows) + result = df.to_csv(quoting=csv.QUOTE_NONNUMERIC) + assert result == expected + + msg = "need to escape, but no escapechar set" + with pytest.raises(csv.Error, match=msg): + df.to_csv(quoting=csv.QUOTE_NONE) + + with pytest.raises(csv.Error, match=msg): + df.to_csv(quoting=csv.QUOTE_NONE, escapechar=None) + + expected_rows = [ + ",c_bool,c_float,c_int,c_string", + "0,True,1.0,42.0,a", + "1,False,3.2,,b!,c", + ] + expected = tm.convert_rows_list_to_csv_str(expected_rows) + result = df.to_csv(quoting=csv.QUOTE_NONE, escapechar="!") + assert result == expected + + expected_rows = [ + ",c_bool,c_ffloat,c_int,c_string", + "0,True,1.0,42.0,a", + "1,False,3.2,,bf,c", + ] + expected = tm.convert_rows_list_to_csv_str(expected_rows) + result = df.to_csv(quoting=csv.QUOTE_NONE, escapechar="f") + assert result == expected + + # see gh-3503: quoting Windows line terminators + # presents with encoding? + text_rows = ["a,b,c", '1,"test \r\n",3'] + text = tm.convert_rows_list_to_csv_str(text_rows) + df = read_csv(StringIO(text)) + + buf = StringIO() + df.to_csv(buf, encoding="utf-8", index=False) + assert buf.getvalue() == text + + # xref gh-7791: make sure the quoting parameter is passed through + # with multi-indexes + df = DataFrame({"a": [1, 2], "b": [3, 4], "c": [5, 6]}) + df = df.set_index(["a", "b"]) + + expected_rows = ['"a","b","c"', '"1","3","5"', '"2","4","6"'] + expected = tm.convert_rows_list_to_csv_str(expected_rows) + assert df.to_csv(quoting=csv.QUOTE_ALL) == expected + + def test_period_index_date_overflow(self): + # see gh-15982 + + dates = ["1990-01-01", "2000-01-01", "3005-01-01"] + index = pd.PeriodIndex(dates, freq="D") + + df = DataFrame([4, 5, 6], index=index) + result = df.to_csv() + + expected_rows = [",0", "1990-01-01,4", "2000-01-01,5", "3005-01-01,6"] + expected = tm.convert_rows_list_to_csv_str(expected_rows) + assert result == expected + + date_format = "%m-%d-%Y" + result = df.to_csv(date_format=date_format) + + expected_rows = [",0", "01-01-1990,4", "01-01-2000,5", "01-01-3005,6"] + expected = tm.convert_rows_list_to_csv_str(expected_rows) + assert result == expected + + # Overflow with pd.NaT + dates = ["1990-01-01", NaT, "3005-01-01"] + index = pd.PeriodIndex(dates, freq="D") + + df = DataFrame([4, 5, 6], index=index) + result = df.to_csv() + + expected_rows = [",0", "1990-01-01,4", ",5", "3005-01-01,6"] + expected = tm.convert_rows_list_to_csv_str(expected_rows) + assert result == expected + + def test_multi_index_header(self): + # see gh-5539 + columns = MultiIndex.from_tuples([("a", 1), ("a", 2), ("b", 1), ("b", 2)]) + df = DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]]) + df.columns = columns + + header = ["a", "b", "c", "d"] + result = df.to_csv(header=header) + + expected_rows = [",a,b,c,d", "0,1,2,3,4", "1,5,6,7,8"] + expected = tm.convert_rows_list_to_csv_str(expected_rows) + assert result == expected + + def test_to_csv_single_level_multi_index(self): + # see gh-26303 + index = Index([(1,), (2,), (3,)]) + df = DataFrame([[1, 2, 3]], columns=index) + df = df.reindex(columns=[(1,), (3,)]) + expected = ",1,3\n0,1,3\n" + result = df.to_csv(lineterminator="\n") + tm.assert_almost_equal(result, expected) + + def test_gz_lineend(self): + # GH 25311 + df = DataFrame({"a": [1, 2]}) + expected_rows = ["a", "1", "2"] + expected = tm.convert_rows_list_to_csv_str(expected_rows) + with tm.ensure_clean("__test_gz_lineend.csv.gz") as path: + df.to_csv(path, index=False) + with tm.decompress_file(path, compression="gzip") as f: + result = f.read().decode("utf-8") + + assert result == expected + + def test_to_csv_numpy_16_bug(self): + frame = DataFrame({"a": date_range("1/1/2000", periods=10)}) + + buf = StringIO() + frame.to_csv(buf) + + result = buf.getvalue() + assert "2000-01-01" in result + + def test_to_csv_na_quoting(self): + # GH 15891 + # Normalize carriage return for Windows OS + result = ( + DataFrame([None, None]) + .to_csv(None, header=False, index=False, na_rep="") + .replace("\r\n", "\n") + ) + expected = '""\n""\n' + assert result == expected + + def test_to_csv_categorical_and_ea(self): + # GH#46812 + df = DataFrame({"a": "x", "b": [1, pd.NA]}) + df["b"] = df["b"].astype("Int16") + df["b"] = df["b"].astype("category") + result = df.to_csv() + expected_rows = [",a,b", "0,x,1", "1,x,"] + expected = tm.convert_rows_list_to_csv_str(expected_rows) + assert result == expected + + def test_to_csv_categorical_and_interval(self): + # GH#46297 + df = DataFrame( + { + "a": [ + pd.Interval( + Timestamp("2020-01-01"), + Timestamp("2020-01-02"), + closed="both", + ) + ] + } + ) + df["a"] = df["a"].astype("category") + result = df.to_csv() + expected_rows = [",a", '0,"[2020-01-01 00:00:00, 2020-01-02 00:00:00]"'] + expected = tm.convert_rows_list_to_csv_str(expected_rows) + assert result == expected diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_to_dict.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_to_dict.py new file mode 100644 index 0000000000000000000000000000000000000000..570f85a4a31ee5f210a6ccd9c8c52a95b5c09b8d --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_to_dict.py @@ -0,0 +1,535 @@ +from collections import ( + OrderedDict, + defaultdict, +) +from datetime import datetime + +import numpy as np +import pytest +import pytz + +from pandas import ( + NA, + DataFrame, + Index, + Interval, + MultiIndex, + Period, + Series, + Timedelta, + Timestamp, +) +import pandas._testing as tm + + +class TestDataFrameToDict: + def test_to_dict_timestamp(self): + # GH#11247 + # split/records producing np.datetime64 rather than Timestamps + # on datetime64[ns] dtypes only + + tsmp = Timestamp("20130101") + test_data = DataFrame({"A": [tsmp, tsmp], "B": [tsmp, tsmp]}) + test_data_mixed = DataFrame({"A": [tsmp, tsmp], "B": [1, 2]}) + + expected_records = [{"A": tsmp, "B": tsmp}, {"A": tsmp, "B": tsmp}] + expected_records_mixed = [{"A": tsmp, "B": 1}, {"A": tsmp, "B": 2}] + + assert test_data.to_dict(orient="records") == expected_records + assert test_data_mixed.to_dict(orient="records") == expected_records_mixed + + expected_series = { + "A": Series([tsmp, tsmp], name="A"), + "B": Series([tsmp, tsmp], name="B"), + } + expected_series_mixed = { + "A": Series([tsmp, tsmp], name="A"), + "B": Series([1, 2], name="B"), + } + + tm.assert_dict_equal(test_data.to_dict(orient="series"), expected_series) + tm.assert_dict_equal( + test_data_mixed.to_dict(orient="series"), expected_series_mixed + ) + + expected_split = { + "index": [0, 1], + "data": [[tsmp, tsmp], [tsmp, tsmp]], + "columns": ["A", "B"], + } + expected_split_mixed = { + "index": [0, 1], + "data": [[tsmp, 1], [tsmp, 2]], + "columns": ["A", "B"], + } + + tm.assert_dict_equal(test_data.to_dict(orient="split"), expected_split) + tm.assert_dict_equal( + test_data_mixed.to_dict(orient="split"), expected_split_mixed + ) + + def test_to_dict_index_not_unique_with_index_orient(self): + # GH#22801 + # Data loss when indexes are not unique. Raise ValueError. + df = DataFrame({"a": [1, 2], "b": [0.5, 0.75]}, index=["A", "A"]) + msg = "DataFrame index must be unique for orient='index'" + with pytest.raises(ValueError, match=msg): + df.to_dict(orient="index") + + def test_to_dict_invalid_orient(self): + df = DataFrame({"A": [0, 1]}) + msg = "orient 'xinvalid' not understood" + with pytest.raises(ValueError, match=msg): + df.to_dict(orient="xinvalid") + + @pytest.mark.parametrize("orient", ["d", "l", "r", "sp", "s", "i"]) + def test_to_dict_short_orient_raises(self, orient): + # GH#32515 + df = DataFrame({"A": [0, 1]}) + with pytest.raises(ValueError, match="not understood"): + df.to_dict(orient=orient) + + @pytest.mark.parametrize("mapping", [dict, defaultdict(list), OrderedDict]) + def test_to_dict(self, mapping): + # orient= should only take the listed options + # see GH#32515 + test_data = {"A": {"1": 1, "2": 2}, "B": {"1": "1", "2": "2", "3": "3"}} + + # GH#16122 + recons_data = DataFrame(test_data).to_dict(into=mapping) + + for k, v in test_data.items(): + for k2, v2 in v.items(): + assert v2 == recons_data[k][k2] + + recons_data = DataFrame(test_data).to_dict("list", into=mapping) + + for k, v in test_data.items(): + for k2, v2 in v.items(): + assert v2 == recons_data[k][int(k2) - 1] + + recons_data = DataFrame(test_data).to_dict("series", into=mapping) + + for k, v in test_data.items(): + for k2, v2 in v.items(): + assert v2 == recons_data[k][k2] + + recons_data = DataFrame(test_data).to_dict("split", into=mapping) + expected_split = { + "columns": ["A", "B"], + "index": ["1", "2", "3"], + "data": [[1.0, "1"], [2.0, "2"], [np.nan, "3"]], + } + tm.assert_dict_equal(recons_data, expected_split) + + recons_data = DataFrame(test_data).to_dict("records", into=mapping) + expected_records = [ + {"A": 1.0, "B": "1"}, + {"A": 2.0, "B": "2"}, + {"A": np.nan, "B": "3"}, + ] + assert isinstance(recons_data, list) + assert len(recons_data) == 3 + for left, right in zip(recons_data, expected_records): + tm.assert_dict_equal(left, right) + + # GH#10844 + recons_data = DataFrame(test_data).to_dict("index") + + for k, v in test_data.items(): + for k2, v2 in v.items(): + assert v2 == recons_data[k2][k] + + df = DataFrame(test_data) + df["duped"] = df[df.columns[0]] + recons_data = df.to_dict("index") + comp_data = test_data.copy() + comp_data["duped"] = comp_data[df.columns[0]] + for k, v in comp_data.items(): + for k2, v2 in v.items(): + assert v2 == recons_data[k2][k] + + @pytest.mark.parametrize("mapping", [list, defaultdict, []]) + def test_to_dict_errors(self, mapping): + # GH#16122 + df = DataFrame(np.random.default_rng(2).standard_normal((3, 3))) + msg = "|".join( + [ + "unsupported type: ", + r"to_dict\(\) only accepts initialized defaultdicts", + ] + ) + with pytest.raises(TypeError, match=msg): + df.to_dict(into=mapping) + + def test_to_dict_not_unique_warning(self): + # GH#16927: When converting to a dict, if a column has a non-unique name + # it will be dropped, throwing a warning. + df = DataFrame([[1, 2, 3]], columns=["a", "a", "b"]) + with tm.assert_produces_warning(UserWarning): + df.to_dict() + + @pytest.mark.filterwarnings("ignore::UserWarning") + @pytest.mark.parametrize( + "orient,expected", + [ + ("list", {"A": [2, 5], "B": [3, 6]}), + ("dict", {"A": {0: 2, 1: 5}, "B": {0: 3, 1: 6}}), + ], + ) + def test_to_dict_not_unique(self, orient, expected): + # GH#54824: This is to make sure that dataframes with non-unique column + # would have uniform behavior throughout different orients + df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["A", "A", "B"]) + result = df.to_dict(orient) + assert result == expected + + # orient - orient argument to to_dict function + # item_getter - function for extracting value from + # the resulting dict using column name and index + @pytest.mark.parametrize( + "orient,item_getter", + [ + ("dict", lambda d, col, idx: d[col][idx]), + ("records", lambda d, col, idx: d[idx][col]), + ("list", lambda d, col, idx: d[col][idx]), + ("split", lambda d, col, idx: d["data"][idx][d["columns"].index(col)]), + ("index", lambda d, col, idx: d[idx][col]), + ], + ) + def test_to_dict_box_scalars(self, orient, item_getter): + # GH#14216, GH#23753 + # make sure that we are boxing properly + df = DataFrame({"a": [1, 2], "b": [0.1, 0.2]}) + result = df.to_dict(orient=orient) + assert isinstance(item_getter(result, "a", 0), int) + assert isinstance(item_getter(result, "b", 0), float) + + def test_to_dict_tz(self): + # GH#18372 When converting to dict with orient='records' columns of + # datetime that are tz-aware were not converted to required arrays + data = [ + (datetime(2017, 11, 18, 21, 53, 0, 219225, tzinfo=pytz.utc),), + (datetime(2017, 11, 18, 22, 6, 30, 61810, tzinfo=pytz.utc),), + ] + df = DataFrame(list(data), columns=["d"]) + + result = df.to_dict(orient="records") + expected = [ + {"d": Timestamp("2017-11-18 21:53:00.219225+0000", tz=pytz.utc)}, + {"d": Timestamp("2017-11-18 22:06:30.061810+0000", tz=pytz.utc)}, + ] + tm.assert_dict_equal(result[0], expected[0]) + tm.assert_dict_equal(result[1], expected[1]) + + @pytest.mark.parametrize( + "into, expected", + [ + ( + dict, + { + 0: {"int_col": 1, "float_col": 1.0}, + 1: {"int_col": 2, "float_col": 2.0}, + 2: {"int_col": 3, "float_col": 3.0}, + }, + ), + ( + OrderedDict, + OrderedDict( + [ + (0, {"int_col": 1, "float_col": 1.0}), + (1, {"int_col": 2, "float_col": 2.0}), + (2, {"int_col": 3, "float_col": 3.0}), + ] + ), + ), + ( + defaultdict(dict), + defaultdict( + dict, + { + 0: {"int_col": 1, "float_col": 1.0}, + 1: {"int_col": 2, "float_col": 2.0}, + 2: {"int_col": 3, "float_col": 3.0}, + }, + ), + ), + ], + ) + def test_to_dict_index_dtypes(self, into, expected): + # GH#18580 + # When using to_dict(orient='index') on a dataframe with int + # and float columns only the int columns were cast to float + + df = DataFrame({"int_col": [1, 2, 3], "float_col": [1.0, 2.0, 3.0]}) + + result = df.to_dict(orient="index", into=into) + cols = ["int_col", "float_col"] + result = DataFrame.from_dict(result, orient="index")[cols] + expected = DataFrame.from_dict(expected, orient="index")[cols] + tm.assert_frame_equal(result, expected) + + def test_to_dict_numeric_names(self): + # GH#24940 + df = DataFrame({str(i): [i] for i in range(5)}) + result = set(df.to_dict("records")[0].keys()) + expected = set(df.columns) + assert result == expected + + def test_to_dict_wide(self): + # GH#24939 + df = DataFrame({(f"A_{i:d}"): [i] for i in range(256)}) + result = df.to_dict("records")[0] + expected = {f"A_{i:d}": i for i in range(256)} + assert result == expected + + @pytest.mark.parametrize( + "data,dtype", + ( + ([True, True, False], bool), + [ + [ + datetime(2018, 1, 1), + datetime(2019, 2, 2), + datetime(2020, 3, 3), + ], + Timestamp, + ], + [[1.0, 2.0, 3.0], float], + [[1, 2, 3], int], + [["X", "Y", "Z"], str], + ), + ) + def test_to_dict_orient_dtype(self, data, dtype): + # GH22620 & GH21256 + + df = DataFrame({"a": data}) + d = df.to_dict(orient="records") + assert all(type(record["a"]) is dtype for record in d) + + @pytest.mark.parametrize( + "data,expected_dtype", + ( + [np.uint64(2), int], + [np.int64(-9), int], + [np.float64(1.1), float], + [np.bool_(True), bool], + [np.datetime64("2005-02-25"), Timestamp], + ), + ) + def test_to_dict_scalar_constructor_orient_dtype(self, data, expected_dtype): + # GH22620 & GH21256 + + df = DataFrame({"a": data}, index=[0]) + d = df.to_dict(orient="records") + result = type(d[0]["a"]) + assert result is expected_dtype + + def test_to_dict_mixed_numeric_frame(self): + # GH 12859 + df = DataFrame({"a": [1.0], "b": [9.0]}) + result = df.reset_index().to_dict("records") + expected = [{"index": 0, "a": 1.0, "b": 9.0}] + assert result == expected + + @pytest.mark.parametrize( + "index", + [ + None, + Index(["aa", "bb"]), + Index(["aa", "bb"], name="cc"), + MultiIndex.from_tuples([("a", "b"), ("a", "c")]), + MultiIndex.from_tuples([("a", "b"), ("a", "c")], names=["n1", "n2"]), + ], + ) + @pytest.mark.parametrize( + "columns", + [ + ["x", "y"], + Index(["x", "y"]), + Index(["x", "y"], name="z"), + MultiIndex.from_tuples([("x", 1), ("y", 2)]), + MultiIndex.from_tuples([("x", 1), ("y", 2)], names=["z1", "z2"]), + ], + ) + def test_to_dict_orient_tight(self, index, columns): + df = DataFrame.from_records( + [[1, 3], [2, 4]], + columns=columns, + index=index, + ) + roundtrip = DataFrame.from_dict(df.to_dict(orient="tight"), orient="tight") + + tm.assert_frame_equal(df, roundtrip) + + @pytest.mark.parametrize( + "orient", + ["dict", "list", "split", "records", "index", "tight"], + ) + @pytest.mark.parametrize( + "data,expected_types", + ( + ( + { + "a": [np.int64(1), 1, np.int64(3)], + "b": [np.float64(1.0), 2.0, np.float64(3.0)], + "c": [np.float64(1.0), 2, np.int64(3)], + "d": [np.float64(1.0), "a", np.int64(3)], + "e": [np.float64(1.0), ["a"], np.int64(3)], + "f": [np.float64(1.0), ("a",), np.int64(3)], + }, + { + "a": [int, int, int], + "b": [float, float, float], + "c": [float, float, float], + "d": [float, str, int], + "e": [float, list, int], + "f": [float, tuple, int], + }, + ), + ( + { + "a": [1, 2, 3], + "b": [1.1, 2.2, 3.3], + }, + { + "a": [int, int, int], + "b": [float, float, float], + }, + ), + ( # Make sure we have one df which is all object type cols + { + "a": [1, "hello", 3], + "b": [1.1, "world", 3.3], + }, + { + "a": [int, str, int], + "b": [float, str, float], + }, + ), + ), + ) + def test_to_dict_returns_native_types(self, orient, data, expected_types): + # GH 46751 + # Tests we get back native types for all orient types + df = DataFrame(data) + result = df.to_dict(orient) + if orient == "dict": + assertion_iterator = ( + (i, key, value) + for key, index_value_map in result.items() + for i, value in index_value_map.items() + ) + elif orient == "list": + assertion_iterator = ( + (i, key, value) + for key, values in result.items() + for i, value in enumerate(values) + ) + elif orient in {"split", "tight"}: + assertion_iterator = ( + (i, key, result["data"][i][j]) + for i in result["index"] + for j, key in enumerate(result["columns"]) + ) + elif orient == "records": + assertion_iterator = ( + (i, key, value) + for i, record in enumerate(result) + for key, value in record.items() + ) + elif orient == "index": + assertion_iterator = ( + (i, key, value) + for i, record in result.items() + for key, value in record.items() + ) + + for i, key, value in assertion_iterator: + assert value == data[key][i] + assert type(value) is expected_types[key][i] + + @pytest.mark.parametrize("orient", ["dict", "list", "series", "records", "index"]) + def test_to_dict_index_false_error(self, orient): + # GH#46398 + df = DataFrame({"col1": [1, 2], "col2": [3, 4]}, index=["row1", "row2"]) + msg = "'index=False' is only valid when 'orient' is 'split' or 'tight'" + with pytest.raises(ValueError, match=msg): + df.to_dict(orient=orient, index=False) + + @pytest.mark.parametrize( + "orient, expected", + [ + ("split", {"columns": ["col1", "col2"], "data": [[1, 3], [2, 4]]}), + ( + "tight", + { + "columns": ["col1", "col2"], + "data": [[1, 3], [2, 4]], + "column_names": [None], + }, + ), + ], + ) + def test_to_dict_index_false(self, orient, expected): + # GH#46398 + df = DataFrame({"col1": [1, 2], "col2": [3, 4]}, index=["row1", "row2"]) + result = df.to_dict(orient=orient, index=False) + tm.assert_dict_equal(result, expected) + + @pytest.mark.parametrize( + "orient, expected", + [ + ("dict", {"a": {0: 1, 1: None}}), + ("list", {"a": [1, None]}), + ("split", {"index": [0, 1], "columns": ["a"], "data": [[1], [None]]}), + ( + "tight", + { + "index": [0, 1], + "columns": ["a"], + "data": [[1], [None]], + "index_names": [None], + "column_names": [None], + }, + ), + ("records", [{"a": 1}, {"a": None}]), + ("index", {0: {"a": 1}, 1: {"a": None}}), + ], + ) + def test_to_dict_na_to_none(self, orient, expected): + # GH#50795 + df = DataFrame({"a": [1, NA]}, dtype="Int64") + result = df.to_dict(orient=orient) + assert result == expected + + def test_to_dict_masked_native_python(self): + # GH#34665 + df = DataFrame({"a": Series([1, 2], dtype="Int64"), "B": 1}) + result = df.to_dict(orient="records") + assert isinstance(result[0]["a"], int) + + df = DataFrame({"a": Series([1, NA], dtype="Int64"), "B": 1}) + result = df.to_dict(orient="records") + assert isinstance(result[0]["a"], int) + + def test_to_dict_pos_args_deprecation(self): + # GH-54229 + df = DataFrame({"a": [1, 2, 3]}) + msg = ( + r"Starting with pandas version 3.0 all arguments of to_dict except for the " + r"argument 'orient' will be keyword-only." + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + df.to_dict("records", {}) + + +@pytest.mark.parametrize( + "val", [Timestamp(2020, 1, 1), Timedelta(1), Period("2020"), Interval(1, 2)] +) +def test_to_dict_list_pd_scalars(val): + # GH 54824 + df = DataFrame({"a": [val]}) + result = df.to_dict(orient="list") + expected = {"a": [val]} + assert result == expected diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_to_dict_of_blocks.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_to_dict_of_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..42858aa412810651d766b03f12af523e37315211 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_to_dict_of_blocks.py @@ -0,0 +1,79 @@ +import numpy as np +import pytest + +from pandas._config import using_string_dtype + +import pandas.util._test_decorators as td + +from pandas import ( + DataFrame, + MultiIndex, +) +import pandas._testing as tm +from pandas.core.arrays import NumpyExtensionArray + +pytestmark = td.skip_array_manager_invalid_test + + +class TestToDictOfBlocks: + @pytest.mark.filterwarnings("ignore:Setting a value on a view:FutureWarning") + def test_no_copy_blocks(self, float_frame, using_copy_on_write): + # GH#9607 + df = DataFrame(float_frame, copy=True) + column = df.columns[0] + + _last_df = None + # use the copy=False, change a column + blocks = df._to_dict_of_blocks() + for _df in blocks.values(): + _last_df = _df + if column in _df: + _df.loc[:, column] = _df[column] + 1 + + if not using_copy_on_write: + # make sure we did change the original DataFrame + assert _last_df is not None and _last_df[column].equals(df[column]) + else: + assert _last_df is not None and not _last_df[column].equals(df[column]) + + +@pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)") +def test_to_dict_of_blocks_item_cache(using_copy_on_write, warn_copy_on_write): + # Calling to_dict_of_blocks should not poison item_cache + df = DataFrame({"a": [1, 2, 3, 4], "b": ["a", "b", "c", "d"]}) + df["c"] = NumpyExtensionArray(np.array([1, 2, None, 3], dtype=object)) + mgr = df._mgr + assert len(mgr.blocks) == 3 # i.e. not consolidated + + ser = df["b"] # populations item_cache["b"] + + df._to_dict_of_blocks() + + if using_copy_on_write: + with pytest.raises(ValueError, match="read-only"): + ser.values[0] = "foo" + elif warn_copy_on_write: + ser.values[0] = "foo" + assert df.loc[0, "b"] == "foo" + # with warning mode, the item cache is disabled + assert df["b"] is not ser + else: + # Check that the to_dict_of_blocks didn't break link between ser and df + ser.values[0] = "foo" + assert df.loc[0, "b"] == "foo" + + assert df["b"] is ser + + +def test_set_change_dtype_slice(): + # GH#8850 + cols = MultiIndex.from_tuples([("1st", "a"), ("2nd", "b"), ("3rd", "c")]) + df = DataFrame([[1.0, 2, 3], [4.0, 5, 6]], columns=cols) + df["2nd"] = df["2nd"] * 2.0 + + blocks = df._to_dict_of_blocks() + assert sorted(blocks.keys()) == ["float64", "int64"] + tm.assert_frame_equal( + blocks["float64"], DataFrame([[1.0, 4.0], [4.0, 10.0]], columns=cols[:2]) + ) + tm.assert_frame_equal(blocks["int64"], DataFrame([[3], [6]], columns=cols[2:])) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_to_numpy.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_to_numpy.py new file mode 100644 index 0000000000000000000000000000000000000000..0731750aed0cf4b46fe7598b87d459036bc68146 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_to_numpy.py @@ -0,0 +1,53 @@ +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +from pandas import ( + DataFrame, + Timestamp, +) +import pandas._testing as tm + + +class TestToNumpy: + def test_to_numpy(self): + df = DataFrame({"A": [1, 2], "B": [3, 4.5]}) + expected = np.array([[1, 3], [2, 4.5]]) + result = df.to_numpy() + tm.assert_numpy_array_equal(result, expected) + + def test_to_numpy_dtype(self): + df = DataFrame({"A": [1, 2], "B": [3, 4.5]}) + expected = np.array([[1, 3], [2, 4]], dtype="int64") + result = df.to_numpy(dtype="int64") + tm.assert_numpy_array_equal(result, expected) + + @td.skip_array_manager_invalid_test + def test_to_numpy_copy(self, using_copy_on_write): + arr = np.random.default_rng(2).standard_normal((4, 3)) + df = DataFrame(arr) + if using_copy_on_write: + assert df.values.base is not arr + assert df.to_numpy(copy=False).base is df.values.base + else: + assert df.values.base is arr + assert df.to_numpy(copy=False).base is arr + assert df.to_numpy(copy=True).base is not arr + + # we still don't want a copy when na_value=np.nan is passed, + # and that can be respected because we are already numpy-float + if using_copy_on_write: + assert df.to_numpy(copy=False).base is df.values.base + else: + assert df.to_numpy(copy=False, na_value=np.nan).base is arr + + @pytest.mark.filterwarnings( + "ignore:invalid value encountered in cast:RuntimeWarning" + ) + def test_to_numpy_mixed_dtype_to_str(self): + # https://github.com/pandas-dev/pandas/issues/35455 + df = DataFrame([[Timestamp("2020-01-01 00:00:00"), 100.0]]) + result = df.to_numpy(dtype=str) + expected = np.array([["2020-01-01 00:00:00", "100.0"]], dtype=str) + tm.assert_numpy_array_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_to_period.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_to_period.py new file mode 100644 index 0000000000000000000000000000000000000000..6a3e6b8c0e0596cfad38bfd1e02fd1b0f34e4ddb --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_to_period.py @@ -0,0 +1,89 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + DatetimeIndex, + PeriodIndex, + Series, + date_range, + period_range, +) +import pandas._testing as tm + + +class TestToPeriod: + def test_to_period(self, frame_or_series): + K = 5 + + dr = date_range("1/1/2000", "1/1/2001", freq="D") + obj = DataFrame( + np.random.default_rng(2).standard_normal((len(dr), K)), + index=dr, + columns=["A", "B", "C", "D", "E"], + ) + obj["mix"] = "a" + obj = tm.get_obj(obj, frame_or_series) + + pts = obj.to_period() + exp = obj.copy() + exp.index = period_range("1/1/2000", "1/1/2001") + tm.assert_equal(pts, exp) + + pts = obj.to_period("M") + exp.index = exp.index.asfreq("M") + tm.assert_equal(pts, exp) + + def test_to_period_without_freq(self, frame_or_series): + # GH#7606 without freq + idx = DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03", "2011-01-04"]) + exp_idx = PeriodIndex( + ["2011-01-01", "2011-01-02", "2011-01-03", "2011-01-04"], freq="D" + ) + + obj = DataFrame( + np.random.default_rng(2).standard_normal((4, 4)), index=idx, columns=idx + ) + obj = tm.get_obj(obj, frame_or_series) + expected = obj.copy() + expected.index = exp_idx + tm.assert_equal(obj.to_period(), expected) + + if frame_or_series is DataFrame: + expected = obj.copy() + expected.columns = exp_idx + tm.assert_frame_equal(obj.to_period(axis=1), expected) + + def test_to_period_columns(self): + dr = date_range("1/1/2000", "1/1/2001") + df = DataFrame(np.random.default_rng(2).standard_normal((len(dr), 5)), index=dr) + df["mix"] = "a" + + df = df.T + pts = df.to_period(axis=1) + exp = df.copy() + exp.columns = period_range("1/1/2000", "1/1/2001") + tm.assert_frame_equal(pts, exp) + + pts = df.to_period("M", axis=1) + tm.assert_index_equal(pts.columns, exp.columns.asfreq("M")) + + def test_to_period_invalid_axis(self): + dr = date_range("1/1/2000", "1/1/2001") + df = DataFrame(np.random.default_rng(2).standard_normal((len(dr), 5)), index=dr) + df["mix"] = "a" + + msg = "No axis named 2 for object type DataFrame" + with pytest.raises(ValueError, match=msg): + df.to_period(axis=2) + + def test_to_period_raises(self, index, frame_or_series): + # https://github.com/pandas-dev/pandas/issues/33327 + obj = Series(index=index, dtype=object) + if frame_or_series is DataFrame: + obj = obj.to_frame() + + if not isinstance(index, DatetimeIndex): + msg = f"unsupported Type {type(index).__name__}" + with pytest.raises(TypeError, match=msg): + obj.to_period() diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_to_records.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_to_records.py new file mode 100644 index 0000000000000000000000000000000000000000..fab90b112fa94c9aa6bf6d8b9f0045e82f3ec92d --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_to_records.py @@ -0,0 +1,523 @@ +from collections import abc +import email +from email.parser import Parser + +import numpy as np +import pytest + +from pandas import ( + CategoricalDtype, + DataFrame, + MultiIndex, + Series, + Timestamp, + date_range, +) +import pandas._testing as tm + + +class TestDataFrameToRecords: + def test_to_records_timeseries(self): + index = date_range("1/1/2000", periods=10) + df = DataFrame( + np.random.default_rng(2).standard_normal((10, 3)), + index=index, + columns=["a", "b", "c"], + ) + + result = df.to_records() + assert result["index"].dtype == "M8[ns]" + + result = df.to_records(index=False) + + def test_to_records_dt64(self): + df = DataFrame( + [["one", "two", "three"], ["four", "five", "six"]], + index=date_range("2012-01-01", "2012-01-02"), + ) + + expected = df.index.values[0] + result = df.to_records()["index"][0] + assert expected == result + + def test_to_records_dt64tz_column(self): + # GH#32535 dont less tz in to_records + df = DataFrame({"A": date_range("2012-01-01", "2012-01-02", tz="US/Eastern")}) + + result = df.to_records() + + assert result.dtype["A"] == object + val = result[0][1] + assert isinstance(val, Timestamp) + assert val == df.loc[0, "A"] + + def test_to_records_with_multindex(self): + # GH#3189 + index = [ + ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"], + ["one", "two", "one", "two", "one", "two", "one", "two"], + ] + data = np.zeros((8, 4)) + df = DataFrame(data, index=index) + r = df.to_records(index=True)["level_0"] + assert "bar" in r + assert "one" not in r + + def test_to_records_with_Mapping_type(self): + abc.Mapping.register(email.message.Message) + + headers = Parser().parsestr( + "From: \n" + "To: \n" + "Subject: Test message\n" + "\n" + "Body would go here\n" + ) + + frame = DataFrame.from_records([headers]) + all(x in frame for x in ["Type", "Subject", "From"]) + + def test_to_records_floats(self): + df = DataFrame(np.random.default_rng(2).random((10, 10))) + df.to_records() + + def test_to_records_index_name(self): + df = DataFrame(np.random.default_rng(2).standard_normal((3, 3))) + df.index.name = "X" + rs = df.to_records() + assert "X" in rs.dtype.fields + + df = DataFrame(np.random.default_rng(2).standard_normal((3, 3))) + rs = df.to_records() + assert "index" in rs.dtype.fields + + df.index = MultiIndex.from_tuples([("a", "x"), ("a", "y"), ("b", "z")]) + df.index.names = ["A", None] + result = df.to_records() + expected = np.rec.fromarrays( + [np.array(["a", "a", "b"]), np.array(["x", "y", "z"])] + + [np.asarray(df.iloc[:, i]) for i in range(3)], + dtype={ + "names": ["A", "level_1", "0", "1", "2"], + "formats": [ + "O", + "O", + f"{tm.ENDIAN}f8", + f"{tm.ENDIAN}f8", + f"{tm.ENDIAN}f8", + ], + }, + ) + tm.assert_numpy_array_equal(result, expected) + + def test_to_records_with_unicode_index(self): + # GH#13172 + # unicode_literals conflict with to_records + result = DataFrame([{"a": "x", "b": "y"}]).set_index("a").to_records() + expected = np.rec.array([("x", "y")], dtype=[("a", "O"), ("b", "O")]) + tm.assert_almost_equal(result, expected) + + def test_to_records_index_dtype(self): + # GH 47263: consistent data types for Index and MultiIndex + df = DataFrame( + { + 1: date_range("2022-01-01", periods=2), + 2: date_range("2022-01-01", periods=2), + 3: date_range("2022-01-01", periods=2), + } + ) + + expected = np.rec.array( + [ + ("2022-01-01", "2022-01-01", "2022-01-01"), + ("2022-01-02", "2022-01-02", "2022-01-02"), + ], + dtype=[ + ("1", f"{tm.ENDIAN}M8[ns]"), + ("2", f"{tm.ENDIAN}M8[ns]"), + ("3", f"{tm.ENDIAN}M8[ns]"), + ], + ) + + result = df.to_records(index=False) + tm.assert_almost_equal(result, expected) + + result = df.set_index(1).to_records(index=True) + tm.assert_almost_equal(result, expected) + + result = df.set_index([1, 2]).to_records(index=True) + tm.assert_almost_equal(result, expected) + + def test_to_records_with_unicode_column_names(self): + # xref issue: https://github.com/numpy/numpy/issues/2407 + # Issue GH#11879. to_records used to raise an exception when used + # with column names containing non-ascii characters in Python 2 + result = DataFrame(data={"accented_name_é": [1.0]}).to_records() + + # Note that numpy allows for unicode field names but dtypes need + # to be specified using dictionary instead of list of tuples. + expected = np.rec.array( + [(0, 1.0)], + dtype={"names": ["index", "accented_name_é"], "formats": ["=i8", "=f8"]}, + ) + tm.assert_almost_equal(result, expected) + + def test_to_records_with_categorical(self): + # GH#8626 + + # dict creation + df = DataFrame({"A": list("abc")}, dtype="category") + expected = Series(list("abc"), dtype="category", name="A") + tm.assert_series_equal(df["A"], expected) + + # list-like creation + df = DataFrame(list("abc"), dtype="category") + expected = Series(list("abc"), dtype="category", name=0) + tm.assert_series_equal(df[0], expected) + + # to record array + # this coerces + result = df.to_records() + expected = np.rec.array( + [(0, "a"), (1, "b"), (2, "c")], dtype=[("index", "=i8"), ("0", "O")] + ) + tm.assert_almost_equal(result, expected) + + @pytest.mark.parametrize( + "kwargs,expected", + [ + # No dtypes --> default to array dtypes. + ( + {}, + np.rec.array( + [(0, 1, 0.2, "a"), (1, 2, 1.5, "bc")], + dtype=[ + ("index", f"{tm.ENDIAN}i8"), + ("A", f"{tm.ENDIAN}i8"), + ("B", f"{tm.ENDIAN}f8"), + ("C", "O"), + ], + ), + ), + # Should have no effect in this case. + ( + {"index": True}, + np.rec.array( + [(0, 1, 0.2, "a"), (1, 2, 1.5, "bc")], + dtype=[ + ("index", f"{tm.ENDIAN}i8"), + ("A", f"{tm.ENDIAN}i8"), + ("B", f"{tm.ENDIAN}f8"), + ("C", "O"), + ], + ), + ), + # Column dtype applied across the board. Index unaffected. + ( + {"column_dtypes": f"{tm.ENDIAN}U4"}, + np.rec.array( + [("0", "1", "0.2", "a"), ("1", "2", "1.5", "bc")], + dtype=[ + ("index", f"{tm.ENDIAN}i8"), + ("A", f"{tm.ENDIAN}U4"), + ("B", f"{tm.ENDIAN}U4"), + ("C", f"{tm.ENDIAN}U4"), + ], + ), + ), + # Index dtype applied across the board. Columns unaffected. + ( + {"index_dtypes": f"{tm.ENDIAN}U1"}, + np.rec.array( + [("0", 1, 0.2, "a"), ("1", 2, 1.5, "bc")], + dtype=[ + ("index", f"{tm.ENDIAN}U1"), + ("A", f"{tm.ENDIAN}i8"), + ("B", f"{tm.ENDIAN}f8"), + ("C", "O"), + ], + ), + ), + # Pass in a type instance. + ( + {"column_dtypes": str}, + np.rec.array( + [("0", "1", "0.2", "a"), ("1", "2", "1.5", "bc")], + dtype=[ + ("index", f"{tm.ENDIAN}i8"), + ("A", f"{tm.ENDIAN}U"), + ("B", f"{tm.ENDIAN}U"), + ("C", f"{tm.ENDIAN}U"), + ], + ), + ), + # Pass in a dtype instance. + ( + {"column_dtypes": np.dtype(np.str_)}, + np.rec.array( + [("0", "1", "0.2", "a"), ("1", "2", "1.5", "bc")], + dtype=[ + ("index", f"{tm.ENDIAN}i8"), + ("A", f"{tm.ENDIAN}U"), + ("B", f"{tm.ENDIAN}U"), + ("C", f"{tm.ENDIAN}U"), + ], + ), + ), + # Pass in a dictionary (name-only). + ( + { + "column_dtypes": { + "A": np.int8, + "B": np.float32, + "C": f"{tm.ENDIAN}U2", + } + }, + np.rec.array( + [("0", "1", "0.2", "a"), ("1", "2", "1.5", "bc")], + dtype=[ + ("index", f"{tm.ENDIAN}i8"), + ("A", "i1"), + ("B", f"{tm.ENDIAN}f4"), + ("C", f"{tm.ENDIAN}U2"), + ], + ), + ), + # Pass in a dictionary (indices-only). + ( + {"index_dtypes": {0: "int16"}}, + np.rec.array( + [(0, 1, 0.2, "a"), (1, 2, 1.5, "bc")], + dtype=[ + ("index", "i2"), + ("A", f"{tm.ENDIAN}i8"), + ("B", f"{tm.ENDIAN}f8"), + ("C", "O"), + ], + ), + ), + # Ignore index mappings if index is not True. + ( + {"index": False, "index_dtypes": f"{tm.ENDIAN}U2"}, + np.rec.array( + [(1, 0.2, "a"), (2, 1.5, "bc")], + dtype=[ + ("A", f"{tm.ENDIAN}i8"), + ("B", f"{tm.ENDIAN}f8"), + ("C", "O"), + ], + ), + ), + # Non-existent names / indices in mapping should not error. + ( + {"index_dtypes": {0: "int16", "not-there": "float32"}}, + np.rec.array( + [(0, 1, 0.2, "a"), (1, 2, 1.5, "bc")], + dtype=[ + ("index", "i2"), + ("A", f"{tm.ENDIAN}i8"), + ("B", f"{tm.ENDIAN}f8"), + ("C", "O"), + ], + ), + ), + # Names / indices not in mapping default to array dtype. + ( + {"column_dtypes": {"A": np.int8, "B": np.float32}}, + np.rec.array( + [("0", "1", "0.2", "a"), ("1", "2", "1.5", "bc")], + dtype=[ + ("index", f"{tm.ENDIAN}i8"), + ("A", "i1"), + ("B", f"{tm.ENDIAN}f4"), + ("C", "O"), + ], + ), + ), + # Names / indices not in dtype mapping default to array dtype. + ( + {"column_dtypes": {"A": np.dtype("int8"), "B": np.dtype("float32")}}, + np.rec.array( + [("0", "1", "0.2", "a"), ("1", "2", "1.5", "bc")], + dtype=[ + ("index", f"{tm.ENDIAN}i8"), + ("A", "i1"), + ("B", f"{tm.ENDIAN}f4"), + ("C", "O"), + ], + ), + ), + # Mixture of everything. + ( + { + "column_dtypes": {"A": np.int8, "B": np.float32}, + "index_dtypes": f"{tm.ENDIAN}U2", + }, + np.rec.array( + [("0", "1", "0.2", "a"), ("1", "2", "1.5", "bc")], + dtype=[ + ("index", f"{tm.ENDIAN}U2"), + ("A", "i1"), + ("B", f"{tm.ENDIAN}f4"), + ("C", "O"), + ], + ), + ), + # Invalid dype values. + ( + {"index": False, "column_dtypes": []}, + (ValueError, "Invalid dtype \\[\\] specified for column A"), + ), + ( + {"index": False, "column_dtypes": {"A": "int32", "B": 5}}, + (ValueError, "Invalid dtype 5 specified for column B"), + ), + # Numpy can't handle EA types, so check error is raised + ( + { + "index": False, + "column_dtypes": {"A": "int32", "B": CategoricalDtype(["a", "b"])}, + }, + (ValueError, "Invalid dtype category specified for column B"), + ), + # Check that bad types raise + ( + {"index": False, "column_dtypes": {"A": "int32", "B": "foo"}}, + (TypeError, "data type [\"']foo[\"'] not understood"), + ), + ], + ) + def test_to_records_dtype(self, kwargs, expected): + # see GH#18146 + df = DataFrame({"A": [1, 2], "B": [0.2, 1.5], "C": ["a", "bc"]}) + + if not isinstance(expected, np.rec.recarray): + with pytest.raises(expected[0], match=expected[1]): + df.to_records(**kwargs) + else: + result = df.to_records(**kwargs) + tm.assert_almost_equal(result, expected) + + @pytest.mark.parametrize( + "df,kwargs,expected", + [ + # MultiIndex in the index. + ( + DataFrame( + [[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=list("abc") + ).set_index(["a", "b"]), + {"column_dtypes": "float64", "index_dtypes": {0: "int32", 1: "int8"}}, + np.rec.array( + [(1, 2, 3.0), (4, 5, 6.0), (7, 8, 9.0)], + dtype=[ + ("a", f"{tm.ENDIAN}i4"), + ("b", "i1"), + ("c", f"{tm.ENDIAN}f8"), + ], + ), + ), + # MultiIndex in the columns. + ( + DataFrame( + [[1, 2, 3], [4, 5, 6], [7, 8, 9]], + columns=MultiIndex.from_tuples( + [("a", "d"), ("b", "e"), ("c", "f")] + ), + ), + { + "column_dtypes": {0: f"{tm.ENDIAN}U1", 2: "float32"}, + "index_dtypes": "float32", + }, + np.rec.array( + [(0.0, "1", 2, 3.0), (1.0, "4", 5, 6.0), (2.0, "7", 8, 9.0)], + dtype=[ + ("index", f"{tm.ENDIAN}f4"), + ("('a', 'd')", f"{tm.ENDIAN}U1"), + ("('b', 'e')", f"{tm.ENDIAN}i8"), + ("('c', 'f')", f"{tm.ENDIAN}f4"), + ], + ), + ), + # MultiIndex in both the columns and index. + ( + DataFrame( + [[1, 2, 3], [4, 5, 6], [7, 8, 9]], + columns=MultiIndex.from_tuples( + [("a", "d"), ("b", "e"), ("c", "f")], names=list("ab") + ), + index=MultiIndex.from_tuples( + [("d", -4), ("d", -5), ("f", -6)], names=list("cd") + ), + ), + { + "column_dtypes": "float64", + "index_dtypes": {0: f"{tm.ENDIAN}U2", 1: "int8"}, + }, + np.rec.array( + [ + ("d", -4, 1.0, 2.0, 3.0), + ("d", -5, 4.0, 5.0, 6.0), + ("f", -6, 7, 8, 9.0), + ], + dtype=[ + ("c", f"{tm.ENDIAN}U2"), + ("d", "i1"), + ("('a', 'd')", f"{tm.ENDIAN}f8"), + ("('b', 'e')", f"{tm.ENDIAN}f8"), + ("('c', 'f')", f"{tm.ENDIAN}f8"), + ], + ), + ), + ], + ) + def test_to_records_dtype_mi(self, df, kwargs, expected): + # see GH#18146 + result = df.to_records(**kwargs) + tm.assert_almost_equal(result, expected) + + def test_to_records_dict_like(self): + # see GH#18146 + class DictLike: + def __init__(self, **kwargs) -> None: + self.d = kwargs.copy() + + def __getitem__(self, key): + return self.d.__getitem__(key) + + def __contains__(self, key) -> bool: + return key in self.d + + def keys(self): + return self.d.keys() + + df = DataFrame({"A": [1, 2], "B": [0.2, 1.5], "C": ["a", "bc"]}) + + dtype_mappings = { + "column_dtypes": DictLike(A=np.int8, B=np.float32), + "index_dtypes": f"{tm.ENDIAN}U2", + } + + result = df.to_records(**dtype_mappings) + expected = np.rec.array( + [("0", "1", "0.2", "a"), ("1", "2", "1.5", "bc")], + dtype=[ + ("index", f"{tm.ENDIAN}U2"), + ("A", "i1"), + ("B", f"{tm.ENDIAN}f4"), + ("C", "O"), + ], + ) + tm.assert_almost_equal(result, expected) + + @pytest.mark.parametrize("tz", ["UTC", "GMT", "US/Eastern"]) + def test_to_records_datetimeindex_with_tz(self, tz): + # GH#13937 + dr = date_range("2016-01-01", periods=10, freq="s", tz=tz) + + df = DataFrame({"datetime": dr}, index=dr) + + expected = df.to_records() + result = df.tz_convert("UTC").to_records() + + # both converted to UTC, so they are equal + tm.assert_numpy_array_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_to_timestamp.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_to_timestamp.py new file mode 100644 index 0000000000000000000000000000000000000000..0e7e1d595d6be9250638932e7690f420b9a12fc0 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_to_timestamp.py @@ -0,0 +1,154 @@ +from datetime import timedelta + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + DatetimeIndex, + PeriodIndex, + Series, + Timedelta, + date_range, + period_range, + to_datetime, +) +import pandas._testing as tm + + +def _get_with_delta(delta, freq="YE-DEC"): + return date_range( + to_datetime("1/1/2001") + delta, + to_datetime("12/31/2009") + delta, + freq=freq, + ) + + +class TestToTimestamp: + def test_to_timestamp(self, frame_or_series): + K = 5 + index = period_range(freq="Y", start="1/1/2001", end="12/1/2009") + obj = DataFrame( + np.random.default_rng(2).standard_normal((len(index), K)), + index=index, + columns=["A", "B", "C", "D", "E"], + ) + obj["mix"] = "a" + obj = tm.get_obj(obj, frame_or_series) + + exp_index = date_range("1/1/2001", end="12/31/2009", freq="YE-DEC") + exp_index = exp_index + Timedelta(1, "D") - Timedelta(1, "ns") + result = obj.to_timestamp("D", "end") + tm.assert_index_equal(result.index, exp_index) + tm.assert_numpy_array_equal(result.values, obj.values) + if frame_or_series is Series: + assert result.name == "A" + + exp_index = date_range("1/1/2001", end="1/1/2009", freq="YS-JAN") + result = obj.to_timestamp("D", "start") + tm.assert_index_equal(result.index, exp_index) + + result = obj.to_timestamp(how="start") + tm.assert_index_equal(result.index, exp_index) + + delta = timedelta(hours=23) + result = obj.to_timestamp("H", "end") + exp_index = _get_with_delta(delta) + exp_index = exp_index + Timedelta(1, "h") - Timedelta(1, "ns") + tm.assert_index_equal(result.index, exp_index) + + delta = timedelta(hours=23, minutes=59) + result = obj.to_timestamp("T", "end") + exp_index = _get_with_delta(delta) + exp_index = exp_index + Timedelta(1, "m") - Timedelta(1, "ns") + tm.assert_index_equal(result.index, exp_index) + + result = obj.to_timestamp("S", "end") + delta = timedelta(hours=23, minutes=59, seconds=59) + exp_index = _get_with_delta(delta) + exp_index = exp_index + Timedelta(1, "s") - Timedelta(1, "ns") + tm.assert_index_equal(result.index, exp_index) + + def test_to_timestamp_columns(self): + K = 5 + index = period_range(freq="Y", start="1/1/2001", end="12/1/2009") + df = DataFrame( + np.random.default_rng(2).standard_normal((len(index), K)), + index=index, + columns=["A", "B", "C", "D", "E"], + ) + df["mix"] = "a" + + # columns + df = df.T + + exp_index = date_range("1/1/2001", end="12/31/2009", freq="YE-DEC") + exp_index = exp_index + Timedelta(1, "D") - Timedelta(1, "ns") + result = df.to_timestamp("D", "end", axis=1) + tm.assert_index_equal(result.columns, exp_index) + tm.assert_numpy_array_equal(result.values, df.values) + + exp_index = date_range("1/1/2001", end="1/1/2009", freq="YS-JAN") + result = df.to_timestamp("D", "start", axis=1) + tm.assert_index_equal(result.columns, exp_index) + + delta = timedelta(hours=23) + result = df.to_timestamp("H", "end", axis=1) + exp_index = _get_with_delta(delta) + exp_index = exp_index + Timedelta(1, "h") - Timedelta(1, "ns") + tm.assert_index_equal(result.columns, exp_index) + + delta = timedelta(hours=23, minutes=59) + result = df.to_timestamp("min", "end", axis=1) + exp_index = _get_with_delta(delta) + exp_index = exp_index + Timedelta(1, "m") - Timedelta(1, "ns") + tm.assert_index_equal(result.columns, exp_index) + + result = df.to_timestamp("S", "end", axis=1) + delta = timedelta(hours=23, minutes=59, seconds=59) + exp_index = _get_with_delta(delta) + exp_index = exp_index + Timedelta(1, "s") - Timedelta(1, "ns") + tm.assert_index_equal(result.columns, exp_index) + + result1 = df.to_timestamp("5min", axis=1) + result2 = df.to_timestamp("min", axis=1) + expected = date_range("2001-01-01", "2009-01-01", freq="YS") + assert isinstance(result1.columns, DatetimeIndex) + assert isinstance(result2.columns, DatetimeIndex) + tm.assert_numpy_array_equal(result1.columns.asi8, expected.asi8) + tm.assert_numpy_array_equal(result2.columns.asi8, expected.asi8) + # PeriodIndex.to_timestamp always use 'infer' + assert result1.columns.freqstr == "YS-JAN" + assert result2.columns.freqstr == "YS-JAN" + + def test_to_timestamp_invalid_axis(self): + index = period_range(freq="Y", start="1/1/2001", end="12/1/2009") + obj = DataFrame( + np.random.default_rng(2).standard_normal((len(index), 5)), index=index + ) + + # invalid axis + with pytest.raises(ValueError, match="axis"): + obj.to_timestamp(axis=2) + + def test_to_timestamp_hourly(self, frame_or_series): + index = period_range(freq="h", start="1/1/2001", end="1/2/2001") + obj = Series(1, index=index, name="foo") + if frame_or_series is not Series: + obj = obj.to_frame() + + exp_index = date_range("1/1/2001 00:59:59", end="1/2/2001 00:59:59", freq="h") + result = obj.to_timestamp(how="end") + exp_index = exp_index + Timedelta(1, "s") - Timedelta(1, "ns") + tm.assert_index_equal(result.index, exp_index) + if frame_or_series is Series: + assert result.name == "foo" + + def test_to_timestamp_raises(self, index, frame_or_series): + # GH#33327 + obj = frame_or_series(index=index, dtype=object) + + if not isinstance(index, PeriodIndex): + msg = f"unsupported Type {type(index).__name__}" + with pytest.raises(TypeError, match=msg): + obj.to_timestamp() diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_transpose.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_transpose.py new file mode 100644 index 0000000000000000000000000000000000000000..3e74094f266d14b8752e562653cf490868dcd0b0 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_transpose.py @@ -0,0 +1,209 @@ +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +import pandas as pd +from pandas import ( + DataFrame, + DatetimeIndex, + Index, + IntervalIndex, + Series, + Timestamp, + bdate_range, + date_range, + timedelta_range, +) +import pandas._testing as tm + + +class TestTranspose: + def test_transpose_td64_intervals(self): + # GH#44917 + tdi = timedelta_range("0 Days", "3 Days") + ii = IntervalIndex.from_breaks(tdi) + ii = ii.insert(-1, np.nan) + df = DataFrame(ii) + + result = df.T + expected = DataFrame({i: ii[i : i + 1] for i in range(len(ii))}) + tm.assert_frame_equal(result, expected) + + def test_transpose_empty_preserves_datetimeindex(self): + # GH#41382 + dti = DatetimeIndex([], dtype="M8[ns]") + df = DataFrame(index=dti) + + expected = DatetimeIndex([], dtype="datetime64[ns]", freq=None) + + result1 = df.T.sum().index + result2 = df.sum(axis=1).index + + tm.assert_index_equal(result1, expected) + tm.assert_index_equal(result2, expected) + + def test_transpose_tzaware_1col_single_tz(self): + # GH#26825 + dti = date_range("2016-04-05 04:30", periods=3, tz="UTC") + + df = DataFrame(dti) + assert (df.dtypes == dti.dtype).all() + res = df.T + assert (res.dtypes == dti.dtype).all() + + def test_transpose_tzaware_2col_single_tz(self): + # GH#26825 + dti = date_range("2016-04-05 04:30", periods=3, tz="UTC") + + df3 = DataFrame({"A": dti, "B": dti}) + assert (df3.dtypes == dti.dtype).all() + res3 = df3.T + assert (res3.dtypes == dti.dtype).all() + + def test_transpose_tzaware_2col_mixed_tz(self): + # GH#26825 + dti = date_range("2016-04-05 04:30", periods=3, tz="UTC") + dti2 = dti.tz_convert("US/Pacific") + + df4 = DataFrame({"A": dti, "B": dti2}) + assert (df4.dtypes == [dti.dtype, dti2.dtype]).all() + assert (df4.T.dtypes == object).all() + tm.assert_frame_equal(df4.T.T, df4.astype(object)) + + @pytest.mark.parametrize("tz", [None, "America/New_York"]) + def test_transpose_preserves_dtindex_equality_with_dst(self, tz): + # GH#19970 + idx = date_range("20161101", "20161130", freq="4h", tz=tz) + df = DataFrame({"a": range(len(idx)), "b": range(len(idx))}, index=idx) + result = df.T == df.T + expected = DataFrame(True, index=list("ab"), columns=idx) + tm.assert_frame_equal(result, expected) + + def test_transpose_object_to_tzaware_mixed_tz(self): + # GH#26825 + dti = date_range("2016-04-05 04:30", periods=3, tz="UTC") + dti2 = dti.tz_convert("US/Pacific") + + # mixed all-tzaware dtypes + df2 = DataFrame([dti, dti2]) + assert (df2.dtypes == object).all() + res2 = df2.T + assert (res2.dtypes == object).all() + + def test_transpose_uint64(self): + df = DataFrame( + {"A": np.arange(3), "B": [2**63, 2**63 + 5, 2**63 + 10]}, + dtype=np.uint64, + ) + result = df.T + expected = DataFrame(df.values.T) + expected.index = ["A", "B"] + tm.assert_frame_equal(result, expected) + + def test_transpose_float(self, float_frame): + frame = float_frame + dft = frame.T + for idx, series in dft.items(): + for col, value in series.items(): + if np.isnan(value): + assert np.isnan(frame[col][idx]) + else: + assert value == frame[col][idx] + + def test_transpose_mixed(self): + # mixed type + mixed = DataFrame( + { + "A": [0.0, 1.0, 2.0, 3.0, 4.0], + "B": [0.0, 1.0, 0.0, 1.0, 0.0], + "C": ["foo1", "foo2", "foo3", "foo4", "foo5"], + "D": bdate_range("1/1/2009", periods=5), + }, + index=Index(["a", "b", "c", "d", "e"], dtype=object), + ) + + mixed_T = mixed.T + for col, s in mixed_T.items(): + assert s.dtype == np.object_ + + @td.skip_array_manager_invalid_test + def test_transpose_get_view(self, float_frame, using_copy_on_write): + dft = float_frame.T + dft.iloc[:, 5:10] = 5 + + if using_copy_on_write: + assert (float_frame.values[5:10] != 5).all() + else: + assert (float_frame.values[5:10] == 5).all() + + @td.skip_array_manager_invalid_test + def test_transpose_get_view_dt64tzget_view(self, using_copy_on_write): + dti = date_range("2016-01-01", periods=6, tz="US/Pacific") + arr = dti._data.reshape(3, 2) + df = DataFrame(arr) + assert df._mgr.nblocks == 1 + + result = df.T + assert result._mgr.nblocks == 1 + + rtrip = result._mgr.blocks[0].values + if using_copy_on_write: + assert np.shares_memory(df._mgr.blocks[0].values._ndarray, rtrip._ndarray) + else: + assert np.shares_memory(arr._ndarray, rtrip._ndarray) + + def test_transpose_not_inferring_dt(self): + # GH#51546 + df = DataFrame( + { + "a": [Timestamp("2019-12-31"), Timestamp("2019-12-31")], + }, + dtype=object, + ) + result = df.T + expected = DataFrame( + [[Timestamp("2019-12-31"), Timestamp("2019-12-31")]], + columns=[0, 1], + index=["a"], + dtype=object, + ) + tm.assert_frame_equal(result, expected) + + def test_transpose_not_inferring_dt_mixed_blocks(self): + # GH#51546 + df = DataFrame( + { + "a": Series( + [Timestamp("2019-12-31"), Timestamp("2019-12-31")], dtype=object + ), + "b": [Timestamp("2019-12-31"), Timestamp("2019-12-31")], + } + ) + result = df.T + expected = DataFrame( + [ + [Timestamp("2019-12-31"), Timestamp("2019-12-31")], + [Timestamp("2019-12-31"), Timestamp("2019-12-31")], + ], + columns=[0, 1], + index=["a", "b"], + dtype=object, + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("dtype1", ["Int64", "Float64"]) + @pytest.mark.parametrize("dtype2", ["Int64", "Float64"]) + def test_transpose(self, dtype1, dtype2): + # GH#57315 - transpose should have F contiguous blocks + df = DataFrame( + { + "a": pd.array([1, 1, 2], dtype=dtype1), + "b": pd.array([3, 4, 5], dtype=dtype2), + } + ) + result = df.T + for blk in result._mgr.blocks: + # When dtypes are unequal, we get NumPy object array + data = blk.values._data if dtype1 == dtype2 else blk.values + assert data.flags["F_CONTIGUOUS"] diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_truncate.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_truncate.py new file mode 100644 index 0000000000000000000000000000000000000000..12077952c2e0300257eb9029d2a1a231d5fa0a5c --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_truncate.py @@ -0,0 +1,154 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + DatetimeIndex, + Index, + Series, + date_range, +) +import pandas._testing as tm + + +class TestDataFrameTruncate: + def test_truncate(self, datetime_frame, frame_or_series): + ts = datetime_frame[::3] + ts = tm.get_obj(ts, frame_or_series) + + start, end = datetime_frame.index[3], datetime_frame.index[6] + + start_missing = datetime_frame.index[2] + end_missing = datetime_frame.index[7] + + # neither specified + truncated = ts.truncate() + tm.assert_equal(truncated, ts) + + # both specified + expected = ts[1:3] + + truncated = ts.truncate(start, end) + tm.assert_equal(truncated, expected) + + truncated = ts.truncate(start_missing, end_missing) + tm.assert_equal(truncated, expected) + + # start specified + expected = ts[1:] + + truncated = ts.truncate(before=start) + tm.assert_equal(truncated, expected) + + truncated = ts.truncate(before=start_missing) + tm.assert_equal(truncated, expected) + + # end specified + expected = ts[:3] + + truncated = ts.truncate(after=end) + tm.assert_equal(truncated, expected) + + truncated = ts.truncate(after=end_missing) + tm.assert_equal(truncated, expected) + + # corner case, empty series/frame returned + truncated = ts.truncate(after=ts.index[0] - ts.index.freq) + assert len(truncated) == 0 + + truncated = ts.truncate(before=ts.index[-1] + ts.index.freq) + assert len(truncated) == 0 + + msg = "Truncate: 2000-01-06 00:00:00 must be after 2000-05-16 00:00:00" + with pytest.raises(ValueError, match=msg): + ts.truncate( + before=ts.index[-1] - ts.index.freq, after=ts.index[0] + ts.index.freq + ) + + def test_truncate_nonsortedindex(self, frame_or_series): + # GH#17935 + + obj = DataFrame({"A": ["a", "b", "c", "d", "e"]}, index=[5, 3, 2, 9, 0]) + obj = tm.get_obj(obj, frame_or_series) + + msg = "truncate requires a sorted index" + with pytest.raises(ValueError, match=msg): + obj.truncate(before=3, after=9) + + def test_sort_values_nonsortedindex(self): + rng = date_range("2011-01-01", "2012-01-01", freq="W") + ts = DataFrame( + { + "A": np.random.default_rng(2).standard_normal(len(rng)), + "B": np.random.default_rng(2).standard_normal(len(rng)), + }, + index=rng, + ) + + decreasing = ts.sort_values("A", ascending=False) + + msg = "truncate requires a sorted index" + with pytest.raises(ValueError, match=msg): + decreasing.truncate(before="2011-11", after="2011-12") + + def test_truncate_nonsortedindex_axis1(self): + # GH#17935 + + df = DataFrame( + { + 3: np.random.default_rng(2).standard_normal(5), + 20: np.random.default_rng(2).standard_normal(5), + 2: np.random.default_rng(2).standard_normal(5), + 0: np.random.default_rng(2).standard_normal(5), + }, + columns=[3, 20, 2, 0], + ) + msg = "truncate requires a sorted index" + with pytest.raises(ValueError, match=msg): + df.truncate(before=2, after=20, axis=1) + + @pytest.mark.parametrize( + "before, after, indices", + [(1, 2, [2, 1]), (None, 2, [2, 1, 0]), (1, None, [3, 2, 1])], + ) + @pytest.mark.parametrize("dtyp", [*tm.ALL_REAL_NUMPY_DTYPES, "datetime64[ns]"]) + def test_truncate_decreasing_index( + self, before, after, indices, dtyp, frame_or_series + ): + # https://github.com/pandas-dev/pandas/issues/33756 + idx = Index([3, 2, 1, 0], dtype=dtyp) + if isinstance(idx, DatetimeIndex): + before = pd.Timestamp(before) if before is not None else None + after = pd.Timestamp(after) if after is not None else None + indices = [pd.Timestamp(i) for i in indices] + values = frame_or_series(range(len(idx)), index=idx) + result = values.truncate(before=before, after=after) + expected = values.loc[indices] + tm.assert_equal(result, expected) + + def test_truncate_multiindex(self, frame_or_series): + # GH 34564 + mi = pd.MultiIndex.from_product([[1, 2, 3, 4], ["A", "B"]], names=["L1", "L2"]) + s1 = DataFrame(range(mi.shape[0]), index=mi, columns=["col"]) + s1 = tm.get_obj(s1, frame_or_series) + + result = s1.truncate(before=2, after=3) + + df = DataFrame.from_dict( + {"L1": [2, 2, 3, 3], "L2": ["A", "B", "A", "B"], "col": [2, 3, 4, 5]} + ) + expected = df.set_index(["L1", "L2"]) + expected = tm.get_obj(expected, frame_or_series) + + tm.assert_equal(result, expected) + + def test_truncate_index_only_one_unique_value(self, frame_or_series): + # GH 42365 + obj = Series(0, index=date_range("2021-06-30", "2021-06-30")).repeat(5) + if frame_or_series is DataFrame: + obj = obj.to_frame(name="a") + + truncated = obj.truncate("2021-06-28", "2021-07-01") + + tm.assert_equal(truncated, obj) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_tz_convert.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_tz_convert.py new file mode 100644 index 0000000000000000000000000000000000000000..bcb8e423980fdc06195846a6d79afa00f8e691fd --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_tz_convert.py @@ -0,0 +1,131 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Index, + MultiIndex, + Series, + date_range, +) +import pandas._testing as tm + + +class TestTZConvert: + def test_tz_convert(self, frame_or_series): + rng = date_range("1/1/2011", periods=200, freq="D", tz="US/Eastern") + + obj = DataFrame({"a": 1}, index=rng) + obj = tm.get_obj(obj, frame_or_series) + + result = obj.tz_convert("Europe/Berlin") + expected = DataFrame({"a": 1}, rng.tz_convert("Europe/Berlin")) + expected = tm.get_obj(expected, frame_or_series) + + assert result.index.tz.zone == "Europe/Berlin" + tm.assert_equal(result, expected) + + def test_tz_convert_axis1(self): + rng = date_range("1/1/2011", periods=200, freq="D", tz="US/Eastern") + + obj = DataFrame({"a": 1}, index=rng) + + obj = obj.T + result = obj.tz_convert("Europe/Berlin", axis=1) + assert result.columns.tz.zone == "Europe/Berlin" + + expected = DataFrame({"a": 1}, rng.tz_convert("Europe/Berlin")) + + tm.assert_equal(result, expected.T) + + def test_tz_convert_naive(self, frame_or_series): + # can't convert tz-naive + rng = date_range("1/1/2011", periods=200, freq="D") + ts = Series(1, index=rng) + ts = frame_or_series(ts) + + with pytest.raises(TypeError, match="Cannot convert tz-naive"): + ts.tz_convert("US/Eastern") + + @pytest.mark.parametrize("fn", ["tz_localize", "tz_convert"]) + def test_tz_convert_and_localize(self, fn): + l0 = date_range("20140701", periods=5, freq="D") + l1 = date_range("20140701", periods=5, freq="D") + + int_idx = Index(range(5)) + + if fn == "tz_convert": + l0 = l0.tz_localize("UTC") + l1 = l1.tz_localize("UTC") + + for idx in [l0, l1]: + l0_expected = getattr(idx, fn)("US/Pacific") + l1_expected = getattr(idx, fn)("US/Pacific") + + df1 = DataFrame(np.ones(5), index=l0) + df1 = getattr(df1, fn)("US/Pacific") + tm.assert_index_equal(df1.index, l0_expected) + + # MultiIndex + # GH7846 + df2 = DataFrame(np.ones(5), MultiIndex.from_arrays([l0, l1])) + + # freq is not preserved in MultiIndex construction + l1_expected = l1_expected._with_freq(None) + l0_expected = l0_expected._with_freq(None) + l1 = l1._with_freq(None) + l0 = l0._with_freq(None) + + df3 = getattr(df2, fn)("US/Pacific", level=0) + assert not df3.index.levels[0].equals(l0) + tm.assert_index_equal(df3.index.levels[0], l0_expected) + tm.assert_index_equal(df3.index.levels[1], l1) + assert not df3.index.levels[1].equals(l1_expected) + + df3 = getattr(df2, fn)("US/Pacific", level=1) + tm.assert_index_equal(df3.index.levels[0], l0) + assert not df3.index.levels[0].equals(l0_expected) + tm.assert_index_equal(df3.index.levels[1], l1_expected) + assert not df3.index.levels[1].equals(l1) + + df4 = DataFrame(np.ones(5), MultiIndex.from_arrays([int_idx, l0])) + + # TODO: untested + getattr(df4, fn)("US/Pacific", level=1) + + tm.assert_index_equal(df3.index.levels[0], l0) + assert not df3.index.levels[0].equals(l0_expected) + tm.assert_index_equal(df3.index.levels[1], l1_expected) + assert not df3.index.levels[1].equals(l1) + + # Bad Inputs + + # Not DatetimeIndex / PeriodIndex + with pytest.raises(TypeError, match="DatetimeIndex"): + df = DataFrame(index=int_idx) + getattr(df, fn)("US/Pacific") + + # Not DatetimeIndex / PeriodIndex + with pytest.raises(TypeError, match="DatetimeIndex"): + df = DataFrame(np.ones(5), MultiIndex.from_arrays([int_idx, l0])) + getattr(df, fn)("US/Pacific", level=0) + + # Invalid level + with pytest.raises(ValueError, match="not valid"): + df = DataFrame(index=l0) + getattr(df, fn)("US/Pacific", level=1) + + @pytest.mark.parametrize("copy", [True, False]) + def test_tz_convert_copy_inplace_mutate(self, copy, frame_or_series): + # GH#6326 + obj = frame_or_series( + np.arange(0, 5), + index=date_range("20131027", periods=5, freq="h", tz="Europe/Berlin"), + ) + orig = obj.copy() + result = obj.tz_convert("UTC", copy=copy) + expected = frame_or_series(np.arange(0, 5), index=obj.index.tz_convert("UTC")) + tm.assert_equal(result, expected) + tm.assert_equal(obj, orig) + assert result.index is not obj.index + assert result is not obj diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_tz_localize.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_tz_localize.py new file mode 100644 index 0000000000000000000000000000000000000000..b167afc17f484cce36c3909222b3f3d80ff4c926 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_tz_localize.py @@ -0,0 +1,68 @@ +from datetime import timezone + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Series, + date_range, +) +import pandas._testing as tm + + +class TestTZLocalize: + # See also: + # test_tz_convert_and_localize in test_tz_convert + + def test_tz_localize(self, frame_or_series): + rng = date_range("1/1/2011", periods=100, freq="h") + + obj = DataFrame({"a": 1}, index=rng) + obj = tm.get_obj(obj, frame_or_series) + + result = obj.tz_localize("utc") + expected = DataFrame({"a": 1}, rng.tz_localize("UTC")) + expected = tm.get_obj(expected, frame_or_series) + + assert result.index.tz is timezone.utc + tm.assert_equal(result, expected) + + def test_tz_localize_axis1(self): + rng = date_range("1/1/2011", periods=100, freq="h") + + df = DataFrame({"a": 1}, index=rng) + + df = df.T + result = df.tz_localize("utc", axis=1) + assert result.columns.tz is timezone.utc + + expected = DataFrame({"a": 1}, rng.tz_localize("UTC")) + + tm.assert_frame_equal(result, expected.T) + + def test_tz_localize_naive(self, frame_or_series): + # Can't localize if already tz-aware + rng = date_range("1/1/2011", periods=100, freq="h", tz="utc") + ts = Series(1, index=rng) + ts = frame_or_series(ts) + + with pytest.raises(TypeError, match="Already tz-aware"): + ts.tz_localize("US/Eastern") + + @pytest.mark.parametrize("copy", [True, False]) + def test_tz_localize_copy_inplace_mutate(self, copy, frame_or_series): + # GH#6326 + obj = frame_or_series( + np.arange(0, 5), index=date_range("20131027", periods=5, freq="1h", tz=None) + ) + orig = obj.copy() + result = obj.tz_localize("UTC", copy=copy) + expected = frame_or_series( + np.arange(0, 5), + index=date_range("20131027", periods=5, freq="1h", tz="UTC"), + ) + tm.assert_equal(result, expected) + tm.assert_equal(obj, orig) + assert result.index is not obj.index + assert result is not obj diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_update.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_update.py new file mode 100644 index 0000000000000000000000000000000000000000..56700ab6bd1f7327ba7622f6d2cb7418c96146ab --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_update.py @@ -0,0 +1,204 @@ +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +import pandas as pd +from pandas import ( + DataFrame, + Series, + date_range, +) +import pandas._testing as tm + + +class TestDataFrameUpdate: + def test_update_nan(self): + # #15593 #15617 + # test 1 + df1 = DataFrame({"A": [1.0, 2, 3], "B": date_range("2000", periods=3)}) + df2 = DataFrame({"A": [None, 2, 3]}) + expected = df1.copy() + df1.update(df2, overwrite=False) + + tm.assert_frame_equal(df1, expected) + + # test 2 + df1 = DataFrame({"A": [1.0, None, 3], "B": date_range("2000", periods=3)}) + df2 = DataFrame({"A": [None, 2, 3]}) + expected = DataFrame({"A": [1.0, 2, 3], "B": date_range("2000", periods=3)}) + df1.update(df2, overwrite=False) + + tm.assert_frame_equal(df1, expected) + + def test_update(self): + df = DataFrame( + [[1.5, np.nan, 3.0], [1.5, np.nan, 3.0], [1.5, np.nan, 3], [1.5, np.nan, 3]] + ) + + other = DataFrame([[3.6, 2.0, np.nan], [np.nan, np.nan, 7]], index=[1, 3]) + + df.update(other) + + expected = DataFrame( + [[1.5, np.nan, 3], [3.6, 2, 3], [1.5, np.nan, 3], [1.5, np.nan, 7.0]] + ) + tm.assert_frame_equal(df, expected) + + def test_update_dtypes(self): + # gh 3016 + df = DataFrame( + [[1.0, 2.0, 1, False, True], [4.0, 5.0, 2, True, False]], + columns=["A", "B", "int", "bool1", "bool2"], + ) + + other = DataFrame( + [[45, 45, 3, True]], index=[0], columns=["A", "B", "int", "bool1"] + ) + df.update(other) + + expected = DataFrame( + [[45.0, 45.0, 3, True, True], [4.0, 5.0, 2, True, False]], + columns=["A", "B", "int", "bool1", "bool2"], + ) + tm.assert_frame_equal(df, expected) + + def test_update_nooverwrite(self): + df = DataFrame( + [[1.5, np.nan, 3.0], [1.5, np.nan, 3.0], [1.5, np.nan, 3], [1.5, np.nan, 3]] + ) + + other = DataFrame([[3.6, 2.0, np.nan], [np.nan, np.nan, 7]], index=[1, 3]) + + df.update(other, overwrite=False) + + expected = DataFrame( + [[1.5, np.nan, 3], [1.5, 2, 3], [1.5, np.nan, 3], [1.5, np.nan, 3.0]] + ) + tm.assert_frame_equal(df, expected) + + def test_update_filtered(self): + df = DataFrame( + [[1.5, np.nan, 3.0], [1.5, np.nan, 3.0], [1.5, np.nan, 3], [1.5, np.nan, 3]] + ) + + other = DataFrame([[3.6, 2.0, np.nan], [np.nan, np.nan, 7]], index=[1, 3]) + + df.update(other, filter_func=lambda x: x > 2) + + expected = DataFrame( + [[1.5, np.nan, 3], [1.5, np.nan, 3], [1.5, np.nan, 3], [1.5, np.nan, 7.0]] + ) + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize( + "bad_kwarg, exception, msg", + [ + # errors must be 'ignore' or 'raise' + ({"errors": "something"}, ValueError, "The parameter errors must.*"), + ({"join": "inner"}, NotImplementedError, "Only left join is supported"), + ], + ) + def test_update_raise_bad_parameter(self, bad_kwarg, exception, msg): + df = DataFrame([[1.5, 1, 3.0]]) + with pytest.raises(exception, match=msg): + df.update(df, **bad_kwarg) + + def test_update_raise_on_overlap(self): + df = DataFrame( + [[1.5, 1, 3.0], [1.5, np.nan, 3.0], [1.5, np.nan, 3], [1.5, np.nan, 3]] + ) + + other = DataFrame([[2.0, np.nan], [np.nan, 7]], index=[1, 3], columns=[1, 2]) + with pytest.raises(ValueError, match="Data overlaps"): + df.update(other, errors="raise") + + def test_update_from_non_df(self): + d = {"a": Series([1, 2, 3, 4]), "b": Series([5, 6, 7, 8])} + df = DataFrame(d) + + d["a"] = Series([5, 6, 7, 8]) + df.update(d) + + expected = DataFrame(d) + + tm.assert_frame_equal(df, expected) + + d = {"a": [1, 2, 3, 4], "b": [5, 6, 7, 8]} + df = DataFrame(d) + + d["a"] = [5, 6, 7, 8] + df.update(d) + + expected = DataFrame(d) + + tm.assert_frame_equal(df, expected) + + def test_update_datetime_tz(self): + # GH 25807 + result = DataFrame([pd.Timestamp("2019", tz="UTC")]) + with tm.assert_produces_warning(None): + result.update(result) + expected = DataFrame([pd.Timestamp("2019", tz="UTC")]) + tm.assert_frame_equal(result, expected) + + def test_update_datetime_tz_in_place(self, using_copy_on_write, warn_copy_on_write): + # https://github.com/pandas-dev/pandas/issues/56227 + result = DataFrame([pd.Timestamp("2019", tz="UTC")]) + orig = result.copy() + view = result[:] + with tm.assert_produces_warning( + FutureWarning if warn_copy_on_write else None, match="Setting a value" + ): + result.update(result + pd.Timedelta(days=1)) + expected = DataFrame([pd.Timestamp("2019-01-02", tz="UTC")]) + tm.assert_frame_equal(result, expected) + if not using_copy_on_write: + tm.assert_frame_equal(view, expected) + else: + tm.assert_frame_equal(view, orig) + + def test_update_with_different_dtype(self, using_copy_on_write): + # GH#3217 + df = DataFrame({"a": [1, 3], "b": [np.nan, 2]}) + df["c"] = np.nan + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + df.update({"c": Series(["foo"], index=[0])}) + + expected = DataFrame( + { + "a": [1, 3], + "b": [np.nan, 2], + "c": Series(["foo", np.nan]), + } + ) + tm.assert_frame_equal(df, expected) + + @td.skip_array_manager_invalid_test + def test_update_modify_view( + self, using_copy_on_write, warn_copy_on_write, using_infer_string + ): + # GH#47188 + df = DataFrame({"A": ["1", np.nan], "B": ["100", np.nan]}) + df2 = DataFrame({"A": ["a", "x"], "B": ["100", "200"]}) + df2_orig = df2.copy() + result_view = df2[:] + # TODO(CoW-warn) better warning message + with tm.assert_cow_warning(warn_copy_on_write): + df2.update(df) + expected = DataFrame({"A": ["1", "x"], "B": ["100", "200"]}) + tm.assert_frame_equal(df2, expected) + if using_copy_on_write or using_infer_string: + tm.assert_frame_equal(result_view, df2_orig) + else: + tm.assert_frame_equal(result_view, expected) + + def test_update_dt_column_with_NaT_create_column(self): + # GH#16713 + df = DataFrame({"A": [1, None], "B": [pd.NaT, pd.to_datetime("2016-01-01")]}) + df2 = DataFrame({"A": [2, 3]}) + df.update(df2, overwrite=False) + expected = DataFrame( + {"A": [1.0, 3.0], "B": [pd.NaT, pd.to_datetime("2016-01-01")]} + ) + tm.assert_frame_equal(df, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_value_counts.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_value_counts.py new file mode 100644 index 0000000000000000000000000000000000000000..4136d641ef67f2d289142b34db7a2616de24ad24 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_value_counts.py @@ -0,0 +1,205 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm + + +def test_data_frame_value_counts_unsorted(): + df = pd.DataFrame( + {"num_legs": [2, 4, 4, 6], "num_wings": [2, 0, 0, 0]}, + index=["falcon", "dog", "cat", "ant"], + ) + + result = df.value_counts(sort=False) + expected = pd.Series( + data=[1, 2, 1], + index=pd.MultiIndex.from_arrays( + [(2, 4, 6), (2, 0, 0)], names=["num_legs", "num_wings"] + ), + name="count", + ) + + tm.assert_series_equal(result, expected) + + +def test_data_frame_value_counts_ascending(): + df = pd.DataFrame( + {"num_legs": [2, 4, 4, 6], "num_wings": [2, 0, 0, 0]}, + index=["falcon", "dog", "cat", "ant"], + ) + + result = df.value_counts(ascending=True) + expected = pd.Series( + data=[1, 1, 2], + index=pd.MultiIndex.from_arrays( + [(2, 6, 4), (2, 0, 0)], names=["num_legs", "num_wings"] + ), + name="count", + ) + + tm.assert_series_equal(result, expected) + + +def test_data_frame_value_counts_default(): + df = pd.DataFrame( + {"num_legs": [2, 4, 4, 6], "num_wings": [2, 0, 0, 0]}, + index=["falcon", "dog", "cat", "ant"], + ) + + result = df.value_counts() + expected = pd.Series( + data=[2, 1, 1], + index=pd.MultiIndex.from_arrays( + [(4, 2, 6), (0, 2, 0)], names=["num_legs", "num_wings"] + ), + name="count", + ) + + tm.assert_series_equal(result, expected) + + +def test_data_frame_value_counts_normalize(): + df = pd.DataFrame( + {"num_legs": [2, 4, 4, 6], "num_wings": [2, 0, 0, 0]}, + index=["falcon", "dog", "cat", "ant"], + ) + + result = df.value_counts(normalize=True) + expected = pd.Series( + data=[0.5, 0.25, 0.25], + index=pd.MultiIndex.from_arrays( + [(4, 2, 6), (0, 2, 0)], names=["num_legs", "num_wings"] + ), + name="proportion", + ) + + tm.assert_series_equal(result, expected) + + +def test_data_frame_value_counts_single_col_default(): + df = pd.DataFrame({"num_legs": [2, 4, 4, 6]}) + + result = df.value_counts() + expected = pd.Series( + data=[2, 1, 1], + index=pd.MultiIndex.from_arrays([[4, 2, 6]], names=["num_legs"]), + name="count", + ) + + tm.assert_series_equal(result, expected) + + +def test_data_frame_value_counts_empty(): + df_no_cols = pd.DataFrame() + + result = df_no_cols.value_counts() + expected = pd.Series( + [], dtype=np.int64, name="count", index=np.array([], dtype=np.intp) + ) + + tm.assert_series_equal(result, expected) + + +def test_data_frame_value_counts_empty_normalize(): + df_no_cols = pd.DataFrame() + + result = df_no_cols.value_counts(normalize=True) + expected = pd.Series( + [], dtype=np.float64, name="proportion", index=np.array([], dtype=np.intp) + ) + + tm.assert_series_equal(result, expected) + + +def test_data_frame_value_counts_dropna_true(nulls_fixture): + # GH 41334 + df = pd.DataFrame( + { + "first_name": ["John", "Anne", "John", "Beth"], + "middle_name": ["Smith", nulls_fixture, nulls_fixture, "Louise"], + }, + ) + result = df.value_counts() + expected = pd.Series( + data=[1, 1], + index=pd.MultiIndex.from_arrays( + [("Beth", "John"), ("Louise", "Smith")], names=["first_name", "middle_name"] + ), + name="count", + ) + + tm.assert_series_equal(result, expected) + + +def test_data_frame_value_counts_dropna_false(nulls_fixture): + # GH 41334 + df = pd.DataFrame( + { + "first_name": ["John", "Anne", "John", "Beth"], + "middle_name": ["Smith", nulls_fixture, nulls_fixture, "Louise"], + }, + ) + + result = df.value_counts(dropna=False) + expected = pd.Series( + data=[1, 1, 1, 1], + index=pd.MultiIndex( + levels=[ + pd.Index(["Anne", "Beth", "John"]), + pd.Index(["Louise", "Smith", np.nan]), + ], + codes=[[0, 1, 2, 2], [2, 0, 1, 2]], + names=["first_name", "middle_name"], + ), + name="count", + ) + + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("columns", (["first_name", "middle_name"], [0, 1])) +def test_data_frame_value_counts_subset(nulls_fixture, columns): + # GH 50829 + df = pd.DataFrame( + { + columns[0]: ["John", "Anne", "John", "Beth"], + columns[1]: ["Smith", nulls_fixture, nulls_fixture, "Louise"], + }, + ) + result = df.value_counts(columns[0]) + expected = pd.Series( + data=[2, 1, 1], + index=pd.Index(["John", "Anne", "Beth"], name=columns[0]), + name="count", + ) + + tm.assert_series_equal(result, expected) + + +def test_value_counts_categorical_future_warning(): + # GH#54775 + df = pd.DataFrame({"a": [1, 2, 3]}, dtype="category") + result = df.value_counts() + expected = pd.Series( + 1, + index=pd.MultiIndex.from_arrays( + [pd.Index([1, 2, 3], name="a", dtype="category")] + ), + name="count", + ) + tm.assert_series_equal(result, expected) + + +def test_value_counts_with_missing_category(): + # GH-54836 + df = pd.DataFrame({"a": pd.Categorical([1, 2, 4], categories=[1, 2, 3, 4])}) + result = df.value_counts() + expected = pd.Series( + [1, 1, 1, 0], + index=pd.MultiIndex.from_arrays( + [pd.CategoricalIndex([1, 2, 4, 3], categories=[1, 2, 3, 4], name="a")] + ), + name="count", + ) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_values.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_values.py new file mode 100644 index 0000000000000000000000000000000000000000..bbca4ee1b88b1b756ea27140d2944d349049c37c --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_values.py @@ -0,0 +1,280 @@ +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +from pandas import ( + DataFrame, + NaT, + Series, + Timestamp, + date_range, + period_range, +) +import pandas._testing as tm + + +class TestDataFrameValues: + @td.skip_array_manager_invalid_test + def test_values(self, float_frame, using_copy_on_write): + if using_copy_on_write: + with pytest.raises(ValueError, match="read-only"): + float_frame.values[:, 0] = 5.0 + assert (float_frame.values[:, 0] != 5).all() + else: + float_frame.values[:, 0] = 5.0 + assert (float_frame.values[:, 0] == 5).all() + + def test_more_values(self, float_string_frame): + values = float_string_frame.values + assert values.shape[1] == len(float_string_frame.columns) + + def test_values_mixed_dtypes(self, float_frame, float_string_frame): + frame = float_frame + arr = frame.values + + frame_cols = frame.columns + for i, row in enumerate(arr): + for j, value in enumerate(row): + col = frame_cols[j] + if np.isnan(value): + assert np.isnan(frame[col].iloc[i]) + else: + assert value == frame[col].iloc[i] + + # mixed type + arr = float_string_frame[["foo", "A"]].values + assert arr[0, 0] == "bar" + + df = DataFrame({"complex": [1j, 2j, 3j], "real": [1, 2, 3]}) + arr = df.values + assert arr[0, 0] == 1j + + def test_values_duplicates(self): + df = DataFrame( + [[1, 2, "a", "b"], [1, 2, "a", "b"]], columns=["one", "one", "two", "two"] + ) + + result = df.values + expected = np.array([[1, 2, "a", "b"], [1, 2, "a", "b"]], dtype=object) + + tm.assert_numpy_array_equal(result, expected) + + def test_values_with_duplicate_columns(self): + df = DataFrame([[1, 2.5], [3, 4.5]], index=[1, 2], columns=["x", "x"]) + result = df.values + expected = np.array([[1, 2.5], [3, 4.5]]) + assert (result == expected).all().all() + + @pytest.mark.parametrize("constructor", [date_range, period_range]) + def test_values_casts_datetimelike_to_object(self, constructor): + series = Series(constructor("2000-01-01", periods=10, freq="D")) + + expected = series.astype("object") + + df = DataFrame( + {"a": series, "b": np.random.default_rng(2).standard_normal(len(series))} + ) + + result = df.values.squeeze() + assert (result[:, 0] == expected.values).all() + + df = DataFrame({"a": series, "b": ["foo"] * len(series)}) + + result = df.values.squeeze() + assert (result[:, 0] == expected.values).all() + + def test_frame_values_with_tz(self): + tz = "US/Central" + df = DataFrame({"A": date_range("2000", periods=4, tz=tz)}) + result = df.values + expected = np.array( + [ + [Timestamp("2000-01-01", tz=tz)], + [Timestamp("2000-01-02", tz=tz)], + [Timestamp("2000-01-03", tz=tz)], + [Timestamp("2000-01-04", tz=tz)], + ] + ) + tm.assert_numpy_array_equal(result, expected) + + # two columns, homogeneous + + df["B"] = df["A"] + result = df.values + expected = np.concatenate([expected, expected], axis=1) + tm.assert_numpy_array_equal(result, expected) + + # three columns, heterogeneous + est = "US/Eastern" + df["C"] = df["A"].dt.tz_convert(est) + + new = np.array( + [ + [Timestamp("2000-01-01T01:00:00", tz=est)], + [Timestamp("2000-01-02T01:00:00", tz=est)], + [Timestamp("2000-01-03T01:00:00", tz=est)], + [Timestamp("2000-01-04T01:00:00", tz=est)], + ] + ) + expected = np.concatenate([expected, new], axis=1) + result = df.values + tm.assert_numpy_array_equal(result, expected) + + def test_interleave_with_tzaware(self, timezone_frame): + # interleave with object + result = timezone_frame.assign(D="foo").values + expected = np.array( + [ + [ + Timestamp("2013-01-01 00:00:00"), + Timestamp("2013-01-02 00:00:00"), + Timestamp("2013-01-03 00:00:00"), + ], + [ + Timestamp("2013-01-01 00:00:00-0500", tz="US/Eastern"), + NaT, + Timestamp("2013-01-03 00:00:00-0500", tz="US/Eastern"), + ], + [ + Timestamp("2013-01-01 00:00:00+0100", tz="CET"), + NaT, + Timestamp("2013-01-03 00:00:00+0100", tz="CET"), + ], + ["foo", "foo", "foo"], + ], + dtype=object, + ).T + tm.assert_numpy_array_equal(result, expected) + + # interleave with only datetime64[ns] + result = timezone_frame.values + expected = np.array( + [ + [ + Timestamp("2013-01-01 00:00:00"), + Timestamp("2013-01-02 00:00:00"), + Timestamp("2013-01-03 00:00:00"), + ], + [ + Timestamp("2013-01-01 00:00:00-0500", tz="US/Eastern"), + NaT, + Timestamp("2013-01-03 00:00:00-0500", tz="US/Eastern"), + ], + [ + Timestamp("2013-01-01 00:00:00+0100", tz="CET"), + NaT, + Timestamp("2013-01-03 00:00:00+0100", tz="CET"), + ], + ], + dtype=object, + ).T + tm.assert_numpy_array_equal(result, expected) + + def test_values_interleave_non_unique_cols(self): + df = DataFrame( + [[Timestamp("20130101"), 3.5], [Timestamp("20130102"), 4.5]], + columns=["x", "x"], + index=[1, 2], + ) + + df_unique = df.copy() + df_unique.columns = ["x", "y"] + assert df_unique.values.shape == df.values.shape + tm.assert_numpy_array_equal(df_unique.values[0], df.values[0]) + tm.assert_numpy_array_equal(df_unique.values[1], df.values[1]) + + def test_values_numeric_cols(self, float_frame): + float_frame["foo"] = "bar" + + values = float_frame[["A", "B", "C", "D"]].values + assert values.dtype == np.float64 + + def test_values_lcd(self, mixed_float_frame, mixed_int_frame): + # mixed lcd + values = mixed_float_frame[["A", "B", "C", "D"]].values + assert values.dtype == np.float64 + + values = mixed_float_frame[["A", "B", "C"]].values + assert values.dtype == np.float32 + + values = mixed_float_frame[["C"]].values + assert values.dtype == np.float16 + + # GH#10364 + # B uint64 forces float because there are other signed int types + values = mixed_int_frame[["A", "B", "C", "D"]].values + assert values.dtype == np.float64 + + values = mixed_int_frame[["A", "D"]].values + assert values.dtype == np.int64 + + # B uint64 forces float because there are other signed int types + values = mixed_int_frame[["A", "B", "C"]].values + assert values.dtype == np.float64 + + # as B and C are both unsigned, no forcing to float is needed + values = mixed_int_frame[["B", "C"]].values + assert values.dtype == np.uint64 + + values = mixed_int_frame[["A", "C"]].values + assert values.dtype == np.int32 + + values = mixed_int_frame[["C", "D"]].values + assert values.dtype == np.int64 + + values = mixed_int_frame[["A"]].values + assert values.dtype == np.int32 + + values = mixed_int_frame[["C"]].values + assert values.dtype == np.uint8 + + +class TestPrivateValues: + @td.skip_array_manager_invalid_test + def test_private_values_dt64tz(self, using_copy_on_write): + dta = date_range("2000", periods=4, tz="US/Central")._data.reshape(-1, 1) + + df = DataFrame(dta, columns=["A"]) + tm.assert_equal(df._values, dta) + + if using_copy_on_write: + assert not np.shares_memory(df._values._ndarray, dta._ndarray) + else: + # we have a view + assert np.shares_memory(df._values._ndarray, dta._ndarray) + + # TimedeltaArray + tda = dta - dta + df2 = df - df + tm.assert_equal(df2._values, tda) + + @td.skip_array_manager_invalid_test + def test_private_values_dt64tz_multicol(self, using_copy_on_write): + dta = date_range("2000", periods=8, tz="US/Central")._data.reshape(-1, 2) + + df = DataFrame(dta, columns=["A", "B"]) + tm.assert_equal(df._values, dta) + + if using_copy_on_write: + assert not np.shares_memory(df._values._ndarray, dta._ndarray) + else: + # we have a view + assert np.shares_memory(df._values._ndarray, dta._ndarray) + + # TimedeltaArray + tda = dta - dta + df2 = df - df + tm.assert_equal(df2._values, tda) + + def test_private_values_dt64_multiblock(self): + dta = date_range("2000", periods=8)._data + + df = DataFrame({"A": dta[:4]}, copy=False) + df["B"] = dta[4:] + + assert len(df._mgr.arrays) == 2 + + result = df._values + expected = dta.reshape(2, 4).T + tm.assert_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/interchange/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/interchange/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/interchange/test_impl.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/interchange/test_impl.py new file mode 100644 index 0000000000000000000000000000000000000000..5563ee8b4caed45bea14ddebaf4ad41f2b846ea6 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/interchange/test_impl.py @@ -0,0 +1,616 @@ +from datetime import ( + datetime, + timezone, +) + +import numpy as np +import pytest + +from pandas._libs.tslibs import iNaT +from pandas.compat import ( + is_ci_environment, + is_platform_windows, +) +from pandas.compat.numpy import np_version_lt1p23 + +import pandas as pd +import pandas._testing as tm +from pandas.core.interchange.column import PandasColumn +from pandas.core.interchange.dataframe_protocol import ( + ColumnNullType, + DtypeKind, +) +from pandas.core.interchange.from_dataframe import from_dataframe +from pandas.core.interchange.utils import ArrowCTypes + + +@pytest.fixture +def data_categorical(): + return { + "ordered": pd.Categorical(list("testdata") * 30, ordered=True), + "unordered": pd.Categorical(list("testdata") * 30, ordered=False), + } + + +@pytest.fixture +def string_data(): + return { + "separator data": [ + "abC|DeF,Hik", + "234,3245.67", + "gSaf,qWer|Gre", + "asd3,4sad|", + np.nan, + ] + } + + +@pytest.mark.parametrize("data", [("ordered", True), ("unordered", False)]) +def test_categorical_dtype(data, data_categorical): + df = pd.DataFrame({"A": (data_categorical[data[0]])}) + + col = df.__dataframe__().get_column_by_name("A") + assert col.dtype[0] == DtypeKind.CATEGORICAL + assert col.null_count == 0 + assert col.describe_null == (ColumnNullType.USE_SENTINEL, -1) + assert col.num_chunks() == 1 + desc_cat = col.describe_categorical + assert desc_cat["is_ordered"] == data[1] + assert desc_cat["is_dictionary"] is True + assert isinstance(desc_cat["categories"], PandasColumn) + tm.assert_series_equal( + desc_cat["categories"]._col, pd.Series(["a", "d", "e", "s", "t"]) + ) + + tm.assert_frame_equal(df, from_dataframe(df.__dataframe__())) + + +def test_categorical_pyarrow(): + # GH 49889 + pa = pytest.importorskip("pyarrow", "11.0.0") + + arr = ["Mon", "Tue", "Mon", "Wed", "Mon", "Thu", "Fri", "Sat", "Sun"] + table = pa.table({"weekday": pa.array(arr).dictionary_encode()}) + exchange_df = table.__dataframe__() + result = from_dataframe(exchange_df) + weekday = pd.Categorical( + arr, categories=["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"] + ) + expected = pd.DataFrame({"weekday": weekday}) + tm.assert_frame_equal(result, expected) + + +def test_empty_categorical_pyarrow(): + # https://github.com/pandas-dev/pandas/issues/53077 + pa = pytest.importorskip("pyarrow", "11.0.0") + + arr = [None] + table = pa.table({"arr": pa.array(arr, "float64").dictionary_encode()}) + exchange_df = table.__dataframe__() + result = pd.api.interchange.from_dataframe(exchange_df) + expected = pd.DataFrame({"arr": pd.Categorical([np.nan])}) + tm.assert_frame_equal(result, expected) + + +def test_large_string_pyarrow(): + # GH 52795 + pa = pytest.importorskip("pyarrow", "11.0.0") + + arr = ["Mon", "Tue"] + table = pa.table({"weekday": pa.array(arr, "large_string")}) + exchange_df = table.__dataframe__() + result = from_dataframe(exchange_df) + expected = pd.DataFrame({"weekday": ["Mon", "Tue"]}) + tm.assert_frame_equal(result, expected) + + # check round-trip + assert pa.Table.equals(pa.interchange.from_dataframe(result), table) + + +@pytest.mark.parametrize( + ("offset", "length", "expected_values"), + [ + (0, None, [3.3, float("nan"), 2.1]), + (1, None, [float("nan"), 2.1]), + (2, None, [2.1]), + (0, 2, [3.3, float("nan")]), + (0, 1, [3.3]), + (1, 1, [float("nan")]), + ], +) +def test_bitmasks_pyarrow(offset, length, expected_values): + # GH 52795 + pa = pytest.importorskip("pyarrow", "11.0.0") + + arr = [3.3, None, 2.1] + table = pa.table({"arr": arr}).slice(offset, length) + exchange_df = table.__dataframe__() + result = from_dataframe(exchange_df) + expected = pd.DataFrame({"arr": expected_values}) + tm.assert_frame_equal(result, expected) + + # check round-trip + assert pa.Table.equals(pa.interchange.from_dataframe(result), table) + + +@pytest.mark.parametrize( + "data", + [ + lambda: np.random.default_rng(2).integers(-100, 100), + lambda: np.random.default_rng(2).integers(1, 100), + lambda: np.random.default_rng(2).random(), + lambda: np.random.default_rng(2).choice([True, False]), + lambda: datetime( + year=np.random.default_rng(2).integers(1900, 2100), + month=np.random.default_rng(2).integers(1, 12), + day=np.random.default_rng(2).integers(1, 20), + ), + ], +) +def test_dataframe(data): + NCOLS, NROWS = 10, 20 + data = { + f"col{int((i - NCOLS / 2) % NCOLS + 1)}": [data() for _ in range(NROWS)] + for i in range(NCOLS) + } + df = pd.DataFrame(data) + + df2 = df.__dataframe__() + + assert df2.num_columns() == NCOLS + assert df2.num_rows() == NROWS + + assert list(df2.column_names()) == list(data.keys()) + + indices = (0, 2) + names = tuple(list(data.keys())[idx] for idx in indices) + + result = from_dataframe(df2.select_columns(indices)) + expected = from_dataframe(df2.select_columns_by_name(names)) + tm.assert_frame_equal(result, expected) + + assert isinstance(result.attrs["_INTERCHANGE_PROTOCOL_BUFFERS"], list) + assert isinstance(expected.attrs["_INTERCHANGE_PROTOCOL_BUFFERS"], list) + + +def test_missing_from_masked(): + df = pd.DataFrame( + { + "x": np.array([1.0, 2.0, 3.0, 4.0, 0.0]), + "y": np.array([1.5, 2.5, 3.5, 4.5, 0]), + "z": np.array([1.0, 0.0, 1.0, 1.0, 1.0]), + } + ) + + rng = np.random.default_rng(2) + dict_null = {col: rng.integers(low=0, high=len(df)) for col in df.columns} + for col, num_nulls in dict_null.items(): + null_idx = df.index[ + rng.choice(np.arange(len(df)), size=num_nulls, replace=False) + ] + df.loc[null_idx, col] = None + + df2 = df.__dataframe__() + + assert df2.get_column_by_name("x").null_count == dict_null["x"] + assert df2.get_column_by_name("y").null_count == dict_null["y"] + assert df2.get_column_by_name("z").null_count == dict_null["z"] + + +@pytest.mark.parametrize( + "data", + [ + {"x": [1.5, 2.5, 3.5], "y": [9.2, 10.5, 11.8]}, + {"x": [1, 2, 0], "y": [9.2, 10.5, 11.8]}, + { + "x": np.array([True, True, False]), + "y": np.array([1, 2, 0]), + "z": np.array([9.2, 10.5, 11.8]), + }, + ], +) +def test_mixed_data(data): + df = pd.DataFrame(data) + df2 = df.__dataframe__() + + for col_name in df.columns: + assert df2.get_column_by_name(col_name).null_count == 0 + + +def test_mixed_missing(): + df = pd.DataFrame( + { + "x": np.array([True, None, False, None, True]), + "y": np.array([None, 2, None, 1, 2]), + "z": np.array([9.2, 10.5, None, 11.8, None]), + } + ) + + df2 = df.__dataframe__() + + for col_name in df.columns: + assert df2.get_column_by_name(col_name).null_count == 2 + + +def test_string(string_data): + test_str_data = string_data["separator data"] + [""] + df = pd.DataFrame({"A": test_str_data}) + col = df.__dataframe__().get_column_by_name("A") + + assert col.size() == 6 + assert col.null_count == 1 + assert col.dtype[0] == DtypeKind.STRING + assert col.describe_null == (ColumnNullType.USE_BYTEMASK, 0) + + df_sliced = df[1:] + col = df_sliced.__dataframe__().get_column_by_name("A") + assert col.size() == 5 + assert col.null_count == 1 + assert col.dtype[0] == DtypeKind.STRING + assert col.describe_null == (ColumnNullType.USE_BYTEMASK, 0) + + +def test_nonstring_object(): + df = pd.DataFrame({"A": ["a", 10, 1.0, ()]}) + col = df.__dataframe__().get_column_by_name("A") + with pytest.raises(NotImplementedError, match="not supported yet"): + col.dtype + + +def test_datetime(): + df = pd.DataFrame({"A": [pd.Timestamp("2022-01-01"), pd.NaT]}) + col = df.__dataframe__().get_column_by_name("A") + + assert col.size() == 2 + assert col.null_count == 1 + assert col.dtype[0] == DtypeKind.DATETIME + assert col.describe_null == (ColumnNullType.USE_SENTINEL, iNaT) + + tm.assert_frame_equal(df, from_dataframe(df.__dataframe__())) + + +@pytest.mark.skipif(np_version_lt1p23, reason="Numpy > 1.23 required") +def test_categorical_to_numpy_dlpack(): + # https://github.com/pandas-dev/pandas/issues/48393 + df = pd.DataFrame({"A": pd.Categorical(["a", "b", "a"])}) + col = df.__dataframe__().get_column_by_name("A") + result = np.from_dlpack(col.get_buffers()["data"][0]) + expected = np.array([0, 1, 0], dtype="int8") + tm.assert_numpy_array_equal(result, expected) + + +@pytest.mark.parametrize("data", [{}, {"a": []}]) +def test_empty_pyarrow(data): + # GH 53155 + pytest.importorskip("pyarrow", "11.0.0") + from pyarrow.interchange import from_dataframe as pa_from_dataframe + + expected = pd.DataFrame(data) + arrow_df = pa_from_dataframe(expected) + result = from_dataframe(arrow_df) + tm.assert_frame_equal(result, expected, check_column_type=False) + + +def test_multi_chunk_pyarrow() -> None: + pa = pytest.importorskip("pyarrow", "11.0.0") + n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) + names = ["n_legs"] + table = pa.table([n_legs], names=names) + with pytest.raises( + RuntimeError, + match="Cannot do zero copy conversion into multi-column DataFrame block", + ): + pd.api.interchange.from_dataframe(table, allow_copy=False) + + +def test_multi_chunk_column() -> None: + pytest.importorskip("pyarrow", "11.0.0") + ser = pd.Series([1, 2, None], dtype="Int64[pyarrow]") + df = pd.concat([ser, ser], ignore_index=True).to_frame("a") + df_orig = df.copy() + with pytest.raises( + RuntimeError, match="Found multi-chunk pyarrow array, but `allow_copy` is False" + ): + pd.api.interchange.from_dataframe(df.__dataframe__(allow_copy=False)) + result = pd.api.interchange.from_dataframe(df.__dataframe__(allow_copy=True)) + # Interchange protocol defaults to creating numpy-backed columns, so currently this + # is 'float64'. + expected = pd.DataFrame({"a": [1.0, 2.0, None, 1.0, 2.0, None]}, dtype="float64") + tm.assert_frame_equal(result, expected) + + # Check that the rechunking we did didn't modify the original DataFrame. + tm.assert_frame_equal(df, df_orig) + assert len(df["a"].array._pa_array.chunks) == 2 + assert len(df_orig["a"].array._pa_array.chunks) == 2 + + +def test_timestamp_ns_pyarrow(): + # GH 56712 + pytest.importorskip("pyarrow", "11.0.0") + timestamp_args = { + "year": 2000, + "month": 1, + "day": 1, + "hour": 1, + "minute": 1, + "second": 1, + } + df = pd.Series( + [datetime(**timestamp_args)], + dtype="timestamp[ns][pyarrow]", + name="col0", + ).to_frame() + + dfi = df.__dataframe__() + result = pd.api.interchange.from_dataframe(dfi)["col0"].item() + + expected = pd.Timestamp(**timestamp_args) + assert result == expected + + +@pytest.mark.parametrize("tz", ["UTC", "US/Pacific"]) +@pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"]) +def test_datetimetzdtype(tz, unit): + # GH 54239 + tz_data = ( + pd.date_range("2018-01-01", periods=5, freq="D").tz_localize(tz).as_unit(unit) + ) + df = pd.DataFrame({"ts_tz": tz_data}) + tm.assert_frame_equal(df, from_dataframe(df.__dataframe__())) + + +def test_interchange_from_non_pandas_tz_aware(request): + # GH 54239, 54287 + pa = pytest.importorskip("pyarrow", "11.0.0") + import pyarrow.compute as pc + + if is_platform_windows() and is_ci_environment(): + mark = pytest.mark.xfail( + raises=pa.ArrowInvalid, + reason=( + "TODO: Set ARROW_TIMEZONE_DATABASE environment variable " + "on CI to path to the tzdata for pyarrow." + ), + ) + request.applymarker(mark) + + arr = pa.array([datetime(2020, 1, 1), None, datetime(2020, 1, 2)]) + arr = pc.assume_timezone(arr, "Asia/Kathmandu") + table = pa.table({"arr": arr}) + exchange_df = table.__dataframe__() + result = from_dataframe(exchange_df) + + expected = pd.DataFrame( + ["2020-01-01 00:00:00+05:45", "NaT", "2020-01-02 00:00:00+05:45"], + columns=["arr"], + dtype="datetime64[us, Asia/Kathmandu]", + ) + tm.assert_frame_equal(expected, result) + + +def test_interchange_from_corrected_buffer_dtypes(monkeypatch) -> None: + # https://github.com/pandas-dev/pandas/issues/54781 + df = pd.DataFrame({"a": ["foo", "bar"]}).__dataframe__() + interchange = df.__dataframe__() + column = interchange.get_column_by_name("a") + buffers = column.get_buffers() + buffers_data = buffers["data"] + buffer_dtype = buffers_data[1] + buffer_dtype = ( + DtypeKind.UINT, + 8, + ArrowCTypes.UINT8, + buffer_dtype[3], + ) + buffers["data"] = (buffers_data[0], buffer_dtype) + column.get_buffers = lambda: buffers + interchange.get_column_by_name = lambda _: column + monkeypatch.setattr(df, "__dataframe__", lambda allow_copy: interchange) + pd.api.interchange.from_dataframe(df) + + +def test_empty_string_column(): + # https://github.com/pandas-dev/pandas/issues/56703 + df = pd.DataFrame({"a": []}, dtype=str) + df2 = df.__dataframe__() + result = pd.api.interchange.from_dataframe(df2) + tm.assert_frame_equal(df, result) + + +def test_large_string(): + # GH#56702 + pytest.importorskip("pyarrow") + df = pd.DataFrame({"a": ["x"]}, dtype="large_string[pyarrow]") + result = pd.api.interchange.from_dataframe(df.__dataframe__()) + expected = pd.DataFrame({"a": ["x"]}, dtype="str") + tm.assert_frame_equal(result, expected) + + +def test_non_str_names(): + # https://github.com/pandas-dev/pandas/issues/56701 + df = pd.Series([1, 2, 3], name=0).to_frame() + names = df.__dataframe__().column_names() + assert names == ["0"] + + +def test_non_str_names_w_duplicates(): + # https://github.com/pandas-dev/pandas/issues/56701 + df = pd.DataFrame({"0": [1, 2, 3], 0: [4, 5, 6]}) + dfi = df.__dataframe__() + with pytest.raises( + TypeError, + match=( + "Expected a Series, got a DataFrame. This likely happened because you " + "called __dataframe__ on a DataFrame which, after converting column " + r"names to string, resulted in duplicated names: Index\(\['0', '0'\], " + r"dtype='(str|object)'\). Please rename these columns before using the " + "interchange protocol." + ), + ): + pd.api.interchange.from_dataframe(dfi, allow_copy=False) + + +@pytest.mark.parametrize( + ("data", "dtype", "expected_dtype"), + [ + ([1, 2, None], "Int64", "int64"), + ([1, 2, None], "Int64[pyarrow]", "int64"), + ([1, 2, None], "Int8", "int8"), + ([1, 2, None], "Int8[pyarrow]", "int8"), + ( + [1, 2, None], + "UInt64", + "uint64", + ), + ( + [1, 2, None], + "UInt64[pyarrow]", + "uint64", + ), + ([1.0, 2.25, None], "Float32", "float32"), + ([1.0, 2.25, None], "Float32[pyarrow]", "float32"), + ([True, False, None], "boolean", "bool"), + ([True, False, None], "boolean[pyarrow]", "bool"), + (["much ado", "about", None], pd.StringDtype(na_value=np.nan), "large_string"), + (["much ado", "about", None], "string[pyarrow]", "large_string"), + ( + [datetime(2020, 1, 1), datetime(2020, 1, 2), None], + "timestamp[ns][pyarrow]", + "timestamp[ns]", + ), + ( + [datetime(2020, 1, 1), datetime(2020, 1, 2), None], + "timestamp[us][pyarrow]", + "timestamp[us]", + ), + ( + [ + datetime(2020, 1, 1, tzinfo=timezone.utc), + datetime(2020, 1, 2, tzinfo=timezone.utc), + None, + ], + "timestamp[us, Asia/Kathmandu][pyarrow]", + "timestamp[us, tz=Asia/Kathmandu]", + ), + ], +) +def test_pandas_nullable_with_missing_values( + data: list, dtype: str, expected_dtype: str +) -> None: + # https://github.com/pandas-dev/pandas/issues/57643 + # https://github.com/pandas-dev/pandas/issues/57664 + pa = pytest.importorskip("pyarrow", "11.0.0") + import pyarrow.interchange as pai + + if expected_dtype == "timestamp[us, tz=Asia/Kathmandu]": + expected_dtype = pa.timestamp("us", "Asia/Kathmandu") + + df = pd.DataFrame({"a": data}, dtype=dtype) + result = pai.from_dataframe(df.__dataframe__())["a"] + assert result.type == expected_dtype + assert result[0].as_py() == data[0] + assert result[1].as_py() == data[1] + assert result[2].as_py() is None + + +@pytest.mark.parametrize( + ("data", "dtype", "expected_dtype"), + [ + ([1, 2, 3], "Int64", "int64"), + ([1, 2, 3], "Int64[pyarrow]", "int64"), + ([1, 2, 3], "Int8", "int8"), + ([1, 2, 3], "Int8[pyarrow]", "int8"), + ( + [1, 2, 3], + "UInt64", + "uint64", + ), + ( + [1, 2, 3], + "UInt64[pyarrow]", + "uint64", + ), + ([1.0, 2.25, 5.0], "Float32", "float32"), + ([1.0, 2.25, 5.0], "Float32[pyarrow]", "float32"), + ([True, False, False], "boolean", "bool"), + ([True, False, False], "boolean[pyarrow]", "bool"), + ( + ["much ado", "about", "nothing"], + pd.StringDtype(na_value=np.nan), + "large_string", + ), + (["much ado", "about", "nothing"], "string[pyarrow]", "large_string"), + ( + [datetime(2020, 1, 1), datetime(2020, 1, 2), datetime(2020, 1, 3)], + "timestamp[ns][pyarrow]", + "timestamp[ns]", + ), + ( + [datetime(2020, 1, 1), datetime(2020, 1, 2), datetime(2020, 1, 3)], + "timestamp[us][pyarrow]", + "timestamp[us]", + ), + ( + [ + datetime(2020, 1, 1, tzinfo=timezone.utc), + datetime(2020, 1, 2, tzinfo=timezone.utc), + datetime(2020, 1, 3, tzinfo=timezone.utc), + ], + "timestamp[us, Asia/Kathmandu][pyarrow]", + "timestamp[us, tz=Asia/Kathmandu]", + ), + ], +) +def test_pandas_nullable_without_missing_values( + data: list, dtype: str, expected_dtype: str +) -> None: + # https://github.com/pandas-dev/pandas/issues/57643 + pa = pytest.importorskip("pyarrow", "11.0.0") + import pyarrow.interchange as pai + + if expected_dtype == "timestamp[us, tz=Asia/Kathmandu]": + expected_dtype = pa.timestamp("us", "Asia/Kathmandu") + + df = pd.DataFrame({"a": data}, dtype=dtype) + result = pai.from_dataframe(df.__dataframe__())["a"] + assert result.type == expected_dtype + assert result[0].as_py() == data[0] + assert result[1].as_py() == data[1] + assert result[2].as_py() == data[2] + + +def test_string_validity_buffer() -> None: + # https://github.com/pandas-dev/pandas/issues/57761 + pytest.importorskip("pyarrow", "11.0.0") + df = pd.DataFrame({"a": ["x"]}, dtype="large_string[pyarrow]") + result = df.__dataframe__().get_column_by_name("a").get_buffers()["validity"] + assert result is None + + +def test_string_validity_buffer_no_missing() -> None: + # https://github.com/pandas-dev/pandas/issues/57762 + pytest.importorskip("pyarrow", "11.0.0") + df = pd.DataFrame({"a": ["x", None]}, dtype="large_string[pyarrow]") + validity = df.__dataframe__().get_column_by_name("a").get_buffers()["validity"] + assert validity is not None + result = validity[1] + expected = (DtypeKind.BOOL, 1, ArrowCTypes.BOOL, "=") + assert result == expected + + +def test_empty_dataframe(): + # https://github.com/pandas-dev/pandas/issues/56700 + df = pd.DataFrame({"a": []}, dtype="int8") + dfi = df.__dataframe__() + result = pd.api.interchange.from_dataframe(dfi, allow_copy=False) + expected = pd.DataFrame({"a": []}, dtype="int8") + tm.assert_frame_equal(result, expected) + + +def test_from_dataframe_list_dtype(): + pa = pytest.importorskip("pyarrow", "14.0.0") + data = {"a": [[1, 2], [4, 5, 6]]} + tbl = pa.table(data) + result = from_dataframe(tbl) + expected = pd.DataFrame(data) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/interchange/test_spec_conformance.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/interchange/test_spec_conformance.py new file mode 100644 index 0000000000000000000000000000000000000000..7c02379c118539032cb79d682d4baa2c7ae1fb81 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/interchange/test_spec_conformance.py @@ -0,0 +1,175 @@ +""" +A verbatim copy (vendored) of the spec tests. +Taken from https://github.com/data-apis/dataframe-api +""" +import ctypes +import math + +import pytest + +import pandas as pd + + +@pytest.fixture +def df_from_dict(): + def maker(dct, is_categorical=False): + df = pd.DataFrame(dct) + return df.astype("category") if is_categorical else df + + return maker + + +@pytest.mark.parametrize( + "test_data", + [ + {"a": ["foo", "bar"], "b": ["baz", "qux"]}, + {"a": [1.5, 2.5, 3.5], "b": [9.2, 10.5, 11.8]}, + {"A": [1, 2, 3, 4], "B": [1, 2, 3, 4]}, + ], + ids=["str_data", "float_data", "int_data"], +) +def test_only_one_dtype(test_data, df_from_dict): + columns = list(test_data.keys()) + df = df_from_dict(test_data) + dfX = df.__dataframe__() + + column_size = len(test_data[columns[0]]) + for column in columns: + null_count = dfX.get_column_by_name(column).null_count + assert null_count == 0 + assert isinstance(null_count, int) + assert dfX.get_column_by_name(column).size() == column_size + assert dfX.get_column_by_name(column).offset == 0 + + +def test_mixed_dtypes(df_from_dict): + df = df_from_dict( + { + "a": [1, 2, 3], # dtype kind INT = 0 + "b": [3, 4, 5], # dtype kind INT = 0 + "c": [1.5, 2.5, 3.5], # dtype kind FLOAT = 2 + "d": [9, 10, 11], # dtype kind INT = 0 + "e": [True, False, True], # dtype kind BOOLEAN = 20 + "f": ["a", "", "c"], # dtype kind STRING = 21 + } + ) + dfX = df.__dataframe__() + # for meanings of dtype[0] see the spec; we cannot import the spec here as this + # file is expected to be vendored *anywhere*; + # values for dtype[0] are explained above + columns = {"a": 0, "b": 0, "c": 2, "d": 0, "e": 20, "f": 21} + + for column, kind in columns.items(): + colX = dfX.get_column_by_name(column) + assert colX.null_count == 0 + assert isinstance(colX.null_count, int) + assert colX.size() == 3 + assert colX.offset == 0 + + assert colX.dtype[0] == kind + + assert dfX.get_column_by_name("c").dtype[1] == 64 + + +def test_na_float(df_from_dict): + df = df_from_dict({"a": [1.0, math.nan, 2.0]}) + dfX = df.__dataframe__() + colX = dfX.get_column_by_name("a") + assert colX.null_count == 1 + assert isinstance(colX.null_count, int) + + +def test_noncategorical(df_from_dict): + df = df_from_dict({"a": [1, 2, 3]}) + dfX = df.__dataframe__() + colX = dfX.get_column_by_name("a") + with pytest.raises(TypeError, match=".*categorical.*"): + colX.describe_categorical + + +def test_categorical(df_from_dict): + df = df_from_dict( + {"weekday": ["Mon", "Tue", "Mon", "Wed", "Mon", "Thu", "Fri", "Sat", "Sun"]}, + is_categorical=True, + ) + + colX = df.__dataframe__().get_column_by_name("weekday") + categorical = colX.describe_categorical + assert isinstance(categorical["is_ordered"], bool) + assert isinstance(categorical["is_dictionary"], bool) + + +def test_dataframe(df_from_dict): + df = df_from_dict( + {"x": [True, True, False], "y": [1, 2, 0], "z": [9.2, 10.5, 11.8]} + ) + dfX = df.__dataframe__() + + assert dfX.num_columns() == 3 + assert dfX.num_rows() == 3 + assert dfX.num_chunks() == 1 + assert list(dfX.column_names()) == ["x", "y", "z"] + assert list(dfX.select_columns((0, 2)).column_names()) == list( + dfX.select_columns_by_name(("x", "z")).column_names() + ) + + +@pytest.mark.parametrize(["size", "n_chunks"], [(10, 3), (12, 3), (12, 5)]) +def test_df_get_chunks(size, n_chunks, df_from_dict): + df = df_from_dict({"x": list(range(size))}) + dfX = df.__dataframe__() + chunks = list(dfX.get_chunks(n_chunks)) + assert len(chunks) == n_chunks + assert sum(chunk.num_rows() for chunk in chunks) == size + + +@pytest.mark.parametrize(["size", "n_chunks"], [(10, 3), (12, 3), (12, 5)]) +def test_column_get_chunks(size, n_chunks, df_from_dict): + df = df_from_dict({"x": list(range(size))}) + dfX = df.__dataframe__() + chunks = list(dfX.get_column(0).get_chunks(n_chunks)) + assert len(chunks) == n_chunks + assert sum(chunk.size() for chunk in chunks) == size + + +def test_get_columns(df_from_dict): + df = df_from_dict({"a": [0, 1], "b": [2.5, 3.5]}) + dfX = df.__dataframe__() + for colX in dfX.get_columns(): + assert colX.size() == 2 + assert colX.num_chunks() == 1 + # for meanings of dtype[0] see the spec; we cannot import the spec here as this + # file is expected to be vendored *anywhere* + assert dfX.get_column(0).dtype[0] == 0 # INT + assert dfX.get_column(1).dtype[0] == 2 # FLOAT + + +def test_buffer(df_from_dict): + arr = [0, 1, -1] + df = df_from_dict({"a": arr}) + dfX = df.__dataframe__() + colX = dfX.get_column(0) + bufX = colX.get_buffers() + + dataBuf, dataDtype = bufX["data"] + + assert dataBuf.bufsize > 0 + assert dataBuf.ptr != 0 + device, _ = dataBuf.__dlpack_device__() + + # for meanings of dtype[0] see the spec; we cannot import the spec here as this + # file is expected to be vendored *anywhere* + assert dataDtype[0] == 0 # INT + + if device == 1: # CPU-only as we're going to directly read memory here + bitwidth = dataDtype[1] + ctype = { + 8: ctypes.c_int8, + 16: ctypes.c_int16, + 32: ctypes.c_int32, + 64: ctypes.c_int64, + }[bitwidth] + + for idx, truth in enumerate(arr): + val = ctype.from_address(dataBuf.ptr + idx * (bitwidth // 8)).value + assert val == truth, f"Buffer at index {idx} mismatch" diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/interchange/test_utils.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/interchange/test_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a47bc2752ff32f5eb7630a3960e7611242cb73e3 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/interchange/test_utils.py @@ -0,0 +1,89 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas.core.interchange.utils import dtype_to_arrow_c_fmt + +# TODO: use ArrowSchema to get reference C-string. +# At the time, there is no way to access ArrowSchema holding a type format string +# from python. The only way to access it is to export the structure to a C-pointer, +# see DataType._export_to_c() method defined in +# https://github.com/apache/arrow/blob/master/python/pyarrow/types.pxi + + +@pytest.mark.parametrize( + "pandas_dtype, c_string", + [ + (np.dtype("bool"), "b"), + (np.dtype("int8"), "c"), + (np.dtype("uint8"), "C"), + (np.dtype("int16"), "s"), + (np.dtype("uint16"), "S"), + (np.dtype("int32"), "i"), + (np.dtype("uint32"), "I"), + (np.dtype("int64"), "l"), + (np.dtype("uint64"), "L"), + (np.dtype("float16"), "e"), + (np.dtype("float32"), "f"), + (np.dtype("float64"), "g"), + (pd.Series(["a"]).dtype, "u"), + ( + pd.Series([0]).astype("datetime64[ns]").dtype, + "tsn:", + ), + (pd.CategoricalDtype(["a"]), "l"), + (np.dtype("O"), "u"), + ], +) +def test_dtype_to_arrow_c_fmt(pandas_dtype, c_string): # PR01 + """Test ``dtype_to_arrow_c_fmt`` utility function.""" + assert dtype_to_arrow_c_fmt(pandas_dtype) == c_string + + +@pytest.mark.parametrize( + "pa_dtype, args_kwargs, c_string", + [ + ["null", {}, "n"], + ["bool_", {}, "b"], + ["uint8", {}, "C"], + ["uint16", {}, "S"], + ["uint32", {}, "I"], + ["uint64", {}, "L"], + ["int8", {}, "c"], + ["int16", {}, "S"], + ["int32", {}, "i"], + ["int64", {}, "l"], + ["float16", {}, "e"], + ["float32", {}, "f"], + ["float64", {}, "g"], + ["string", {}, "u"], + ["binary", {}, "z"], + ["time32", ("s",), "tts"], + ["time32", ("ms",), "ttm"], + ["time64", ("us",), "ttu"], + ["time64", ("ns",), "ttn"], + ["date32", {}, "tdD"], + ["date64", {}, "tdm"], + ["timestamp", {"unit": "s"}, "tss:"], + ["timestamp", {"unit": "ms"}, "tsm:"], + ["timestamp", {"unit": "us"}, "tsu:"], + ["timestamp", {"unit": "ns"}, "tsn:"], + ["timestamp", {"unit": "ns", "tz": "UTC"}, "tsn:UTC"], + ["duration", ("s",), "tDs"], + ["duration", ("ms",), "tDm"], + ["duration", ("us",), "tDu"], + ["duration", ("ns",), "tDn"], + ["decimal128", {"precision": 4, "scale": 2}, "d:4,2"], + ], +) +def test_dtype_to_arrow_c_fmt_arrowdtype(pa_dtype, args_kwargs, c_string): + # GH 52323 + pa = pytest.importorskip("pyarrow") + if not args_kwargs: + pa_type = getattr(pa, pa_dtype)() + elif isinstance(args_kwargs, tuple): + pa_type = getattr(pa, pa_dtype)(*args_kwargs) + else: + pa_type = getattr(pa, pa_dtype)(**args_kwargs) + arrow_type = pd.ArrowDtype(pa_type) + assert dtype_to_arrow_c_fmt(arrow_type) == c_string diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/internals/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/internals/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/internals/test_api.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/internals/test_api.py new file mode 100644 index 0000000000000000000000000000000000000000..1251a6ae97a1cb9304de036dba252de54e7fb10b --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/internals/test_api.py @@ -0,0 +1,86 @@ +""" +Tests for the pseudo-public API implemented in internals/api.py and exposed +in core.internals +""" + +import pytest + +import pandas as pd +import pandas._testing as tm +from pandas.core import internals +from pandas.core.internals import api + + +def test_internals_api(): + assert internals.make_block is api.make_block + + +def test_namespace(): + # SUBJECT TO CHANGE + + modules = [ + "blocks", + "concat", + "managers", + "construction", + "array_manager", + "base", + "api", + "ops", + ] + expected = [ + "make_block", + "DataManager", + "ArrayManager", + "BlockManager", + "SingleDataManager", + "SingleBlockManager", + "SingleArrayManager", + "concatenate_managers", + ] + + result = [x for x in dir(internals) if not x.startswith("__")] + assert set(result) == set(expected + modules) + + +@pytest.mark.parametrize( + "name", + [ + "NumericBlock", + "ObjectBlock", + "Block", + "ExtensionBlock", + "DatetimeTZBlock", + ], +) +def test_deprecations(name): + # GH#55139 + msg = f"{name} is deprecated.* Use public APIs instead" + with tm.assert_produces_warning(DeprecationWarning, match=msg): + getattr(internals, name) + + if name not in ["NumericBlock", "ObjectBlock"]: + # NumericBlock and ObjectBlock are not in the internals.api namespace + with tm.assert_produces_warning(DeprecationWarning, match=msg): + getattr(api, name) + + +def test_make_block_2d_with_dti(): + # GH#41168 + dti = pd.date_range("2012", periods=3, tz="UTC") + blk = api.make_block(dti, placement=[0]) + + assert blk.shape == (1, 3) + assert blk.values.shape == (1, 3) + + +def test_create_block_manager_from_blocks_deprecated(): + # GH#33892 + # If they must, downstream packages should get this from internals.api, + # not internals. + msg = ( + "create_block_manager_from_blocks is deprecated and will be " + "removed in a future version. Use public APIs instead" + ) + with tm.assert_produces_warning(DeprecationWarning, match=msg): + internals.create_block_manager_from_blocks diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/internals/test_internals.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/internals/test_internals.py new file mode 100644 index 0000000000000000000000000000000000000000..30c5d3177c5a569676c20b47e2776f266bb308cc --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/internals/test_internals.py @@ -0,0 +1,1422 @@ +from datetime import ( + date, + datetime, +) +import itertools +import re + +import numpy as np +import pytest + +from pandas._libs.internals import BlockPlacement +from pandas.compat import IS64 +import pandas.util._test_decorators as td + +from pandas.core.dtypes.common import is_scalar + +import pandas as pd +from pandas import ( + Categorical, + DataFrame, + DatetimeIndex, + Index, + IntervalIndex, + Series, + Timedelta, + Timestamp, + period_range, +) +import pandas._testing as tm +import pandas.core.algorithms as algos +from pandas.core.arrays import ( + DatetimeArray, + SparseArray, + TimedeltaArray, +) +from pandas.core.internals import ( + BlockManager, + SingleBlockManager, + make_block, +) +from pandas.core.internals.blocks import ( + ensure_block_shape, + maybe_coerce_values, + new_block, +) + +# this file contains BlockManager specific tests +# TODO(ArrayManager) factor out interleave_dtype tests +pytestmark = td.skip_array_manager_invalid_test + + +@pytest.fixture(params=[new_block, make_block]) +def block_maker(request): + """ + Fixture to test both the internal new_block and pseudo-public make_block. + """ + return request.param + + +@pytest.fixture +def mgr(): + return create_mgr( + "a: f8; b: object; c: f8; d: object; e: f8;" + "f: bool; g: i8; h: complex; i: datetime-1; j: datetime-2;" + "k: M8[ns, US/Eastern]; l: M8[ns, CET];" + ) + + +def assert_block_equal(left, right): + tm.assert_numpy_array_equal(left.values, right.values) + assert left.dtype == right.dtype + assert isinstance(left.mgr_locs, BlockPlacement) + assert isinstance(right.mgr_locs, BlockPlacement) + tm.assert_numpy_array_equal(left.mgr_locs.as_array, right.mgr_locs.as_array) + + +def get_numeric_mat(shape): + arr = np.arange(shape[0]) + return np.lib.stride_tricks.as_strided( + x=arr, shape=shape, strides=(arr.itemsize,) + (0,) * (len(shape) - 1) + ).copy() + + +N = 10 + + +def create_block(typestr, placement, item_shape=None, num_offset=0, maker=new_block): + """ + Supported typestr: + + * float, f8, f4, f2 + * int, i8, i4, i2, i1 + * uint, u8, u4, u2, u1 + * complex, c16, c8 + * bool + * object, string, O + * datetime, dt, M8[ns], M8[ns, tz] + * timedelta, td, m8[ns] + * sparse (SparseArray with fill_value=0.0) + * sparse_na (SparseArray with fill_value=np.nan) + * category, category2 + + """ + placement = BlockPlacement(placement) + num_items = len(placement) + + if item_shape is None: + item_shape = (N,) + + shape = (num_items,) + item_shape + + mat = get_numeric_mat(shape) + + if typestr in ( + "float", + "f8", + "f4", + "f2", + "int", + "i8", + "i4", + "i2", + "i1", + "uint", + "u8", + "u4", + "u2", + "u1", + ): + values = mat.astype(typestr) + num_offset + elif typestr in ("complex", "c16", "c8"): + values = 1.0j * (mat.astype(typestr) + num_offset) + elif typestr in ("object", "string", "O"): + values = np.reshape([f"A{i:d}" for i in mat.ravel() + num_offset], shape) + elif typestr in ("b", "bool"): + values = np.ones(shape, dtype=np.bool_) + elif typestr in ("datetime", "dt", "M8[ns]"): + values = (mat * 1e9).astype("M8[ns]") + elif typestr.startswith("M8[ns"): + # datetime with tz + m = re.search(r"M8\[ns,\s*(\w+\/?\w*)\]", typestr) + assert m is not None, f"incompatible typestr -> {typestr}" + tz = m.groups()[0] + assert num_items == 1, "must have only 1 num items for a tz-aware" + values = DatetimeIndex(np.arange(N) * 10**9, tz=tz)._data + values = ensure_block_shape(values, ndim=len(shape)) + elif typestr in ("timedelta", "td", "m8[ns]"): + values = (mat * 1).astype("m8[ns]") + elif typestr in ("category",): + values = Categorical([1, 1, 2, 2, 3, 3, 3, 3, 4, 4]) + elif typestr in ("category2",): + values = Categorical(["a", "a", "a", "a", "b", "b", "c", "c", "c", "d"]) + elif typestr in ("sparse", "sparse_na"): + if shape[-1] != 10: + # We also are implicitly assuming this in the category cases above + raise NotImplementedError + + assert all(s == 1 for s in shape[:-1]) + if typestr.endswith("_na"): + fill_value = np.nan + else: + fill_value = 0.0 + values = SparseArray( + [fill_value, fill_value, 1, 2, 3, fill_value, 4, 5, fill_value, 6], + fill_value=fill_value, + ) + arr = values.sp_values.view() + arr += num_offset - 1 + else: + raise ValueError(f'Unsupported typestr: "{typestr}"') + + values = maybe_coerce_values(values) + return maker(values, placement=placement, ndim=len(shape)) + + +def create_single_mgr(typestr, num_rows=None): + if num_rows is None: + num_rows = N + + return SingleBlockManager( + create_block(typestr, placement=slice(0, num_rows), item_shape=()), + Index(np.arange(num_rows)), + ) + + +def create_mgr(descr, item_shape=None): + """ + Construct BlockManager from string description. + + String description syntax looks similar to np.matrix initializer. It looks + like this:: + + a,b,c: f8; d,e,f: i8 + + Rules are rather simple: + + * see list of supported datatypes in `create_block` method + * components are semicolon-separated + * each component is `NAME,NAME,NAME: DTYPE_ID` + * whitespace around colons & semicolons are removed + * components with same DTYPE_ID are combined into single block + * to force multiple blocks with same dtype, use '-SUFFIX':: + + 'a:f8-1; b:f8-2; c:f8-foobar' + + """ + if item_shape is None: + item_shape = (N,) + + offset = 0 + mgr_items = [] + block_placements = {} + for d in descr.split(";"): + d = d.strip() + if not len(d): + continue + names, blockstr = d.partition(":")[::2] + blockstr = blockstr.strip() + names = names.strip().split(",") + + mgr_items.extend(names) + placement = list(np.arange(len(names)) + offset) + try: + block_placements[blockstr].extend(placement) + except KeyError: + block_placements[blockstr] = placement + offset += len(names) + + mgr_items = Index(mgr_items) + + blocks = [] + num_offset = 0 + for blockstr, placement in block_placements.items(): + typestr = blockstr.split("-")[0] + blocks.append( + create_block( + typestr, placement, item_shape=item_shape, num_offset=num_offset + ) + ) + num_offset += len(placement) + + sblocks = sorted(blocks, key=lambda b: b.mgr_locs[0]) + return BlockManager( + tuple(sblocks), + [mgr_items] + [Index(np.arange(n)) for n in item_shape], + ) + + +@pytest.fixture +def fblock(): + return create_block("float", [0, 2, 4]) + + +class TestBlock: + def test_constructor(self): + int32block = create_block("i4", [0]) + assert int32block.dtype == np.int32 + + @pytest.mark.parametrize( + "typ, data", + [ + ["float", [0, 2, 4]], + ["complex", [7]], + ["object", [1, 3]], + ["bool", [5]], + ], + ) + def test_pickle(self, typ, data): + blk = create_block(typ, data) + assert_block_equal(tm.round_trip_pickle(blk), blk) + + def test_mgr_locs(self, fblock): + assert isinstance(fblock.mgr_locs, BlockPlacement) + tm.assert_numpy_array_equal( + fblock.mgr_locs.as_array, np.array([0, 2, 4], dtype=np.intp) + ) + + def test_attrs(self, fblock): + assert fblock.shape == fblock.values.shape + assert fblock.dtype == fblock.values.dtype + assert len(fblock) == len(fblock.values) + + def test_copy(self, fblock): + cop = fblock.copy() + assert cop is not fblock + assert_block_equal(fblock, cop) + + def test_delete(self, fblock): + newb = fblock.copy() + locs = newb.mgr_locs + nb = newb.delete(0)[0] + assert newb.mgr_locs is locs + + assert nb is not newb + + tm.assert_numpy_array_equal( + nb.mgr_locs.as_array, np.array([2, 4], dtype=np.intp) + ) + assert not (newb.values[0] == 1).all() + assert (nb.values[0] == 1).all() + + newb = fblock.copy() + locs = newb.mgr_locs + nb = newb.delete(1) + assert len(nb) == 2 + assert newb.mgr_locs is locs + + tm.assert_numpy_array_equal( + nb[0].mgr_locs.as_array, np.array([0], dtype=np.intp) + ) + tm.assert_numpy_array_equal( + nb[1].mgr_locs.as_array, np.array([4], dtype=np.intp) + ) + assert not (newb.values[1] == 2).all() + assert (nb[1].values[0] == 2).all() + + newb = fblock.copy() + nb = newb.delete(2) + assert len(nb) == 1 + tm.assert_numpy_array_equal( + nb[0].mgr_locs.as_array, np.array([0, 2], dtype=np.intp) + ) + assert (nb[0].values[1] == 1).all() + + newb = fblock.copy() + + with pytest.raises(IndexError, match=None): + newb.delete(3) + + def test_delete_datetimelike(self): + # dont use np.delete on values, as that will coerce from DTA/TDA to ndarray + arr = np.arange(20, dtype="i8").reshape(5, 4).view("m8[ns]") + df = DataFrame(arr) + blk = df._mgr.blocks[0] + assert isinstance(blk.values, TimedeltaArray) + + nb = blk.delete(1) + assert len(nb) == 2 + assert isinstance(nb[0].values, TimedeltaArray) + assert isinstance(nb[1].values, TimedeltaArray) + + df = DataFrame(arr.view("M8[ns]")) + blk = df._mgr.blocks[0] + assert isinstance(blk.values, DatetimeArray) + + nb = blk.delete([1, 3]) + assert len(nb) == 2 + assert isinstance(nb[0].values, DatetimeArray) + assert isinstance(nb[1].values, DatetimeArray) + + def test_split(self): + # GH#37799 + values = np.random.default_rng(2).standard_normal((3, 4)) + blk = new_block(values, placement=BlockPlacement([3, 1, 6]), ndim=2) + result = blk._split() + + # check that we get views, not copies + values[:] = -9999 + assert (blk.values == -9999).all() + + assert len(result) == 3 + expected = [ + new_block(values[[0]], placement=BlockPlacement([3]), ndim=2), + new_block(values[[1]], placement=BlockPlacement([1]), ndim=2), + new_block(values[[2]], placement=BlockPlacement([6]), ndim=2), + ] + for res, exp in zip(result, expected): + assert_block_equal(res, exp) + + +class TestBlockManager: + def test_attrs(self): + mgr = create_mgr("a,b,c: f8-1; d,e,f: f8-2") + assert mgr.nblocks == 2 + assert len(mgr) == 6 + + def test_duplicate_ref_loc_failure(self): + tmp_mgr = create_mgr("a:bool; a: f8") + + axes, blocks = tmp_mgr.axes, tmp_mgr.blocks + + blocks[0].mgr_locs = BlockPlacement(np.array([0])) + blocks[1].mgr_locs = BlockPlacement(np.array([0])) + + # test trying to create block manager with overlapping ref locs + + msg = "Gaps in blk ref_locs" + + with pytest.raises(AssertionError, match=msg): + mgr = BlockManager(blocks, axes) + mgr._rebuild_blknos_and_blklocs() + + blocks[0].mgr_locs = BlockPlacement(np.array([0])) + blocks[1].mgr_locs = BlockPlacement(np.array([1])) + mgr = BlockManager(blocks, axes) + mgr.iget(1) + + def test_pickle(self, mgr): + mgr2 = tm.round_trip_pickle(mgr) + tm.assert_frame_equal( + DataFrame._from_mgr(mgr, axes=mgr.axes), + DataFrame._from_mgr(mgr2, axes=mgr2.axes), + ) + + # GH2431 + assert hasattr(mgr2, "_is_consolidated") + assert hasattr(mgr2, "_known_consolidated") + + # reset to False on load + assert not mgr2._is_consolidated + assert not mgr2._known_consolidated + + @pytest.mark.parametrize("mgr_string", ["a,a,a:f8", "a: f8; a: i8"]) + def test_non_unique_pickle(self, mgr_string): + mgr = create_mgr(mgr_string) + mgr2 = tm.round_trip_pickle(mgr) + tm.assert_frame_equal( + DataFrame._from_mgr(mgr, axes=mgr.axes), + DataFrame._from_mgr(mgr2, axes=mgr2.axes), + ) + + def test_categorical_block_pickle(self): + mgr = create_mgr("a: category") + mgr2 = tm.round_trip_pickle(mgr) + tm.assert_frame_equal( + DataFrame._from_mgr(mgr, axes=mgr.axes), + DataFrame._from_mgr(mgr2, axes=mgr2.axes), + ) + + smgr = create_single_mgr("category") + smgr2 = tm.round_trip_pickle(smgr) + tm.assert_series_equal( + Series()._constructor_from_mgr(smgr, axes=smgr.axes), + Series()._constructor_from_mgr(smgr2, axes=smgr2.axes), + ) + + def test_iget(self): + cols = Index(list("abc")) + values = np.random.default_rng(2).random((3, 3)) + block = new_block( + values=values.copy(), + placement=BlockPlacement(np.arange(3, dtype=np.intp)), + ndim=values.ndim, + ) + mgr = BlockManager(blocks=(block,), axes=[cols, Index(np.arange(3))]) + + tm.assert_almost_equal(mgr.iget(0).internal_values(), values[0]) + tm.assert_almost_equal(mgr.iget(1).internal_values(), values[1]) + tm.assert_almost_equal(mgr.iget(2).internal_values(), values[2]) + + def test_set(self): + mgr = create_mgr("a,b,c: int", item_shape=(3,)) + + mgr.insert(len(mgr.items), "d", np.array(["foo"] * 3)) + mgr.iset(1, np.array(["bar"] * 3)) + tm.assert_numpy_array_equal(mgr.iget(0).internal_values(), np.array([0] * 3)) + tm.assert_numpy_array_equal( + mgr.iget(1).internal_values(), np.array(["bar"] * 3, dtype=np.object_) + ) + tm.assert_numpy_array_equal(mgr.iget(2).internal_values(), np.array([2] * 3)) + tm.assert_numpy_array_equal( + mgr.iget(3).internal_values(), np.array(["foo"] * 3, dtype=np.object_) + ) + + def test_set_change_dtype(self, mgr): + mgr.insert(len(mgr.items), "baz", np.zeros(N, dtype=bool)) + + mgr.iset(mgr.items.get_loc("baz"), np.repeat("foo", N)) + idx = mgr.items.get_loc("baz") + assert mgr.iget(idx).dtype == np.object_ + + mgr2 = mgr.consolidate() + mgr2.iset(mgr2.items.get_loc("baz"), np.repeat("foo", N)) + idx = mgr2.items.get_loc("baz") + assert mgr2.iget(idx).dtype == np.object_ + + mgr2.insert( + len(mgr2.items), + "quux", + np.random.default_rng(2).standard_normal(N).astype(int), + ) + idx = mgr2.items.get_loc("quux") + assert mgr2.iget(idx).dtype == np.dtype(int) + + mgr2.iset( + mgr2.items.get_loc("quux"), np.random.default_rng(2).standard_normal(N) + ) + assert mgr2.iget(idx).dtype == np.float64 + + def test_copy(self, mgr): + cp = mgr.copy(deep=False) + for blk, cp_blk in zip(mgr.blocks, cp.blocks): + # view assertion + tm.assert_equal(cp_blk.values, blk.values) + if isinstance(blk.values, np.ndarray): + assert cp_blk.values.base is blk.values.base + else: + # DatetimeTZBlock has DatetimeIndex values + assert cp_blk.values._ndarray.base is blk.values._ndarray.base + + # copy(deep=True) consolidates, so the block-wise assertions will + # fail is mgr is not consolidated + mgr._consolidate_inplace() + cp = mgr.copy(deep=True) + for blk, cp_blk in zip(mgr.blocks, cp.blocks): + bvals = blk.values + cpvals = cp_blk.values + + tm.assert_equal(cpvals, bvals) + + if isinstance(cpvals, np.ndarray): + lbase = cpvals.base + rbase = bvals.base + else: + lbase = cpvals._ndarray.base + rbase = bvals._ndarray.base + + # copy assertion we either have a None for a base or in case of + # some blocks it is an array (e.g. datetimetz), but was copied + if isinstance(cpvals, DatetimeArray): + assert (lbase is None and rbase is None) or (lbase is not rbase) + elif not isinstance(cpvals, np.ndarray): + assert lbase is not rbase + else: + assert lbase is None and rbase is None + + def test_sparse(self): + mgr = create_mgr("a: sparse-1; b: sparse-2") + assert mgr.as_array().dtype == np.float64 + + def test_sparse_mixed(self): + mgr = create_mgr("a: sparse-1; b: sparse-2; c: f8") + assert len(mgr.blocks) == 3 + assert isinstance(mgr, BlockManager) + + @pytest.mark.parametrize( + "mgr_string, dtype", + [("c: f4; d: f2", np.float32), ("c: f4; d: f2; e: f8", np.float64)], + ) + def test_as_array_float(self, mgr_string, dtype): + mgr = create_mgr(mgr_string) + assert mgr.as_array().dtype == dtype + + @pytest.mark.parametrize( + "mgr_string, dtype", + [ + ("a: bool-1; b: bool-2", np.bool_), + ("a: i8-1; b: i8-2; c: i4; d: i2; e: u1", np.int64), + ("c: i4; d: i2; e: u1", np.int32), + ], + ) + def test_as_array_int_bool(self, mgr_string, dtype): + mgr = create_mgr(mgr_string) + assert mgr.as_array().dtype == dtype + + def test_as_array_datetime(self): + mgr = create_mgr("h: datetime-1; g: datetime-2") + assert mgr.as_array().dtype == "M8[ns]" + + def test_as_array_datetime_tz(self): + mgr = create_mgr("h: M8[ns, US/Eastern]; g: M8[ns, CET]") + assert mgr.iget(0).dtype == "datetime64[ns, US/Eastern]" + assert mgr.iget(1).dtype == "datetime64[ns, CET]" + assert mgr.as_array().dtype == "object" + + @pytest.mark.parametrize("t", ["float16", "float32", "float64", "int32", "int64"]) + def test_astype(self, t): + # coerce all + mgr = create_mgr("c: f4; d: f2; e: f8") + + t = np.dtype(t) + tmgr = mgr.astype(t) + assert tmgr.iget(0).dtype.type == t + assert tmgr.iget(1).dtype.type == t + assert tmgr.iget(2).dtype.type == t + + # mixed + mgr = create_mgr("a,b: object; c: bool; d: datetime; e: f4; f: f2; g: f8") + + t = np.dtype(t) + tmgr = mgr.astype(t, errors="ignore") + assert tmgr.iget(2).dtype.type == t + assert tmgr.iget(4).dtype.type == t + assert tmgr.iget(5).dtype.type == t + assert tmgr.iget(6).dtype.type == t + + assert tmgr.iget(0).dtype.type == np.object_ + assert tmgr.iget(1).dtype.type == np.object_ + if t != np.int64: + assert tmgr.iget(3).dtype.type == np.datetime64 + else: + assert tmgr.iget(3).dtype.type == t + + def test_convert(self, using_infer_string): + def _compare(old_mgr, new_mgr): + """compare the blocks, numeric compare ==, object don't""" + old_blocks = set(old_mgr.blocks) + new_blocks = set(new_mgr.blocks) + assert len(old_blocks) == len(new_blocks) + + # compare non-numeric + for b in old_blocks: + found = False + for nb in new_blocks: + if (b.values == nb.values).all(): + found = True + break + assert found + + for b in new_blocks: + found = False + for ob in old_blocks: + if (b.values == ob.values).all(): + found = True + break + assert found + + # noops + mgr = create_mgr("f: i8; g: f8") + new_mgr = mgr.convert(copy=True) + _compare(mgr, new_mgr) + + # convert + mgr = create_mgr("a,b,foo: object; f: i8; g: f8") + mgr.iset(0, np.array(["1"] * N, dtype=np.object_)) + mgr.iset(1, np.array(["2."] * N, dtype=np.object_)) + mgr.iset(2, np.array(["foo."] * N, dtype=np.object_)) + new_mgr = mgr.convert(copy=True) + dtype = "str" if using_infer_string else np.object_ + assert new_mgr.iget(0).dtype == dtype + assert new_mgr.iget(1).dtype == dtype + assert new_mgr.iget(2).dtype == dtype + assert new_mgr.iget(3).dtype == np.int64 + assert new_mgr.iget(4).dtype == np.float64 + + mgr = create_mgr( + "a,b,foo: object; f: i4; bool: bool; dt: datetime; i: i8; g: f8; h: f2" + ) + mgr.iset(0, np.array(["1"] * N, dtype=np.object_)) + mgr.iset(1, np.array(["2."] * N, dtype=np.object_)) + mgr.iset(2, np.array(["foo."] * N, dtype=np.object_)) + new_mgr = mgr.convert(copy=True) + assert new_mgr.iget(0).dtype == dtype + assert new_mgr.iget(1).dtype == dtype + assert new_mgr.iget(2).dtype == dtype + assert new_mgr.iget(3).dtype == np.int32 + assert new_mgr.iget(4).dtype == np.bool_ + assert new_mgr.iget(5).dtype.type, np.datetime64 + assert new_mgr.iget(6).dtype == np.int64 + assert new_mgr.iget(7).dtype == np.float64 + assert new_mgr.iget(8).dtype == np.float16 + + def test_interleave(self): + # self + for dtype in ["f8", "i8", "object", "bool", "complex", "M8[ns]", "m8[ns]"]: + mgr = create_mgr(f"a: {dtype}") + assert mgr.as_array().dtype == dtype + mgr = create_mgr(f"a: {dtype}; b: {dtype}") + assert mgr.as_array().dtype == dtype + + @pytest.mark.parametrize( + "mgr_string, dtype", + [ + ("a: category", "i8"), + ("a: category; b: category", "i8"), + ("a: category; b: category2", "object"), + ("a: category2", "object"), + ("a: category2; b: category2", "object"), + ("a: f8", "f8"), + ("a: f8; b: i8", "f8"), + ("a: f4; b: i8", "f8"), + ("a: f4; b: i8; d: object", "object"), + ("a: bool; b: i8", "object"), + ("a: complex", "complex"), + ("a: f8; b: category", "object"), + ("a: M8[ns]; b: category", "object"), + ("a: M8[ns]; b: bool", "object"), + ("a: M8[ns]; b: i8", "object"), + ("a: m8[ns]; b: bool", "object"), + ("a: m8[ns]; b: i8", "object"), + ("a: M8[ns]; b: m8[ns]", "object"), + ], + ) + def test_interleave_dtype(self, mgr_string, dtype): + # will be converted according the actual dtype of the underlying + mgr = create_mgr("a: category") + assert mgr.as_array().dtype == "i8" + mgr = create_mgr("a: category; b: category2") + assert mgr.as_array().dtype == "object" + mgr = create_mgr("a: category2") + assert mgr.as_array().dtype == "object" + + # combinations + mgr = create_mgr("a: f8") + assert mgr.as_array().dtype == "f8" + mgr = create_mgr("a: f8; b: i8") + assert mgr.as_array().dtype == "f8" + mgr = create_mgr("a: f4; b: i8") + assert mgr.as_array().dtype == "f8" + mgr = create_mgr("a: f4; b: i8; d: object") + assert mgr.as_array().dtype == "object" + mgr = create_mgr("a: bool; b: i8") + assert mgr.as_array().dtype == "object" + mgr = create_mgr("a: complex") + assert mgr.as_array().dtype == "complex" + mgr = create_mgr("a: f8; b: category") + assert mgr.as_array().dtype == "f8" + mgr = create_mgr("a: M8[ns]; b: category") + assert mgr.as_array().dtype == "object" + mgr = create_mgr("a: M8[ns]; b: bool") + assert mgr.as_array().dtype == "object" + mgr = create_mgr("a: M8[ns]; b: i8") + assert mgr.as_array().dtype == "object" + mgr = create_mgr("a: m8[ns]; b: bool") + assert mgr.as_array().dtype == "object" + mgr = create_mgr("a: m8[ns]; b: i8") + assert mgr.as_array().dtype == "object" + mgr = create_mgr("a: M8[ns]; b: m8[ns]") + assert mgr.as_array().dtype == "object" + + def test_consolidate_ordering_issues(self, mgr): + mgr.iset(mgr.items.get_loc("f"), np.random.default_rng(2).standard_normal(N)) + mgr.iset(mgr.items.get_loc("d"), np.random.default_rng(2).standard_normal(N)) + mgr.iset(mgr.items.get_loc("b"), np.random.default_rng(2).standard_normal(N)) + mgr.iset(mgr.items.get_loc("g"), np.random.default_rng(2).standard_normal(N)) + mgr.iset(mgr.items.get_loc("h"), np.random.default_rng(2).standard_normal(N)) + + # we have datetime/tz blocks in mgr + cons = mgr.consolidate() + assert cons.nblocks == 4 + cons = mgr.consolidate().get_numeric_data() + assert cons.nblocks == 1 + assert isinstance(cons.blocks[0].mgr_locs, BlockPlacement) + tm.assert_numpy_array_equal( + cons.blocks[0].mgr_locs.as_array, np.arange(len(cons.items), dtype=np.intp) + ) + + def test_reindex_items(self): + # mgr is not consolidated, f8 & f8-2 blocks + mgr = create_mgr("a: f8; b: i8; c: f8; d: i8; e: f8; f: bool; g: f8-2") + + reindexed = mgr.reindex_axis(["g", "c", "a", "d"], axis=0) + # reindex_axis does not consolidate_inplace, as that risks failing to + # invalidate _item_cache + assert not reindexed.is_consolidated() + + tm.assert_index_equal(reindexed.items, Index(["g", "c", "a", "d"])) + tm.assert_almost_equal( + mgr.iget(6).internal_values(), reindexed.iget(0).internal_values() + ) + tm.assert_almost_equal( + mgr.iget(2).internal_values(), reindexed.iget(1).internal_values() + ) + tm.assert_almost_equal( + mgr.iget(0).internal_values(), reindexed.iget(2).internal_values() + ) + tm.assert_almost_equal( + mgr.iget(3).internal_values(), reindexed.iget(3).internal_values() + ) + + def test_get_numeric_data(self, using_copy_on_write): + mgr = create_mgr( + "int: int; float: float; complex: complex;" + "str: object; bool: bool; obj: object; dt: datetime", + item_shape=(3,), + ) + mgr.iset(5, np.array([1, 2, 3], dtype=np.object_)) + + numeric = mgr.get_numeric_data() + tm.assert_index_equal(numeric.items, Index(["int", "float", "complex", "bool"])) + tm.assert_almost_equal( + mgr.iget(mgr.items.get_loc("float")).internal_values(), + numeric.iget(numeric.items.get_loc("float")).internal_values(), + ) + + # Check sharing + numeric.iset( + numeric.items.get_loc("float"), + np.array([100.0, 200.0, 300.0]), + inplace=True, + ) + if using_copy_on_write: + tm.assert_almost_equal( + mgr.iget(mgr.items.get_loc("float")).internal_values(), + np.array([1.0, 1.0, 1.0]), + ) + else: + tm.assert_almost_equal( + mgr.iget(mgr.items.get_loc("float")).internal_values(), + np.array([100.0, 200.0, 300.0]), + ) + + def test_get_bool_data(self, using_copy_on_write): + mgr = create_mgr( + "int: int; float: float; complex: complex;" + "str: object; bool: bool; obj: object; dt: datetime", + item_shape=(3,), + ) + mgr.iset(6, np.array([True, False, True], dtype=np.object_)) + + bools = mgr.get_bool_data() + tm.assert_index_equal(bools.items, Index(["bool"])) + tm.assert_almost_equal( + mgr.iget(mgr.items.get_loc("bool")).internal_values(), + bools.iget(bools.items.get_loc("bool")).internal_values(), + ) + + bools.iset(0, np.array([True, False, True]), inplace=True) + if using_copy_on_write: + tm.assert_numpy_array_equal( + mgr.iget(mgr.items.get_loc("bool")).internal_values(), + np.array([True, True, True]), + ) + else: + tm.assert_numpy_array_equal( + mgr.iget(mgr.items.get_loc("bool")).internal_values(), + np.array([True, False, True]), + ) + + def test_unicode_repr_doesnt_raise(self): + repr(create_mgr("b,\u05d0: object")) + + @pytest.mark.parametrize( + "mgr_string", ["a,b,c: i8-1; d,e,f: i8-2", "a,a,a: i8-1; b,b,b: i8-2"] + ) + def test_equals(self, mgr_string): + # unique items + bm1 = create_mgr(mgr_string) + bm2 = BlockManager(bm1.blocks[::-1], bm1.axes) + assert bm1.equals(bm2) + + @pytest.mark.parametrize( + "mgr_string", + [ + "a:i8;b:f8", # basic case + "a:i8;b:f8;c:c8;d:b", # many types + "a:i8;e:dt;f:td;g:string", # more types + "a:i8;b:category;c:category2", # categories + "c:sparse;d:sparse_na;b:f8", # sparse + ], + ) + def test_equals_block_order_different_dtypes(self, mgr_string): + # GH 9330 + bm = create_mgr(mgr_string) + block_perms = itertools.permutations(bm.blocks) + for bm_perm in block_perms: + bm_this = BlockManager(tuple(bm_perm), bm.axes) + assert bm.equals(bm_this) + assert bm_this.equals(bm) + + def test_single_mgr_ctor(self): + mgr = create_single_mgr("f8", num_rows=5) + assert mgr.external_values().tolist() == [0.0, 1.0, 2.0, 3.0, 4.0] + + @pytest.mark.parametrize("value", [1, "True", [1, 2, 3], 5.0]) + def test_validate_bool_args(self, value): + bm1 = create_mgr("a,b,c: i8-1; d,e,f: i8-2") + + msg = ( + 'For argument "inplace" expected type bool, ' + f"received type {type(value).__name__}." + ) + with pytest.raises(ValueError, match=msg): + bm1.replace_list([1], [2], inplace=value) + + def test_iset_split_block(self): + bm = create_mgr("a,b,c: i8; d: f8") + bm._iset_split_block(0, np.array([0])) + tm.assert_numpy_array_equal( + bm.blklocs, np.array([0, 0, 1, 0], dtype="int64" if IS64 else "int32") + ) + # First indexer currently does not have a block associated with it in case + tm.assert_numpy_array_equal( + bm.blknos, np.array([0, 0, 0, 1], dtype="int64" if IS64 else "int32") + ) + assert len(bm.blocks) == 2 + + def test_iset_split_block_values(self): + bm = create_mgr("a,b,c: i8; d: f8") + bm._iset_split_block(0, np.array([0]), np.array([list(range(10))])) + tm.assert_numpy_array_equal( + bm.blklocs, np.array([0, 0, 1, 0], dtype="int64" if IS64 else "int32") + ) + # First indexer currently does not have a block associated with it in case + tm.assert_numpy_array_equal( + bm.blknos, np.array([0, 2, 2, 1], dtype="int64" if IS64 else "int32") + ) + assert len(bm.blocks) == 3 + + +def _as_array(mgr): + if mgr.ndim == 1: + return mgr.external_values() + return mgr.as_array().T + + +class TestIndexing: + # Nosetests-style data-driven tests. + # + # This test applies different indexing routines to block managers and + # compares the outcome to the result of same operations on np.ndarray. + # + # NOTE: sparse (SparseBlock with fill_value != np.nan) fail a lot of tests + # and are disabled. + + MANAGERS = [ + create_single_mgr("f8", N), + create_single_mgr("i8", N), + # 2-dim + create_mgr("a,b,c,d,e,f: f8", item_shape=(N,)), + create_mgr("a,b,c,d,e,f: i8", item_shape=(N,)), + create_mgr("a,b: f8; c,d: i8; e,f: string", item_shape=(N,)), + create_mgr("a,b: f8; c,d: i8; e,f: f8", item_shape=(N,)), + ] + + @pytest.mark.parametrize("mgr", MANAGERS) + def test_get_slice(self, mgr): + def assert_slice_ok(mgr, axis, slobj): + mat = _as_array(mgr) + + # we maybe using an ndarray to test slicing and + # might not be the full length of the axis + if isinstance(slobj, np.ndarray): + ax = mgr.axes[axis] + if len(ax) and len(slobj) and len(slobj) != len(ax): + slobj = np.concatenate( + [slobj, np.zeros(len(ax) - len(slobj), dtype=bool)] + ) + + if isinstance(slobj, slice): + sliced = mgr.get_slice(slobj, axis=axis) + elif ( + mgr.ndim == 1 + and axis == 0 + and isinstance(slobj, np.ndarray) + and slobj.dtype == bool + ): + sliced = mgr.get_rows_with_mask(slobj) + else: + # BlockManager doesn't support non-slice, SingleBlockManager + # doesn't support axis > 0 + raise TypeError(slobj) + + mat_slobj = (slice(None),) * axis + (slobj,) + tm.assert_numpy_array_equal( + mat[mat_slobj], _as_array(sliced), check_dtype=False + ) + tm.assert_index_equal(mgr.axes[axis][slobj], sliced.axes[axis]) + + assert mgr.ndim <= 2, mgr.ndim + for ax in range(mgr.ndim): + # slice + assert_slice_ok(mgr, ax, slice(None)) + assert_slice_ok(mgr, ax, slice(3)) + assert_slice_ok(mgr, ax, slice(100)) + assert_slice_ok(mgr, ax, slice(1, 4)) + assert_slice_ok(mgr, ax, slice(3, 0, -2)) + + if mgr.ndim < 2: + # 2D only support slice objects + + # boolean mask + assert_slice_ok(mgr, ax, np.ones(mgr.shape[ax], dtype=np.bool_)) + assert_slice_ok(mgr, ax, np.zeros(mgr.shape[ax], dtype=np.bool_)) + + if mgr.shape[ax] >= 3: + assert_slice_ok(mgr, ax, np.arange(mgr.shape[ax]) % 3 == 0) + assert_slice_ok( + mgr, ax, np.array([True, True, False], dtype=np.bool_) + ) + + @pytest.mark.parametrize("mgr", MANAGERS) + def test_take(self, mgr): + def assert_take_ok(mgr, axis, indexer): + mat = _as_array(mgr) + taken = mgr.take(indexer, axis) + tm.assert_numpy_array_equal( + np.take(mat, indexer, axis), _as_array(taken), check_dtype=False + ) + tm.assert_index_equal(mgr.axes[axis].take(indexer), taken.axes[axis]) + + for ax in range(mgr.ndim): + # take/fancy indexer + assert_take_ok(mgr, ax, indexer=np.array([], dtype=np.intp)) + assert_take_ok(mgr, ax, indexer=np.array([0, 0, 0], dtype=np.intp)) + assert_take_ok( + mgr, ax, indexer=np.array(list(range(mgr.shape[ax])), dtype=np.intp) + ) + + if mgr.shape[ax] >= 3: + assert_take_ok(mgr, ax, indexer=np.array([0, 1, 2], dtype=np.intp)) + assert_take_ok(mgr, ax, indexer=np.array([-1, -2, -3], dtype=np.intp)) + + @pytest.mark.parametrize("mgr", MANAGERS) + @pytest.mark.parametrize("fill_value", [None, np.nan, 100.0]) + def test_reindex_axis(self, fill_value, mgr): + def assert_reindex_axis_is_ok(mgr, axis, new_labels, fill_value): + mat = _as_array(mgr) + indexer = mgr.axes[axis].get_indexer_for(new_labels) + + reindexed = mgr.reindex_axis(new_labels, axis, fill_value=fill_value) + tm.assert_numpy_array_equal( + algos.take_nd(mat, indexer, axis, fill_value=fill_value), + _as_array(reindexed), + check_dtype=False, + ) + tm.assert_index_equal(reindexed.axes[axis], new_labels) + + for ax in range(mgr.ndim): + assert_reindex_axis_is_ok(mgr, ax, Index([]), fill_value) + assert_reindex_axis_is_ok(mgr, ax, mgr.axes[ax], fill_value) + assert_reindex_axis_is_ok(mgr, ax, mgr.axes[ax][[0, 0, 0]], fill_value) + assert_reindex_axis_is_ok(mgr, ax, Index(["foo", "bar", "baz"]), fill_value) + assert_reindex_axis_is_ok( + mgr, ax, Index(["foo", mgr.axes[ax][0], "baz"]), fill_value + ) + + if mgr.shape[ax] >= 3: + assert_reindex_axis_is_ok(mgr, ax, mgr.axes[ax][:-3], fill_value) + assert_reindex_axis_is_ok(mgr, ax, mgr.axes[ax][-3::-1], fill_value) + assert_reindex_axis_is_ok( + mgr, ax, mgr.axes[ax][[0, 1, 2, 0, 1, 2]], fill_value + ) + + @pytest.mark.parametrize("mgr", MANAGERS) + @pytest.mark.parametrize("fill_value", [None, np.nan, 100.0]) + def test_reindex_indexer(self, fill_value, mgr): + def assert_reindex_indexer_is_ok(mgr, axis, new_labels, indexer, fill_value): + mat = _as_array(mgr) + reindexed_mat = algos.take_nd(mat, indexer, axis, fill_value=fill_value) + reindexed = mgr.reindex_indexer( + new_labels, indexer, axis, fill_value=fill_value + ) + tm.assert_numpy_array_equal( + reindexed_mat, _as_array(reindexed), check_dtype=False + ) + tm.assert_index_equal(reindexed.axes[axis], new_labels) + + for ax in range(mgr.ndim): + assert_reindex_indexer_is_ok( + mgr, ax, Index([]), np.array([], dtype=np.intp), fill_value + ) + assert_reindex_indexer_is_ok( + mgr, ax, mgr.axes[ax], np.arange(mgr.shape[ax]), fill_value + ) + assert_reindex_indexer_is_ok( + mgr, + ax, + Index(["foo"] * mgr.shape[ax]), + np.arange(mgr.shape[ax]), + fill_value, + ) + assert_reindex_indexer_is_ok( + mgr, ax, mgr.axes[ax][::-1], np.arange(mgr.shape[ax]), fill_value + ) + assert_reindex_indexer_is_ok( + mgr, ax, mgr.axes[ax], np.arange(mgr.shape[ax])[::-1], fill_value + ) + assert_reindex_indexer_is_ok( + mgr, ax, Index(["foo", "bar", "baz"]), np.array([0, 0, 0]), fill_value + ) + assert_reindex_indexer_is_ok( + mgr, ax, Index(["foo", "bar", "baz"]), np.array([-1, 0, -1]), fill_value + ) + assert_reindex_indexer_is_ok( + mgr, + ax, + Index(["foo", mgr.axes[ax][0], "baz"]), + np.array([-1, -1, -1]), + fill_value, + ) + + if mgr.shape[ax] >= 3: + assert_reindex_indexer_is_ok( + mgr, + ax, + Index(["foo", "bar", "baz"]), + np.array([0, 1, 2]), + fill_value, + ) + + +class TestBlockPlacement: + @pytest.mark.parametrize( + "slc, expected", + [ + (slice(0, 4), 4), + (slice(0, 4, 2), 2), + (slice(0, 3, 2), 2), + (slice(0, 1, 2), 1), + (slice(1, 0, -1), 1), + ], + ) + def test_slice_len(self, slc, expected): + assert len(BlockPlacement(slc)) == expected + + @pytest.mark.parametrize("slc", [slice(1, 1, 0), slice(1, 2, 0)]) + def test_zero_step_raises(self, slc): + msg = "slice step cannot be zero" + with pytest.raises(ValueError, match=msg): + BlockPlacement(slc) + + def test_slice_canonize_negative_stop(self): + # GH#37524 negative stop is OK with negative step and positive start + slc = slice(3, -1, -2) + + bp = BlockPlacement(slc) + assert bp.indexer == slice(3, None, -2) + + @pytest.mark.parametrize( + "slc", + [ + slice(None, None), + slice(10, None), + slice(None, None, -1), + slice(None, 10, -1), + # These are "unbounded" because negative index will + # change depending on container shape. + slice(-1, None), + slice(None, -1), + slice(-1, -1), + slice(-1, None, -1), + slice(None, -1, -1), + slice(-1, -1, -1), + ], + ) + def test_unbounded_slice_raises(self, slc): + msg = "unbounded slice" + with pytest.raises(ValueError, match=msg): + BlockPlacement(slc) + + @pytest.mark.parametrize( + "slc", + [ + slice(0, 0), + slice(100, 0), + slice(100, 100), + slice(100, 100, -1), + slice(0, 100, -1), + ], + ) + def test_not_slice_like_slices(self, slc): + assert not BlockPlacement(slc).is_slice_like + + @pytest.mark.parametrize( + "arr, slc", + [ + ([0], slice(0, 1, 1)), + ([100], slice(100, 101, 1)), + ([0, 1, 2], slice(0, 3, 1)), + ([0, 5, 10], slice(0, 15, 5)), + ([0, 100], slice(0, 200, 100)), + ([2, 1], slice(2, 0, -1)), + ], + ) + def test_array_to_slice_conversion(self, arr, slc): + assert BlockPlacement(arr).as_slice == slc + + @pytest.mark.parametrize( + "arr", + [ + [], + [-1], + [-1, -2, -3], + [-10], + [-1], + [-1, 0, 1, 2], + [-2, 0, 2, 4], + [1, 0, -1], + [1, 1, 1], + ], + ) + def test_not_slice_like_arrays(self, arr): + assert not BlockPlacement(arr).is_slice_like + + @pytest.mark.parametrize( + "slc, expected", + [(slice(0, 3), [0, 1, 2]), (slice(0, 0), []), (slice(3, 0), [])], + ) + def test_slice_iter(self, slc, expected): + assert list(BlockPlacement(slc)) == expected + + @pytest.mark.parametrize( + "slc, arr", + [ + (slice(0, 3), [0, 1, 2]), + (slice(0, 0), []), + (slice(3, 0), []), + (slice(3, 0, -1), [3, 2, 1]), + ], + ) + def test_slice_to_array_conversion(self, slc, arr): + tm.assert_numpy_array_equal( + BlockPlacement(slc).as_array, np.asarray(arr, dtype=np.intp) + ) + + def test_blockplacement_add(self): + bpl = BlockPlacement(slice(0, 5)) + assert bpl.add(1).as_slice == slice(1, 6, 1) + assert bpl.add(np.arange(5)).as_slice == slice(0, 10, 2) + assert list(bpl.add(np.arange(5, 0, -1))) == [5, 5, 5, 5, 5] + + @pytest.mark.parametrize( + "val, inc, expected", + [ + (slice(0, 0), 0, []), + (slice(1, 4), 0, [1, 2, 3]), + (slice(3, 0, -1), 0, [3, 2, 1]), + ([1, 2, 4], 0, [1, 2, 4]), + (slice(0, 0), 10, []), + (slice(1, 4), 10, [11, 12, 13]), + (slice(3, 0, -1), 10, [13, 12, 11]), + ([1, 2, 4], 10, [11, 12, 14]), + (slice(0, 0), -1, []), + (slice(1, 4), -1, [0, 1, 2]), + ([1, 2, 4], -1, [0, 1, 3]), + ], + ) + def test_blockplacement_add_int(self, val, inc, expected): + assert list(BlockPlacement(val).add(inc)) == expected + + @pytest.mark.parametrize("val", [slice(1, 4), [1, 2, 4]]) + def test_blockplacement_add_int_raises(self, val): + msg = "iadd causes length change" + with pytest.raises(ValueError, match=msg): + BlockPlacement(val).add(-10) + + +class TestCanHoldElement: + @pytest.fixture( + params=[ + lambda x: x, + lambda x: x.to_series(), + lambda x: x._data, + lambda x: list(x), + lambda x: x.astype(object), + lambda x: np.asarray(x), + lambda x: x[0], + lambda x: x[:0], + ] + ) + def element(self, request): + """ + Functions that take an Index and return an element that should have + blk._can_hold_element(element) for a Block with this index's dtype. + """ + return request.param + + def test_datetime_block_can_hold_element(self): + block = create_block("datetime", [0]) + + assert block._can_hold_element([]) + + # We will check that block._can_hold_element iff arr.__setitem__ works + arr = pd.array(block.values.ravel()) + + # coerce None + assert block._can_hold_element(None) + arr[0] = None + assert arr[0] is pd.NaT + + # coerce different types of datetime objects + vals = [np.datetime64("2010-10-10"), datetime(2010, 10, 10)] + for val in vals: + assert block._can_hold_element(val) + arr[0] = val + + val = date(2010, 10, 10) + assert not block._can_hold_element(val) + + msg = ( + "value should be a 'Timestamp', 'NaT', " + "or array of those. Got 'date' instead." + ) + with pytest.raises(TypeError, match=msg): + arr[0] = val + + @pytest.mark.parametrize("dtype", [np.int64, np.uint64, np.float64]) + def test_interval_can_hold_element_emptylist(self, dtype, element): + arr = np.array([1, 3, 4], dtype=dtype) + ii = IntervalIndex.from_breaks(arr) + blk = new_block(ii._data, BlockPlacement([1]), ndim=2) + + assert blk._can_hold_element([]) + # TODO: check this holds for all blocks + + @pytest.mark.parametrize("dtype", [np.int64, np.uint64, np.float64]) + def test_interval_can_hold_element(self, dtype, element): + arr = np.array([1, 3, 4, 9], dtype=dtype) + ii = IntervalIndex.from_breaks(arr) + blk = new_block(ii._data, BlockPlacement([1]), ndim=2) + + elem = element(ii) + self.check_series_setitem(elem, ii, True) + assert blk._can_hold_element(elem) + + # Careful: to get the expected Series-inplace behavior we need + # `elem` to not have the same length as `arr` + ii2 = IntervalIndex.from_breaks(arr[:-1], closed="neither") + elem = element(ii2) + with tm.assert_produces_warning(FutureWarning): + self.check_series_setitem(elem, ii, False) + assert not blk._can_hold_element(elem) + + ii3 = IntervalIndex.from_breaks([Timestamp(1), Timestamp(3), Timestamp(4)]) + elem = element(ii3) + with tm.assert_produces_warning(FutureWarning): + self.check_series_setitem(elem, ii, False) + assert not blk._can_hold_element(elem) + + ii4 = IntervalIndex.from_breaks([Timedelta(1), Timedelta(3), Timedelta(4)]) + elem = element(ii4) + with tm.assert_produces_warning(FutureWarning): + self.check_series_setitem(elem, ii, False) + assert not blk._can_hold_element(elem) + + def test_period_can_hold_element_emptylist(self): + pi = period_range("2016", periods=3, freq="Y") + blk = new_block(pi._data.reshape(1, 3), BlockPlacement([1]), ndim=2) + + assert blk._can_hold_element([]) + + def test_period_can_hold_element(self, element): + pi = period_range("2016", periods=3, freq="Y") + + elem = element(pi) + self.check_series_setitem(elem, pi, True) + + # Careful: to get the expected Series-inplace behavior we need + # `elem` to not have the same length as `arr` + pi2 = pi.asfreq("D")[:-1] + elem = element(pi2) + with tm.assert_produces_warning(FutureWarning): + self.check_series_setitem(elem, pi, False) + + dti = pi.to_timestamp("s")[:-1] + elem = element(dti) + with tm.assert_produces_warning(FutureWarning): + self.check_series_setitem(elem, pi, False) + + def check_can_hold_element(self, obj, elem, inplace: bool): + blk = obj._mgr.blocks[0] + if inplace: + assert blk._can_hold_element(elem) + else: + assert not blk._can_hold_element(elem) + + def check_series_setitem(self, elem, index: Index, inplace: bool): + arr = index._data.copy() + ser = Series(arr, copy=False) + + self.check_can_hold_element(ser, elem, inplace) + + if is_scalar(elem): + ser[0] = elem + else: + ser[: len(elem)] = elem + + if inplace: + assert ser.array is arr # i.e. setting was done inplace + else: + assert ser.dtype == object + + +class TestShouldStore: + def test_should_store_categorical(self): + cat = Categorical(["A", "B", "C"]) + df = DataFrame(cat) + blk = df._mgr.blocks[0] + + # matching dtype + assert blk.should_store(cat) + assert blk.should_store(cat[:-1]) + + # different dtype + assert not blk.should_store(cat.as_ordered()) + + # ndarray instead of Categorical + assert not blk.should_store(np.asarray(cat)) + + +def test_validate_ndim(): + values = np.array([1.0, 2.0]) + placement = BlockPlacement(slice(2)) + msg = r"Wrong number of dimensions. values.ndim != ndim \[1 != 2\]" + + with pytest.raises(ValueError, match=msg): + make_block(values, placement, ndim=2) + + +def test_block_shape(): + idx = Index([0, 1, 2, 3, 4]) + a = Series([1, 2, 3]).reindex(idx) + b = Series(Categorical([1, 2, 3])).reindex(idx) + + assert a._mgr.blocks[0].mgr_locs.indexer == b._mgr.blocks[0].mgr_locs.indexer + + +def test_make_block_no_pandas_array(block_maker): + # https://github.com/pandas-dev/pandas/pull/24866 + arr = pd.arrays.NumpyExtensionArray(np.array([1, 2])) + + # NumpyExtensionArray, no dtype + result = block_maker(arr, BlockPlacement(slice(len(arr))), ndim=arr.ndim) + assert result.dtype.kind in ["i", "u"] + + if block_maker is make_block: + # new_block requires caller to unwrap NumpyExtensionArray + assert result.is_extension is False + + # NumpyExtensionArray, NumpyEADtype + result = block_maker(arr, slice(len(arr)), dtype=arr.dtype, ndim=arr.ndim) + assert result.dtype.kind in ["i", "u"] + assert result.is_extension is False + + # new_block no longer taked dtype keyword + # ndarray, NumpyEADtype + result = block_maker( + arr.to_numpy(), slice(len(arr)), dtype=arr.dtype, ndim=arr.ndim + ) + assert result.dtype.kind in ["i", "u"] + assert result.is_extension is False diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/internals/test_managers.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/internals/test_managers.py new file mode 100644 index 0000000000000000000000000000000000000000..f40362c299717be5f2e8665e4547276c2af05fb0 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/internals/test_managers.py @@ -0,0 +1,103 @@ +""" +Testing interaction between the different managers (BlockManager, ArrayManager) +""" +import os +import subprocess +import sys + +import pytest + +from pandas.core.dtypes.missing import array_equivalent + +import pandas as pd +import pandas._testing as tm +from pandas.core.internals import ( + ArrayManager, + BlockManager, + SingleArrayManager, + SingleBlockManager, +) + + +def test_dataframe_creation(): + msg = "data_manager option is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + with pd.option_context("mode.data_manager", "block"): + df_block = pd.DataFrame( + {"a": [1, 2, 3], "b": [0.1, 0.2, 0.3], "c": [4, 5, 6]} + ) + assert isinstance(df_block._mgr, BlockManager) + + with tm.assert_produces_warning(FutureWarning, match=msg): + with pd.option_context("mode.data_manager", "array"): + df_array = pd.DataFrame( + {"a": [1, 2, 3], "b": [0.1, 0.2, 0.3], "c": [4, 5, 6]} + ) + assert isinstance(df_array._mgr, ArrayManager) + + # also ensure both are seen as equal + tm.assert_frame_equal(df_block, df_array) + + # conversion from one manager to the other + result = df_block._as_manager("block") + assert isinstance(result._mgr, BlockManager) + result = df_block._as_manager("array") + assert isinstance(result._mgr, ArrayManager) + tm.assert_frame_equal(result, df_block) + assert all( + array_equivalent(left, right) + for left, right in zip(result._mgr.arrays, df_array._mgr.arrays) + ) + + result = df_array._as_manager("array") + assert isinstance(result._mgr, ArrayManager) + result = df_array._as_manager("block") + assert isinstance(result._mgr, BlockManager) + tm.assert_frame_equal(result, df_array) + assert len(result._mgr.blocks) == 2 + + +def test_series_creation(): + msg = "data_manager option is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + with pd.option_context("mode.data_manager", "block"): + s_block = pd.Series([1, 2, 3], name="A", index=["a", "b", "c"]) + assert isinstance(s_block._mgr, SingleBlockManager) + + with tm.assert_produces_warning(FutureWarning, match=msg): + with pd.option_context("mode.data_manager", "array"): + s_array = pd.Series([1, 2, 3], name="A", index=["a", "b", "c"]) + assert isinstance(s_array._mgr, SingleArrayManager) + + # also ensure both are seen as equal + tm.assert_series_equal(s_block, s_array) + + # conversion from one manager to the other + result = s_block._as_manager("block") + assert isinstance(result._mgr, SingleBlockManager) + result = s_block._as_manager("array") + assert isinstance(result._mgr, SingleArrayManager) + tm.assert_series_equal(result, s_block) + + result = s_array._as_manager("array") + assert isinstance(result._mgr, SingleArrayManager) + result = s_array._as_manager("block") + assert isinstance(result._mgr, SingleBlockManager) + tm.assert_series_equal(result, s_array) + + +@pytest.mark.single_cpu +@pytest.mark.parametrize("manager", ["block", "array"]) +def test_array_manager_depr_env_var(manager): + # GH#55043 + test_env = os.environ.copy() + test_env["PANDAS_DATA_MANAGER"] = manager + response = subprocess.run( + [sys.executable, "-c", "import pandas"], + capture_output=True, + env=test_env, + check=True, + ) + msg = "FutureWarning: The env variable PANDAS_DATA_MANAGER is set" + stderr_msg = response.stderr.decode("utf-8") + assert msg in stderr_msg, stderr_msg diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_merge_ordered.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_merge_ordered.py new file mode 100644 index 0000000000000000000000000000000000000000..0bd3ca3cf2c1bd8ceae38d8a04cd6d48c4c69f68 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/reshape/merge/test_merge_ordered.py @@ -0,0 +1,244 @@ +import re + +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + merge_ordered, +) +import pandas._testing as tm + + +@pytest.fixture +def left(): + return DataFrame({"key": ["a", "c", "e"], "lvalue": [1, 2.0, 3]}) + + +@pytest.fixture +def right(): + return DataFrame({"key": ["b", "c", "d", "f"], "rvalue": [1, 2, 3.0, 4]}) + + +class TestMergeOrdered: + def test_basic(self, left, right): + result = merge_ordered(left, right, on="key") + expected = DataFrame( + { + "key": ["a", "b", "c", "d", "e", "f"], + "lvalue": [1, np.nan, 2, np.nan, 3, np.nan], + "rvalue": [np.nan, 1, 2, 3, np.nan, 4], + } + ) + + tm.assert_frame_equal(result, expected) + + def test_ffill(self, left, right): + result = merge_ordered(left, right, on="key", fill_method="ffill") + expected = DataFrame( + { + "key": ["a", "b", "c", "d", "e", "f"], + "lvalue": [1.0, 1, 2, 2, 3, 3.0], + "rvalue": [np.nan, 1, 2, 3, 3, 4], + } + ) + tm.assert_frame_equal(result, expected) + + def test_multigroup(self, left, right): + left = pd.concat([left, left], ignore_index=True) + + left["group"] = ["a"] * 3 + ["b"] * 3 + + result = merge_ordered( + left, right, on="key", left_by="group", fill_method="ffill" + ) + expected = DataFrame( + { + "key": ["a", "b", "c", "d", "e", "f"] * 2, + "lvalue": [1.0, 1, 2, 2, 3, 3.0] * 2, + "rvalue": [np.nan, 1, 2, 3, 3, 4] * 2, + } + ) + expected["group"] = ["a"] * 6 + ["b"] * 6 + + tm.assert_frame_equal(result, expected.loc[:, result.columns]) + + result2 = merge_ordered( + right, left, on="key", right_by="group", fill_method="ffill" + ) + tm.assert_frame_equal(result, result2.loc[:, result.columns]) + + result = merge_ordered(left, right, on="key", left_by="group") + assert result["group"].notna().all() + + @pytest.mark.filterwarnings( + "ignore:Passing a BlockManager|Passing a SingleBlockManager:DeprecationWarning" + ) + def test_merge_type(self, left, right): + class NotADataFrame(DataFrame): + @property + def _constructor(self): + return NotADataFrame + + nad = NotADataFrame(left) + result = nad.merge(right, on="key") + + assert isinstance(result, NotADataFrame) + + @pytest.mark.parametrize( + "df_seq, pattern", + [ + ((), "[Nn]o objects"), + ([], "[Nn]o objects"), + ({}, "[Nn]o objects"), + ([None], "objects.*None"), + ([None, None], "objects.*None"), + ], + ) + def test_empty_sequence_concat(self, df_seq, pattern): + # GH 9157 + with pytest.raises(ValueError, match=pattern): + pd.concat(df_seq) + + @pytest.mark.parametrize( + "arg", [[DataFrame()], [None, DataFrame()], [DataFrame(), None]] + ) + def test_empty_sequence_concat_ok(self, arg): + pd.concat(arg) + + def test_doc_example(self): + left = DataFrame( + { + "group": list("aaabbb"), + "key": ["a", "c", "e", "a", "c", "e"], + "lvalue": [1, 2, 3] * 2, + } + ) + + right = DataFrame({"key": ["b", "c", "d"], "rvalue": [1, 2, 3]}) + + result = merge_ordered(left, right, fill_method="ffill", left_by="group") + + expected = DataFrame( + { + "group": list("aaaaabbbbb"), + "key": ["a", "b", "c", "d", "e"] * 2, + "lvalue": [1, 1, 2, 2, 3] * 2, + "rvalue": [np.nan, 1, 2, 3, 3] * 2, + } + ) + + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "left, right, on, left_by, right_by, expected", + [ + ( + DataFrame({"G": ["g", "g"], "H": ["h", "h"], "T": [1, 3]}), + DataFrame({"T": [2], "E": [1]}), + ["T"], + ["G", "H"], + None, + DataFrame( + { + "G": ["g"] * 3, + "H": ["h"] * 3, + "T": [1, 2, 3], + "E": [np.nan, 1.0, np.nan], + } + ), + ), + ( + DataFrame({"G": ["g", "g"], "H": ["h", "h"], "T": [1, 3]}), + DataFrame({"T": [2], "E": [1]}), + "T", + ["G", "H"], + None, + DataFrame( + { + "G": ["g"] * 3, + "H": ["h"] * 3, + "T": [1, 2, 3], + "E": [np.nan, 1.0, np.nan], + } + ), + ), + ( + DataFrame({"T": [2], "E": [1]}), + DataFrame({"G": ["g", "g"], "H": ["h", "h"], "T": [1, 3]}), + ["T"], + None, + ["G", "H"], + DataFrame( + { + "T": [1, 2, 3], + "E": [np.nan, 1.0, np.nan], + "G": ["g"] * 3, + "H": ["h"] * 3, + } + ), + ), + ], + ) + def test_list_type_by(self, left, right, on, left_by, right_by, expected): + # GH 35269 + result = merge_ordered( + left=left, + right=right, + on=on, + left_by=left_by, + right_by=right_by, + ) + + tm.assert_frame_equal(result, expected) + + def test_left_by_length_equals_to_right_shape0(self): + # GH 38166 + left = DataFrame([["g", "h", 1], ["g", "h", 3]], columns=list("GHE")) + right = DataFrame([[2, 1]], columns=list("ET")) + result = merge_ordered(left, right, on="E", left_by=["G", "H"]) + expected = DataFrame( + {"G": ["g"] * 3, "H": ["h"] * 3, "E": [1, 2, 3], "T": [np.nan, 1.0, np.nan]} + ) + + tm.assert_frame_equal(result, expected) + + def test_elements_not_in_by_but_in_df(self): + # GH 38167 + left = DataFrame([["g", "h", 1], ["g", "h", 3]], columns=list("GHE")) + right = DataFrame([[2, 1]], columns=list("ET")) + msg = r"\{'h'\} not found in left columns" + with pytest.raises(KeyError, match=msg): + merge_ordered(left, right, on="E", left_by=["G", "h"]) + + @pytest.mark.parametrize("invalid_method", ["linear", "carrot"]) + def test_ffill_validate_fill_method(self, left, right, invalid_method): + # GH 55884 + with pytest.raises( + ValueError, match=re.escape("fill_method must be 'ffill' or None") + ): + merge_ordered(left, right, on="key", fill_method=invalid_method) + + def test_ffill_left_merge(self): + # GH 57010 + df1 = DataFrame( + { + "key": ["a", "c", "e", "a", "c", "e"], + "lvalue": [1, 2, 3, 1, 2, 3], + "group": ["a", "a", "a", "b", "b", "b"], + } + ) + df2 = DataFrame({"key": ["b", "c", "d"], "rvalue": [1, 2, 3]}) + result = merge_ordered( + df1, df2, fill_method="ffill", left_by="group", how="left" + ) + expected = DataFrame( + { + "key": ["a", "c", "e", "a", "c", "e"], + "lvalue": [1, 2, 3, 1, 2, 3], + "group": ["a", "a", "a", "b", "b", "b"], + "rvalue": [np.nan, 2.0, 2.0, np.nan, 2.0, 2.0], + } + ) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/interval/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/interval/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_arithmetic.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_arithmetic.py new file mode 100644 index 0000000000000000000000000000000000000000..603763227cb888cb716692ddb82d206ba6812c90 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_arithmetic.py @@ -0,0 +1,192 @@ +from datetime import timedelta + +import numpy as np +import pytest + +from pandas import ( + Interval, + Timedelta, + Timestamp, +) +import pandas._testing as tm + + +class TestIntervalArithmetic: + def test_interval_add(self, closed): + interval = Interval(0, 1, closed=closed) + expected = Interval(1, 2, closed=closed) + + result = interval + 1 + assert result == expected + + result = 1 + interval + assert result == expected + + result = interval + result += 1 + assert result == expected + + msg = r"unsupported operand type\(s\) for \+" + with pytest.raises(TypeError, match=msg): + interval + interval + + with pytest.raises(TypeError, match=msg): + interval + "foo" + + def test_interval_sub(self, closed): + interval = Interval(0, 1, closed=closed) + expected = Interval(-1, 0, closed=closed) + + result = interval - 1 + assert result == expected + + result = interval + result -= 1 + assert result == expected + + msg = r"unsupported operand type\(s\) for -" + with pytest.raises(TypeError, match=msg): + interval - interval + + with pytest.raises(TypeError, match=msg): + interval - "foo" + + def test_interval_mult(self, closed): + interval = Interval(0, 1, closed=closed) + expected = Interval(0, 2, closed=closed) + + result = interval * 2 + assert result == expected + + result = 2 * interval + assert result == expected + + result = interval + result *= 2 + assert result == expected + + msg = r"unsupported operand type\(s\) for \*" + with pytest.raises(TypeError, match=msg): + interval * interval + + msg = r"can\'t multiply sequence by non-int" + with pytest.raises(TypeError, match=msg): + interval * "foo" + + def test_interval_div(self, closed): + interval = Interval(0, 1, closed=closed) + expected = Interval(0, 0.5, closed=closed) + + result = interval / 2.0 + assert result == expected + + result = interval + result /= 2.0 + assert result == expected + + msg = r"unsupported operand type\(s\) for /" + with pytest.raises(TypeError, match=msg): + interval / interval + + with pytest.raises(TypeError, match=msg): + interval / "foo" + + def test_interval_floordiv(self, closed): + interval = Interval(1, 2, closed=closed) + expected = Interval(0, 1, closed=closed) + + result = interval // 2 + assert result == expected + + result = interval + result //= 2 + assert result == expected + + msg = r"unsupported operand type\(s\) for //" + with pytest.raises(TypeError, match=msg): + interval // interval + + with pytest.raises(TypeError, match=msg): + interval // "foo" + + @pytest.mark.parametrize("method", ["__add__", "__sub__"]) + @pytest.mark.parametrize( + "interval", + [ + Interval( + Timestamp("2017-01-01 00:00:00"), Timestamp("2018-01-01 00:00:00") + ), + Interval(Timedelta(days=7), Timedelta(days=14)), + ], + ) + @pytest.mark.parametrize( + "delta", [Timedelta(days=7), timedelta(7), np.timedelta64(7, "D")] + ) + def test_time_interval_add_subtract_timedelta(self, interval, delta, method): + # https://github.com/pandas-dev/pandas/issues/32023 + result = getattr(interval, method)(delta) + left = getattr(interval.left, method)(delta) + right = getattr(interval.right, method)(delta) + expected = Interval(left, right) + + assert result == expected + + @pytest.mark.parametrize("interval", [Interval(1, 2), Interval(1.0, 2.0)]) + @pytest.mark.parametrize( + "delta", [Timedelta(days=7), timedelta(7), np.timedelta64(7, "D")] + ) + def test_numeric_interval_add_timedelta_raises(self, interval, delta): + # https://github.com/pandas-dev/pandas/issues/32023 + msg = "|".join( + [ + "unsupported operand", + "cannot use operands", + "Only numeric, Timestamp and Timedelta endpoints are allowed", + ] + ) + with pytest.raises((TypeError, ValueError), match=msg): + interval + delta + + with pytest.raises((TypeError, ValueError), match=msg): + delta + interval + + @pytest.mark.parametrize("klass", [timedelta, np.timedelta64, Timedelta]) + def test_timedelta_add_timestamp_interval(self, klass): + delta = klass(0) + expected = Interval(Timestamp("2020-01-01"), Timestamp("2020-02-01")) + + result = delta + expected + assert result == expected + + result = expected + delta + assert result == expected + + +class TestIntervalComparisons: + def test_interval_equal(self): + assert Interval(0, 1) == Interval(0, 1, closed="right") + assert Interval(0, 1) != Interval(0, 1, closed="left") + assert Interval(0, 1) != 0 + + def test_interval_comparison(self): + msg = ( + "'<' not supported between instances of " + "'pandas._libs.interval.Interval' and 'int'" + ) + with pytest.raises(TypeError, match=msg): + Interval(0, 1) < 2 + + assert Interval(0, 1) < Interval(1, 2) + assert Interval(0, 1) < Interval(0, 2) + assert Interval(0, 1) < Interval(0.5, 1.5) + assert Interval(0, 1) <= Interval(0, 1) + assert Interval(0, 1) > Interval(-1, 2) + assert Interval(0, 1) >= Interval(0, 1) + + def test_equality_comparison_broadcasts_over_array(self): + # https://github.com/pandas-dev/pandas/issues/35931 + interval = Interval(0, 1) + arr = np.array([interval, interval]) + result = interval == arr + expected = np.array([True, True]) + tm.assert_numpy_array_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_constructors.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_constructors.py new file mode 100644 index 0000000000000000000000000000000000000000..a4bc00b923434f8d62c8332b3104696051fd287e --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_constructors.py @@ -0,0 +1,51 @@ +import pytest + +from pandas import ( + Interval, + Period, + Timestamp, +) + + +class TestIntervalConstructors: + @pytest.mark.parametrize( + "left, right", + [ + ("a", "z"), + (("a", "b"), ("c", "d")), + (list("AB"), list("ab")), + (Interval(0, 1), Interval(1, 2)), + (Period("2018Q1", freq="Q"), Period("2018Q1", freq="Q")), + ], + ) + def test_construct_errors(self, left, right): + # GH#23013 + msg = "Only numeric, Timestamp and Timedelta endpoints are allowed" + with pytest.raises(ValueError, match=msg): + Interval(left, right) + + def test_constructor_errors(self): + msg = "invalid option for 'closed': foo" + with pytest.raises(ValueError, match=msg): + Interval(0, 1, closed="foo") + + msg = "left side of interval must be <= right side" + with pytest.raises(ValueError, match=msg): + Interval(1, 0) + + @pytest.mark.parametrize( + "tz_left, tz_right", [(None, "UTC"), ("UTC", None), ("UTC", "US/Eastern")] + ) + def test_constructor_errors_tz(self, tz_left, tz_right): + # GH#18538 + left = Timestamp("2017-01-01", tz=tz_left) + right = Timestamp("2017-01-02", tz=tz_right) + + if tz_left is None or tz_right is None: + error = TypeError + msg = "Cannot compare tz-naive and tz-aware timestamps" + else: + error = ValueError + msg = "left and right must have the same time zone" + with pytest.raises(error, match=msg): + Interval(left, right) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_contains.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_contains.py new file mode 100644 index 0000000000000000000000000000000000000000..8dfca117a658b2a163ef35699c903ad14a032062 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_contains.py @@ -0,0 +1,73 @@ +import pytest + +from pandas import ( + Interval, + Timedelta, + Timestamp, +) + + +class TestContains: + def test_contains(self): + interval = Interval(0, 1) + assert 0.5 in interval + assert 1 in interval + assert 0 not in interval + + interval_both = Interval(0, 1, "both") + assert 0 in interval_both + assert 1 in interval_both + + interval_neither = Interval(0, 1, closed="neither") + assert 0 not in interval_neither + assert 0.5 in interval_neither + assert 1 not in interval_neither + + def test_contains_interval(self, inclusive_endpoints_fixture): + interval1 = Interval(0, 1, "both") + interval2 = Interval(0, 1, inclusive_endpoints_fixture) + assert interval1 in interval1 + assert interval2 in interval2 + assert interval2 in interval1 + assert interval1 not in interval2 or inclusive_endpoints_fixture == "both" + + def test_contains_infinite_length(self): + interval1 = Interval(0, 1, "both") + interval2 = Interval(float("-inf"), float("inf"), "neither") + assert interval1 in interval2 + assert interval2 not in interval1 + + def test_contains_zero_length(self): + interval1 = Interval(0, 1, "both") + interval2 = Interval(-1, -1, "both") + interval3 = Interval(0.5, 0.5, "both") + assert interval2 not in interval1 + assert interval3 in interval1 + assert interval2 not in interval3 and interval3 not in interval2 + assert interval1 not in interval2 and interval1 not in interval3 + + @pytest.mark.parametrize( + "type1", + [ + (0, 1), + (Timestamp(2000, 1, 1, 0), Timestamp(2000, 1, 1, 1)), + (Timedelta("0h"), Timedelta("1h")), + ], + ) + @pytest.mark.parametrize( + "type2", + [ + (0, 1), + (Timestamp(2000, 1, 1, 0), Timestamp(2000, 1, 1, 1)), + (Timedelta("0h"), Timedelta("1h")), + ], + ) + def test_contains_mixed_types(self, type1, type2): + interval1 = Interval(*type1) + interval2 = Interval(*type2) + if type1 == type2: + assert interval1 in interval2 + else: + msg = "^'<=' not supported between instances of" + with pytest.raises(TypeError, match=msg): + interval1 in interval2 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_formats.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_formats.py new file mode 100644 index 0000000000000000000000000000000000000000..6bf7aa91df3cebc41712c611aaa3781b638009d1 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_formats.py @@ -0,0 +1,11 @@ +from pandas import Interval + + +def test_interval_repr(): + interval = Interval(0, 1) + assert repr(interval) == "Interval(0, 1, closed='right')" + assert str(interval) == "(0, 1]" + + interval_left = Interval(0, 1, closed="left") + assert repr(interval_left) == "Interval(0, 1, closed='left')" + assert str(interval_left) == "[0, 1)" diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_interval.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_interval.py new file mode 100644 index 0000000000000000000000000000000000000000..91b31e82f9c524f87e2849360cfd44b2f77b0c9c --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_interval.py @@ -0,0 +1,87 @@ +import numpy as np +import pytest + +from pandas import ( + Interval, + Timedelta, + Timestamp, +) + + +@pytest.fixture +def interval(): + return Interval(0, 1) + + +class TestInterval: + def test_properties(self, interval): + assert interval.closed == "right" + assert interval.left == 0 + assert interval.right == 1 + assert interval.mid == 0.5 + + def test_hash(self, interval): + # should not raise + hash(interval) + + @pytest.mark.parametrize( + "left, right, expected", + [ + (0, 5, 5), + (-2, 5.5, 7.5), + (10, 10, 0), + (10, np.inf, np.inf), + (-np.inf, -5, np.inf), + (-np.inf, np.inf, np.inf), + (Timedelta("0 days"), Timedelta("5 days"), Timedelta("5 days")), + (Timedelta("10 days"), Timedelta("10 days"), Timedelta("0 days")), + (Timedelta("1h10min"), Timedelta("5h5min"), Timedelta("3h55min")), + (Timedelta("5s"), Timedelta("1h"), Timedelta("59min55s")), + ], + ) + def test_length(self, left, right, expected): + # GH 18789 + iv = Interval(left, right) + result = iv.length + assert result == expected + + @pytest.mark.parametrize( + "left, right, expected", + [ + ("2017-01-01", "2017-01-06", "5 days"), + ("2017-01-01", "2017-01-01 12:00:00", "12 hours"), + ("2017-01-01 12:00", "2017-01-01 12:00:00", "0 days"), + ("2017-01-01 12:01", "2017-01-05 17:31:00", "4 days 5 hours 30 min"), + ], + ) + @pytest.mark.parametrize("tz", (None, "UTC", "CET", "US/Eastern")) + def test_length_timestamp(self, tz, left, right, expected): + # GH 18789 + iv = Interval(Timestamp(left, tz=tz), Timestamp(right, tz=tz)) + result = iv.length + expected = Timedelta(expected) + assert result == expected + + @pytest.mark.parametrize( + "left, right", + [ + (0, 1), + (Timedelta("0 days"), Timedelta("1 day")), + (Timestamp("2018-01-01"), Timestamp("2018-01-02")), + ( + Timestamp("2018-01-01", tz="US/Eastern"), + Timestamp("2018-01-02", tz="US/Eastern"), + ), + ], + ) + def test_is_empty(self, left, right, closed): + # GH27219 + # non-empty always return False + iv = Interval(left, right, closed) + assert iv.is_empty is False + + # same endpoint is empty except when closed='both' (contains one point) + iv = Interval(left, left, closed) + result = iv.is_empty + expected = closed != "both" + assert result is expected diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_overlaps.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_overlaps.py new file mode 100644 index 0000000000000000000000000000000000000000..7fcf59d7bb4afc0077884de68dc335aff25c2cc5 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/interval/test_overlaps.py @@ -0,0 +1,67 @@ +import pytest + +from pandas import ( + Interval, + Timedelta, + Timestamp, +) + + +@pytest.fixture( + params=[ + (Timedelta("0 days"), Timedelta("1 day")), + (Timestamp("2018-01-01"), Timedelta("1 day")), + (0, 1), + ], + ids=lambda x: type(x[0]).__name__, +) +def start_shift(request): + """ + Fixture for generating intervals of types from a start value and a shift + value that can be added to start to generate an endpoint + """ + return request.param + + +class TestOverlaps: + def test_overlaps_self(self, start_shift, closed): + start, shift = start_shift + interval = Interval(start, start + shift, closed) + assert interval.overlaps(interval) + + def test_overlaps_nested(self, start_shift, closed, other_closed): + start, shift = start_shift + interval1 = Interval(start, start + 3 * shift, other_closed) + interval2 = Interval(start + shift, start + 2 * shift, closed) + + # nested intervals should always overlap + assert interval1.overlaps(interval2) + + def test_overlaps_disjoint(self, start_shift, closed, other_closed): + start, shift = start_shift + interval1 = Interval(start, start + shift, other_closed) + interval2 = Interval(start + 2 * shift, start + 3 * shift, closed) + + # disjoint intervals should never overlap + assert not interval1.overlaps(interval2) + + def test_overlaps_endpoint(self, start_shift, closed, other_closed): + start, shift = start_shift + interval1 = Interval(start, start + shift, other_closed) + interval2 = Interval(start + shift, start + 2 * shift, closed) + + # overlap if shared endpoint is closed for both (overlap at a point) + result = interval1.overlaps(interval2) + expected = interval1.closed_right and interval2.closed_left + assert result == expected + + @pytest.mark.parametrize( + "other", + [10, True, "foo", Timedelta("1 day"), Timestamp("2018-01-01")], + ids=lambda x: type(x).__name__, + ) + def test_overlaps_invalid_type(self, other): + interval = Interval(0, 1) + msg = f"`other` must be an Interval, got {type(other).__name__}" + with pytest.raises(TypeError, match=msg): + interval.overlaps(other) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/period/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/period/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/period/test_arithmetic.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/period/test_arithmetic.py new file mode 100644 index 0000000000000000000000000000000000000000..5dc0858de466c136dd186f35505226aa5208de70 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/period/test_arithmetic.py @@ -0,0 +1,486 @@ +from datetime import timedelta + +import numpy as np +import pytest + +from pandas._libs.tslibs.period import IncompatibleFrequency + +from pandas import ( + NaT, + Period, + Timedelta, + Timestamp, + offsets, +) + + +class TestPeriodArithmetic: + def test_add_overflow_raises(self): + # GH#55503 + per = Timestamp.max.to_period("ns") + + msg = "|".join( + [ + "Python int too large to convert to C long", + # windows, 32bit linux builds + "int too big to convert", + ] + ) + with pytest.raises(OverflowError, match=msg): + per + 1 + + msg = "value too large" + with pytest.raises(OverflowError, match=msg): + per + Timedelta(1) + with pytest.raises(OverflowError, match=msg): + per + offsets.Nano(1) + + def test_period_add_integer(self): + per1 = Period(freq="D", year=2008, month=1, day=1) + per2 = Period(freq="D", year=2008, month=1, day=2) + assert per1 + 1 == per2 + assert 1 + per1 == per2 + + def test_period_add_invalid(self): + # GH#4731 + per1 = Period(freq="D", year=2008, month=1, day=1) + per2 = Period(freq="D", year=2008, month=1, day=2) + + msg = "|".join( + [ + r"unsupported operand type\(s\)", + "can only concatenate str", + "must be str, not Period", + ] + ) + with pytest.raises(TypeError, match=msg): + per1 + "str" + with pytest.raises(TypeError, match=msg): + "str" + per1 + with pytest.raises(TypeError, match=msg): + per1 + per2 + + def test_period_sub_period_annual(self): + left, right = Period("2011", freq="Y"), Period("2007", freq="Y") + result = left - right + assert result == 4 * right.freq + + msg = r"Input has different freq=M from Period\(freq=Y-DEC\)" + with pytest.raises(IncompatibleFrequency, match=msg): + left - Period("2007-01", freq="M") + + def test_period_sub_period(self): + per1 = Period("2011-01-01", freq="D") + per2 = Period("2011-01-15", freq="D") + + off = per1.freq + assert per1 - per2 == -14 * off + assert per2 - per1 == 14 * off + + msg = r"Input has different freq=M from Period\(freq=D\)" + with pytest.raises(IncompatibleFrequency, match=msg): + per1 - Period("2011-02", freq="M") + + @pytest.mark.parametrize("n", [1, 2, 3, 4]) + def test_sub_n_gt_1_ticks(self, tick_classes, n): + # GH#23878 + p1 = Period("19910905", freq=tick_classes(n)) + p2 = Period("19920406", freq=tick_classes(n)) + + expected = Period(str(p2), freq=p2.freq.base) - Period( + str(p1), freq=p1.freq.base + ) + + assert (p2 - p1) == expected + + @pytest.mark.parametrize("normalize", [True, False]) + @pytest.mark.parametrize("n", [1, 2, 3, 4]) + @pytest.mark.parametrize( + "offset, kwd_name", + [ + (offsets.YearEnd, "month"), + (offsets.QuarterEnd, "startingMonth"), + (offsets.MonthEnd, None), + (offsets.Week, "weekday"), + ], + ) + def test_sub_n_gt_1_offsets(self, offset, kwd_name, n, normalize): + # GH#23878 + kwds = {kwd_name: 3} if kwd_name is not None else {} + p1_d = "19910905" + p2_d = "19920406" + p1 = Period(p1_d, freq=offset(n, normalize, **kwds)) + p2 = Period(p2_d, freq=offset(n, normalize, **kwds)) + + expected = Period(p2_d, freq=p2.freq.base) - Period(p1_d, freq=p1.freq.base) + + assert (p2 - p1) == expected + + def test_period_add_offset(self): + # freq is DateOffset + for freq in ["Y", "2Y", "3Y"]: + per = Period("2011", freq=freq) + exp = Period("2013", freq=freq) + assert per + offsets.YearEnd(2) == exp + assert offsets.YearEnd(2) + per == exp + + for off in [ + offsets.YearBegin(2), + offsets.MonthBegin(1), + offsets.Minute(), + np.timedelta64(365, "D"), + timedelta(365), + ]: + msg = "Input has different freq|Input cannot be converted to Period" + with pytest.raises(IncompatibleFrequency, match=msg): + per + off + with pytest.raises(IncompatibleFrequency, match=msg): + off + per + + for freq in ["M", "2M", "3M"]: + per = Period("2011-03", freq=freq) + exp = Period("2011-05", freq=freq) + assert per + offsets.MonthEnd(2) == exp + assert offsets.MonthEnd(2) + per == exp + + exp = Period("2012-03", freq=freq) + assert per + offsets.MonthEnd(12) == exp + assert offsets.MonthEnd(12) + per == exp + + msg = "|".join( + [ + "Input has different freq", + "Input cannot be converted to Period", + ] + ) + + for off in [ + offsets.YearBegin(2), + offsets.MonthBegin(1), + offsets.Minute(), + np.timedelta64(365, "D"), + timedelta(365), + ]: + with pytest.raises(IncompatibleFrequency, match=msg): + per + off + with pytest.raises(IncompatibleFrequency, match=msg): + off + per + + # freq is Tick + for freq in ["D", "2D", "3D"]: + per = Period("2011-04-01", freq=freq) + + exp = Period("2011-04-06", freq=freq) + assert per + offsets.Day(5) == exp + assert offsets.Day(5) + per == exp + + exp = Period("2011-04-02", freq=freq) + assert per + offsets.Hour(24) == exp + assert offsets.Hour(24) + per == exp + + exp = Period("2011-04-03", freq=freq) + assert per + np.timedelta64(2, "D") == exp + assert np.timedelta64(2, "D") + per == exp + + exp = Period("2011-04-02", freq=freq) + assert per + np.timedelta64(3600 * 24, "s") == exp + assert np.timedelta64(3600 * 24, "s") + per == exp + + exp = Period("2011-03-30", freq=freq) + assert per + timedelta(-2) == exp + assert timedelta(-2) + per == exp + + exp = Period("2011-04-03", freq=freq) + assert per + timedelta(hours=48) == exp + assert timedelta(hours=48) + per == exp + + msg = "|".join( + [ + "Input has different freq", + "Input cannot be converted to Period", + ] + ) + + for off in [ + offsets.YearBegin(2), + offsets.MonthBegin(1), + offsets.Minute(), + np.timedelta64(4, "h"), + timedelta(hours=23), + ]: + with pytest.raises(IncompatibleFrequency, match=msg): + per + off + with pytest.raises(IncompatibleFrequency, match=msg): + off + per + + for freq in ["h", "2h", "3h"]: + per = Period("2011-04-01 09:00", freq=freq) + + exp = Period("2011-04-03 09:00", freq=freq) + assert per + offsets.Day(2) == exp + assert offsets.Day(2) + per == exp + + exp = Period("2011-04-01 12:00", freq=freq) + assert per + offsets.Hour(3) == exp + assert offsets.Hour(3) + per == exp + + msg = "cannot use operands with types" + exp = Period("2011-04-01 12:00", freq=freq) + assert per + np.timedelta64(3, "h") == exp + assert np.timedelta64(3, "h") + per == exp + + exp = Period("2011-04-01 10:00", freq=freq) + assert per + np.timedelta64(3600, "s") == exp + assert np.timedelta64(3600, "s") + per == exp + + exp = Period("2011-04-01 11:00", freq=freq) + assert per + timedelta(minutes=120) == exp + assert timedelta(minutes=120) + per == exp + + exp = Period("2011-04-05 12:00", freq=freq) + assert per + timedelta(days=4, minutes=180) == exp + assert timedelta(days=4, minutes=180) + per == exp + + msg = "|".join( + [ + "Input has different freq", + "Input cannot be converted to Period", + ] + ) + + for off in [ + offsets.YearBegin(2), + offsets.MonthBegin(1), + offsets.Minute(), + np.timedelta64(3200, "s"), + timedelta(hours=23, minutes=30), + ]: + with pytest.raises(IncompatibleFrequency, match=msg): + per + off + with pytest.raises(IncompatibleFrequency, match=msg): + off + per + + def test_period_sub_offset(self): + # freq is DateOffset + msg = "|".join( + [ + "Input has different freq", + "Input cannot be converted to Period", + ] + ) + + for freq in ["Y", "2Y", "3Y"]: + per = Period("2011", freq=freq) + assert per - offsets.YearEnd(2) == Period("2009", freq=freq) + + for off in [ + offsets.YearBegin(2), + offsets.MonthBegin(1), + offsets.Minute(), + np.timedelta64(365, "D"), + timedelta(365), + ]: + with pytest.raises(IncompatibleFrequency, match=msg): + per - off + + for freq in ["M", "2M", "3M"]: + per = Period("2011-03", freq=freq) + assert per - offsets.MonthEnd(2) == Period("2011-01", freq=freq) + assert per - offsets.MonthEnd(12) == Period("2010-03", freq=freq) + + for off in [ + offsets.YearBegin(2), + offsets.MonthBegin(1), + offsets.Minute(), + np.timedelta64(365, "D"), + timedelta(365), + ]: + with pytest.raises(IncompatibleFrequency, match=msg): + per - off + + # freq is Tick + for freq in ["D", "2D", "3D"]: + per = Period("2011-04-01", freq=freq) + assert per - offsets.Day(5) == Period("2011-03-27", freq=freq) + assert per - offsets.Hour(24) == Period("2011-03-31", freq=freq) + assert per - np.timedelta64(2, "D") == Period("2011-03-30", freq=freq) + assert per - np.timedelta64(3600 * 24, "s") == Period( + "2011-03-31", freq=freq + ) + assert per - timedelta(-2) == Period("2011-04-03", freq=freq) + assert per - timedelta(hours=48) == Period("2011-03-30", freq=freq) + + for off in [ + offsets.YearBegin(2), + offsets.MonthBegin(1), + offsets.Minute(), + np.timedelta64(4, "h"), + timedelta(hours=23), + ]: + with pytest.raises(IncompatibleFrequency, match=msg): + per - off + + for freq in ["h", "2h", "3h"]: + per = Period("2011-04-01 09:00", freq=freq) + assert per - offsets.Day(2) == Period("2011-03-30 09:00", freq=freq) + assert per - offsets.Hour(3) == Period("2011-04-01 06:00", freq=freq) + assert per - np.timedelta64(3, "h") == Period("2011-04-01 06:00", freq=freq) + assert per - np.timedelta64(3600, "s") == Period( + "2011-04-01 08:00", freq=freq + ) + assert per - timedelta(minutes=120) == Period("2011-04-01 07:00", freq=freq) + assert per - timedelta(days=4, minutes=180) == Period( + "2011-03-28 06:00", freq=freq + ) + + for off in [ + offsets.YearBegin(2), + offsets.MonthBegin(1), + offsets.Minute(), + np.timedelta64(3200, "s"), + timedelta(hours=23, minutes=30), + ]: + with pytest.raises(IncompatibleFrequency, match=msg): + per - off + + @pytest.mark.parametrize("freq", ["M", "2M", "3M"]) + def test_period_addsub_nat(self, freq): + # GH#13071 + per = Period("2011-01", freq=freq) + + # For subtraction, NaT is treated as another Period object + assert NaT - per is NaT + assert per - NaT is NaT + + # For addition, NaT is treated as offset-like + assert NaT + per is NaT + assert per + NaT is NaT + + @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s", "m"]) + def test_period_add_sub_td64_nat(self, unit): + # GH#47196 + per = Period("2022-06-01", "D") + nat = np.timedelta64("NaT", unit) + + assert per + nat is NaT + assert nat + per is NaT + assert per - nat is NaT + + with pytest.raises(TypeError, match="unsupported operand"): + nat - per + + def test_period_ops_offset(self): + per = Period("2011-04-01", freq="D") + result = per + offsets.Day() + exp = Period("2011-04-02", freq="D") + assert result == exp + + result = per - offsets.Day(2) + exp = Period("2011-03-30", freq="D") + assert result == exp + + msg = r"Input cannot be converted to Period\(freq=D\)" + with pytest.raises(IncompatibleFrequency, match=msg): + per + offsets.Hour(2) + + with pytest.raises(IncompatibleFrequency, match=msg): + per - offsets.Hour(2) + + def test_period_add_timestamp_raises(self): + # GH#17983 + ts = Timestamp("2017") + per = Period("2017", freq="M") + + msg = r"unsupported operand type\(s\) for \+: 'Timestamp' and 'Period'" + with pytest.raises(TypeError, match=msg): + ts + per + + msg = r"unsupported operand type\(s\) for \+: 'Period' and 'Timestamp'" + with pytest.raises(TypeError, match=msg): + per + ts + + +class TestPeriodComparisons: + def test_period_comparison_same_freq(self): + jan = Period("2000-01", "M") + feb = Period("2000-02", "M") + + assert not jan == feb + assert jan != feb + assert jan < feb + assert jan <= feb + assert not jan > feb + assert not jan >= feb + + def test_period_comparison_same_period_different_object(self): + # Separate Period objects for the same period + left = Period("2000-01", "M") + right = Period("2000-01", "M") + + assert left == right + assert left >= right + assert left <= right + assert not left < right + assert not left > right + + def test_period_comparison_mismatched_freq(self): + jan = Period("2000-01", "M") + day = Period("2012-01-01", "D") + + assert not jan == day + assert jan != day + msg = r"Input has different freq=D from Period\(freq=M\)" + with pytest.raises(IncompatibleFrequency, match=msg): + jan < day + with pytest.raises(IncompatibleFrequency, match=msg): + jan <= day + with pytest.raises(IncompatibleFrequency, match=msg): + jan > day + with pytest.raises(IncompatibleFrequency, match=msg): + jan >= day + + def test_period_comparison_invalid_type(self): + jan = Period("2000-01", "M") + + assert not jan == 1 + assert jan != 1 + + int_or_per = "'(Period|int)'" + msg = f"not supported between instances of {int_or_per} and {int_or_per}" + for left, right in [(jan, 1), (1, jan)]: + with pytest.raises(TypeError, match=msg): + left > right + with pytest.raises(TypeError, match=msg): + left >= right + with pytest.raises(TypeError, match=msg): + left < right + with pytest.raises(TypeError, match=msg): + left <= right + + def test_period_comparison_nat(self): + per = Period("2011-01-01", freq="D") + + ts = Timestamp("2011-01-01") + # confirm Period('NaT') work identical with Timestamp('NaT') + for left, right in [ + (NaT, per), + (per, NaT), + (NaT, ts), + (ts, NaT), + ]: + assert not left < right + assert not left > right + assert not left == right + assert left != right + assert not left <= right + assert not left >= right + + @pytest.mark.parametrize( + "zerodim_arr, expected", + ((np.array(0), False), (np.array(Period("2000-01", "M")), True)), + ) + def test_period_comparison_numpy_zerodim_arr(self, zerodim_arr, expected): + per = Period("2000-01", "M") + + assert (per == zerodim_arr) is expected + assert (zerodim_arr == per) is expected diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/period/test_asfreq.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/period/test_asfreq.py new file mode 100644 index 0000000000000000000000000000000000000000..73c4d8061c25789c1ec2a5e4d2c2851d4066a90e --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/period/test_asfreq.py @@ -0,0 +1,828 @@ +import pytest + +from pandas._libs.tslibs.period import INVALID_FREQ_ERR_MSG +from pandas.errors import OutOfBoundsDatetime + +from pandas import ( + Period, + Timestamp, + offsets, +) +import pandas._testing as tm + +bday_msg = "Period with BDay freq is deprecated" + + +class TestFreqConversion: + """Test frequency conversion of date objects""" + + @pytest.mark.filterwarnings("ignore:Period with BDay:FutureWarning") + @pytest.mark.parametrize("freq", ["Y", "Q", "M", "W", "B", "D"]) + def test_asfreq_near_zero(self, freq): + # GH#19643, GH#19650 + per = Period("0001-01-01", freq=freq) + tup1 = (per.year, per.hour, per.day) + + prev = per - 1 + assert prev.ordinal == per.ordinal - 1 + tup2 = (prev.year, prev.month, prev.day) + assert tup2 < tup1 + + def test_asfreq_near_zero_weekly(self): + # GH#19834 + per1 = Period("0001-01-01", "D") + 6 + per2 = Period("0001-01-01", "D") - 6 + week1 = per1.asfreq("W") + week2 = per2.asfreq("W") + assert week1 != week2 + assert week1.asfreq("D", "E") >= per1 + assert week2.asfreq("D", "S") <= per2 + + def test_to_timestamp_out_of_bounds(self): + # GH#19643, used to incorrectly give Timestamp in 1754 + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + per = Period("0001-01-01", freq="B") + msg = "Out of bounds nanosecond timestamp" + with pytest.raises(OutOfBoundsDatetime, match=msg): + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + per.to_timestamp() + + def test_asfreq_corner(self): + val = Period(freq="Y", year=2007) + result1 = val.asfreq("5min") + result2 = val.asfreq("min") + expected = Period("2007-12-31 23:59", freq="min") + assert result1.ordinal == expected.ordinal + assert result1.freqstr == "5min" + assert result2.ordinal == expected.ordinal + assert result2.freqstr == "min" + + def test_conv_annual(self): + # frequency conversion tests: from Annual Frequency + + ival_A = Period(freq="Y", year=2007) + + ival_AJAN = Period(freq="Y-JAN", year=2007) + ival_AJUN = Period(freq="Y-JUN", year=2007) + ival_ANOV = Period(freq="Y-NOV", year=2007) + + ival_A_to_Q_start = Period(freq="Q", year=2007, quarter=1) + ival_A_to_Q_end = Period(freq="Q", year=2007, quarter=4) + ival_A_to_M_start = Period(freq="M", year=2007, month=1) + ival_A_to_M_end = Period(freq="M", year=2007, month=12) + ival_A_to_W_start = Period(freq="W", year=2007, month=1, day=1) + ival_A_to_W_end = Period(freq="W", year=2007, month=12, day=31) + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + ival_A_to_B_start = Period(freq="B", year=2007, month=1, day=1) + ival_A_to_B_end = Period(freq="B", year=2007, month=12, day=31) + ival_A_to_D_start = Period(freq="D", year=2007, month=1, day=1) + ival_A_to_D_end = Period(freq="D", year=2007, month=12, day=31) + ival_A_to_H_start = Period(freq="h", year=2007, month=1, day=1, hour=0) + ival_A_to_H_end = Period(freq="h", year=2007, month=12, day=31, hour=23) + ival_A_to_T_start = Period( + freq="Min", year=2007, month=1, day=1, hour=0, minute=0 + ) + ival_A_to_T_end = Period( + freq="Min", year=2007, month=12, day=31, hour=23, minute=59 + ) + ival_A_to_S_start = Period( + freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=0 + ) + ival_A_to_S_end = Period( + freq="s", year=2007, month=12, day=31, hour=23, minute=59, second=59 + ) + + ival_AJAN_to_D_end = Period(freq="D", year=2007, month=1, day=31) + ival_AJAN_to_D_start = Period(freq="D", year=2006, month=2, day=1) + ival_AJUN_to_D_end = Period(freq="D", year=2007, month=6, day=30) + ival_AJUN_to_D_start = Period(freq="D", year=2006, month=7, day=1) + ival_ANOV_to_D_end = Period(freq="D", year=2007, month=11, day=30) + ival_ANOV_to_D_start = Period(freq="D", year=2006, month=12, day=1) + + assert ival_A.asfreq("Q", "s") == ival_A_to_Q_start + assert ival_A.asfreq("Q", "e") == ival_A_to_Q_end + assert ival_A.asfreq("M", "s") == ival_A_to_M_start + assert ival_A.asfreq("M", "E") == ival_A_to_M_end + assert ival_A.asfreq("W", "s") == ival_A_to_W_start + assert ival_A.asfreq("W", "E") == ival_A_to_W_end + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + assert ival_A.asfreq("B", "s") == ival_A_to_B_start + assert ival_A.asfreq("B", "E") == ival_A_to_B_end + assert ival_A.asfreq("D", "s") == ival_A_to_D_start + assert ival_A.asfreq("D", "E") == ival_A_to_D_end + msg = "'H' is deprecated and will be removed in a future version." + with tm.assert_produces_warning(FutureWarning, match=msg): + assert ival_A.asfreq("H", "s") == ival_A_to_H_start + assert ival_A.asfreq("H", "E") == ival_A_to_H_end + assert ival_A.asfreq("min", "s") == ival_A_to_T_start + assert ival_A.asfreq("min", "E") == ival_A_to_T_end + msg = "'T' is deprecated and will be removed in a future version." + with tm.assert_produces_warning(FutureWarning, match=msg): + assert ival_A.asfreq("T", "s") == ival_A_to_T_start + assert ival_A.asfreq("T", "E") == ival_A_to_T_end + msg = "'S' is deprecated and will be removed in a future version." + with tm.assert_produces_warning(FutureWarning, match=msg): + assert ival_A.asfreq("S", "S") == ival_A_to_S_start + assert ival_A.asfreq("S", "E") == ival_A_to_S_end + + assert ival_AJAN.asfreq("D", "s") == ival_AJAN_to_D_start + assert ival_AJAN.asfreq("D", "E") == ival_AJAN_to_D_end + + assert ival_AJUN.asfreq("D", "s") == ival_AJUN_to_D_start + assert ival_AJUN.asfreq("D", "E") == ival_AJUN_to_D_end + + assert ival_ANOV.asfreq("D", "s") == ival_ANOV_to_D_start + assert ival_ANOV.asfreq("D", "E") == ival_ANOV_to_D_end + + assert ival_A.asfreq("Y") == ival_A + + def test_conv_quarterly(self): + # frequency conversion tests: from Quarterly Frequency + + ival_Q = Period(freq="Q", year=2007, quarter=1) + ival_Q_end_of_year = Period(freq="Q", year=2007, quarter=4) + + ival_QEJAN = Period(freq="Q-JAN", year=2007, quarter=1) + ival_QEJUN = Period(freq="Q-JUN", year=2007, quarter=1) + + ival_Q_to_A = Period(freq="Y", year=2007) + ival_Q_to_M_start = Period(freq="M", year=2007, month=1) + ival_Q_to_M_end = Period(freq="M", year=2007, month=3) + ival_Q_to_W_start = Period(freq="W", year=2007, month=1, day=1) + ival_Q_to_W_end = Period(freq="W", year=2007, month=3, day=31) + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + ival_Q_to_B_start = Period(freq="B", year=2007, month=1, day=1) + ival_Q_to_B_end = Period(freq="B", year=2007, month=3, day=30) + ival_Q_to_D_start = Period(freq="D", year=2007, month=1, day=1) + ival_Q_to_D_end = Period(freq="D", year=2007, month=3, day=31) + ival_Q_to_H_start = Period(freq="h", year=2007, month=1, day=1, hour=0) + ival_Q_to_H_end = Period(freq="h", year=2007, month=3, day=31, hour=23) + ival_Q_to_T_start = Period( + freq="Min", year=2007, month=1, day=1, hour=0, minute=0 + ) + ival_Q_to_T_end = Period( + freq="Min", year=2007, month=3, day=31, hour=23, minute=59 + ) + ival_Q_to_S_start = Period( + freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=0 + ) + ival_Q_to_S_end = Period( + freq="s", year=2007, month=3, day=31, hour=23, minute=59, second=59 + ) + + ival_QEJAN_to_D_start = Period(freq="D", year=2006, month=2, day=1) + ival_QEJAN_to_D_end = Period(freq="D", year=2006, month=4, day=30) + + ival_QEJUN_to_D_start = Period(freq="D", year=2006, month=7, day=1) + ival_QEJUN_to_D_end = Period(freq="D", year=2006, month=9, day=30) + + assert ival_Q.asfreq("Y") == ival_Q_to_A + assert ival_Q_end_of_year.asfreq("Y") == ival_Q_to_A + + assert ival_Q.asfreq("M", "s") == ival_Q_to_M_start + assert ival_Q.asfreq("M", "E") == ival_Q_to_M_end + assert ival_Q.asfreq("W", "s") == ival_Q_to_W_start + assert ival_Q.asfreq("W", "E") == ival_Q_to_W_end + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + assert ival_Q.asfreq("B", "s") == ival_Q_to_B_start + assert ival_Q.asfreq("B", "E") == ival_Q_to_B_end + assert ival_Q.asfreq("D", "s") == ival_Q_to_D_start + assert ival_Q.asfreq("D", "E") == ival_Q_to_D_end + assert ival_Q.asfreq("h", "s") == ival_Q_to_H_start + assert ival_Q.asfreq("h", "E") == ival_Q_to_H_end + assert ival_Q.asfreq("Min", "s") == ival_Q_to_T_start + assert ival_Q.asfreq("Min", "E") == ival_Q_to_T_end + assert ival_Q.asfreq("s", "s") == ival_Q_to_S_start + assert ival_Q.asfreq("s", "E") == ival_Q_to_S_end + + assert ival_QEJAN.asfreq("D", "s") == ival_QEJAN_to_D_start + assert ival_QEJAN.asfreq("D", "E") == ival_QEJAN_to_D_end + assert ival_QEJUN.asfreq("D", "s") == ival_QEJUN_to_D_start + assert ival_QEJUN.asfreq("D", "E") == ival_QEJUN_to_D_end + + assert ival_Q.asfreq("Q") == ival_Q + + def test_conv_monthly(self): + # frequency conversion tests: from Monthly Frequency + + ival_M = Period(freq="M", year=2007, month=1) + ival_M_end_of_year = Period(freq="M", year=2007, month=12) + ival_M_end_of_quarter = Period(freq="M", year=2007, month=3) + ival_M_to_A = Period(freq="Y", year=2007) + ival_M_to_Q = Period(freq="Q", year=2007, quarter=1) + ival_M_to_W_start = Period(freq="W", year=2007, month=1, day=1) + ival_M_to_W_end = Period(freq="W", year=2007, month=1, day=31) + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + ival_M_to_B_start = Period(freq="B", year=2007, month=1, day=1) + ival_M_to_B_end = Period(freq="B", year=2007, month=1, day=31) + ival_M_to_D_start = Period(freq="D", year=2007, month=1, day=1) + ival_M_to_D_end = Period(freq="D", year=2007, month=1, day=31) + ival_M_to_H_start = Period(freq="h", year=2007, month=1, day=1, hour=0) + ival_M_to_H_end = Period(freq="h", year=2007, month=1, day=31, hour=23) + ival_M_to_T_start = Period( + freq="Min", year=2007, month=1, day=1, hour=0, minute=0 + ) + ival_M_to_T_end = Period( + freq="Min", year=2007, month=1, day=31, hour=23, minute=59 + ) + ival_M_to_S_start = Period( + freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=0 + ) + ival_M_to_S_end = Period( + freq="s", year=2007, month=1, day=31, hour=23, minute=59, second=59 + ) + + assert ival_M.asfreq("Y") == ival_M_to_A + assert ival_M_end_of_year.asfreq("Y") == ival_M_to_A + assert ival_M.asfreq("Q") == ival_M_to_Q + assert ival_M_end_of_quarter.asfreq("Q") == ival_M_to_Q + + assert ival_M.asfreq("W", "s") == ival_M_to_W_start + assert ival_M.asfreq("W", "E") == ival_M_to_W_end + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + assert ival_M.asfreq("B", "s") == ival_M_to_B_start + assert ival_M.asfreq("B", "E") == ival_M_to_B_end + assert ival_M.asfreq("D", "s") == ival_M_to_D_start + assert ival_M.asfreq("D", "E") == ival_M_to_D_end + assert ival_M.asfreq("h", "s") == ival_M_to_H_start + assert ival_M.asfreq("h", "E") == ival_M_to_H_end + assert ival_M.asfreq("Min", "s") == ival_M_to_T_start + assert ival_M.asfreq("Min", "E") == ival_M_to_T_end + assert ival_M.asfreq("s", "s") == ival_M_to_S_start + assert ival_M.asfreq("s", "E") == ival_M_to_S_end + + assert ival_M.asfreq("M") == ival_M + + def test_conv_weekly(self): + # frequency conversion tests: from Weekly Frequency + ival_W = Period(freq="W", year=2007, month=1, day=1) + + ival_WSUN = Period(freq="W", year=2007, month=1, day=7) + ival_WSAT = Period(freq="W-SAT", year=2007, month=1, day=6) + ival_WFRI = Period(freq="W-FRI", year=2007, month=1, day=5) + ival_WTHU = Period(freq="W-THU", year=2007, month=1, day=4) + ival_WWED = Period(freq="W-WED", year=2007, month=1, day=3) + ival_WTUE = Period(freq="W-TUE", year=2007, month=1, day=2) + ival_WMON = Period(freq="W-MON", year=2007, month=1, day=1) + + ival_WSUN_to_D_start = Period(freq="D", year=2007, month=1, day=1) + ival_WSUN_to_D_end = Period(freq="D", year=2007, month=1, day=7) + ival_WSAT_to_D_start = Period(freq="D", year=2006, month=12, day=31) + ival_WSAT_to_D_end = Period(freq="D", year=2007, month=1, day=6) + ival_WFRI_to_D_start = Period(freq="D", year=2006, month=12, day=30) + ival_WFRI_to_D_end = Period(freq="D", year=2007, month=1, day=5) + ival_WTHU_to_D_start = Period(freq="D", year=2006, month=12, day=29) + ival_WTHU_to_D_end = Period(freq="D", year=2007, month=1, day=4) + ival_WWED_to_D_start = Period(freq="D", year=2006, month=12, day=28) + ival_WWED_to_D_end = Period(freq="D", year=2007, month=1, day=3) + ival_WTUE_to_D_start = Period(freq="D", year=2006, month=12, day=27) + ival_WTUE_to_D_end = Period(freq="D", year=2007, month=1, day=2) + ival_WMON_to_D_start = Period(freq="D", year=2006, month=12, day=26) + ival_WMON_to_D_end = Period(freq="D", year=2007, month=1, day=1) + + ival_W_end_of_year = Period(freq="W", year=2007, month=12, day=31) + ival_W_end_of_quarter = Period(freq="W", year=2007, month=3, day=31) + ival_W_end_of_month = Period(freq="W", year=2007, month=1, day=31) + ival_W_to_A = Period(freq="Y", year=2007) + ival_W_to_Q = Period(freq="Q", year=2007, quarter=1) + ival_W_to_M = Period(freq="M", year=2007, month=1) + + if Period(freq="D", year=2007, month=12, day=31).weekday == 6: + ival_W_to_A_end_of_year = Period(freq="Y", year=2007) + else: + ival_W_to_A_end_of_year = Period(freq="Y", year=2008) + + if Period(freq="D", year=2007, month=3, day=31).weekday == 6: + ival_W_to_Q_end_of_quarter = Period(freq="Q", year=2007, quarter=1) + else: + ival_W_to_Q_end_of_quarter = Period(freq="Q", year=2007, quarter=2) + + if Period(freq="D", year=2007, month=1, day=31).weekday == 6: + ival_W_to_M_end_of_month = Period(freq="M", year=2007, month=1) + else: + ival_W_to_M_end_of_month = Period(freq="M", year=2007, month=2) + + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + ival_W_to_B_start = Period(freq="B", year=2007, month=1, day=1) + ival_W_to_B_end = Period(freq="B", year=2007, month=1, day=5) + ival_W_to_D_start = Period(freq="D", year=2007, month=1, day=1) + ival_W_to_D_end = Period(freq="D", year=2007, month=1, day=7) + ival_W_to_H_start = Period(freq="h", year=2007, month=1, day=1, hour=0) + ival_W_to_H_end = Period(freq="h", year=2007, month=1, day=7, hour=23) + ival_W_to_T_start = Period( + freq="Min", year=2007, month=1, day=1, hour=0, minute=0 + ) + ival_W_to_T_end = Period( + freq="Min", year=2007, month=1, day=7, hour=23, minute=59 + ) + ival_W_to_S_start = Period( + freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=0 + ) + ival_W_to_S_end = Period( + freq="s", year=2007, month=1, day=7, hour=23, minute=59, second=59 + ) + + assert ival_W.asfreq("Y") == ival_W_to_A + assert ival_W_end_of_year.asfreq("Y") == ival_W_to_A_end_of_year + + assert ival_W.asfreq("Q") == ival_W_to_Q + assert ival_W_end_of_quarter.asfreq("Q") == ival_W_to_Q_end_of_quarter + + assert ival_W.asfreq("M") == ival_W_to_M + assert ival_W_end_of_month.asfreq("M") == ival_W_to_M_end_of_month + + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + assert ival_W.asfreq("B", "s") == ival_W_to_B_start + assert ival_W.asfreq("B", "E") == ival_W_to_B_end + + assert ival_W.asfreq("D", "s") == ival_W_to_D_start + assert ival_W.asfreq("D", "E") == ival_W_to_D_end + + assert ival_WSUN.asfreq("D", "s") == ival_WSUN_to_D_start + assert ival_WSUN.asfreq("D", "E") == ival_WSUN_to_D_end + assert ival_WSAT.asfreq("D", "s") == ival_WSAT_to_D_start + assert ival_WSAT.asfreq("D", "E") == ival_WSAT_to_D_end + assert ival_WFRI.asfreq("D", "s") == ival_WFRI_to_D_start + assert ival_WFRI.asfreq("D", "E") == ival_WFRI_to_D_end + assert ival_WTHU.asfreq("D", "s") == ival_WTHU_to_D_start + assert ival_WTHU.asfreq("D", "E") == ival_WTHU_to_D_end + assert ival_WWED.asfreq("D", "s") == ival_WWED_to_D_start + assert ival_WWED.asfreq("D", "E") == ival_WWED_to_D_end + assert ival_WTUE.asfreq("D", "s") == ival_WTUE_to_D_start + assert ival_WTUE.asfreq("D", "E") == ival_WTUE_to_D_end + assert ival_WMON.asfreq("D", "s") == ival_WMON_to_D_start + assert ival_WMON.asfreq("D", "E") == ival_WMON_to_D_end + + assert ival_W.asfreq("h", "s") == ival_W_to_H_start + assert ival_W.asfreq("h", "E") == ival_W_to_H_end + assert ival_W.asfreq("Min", "s") == ival_W_to_T_start + assert ival_W.asfreq("Min", "E") == ival_W_to_T_end + assert ival_W.asfreq("s", "s") == ival_W_to_S_start + assert ival_W.asfreq("s", "E") == ival_W_to_S_end + + assert ival_W.asfreq("W") == ival_W + + msg = INVALID_FREQ_ERR_MSG + with pytest.raises(ValueError, match=msg): + ival_W.asfreq("WK") + + def test_conv_weekly_legacy(self): + # frequency conversion tests: from Weekly Frequency + msg = INVALID_FREQ_ERR_MSG + with pytest.raises(ValueError, match=msg): + Period(freq="WK", year=2007, month=1, day=1) + + with pytest.raises(ValueError, match=msg): + Period(freq="WK-SAT", year=2007, month=1, day=6) + with pytest.raises(ValueError, match=msg): + Period(freq="WK-FRI", year=2007, month=1, day=5) + with pytest.raises(ValueError, match=msg): + Period(freq="WK-THU", year=2007, month=1, day=4) + with pytest.raises(ValueError, match=msg): + Period(freq="WK-WED", year=2007, month=1, day=3) + with pytest.raises(ValueError, match=msg): + Period(freq="WK-TUE", year=2007, month=1, day=2) + with pytest.raises(ValueError, match=msg): + Period(freq="WK-MON", year=2007, month=1, day=1) + + def test_conv_business(self): + # frequency conversion tests: from Business Frequency" + + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + ival_B = Period(freq="B", year=2007, month=1, day=1) + ival_B_end_of_year = Period(freq="B", year=2007, month=12, day=31) + ival_B_end_of_quarter = Period(freq="B", year=2007, month=3, day=30) + ival_B_end_of_month = Period(freq="B", year=2007, month=1, day=31) + ival_B_end_of_week = Period(freq="B", year=2007, month=1, day=5) + + ival_B_to_A = Period(freq="Y", year=2007) + ival_B_to_Q = Period(freq="Q", year=2007, quarter=1) + ival_B_to_M = Period(freq="M", year=2007, month=1) + ival_B_to_W = Period(freq="W", year=2007, month=1, day=7) + ival_B_to_D = Period(freq="D", year=2007, month=1, day=1) + ival_B_to_H_start = Period(freq="h", year=2007, month=1, day=1, hour=0) + ival_B_to_H_end = Period(freq="h", year=2007, month=1, day=1, hour=23) + ival_B_to_T_start = Period( + freq="Min", year=2007, month=1, day=1, hour=0, minute=0 + ) + ival_B_to_T_end = Period( + freq="Min", year=2007, month=1, day=1, hour=23, minute=59 + ) + ival_B_to_S_start = Period( + freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=0 + ) + ival_B_to_S_end = Period( + freq="s", year=2007, month=1, day=1, hour=23, minute=59, second=59 + ) + + assert ival_B.asfreq("Y") == ival_B_to_A + assert ival_B_end_of_year.asfreq("Y") == ival_B_to_A + assert ival_B.asfreq("Q") == ival_B_to_Q + assert ival_B_end_of_quarter.asfreq("Q") == ival_B_to_Q + assert ival_B.asfreq("M") == ival_B_to_M + assert ival_B_end_of_month.asfreq("M") == ival_B_to_M + assert ival_B.asfreq("W") == ival_B_to_W + assert ival_B_end_of_week.asfreq("W") == ival_B_to_W + + assert ival_B.asfreq("D") == ival_B_to_D + + assert ival_B.asfreq("h", "s") == ival_B_to_H_start + assert ival_B.asfreq("h", "E") == ival_B_to_H_end + assert ival_B.asfreq("Min", "s") == ival_B_to_T_start + assert ival_B.asfreq("Min", "E") == ival_B_to_T_end + assert ival_B.asfreq("s", "s") == ival_B_to_S_start + assert ival_B.asfreq("s", "E") == ival_B_to_S_end + + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + assert ival_B.asfreq("B") == ival_B + + def test_conv_daily(self): + # frequency conversion tests: from Business Frequency" + + ival_D = Period(freq="D", year=2007, month=1, day=1) + ival_D_end_of_year = Period(freq="D", year=2007, month=12, day=31) + ival_D_end_of_quarter = Period(freq="D", year=2007, month=3, day=31) + ival_D_end_of_month = Period(freq="D", year=2007, month=1, day=31) + ival_D_end_of_week = Period(freq="D", year=2007, month=1, day=7) + + ival_D_friday = Period(freq="D", year=2007, month=1, day=5) + ival_D_saturday = Period(freq="D", year=2007, month=1, day=6) + ival_D_sunday = Period(freq="D", year=2007, month=1, day=7) + + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + ival_B_friday = Period(freq="B", year=2007, month=1, day=5) + ival_B_monday = Period(freq="B", year=2007, month=1, day=8) + + ival_D_to_A = Period(freq="Y", year=2007) + + ival_Deoq_to_AJAN = Period(freq="Y-JAN", year=2008) + ival_Deoq_to_AJUN = Period(freq="Y-JUN", year=2007) + ival_Deoq_to_ADEC = Period(freq="Y-DEC", year=2007) + + ival_D_to_QEJAN = Period(freq="Q-JAN", year=2007, quarter=4) + ival_D_to_QEJUN = Period(freq="Q-JUN", year=2007, quarter=3) + ival_D_to_QEDEC = Period(freq="Q-DEC", year=2007, quarter=1) + + ival_D_to_M = Period(freq="M", year=2007, month=1) + ival_D_to_W = Period(freq="W", year=2007, month=1, day=7) + + ival_D_to_H_start = Period(freq="h", year=2007, month=1, day=1, hour=0) + ival_D_to_H_end = Period(freq="h", year=2007, month=1, day=1, hour=23) + ival_D_to_T_start = Period( + freq="Min", year=2007, month=1, day=1, hour=0, minute=0 + ) + ival_D_to_T_end = Period( + freq="Min", year=2007, month=1, day=1, hour=23, minute=59 + ) + ival_D_to_S_start = Period( + freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=0 + ) + ival_D_to_S_end = Period( + freq="s", year=2007, month=1, day=1, hour=23, minute=59, second=59 + ) + + assert ival_D.asfreq("Y") == ival_D_to_A + + assert ival_D_end_of_quarter.asfreq("Y-JAN") == ival_Deoq_to_AJAN + assert ival_D_end_of_quarter.asfreq("Y-JUN") == ival_Deoq_to_AJUN + assert ival_D_end_of_quarter.asfreq("Y-DEC") == ival_Deoq_to_ADEC + + assert ival_D_end_of_year.asfreq("Y") == ival_D_to_A + assert ival_D_end_of_quarter.asfreq("Q") == ival_D_to_QEDEC + assert ival_D.asfreq("Q-JAN") == ival_D_to_QEJAN + assert ival_D.asfreq("Q-JUN") == ival_D_to_QEJUN + assert ival_D.asfreq("Q-DEC") == ival_D_to_QEDEC + assert ival_D.asfreq("M") == ival_D_to_M + assert ival_D_end_of_month.asfreq("M") == ival_D_to_M + assert ival_D.asfreq("W") == ival_D_to_W + assert ival_D_end_of_week.asfreq("W") == ival_D_to_W + + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + assert ival_D_friday.asfreq("B") == ival_B_friday + assert ival_D_saturday.asfreq("B", "s") == ival_B_friday + assert ival_D_saturday.asfreq("B", "E") == ival_B_monday + assert ival_D_sunday.asfreq("B", "s") == ival_B_friday + assert ival_D_sunday.asfreq("B", "E") == ival_B_monday + + assert ival_D.asfreq("h", "s") == ival_D_to_H_start + assert ival_D.asfreq("h", "E") == ival_D_to_H_end + assert ival_D.asfreq("Min", "s") == ival_D_to_T_start + assert ival_D.asfreq("Min", "E") == ival_D_to_T_end + assert ival_D.asfreq("s", "s") == ival_D_to_S_start + assert ival_D.asfreq("s", "E") == ival_D_to_S_end + + assert ival_D.asfreq("D") == ival_D + + def test_conv_hourly(self): + # frequency conversion tests: from Hourly Frequency" + + ival_H = Period(freq="h", year=2007, month=1, day=1, hour=0) + ival_H_end_of_year = Period(freq="h", year=2007, month=12, day=31, hour=23) + ival_H_end_of_quarter = Period(freq="h", year=2007, month=3, day=31, hour=23) + ival_H_end_of_month = Period(freq="h", year=2007, month=1, day=31, hour=23) + ival_H_end_of_week = Period(freq="h", year=2007, month=1, day=7, hour=23) + ival_H_end_of_day = Period(freq="h", year=2007, month=1, day=1, hour=23) + ival_H_end_of_bus = Period(freq="h", year=2007, month=1, day=1, hour=23) + + ival_H_to_A = Period(freq="Y", year=2007) + ival_H_to_Q = Period(freq="Q", year=2007, quarter=1) + ival_H_to_M = Period(freq="M", year=2007, month=1) + ival_H_to_W = Period(freq="W", year=2007, month=1, day=7) + ival_H_to_D = Period(freq="D", year=2007, month=1, day=1) + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + ival_H_to_B = Period(freq="B", year=2007, month=1, day=1) + + ival_H_to_T_start = Period( + freq="Min", year=2007, month=1, day=1, hour=0, minute=0 + ) + ival_H_to_T_end = Period( + freq="Min", year=2007, month=1, day=1, hour=0, minute=59 + ) + ival_H_to_S_start = Period( + freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=0 + ) + ival_H_to_S_end = Period( + freq="s", year=2007, month=1, day=1, hour=0, minute=59, second=59 + ) + + assert ival_H.asfreq("Y") == ival_H_to_A + assert ival_H_end_of_year.asfreq("Y") == ival_H_to_A + assert ival_H.asfreq("Q") == ival_H_to_Q + assert ival_H_end_of_quarter.asfreq("Q") == ival_H_to_Q + assert ival_H.asfreq("M") == ival_H_to_M + assert ival_H_end_of_month.asfreq("M") == ival_H_to_M + assert ival_H.asfreq("W") == ival_H_to_W + assert ival_H_end_of_week.asfreq("W") == ival_H_to_W + assert ival_H.asfreq("D") == ival_H_to_D + assert ival_H_end_of_day.asfreq("D") == ival_H_to_D + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + assert ival_H.asfreq("B") == ival_H_to_B + assert ival_H_end_of_bus.asfreq("B") == ival_H_to_B + + assert ival_H.asfreq("Min", "s") == ival_H_to_T_start + assert ival_H.asfreq("Min", "E") == ival_H_to_T_end + assert ival_H.asfreq("s", "s") == ival_H_to_S_start + assert ival_H.asfreq("s", "E") == ival_H_to_S_end + + assert ival_H.asfreq("h") == ival_H + + def test_conv_minutely(self): + # frequency conversion tests: from Minutely Frequency" + + ival_T = Period(freq="Min", year=2007, month=1, day=1, hour=0, minute=0) + ival_T_end_of_year = Period( + freq="Min", year=2007, month=12, day=31, hour=23, minute=59 + ) + ival_T_end_of_quarter = Period( + freq="Min", year=2007, month=3, day=31, hour=23, minute=59 + ) + ival_T_end_of_month = Period( + freq="Min", year=2007, month=1, day=31, hour=23, minute=59 + ) + ival_T_end_of_week = Period( + freq="Min", year=2007, month=1, day=7, hour=23, minute=59 + ) + ival_T_end_of_day = Period( + freq="Min", year=2007, month=1, day=1, hour=23, minute=59 + ) + ival_T_end_of_bus = Period( + freq="Min", year=2007, month=1, day=1, hour=23, minute=59 + ) + ival_T_end_of_hour = Period( + freq="Min", year=2007, month=1, day=1, hour=0, minute=59 + ) + + ival_T_to_A = Period(freq="Y", year=2007) + ival_T_to_Q = Period(freq="Q", year=2007, quarter=1) + ival_T_to_M = Period(freq="M", year=2007, month=1) + ival_T_to_W = Period(freq="W", year=2007, month=1, day=7) + ival_T_to_D = Period(freq="D", year=2007, month=1, day=1) + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + ival_T_to_B = Period(freq="B", year=2007, month=1, day=1) + ival_T_to_H = Period(freq="h", year=2007, month=1, day=1, hour=0) + + ival_T_to_S_start = Period( + freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=0 + ) + ival_T_to_S_end = Period( + freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=59 + ) + + assert ival_T.asfreq("Y") == ival_T_to_A + assert ival_T_end_of_year.asfreq("Y") == ival_T_to_A + assert ival_T.asfreq("Q") == ival_T_to_Q + assert ival_T_end_of_quarter.asfreq("Q") == ival_T_to_Q + assert ival_T.asfreq("M") == ival_T_to_M + assert ival_T_end_of_month.asfreq("M") == ival_T_to_M + assert ival_T.asfreq("W") == ival_T_to_W + assert ival_T_end_of_week.asfreq("W") == ival_T_to_W + assert ival_T.asfreq("D") == ival_T_to_D + assert ival_T_end_of_day.asfreq("D") == ival_T_to_D + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + assert ival_T.asfreq("B") == ival_T_to_B + assert ival_T_end_of_bus.asfreq("B") == ival_T_to_B + assert ival_T.asfreq("h") == ival_T_to_H + assert ival_T_end_of_hour.asfreq("h") == ival_T_to_H + + assert ival_T.asfreq("s", "s") == ival_T_to_S_start + assert ival_T.asfreq("s", "E") == ival_T_to_S_end + + assert ival_T.asfreq("Min") == ival_T + + def test_conv_secondly(self): + # frequency conversion tests: from Secondly Frequency" + + ival_S = Period(freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=0) + ival_S_end_of_year = Period( + freq="s", year=2007, month=12, day=31, hour=23, minute=59, second=59 + ) + ival_S_end_of_quarter = Period( + freq="s", year=2007, month=3, day=31, hour=23, minute=59, second=59 + ) + ival_S_end_of_month = Period( + freq="s", year=2007, month=1, day=31, hour=23, minute=59, second=59 + ) + ival_S_end_of_week = Period( + freq="s", year=2007, month=1, day=7, hour=23, minute=59, second=59 + ) + ival_S_end_of_day = Period( + freq="s", year=2007, month=1, day=1, hour=23, minute=59, second=59 + ) + ival_S_end_of_bus = Period( + freq="s", year=2007, month=1, day=1, hour=23, minute=59, second=59 + ) + ival_S_end_of_hour = Period( + freq="s", year=2007, month=1, day=1, hour=0, minute=59, second=59 + ) + ival_S_end_of_minute = Period( + freq="s", year=2007, month=1, day=1, hour=0, minute=0, second=59 + ) + + ival_S_to_A = Period(freq="Y", year=2007) + ival_S_to_Q = Period(freq="Q", year=2007, quarter=1) + ival_S_to_M = Period(freq="M", year=2007, month=1) + ival_S_to_W = Period(freq="W", year=2007, month=1, day=7) + ival_S_to_D = Period(freq="D", year=2007, month=1, day=1) + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + ival_S_to_B = Period(freq="B", year=2007, month=1, day=1) + ival_S_to_H = Period(freq="h", year=2007, month=1, day=1, hour=0) + ival_S_to_T = Period(freq="Min", year=2007, month=1, day=1, hour=0, minute=0) + + assert ival_S.asfreq("Y") == ival_S_to_A + assert ival_S_end_of_year.asfreq("Y") == ival_S_to_A + assert ival_S.asfreq("Q") == ival_S_to_Q + assert ival_S_end_of_quarter.asfreq("Q") == ival_S_to_Q + assert ival_S.asfreq("M") == ival_S_to_M + assert ival_S_end_of_month.asfreq("M") == ival_S_to_M + assert ival_S.asfreq("W") == ival_S_to_W + assert ival_S_end_of_week.asfreq("W") == ival_S_to_W + assert ival_S.asfreq("D") == ival_S_to_D + assert ival_S_end_of_day.asfreq("D") == ival_S_to_D + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + assert ival_S.asfreq("B") == ival_S_to_B + assert ival_S_end_of_bus.asfreq("B") == ival_S_to_B + assert ival_S.asfreq("h") == ival_S_to_H + assert ival_S_end_of_hour.asfreq("h") == ival_S_to_H + assert ival_S.asfreq("Min") == ival_S_to_T + assert ival_S_end_of_minute.asfreq("Min") == ival_S_to_T + + assert ival_S.asfreq("s") == ival_S + + def test_conv_microsecond(self): + # GH#31475 Avoid floating point errors dropping the start_time to + # before the beginning of the Period + per = Period("2020-01-30 15:57:27.576166", freq="us") + assert per.ordinal == 1580399847576166 + + start = per.start_time + expected = Timestamp("2020-01-30 15:57:27.576166") + assert start == expected + assert start._value == per.ordinal * 1000 + + per2 = Period("2300-01-01", "us") + msg = "2300-01-01" + with pytest.raises(OutOfBoundsDatetime, match=msg): + per2.start_time + with pytest.raises(OutOfBoundsDatetime, match=msg): + per2.end_time + + def test_asfreq_mult(self): + # normal freq to mult freq + p = Period(freq="Y", year=2007) + # ordinal will not change + for freq in ["3Y", offsets.YearEnd(3)]: + result = p.asfreq(freq) + expected = Period("2007", freq="3Y") + + assert result == expected + assert result.ordinal == expected.ordinal + assert result.freq == expected.freq + # ordinal will not change + for freq in ["3Y", offsets.YearEnd(3)]: + result = p.asfreq(freq, how="S") + expected = Period("2007", freq="3Y") + + assert result == expected + assert result.ordinal == expected.ordinal + assert result.freq == expected.freq + + # mult freq to normal freq + p = Period(freq="3Y", year=2007) + # ordinal will change because how=E is the default + for freq in ["Y", offsets.YearEnd()]: + result = p.asfreq(freq) + expected = Period("2009", freq="Y") + + assert result == expected + assert result.ordinal == expected.ordinal + assert result.freq == expected.freq + # ordinal will not change + for freq in ["Y", offsets.YearEnd()]: + result = p.asfreq(freq, how="s") + expected = Period("2007", freq="Y") + + assert result == expected + assert result.ordinal == expected.ordinal + assert result.freq == expected.freq + + p = Period(freq="Y", year=2007) + for freq in ["2M", offsets.MonthEnd(2)]: + result = p.asfreq(freq) + expected = Period("2007-12", freq="2M") + + assert result == expected + assert result.ordinal == expected.ordinal + assert result.freq == expected.freq + for freq in ["2M", offsets.MonthEnd(2)]: + result = p.asfreq(freq, how="s") + expected = Period("2007-01", freq="2M") + + assert result == expected + assert result.ordinal == expected.ordinal + assert result.freq == expected.freq + + p = Period(freq="3Y", year=2007) + for freq in ["2M", offsets.MonthEnd(2)]: + result = p.asfreq(freq) + expected = Period("2009-12", freq="2M") + + assert result == expected + assert result.ordinal == expected.ordinal + assert result.freq == expected.freq + for freq in ["2M", offsets.MonthEnd(2)]: + result = p.asfreq(freq, how="s") + expected = Period("2007-01", freq="2M") + + assert result == expected + assert result.ordinal == expected.ordinal + assert result.freq == expected.freq + + def test_asfreq_combined(self): + # normal freq to combined freq + p = Period("2007", freq="h") + + # ordinal will not change + expected = Period("2007", freq="25h") + for freq, how in zip(["1D1h", "1h1D"], ["E", "S"]): + result = p.asfreq(freq, how=how) + assert result == expected + assert result.ordinal == expected.ordinal + assert result.freq == expected.freq + + # combined freq to normal freq + p1 = Period(freq="1D1h", year=2007) + p2 = Period(freq="1h1D", year=2007) + + # ordinal will change because how=E is the default + result1 = p1.asfreq("h") + result2 = p2.asfreq("h") + expected = Period("2007-01-02", freq="h") + assert result1 == expected + assert result1.ordinal == expected.ordinal + assert result1.freq == expected.freq + assert result2 == expected + assert result2.ordinal == expected.ordinal + assert result2.freq == expected.freq + + # ordinal will not change + result1 = p1.asfreq("h", how="S") + result2 = p2.asfreq("h", how="S") + expected = Period("2007-01-01", freq="h") + assert result1 == expected + assert result1.ordinal == expected.ordinal + assert result1.freq == expected.freq + assert result2 == expected + assert result2.ordinal == expected.ordinal + assert result2.freq == expected.freq + + def test_asfreq_MS(self): + initial = Period("2013") + + assert initial.asfreq(freq="M", how="S") == Period("2013-01", "M") + + msg = "MS is not supported as period frequency" + with pytest.raises(ValueError, match=msg): + initial.asfreq(freq="MS", how="S") + + with pytest.raises(ValueError, match=msg): + Period("2013-01", "MS") diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/period/test_period.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/period/test_period.py new file mode 100644 index 0000000000000000000000000000000000000000..08378e1f2fb460041193e078fc8a56bd39cb9874 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/period/test_period.py @@ -0,0 +1,1158 @@ +from datetime import ( + date, + datetime, + timedelta, +) +import re + +import numpy as np +import pytest + +from pandas._libs.tslibs import iNaT +from pandas._libs.tslibs.ccalendar import ( + DAYS, + MONTHS, +) +from pandas._libs.tslibs.np_datetime import OutOfBoundsDatetime +from pandas._libs.tslibs.parsing import DateParseError +from pandas._libs.tslibs.period import INVALID_FREQ_ERR_MSG +from pandas.compat import PY314 + +from pandas import ( + NaT, + Period, + Timedelta, + Timestamp, + offsets, +) +import pandas._testing as tm + +bday_msg = "Period with BDay freq is deprecated" + + +class TestPeriodDisallowedFreqs: + @pytest.mark.parametrize( + "freq, freq_msg", + [ + (offsets.BYearBegin(), "BYearBegin"), + (offsets.YearBegin(2), "YearBegin"), + (offsets.QuarterBegin(startingMonth=12), "QuarterBegin"), + (offsets.BusinessMonthEnd(2), "BusinessMonthEnd"), + ], + ) + def test_offsets_not_supported(self, freq, freq_msg): + # GH#55785 + msg = re.escape(f"{freq} is not supported as period frequency") + with pytest.raises(ValueError, match=msg): + Period(year=2014, freq=freq) + + def test_custom_business_day_freq_raises(self): + # GH#52534 + msg = "C is not supported as period frequency" + with pytest.raises(ValueError, match=msg): + Period("2023-04-10", freq="C") + msg = f"{offsets.CustomBusinessDay().base} is not supported as period frequency" + with pytest.raises(ValueError, match=msg): + Period("2023-04-10", freq=offsets.CustomBusinessDay()) + + def test_invalid_frequency_error_message(self): + msg = "WOM-1MON is not supported as period frequency" + with pytest.raises(ValueError, match=msg): + Period("2012-01-02", freq="WOM-1MON") + + def test_invalid_frequency_period_error_message(self): + msg = "for Period, please use 'M' instead of 'ME'" + with pytest.raises(ValueError, match=msg): + Period("2012-01-02", freq="ME") + + +class TestPeriodConstruction: + def test_from_td64nat_raises(self): + # GH#44507 + td = NaT.to_numpy("m8[ns]") + + msg = "Value must be Period, string, integer, or datetime" + with pytest.raises(ValueError, match=msg): + Period(td) + + with pytest.raises(ValueError, match=msg): + Period(td, freq="D") + + def test_construction(self): + i1 = Period("1/1/2005", freq="M") + i2 = Period("Jan 2005") + + assert i1 == i2 + + # GH#54105 - Period can be confusingly instantiated with lowercase freq + # TODO: raise in the future an error when passing lowercase freq + i1 = Period("2005", freq="Y") + i2 = Period("2005") + + assert i1 == i2 + + i4 = Period("2005", freq="M") + assert i1 != i4 + + i1 = Period.now(freq="Q") + i2 = Period(datetime.now(), freq="Q") + + assert i1 == i2 + + # Pass in freq as a keyword argument sometimes as a test for + # https://github.com/pandas-dev/pandas/issues/53369 + i1 = Period.now(freq="D") + i2 = Period(datetime.now(), freq="D") + i3 = Period.now(offsets.Day()) + + assert i1 == i2 + assert i1 == i3 + + i1 = Period("1982", freq="min") + msg = "'MIN' is deprecated and will be removed in a future version." + with tm.assert_produces_warning(FutureWarning, match=msg): + i2 = Period("1982", freq="MIN") + assert i1 == i2 + + i1 = Period(year=2005, month=3, day=1, freq="D") + i2 = Period("3/1/2005", freq="D") + assert i1 == i2 + + i3 = Period(year=2005, month=3, day=1, freq="d") + assert i1 == i3 + + i1 = Period("2007-01-01 09:00:00.001") + expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1000), freq="ms") + assert i1 == expected + + expected = Period("2007-01-01 09:00:00.001", freq="ms") + assert i1 == expected + + i1 = Period("2007-01-01 09:00:00.00101") + expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1010), freq="us") + assert i1 == expected + + expected = Period("2007-01-01 09:00:00.00101", freq="us") + assert i1 == expected + + msg = "Must supply freq for ordinal value" + with pytest.raises(ValueError, match=msg): + Period(ordinal=200701) + + msg = "Invalid frequency: X" + with pytest.raises(ValueError, match=msg): + Period("2007-1-1", freq="X") + + def test_tuple_freq_disallowed(self): + # GH#34703 tuple freq disallowed + with pytest.raises(TypeError, match="pass as a string instead"): + Period("1982", freq=("Min", 1)) + + with pytest.raises(TypeError, match="pass as a string instead"): + Period("2006-12-31", ("w", 1)) + + def test_construction_from_timestamp_nanos(self): + # GH#46811 don't drop nanos from Timestamp + ts = Timestamp("2022-04-20 09:23:24.123456789") + per = Period(ts, freq="ns") + + # should losslessly round-trip, not lose the 789 + rt = per.to_timestamp() + assert rt == ts + + # same thing but from a datetime64 object + dt64 = ts.asm8 + per2 = Period(dt64, freq="ns") + rt2 = per2.to_timestamp() + assert rt2.asm8 == dt64 + + def test_construction_bday(self): + # Biz day construction, roll forward if non-weekday + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + i1 = Period("3/10/12", freq="B") + i2 = Period("3/10/12", freq="D") + assert i1 == i2.asfreq("B") + i2 = Period("3/11/12", freq="D") + assert i1 == i2.asfreq("B") + i2 = Period("3/12/12", freq="D") + assert i1 == i2.asfreq("B") + + i3 = Period("3/10/12", freq="b") + assert i1 == i3 + + i1 = Period(year=2012, month=3, day=10, freq="B") + i2 = Period("3/12/12", freq="B") + assert i1 == i2 + + def test_construction_quarter(self): + i1 = Period(year=2005, quarter=1, freq="Q") + i2 = Period("1/1/2005", freq="Q") + assert i1 == i2 + + i1 = Period(year=2005, quarter=3, freq="Q") + i2 = Period("9/1/2005", freq="Q") + assert i1 == i2 + + i1 = Period("2005Q1") + i2 = Period(year=2005, quarter=1, freq="Q") + i3 = Period("2005q1") + assert i1 == i2 + assert i1 == i3 + + i1 = Period("05Q1") + assert i1 == i2 + lower = Period("05q1") + assert i1 == lower + + i1 = Period("1Q2005") + assert i1 == i2 + lower = Period("1q2005") + assert i1 == lower + + i1 = Period("1Q05") + assert i1 == i2 + lower = Period("1q05") + assert i1 == lower + + i1 = Period("4Q1984") + assert i1.year == 1984 + lower = Period("4q1984") + assert i1 == lower + + def test_construction_month(self): + expected = Period("2007-01", freq="M") + i1 = Period("200701", freq="M") + assert i1 == expected + + i1 = Period("200701", freq="M") + assert i1 == expected + + i1 = Period(200701, freq="M") + assert i1 == expected + + i1 = Period(ordinal=200701, freq="M") + assert i1.year == 18695 + + i1 = Period(datetime(2007, 1, 1), freq="M") + i2 = Period("200701", freq="M") + assert i1 == i2 + + i1 = Period(date(2007, 1, 1), freq="M") + i2 = Period(datetime(2007, 1, 1), freq="M") + i3 = Period(np.datetime64("2007-01-01"), freq="M") + i4 = Period("2007-01-01 00:00:00", freq="M") + i5 = Period("2007-01-01 00:00:00.000", freq="M") + assert i1 == i2 + assert i1 == i3 + assert i1 == i4 + assert i1 == i5 + + def test_period_constructor_offsets(self): + assert Period("1/1/2005", freq=offsets.MonthEnd()) == Period( + "1/1/2005", freq="M" + ) + assert Period("2005", freq=offsets.YearEnd()) == Period("2005", freq="Y") + assert Period("2005", freq=offsets.MonthEnd()) == Period("2005", freq="M") + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + assert Period("3/10/12", freq=offsets.BusinessDay()) == Period( + "3/10/12", freq="B" + ) + assert Period("3/10/12", freq=offsets.Day()) == Period("3/10/12", freq="D") + + assert Period( + year=2005, quarter=1, freq=offsets.QuarterEnd(startingMonth=12) + ) == Period(year=2005, quarter=1, freq="Q") + assert Period( + year=2005, quarter=2, freq=offsets.QuarterEnd(startingMonth=12) + ) == Period(year=2005, quarter=2, freq="Q") + + assert Period(year=2005, month=3, day=1, freq=offsets.Day()) == Period( + year=2005, month=3, day=1, freq="D" + ) + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + assert Period(year=2012, month=3, day=10, freq=offsets.BDay()) == Period( + year=2012, month=3, day=10, freq="B" + ) + + expected = Period("2005-03-01", freq="3D") + assert Period(year=2005, month=3, day=1, freq=offsets.Day(3)) == expected + assert Period(year=2005, month=3, day=1, freq="3D") == expected + + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + assert Period(year=2012, month=3, day=10, freq=offsets.BDay(3)) == Period( + year=2012, month=3, day=10, freq="3B" + ) + + assert Period(200701, freq=offsets.MonthEnd()) == Period(200701, freq="M") + + i1 = Period(ordinal=200701, freq=offsets.MonthEnd()) + i2 = Period(ordinal=200701, freq="M") + assert i1 == i2 + assert i1.year == 18695 + assert i2.year == 18695 + + i1 = Period(datetime(2007, 1, 1), freq="M") + i2 = Period("200701", freq="M") + assert i1 == i2 + + i1 = Period(date(2007, 1, 1), freq="M") + i2 = Period(datetime(2007, 1, 1), freq="M") + i3 = Period(np.datetime64("2007-01-01"), freq="M") + i4 = Period("2007-01-01 00:00:00", freq="M") + i5 = Period("2007-01-01 00:00:00.000", freq="M") + assert i1 == i2 + assert i1 == i3 + assert i1 == i4 + assert i1 == i5 + + i1 = Period("2007-01-01 09:00:00.001") + expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1000), freq="ms") + assert i1 == expected + + expected = Period("2007-01-01 09:00:00.001", freq="ms") + assert i1 == expected + + i1 = Period("2007-01-01 09:00:00.00101") + expected = Period(datetime(2007, 1, 1, 9, 0, 0, 1010), freq="us") + assert i1 == expected + + expected = Period("2007-01-01 09:00:00.00101", freq="us") + assert i1 == expected + + def test_invalid_arguments(self): + msg = "Must supply freq for datetime value" + with pytest.raises(ValueError, match=msg): + Period(datetime.now()) + with pytest.raises(ValueError, match=msg): + Period(datetime.now().date()) + + msg = "Value must be Period, string, integer, or datetime" + with pytest.raises(ValueError, match=msg): + Period(1.6, freq="D") + msg = "Ordinal must be an integer" + with pytest.raises(ValueError, match=msg): + Period(ordinal=1.6, freq="D") + msg = "Only value or ordinal but not both should be given but not both" + with pytest.raises(ValueError, match=msg): + Period(ordinal=2, value=1, freq="D") + + msg = "If value is None, freq cannot be None" + with pytest.raises(ValueError, match=msg): + Period(month=1) + + msg = '^Given date string "-2000" not likely a datetime$' + with pytest.raises(ValueError, match=msg): + Period("-2000", "Y") + if PY314: + msg = "day 0 must be in range 1..31 for month 1 in year 1: 0" + else: + msg = "day is out of range for month" + with pytest.raises(DateParseError, match=msg): + Period("0", "Y") + msg = "Unknown datetime string format, unable to parse" + with pytest.raises(DateParseError, match=msg): + Period("1/1/-2000", "Y") + + def test_constructor_corner(self): + expected = Period("2007-01", freq="2M") + assert Period(year=2007, month=1, freq="2M") == expected + + assert Period(None) is NaT + + p = Period("2007-01-01", freq="D") + + result = Period(p, freq="Y") + exp = Period("2007", freq="Y") + assert result == exp + + def test_constructor_infer_freq(self): + p = Period("2007-01-01") + assert p.freq == "D" + + p = Period("2007-01-01 07") + assert p.freq == "h" + + p = Period("2007-01-01 07:10") + assert p.freq == "min" + + p = Period("2007-01-01 07:10:15") + assert p.freq == "s" + + p = Period("2007-01-01 07:10:15.123") + assert p.freq == "ms" + + # We see that there are 6 digits after the decimal, so get microsecond + # even though they are all zeros. + p = Period("2007-01-01 07:10:15.123000") + assert p.freq == "us" + + p = Period("2007-01-01 07:10:15.123400") + assert p.freq == "us" + + def test_multiples(self): + result1 = Period("1989", freq="2Y") + result2 = Period("1989", freq="Y") + assert result1.ordinal == result2.ordinal + assert result1.freqstr == "2Y-DEC" + assert result2.freqstr == "Y-DEC" + assert result1.freq == offsets.YearEnd(2) + assert result2.freq == offsets.YearEnd() + + assert (result1 + 1).ordinal == result1.ordinal + 2 + assert (1 + result1).ordinal == result1.ordinal + 2 + assert (result1 - 1).ordinal == result2.ordinal - 2 + assert (-1 + result1).ordinal == result2.ordinal - 2 + + @pytest.mark.parametrize("month", MONTHS) + def test_period_cons_quarterly(self, month): + # bugs in scikits.timeseries + freq = f"Q-{month}" + exp = Period("1989Q3", freq=freq) + assert "1989Q3" in str(exp) + stamp = exp.to_timestamp("D", how="end") + p = Period(stamp, freq=freq) + assert p == exp + + stamp = exp.to_timestamp("3D", how="end") + p = Period(stamp, freq=freq) + assert p == exp + + @pytest.mark.parametrize("month", MONTHS) + def test_period_cons_annual(self, month): + # bugs in scikits.timeseries + freq = f"Y-{month}" + exp = Period("1989", freq=freq) + stamp = exp.to_timestamp("D", how="end") + timedelta(days=30) + p = Period(stamp, freq=freq) + + assert p == exp + 1 + assert isinstance(p, Period) + + @pytest.mark.parametrize("day", DAYS) + @pytest.mark.parametrize("num", range(10, 17)) + def test_period_cons_weekly(self, num, day): + daystr = f"2011-02-{num}" + freq = f"W-{day}" + + result = Period(daystr, freq=freq) + expected = Period(daystr, freq="D").asfreq(freq) + assert result == expected + assert isinstance(result, Period) + + def test_parse_week_str_roundstrip(self): + # GH#50803 + per = Period("2017-01-23/2017-01-29") + assert per.freq.freqstr == "W-SUN" + + per = Period("2017-01-24/2017-01-30") + assert per.freq.freqstr == "W-MON" + + msg = "Could not parse as weekly-freq Period" + with pytest.raises(ValueError, match=msg): + # not 6 days apart + Period("2016-01-23/2017-01-29") + + def test_period_from_ordinal(self): + p = Period("2011-01", freq="M") + res = Period._from_ordinal(p.ordinal, freq=p.freq) + assert p == res + assert isinstance(res, Period) + + @pytest.mark.parametrize("freq", ["Y", "M", "D", "h"]) + def test_construct_from_nat_string_and_freq(self, freq): + per = Period("NaT", freq=freq) + assert per is NaT + + per = Period("NaT", freq="2" + freq) + assert per is NaT + + per = Period("NaT", freq="3" + freq) + assert per is NaT + + def test_period_cons_nat(self): + p = Period("nat", freq="W-SUN") + assert p is NaT + + p = Period(iNaT, freq="D") + assert p is NaT + + p = Period(iNaT, freq="3D") + assert p is NaT + + p = Period(iNaT, freq="1D1h") + assert p is NaT + + p = Period("NaT") + assert p is NaT + + p = Period(iNaT) + assert p is NaT + + def test_period_cons_mult(self): + p1 = Period("2011-01", freq="3M") + p2 = Period("2011-01", freq="M") + assert p1.ordinal == p2.ordinal + + assert p1.freq == offsets.MonthEnd(3) + assert p1.freqstr == "3M" + + assert p2.freq == offsets.MonthEnd() + assert p2.freqstr == "M" + + result = p1 + 1 + assert result.ordinal == (p2 + 3).ordinal + + assert result.freq == p1.freq + assert result.freqstr == "3M" + + result = p1 - 1 + assert result.ordinal == (p2 - 3).ordinal + assert result.freq == p1.freq + assert result.freqstr == "3M" + + msg = "Frequency must be positive, because it represents span: -3M" + with pytest.raises(ValueError, match=msg): + Period("2011-01", freq="-3M") + + msg = "Frequency must be positive, because it represents span: 0M" + with pytest.raises(ValueError, match=msg): + Period("2011-01", freq="0M") + + def test_period_cons_combined(self): + p = [ + ( + Period("2011-01", freq="1D1h"), + Period("2011-01", freq="1h1D"), + Period("2011-01", freq="h"), + ), + ( + Period(ordinal=1, freq="1D1h"), + Period(ordinal=1, freq="1h1D"), + Period(ordinal=1, freq="h"), + ), + ] + + for p1, p2, p3 in p: + assert p1.ordinal == p3.ordinal + assert p2.ordinal == p3.ordinal + + assert p1.freq == offsets.Hour(25) + assert p1.freqstr == "25h" + + assert p2.freq == offsets.Hour(25) + assert p2.freqstr == "25h" + + assert p3.freq == offsets.Hour() + assert p3.freqstr == "h" + + result = p1 + 1 + assert result.ordinal == (p3 + 25).ordinal + assert result.freq == p1.freq + assert result.freqstr == "25h" + + result = p2 + 1 + assert result.ordinal == (p3 + 25).ordinal + assert result.freq == p2.freq + assert result.freqstr == "25h" + + result = p1 - 1 + assert result.ordinal == (p3 - 25).ordinal + assert result.freq == p1.freq + assert result.freqstr == "25h" + + result = p2 - 1 + assert result.ordinal == (p3 - 25).ordinal + assert result.freq == p2.freq + assert result.freqstr == "25h" + + msg = "Frequency must be positive, because it represents span: -25h" + with pytest.raises(ValueError, match=msg): + Period("2011-01", freq="-1D1h") + with pytest.raises(ValueError, match=msg): + Period("2011-01", freq="-1h1D") + with pytest.raises(ValueError, match=msg): + Period(ordinal=1, freq="-1D1h") + with pytest.raises(ValueError, match=msg): + Period(ordinal=1, freq="-1h1D") + + msg = "Frequency must be positive, because it represents span: 0D" + with pytest.raises(ValueError, match=msg): + Period("2011-01", freq="0D0h") + with pytest.raises(ValueError, match=msg): + Period(ordinal=1, freq="0D0h") + + # You can only combine together day and intraday offsets + msg = "Invalid frequency: 1W1D" + with pytest.raises(ValueError, match=msg): + Period("2011-01", freq="1W1D") + msg = "Invalid frequency: 1D1W" + with pytest.raises(ValueError, match=msg): + Period("2011-01", freq="1D1W") + + @pytest.mark.parametrize("day", ["1970/01/01 ", "2020-12-31 ", "1981/09/13 "]) + @pytest.mark.parametrize("hour", ["00:00:00", "00:00:01", "23:59:59", "12:00:59"]) + @pytest.mark.parametrize( + "sec_float, expected", + [ + (".000000001", 1), + (".000000999", 999), + (".123456789", 789), + (".999999999", 999), + (".999999000", 0), + # Test femtoseconds, attoseconds, picoseconds are dropped like Timestamp + (".999999001123", 1), + (".999999001123456", 1), + (".999999001123456789", 1), + ], + ) + def test_period_constructor_nanosecond(self, day, hour, sec_float, expected): + # GH 34621 + + assert Period(day + hour + sec_float).start_time.nanosecond == expected + + @pytest.mark.parametrize("hour", range(24)) + def test_period_large_ordinal(self, hour): + # Issue #36430 + # Integer overflow for Period over the maximum timestamp + p = Period(ordinal=2562048 + hour, freq="1h") + assert p.hour == hour + + +class TestPeriodMethods: + def test_round_trip(self): + p = Period("2000Q1") + new_p = tm.round_trip_pickle(p) + assert new_p == p + + def test_hash(self): + assert hash(Period("2011-01", freq="M")) == hash(Period("2011-01", freq="M")) + + assert hash(Period("2011-01-01", freq="D")) != hash(Period("2011-01", freq="M")) + + assert hash(Period("2011-01", freq="3M")) != hash(Period("2011-01", freq="2M")) + + assert hash(Period("2011-01", freq="M")) != hash(Period("2011-02", freq="M")) + + # -------------------------------------------------------------- + # to_timestamp + + def test_to_timestamp_mult(self): + p = Period("2011-01", freq="M") + assert p.to_timestamp(how="S") == Timestamp("2011-01-01") + expected = Timestamp("2011-02-01") - Timedelta(1, "ns") + assert p.to_timestamp(how="E") == expected + + p = Period("2011-01", freq="3M") + assert p.to_timestamp(how="S") == Timestamp("2011-01-01") + expected = Timestamp("2011-04-01") - Timedelta(1, "ns") + assert p.to_timestamp(how="E") == expected + + @pytest.mark.filterwarnings( + "ignore:Period with BDay freq is deprecated:FutureWarning" + ) + def test_to_timestamp(self): + p = Period("1982", freq="Y") + start_ts = p.to_timestamp(how="S") + aliases = ["s", "StarT", "BEGIn"] + for a in aliases: + assert start_ts == p.to_timestamp("D", how=a) + # freq with mult should not affect to the result + assert start_ts == p.to_timestamp("3D", how=a) + + end_ts = p.to_timestamp(how="E") + aliases = ["e", "end", "FINIsH"] + for a in aliases: + assert end_ts == p.to_timestamp("D", how=a) + assert end_ts == p.to_timestamp("3D", how=a) + + from_lst = ["Y", "Q", "M", "W", "B", "D", "h", "Min", "s"] + + def _ex(p): + if p.freq == "B": + return p.start_time + Timedelta(days=1, nanoseconds=-1) + return Timestamp((p + p.freq).start_time._value - 1) + + for fcode in from_lst: + p = Period("1982", freq=fcode) + result = p.to_timestamp().to_period(fcode) + assert result == p + + assert p.start_time == p.to_timestamp(how="S") + + assert p.end_time == _ex(p) + + # Frequency other than daily + + p = Period("1985", freq="Y") + + result = p.to_timestamp("h", how="end") + expected = Timestamp(1986, 1, 1) - Timedelta(1, "ns") + assert result == expected + result = p.to_timestamp("3h", how="end") + assert result == expected + + result = p.to_timestamp("min", how="end") + expected = Timestamp(1986, 1, 1) - Timedelta(1, "ns") + assert result == expected + result = p.to_timestamp("2min", how="end") + assert result == expected + + result = p.to_timestamp(how="end") + expected = Timestamp(1986, 1, 1) - Timedelta(1, "ns") + assert result == expected + + expected = datetime(1985, 1, 1) + result = p.to_timestamp("h", how="start") + assert result == expected + result = p.to_timestamp("min", how="start") + assert result == expected + result = p.to_timestamp("s", how="start") + assert result == expected + result = p.to_timestamp("3h", how="start") + assert result == expected + result = p.to_timestamp("5s", how="start") + assert result == expected + + def test_to_timestamp_business_end(self): + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + per = Period("1990-01-05", "B") # Friday + result = per.to_timestamp("B", how="E") + + expected = Timestamp("1990-01-06") - Timedelta(nanoseconds=1) + assert result == expected + + @pytest.mark.parametrize( + "ts, expected", + [ + ("1970-01-01 00:00:00", 0), + ("1970-01-01 00:00:00.000001", 1), + ("1970-01-01 00:00:00.00001", 10), + ("1970-01-01 00:00:00.499", 499000), + ("1999-12-31 23:59:59.999", 999000), + ("1999-12-31 23:59:59.999999", 999999), + ("2050-12-31 23:59:59.5", 500000), + ("2050-12-31 23:59:59.500001", 500001), + ("2050-12-31 23:59:59.123456", 123456), + ], + ) + @pytest.mark.parametrize("freq", [None, "us", "ns"]) + def test_to_timestamp_microsecond(self, ts, expected, freq): + # GH 24444 + result = Period(ts).to_timestamp(freq=freq).microsecond + assert result == expected + + # -------------------------------------------------------------- + # Rendering: __repr__, strftime, etc + + @pytest.mark.parametrize( + "str_ts,freq,str_res,str_freq", + ( + ("Jan-2000", None, "2000-01", "M"), + ("2000-12-15", None, "2000-12-15", "D"), + ( + "2000-12-15 13:45:26.123456789", + "ns", + "2000-12-15 13:45:26.123456789", + "ns", + ), + ("2000-12-15 13:45:26.123456789", "us", "2000-12-15 13:45:26.123456", "us"), + ("2000-12-15 13:45:26.123456", None, "2000-12-15 13:45:26.123456", "us"), + ("2000-12-15 13:45:26.123456789", "ms", "2000-12-15 13:45:26.123", "ms"), + ("2000-12-15 13:45:26.123", None, "2000-12-15 13:45:26.123", "ms"), + ("2000-12-15 13:45:26", "s", "2000-12-15 13:45:26", "s"), + ("2000-12-15 13:45:26", "min", "2000-12-15 13:45", "min"), + ("2000-12-15 13:45:26", "h", "2000-12-15 13:00", "h"), + ("2000-12-15", "Y", "2000", "Y-DEC"), + ("2000-12-15", "Q", "2000Q4", "Q-DEC"), + ("2000-12-15", "M", "2000-12", "M"), + ("2000-12-15", "W", "2000-12-11/2000-12-17", "W-SUN"), + ("2000-12-15", "D", "2000-12-15", "D"), + ("2000-12-15", "B", "2000-12-15", "B"), + ), + ) + @pytest.mark.filterwarnings( + "ignore:Period with BDay freq is deprecated:FutureWarning" + ) + def test_repr(self, str_ts, freq, str_res, str_freq): + p = Period(str_ts, freq=freq) + assert str(p) == str_res + assert repr(p) == f"Period('{str_res}', '{str_freq}')" + + def test_repr_nat(self): + p = Period("nat", freq="M") + assert repr(NaT) in repr(p) + + def test_strftime(self): + # GH#3363 + p = Period("2000-1-1 12:34:12", freq="s") + res = p.strftime("%Y-%m-%d %H:%M:%S") + assert res == "2000-01-01 12:34:12" + assert isinstance(res, str) + + +class TestPeriodProperties: + """Test properties such as year, month, weekday, etc....""" + + @pytest.mark.parametrize("freq", ["Y", "M", "D", "h"]) + def test_is_leap_year(self, freq): + # GH 13727 + p = Period("2000-01-01 00:00:00", freq=freq) + assert p.is_leap_year + assert isinstance(p.is_leap_year, bool) + + p = Period("1999-01-01 00:00:00", freq=freq) + assert not p.is_leap_year + + p = Period("2004-01-01 00:00:00", freq=freq) + assert p.is_leap_year + + p = Period("2100-01-01 00:00:00", freq=freq) + assert not p.is_leap_year + + def test_quarterly_negative_ordinals(self): + p = Period(ordinal=-1, freq="Q-DEC") + assert p.year == 1969 + assert p.quarter == 4 + assert isinstance(p, Period) + + p = Period(ordinal=-2, freq="Q-DEC") + assert p.year == 1969 + assert p.quarter == 3 + assert isinstance(p, Period) + + p = Period(ordinal=-2, freq="M") + assert p.year == 1969 + assert p.month == 11 + assert isinstance(p, Period) + + def test_freq_str(self): + i1 = Period("1982", freq="Min") + assert i1.freq == offsets.Minute() + assert i1.freqstr == "min" + + @pytest.mark.filterwarnings( + "ignore:Period with BDay freq is deprecated:FutureWarning" + ) + def test_period_deprecated_freq(self): + cases = { + "M": ["MTH", "MONTH", "MONTHLY", "Mth", "month", "monthly"], + "B": ["BUS", "BUSINESS", "BUSINESSLY", "WEEKDAY", "bus"], + "D": ["DAY", "DLY", "DAILY", "Day", "Dly", "Daily"], + "h": ["HR", "HOUR", "HRLY", "HOURLY", "hr", "Hour", "HRly"], + "min": ["minute", "MINUTE", "MINUTELY", "minutely"], + "s": ["sec", "SEC", "SECOND", "SECONDLY", "second"], + "ms": ["MILLISECOND", "MILLISECONDLY", "millisecond"], + "us": ["MICROSECOND", "MICROSECONDLY", "microsecond"], + "ns": ["NANOSECOND", "NANOSECONDLY", "nanosecond"], + } + + msg = INVALID_FREQ_ERR_MSG + for exp, freqs in cases.items(): + for freq in freqs: + with pytest.raises(ValueError, match=msg): + Period("2016-03-01 09:00", freq=freq) + with pytest.raises(ValueError, match=msg): + Period(ordinal=1, freq=freq) + + # check supported freq-aliases still works + p1 = Period("2016-03-01 09:00", freq=exp) + p2 = Period(ordinal=1, freq=exp) + assert isinstance(p1, Period) + assert isinstance(p2, Period) + + @staticmethod + def _period_constructor(bound, offset): + return Period( + year=bound.year, + month=bound.month, + day=bound.day, + hour=bound.hour, + minute=bound.minute, + second=bound.second + offset, + freq="us", + ) + + @pytest.mark.parametrize("bound, offset", [(Timestamp.min, -1), (Timestamp.max, 1)]) + @pytest.mark.parametrize("period_property", ["start_time", "end_time"]) + def test_outer_bounds_start_and_end_time(self, bound, offset, period_property): + # GH #13346 + period = TestPeriodProperties._period_constructor(bound, offset) + with pytest.raises(OutOfBoundsDatetime, match="Out of bounds nanosecond"): + getattr(period, period_property) + + @pytest.mark.parametrize("bound, offset", [(Timestamp.min, -1), (Timestamp.max, 1)]) + @pytest.mark.parametrize("period_property", ["start_time", "end_time"]) + def test_inner_bounds_start_and_end_time(self, bound, offset, period_property): + # GH #13346 + period = TestPeriodProperties._period_constructor(bound, -offset) + expected = period.to_timestamp().round(freq="s") + assert getattr(period, period_property).round(freq="s") == expected + expected = (bound - offset * Timedelta(1, unit="s")).floor("s") + assert getattr(period, period_property).floor("s") == expected + + def test_start_time(self): + freq_lst = ["Y", "Q", "M", "D", "h", "min", "s"] + xp = datetime(2012, 1, 1) + for f in freq_lst: + p = Period("2012", freq=f) + assert p.start_time == xp + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + assert Period("2012", freq="B").start_time == datetime(2012, 1, 2) + assert Period("2012", freq="W").start_time == datetime(2011, 12, 26) + + def test_end_time(self): + p = Period("2012", freq="Y") + + def _ex(*args): + return Timestamp(Timestamp(datetime(*args)).as_unit("ns")._value - 1) + + xp = _ex(2013, 1, 1) + assert xp == p.end_time + + p = Period("2012", freq="Q") + xp = _ex(2012, 4, 1) + assert xp == p.end_time + + p = Period("2012", freq="M") + xp = _ex(2012, 2, 1) + assert xp == p.end_time + + p = Period("2012", freq="D") + xp = _ex(2012, 1, 2) + assert xp == p.end_time + + p = Period("2012", freq="h") + xp = _ex(2012, 1, 1, 1) + assert xp == p.end_time + + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + p = Period("2012", freq="B") + xp = _ex(2012, 1, 3) + assert xp == p.end_time + + p = Period("2012", freq="W") + xp = _ex(2012, 1, 2) + assert xp == p.end_time + + # Test for GH 11738 + p = Period("2012", freq="15D") + xp = _ex(2012, 1, 16) + assert xp == p.end_time + + p = Period("2012", freq="1D1h") + xp = _ex(2012, 1, 2, 1) + assert xp == p.end_time + + p = Period("2012", freq="1h1D") + xp = _ex(2012, 1, 2, 1) + assert xp == p.end_time + + def test_end_time_business_friday(self): + # GH#34449 + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + per = Period("1990-01-05", "B") + result = per.end_time + + expected = Timestamp("1990-01-06") - Timedelta(nanoseconds=1) + assert result == expected + + def test_anchor_week_end_time(self): + def _ex(*args): + return Timestamp(Timestamp(datetime(*args)).as_unit("ns")._value - 1) + + p = Period("2013-1-1", "W-SAT") + xp = _ex(2013, 1, 6) + assert p.end_time == xp + + def test_properties_annually(self): + # Test properties on Periods with annually frequency. + a_date = Period(freq="Y", year=2007) + assert a_date.year == 2007 + + def test_properties_quarterly(self): + # Test properties on Periods with daily frequency. + qedec_date = Period(freq="Q-DEC", year=2007, quarter=1) + qejan_date = Period(freq="Q-JAN", year=2007, quarter=1) + qejun_date = Period(freq="Q-JUN", year=2007, quarter=1) + # + for x in range(3): + for qd in (qedec_date, qejan_date, qejun_date): + assert (qd + x).qyear == 2007 + assert (qd + x).quarter == x + 1 + + def test_properties_monthly(self): + # Test properties on Periods with daily frequency. + m_date = Period(freq="M", year=2007, month=1) + for x in range(11): + m_ival_x = m_date + x + assert m_ival_x.year == 2007 + if 1 <= x + 1 <= 3: + assert m_ival_x.quarter == 1 + elif 4 <= x + 1 <= 6: + assert m_ival_x.quarter == 2 + elif 7 <= x + 1 <= 9: + assert m_ival_x.quarter == 3 + elif 10 <= x + 1 <= 12: + assert m_ival_x.quarter == 4 + assert m_ival_x.month == x + 1 + + def test_properties_weekly(self): + # Test properties on Periods with daily frequency. + w_date = Period(freq="W", year=2007, month=1, day=7) + # + assert w_date.year == 2007 + assert w_date.quarter == 1 + assert w_date.month == 1 + assert w_date.week == 1 + assert (w_date - 1).week == 52 + assert w_date.days_in_month == 31 + assert Period(freq="W", year=2012, month=2, day=1).days_in_month == 29 + + def test_properties_weekly_legacy(self): + # Test properties on Periods with daily frequency. + w_date = Period(freq="W", year=2007, month=1, day=7) + assert w_date.year == 2007 + assert w_date.quarter == 1 + assert w_date.month == 1 + assert w_date.week == 1 + assert (w_date - 1).week == 52 + assert w_date.days_in_month == 31 + + exp = Period(freq="W", year=2012, month=2, day=1) + assert exp.days_in_month == 29 + + msg = INVALID_FREQ_ERR_MSG + with pytest.raises(ValueError, match=msg): + Period(freq="WK", year=2007, month=1, day=7) + + def test_properties_daily(self): + # Test properties on Periods with daily frequency. + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + b_date = Period(freq="B", year=2007, month=1, day=1) + # + assert b_date.year == 2007 + assert b_date.quarter == 1 + assert b_date.month == 1 + assert b_date.day == 1 + assert b_date.weekday == 0 + assert b_date.dayofyear == 1 + assert b_date.days_in_month == 31 + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + assert Period(freq="B", year=2012, month=2, day=1).days_in_month == 29 + + d_date = Period(freq="D", year=2007, month=1, day=1) + + assert d_date.year == 2007 + assert d_date.quarter == 1 + assert d_date.month == 1 + assert d_date.day == 1 + assert d_date.weekday == 0 + assert d_date.dayofyear == 1 + assert d_date.days_in_month == 31 + assert Period(freq="D", year=2012, month=2, day=1).days_in_month == 29 + + def test_properties_hourly(self): + # Test properties on Periods with hourly frequency. + h_date1 = Period(freq="h", year=2007, month=1, day=1, hour=0) + h_date2 = Period(freq="2h", year=2007, month=1, day=1, hour=0) + + for h_date in [h_date1, h_date2]: + assert h_date.year == 2007 + assert h_date.quarter == 1 + assert h_date.month == 1 + assert h_date.day == 1 + assert h_date.weekday == 0 + assert h_date.dayofyear == 1 + assert h_date.hour == 0 + assert h_date.days_in_month == 31 + assert ( + Period(freq="h", year=2012, month=2, day=1, hour=0).days_in_month == 29 + ) + + def test_properties_minutely(self): + # Test properties on Periods with minutely frequency. + t_date = Period(freq="Min", year=2007, month=1, day=1, hour=0, minute=0) + # + assert t_date.quarter == 1 + assert t_date.month == 1 + assert t_date.day == 1 + assert t_date.weekday == 0 + assert t_date.dayofyear == 1 + assert t_date.hour == 0 + assert t_date.minute == 0 + assert t_date.days_in_month == 31 + assert ( + Period(freq="D", year=2012, month=2, day=1, hour=0, minute=0).days_in_month + == 29 + ) + + def test_properties_secondly(self): + # Test properties on Periods with secondly frequency. + s_date = Period( + freq="Min", year=2007, month=1, day=1, hour=0, minute=0, second=0 + ) + # + assert s_date.year == 2007 + assert s_date.quarter == 1 + assert s_date.month == 1 + assert s_date.day == 1 + assert s_date.weekday == 0 + assert s_date.dayofyear == 1 + assert s_date.hour == 0 + assert s_date.minute == 0 + assert s_date.second == 0 + assert s_date.days_in_month == 31 + assert ( + Period( + freq="Min", year=2012, month=2, day=1, hour=0, minute=0, second=0 + ).days_in_month + == 29 + ) + + +class TestPeriodComparisons: + def test_sort_periods(self): + jan = Period("2000-01", "M") + feb = Period("2000-02", "M") + mar = Period("2000-03", "M") + periods = [mar, jan, feb] + correctPeriods = [jan, feb, mar] + assert sorted(periods) == correctPeriods + + +def test_period_immutable(): + # see gh-17116 + msg = "not writable" + + per = Period("2014Q1") + with pytest.raises(AttributeError, match=msg): + per.ordinal = 14 + + freq = per.freq + with pytest.raises(AttributeError, match=msg): + per.freq = 2 * freq + + +def test_small_year_parsing(): + per1 = Period("0001-01-07", "D") + assert per1.year == 1 + assert per1.day == 7 + + +def test_negone_ordinals(): + freqs = ["Y", "M", "Q", "D", "h", "min", "s"] + + period = Period(ordinal=-1, freq="D") + for freq in freqs: + repr(period.asfreq(freq)) + + for freq in freqs: + period = Period(ordinal=-1, freq=freq) + repr(period) + assert period.year == 1969 + + with tm.assert_produces_warning(FutureWarning, match=bday_msg): + period = Period(ordinal=-1, freq="B") + repr(period) + period = Period(ordinal=-1, freq="W") + repr(period) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/test_na_scalar.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/test_na_scalar.py new file mode 100644 index 0000000000000000000000000000000000000000..287b7557f50f9f6a81763f86d0eb616cfd730f8c --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/test_na_scalar.py @@ -0,0 +1,316 @@ +from datetime import ( + date, + time, + timedelta, +) +import pickle + +import numpy as np +import pytest + +from pandas._libs.missing import NA + +from pandas.core.dtypes.common import is_scalar + +import pandas as pd +import pandas._testing as tm + + +def test_singleton(): + assert NA is NA + new_NA = type(NA)() + assert new_NA is NA + + +def test_repr(): + assert repr(NA) == "" + assert str(NA) == "" + + +def test_format(): + # GH-34740 + assert format(NA) == "" + assert format(NA, ">10") == " " + assert format(NA, "xxx") == "" # NA is flexible, accept any format spec + + assert f"{NA}" == "" + assert f"{NA:>10}" == " " + assert f"{NA:xxx}" == "" + + +def test_truthiness(): + msg = "boolean value of NA is ambiguous" + + with pytest.raises(TypeError, match=msg): + bool(NA) + + with pytest.raises(TypeError, match=msg): + not NA + + +def test_hashable(): + assert hash(NA) == hash(NA) + d = {NA: "test"} + assert d[NA] == "test" + + +@pytest.mark.parametrize( + "other", [NA, 1, 1.0, "a", b"a", np.int64(1), np.nan], ids=repr +) +def test_arithmetic_ops(all_arithmetic_functions, other): + op = all_arithmetic_functions + + if op.__name__ in ("pow", "rpow", "rmod") and isinstance(other, (str, bytes)): + pytest.skip(reason=f"{op.__name__} with NA and {other} not defined.") + if op.__name__ in ("divmod", "rdivmod"): + assert op(NA, other) is (NA, NA) + else: + if op.__name__ == "rpow": + # avoid special case + other += 1 + assert op(NA, other) is NA + + +@pytest.mark.parametrize( + "other", + [ + NA, + 1, + 1.0, + "a", + b"a", + np.int64(1), + np.nan, + np.bool_(True), + time(0), + date(1, 2, 3), + timedelta(1), + pd.NaT, + ], +) +def test_comparison_ops(comparison_op, other): + assert comparison_op(NA, other) is NA + assert comparison_op(other, NA) is NA + + +@pytest.mark.parametrize( + "value", + [ + 0, + 0.0, + -0, + -0.0, + False, + np.bool_(False), + np.int_(0), + np.float64(0), + np.int_(-0), + np.float64(-0), + ], +) +@pytest.mark.parametrize("asarray", [True, False]) +def test_pow_special(value, asarray): + if asarray: + value = np.array([value]) + result = NA**value + + if asarray: + result = result[0] + else: + # this assertion isn't possible for ndarray. + assert isinstance(result, type(value)) + assert result == 1 + + +@pytest.mark.parametrize( + "value", [1, 1.0, True, np.bool_(True), np.int_(1), np.float64(1)] +) +@pytest.mark.parametrize("asarray", [True, False]) +def test_rpow_special(value, asarray): + if asarray: + value = np.array([value]) + result = value**NA + + if asarray: + result = result[0] + elif not isinstance(value, (np.float64, np.bool_, np.int_)): + # this assertion isn't possible with asarray=True + assert isinstance(result, type(value)) + + assert result == value + + +@pytest.mark.parametrize("value", [-1, -1.0, np.int_(-1), np.float64(-1)]) +@pytest.mark.parametrize("asarray", [True, False]) +def test_rpow_minus_one(value, asarray): + if asarray: + value = np.array([value]) + result = value**NA + + if asarray: + result = result[0] + + assert pd.isna(result) + + +def test_unary_ops(): + assert +NA is NA + assert -NA is NA + assert abs(NA) is NA + assert ~NA is NA + + +def test_logical_and(): + assert NA & True is NA + assert True & NA is NA + assert NA & False is False + assert False & NA is False + assert NA & NA is NA + + msg = "unsupported operand type" + with pytest.raises(TypeError, match=msg): + NA & 5 + + +def test_logical_or(): + assert NA | True is True + assert True | NA is True + assert NA | False is NA + assert False | NA is NA + assert NA | NA is NA + + msg = "unsupported operand type" + with pytest.raises(TypeError, match=msg): + NA | 5 + + +def test_logical_xor(): + assert NA ^ True is NA + assert True ^ NA is NA + assert NA ^ False is NA + assert False ^ NA is NA + assert NA ^ NA is NA + + msg = "unsupported operand type" + with pytest.raises(TypeError, match=msg): + NA ^ 5 + + +def test_logical_not(): + assert ~NA is NA + + +@pytest.mark.parametrize("shape", [(3,), (3, 3), (1, 2, 3)]) +def test_arithmetic_ndarray(shape, all_arithmetic_functions): + op = all_arithmetic_functions + a = np.zeros(shape) + if op.__name__ == "pow": + a += 5 + result = op(NA, a) + expected = np.full(a.shape, NA, dtype=object) + tm.assert_numpy_array_equal(result, expected) + + +def test_is_scalar(): + assert is_scalar(NA) is True + + +def test_isna(): + assert pd.isna(NA) is True + assert pd.notna(NA) is False + + +def test_series_isna(): + s = pd.Series([1, NA], dtype=object) + expected = pd.Series([False, True]) + tm.assert_series_equal(s.isna(), expected) + + +def test_ufunc(): + assert np.log(NA) is NA + assert np.add(NA, 1) is NA + result = np.divmod(NA, 1) + assert result[0] is NA and result[1] is NA + + result = np.frexp(NA) + assert result[0] is NA and result[1] is NA + + +def test_ufunc_raises(): + msg = "ufunc method 'at'" + with pytest.raises(ValueError, match=msg): + np.log.at(NA, 0) + + +def test_binary_input_not_dunder(): + a = np.array([1, 2, 3]) + expected = np.array([NA, NA, NA], dtype=object) + result = np.logaddexp(a, NA) + tm.assert_numpy_array_equal(result, expected) + + result = np.logaddexp(NA, a) + tm.assert_numpy_array_equal(result, expected) + + # all NA, multiple inputs + assert np.logaddexp(NA, NA) is NA + + result = np.modf(NA, NA) + assert len(result) == 2 + assert all(x is NA for x in result) + + +def test_divmod_ufunc(): + # binary in, binary out. + a = np.array([1, 2, 3]) + expected = np.array([NA, NA, NA], dtype=object) + + result = np.divmod(a, NA) + assert isinstance(result, tuple) + for arr in result: + tm.assert_numpy_array_equal(arr, expected) + tm.assert_numpy_array_equal(arr, expected) + + result = np.divmod(NA, a) + for arr in result: + tm.assert_numpy_array_equal(arr, expected) + tm.assert_numpy_array_equal(arr, expected) + + +def test_integer_hash_collision_dict(): + # GH 30013 + result = {NA: "foo", hash(NA): "bar"} + + assert result[NA] == "foo" + assert result[hash(NA)] == "bar" + + +def test_integer_hash_collision_set(): + # GH 30013 + result = {NA, hash(NA)} + + assert len(result) == 2 + assert NA in result + assert hash(NA) in result + + +def test_pickle_roundtrip(): + # https://github.com/pandas-dev/pandas/issues/31847 + result = pickle.loads(pickle.dumps(NA)) + assert result is NA + + +def test_pickle_roundtrip_pandas(): + result = tm.round_trip_pickle(NA) + assert result is NA + + +@pytest.mark.parametrize( + "values, dtype", [([1, 2, NA], "Int64"), (["A", "B", NA], "string")] +) +@pytest.mark.parametrize("as_frame", [True, False]) +def test_pickle_roundtrip_containers(as_frame, values, dtype): + s = pd.Series(pd.array(values, dtype=dtype)) + if as_frame: + s = s.to_frame(name="A") + result = tm.round_trip_pickle(s) + tm.assert_equal(result, s) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/test_nat.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/test_nat.py new file mode 100644 index 0000000000000000000000000000000000000000..cb046e0133245689f75c2def996bfabfb18e6185 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/test_nat.py @@ -0,0 +1,709 @@ +from datetime import ( + datetime, + timedelta, +) +import operator + +import numpy as np +import pytest +import pytz + +from pandas._libs.tslibs import iNaT +from pandas.compat.numpy import np_version_gte1p24p3 + +from pandas import ( + DatetimeIndex, + DatetimeTZDtype, + Index, + NaT, + Period, + Series, + Timedelta, + TimedeltaIndex, + Timestamp, + isna, + offsets, +) +import pandas._testing as tm +from pandas.core import roperator +from pandas.core.arrays import ( + DatetimeArray, + PeriodArray, + TimedeltaArray, +) + + +class TestNaTFormatting: + def test_repr(self): + assert repr(NaT) == "NaT" + + def test_str(self): + assert str(NaT) == "NaT" + + def test_isoformat(self): + assert NaT.isoformat() == "NaT" + + +@pytest.mark.parametrize( + "nat,idx", + [ + (Timestamp("NaT"), DatetimeArray), + (Timedelta("NaT"), TimedeltaArray), + (Period("NaT", freq="M"), PeriodArray), + ], +) +def test_nat_fields(nat, idx): + for field in idx._field_ops: + # weekday is a property of DTI, but a method + # on NaT/Timestamp for compat with datetime + if field == "weekday": + continue + + result = getattr(NaT, field) + assert np.isnan(result) + + result = getattr(nat, field) + assert np.isnan(result) + + for field in idx._bool_ops: + result = getattr(NaT, field) + assert result is False + + result = getattr(nat, field) + assert result is False + + +def test_nat_vector_field_access(): + idx = DatetimeIndex(["1/1/2000", None, None, "1/4/2000"]) + + for field in DatetimeArray._field_ops: + # weekday is a property of DTI, but a method + # on NaT/Timestamp for compat with datetime + if field == "weekday": + continue + + result = getattr(idx, field) + expected = Index([getattr(x, field) for x in idx]) + tm.assert_index_equal(result, expected) + + ser = Series(idx) + + for field in DatetimeArray._field_ops: + # weekday is a property of DTI, but a method + # on NaT/Timestamp for compat with datetime + if field == "weekday": + continue + + result = getattr(ser.dt, field) + expected = [getattr(x, field) for x in idx] + tm.assert_series_equal(result, Series(expected)) + + for field in DatetimeArray._bool_ops: + result = getattr(ser.dt, field) + expected = [getattr(x, field) for x in idx] + tm.assert_series_equal(result, Series(expected)) + + +@pytest.mark.parametrize("klass", [Timestamp, Timedelta, Period]) +@pytest.mark.parametrize( + "value", [None, np.nan, iNaT, float("nan"), NaT, "NaT", "nat", "", "NAT"] +) +def test_identity(klass, value): + assert klass(value) is NaT + + +@pytest.mark.parametrize("klass", [Timestamp, Timedelta]) +@pytest.mark.parametrize("method", ["round", "floor", "ceil"]) +@pytest.mark.parametrize("freq", ["s", "5s", "min", "5min", "h", "5h"]) +def test_round_nat(klass, method, freq): + # see gh-14940 + ts = klass("nat") + + round_method = getattr(ts, method) + assert round_method(freq) is ts + + +@pytest.mark.parametrize( + "method", + [ + "astimezone", + "combine", + "ctime", + "dst", + "fromordinal", + "fromtimestamp", + "fromisocalendar", + "isocalendar", + "strftime", + "strptime", + "time", + "timestamp", + "timetuple", + "timetz", + "toordinal", + "tzname", + "utcfromtimestamp", + "utcnow", + "utcoffset", + "utctimetuple", + "timestamp", + ], +) +def test_nat_methods_raise(method): + # see gh-9513, gh-17329 + msg = f"NaTType does not support {method}" + + with pytest.raises(ValueError, match=msg): + getattr(NaT, method)() + + +@pytest.mark.parametrize("method", ["weekday", "isoweekday"]) +def test_nat_methods_nan(method): + # see gh-9513, gh-17329 + assert np.isnan(getattr(NaT, method)()) + + +@pytest.mark.parametrize( + "method", ["date", "now", "replace", "today", "tz_convert", "tz_localize"] +) +def test_nat_methods_nat(method): + # see gh-8254, gh-9513, gh-17329 + assert getattr(NaT, method)() is NaT + + +@pytest.mark.parametrize( + "get_nat", [lambda x: NaT, lambda x: Timedelta(x), lambda x: Timestamp(x)] +) +def test_nat_iso_format(get_nat): + # see gh-12300 + assert get_nat("NaT").isoformat() == "NaT" + assert get_nat("NaT").isoformat(timespec="nanoseconds") == "NaT" + + +@pytest.mark.parametrize( + "klass,expected", + [ + (Timestamp, ["normalize", "to_julian_date", "to_period", "unit"]), + ( + Timedelta, + [ + "components", + "resolution_string", + "to_pytimedelta", + "to_timedelta64", + "unit", + "view", + ], + ), + ], +) +def test_missing_public_nat_methods(klass, expected): + # see gh-17327 + # + # NaT should have *most* of the Timestamp and Timedelta methods. + # Here, we check which public methods NaT does not have. We + # ignore any missing private methods. + nat_names = dir(NaT) + klass_names = dir(klass) + + missing = [x for x in klass_names if x not in nat_names and not x.startswith("_")] + missing.sort() + + assert missing == expected + + +def _get_overlap_public_nat_methods(klass, as_tuple=False): + """ + Get overlapping public methods between NaT and another class. + + Parameters + ---------- + klass : type + The class to compare with NaT + as_tuple : bool, default False + Whether to return a list of tuples of the form (klass, method). + + Returns + ------- + overlap : list + """ + nat_names = dir(NaT) + klass_names = dir(klass) + + overlap = [ + x + for x in nat_names + if x in klass_names and not x.startswith("_") and callable(getattr(klass, x)) + ] + + # Timestamp takes precedence over Timedelta in terms of overlap. + if klass is Timedelta: + ts_names = dir(Timestamp) + overlap = [x for x in overlap if x not in ts_names] + + if as_tuple: + overlap = [(klass, method) for method in overlap] + + overlap.sort() + return overlap + + +@pytest.mark.parametrize( + "klass,expected", + [ + ( + Timestamp, + [ + "as_unit", + "astimezone", + "ceil", + "combine", + "ctime", + "date", + "day_name", + "dst", + "floor", + "fromisocalendar", + "fromisoformat", + "fromordinal", + "fromtimestamp", + "isocalendar", + "isoformat", + "isoweekday", + "month_name", + "now", + "replace", + "round", + "strftime", + "strptime", + "time", + "timestamp", + "timetuple", + "timetz", + "to_datetime64", + "to_numpy", + "to_pydatetime", + "today", + "toordinal", + "tz_convert", + "tz_localize", + "tzname", + "utcfromtimestamp", + "utcnow", + "utcoffset", + "utctimetuple", + "weekday", + ], + ), + (Timedelta, ["total_seconds"]), + ], +) +def test_overlap_public_nat_methods(klass, expected): + # see gh-17327 + # + # NaT should have *most* of the Timestamp and Timedelta methods. + # In case when Timestamp, Timedelta, and NaT are overlap, the overlap + # is considered to be with Timestamp and NaT, not Timedelta. + assert _get_overlap_public_nat_methods(klass) == expected + + +@pytest.mark.parametrize( + "compare", + ( + _get_overlap_public_nat_methods(Timestamp, True) + + _get_overlap_public_nat_methods(Timedelta, True) + ), + ids=lambda x: f"{x[0].__name__}.{x[1]}", +) +def test_nat_doc_strings(compare): + # see gh-17327 + # + # The docstrings for overlapping methods should match. + klass, method = compare + klass_doc = getattr(klass, method).__doc__ + + if klass == Timestamp and method == "isoformat": + pytest.skip( + "Ignore differences with Timestamp.isoformat() as they're intentional" + ) + + if method == "to_numpy": + # GH#44460 can return either dt64 or td64 depending on dtype, + # different docstring is intentional + pytest.skip(f"different docstring for {method} is intentional") + + nat_doc = getattr(NaT, method).__doc__ + assert klass_doc == nat_doc + + +_ops = { + "left_plus_right": lambda a, b: a + b, + "right_plus_left": lambda a, b: b + a, + "left_minus_right": lambda a, b: a - b, + "right_minus_left": lambda a, b: b - a, + "left_times_right": lambda a, b: a * b, + "right_times_left": lambda a, b: b * a, + "left_div_right": lambda a, b: a / b, + "right_div_left": lambda a, b: b / a, +} + + +@pytest.mark.parametrize("op_name", list(_ops.keys())) +@pytest.mark.parametrize( + "value,val_type", + [ + (2, "scalar"), + (1.5, "floating"), + (np.nan, "floating"), + ("foo", "str"), + (timedelta(3600), "timedelta"), + (Timedelta("5s"), "timedelta"), + (datetime(2014, 1, 1), "timestamp"), + (Timestamp("2014-01-01"), "timestamp"), + (Timestamp("2014-01-01", tz="UTC"), "timestamp"), + (Timestamp("2014-01-01", tz="US/Eastern"), "timestamp"), + (pytz.timezone("Asia/Tokyo").localize(datetime(2014, 1, 1)), "timestamp"), + ], +) +def test_nat_arithmetic_scalar(op_name, value, val_type): + # see gh-6873 + invalid_ops = { + "scalar": {"right_div_left"}, + "floating": { + "right_div_left", + "left_minus_right", + "right_minus_left", + "left_plus_right", + "right_plus_left", + }, + "str": set(_ops.keys()), + "timedelta": {"left_times_right", "right_times_left"}, + "timestamp": { + "left_times_right", + "right_times_left", + "left_div_right", + "right_div_left", + }, + } + + op = _ops[op_name] + + if op_name in invalid_ops.get(val_type, set()): + if ( + val_type == "timedelta" + and "times" in op_name + and isinstance(value, Timedelta) + ): + typs = "(Timedelta|NaTType)" + msg = rf"unsupported operand type\(s\) for \*: '{typs}' and '{typs}'" + elif val_type == "str": + # un-specific check here because the message comes from str + # and varies by method + msg = "|".join( + [ + "can only concatenate str", + "unsupported operand type", + "can't multiply sequence", + "Can't convert 'NaTType'", + "must be str, not NaTType", + ] + ) + else: + msg = "unsupported operand type" + + with pytest.raises(TypeError, match=msg): + op(NaT, value) + else: + if val_type == "timedelta" and "div" in op_name: + expected = np.nan + else: + expected = NaT + + assert op(NaT, value) is expected + + +@pytest.mark.parametrize( + "val,expected", [(np.nan, NaT), (NaT, np.nan), (np.timedelta64("NaT"), np.nan)] +) +def test_nat_rfloordiv_timedelta(val, expected): + # see gh-#18846 + # + # See also test_timedelta.TestTimedeltaArithmetic.test_floordiv + td = Timedelta(hours=3, minutes=4) + assert td // val is expected + + +@pytest.mark.parametrize( + "op_name", + ["left_plus_right", "right_plus_left", "left_minus_right", "right_minus_left"], +) +@pytest.mark.parametrize( + "value", + [ + DatetimeIndex(["2011-01-01", "2011-01-02"], name="x"), + DatetimeIndex(["2011-01-01", "2011-01-02"], tz="US/Eastern", name="x"), + DatetimeArray._from_sequence(["2011-01-01", "2011-01-02"], dtype="M8[ns]"), + DatetimeArray._from_sequence( + ["2011-01-01", "2011-01-02"], dtype=DatetimeTZDtype(tz="US/Pacific") + ), + TimedeltaIndex(["1 day", "2 day"], name="x"), + ], +) +def test_nat_arithmetic_index(op_name, value): + # see gh-11718 + exp_name = "x" + exp_data = [NaT] * 2 + + if value.dtype.kind == "M" and "plus" in op_name: + expected = DatetimeIndex(exp_data, tz=value.tz, name=exp_name) + else: + expected = TimedeltaIndex(exp_data, name=exp_name) + expected = expected.as_unit(value.unit) + + if not isinstance(value, Index): + expected = expected.array + + op = _ops[op_name] + result = op(NaT, value) + tm.assert_equal(result, expected) + + +@pytest.mark.parametrize( + "op_name", + ["left_plus_right", "right_plus_left", "left_minus_right", "right_minus_left"], +) +@pytest.mark.parametrize("box", [TimedeltaIndex, Series, TimedeltaArray._from_sequence]) +def test_nat_arithmetic_td64_vector(op_name, box): + # see gh-19124 + vec = box(["1 day", "2 day"], dtype="timedelta64[ns]") + box_nat = box([NaT, NaT], dtype="timedelta64[ns]") + tm.assert_equal(_ops[op_name](vec, NaT), box_nat) + + +@pytest.mark.parametrize( + "dtype,op,out_dtype", + [ + ("datetime64[ns]", operator.add, "datetime64[ns]"), + ("datetime64[ns]", roperator.radd, "datetime64[ns]"), + ("datetime64[ns]", operator.sub, "timedelta64[ns]"), + ("datetime64[ns]", roperator.rsub, "timedelta64[ns]"), + ("timedelta64[ns]", operator.add, "datetime64[ns]"), + ("timedelta64[ns]", roperator.radd, "datetime64[ns]"), + ("timedelta64[ns]", operator.sub, "datetime64[ns]"), + ("timedelta64[ns]", roperator.rsub, "timedelta64[ns]"), + ], +) +def test_nat_arithmetic_ndarray(dtype, op, out_dtype): + other = np.arange(10).astype(dtype) + result = op(NaT, other) + + expected = np.empty(other.shape, dtype=out_dtype) + expected.fill("NaT") + tm.assert_numpy_array_equal(result, expected) + + +def test_nat_pinned_docstrings(): + # see gh-17327 + assert NaT.ctime.__doc__ == Timestamp.ctime.__doc__ + + +def test_to_numpy_alias(): + # GH 24653: alias .to_numpy() for scalars + expected = NaT.to_datetime64() + result = NaT.to_numpy() + + assert isna(expected) and isna(result) + + # GH#44460 + result = NaT.to_numpy("M8[s]") + assert isinstance(result, np.datetime64) + assert result.dtype == "M8[s]" + + result = NaT.to_numpy("m8[ns]") + assert isinstance(result, np.timedelta64) + assert result.dtype == "m8[ns]" + + result = NaT.to_numpy("m8[s]") + assert isinstance(result, np.timedelta64) + assert result.dtype == "m8[s]" + + with pytest.raises(ValueError, match="NaT.to_numpy dtype must be a "): + NaT.to_numpy(np.int64) + + +@pytest.mark.parametrize( + "other", + [ + Timedelta(0), + Timedelta(0).to_pytimedelta(), + pytest.param( + Timedelta(0).to_timedelta64(), + marks=pytest.mark.xfail( + not np_version_gte1p24p3, + reason="td64 doesn't return NotImplemented, see numpy#17017", + # When this xfail is fixed, test_nat_comparisons_numpy + # can be removed. + ), + ), + Timestamp(0), + Timestamp(0).to_pydatetime(), + pytest.param( + Timestamp(0).to_datetime64(), + marks=pytest.mark.xfail( + not np_version_gte1p24p3, + reason="dt64 doesn't return NotImplemented, see numpy#17017", + ), + ), + Timestamp(0).tz_localize("UTC"), + NaT, + ], +) +def test_nat_comparisons(compare_operators_no_eq_ne, other): + # GH 26039 + opname = compare_operators_no_eq_ne + + assert getattr(NaT, opname)(other) is False + + op = getattr(operator, opname.strip("_")) + assert op(NaT, other) is False + assert op(other, NaT) is False + + +@pytest.mark.parametrize("other", [np.timedelta64(0, "ns"), np.datetime64("now", "ns")]) +def test_nat_comparisons_numpy(other): + # Once numpy#17017 is fixed and the xfailed cases in test_nat_comparisons + # pass, this test can be removed + assert not NaT == other + assert NaT != other + assert not NaT < other + assert not NaT > other + assert not NaT <= other + assert not NaT >= other + + +@pytest.mark.parametrize("other_and_type", [("foo", "str"), (2, "int"), (2.0, "float")]) +@pytest.mark.parametrize( + "symbol_and_op", + [("<=", operator.le), ("<", operator.lt), (">=", operator.ge), (">", operator.gt)], +) +def test_nat_comparisons_invalid(other_and_type, symbol_and_op): + # GH#35585 + other, other_type = other_and_type + symbol, op = symbol_and_op + + assert not NaT == other + assert not other == NaT + + assert NaT != other + assert other != NaT + + msg = f"'{symbol}' not supported between instances of 'NaTType' and '{other_type}'" + with pytest.raises(TypeError, match=msg): + op(NaT, other) + + msg = f"'{symbol}' not supported between instances of '{other_type}' and 'NaTType'" + with pytest.raises(TypeError, match=msg): + op(other, NaT) + + +@pytest.mark.parametrize( + "other", + [ + np.array(["foo"] * 2, dtype=object), + np.array([2, 3], dtype="int64"), + np.array([2.0, 3.5], dtype="float64"), + ], + ids=["str", "int", "float"], +) +def test_nat_comparisons_invalid_ndarray(other): + # GH#40722 + expected = np.array([False, False]) + result = NaT == other + tm.assert_numpy_array_equal(result, expected) + result = other == NaT + tm.assert_numpy_array_equal(result, expected) + + expected = np.array([True, True]) + result = NaT != other + tm.assert_numpy_array_equal(result, expected) + result = other != NaT + tm.assert_numpy_array_equal(result, expected) + + for symbol, op in [ + ("<=", operator.le), + ("<", operator.lt), + (">=", operator.ge), + (">", operator.gt), + ]: + msg = f"'{symbol}' not supported between" + + with pytest.raises(TypeError, match=msg): + op(NaT, other) + + if other.dtype == np.dtype("object"): + # uses the reverse operator, so symbol changes + msg = None + with pytest.raises(TypeError, match=msg): + op(other, NaT) + + +def test_compare_date(fixed_now_ts): + # GH#39151 comparing NaT with date object is deprecated + # See also: tests.scalar.timestamps.test_comparisons::test_compare_date + + dt = fixed_now_ts.to_pydatetime().date() + + msg = "Cannot compare NaT with datetime.date object" + for left, right in [(NaT, dt), (dt, NaT)]: + assert not left == right + assert left != right + + with pytest.raises(TypeError, match=msg): + left < right + with pytest.raises(TypeError, match=msg): + left <= right + with pytest.raises(TypeError, match=msg): + left > right + with pytest.raises(TypeError, match=msg): + left >= right + + +@pytest.mark.parametrize( + "obj", + [ + offsets.YearEnd(2), + offsets.YearBegin(2), + offsets.MonthBegin(1), + offsets.MonthEnd(2), + offsets.MonthEnd(12), + offsets.Day(2), + offsets.Day(5), + offsets.Hour(24), + offsets.Hour(3), + offsets.Minute(), + np.timedelta64(3, "h"), + np.timedelta64(4, "h"), + np.timedelta64(3200, "s"), + np.timedelta64(3600, "s"), + np.timedelta64(3600 * 24, "s"), + np.timedelta64(2, "D"), + np.timedelta64(365, "D"), + timedelta(-2), + timedelta(365), + timedelta(minutes=120), + timedelta(days=4, minutes=180), + timedelta(hours=23), + timedelta(hours=23, minutes=30), + timedelta(hours=48), + ], +) +def test_nat_addsub_tdlike_scalar(obj): + assert NaT + obj is NaT + assert obj + NaT is NaT + assert NaT - obj is NaT + + +def test_pickle(): + # GH#4606 + p = tm.round_trip_pickle(NaT) + assert p is NaT diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/methods/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/methods/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/methods/test_as_unit.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/methods/test_as_unit.py new file mode 100644 index 0000000000000000000000000000000000000000..8660141e5a5372d4bb8e921bc8b3a5ee148e8900 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/methods/test_as_unit.py @@ -0,0 +1,80 @@ +import pytest + +from pandas._libs.tslibs.dtypes import NpyDatetimeUnit +from pandas.errors import OutOfBoundsTimedelta + +from pandas import Timedelta + + +class TestAsUnit: + def test_as_unit(self): + td = Timedelta(days=1) + + assert td.as_unit("ns") is td + + res = td.as_unit("us") + assert res._value == td._value // 1000 + assert res._creso == NpyDatetimeUnit.NPY_FR_us.value + + rt = res.as_unit("ns") + assert rt._value == td._value + assert rt._creso == td._creso + + res = td.as_unit("ms") + assert res._value == td._value // 1_000_000 + assert res._creso == NpyDatetimeUnit.NPY_FR_ms.value + + rt = res.as_unit("ns") + assert rt._value == td._value + assert rt._creso == td._creso + + res = td.as_unit("s") + assert res._value == td._value // 1_000_000_000 + assert res._creso == NpyDatetimeUnit.NPY_FR_s.value + + rt = res.as_unit("ns") + assert rt._value == td._value + assert rt._creso == td._creso + + def test_as_unit_overflows(self): + # microsecond that would be just out of bounds for nano + us = 9223372800000000 + td = Timedelta._from_value_and_reso(us, NpyDatetimeUnit.NPY_FR_us.value) + + msg = "Cannot cast 106752 days 00:00:00 to unit='ns' without overflow" + with pytest.raises(OutOfBoundsTimedelta, match=msg): + td.as_unit("ns") + + res = td.as_unit("ms") + assert res._value == us // 1000 + assert res._creso == NpyDatetimeUnit.NPY_FR_ms.value + + def test_as_unit_rounding(self): + td = Timedelta(microseconds=1500) + res = td.as_unit("ms") + + expected = Timedelta(milliseconds=1) + assert res == expected + + assert res._creso == NpyDatetimeUnit.NPY_FR_ms.value + assert res._value == 1 + + with pytest.raises(ValueError, match="Cannot losslessly convert units"): + td.as_unit("ms", round_ok=False) + + def test_as_unit_non_nano(self): + # case where we are going neither to nor from nano + td = Timedelta(days=1).as_unit("ms") + assert td.days == 1 + assert td._value == 86_400_000 + assert td.components.days == 1 + assert td._d == 1 + assert td.total_seconds() == 86400 + + res = td.as_unit("us") + assert res._value == 86_400_000_000 + assert res.components.days == 1 + assert res.components.hours == 0 + assert res._d == 1 + assert res._h == 0 + assert res.total_seconds() == 86400 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/methods/test_round.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/methods/test_round.py new file mode 100644 index 0000000000000000000000000000000000000000..e54adb27d126bf13b454c513cecf847cdbb623bb --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/methods/test_round.py @@ -0,0 +1,187 @@ +from hypothesis import ( + given, + strategies as st, +) +import numpy as np +import pytest + +from pandas._libs import lib +from pandas._libs.tslibs import iNaT +from pandas.errors import OutOfBoundsTimedelta + +from pandas import Timedelta + + +class TestTimedeltaRound: + @pytest.mark.parametrize( + "freq,s1,s2", + [ + # This first case has s1, s2 being the same as t1,t2 below + ( + "ns", + Timedelta("1 days 02:34:56.789123456"), + Timedelta("-1 days 02:34:56.789123456"), + ), + ( + "us", + Timedelta("1 days 02:34:56.789123000"), + Timedelta("-1 days 02:34:56.789123000"), + ), + ( + "ms", + Timedelta("1 days 02:34:56.789000000"), + Timedelta("-1 days 02:34:56.789000000"), + ), + ("s", Timedelta("1 days 02:34:57"), Timedelta("-1 days 02:34:57")), + ("2s", Timedelta("1 days 02:34:56"), Timedelta("-1 days 02:34:56")), + ("5s", Timedelta("1 days 02:34:55"), Timedelta("-1 days 02:34:55")), + ("min", Timedelta("1 days 02:35:00"), Timedelta("-1 days 02:35:00")), + ("12min", Timedelta("1 days 02:36:00"), Timedelta("-1 days 02:36:00")), + ("h", Timedelta("1 days 03:00:00"), Timedelta("-1 days 03:00:00")), + ("d", Timedelta("1 days"), Timedelta("-1 days")), + ], + ) + def test_round(self, freq, s1, s2): + t1 = Timedelta("1 days 02:34:56.789123456") + t2 = Timedelta("-1 days 02:34:56.789123456") + + r1 = t1.round(freq) + assert r1 == s1 + r2 = t2.round(freq) + assert r2 == s2 + + def test_round_invalid(self): + t1 = Timedelta("1 days 02:34:56.789123456") + + for freq, msg in [ + ("YE", " is a non-fixed frequency"), + ("ME", " is a non-fixed frequency"), + ("foobar", "Invalid frequency: foobar"), + ]: + with pytest.raises(ValueError, match=msg): + t1.round(freq) + + @pytest.mark.skip_ubsan + def test_round_implementation_bounds(self): + # See also: analogous test for Timestamp + # GH#38964 + result = Timedelta.min.ceil("s") + expected = Timedelta.min + Timedelta(seconds=1) - Timedelta(145224193) + assert result == expected + + result = Timedelta.max.floor("s") + expected = Timedelta.max - Timedelta(854775807) + assert result == expected + + msg = ( + r"Cannot round -106752 days \+00:12:43.145224193 to freq=s without overflow" + ) + with pytest.raises(OutOfBoundsTimedelta, match=msg): + Timedelta.min.floor("s") + with pytest.raises(OutOfBoundsTimedelta, match=msg): + Timedelta.min.round("s") + + msg = "Cannot round 106751 days 23:47:16.854775807 to freq=s without overflow" + with pytest.raises(OutOfBoundsTimedelta, match=msg): + Timedelta.max.ceil("s") + with pytest.raises(OutOfBoundsTimedelta, match=msg): + Timedelta.max.round("s") + + @pytest.mark.skip_ubsan + @given(val=st.integers(min_value=iNaT + 1, max_value=lib.i8max)) + @pytest.mark.parametrize( + "method", [Timedelta.round, Timedelta.floor, Timedelta.ceil] + ) + def test_round_sanity(self, val, method): + cls = Timedelta + err_cls = OutOfBoundsTimedelta + + val = np.int64(val) + td = cls(val) + + def checker(ts, nanos, unit): + # First check that we do raise in cases where we should + if nanos == 1: + pass + else: + div, mod = divmod(ts._value, nanos) + diff = int(nanos - mod) + lb = ts._value - mod + assert lb <= ts._value # i.e. no overflows with python ints + ub = ts._value + diff + assert ub > ts._value # i.e. no overflows with python ints + + msg = "without overflow" + if mod == 0: + # We should never be raising in this + pass + elif method is cls.ceil: + if ub > cls.max._value: + with pytest.raises(err_cls, match=msg): + method(ts, unit) + return + elif method is cls.floor: + if lb < cls.min._value: + with pytest.raises(err_cls, match=msg): + method(ts, unit) + return + elif mod >= diff: + if ub > cls.max._value: + with pytest.raises(err_cls, match=msg): + method(ts, unit) + return + elif lb < cls.min._value: + with pytest.raises(err_cls, match=msg): + method(ts, unit) + return + + res = method(ts, unit) + + td = res - ts + diff = abs(td._value) + assert diff < nanos + assert res._value % nanos == 0 + + if method is cls.round: + assert diff <= nanos / 2 + elif method is cls.floor: + assert res <= ts + elif method is cls.ceil: + assert res >= ts + + nanos = 1 + checker(td, nanos, "ns") + + nanos = 1000 + checker(td, nanos, "us") + + nanos = 1_000_000 + checker(td, nanos, "ms") + + nanos = 1_000_000_000 + checker(td, nanos, "s") + + nanos = 60 * 1_000_000_000 + checker(td, nanos, "min") + + nanos = 60 * 60 * 1_000_000_000 + checker(td, nanos, "h") + + nanos = 24 * 60 * 60 * 1_000_000_000 + checker(td, nanos, "D") + + @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s"]) + def test_round_non_nano(self, unit): + td = Timedelta("1 days 02:34:57").as_unit(unit) + + res = td.round("min") + assert res == Timedelta("1 days 02:35:00") + assert res._creso == td._creso + + res = td.floor("min") + assert res == Timedelta("1 days 02:34:00") + assert res._creso == td._creso + + res = td.ceil("min") + assert res == Timedelta("1 days 02:35:00") + assert res._creso == td._creso diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/test_arithmetic.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/test_arithmetic.py new file mode 100644 index 0000000000000000000000000000000000000000..a4d846f068d0012fb67f066e68950b6128ee4030 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/test_arithmetic.py @@ -0,0 +1,1183 @@ +""" +Tests for scalar Timedelta arithmetic ops +""" +from datetime import ( + datetime, + timedelta, +) +import operator + +import numpy as np +import pytest + +from pandas.errors import OutOfBoundsTimedelta + +import pandas as pd +from pandas import ( + NaT, + Timedelta, + Timestamp, + offsets, +) +import pandas._testing as tm +from pandas.core import ops + + +class TestTimedeltaAdditionSubtraction: + """ + Tests for Timedelta methods: + + __add__, __radd__, + __sub__, __rsub__ + """ + + @pytest.mark.parametrize( + "ten_seconds", + [ + Timedelta(10, unit="s"), + timedelta(seconds=10), + np.timedelta64(10, "s"), + np.timedelta64(10000000000, "ns"), + offsets.Second(10), + ], + ) + def test_td_add_sub_ten_seconds(self, ten_seconds): + # GH#6808 + base = Timestamp("20130101 09:01:12.123456") + expected_add = Timestamp("20130101 09:01:22.123456") + expected_sub = Timestamp("20130101 09:01:02.123456") + + result = base + ten_seconds + assert result == expected_add + + result = base - ten_seconds + assert result == expected_sub + + @pytest.mark.parametrize( + "one_day_ten_secs", + [ + Timedelta("1 day, 00:00:10"), + Timedelta("1 days, 00:00:10"), + timedelta(days=1, seconds=10), + np.timedelta64(1, "D") + np.timedelta64(10, "s"), + offsets.Day() + offsets.Second(10), + ], + ) + def test_td_add_sub_one_day_ten_seconds(self, one_day_ten_secs): + # GH#6808 + base = Timestamp("20130102 09:01:12.123456") + expected_add = Timestamp("20130103 09:01:22.123456") + expected_sub = Timestamp("20130101 09:01:02.123456") + + result = base + one_day_ten_secs + assert result == expected_add + + result = base - one_day_ten_secs + assert result == expected_sub + + @pytest.mark.parametrize("op", [operator.add, ops.radd]) + def test_td_add_datetimelike_scalar(self, op): + # GH#19738 + td = Timedelta(10, unit="d") + + result = op(td, datetime(2016, 1, 1)) + if op is operator.add: + # datetime + Timedelta does _not_ call Timedelta.__radd__, + # so we get a datetime back instead of a Timestamp + assert isinstance(result, Timestamp) + assert result == Timestamp(2016, 1, 11) + + result = op(td, Timestamp("2018-01-12 18:09")) + assert isinstance(result, Timestamp) + assert result == Timestamp("2018-01-22 18:09") + + result = op(td, np.datetime64("2018-01-12")) + assert isinstance(result, Timestamp) + assert result == Timestamp("2018-01-22") + + result = op(td, NaT) + assert result is NaT + + def test_td_add_timestamp_overflow(self): + ts = Timestamp("1700-01-01").as_unit("ns") + msg = "Cannot cast 259987 from D to 'ns' without overflow." + with pytest.raises(OutOfBoundsTimedelta, match=msg): + ts + Timedelta(13 * 19999, unit="D") + + msg = "Cannot cast 259987 days 00:00:00 to unit='ns' without overflow" + with pytest.raises(OutOfBoundsTimedelta, match=msg): + ts + timedelta(days=13 * 19999) + + @pytest.mark.parametrize("op", [operator.add, ops.radd]) + def test_td_add_td(self, op): + td = Timedelta(10, unit="d") + + result = op(td, Timedelta(days=10)) + assert isinstance(result, Timedelta) + assert result == Timedelta(days=20) + + @pytest.mark.parametrize("op", [operator.add, ops.radd]) + def test_td_add_pytimedelta(self, op): + td = Timedelta(10, unit="d") + result = op(td, timedelta(days=9)) + assert isinstance(result, Timedelta) + assert result == Timedelta(days=19) + + @pytest.mark.parametrize("op", [operator.add, ops.radd]) + def test_td_add_timedelta64(self, op): + td = Timedelta(10, unit="d") + result = op(td, np.timedelta64(-4, "D")) + assert isinstance(result, Timedelta) + assert result == Timedelta(days=6) + + @pytest.mark.parametrize("op", [operator.add, ops.radd]) + def test_td_add_offset(self, op): + td = Timedelta(10, unit="d") + + result = op(td, offsets.Hour(6)) + assert isinstance(result, Timedelta) + assert result == Timedelta(days=10, hours=6) + + def test_td_sub_td(self): + td = Timedelta(10, unit="d") + expected = Timedelta(0, unit="ns") + result = td - td + assert isinstance(result, Timedelta) + assert result == expected + + def test_td_sub_pytimedelta(self): + td = Timedelta(10, unit="d") + expected = Timedelta(0, unit="ns") + + result = td - td.to_pytimedelta() + assert isinstance(result, Timedelta) + assert result == expected + + result = td.to_pytimedelta() - td + assert isinstance(result, Timedelta) + assert result == expected + + def test_td_sub_timedelta64(self): + td = Timedelta(10, unit="d") + expected = Timedelta(0, unit="ns") + + result = td - td.to_timedelta64() + assert isinstance(result, Timedelta) + assert result == expected + + result = td.to_timedelta64() - td + assert isinstance(result, Timedelta) + assert result == expected + + def test_td_sub_nat(self): + # In this context pd.NaT is treated as timedelta-like + td = Timedelta(10, unit="d") + result = td - NaT + assert result is NaT + + def test_td_sub_td64_nat(self): + td = Timedelta(10, unit="d") + td_nat = np.timedelta64("NaT") + + result = td - td_nat + assert result is NaT + + result = td_nat - td + assert result is NaT + + def test_td_sub_offset(self): + td = Timedelta(10, unit="d") + result = td - offsets.Hour(1) + assert isinstance(result, Timedelta) + assert result == Timedelta(239, unit="h") + + def test_td_add_sub_numeric_raises(self): + td = Timedelta(10, unit="d") + msg = "unsupported operand type" + for other in [2, 2.0, np.int64(2), np.float64(2)]: + with pytest.raises(TypeError, match=msg): + td + other + with pytest.raises(TypeError, match=msg): + other + td + with pytest.raises(TypeError, match=msg): + td - other + with pytest.raises(TypeError, match=msg): + other - td + + def test_td_add_sub_int_ndarray(self): + td = Timedelta("1 day") + other = np.array([1]) + + msg = r"unsupported operand type\(s\) for \+: 'Timedelta' and 'int'" + with pytest.raises(TypeError, match=msg): + td + np.array([1]) + + msg = "|".join( + [ + ( + r"unsupported operand type\(s\) for \+: 'numpy.ndarray' " + "and 'Timedelta'" + ), + # This message goes on to say "Please do not rely on this error; + # it may not be given on all Python implementations" + "Concatenation operation is not implemented for NumPy arrays", + ] + ) + with pytest.raises(TypeError, match=msg): + other + td + msg = r"unsupported operand type\(s\) for -: 'Timedelta' and 'int'" + with pytest.raises(TypeError, match=msg): + td - other + msg = r"unsupported operand type\(s\) for -: 'numpy.ndarray' and 'Timedelta'" + with pytest.raises(TypeError, match=msg): + other - td + + def test_td_rsub_nat(self): + td = Timedelta(10, unit="d") + result = NaT - td + assert result is NaT + + result = np.datetime64("NaT") - td + assert result is NaT + + def test_td_rsub_offset(self): + result = offsets.Hour(1) - Timedelta(10, unit="d") + assert isinstance(result, Timedelta) + assert result == Timedelta(-239, unit="h") + + def test_td_sub_timedeltalike_object_dtype_array(self): + # GH#21980 + arr = np.array([Timestamp("20130101 9:01"), Timestamp("20121230 9:02")]) + exp = np.array([Timestamp("20121231 9:01"), Timestamp("20121229 9:02")]) + res = arr - Timedelta("1D") + tm.assert_numpy_array_equal(res, exp) + + def test_td_sub_mixed_most_timedeltalike_object_dtype_array(self): + # GH#21980 + now = Timestamp("2021-11-09 09:54:00") + arr = np.array([now, Timedelta("1D"), np.timedelta64(2, "h")]) + exp = np.array( + [ + now - Timedelta("1D"), + Timedelta("0D"), + np.timedelta64(2, "h") - Timedelta("1D"), + ] + ) + res = arr - Timedelta("1D") + tm.assert_numpy_array_equal(res, exp) + + def test_td_rsub_mixed_most_timedeltalike_object_dtype_array(self): + # GH#21980 + now = Timestamp("2021-11-09 09:54:00") + arr = np.array([now, Timedelta("1D"), np.timedelta64(2, "h")]) + msg = r"unsupported operand type\(s\) for \-: 'Timedelta' and 'Timestamp'" + with pytest.raises(TypeError, match=msg): + Timedelta("1D") - arr + + @pytest.mark.parametrize("op", [operator.add, ops.radd]) + def test_td_add_timedeltalike_object_dtype_array(self, op): + # GH#21980 + arr = np.array([Timestamp("20130101 9:01"), Timestamp("20121230 9:02")]) + exp = np.array([Timestamp("20130102 9:01"), Timestamp("20121231 9:02")]) + res = op(arr, Timedelta("1D")) + tm.assert_numpy_array_equal(res, exp) + + @pytest.mark.parametrize("op", [operator.add, ops.radd]) + def test_td_add_mixed_timedeltalike_object_dtype_array(self, op): + # GH#21980 + now = Timestamp("2021-11-09 09:54:00") + arr = np.array([now, Timedelta("1D")]) + exp = np.array([now + Timedelta("1D"), Timedelta("2D")]) + res = op(arr, Timedelta("1D")) + tm.assert_numpy_array_equal(res, exp) + + def test_td_add_sub_td64_ndarray(self): + td = Timedelta("1 day") + + other = np.array([td.to_timedelta64()]) + expected = np.array([Timedelta("2 Days").to_timedelta64()]) + + result = td + other + tm.assert_numpy_array_equal(result, expected) + result = other + td + tm.assert_numpy_array_equal(result, expected) + + result = td - other + tm.assert_numpy_array_equal(result, expected * 0) + result = other - td + tm.assert_numpy_array_equal(result, expected * 0) + + def test_td_add_sub_dt64_ndarray(self): + td = Timedelta("1 day") + other = np.array(["2000-01-01"], dtype="M8[ns]") + + expected = np.array(["2000-01-02"], dtype="M8[ns]") + tm.assert_numpy_array_equal(td + other, expected) + tm.assert_numpy_array_equal(other + td, expected) + + expected = np.array(["1999-12-31"], dtype="M8[ns]") + tm.assert_numpy_array_equal(-td + other, expected) + tm.assert_numpy_array_equal(other - td, expected) + + def test_td_add_sub_ndarray_0d(self): + td = Timedelta("1 day") + other = np.array(td.asm8) + + result = td + other + assert isinstance(result, Timedelta) + assert result == 2 * td + + result = other + td + assert isinstance(result, Timedelta) + assert result == 2 * td + + result = other - td + assert isinstance(result, Timedelta) + assert result == 0 * td + + result = td - other + assert isinstance(result, Timedelta) + assert result == 0 * td + + +class TestTimedeltaMultiplicationDivision: + """ + Tests for Timedelta methods: + + __mul__, __rmul__, + __div__, __rdiv__, + __truediv__, __rtruediv__, + __floordiv__, __rfloordiv__, + __mod__, __rmod__, + __divmod__, __rdivmod__ + """ + + # --------------------------------------------------------------- + # Timedelta.__mul__, __rmul__ + + @pytest.mark.parametrize( + "td_nat", [NaT, np.timedelta64("NaT", "ns"), np.timedelta64("NaT")] + ) + @pytest.mark.parametrize("op", [operator.mul, ops.rmul]) + def test_td_mul_nat(self, op, td_nat): + # GH#19819 + td = Timedelta(10, unit="d") + typs = "|".join(["numpy.timedelta64", "NaTType", "Timedelta"]) + msg = "|".join( + [ + rf"unsupported operand type\(s\) for \*: '{typs}' and '{typs}'", + r"ufunc '?multiply'? cannot use operands with types", + ] + ) + with pytest.raises(TypeError, match=msg): + op(td, td_nat) + + @pytest.mark.parametrize("nan", [np.nan, np.float64("NaN"), float("nan")]) + @pytest.mark.parametrize("op", [operator.mul, ops.rmul]) + def test_td_mul_nan(self, op, nan): + # np.float64('NaN') has a 'dtype' attr, avoid treating as array + td = Timedelta(10, unit="d") + result = op(td, nan) + assert result is NaT + + @pytest.mark.parametrize("op", [operator.mul, ops.rmul]) + def test_td_mul_scalar(self, op): + # GH#19738 + td = Timedelta(minutes=3) + + result = op(td, 2) + assert result == Timedelta(minutes=6) + + result = op(td, 1.5) + assert result == Timedelta(minutes=4, seconds=30) + + assert op(td, np.nan) is NaT + + assert op(-1, td)._value == -1 * td._value + assert op(-1.0, td)._value == -1.0 * td._value + + msg = "unsupported operand type" + with pytest.raises(TypeError, match=msg): + # timedelta * datetime is gibberish + op(td, Timestamp(2016, 1, 2)) + + with pytest.raises(TypeError, match=msg): + # invalid multiply with another timedelta + op(td, td) + + def test_td_mul_numeric_ndarray(self): + td = Timedelta("1 day") + other = np.array([2]) + expected = np.array([Timedelta("2 Days").to_timedelta64()]) + + result = td * other + tm.assert_numpy_array_equal(result, expected) + + result = other * td + tm.assert_numpy_array_equal(result, expected) + + def test_td_mul_numeric_ndarray_0d(self): + td = Timedelta("1 day") + other = np.array(2, dtype=np.int64) + assert other.ndim == 0 + expected = Timedelta("2 days") + + res = td * other + assert type(res) is Timedelta + assert res == expected + + res = other * td + assert type(res) is Timedelta + assert res == expected + + def test_td_mul_td64_ndarray_invalid(self): + td = Timedelta("1 day") + other = np.array([Timedelta("2 Days").to_timedelta64()]) + + msg = ( + "ufunc '?multiply'? cannot use operands with types " + rf"dtype\('{tm.ENDIAN}m8\[ns\]'\) and dtype\('{tm.ENDIAN}m8\[ns\]'\)" + ) + with pytest.raises(TypeError, match=msg): + td * other + with pytest.raises(TypeError, match=msg): + other * td + + # --------------------------------------------------------------- + # Timedelta.__div__, __truediv__ + + def test_td_div_timedeltalike_scalar(self): + # GH#19738 + td = Timedelta(10, unit="d") + + result = td / offsets.Hour(1) + assert result == 240 + + assert td / td == 1 + assert td / np.timedelta64(60, "h") == 4 + + assert np.isnan(td / NaT) + + def test_td_div_td64_non_nano(self): + # truediv + td = Timedelta("1 days 2 hours 3 ns") + result = td / np.timedelta64(1, "D") + assert result == td._value / (86400 * 10**9) + result = td / np.timedelta64(1, "s") + assert result == td._value / 10**9 + result = td / np.timedelta64(1, "ns") + assert result == td._value + + # floordiv + td = Timedelta("1 days 2 hours 3 ns") + result = td // np.timedelta64(1, "D") + assert result == 1 + result = td // np.timedelta64(1, "s") + assert result == 93600 + result = td // np.timedelta64(1, "ns") + assert result == td._value + + def test_td_div_numeric_scalar(self): + # GH#19738 + td = Timedelta(10, unit="d") + + result = td / 2 + assert isinstance(result, Timedelta) + assert result == Timedelta(days=5) + + result = td / 5 + assert isinstance(result, Timedelta) + assert result == Timedelta(days=2) + + @pytest.mark.parametrize( + "nan", + [ + np.nan, + np.float64("NaN"), + float("nan"), + ], + ) + def test_td_div_nan(self, nan): + # np.float64('NaN') has a 'dtype' attr, avoid treating as array + td = Timedelta(10, unit="d") + result = td / nan + assert result is NaT + + result = td // nan + assert result is NaT + + def test_td_div_td64_ndarray(self): + td = Timedelta("1 day") + + other = np.array([Timedelta("2 Days").to_timedelta64()]) + expected = np.array([0.5]) + + result = td / other + tm.assert_numpy_array_equal(result, expected) + + result = other / td + tm.assert_numpy_array_equal(result, expected * 4) + + def test_td_div_ndarray_0d(self): + td = Timedelta("1 day") + + other = np.array(1) + res = td / other + assert isinstance(res, Timedelta) + assert res == td + + # --------------------------------------------------------------- + # Timedelta.__rdiv__ + + def test_td_rdiv_timedeltalike_scalar(self): + # GH#19738 + td = Timedelta(10, unit="d") + result = offsets.Hour(1) / td + assert result == 1 / 240.0 + + assert np.timedelta64(60, "h") / td == 0.25 + + def test_td_rdiv_na_scalar(self): + # GH#31869 None gets cast to NaT + td = Timedelta(10, unit="d") + + result = NaT / td + assert np.isnan(result) + + result = None / td + assert np.isnan(result) + + result = np.timedelta64("NaT") / td + assert np.isnan(result) + + msg = r"unsupported operand type\(s\) for /: 'numpy.datetime64' and 'Timedelta'" + with pytest.raises(TypeError, match=msg): + np.datetime64("NaT") / td + + msg = r"unsupported operand type\(s\) for /: 'float' and 'Timedelta'" + with pytest.raises(TypeError, match=msg): + np.nan / td + + def test_td_rdiv_ndarray(self): + td = Timedelta(10, unit="d") + + arr = np.array([td], dtype=object) + result = arr / td + expected = np.array([1], dtype=np.float64) + tm.assert_numpy_array_equal(result, expected) + + arr = np.array([None]) + result = arr / td + expected = np.array([np.nan]) + tm.assert_numpy_array_equal(result, expected) + + arr = np.array([np.nan], dtype=object) + msg = r"unsupported operand type\(s\) for /: 'float' and 'Timedelta'" + with pytest.raises(TypeError, match=msg): + arr / td + + arr = np.array([np.nan], dtype=np.float64) + msg = "cannot use operands with types dtype" + with pytest.raises(TypeError, match=msg): + arr / td + + def test_td_rdiv_ndarray_0d(self): + td = Timedelta(10, unit="d") + + arr = np.array(td.asm8) + + assert arr / td == 1 + + # --------------------------------------------------------------- + # Timedelta.__floordiv__ + + def test_td_floordiv_timedeltalike_scalar(self): + # GH#18846 + td = Timedelta(hours=3, minutes=4) + scalar = Timedelta(hours=3, minutes=3) + + assert td // scalar == 1 + assert -td // scalar.to_pytimedelta() == -2 + assert (2 * td) // scalar.to_timedelta64() == 2 + + def test_td_floordiv_null_scalar(self): + # GH#18846 + td = Timedelta(hours=3, minutes=4) + + assert td // np.nan is NaT + assert np.isnan(td // NaT) + assert np.isnan(td // np.timedelta64("NaT")) + + def test_td_floordiv_offsets(self): + # GH#19738 + td = Timedelta(hours=3, minutes=4) + assert td // offsets.Hour(1) == 3 + assert td // offsets.Minute(2) == 92 + + def test_td_floordiv_invalid_scalar(self): + # GH#18846 + td = Timedelta(hours=3, minutes=4) + + msg = "|".join( + [ + r"Invalid dtype datetime64\[D\] for __floordiv__", + "'dtype' is an invalid keyword argument for this function", + "this function got an unexpected keyword argument 'dtype'", + r"ufunc '?floor_divide'? cannot use operands with types", + ] + ) + with pytest.raises(TypeError, match=msg): + td // np.datetime64("2016-01-01", dtype="datetime64[us]") + + def test_td_floordiv_numeric_scalar(self): + # GH#18846 + td = Timedelta(hours=3, minutes=4) + + expected = Timedelta(hours=1, minutes=32) + assert td // 2 == expected + assert td // 2.0 == expected + assert td // np.float64(2.0) == expected + assert td // np.int32(2.0) == expected + assert td // np.uint8(2.0) == expected + + def test_td_floordiv_timedeltalike_array(self): + # GH#18846 + td = Timedelta(hours=3, minutes=4) + scalar = Timedelta(hours=3, minutes=3) + + # Array-like others + assert td // np.array(scalar.to_timedelta64()) == 1 + + res = (3 * td) // np.array([scalar.to_timedelta64()]) + expected = np.array([3], dtype=np.int64) + tm.assert_numpy_array_equal(res, expected) + + res = (10 * td) // np.array([scalar.to_timedelta64(), np.timedelta64("NaT")]) + expected = np.array([10, np.nan]) + tm.assert_numpy_array_equal(res, expected) + + def test_td_floordiv_numeric_series(self): + # GH#18846 + td = Timedelta(hours=3, minutes=4) + ser = pd.Series([1], dtype=np.int64) + res = td // ser + assert res.dtype.kind == "m" + + # --------------------------------------------------------------- + # Timedelta.__rfloordiv__ + + def test_td_rfloordiv_timedeltalike_scalar(self): + # GH#18846 + td = Timedelta(hours=3, minutes=3) + scalar = Timedelta(hours=3, minutes=4) + + # scalar others + # x // Timedelta is defined only for timedelta-like x. int-like, + # float-like, and date-like, in particular, should all either + # a) raise TypeError directly or + # b) return NotImplemented, following which the reversed + # operation will raise TypeError. + assert td.__rfloordiv__(scalar) == 1 + assert (-td).__rfloordiv__(scalar.to_pytimedelta()) == -2 + assert (2 * td).__rfloordiv__(scalar.to_timedelta64()) == 0 + + def test_td_rfloordiv_null_scalar(self): + # GH#18846 + td = Timedelta(hours=3, minutes=3) + + assert np.isnan(td.__rfloordiv__(NaT)) + assert np.isnan(td.__rfloordiv__(np.timedelta64("NaT"))) + + def test_td_rfloordiv_offsets(self): + # GH#19738 + assert offsets.Hour(1) // Timedelta(minutes=25) == 2 + + def test_td_rfloordiv_invalid_scalar(self): + # GH#18846 + td = Timedelta(hours=3, minutes=3) + + dt64 = np.datetime64("2016-01-01", "us") + + assert td.__rfloordiv__(dt64) is NotImplemented + + msg = ( + r"unsupported operand type\(s\) for //: 'numpy.datetime64' and 'Timedelta'" + ) + with pytest.raises(TypeError, match=msg): + dt64 // td + + def test_td_rfloordiv_numeric_scalar(self): + # GH#18846 + td = Timedelta(hours=3, minutes=3) + + assert td.__rfloordiv__(np.nan) is NotImplemented + assert td.__rfloordiv__(3.5) is NotImplemented + assert td.__rfloordiv__(2) is NotImplemented + assert td.__rfloordiv__(np.float64(2.0)) is NotImplemented + assert td.__rfloordiv__(np.uint8(9)) is NotImplemented + assert td.__rfloordiv__(np.int32(2.0)) is NotImplemented + + msg = r"unsupported operand type\(s\) for //: '.*' and 'Timedelta" + with pytest.raises(TypeError, match=msg): + np.float64(2.0) // td + with pytest.raises(TypeError, match=msg): + np.uint8(9) // td + with pytest.raises(TypeError, match=msg): + # deprecated GH#19761, enforced GH#29797 + np.int32(2.0) // td + + def test_td_rfloordiv_timedeltalike_array(self): + # GH#18846 + td = Timedelta(hours=3, minutes=3) + scalar = Timedelta(hours=3, minutes=4) + + # Array-like others + assert td.__rfloordiv__(np.array(scalar.to_timedelta64())) == 1 + + res = td.__rfloordiv__(np.array([(3 * scalar).to_timedelta64()])) + expected = np.array([3], dtype=np.int64) + tm.assert_numpy_array_equal(res, expected) + + arr = np.array([(10 * scalar).to_timedelta64(), np.timedelta64("NaT")]) + res = td.__rfloordiv__(arr) + expected = np.array([10, np.nan]) + tm.assert_numpy_array_equal(res, expected) + + def test_td_rfloordiv_intarray(self): + # deprecated GH#19761, enforced GH#29797 + ints = np.array([1349654400, 1349740800, 1349827200, 1349913600]) * 10**9 + + msg = "Invalid dtype" + with pytest.raises(TypeError, match=msg): + ints // Timedelta(1, unit="s") + + def test_td_rfloordiv_numeric_series(self): + # GH#18846 + td = Timedelta(hours=3, minutes=3) + ser = pd.Series([1], dtype=np.int64) + res = td.__rfloordiv__(ser) + assert res is NotImplemented + + msg = "Invalid dtype" + with pytest.raises(TypeError, match=msg): + # Deprecated GH#19761, enforced GH#29797 + ser // td + + # ---------------------------------------------------------------- + # Timedelta.__mod__, __rmod__ + + def test_mod_timedeltalike(self): + # GH#19365 + td = Timedelta(hours=37) + + # Timedelta-like others + result = td % Timedelta(hours=6) + assert isinstance(result, Timedelta) + assert result == Timedelta(hours=1) + + result = td % timedelta(minutes=60) + assert isinstance(result, Timedelta) + assert result == Timedelta(0) + + result = td % NaT + assert result is NaT + + def test_mod_timedelta64_nat(self): + # GH#19365 + td = Timedelta(hours=37) + + result = td % np.timedelta64("NaT", "ns") + assert result is NaT + + def test_mod_timedelta64(self): + # GH#19365 + td = Timedelta(hours=37) + + result = td % np.timedelta64(2, "h") + assert isinstance(result, Timedelta) + assert result == Timedelta(hours=1) + + def test_mod_offset(self): + # GH#19365 + td = Timedelta(hours=37) + + result = td % offsets.Hour(5) + assert isinstance(result, Timedelta) + assert result == Timedelta(hours=2) + + def test_mod_numeric(self): + # GH#19365 + td = Timedelta(hours=37) + + # Numeric Others + result = td % 2 + assert isinstance(result, Timedelta) + assert result == Timedelta(0) + + result = td % 1e12 + assert isinstance(result, Timedelta) + assert result == Timedelta(minutes=3, seconds=20) + + result = td % int(1e12) + assert isinstance(result, Timedelta) + assert result == Timedelta(minutes=3, seconds=20) + + def test_mod_invalid(self): + # GH#19365 + td = Timedelta(hours=37) + msg = "unsupported operand type" + with pytest.raises(TypeError, match=msg): + td % Timestamp("2018-01-22") + + with pytest.raises(TypeError, match=msg): + td % [] + + def test_rmod_pytimedelta(self): + # GH#19365 + td = Timedelta(minutes=3) + + result = timedelta(minutes=4) % td + assert isinstance(result, Timedelta) + assert result == Timedelta(minutes=1) + + def test_rmod_timedelta64(self): + # GH#19365 + td = Timedelta(minutes=3) + result = np.timedelta64(5, "m") % td + assert isinstance(result, Timedelta) + assert result == Timedelta(minutes=2) + + def test_rmod_invalid(self): + # GH#19365 + td = Timedelta(minutes=3) + + msg = "unsupported operand" + with pytest.raises(TypeError, match=msg): + Timestamp("2018-01-22") % td + + with pytest.raises(TypeError, match=msg): + 15 % td + + with pytest.raises(TypeError, match=msg): + 16.0 % td + + msg = "Invalid dtype int" + with pytest.raises(TypeError, match=msg): + np.array([22, 24]) % td + + # ---------------------------------------------------------------- + # Timedelta.__divmod__, __rdivmod__ + + def test_divmod_numeric(self): + # GH#19365 + td = Timedelta(days=2, hours=6) + + result = divmod(td, 53 * 3600 * 1e9) + assert result[0] == Timedelta(1, unit="ns") + assert isinstance(result[1], Timedelta) + assert result[1] == Timedelta(hours=1) + + assert result + result = divmod(td, np.nan) + assert result[0] is NaT + assert result[1] is NaT + + def test_divmod(self): + # GH#19365 + td = Timedelta(days=2, hours=6) + + result = divmod(td, timedelta(days=1)) + assert result[0] == 2 + assert isinstance(result[1], Timedelta) + assert result[1] == Timedelta(hours=6) + + result = divmod(td, 54) + assert result[0] == Timedelta(hours=1) + assert isinstance(result[1], Timedelta) + assert result[1] == Timedelta(0) + + result = divmod(td, NaT) + assert np.isnan(result[0]) + assert result[1] is NaT + + def test_divmod_offset(self): + # GH#19365 + td = Timedelta(days=2, hours=6) + + result = divmod(td, offsets.Hour(-4)) + assert result[0] == -14 + assert isinstance(result[1], Timedelta) + assert result[1] == Timedelta(hours=-2) + + def test_divmod_invalid(self): + # GH#19365 + td = Timedelta(days=2, hours=6) + + msg = r"unsupported operand type\(s\) for //: 'Timedelta' and 'Timestamp'" + with pytest.raises(TypeError, match=msg): + divmod(td, Timestamp("2018-01-22")) + + def test_rdivmod_pytimedelta(self): + # GH#19365 + result = divmod(timedelta(days=2, hours=6), Timedelta(days=1)) + assert result[0] == 2 + assert isinstance(result[1], Timedelta) + assert result[1] == Timedelta(hours=6) + + def test_rdivmod_offset(self): + result = divmod(offsets.Hour(54), Timedelta(hours=-4)) + assert result[0] == -14 + assert isinstance(result[1], Timedelta) + assert result[1] == Timedelta(hours=-2) + + def test_rdivmod_invalid(self): + # GH#19365 + td = Timedelta(minutes=3) + msg = "unsupported operand type" + + with pytest.raises(TypeError, match=msg): + divmod(Timestamp("2018-01-22"), td) + + with pytest.raises(TypeError, match=msg): + divmod(15, td) + + with pytest.raises(TypeError, match=msg): + divmod(16.0, td) + + msg = "Invalid dtype int" + with pytest.raises(TypeError, match=msg): + divmod(np.array([22, 24]), td) + + # ---------------------------------------------------------------- + + @pytest.mark.parametrize( + "op", [operator.mul, ops.rmul, operator.truediv, ops.rdiv, ops.rsub] + ) + @pytest.mark.parametrize( + "arr", + [ + np.array([Timestamp("20130101 9:01"), Timestamp("20121230 9:02")]), + np.array([Timestamp("2021-11-09 09:54:00"), Timedelta("1D")]), + ], + ) + def test_td_op_timedelta_timedeltalike_array(self, op, arr): + msg = "unsupported operand type|cannot use operands with types" + with pytest.raises(TypeError, match=msg): + op(arr, Timedelta("1D")) + + +class TestTimedeltaComparison: + @pytest.mark.skip_ubsan + def test_compare_pytimedelta_bounds(self): + # GH#49021 don't overflow on comparison with very large pytimedeltas + + for unit in ["ns", "us"]: + tdmax = Timedelta.max.as_unit(unit).max + tdmin = Timedelta.min.as_unit(unit).min + + assert tdmax < timedelta.max + assert tdmax <= timedelta.max + assert not tdmax > timedelta.max + assert not tdmax >= timedelta.max + assert tdmax != timedelta.max + assert not tdmax == timedelta.max + + assert tdmin > timedelta.min + assert tdmin >= timedelta.min + assert not tdmin < timedelta.min + assert not tdmin <= timedelta.min + assert tdmin != timedelta.min + assert not tdmin == timedelta.min + + # But the "ms" and "s"-reso bounds extend pass pytimedelta + for unit in ["ms", "s"]: + tdmax = Timedelta.max.as_unit(unit).max + tdmin = Timedelta.min.as_unit(unit).min + + assert tdmax > timedelta.max + assert tdmax >= timedelta.max + assert not tdmax < timedelta.max + assert not tdmax <= timedelta.max + assert tdmax != timedelta.max + assert not tdmax == timedelta.max + + assert tdmin < timedelta.min + assert tdmin <= timedelta.min + assert not tdmin > timedelta.min + assert not tdmin >= timedelta.min + assert tdmin != timedelta.min + assert not tdmin == timedelta.min + + def test_compare_pytimedelta_bounds2(self): + # a pytimedelta outside the microsecond bounds + pytd = timedelta(days=999999999, seconds=86399) + # NB: np.timedelta64(td, "s"") incorrectly overflows + td64 = np.timedelta64(pytd.days, "D") + np.timedelta64(pytd.seconds, "s") + td = Timedelta(td64) + assert td.days == pytd.days + assert td.seconds == pytd.seconds + + assert td == pytd + assert not td != pytd + assert not td < pytd + assert not td > pytd + assert td <= pytd + assert td >= pytd + + td2 = td - Timedelta(seconds=1).as_unit("s") + assert td2 != pytd + assert not td2 == pytd + assert td2 < pytd + assert td2 <= pytd + assert not td2 > pytd + assert not td2 >= pytd + + def test_compare_tick(self, tick_classes): + cls = tick_classes + + off = cls(4) + td = off._as_pd_timedelta + assert isinstance(td, Timedelta) + + assert td == off + assert not td != off + assert td <= off + assert td >= off + assert not td < off + assert not td > off + + assert not td == 2 * off + assert td != 2 * off + assert td <= 2 * off + assert td < 2 * off + assert not td >= 2 * off + assert not td > 2 * off + + def test_comparison_object_array(self): + # analogous to GH#15183 + td = Timedelta("2 days") + other = Timedelta("3 hours") + + arr = np.array([other, td], dtype=object) + res = arr == td + expected = np.array([False, True], dtype=bool) + assert (res == expected).all() + + # 2D case + arr = np.array([[other, td], [td, other]], dtype=object) + res = arr != td + expected = np.array([[True, False], [False, True]], dtype=bool) + assert res.shape == expected.shape + assert (res == expected).all() + + def test_compare_timedelta_ndarray(self): + # GH#11835 + periods = [Timedelta("0 days 01:00:00"), Timedelta("0 days 01:00:00")] + arr = np.array(periods) + result = arr[0] > arr + expected = np.array([False, False]) + tm.assert_numpy_array_equal(result, expected) + + def test_compare_td64_ndarray(self): + # GG#33441 + arr = np.arange(5).astype("timedelta64[ns]") + td = Timedelta(arr[1]) + + expected = np.array([False, True, False, False, False], dtype=bool) + + result = td == arr + tm.assert_numpy_array_equal(result, expected) + + result = arr == td + tm.assert_numpy_array_equal(result, expected) + + result = td != arr + tm.assert_numpy_array_equal(result, ~expected) + + result = arr != td + tm.assert_numpy_array_equal(result, ~expected) + + def test_compare_custom_object(self): + """ + Make sure non supported operations on Timedelta returns NonImplemented + and yields to other operand (GH#20829). + """ + + class CustomClass: + def __init__(self, cmp_result=None) -> None: + self.cmp_result = cmp_result + + def generic_result(self): + if self.cmp_result is None: + return NotImplemented + else: + return self.cmp_result + + def __eq__(self, other): + return self.generic_result() + + def __gt__(self, other): + return self.generic_result() + + t = Timedelta("1s") + + assert t != "string" + assert t != 1 + assert t != CustomClass() + assert t != CustomClass(cmp_result=False) + + assert t < CustomClass(cmp_result=True) + assert not t < CustomClass(cmp_result=False) + + assert t == CustomClass(cmp_result=True) + + @pytest.mark.parametrize("val", ["string", 1]) + def test_compare_unknown_type(self, val): + # GH#20829 + t = Timedelta("1s") + msg = "not supported between instances of 'Timedelta' and '(int|str)'" + with pytest.raises(TypeError, match=msg): + t >= val + with pytest.raises(TypeError, match=msg): + t > val + with pytest.raises(TypeError, match=msg): + t <= val + with pytest.raises(TypeError, match=msg): + t < val + + +def test_ops_notimplemented(): + class Other: + pass + + other = Other() + + td = Timedelta("1 day") + assert td.__add__(other) is NotImplemented + assert td.__sub__(other) is NotImplemented + assert td.__truediv__(other) is NotImplemented + assert td.__mul__(other) is NotImplemented + assert td.__floordiv__(other) is NotImplemented + + +def test_ops_error_str(): + # GH#13624 + td = Timedelta("1 day") + + for left, right in [(td, "a"), ("a", td)]: + msg = "|".join( + [ + "unsupported operand type", + r'can only concatenate str \(not "Timedelta"\) to str', + "must be str, not Timedelta", + ] + ) + with pytest.raises(TypeError, match=msg): + left + right + + msg = "not supported between instances of" + with pytest.raises(TypeError, match=msg): + left > right + + assert not left == right # pylint: disable=unneeded-not + assert left != right diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/test_constructors.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/test_constructors.py new file mode 100644 index 0000000000000000000000000000000000000000..4663f8cb719616cadd6e946c987e76bc3d979b01 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/test_constructors.py @@ -0,0 +1,698 @@ +from datetime import timedelta +from itertools import product + +import numpy as np +import pytest + +from pandas._libs.tslibs import OutOfBoundsTimedelta +from pandas._libs.tslibs.dtypes import NpyDatetimeUnit + +from pandas import ( + Index, + NaT, + Timedelta, + TimedeltaIndex, + offsets, + to_timedelta, +) +import pandas._testing as tm + + +class TestTimedeltaConstructorUnitKeyword: + @pytest.mark.parametrize("unit", ["Y", "y", "M"]) + def test_unit_m_y_raises(self, unit): + msg = "Units 'M', 'Y', and 'y' are no longer supported" + + with pytest.raises(ValueError, match=msg): + Timedelta(10, unit) + + with pytest.raises(ValueError, match=msg): + to_timedelta(10, unit) + + with pytest.raises(ValueError, match=msg): + to_timedelta([1, 2], unit) + + @pytest.mark.parametrize( + "unit,unit_depr", + [ + ("h", "H"), + ("min", "T"), + ("s", "S"), + ("ms", "L"), + ("ns", "N"), + ("us", "U"), + ], + ) + def test_units_H_T_S_L_N_U_deprecated(self, unit, unit_depr): + # GH#52536 + msg = f"'{unit_depr}' is deprecated and will be removed in a future version." + + expected = Timedelta(1, unit=unit) + with tm.assert_produces_warning(FutureWarning, match=msg): + result = Timedelta(1, unit=unit_depr) + tm.assert_equal(result, expected) + + @pytest.mark.parametrize( + "unit, np_unit", + [(value, "W") for value in ["W", "w"]] + + [(value, "D") for value in ["D", "d", "days", "day", "Days", "Day"]] + + [ + (value, "m") + for value in [ + "m", + "minute", + "min", + "minutes", + "Minute", + "Min", + "Minutes", + ] + ] + + [ + (value, "s") + for value in [ + "s", + "seconds", + "sec", + "second", + "Seconds", + "Sec", + "Second", + ] + ] + + [ + (value, "ms") + for value in [ + "ms", + "milliseconds", + "millisecond", + "milli", + "millis", + "MS", + "Milliseconds", + "Millisecond", + "Milli", + "Millis", + ] + ] + + [ + (value, "us") + for value in [ + "us", + "microseconds", + "microsecond", + "micro", + "micros", + "u", + "US", + "Microseconds", + "Microsecond", + "Micro", + "Micros", + "U", + ] + ] + + [ + (value, "ns") + for value in [ + "ns", + "nanoseconds", + "nanosecond", + "nano", + "nanos", + "n", + "NS", + "Nanoseconds", + "Nanosecond", + "Nano", + "Nanos", + "N", + ] + ], + ) + @pytest.mark.parametrize("wrapper", [np.array, list, Index]) + def test_unit_parser(self, unit, np_unit, wrapper): + # validate all units, GH 6855, GH 21762 + # array-likes + expected = TimedeltaIndex( + [np.timedelta64(i, np_unit) for i in np.arange(5).tolist()], + dtype="m8[ns]", + ) + # TODO(2.0): the desired output dtype may have non-nano resolution + msg = f"'{unit}' is deprecated and will be removed in a future version." + + if (unit, np_unit) in (("u", "us"), ("U", "us"), ("n", "ns"), ("N", "ns")): + warn = FutureWarning + else: + warn = FutureWarning + msg = "The 'unit' keyword in TimedeltaIndex construction is deprecated" + with tm.assert_produces_warning(warn, match=msg): + result = to_timedelta(wrapper(range(5)), unit=unit) + tm.assert_index_equal(result, expected) + result = TimedeltaIndex(wrapper(range(5)), unit=unit) + tm.assert_index_equal(result, expected) + + str_repr = [f"{x}{unit}" for x in np.arange(5)] + result = to_timedelta(wrapper(str_repr)) + tm.assert_index_equal(result, expected) + result = to_timedelta(wrapper(str_repr)) + tm.assert_index_equal(result, expected) + + # scalar + expected = Timedelta(np.timedelta64(2, np_unit).astype("timedelta64[ns]")) + result = to_timedelta(2, unit=unit) + assert result == expected + result = Timedelta(2, unit=unit) + assert result == expected + + result = to_timedelta(f"2{unit}") + assert result == expected + result = Timedelta(f"2{unit}") + assert result == expected + + +def test_construct_from_kwargs_overflow(): + # GH#55503 + msg = "seconds=86400000000000000000, milliseconds=0, microseconds=0, nanoseconds=0" + with pytest.raises(OutOfBoundsTimedelta, match=msg): + Timedelta(days=10**6) + msg = "seconds=60000000000000000000, milliseconds=0, microseconds=0, nanoseconds=0" + with pytest.raises(OutOfBoundsTimedelta, match=msg): + Timedelta(minutes=10**9) + + +def test_construct_with_weeks_unit_overflow(): + # GH#47268 don't silently wrap around + with pytest.raises(OutOfBoundsTimedelta, match="without overflow"): + Timedelta(1000000000000000000, unit="W") + + with pytest.raises(OutOfBoundsTimedelta, match="without overflow"): + Timedelta(1000000000000000000.0, unit="W") + + +def test_construct_from_td64_with_unit(): + # ignore the unit, as it may cause silently overflows leading to incorrect + # results, and in non-overflow cases is irrelevant GH#46827 + obj = np.timedelta64(123456789000000000, "h") + + with pytest.raises(OutOfBoundsTimedelta, match="123456789000000000 hours"): + Timedelta(obj, unit="ps") + + with pytest.raises(OutOfBoundsTimedelta, match="123456789000000000 hours"): + Timedelta(obj, unit="ns") + + with pytest.raises(OutOfBoundsTimedelta, match="123456789000000000 hours"): + Timedelta(obj) + + +def test_from_td64_retain_resolution(): + # case where we retain millisecond resolution + obj = np.timedelta64(12345, "ms") + + td = Timedelta(obj) + assert td._value == obj.view("i8") + assert td._creso == NpyDatetimeUnit.NPY_FR_ms.value + + # Case where we cast to nearest-supported reso + obj2 = np.timedelta64(1234, "D") + td2 = Timedelta(obj2) + assert td2._creso == NpyDatetimeUnit.NPY_FR_s.value + assert td2 == obj2 + assert td2.days == 1234 + + # Case that _would_ overflow if we didn't support non-nano + obj3 = np.timedelta64(1000000000000000000, "us") + td3 = Timedelta(obj3) + assert td3.total_seconds() == 1000000000000 + assert td3._creso == NpyDatetimeUnit.NPY_FR_us.value + + +def test_from_pytimedelta_us_reso(): + # pytimedelta has microsecond resolution, so Timedelta(pytd) inherits that + td = timedelta(days=4, minutes=3) + result = Timedelta(td) + assert result.to_pytimedelta() == td + assert result._creso == NpyDatetimeUnit.NPY_FR_us.value + + +def test_from_tick_reso(): + tick = offsets.Nano() + assert Timedelta(tick)._creso == NpyDatetimeUnit.NPY_FR_ns.value + + tick = offsets.Micro() + assert Timedelta(tick)._creso == NpyDatetimeUnit.NPY_FR_us.value + + tick = offsets.Milli() + assert Timedelta(tick)._creso == NpyDatetimeUnit.NPY_FR_ms.value + + tick = offsets.Second() + assert Timedelta(tick)._creso == NpyDatetimeUnit.NPY_FR_s.value + + # everything above Second gets cast to the closest supported reso: second + tick = offsets.Minute() + assert Timedelta(tick)._creso == NpyDatetimeUnit.NPY_FR_s.value + + tick = offsets.Hour() + assert Timedelta(tick)._creso == NpyDatetimeUnit.NPY_FR_s.value + + tick = offsets.Day() + assert Timedelta(tick)._creso == NpyDatetimeUnit.NPY_FR_s.value + + +def test_construction(): + expected = np.timedelta64(10, "D").astype("m8[ns]").view("i8") + assert Timedelta(10, unit="d")._value == expected + assert Timedelta(10.0, unit="d")._value == expected + assert Timedelta("10 days")._value == expected + assert Timedelta(days=10)._value == expected + assert Timedelta(days=10.0)._value == expected + + expected += np.timedelta64(10, "s").astype("m8[ns]").view("i8") + assert Timedelta("10 days 00:00:10")._value == expected + assert Timedelta(days=10, seconds=10)._value == expected + assert Timedelta(days=10, milliseconds=10 * 1000)._value == expected + assert Timedelta(days=10, microseconds=10 * 1000 * 1000)._value == expected + + # rounding cases + assert Timedelta(82739999850000)._value == 82739999850000 + assert "0 days 22:58:59.999850" in str(Timedelta(82739999850000)) + assert Timedelta(123072001000000)._value == 123072001000000 + assert "1 days 10:11:12.001" in str(Timedelta(123072001000000)) + + # string conversion with/without leading zero + # GH#9570 + assert Timedelta("0:00:00") == timedelta(hours=0) + assert Timedelta("00:00:00") == timedelta(hours=0) + assert Timedelta("-1:00:00") == -timedelta(hours=1) + assert Timedelta("-01:00:00") == -timedelta(hours=1) + + # more strings & abbrevs + # GH#8190 + assert Timedelta("1 h") == timedelta(hours=1) + assert Timedelta("1 hour") == timedelta(hours=1) + assert Timedelta("1 hr") == timedelta(hours=1) + assert Timedelta("1 hours") == timedelta(hours=1) + assert Timedelta("-1 hours") == -timedelta(hours=1) + assert Timedelta("1 m") == timedelta(minutes=1) + assert Timedelta("1.5 m") == timedelta(seconds=90) + assert Timedelta("1 minute") == timedelta(minutes=1) + assert Timedelta("1 minutes") == timedelta(minutes=1) + assert Timedelta("1 s") == timedelta(seconds=1) + assert Timedelta("1 second") == timedelta(seconds=1) + assert Timedelta("1 seconds") == timedelta(seconds=1) + assert Timedelta("1 ms") == timedelta(milliseconds=1) + assert Timedelta("1 milli") == timedelta(milliseconds=1) + assert Timedelta("1 millisecond") == timedelta(milliseconds=1) + assert Timedelta("1 us") == timedelta(microseconds=1) + assert Timedelta("1 µs") == timedelta(microseconds=1) + assert Timedelta("1 micros") == timedelta(microseconds=1) + assert Timedelta("1 microsecond") == timedelta(microseconds=1) + assert Timedelta("1.5 microsecond") == Timedelta("00:00:00.000001500") + assert Timedelta("1 ns") == Timedelta("00:00:00.000000001") + assert Timedelta("1 nano") == Timedelta("00:00:00.000000001") + assert Timedelta("1 nanosecond") == Timedelta("00:00:00.000000001") + + # combos + assert Timedelta("10 days 1 hour") == timedelta(days=10, hours=1) + assert Timedelta("10 days 1 h") == timedelta(days=10, hours=1) + assert Timedelta("10 days 1 h 1m 1s") == timedelta( + days=10, hours=1, minutes=1, seconds=1 + ) + assert Timedelta("-10 days 1 h 1m 1s") == -timedelta( + days=10, hours=1, minutes=1, seconds=1 + ) + assert Timedelta("-10 days 1 h 1m 1s") == -timedelta( + days=10, hours=1, minutes=1, seconds=1 + ) + assert Timedelta("-10 days 1 h 1m 1s 3us") == -timedelta( + days=10, hours=1, minutes=1, seconds=1, microseconds=3 + ) + assert Timedelta("-10 days 1 h 1.5m 1s 3us") == -timedelta( + days=10, hours=1, minutes=1, seconds=31, microseconds=3 + ) + + # Currently invalid as it has a - on the hh:mm:dd part + # (only allowed on the days) + msg = "only leading negative signs are allowed" + with pytest.raises(ValueError, match=msg): + Timedelta("-10 days -1 h 1.5m 1s 3us") + + # only leading neg signs are allowed + with pytest.raises(ValueError, match=msg): + Timedelta("10 days -1 h 1.5m 1s 3us") + + # no units specified + msg = "no units specified" + with pytest.raises(ValueError, match=msg): + Timedelta("3.1415") + + # invalid construction + msg = "cannot construct a Timedelta" + with pytest.raises(ValueError, match=msg): + Timedelta() + + msg = "unit abbreviation w/o a number" + with pytest.raises(ValueError, match=msg): + Timedelta("foo") + + msg = ( + "cannot construct a Timedelta from " + "the passed arguments, allowed keywords are " + ) + with pytest.raises(ValueError, match=msg): + Timedelta(day=10) + + # floats + expected = np.timedelta64(10, "s").astype("m8[ns]").view("i8") + np.timedelta64( + 500, "ms" + ).astype("m8[ns]").view("i8") + assert Timedelta(10.5, unit="s")._value == expected + + # offset + assert to_timedelta(offsets.Hour(2)) == Timedelta(hours=2) + assert Timedelta(offsets.Hour(2)) == Timedelta(hours=2) + assert Timedelta(offsets.Second(2)) == Timedelta(seconds=2) + + # GH#11995: unicode + expected = Timedelta("1h") + result = Timedelta("1h") + assert result == expected + assert to_timedelta(offsets.Hour(2)) == Timedelta("0 days, 02:00:00") + + msg = "unit abbreviation w/o a number" + with pytest.raises(ValueError, match=msg): + Timedelta("foo bar") + + +@pytest.mark.parametrize( + "item", + list( + { + "days": "D", + "seconds": "s", + "microseconds": "us", + "milliseconds": "ms", + "minutes": "m", + "hours": "h", + "weeks": "W", + }.items() + ), +) +@pytest.mark.parametrize( + "npdtype", [np.int64, np.int32, np.int16, np.float64, np.float32, np.float16] +) +def test_td_construction_with_np_dtypes(npdtype, item): + # GH#8757: test construction with np dtypes + pykwarg, npkwarg = item + expected = np.timedelta64(1, npkwarg).astype("m8[ns]").view("i8") + assert Timedelta(**{pykwarg: npdtype(1)})._value == expected + + +@pytest.mark.parametrize( + "val", + [ + "1s", + "-1s", + "1us", + "-1us", + "1 day", + "-1 day", + "-23:59:59.999999", + "-1 days +23:59:59.999999", + "-1ns", + "1ns", + "-23:59:59.999999999", + ], +) +def test_td_from_repr_roundtrip(val): + # round-trip both for string and value + td = Timedelta(val) + assert Timedelta(td._value) == td + + assert Timedelta(str(td)) == td + assert Timedelta(td._repr_base(format="all")) == td + assert Timedelta(td._repr_base()) == td + + +def test_overflow_on_construction(): + # GH#3374 + value = Timedelta("1day")._value * 20169940 + msg = "Cannot cast 1742682816000000000000 from ns to 'ns' without overflow" + with pytest.raises(OutOfBoundsTimedelta, match=msg): + Timedelta(value) + + # xref GH#17637 + msg = "Cannot cast 139993 from D to 'ns' without overflow" + with pytest.raises(OutOfBoundsTimedelta, match=msg): + Timedelta(7 * 19999, unit="D") + + # used to overflow before non-ns support + td = Timedelta(timedelta(days=13 * 19999)) + assert td._creso == NpyDatetimeUnit.NPY_FR_us.value + assert td.days == 13 * 19999 + + +@pytest.mark.parametrize( + "val, unit", + [ + (15251, "W"), # 1 + (106752, "D"), # change from previous: + (2562048, "h"), # 0 hours + (153722868, "m"), # 13 minutes + (9223372037, "s"), # 44 seconds + ], +) +def test_construction_out_of_bounds_td64ns(val, unit): + # TODO: parametrize over units just above/below the implementation bounds + # once GH#38964 is resolved + + # Timedelta.max is just under 106752 days + td64 = np.timedelta64(val, unit) + assert td64.astype("m8[ns]").view("i8") < 0 # i.e. naive astype will be wrong + + td = Timedelta(td64) + if unit != "M": + # with unit="M" the conversion to "s" is poorly defined + # (and numpy issues DeprecationWarning) + assert td.asm8 == td64 + assert td.asm8.dtype == "m8[s]" + msg = r"Cannot cast 1067\d\d days .* to unit='ns' without overflow" + with pytest.raises(OutOfBoundsTimedelta, match=msg): + td.as_unit("ns") + + # But just back in bounds and we are OK + assert Timedelta(td64 - 1) == td64 - 1 + + td64 *= -1 + assert td64.astype("m8[ns]").view("i8") > 0 # i.e. naive astype will be wrong + + td2 = Timedelta(td64) + msg = r"Cannot cast -1067\d\d days .* to unit='ns' without overflow" + with pytest.raises(OutOfBoundsTimedelta, match=msg): + td2.as_unit("ns") + + # But just back in bounds and we are OK + assert Timedelta(td64 + 1) == td64 + 1 + + +@pytest.mark.parametrize( + "val, unit", + [ + (15251 * 10**9, "W"), + (106752 * 10**9, "D"), + (2562048 * 10**9, "h"), + (153722868 * 10**9, "m"), + ], +) +def test_construction_out_of_bounds_td64s(val, unit): + td64 = np.timedelta64(val, unit) + with pytest.raises(OutOfBoundsTimedelta, match=str(td64)): + Timedelta(td64) + + # But just back in bounds and we are OK + assert Timedelta(td64 - 10**9) == td64 - 10**9 + + +@pytest.mark.parametrize( + "fmt,exp", + [ + ( + "P6DT0H50M3.010010012S", + Timedelta( + days=6, + minutes=50, + seconds=3, + milliseconds=10, + microseconds=10, + nanoseconds=12, + ), + ), + ( + "P-6DT0H50M3.010010012S", + Timedelta( + days=-6, + minutes=50, + seconds=3, + milliseconds=10, + microseconds=10, + nanoseconds=12, + ), + ), + ("P4DT12H30M5S", Timedelta(days=4, hours=12, minutes=30, seconds=5)), + ("P0DT0H0M0.000000123S", Timedelta(nanoseconds=123)), + ("P0DT0H0M0.00001S", Timedelta(microseconds=10)), + ("P0DT0H0M0.001S", Timedelta(milliseconds=1)), + ("P0DT0H1M0S", Timedelta(minutes=1)), + ("P1DT25H61M61S", Timedelta(days=1, hours=25, minutes=61, seconds=61)), + ("PT1S", Timedelta(seconds=1)), + ("PT0S", Timedelta(seconds=0)), + ("P1WT0S", Timedelta(days=7, seconds=0)), + ("P1D", Timedelta(days=1)), + ("P1DT1H", Timedelta(days=1, hours=1)), + ("P1W", Timedelta(days=7)), + ("PT300S", Timedelta(seconds=300)), + ("P1DT0H0M00000000000S", Timedelta(days=1)), + ("PT-6H3M", Timedelta(hours=-6, minutes=3)), + ("-PT6H3M", Timedelta(hours=-6, minutes=-3)), + ("-PT-6H+3M", Timedelta(hours=6, minutes=-3)), + ], +) +def test_iso_constructor(fmt, exp): + assert Timedelta(fmt) == exp + + +@pytest.mark.parametrize( + "fmt", + [ + "PPPPPPPPPPPP", + "PDTHMS", + "P0DT999H999M999S", + "P1DT0H0M0.0000000000000S", + "P1DT0H0M0.S", + "P", + "-P", + ], +) +def test_iso_constructor_raises(fmt): + msg = f"Invalid ISO 8601 Duration format - {fmt}" + with pytest.raises(ValueError, match=msg): + Timedelta(fmt) + + +@pytest.mark.parametrize( + "constructed_td, conversion", + [ + (Timedelta(nanoseconds=100), "100ns"), + ( + Timedelta( + days=1, + hours=1, + minutes=1, + weeks=1, + seconds=1, + milliseconds=1, + microseconds=1, + nanoseconds=1, + ), + 694861001001001, + ), + (Timedelta(microseconds=1) + Timedelta(nanoseconds=1), "1us1ns"), + (Timedelta(microseconds=1) - Timedelta(nanoseconds=1), "999ns"), + (Timedelta(microseconds=1) + 5 * Timedelta(nanoseconds=-2), "990ns"), + ], +) +def test_td_constructor_on_nanoseconds(constructed_td, conversion): + # GH#9273 + assert constructed_td == Timedelta(conversion) + + +def test_td_constructor_value_error(): + msg = "Invalid type . Must be int or float." + with pytest.raises(TypeError, match=msg): + Timedelta(nanoseconds="abc") + + +def test_timedelta_constructor_identity(): + # Test for #30543 + expected = Timedelta(np.timedelta64(1, "s")) + result = Timedelta(expected) + assert result is expected + + +def test_timedelta_pass_td_and_kwargs_raises(): + # don't silently ignore the kwargs GH#48898 + td = Timedelta(days=1) + msg = ( + "Cannot pass both a Timedelta input and timedelta keyword arguments, " + r"got \['days'\]" + ) + with pytest.raises(ValueError, match=msg): + Timedelta(td, days=2) + + +@pytest.mark.parametrize( + "constructor, value, unit, expectation", + [ + (Timedelta, "10s", "ms", (ValueError, "unit must not be specified")), + (to_timedelta, "10s", "ms", (ValueError, "unit must not be specified")), + (to_timedelta, ["1", 2, 3], "s", (ValueError, "unit must not be specified")), + ], +) +def test_string_with_unit(constructor, value, unit, expectation): + exp, match = expectation + with pytest.raises(exp, match=match): + _ = constructor(value, unit=unit) + + +@pytest.mark.parametrize( + "value", + [ + "".join(elements) + for repetition in (1, 2) + for elements in product("+-, ", repeat=repetition) + ], +) +def test_string_without_numbers(value): + # GH39710 Timedelta input string with only symbols and no digits raises an error + msg = ( + "symbols w/o a number" + if value != "--" + else "only leading negative signs are allowed" + ) + with pytest.raises(ValueError, match=msg): + Timedelta(value) + + +def test_timedelta_new_npnat(): + # GH#48898 + nat = np.timedelta64("NaT", "h") + assert Timedelta(nat) is NaT + + +def test_subclass_respected(): + # GH#49579 + class MyCustomTimedelta(Timedelta): + pass + + td = MyCustomTimedelta("1 minute") + assert isinstance(td, MyCustomTimedelta) + + +def test_non_nano_value(): + # https://github.com/pandas-dev/pandas/issues/49076 + result = Timedelta(10, unit="D").as_unit("s").value + # `.value` shows nanoseconds, even though unit is 's' + assert result == 864000000000000 + + # out-of-nanoseconds-bounds `.value` raises informative message + msg = ( + r"Cannot convert Timedelta to nanoseconds without overflow. " + r"Use `.asm8.view\('i8'\)` to cast represent Timedelta in its " + r"own unit \(here, s\).$" + ) + td = Timedelta(1_000, "D").as_unit("s") * 1_000 + with pytest.raises(OverflowError, match=msg): + td.value + # check that the suggested workaround actually works + result = td.asm8.view("i8") + assert result == 86400000000 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/test_formats.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/test_formats.py new file mode 100644 index 0000000000000000000000000000000000000000..e1b0076d5b7b99f4baba3f18ccf84d8054a31087 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/test_formats.py @@ -0,0 +1,109 @@ +import pytest + +from pandas import Timedelta + + +@pytest.mark.parametrize( + "td, expected_repr", + [ + (Timedelta(10, unit="d"), "Timedelta('10 days 00:00:00')"), + (Timedelta(10, unit="s"), "Timedelta('0 days 00:00:10')"), + (Timedelta(10, unit="ms"), "Timedelta('0 days 00:00:00.010000')"), + (Timedelta(-10, unit="ms"), "Timedelta('-1 days +23:59:59.990000')"), + ], +) +def test_repr(td, expected_repr): + assert repr(td) == expected_repr + + +@pytest.mark.parametrize( + "td, expected_iso", + [ + ( + Timedelta( + days=6, + minutes=50, + seconds=3, + milliseconds=10, + microseconds=10, + nanoseconds=12, + ), + "P6DT0H50M3.010010012S", + ), + (Timedelta(days=4, hours=12, minutes=30, seconds=5), "P4DT12H30M5S"), + (Timedelta(nanoseconds=123), "P0DT0H0M0.000000123S"), + # trim nano + (Timedelta(microseconds=10), "P0DT0H0M0.00001S"), + # trim micro + (Timedelta(milliseconds=1), "P0DT0H0M0.001S"), + # don't strip every 0 + (Timedelta(minutes=1), "P0DT0H1M0S"), + ], +) +def test_isoformat(td, expected_iso): + assert td.isoformat() == expected_iso + + +class TestReprBase: + def test_none(self): + delta_1d = Timedelta(1, unit="D") + delta_0d = Timedelta(0, unit="D") + delta_1s = Timedelta(1, unit="s") + delta_500ms = Timedelta(500, unit="ms") + + drepr = lambda x: x._repr_base() + assert drepr(delta_1d) == "1 days" + assert drepr(-delta_1d) == "-1 days" + assert drepr(delta_0d) == "0 days" + assert drepr(delta_1s) == "0 days 00:00:01" + assert drepr(delta_500ms) == "0 days 00:00:00.500000" + assert drepr(delta_1d + delta_1s) == "1 days 00:00:01" + assert drepr(-delta_1d + delta_1s) == "-1 days +00:00:01" + assert drepr(delta_1d + delta_500ms) == "1 days 00:00:00.500000" + assert drepr(-delta_1d + delta_500ms) == "-1 days +00:00:00.500000" + + def test_sub_day(self): + delta_1d = Timedelta(1, unit="D") + delta_0d = Timedelta(0, unit="D") + delta_1s = Timedelta(1, unit="s") + delta_500ms = Timedelta(500, unit="ms") + + drepr = lambda x: x._repr_base(format="sub_day") + assert drepr(delta_1d) == "1 days" + assert drepr(-delta_1d) == "-1 days" + assert drepr(delta_0d) == "00:00:00" + assert drepr(delta_1s) == "00:00:01" + assert drepr(delta_500ms) == "00:00:00.500000" + assert drepr(delta_1d + delta_1s) == "1 days 00:00:01" + assert drepr(-delta_1d + delta_1s) == "-1 days +00:00:01" + assert drepr(delta_1d + delta_500ms) == "1 days 00:00:00.500000" + assert drepr(-delta_1d + delta_500ms) == "-1 days +00:00:00.500000" + + def test_long(self): + delta_1d = Timedelta(1, unit="D") + delta_0d = Timedelta(0, unit="D") + delta_1s = Timedelta(1, unit="s") + delta_500ms = Timedelta(500, unit="ms") + + drepr = lambda x: x._repr_base(format="long") + assert drepr(delta_1d) == "1 days 00:00:00" + assert drepr(-delta_1d) == "-1 days +00:00:00" + assert drepr(delta_0d) == "0 days 00:00:00" + assert drepr(delta_1s) == "0 days 00:00:01" + assert drepr(delta_500ms) == "0 days 00:00:00.500000" + assert drepr(delta_1d + delta_1s) == "1 days 00:00:01" + assert drepr(-delta_1d + delta_1s) == "-1 days +00:00:01" + assert drepr(delta_1d + delta_500ms) == "1 days 00:00:00.500000" + assert drepr(-delta_1d + delta_500ms) == "-1 days +00:00:00.500000" + + def test_all(self): + delta_1d = Timedelta(1, unit="D") + delta_0d = Timedelta(0, unit="D") + delta_1ns = Timedelta(1, unit="ns") + + drepr = lambda x: x._repr_base(format="all") + assert drepr(delta_1d) == "1 days 00:00:00.000000000" + assert drepr(-delta_1d) == "-1 days +00:00:00.000000000" + assert drepr(delta_0d) == "0 days 00:00:00.000000000" + assert drepr(delta_1ns) == "0 days 00:00:00.000000001" + assert drepr(-delta_1d + delta_1ns) == "-1 days +00:00:00.000000001" diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/test_timedelta.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/test_timedelta.py new file mode 100644 index 0000000000000000000000000000000000000000..d4398f66e6f890a26dc90d540766d71d7857ad67 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timedelta/test_timedelta.py @@ -0,0 +1,666 @@ +""" test the scalar Timedelta """ +from datetime import timedelta +import sys + +from hypothesis import ( + given, + strategies as st, +) +import numpy as np +import pytest + +from pandas._libs import lib +from pandas._libs.tslibs import ( + NaT, + iNaT, +) +from pandas._libs.tslibs.dtypes import NpyDatetimeUnit +from pandas.errors import OutOfBoundsTimedelta + +from pandas import ( + Timedelta, + to_timedelta, +) +import pandas._testing as tm + + +class TestNonNano: + @pytest.fixture(params=["s", "ms", "us"]) + def unit_str(self, request): + return request.param + + @pytest.fixture + def unit(self, unit_str): + # 7, 8, 9 correspond to second, millisecond, and microsecond, respectively + attr = f"NPY_FR_{unit_str}" + return getattr(NpyDatetimeUnit, attr).value + + @pytest.fixture + def val(self, unit): + # microsecond that would be just out of bounds for nano + us = 9223372800000000 + if unit == NpyDatetimeUnit.NPY_FR_us.value: + value = us + elif unit == NpyDatetimeUnit.NPY_FR_ms.value: + value = us // 1000 + else: + value = us // 1_000_000 + return value + + @pytest.fixture + def td(self, unit, val): + return Timedelta._from_value_and_reso(val, unit) + + def test_from_value_and_reso(self, unit, val): + # Just checking that the fixture is giving us what we asked for + td = Timedelta._from_value_and_reso(val, unit) + assert td._value == val + assert td._creso == unit + assert td.days == 106752 + + def test_unary_non_nano(self, td, unit): + assert abs(td)._creso == unit + assert (-td)._creso == unit + assert (+td)._creso == unit + + def test_sub_preserves_reso(self, td, unit): + res = td - td + expected = Timedelta._from_value_and_reso(0, unit) + assert res == expected + assert res._creso == unit + + def test_mul_preserves_reso(self, td, unit): + # The td fixture should always be far from the implementation + # bound, so doubling does not risk overflow. + res = td * 2 + assert res._value == td._value * 2 + assert res._creso == unit + + def test_cmp_cross_reso(self, td): + # numpy gets this wrong because of silent overflow + other = Timedelta(days=106751, unit="ns") + assert other < td + assert td > other + assert not other == td + assert td != other + + def test_to_pytimedelta(self, td): + res = td.to_pytimedelta() + expected = timedelta(days=106752) + assert type(res) is timedelta + assert res == expected + + def test_to_timedelta64(self, td, unit): + for res in [td.to_timedelta64(), td.to_numpy(), td.asm8]: + assert isinstance(res, np.timedelta64) + assert res.view("i8") == td._value + if unit == NpyDatetimeUnit.NPY_FR_s.value: + assert res.dtype == "m8[s]" + elif unit == NpyDatetimeUnit.NPY_FR_ms.value: + assert res.dtype == "m8[ms]" + elif unit == NpyDatetimeUnit.NPY_FR_us.value: + assert res.dtype == "m8[us]" + + def test_truediv_timedeltalike(self, td): + assert td / td == 1 + assert (2.5 * td) / td == 2.5 + + other = Timedelta(td._value) + msg = "Cannot cast 106752 days 00:00:00 to unit='ns' without overflow." + with pytest.raises(OutOfBoundsTimedelta, match=msg): + td / other + + # Timedelta(other.to_pytimedelta()) has microsecond resolution, + # so the division doesn't require casting all the way to nanos, + # so succeeds + res = other.to_pytimedelta() / td + expected = other.to_pytimedelta() / td.to_pytimedelta() + assert res == expected + + # if there's no overflow, we cast to the higher reso + left = Timedelta._from_value_and_reso(50, NpyDatetimeUnit.NPY_FR_us.value) + right = Timedelta._from_value_and_reso(50, NpyDatetimeUnit.NPY_FR_ms.value) + result = left / right + assert result == 0.001 + + result = right / left + assert result == 1000 + + def test_truediv_numeric(self, td): + assert td / np.nan is NaT + + res = td / 2 + assert res._value == td._value / 2 + assert res._creso == td._creso + + res = td / 2.0 + assert res._value == td._value / 2 + assert res._creso == td._creso + + def test_floordiv_timedeltalike(self, td): + assert td // td == 1 + assert (2.5 * td) // td == 2 + + other = Timedelta(td._value) + msg = "Cannot cast 106752 days 00:00:00 to unit='ns' without overflow" + with pytest.raises(OutOfBoundsTimedelta, match=msg): + td // other + + # Timedelta(other.to_pytimedelta()) has microsecond resolution, + # so the floordiv doesn't require casting all the way to nanos, + # so succeeds + res = other.to_pytimedelta() // td + assert res == 0 + + # if there's no overflow, we cast to the higher reso + left = Timedelta._from_value_and_reso(50050, NpyDatetimeUnit.NPY_FR_us.value) + right = Timedelta._from_value_and_reso(50, NpyDatetimeUnit.NPY_FR_ms.value) + result = left // right + assert result == 1 + result = right // left + assert result == 0 + + def test_floordiv_numeric(self, td): + assert td // np.nan is NaT + + res = td // 2 + assert res._value == td._value // 2 + assert res._creso == td._creso + + res = td // 2.0 + assert res._value == td._value // 2 + assert res._creso == td._creso + + assert td // np.array(np.nan) is NaT + + res = td // np.array(2) + assert res._value == td._value // 2 + assert res._creso == td._creso + + res = td // np.array(2.0) + assert res._value == td._value // 2 + assert res._creso == td._creso + + def test_addsub_mismatched_reso(self, td): + # need to cast to since td is out of bounds for ns, so + # so we would raise OverflowError without casting + other = Timedelta(days=1).as_unit("us") + + # td is out of bounds for ns + result = td + other + assert result._creso == other._creso + assert result.days == td.days + 1 + + result = other + td + assert result._creso == other._creso + assert result.days == td.days + 1 + + result = td - other + assert result._creso == other._creso + assert result.days == td.days - 1 + + result = other - td + assert result._creso == other._creso + assert result.days == 1 - td.days + + other2 = Timedelta(500) + msg = "Cannot cast 106752 days 00:00:00 to unit='ns' without overflow" + with pytest.raises(OutOfBoundsTimedelta, match=msg): + td + other2 + with pytest.raises(OutOfBoundsTimedelta, match=msg): + other2 + td + with pytest.raises(OutOfBoundsTimedelta, match=msg): + td - other2 + with pytest.raises(OutOfBoundsTimedelta, match=msg): + other2 - td + + def test_min(self, td): + assert td.min <= td + assert td.min._creso == td._creso + assert td.min._value == NaT._value + 1 + + def test_max(self, td): + assert td.max >= td + assert td.max._creso == td._creso + assert td.max._value == np.iinfo(np.int64).max + + def test_resolution(self, td): + expected = Timedelta._from_value_and_reso(1, td._creso) + result = td.resolution + assert result == expected + assert result._creso == expected._creso + + def test_hash(self) -> None: + # GH#54037 + second_resolution_max = Timedelta(0).as_unit("s").max + + assert hash(second_resolution_max) + + +def test_timedelta_class_min_max_resolution(): + # when accessed on the class (as opposed to an instance), we default + # to nanoseconds + assert Timedelta.min == Timedelta(NaT._value + 1) + assert Timedelta.min._creso == NpyDatetimeUnit.NPY_FR_ns.value + + assert Timedelta.max == Timedelta(np.iinfo(np.int64).max) + assert Timedelta.max._creso == NpyDatetimeUnit.NPY_FR_ns.value + + assert Timedelta.resolution == Timedelta(1) + assert Timedelta.resolution._creso == NpyDatetimeUnit.NPY_FR_ns.value + + +class TestTimedeltaUnaryOps: + def test_invert(self): + td = Timedelta(10, unit="d") + + msg = "bad operand type for unary ~" + with pytest.raises(TypeError, match=msg): + ~td + + # check this matches pytimedelta and timedelta64 + with pytest.raises(TypeError, match=msg): + ~(td.to_pytimedelta()) + + umsg = "ufunc 'invert' not supported for the input types" + with pytest.raises(TypeError, match=umsg): + ~(td.to_timedelta64()) + + def test_unary_ops(self): + td = Timedelta(10, unit="d") + + # __neg__, __pos__ + assert -td == Timedelta(-10, unit="d") + assert -td == Timedelta("-10d") + assert +td == Timedelta(10, unit="d") + + # __abs__, __abs__(__neg__) + assert abs(td) == td + assert abs(-td) == td + assert abs(-td) == Timedelta("10d") + + +class TestTimedeltas: + @pytest.mark.parametrize( + "unit, value, expected", + [ + ("us", 9.999, 9999), + ("ms", 9.999999, 9999999), + ("s", 9.999999999, 9999999999), + ], + ) + def test_rounding_on_int_unit_construction(self, unit, value, expected): + # GH 12690 + result = Timedelta(value, unit=unit) + assert result._value == expected + result = Timedelta(str(value) + unit) + assert result._value == expected + + def test_total_seconds_scalar(self): + # see gh-10939 + rng = Timedelta("1 days, 10:11:12.100123456") + expt = 1 * 86400 + 10 * 3600 + 11 * 60 + 12 + 100123456.0 / 1e9 + tm.assert_almost_equal(rng.total_seconds(), expt) + + rng = Timedelta(np.nan) + assert np.isnan(rng.total_seconds()) + + def test_conversion(self): + for td in [Timedelta(10, unit="d"), Timedelta("1 days, 10:11:12.012345")]: + pydt = td.to_pytimedelta() + assert td == Timedelta(pydt) + assert td == pydt + assert isinstance(pydt, timedelta) and not isinstance(pydt, Timedelta) + + assert td == np.timedelta64(td._value, "ns") + td64 = td.to_timedelta64() + + assert td64 == np.timedelta64(td._value, "ns") + assert td == td64 + + assert isinstance(td64, np.timedelta64) + + # this is NOT equal and cannot be roundtripped (because of the nanos) + td = Timedelta("1 days, 10:11:12.012345678") + assert td != td.to_pytimedelta() + + def test_fields(self): + def check(value): + # that we are int + assert isinstance(value, int) + + # compat to datetime.timedelta + rng = to_timedelta("1 days, 10:11:12") + assert rng.days == 1 + assert rng.seconds == 10 * 3600 + 11 * 60 + 12 + assert rng.microseconds == 0 + assert rng.nanoseconds == 0 + + msg = "'Timedelta' object has no attribute '{}'" + with pytest.raises(AttributeError, match=msg.format("hours")): + rng.hours + with pytest.raises(AttributeError, match=msg.format("minutes")): + rng.minutes + with pytest.raises(AttributeError, match=msg.format("milliseconds")): + rng.milliseconds + + # GH 10050 + check(rng.days) + check(rng.seconds) + check(rng.microseconds) + check(rng.nanoseconds) + + td = Timedelta("-1 days, 10:11:12") + assert abs(td) == Timedelta("13:48:48") + assert str(td) == "-1 days +10:11:12" + assert -td == Timedelta("0 days 13:48:48") + assert -Timedelta("-1 days, 10:11:12")._value == 49728000000000 + assert Timedelta("-1 days, 10:11:12")._value == -49728000000000 + + rng = to_timedelta("-1 days, 10:11:12.100123456") + assert rng.days == -1 + assert rng.seconds == 10 * 3600 + 11 * 60 + 12 + assert rng.microseconds == 100 * 1000 + 123 + assert rng.nanoseconds == 456 + msg = "'Timedelta' object has no attribute '{}'" + with pytest.raises(AttributeError, match=msg.format("hours")): + rng.hours + with pytest.raises(AttributeError, match=msg.format("minutes")): + rng.minutes + with pytest.raises(AttributeError, match=msg.format("milliseconds")): + rng.milliseconds + + # components + tup = to_timedelta(-1, "us").components + assert tup.days == -1 + assert tup.hours == 23 + assert tup.minutes == 59 + assert tup.seconds == 59 + assert tup.milliseconds == 999 + assert tup.microseconds == 999 + assert tup.nanoseconds == 0 + + # GH 10050 + check(tup.days) + check(tup.hours) + check(tup.minutes) + check(tup.seconds) + check(tup.milliseconds) + check(tup.microseconds) + check(tup.nanoseconds) + + tup = Timedelta("-1 days 1 us").components + assert tup.days == -2 + assert tup.hours == 23 + assert tup.minutes == 59 + assert tup.seconds == 59 + assert tup.milliseconds == 999 + assert tup.microseconds == 999 + assert tup.nanoseconds == 0 + + # TODO: this is a test of to_timedelta string parsing + def test_iso_conversion(self): + # GH #21877 + expected = Timedelta(1, unit="s") + assert to_timedelta("P0DT0H0M1S") == expected + + # TODO: this is a test of to_timedelta returning NaT + def test_nat_converters(self): + result = to_timedelta("nat").to_numpy() + assert result.dtype.kind == "M" + assert result.astype("int64") == iNaT + + result = to_timedelta("nan").to_numpy() + assert result.dtype.kind == "M" + assert result.astype("int64") == iNaT + + def test_numeric_conversions(self): + assert Timedelta(0) == np.timedelta64(0, "ns") + assert Timedelta(10) == np.timedelta64(10, "ns") + assert Timedelta(10, unit="ns") == np.timedelta64(10, "ns") + + assert Timedelta(10, unit="us") == np.timedelta64(10, "us") + assert Timedelta(10, unit="ms") == np.timedelta64(10, "ms") + assert Timedelta(10, unit="s") == np.timedelta64(10, "s") + assert Timedelta(10, unit="d") == np.timedelta64(10, "D") + + def test_timedelta_conversions(self): + assert Timedelta(timedelta(seconds=1)) == np.timedelta64(1, "s").astype( + "m8[ns]" + ) + assert Timedelta(timedelta(microseconds=1)) == np.timedelta64(1, "us").astype( + "m8[ns]" + ) + assert Timedelta(timedelta(days=1)) == np.timedelta64(1, "D").astype("m8[ns]") + + def test_to_numpy_alias(self): + # GH 24653: alias .to_numpy() for scalars + td = Timedelta("10m7s") + assert td.to_timedelta64() == td.to_numpy() + + # GH#44460 + msg = "dtype and copy arguments are ignored" + with pytest.raises(ValueError, match=msg): + td.to_numpy("m8[s]") + with pytest.raises(ValueError, match=msg): + td.to_numpy(copy=True) + + def test_identity(self): + td = Timedelta(10, unit="d") + assert isinstance(td, Timedelta) + assert isinstance(td, timedelta) + + def test_short_format_converters(self): + def conv(v): + return v.astype("m8[ns]") + + assert Timedelta("10") == np.timedelta64(10, "ns") + assert Timedelta("10ns") == np.timedelta64(10, "ns") + assert Timedelta("100") == np.timedelta64(100, "ns") + assert Timedelta("100ns") == np.timedelta64(100, "ns") + + assert Timedelta("1000") == np.timedelta64(1000, "ns") + assert Timedelta("1000ns") == np.timedelta64(1000, "ns") + assert Timedelta("1000NS") == np.timedelta64(1000, "ns") + + assert Timedelta("10us") == np.timedelta64(10000, "ns") + assert Timedelta("100us") == np.timedelta64(100000, "ns") + assert Timedelta("1000us") == np.timedelta64(1000000, "ns") + assert Timedelta("1000Us") == np.timedelta64(1000000, "ns") + assert Timedelta("1000uS") == np.timedelta64(1000000, "ns") + + assert Timedelta("1ms") == np.timedelta64(1000000, "ns") + assert Timedelta("10ms") == np.timedelta64(10000000, "ns") + assert Timedelta("100ms") == np.timedelta64(100000000, "ns") + assert Timedelta("1000ms") == np.timedelta64(1000000000, "ns") + + assert Timedelta("-1s") == -np.timedelta64(1000000000, "ns") + assert Timedelta("1s") == np.timedelta64(1000000000, "ns") + assert Timedelta("10s") == np.timedelta64(10000000000, "ns") + assert Timedelta("100s") == np.timedelta64(100000000000, "ns") + assert Timedelta("1000s") == np.timedelta64(1000000000000, "ns") + + assert Timedelta("1d") == conv(np.timedelta64(1, "D")) + assert Timedelta("-1d") == -conv(np.timedelta64(1, "D")) + assert Timedelta("1D") == conv(np.timedelta64(1, "D")) + assert Timedelta("10D") == conv(np.timedelta64(10, "D")) + assert Timedelta("100D") == conv(np.timedelta64(100, "D")) + assert Timedelta("1000D") == conv(np.timedelta64(1000, "D")) + assert Timedelta("10000D") == conv(np.timedelta64(10000, "D")) + + # space + assert Timedelta(" 10000D ") == conv(np.timedelta64(10000, "D")) + assert Timedelta(" - 10000D ") == -conv(np.timedelta64(10000, "D")) + + # invalid + msg = "invalid unit abbreviation" + with pytest.raises(ValueError, match=msg): + Timedelta("1foo") + msg = "unit abbreviation w/o a number" + with pytest.raises(ValueError, match=msg): + Timedelta("foo") + + def test_full_format_converters(self): + def conv(v): + return v.astype("m8[ns]") + + d1 = np.timedelta64(1, "D") + + assert Timedelta("1days") == conv(d1) + assert Timedelta("1days,") == conv(d1) + assert Timedelta("- 1days,") == -conv(d1) + + assert Timedelta("00:00:01") == conv(np.timedelta64(1, "s")) + assert Timedelta("06:00:01") == conv(np.timedelta64(6 * 3600 + 1, "s")) + assert Timedelta("06:00:01.0") == conv(np.timedelta64(6 * 3600 + 1, "s")) + assert Timedelta("06:00:01.01") == conv( + np.timedelta64(1000 * (6 * 3600 + 1) + 10, "ms") + ) + + assert Timedelta("- 1days, 00:00:01") == conv(-d1 + np.timedelta64(1, "s")) + assert Timedelta("1days, 06:00:01") == conv( + d1 + np.timedelta64(6 * 3600 + 1, "s") + ) + assert Timedelta("1days, 06:00:01.01") == conv( + d1 + np.timedelta64(1000 * (6 * 3600 + 1) + 10, "ms") + ) + + # invalid + msg = "have leftover units" + with pytest.raises(ValueError, match=msg): + Timedelta("- 1days, 00") + + def test_pickle(self): + v = Timedelta("1 days 10:11:12.0123456") + v_p = tm.round_trip_pickle(v) + assert v == v_p + + def test_timedelta_hash_equality(self): + # GH 11129 + v = Timedelta(1, "D") + td = timedelta(days=1) + assert hash(v) == hash(td) + + d = {td: 2} + assert d[v] == 2 + + tds = [Timedelta(seconds=1) + Timedelta(days=n) for n in range(20)] + assert all(hash(td) == hash(td.to_pytimedelta()) for td in tds) + + # python timedeltas drop ns resolution + ns_td = Timedelta(1, "ns") + assert hash(ns_td) != hash(ns_td.to_pytimedelta()) + + @pytest.mark.skip_ubsan + @pytest.mark.xfail( + reason="pd.Timedelta violates the Python hash invariant (GH#44504).", + ) + @given( + st.integers( + min_value=(-sys.maxsize - 1) // 500, + max_value=sys.maxsize // 500, + ) + ) + def test_hash_equality_invariance(self, half_microseconds: int) -> None: + # GH#44504 + + nanoseconds = half_microseconds * 500 + + pandas_timedelta = Timedelta(nanoseconds) + numpy_timedelta = np.timedelta64(nanoseconds) + + # See: https://docs.python.org/3/glossary.html#term-hashable + # Hashable objects which compare equal must have the same hash value. + assert pandas_timedelta != numpy_timedelta or hash(pandas_timedelta) == hash( + numpy_timedelta + ) + + def test_implementation_limits(self): + min_td = Timedelta(Timedelta.min) + max_td = Timedelta(Timedelta.max) + + # GH 12727 + # timedelta limits correspond to int64 boundaries + assert min_td._value == iNaT + 1 + assert max_td._value == lib.i8max + + # Beyond lower limit, a NAT before the Overflow + assert (min_td - Timedelta(1, "ns")) is NaT + + msg = "int too (large|big) to convert" + with pytest.raises(OverflowError, match=msg): + min_td - Timedelta(2, "ns") + + with pytest.raises(OverflowError, match=msg): + max_td + Timedelta(1, "ns") + + # Same tests using the internal nanosecond values + td = Timedelta(min_td._value - 1, "ns") + assert td is NaT + + msg = "Cannot cast -9223372036854775809 from ns to 'ns' without overflow" + with pytest.raises(OutOfBoundsTimedelta, match=msg): + Timedelta(min_td._value - 2, "ns") + + msg = "Cannot cast 9223372036854775808 from ns to 'ns' without overflow" + with pytest.raises(OutOfBoundsTimedelta, match=msg): + Timedelta(max_td._value + 1, "ns") + + def test_total_seconds_precision(self): + # GH 19458 + assert Timedelta("30s").total_seconds() == 30.0 + assert Timedelta("0").total_seconds() == 0.0 + assert Timedelta("-2s").total_seconds() == -2.0 + assert Timedelta("5.324s").total_seconds() == 5.324 + assert (Timedelta("30s").total_seconds() - 30.0) < 1e-20 + assert (30.0 - Timedelta("30s").total_seconds()) < 1e-20 + + def test_resolution_string(self): + assert Timedelta(days=1).resolution_string == "D" + assert Timedelta(days=1, hours=6).resolution_string == "h" + assert Timedelta(days=1, minutes=6).resolution_string == "min" + assert Timedelta(days=1, seconds=6).resolution_string == "s" + assert Timedelta(days=1, milliseconds=6).resolution_string == "ms" + assert Timedelta(days=1, microseconds=6).resolution_string == "us" + assert Timedelta(days=1, nanoseconds=6).resolution_string == "ns" + + def test_resolution_deprecated(self): + # GH#21344 + td = Timedelta(days=4, hours=3) + result = td.resolution + assert result == Timedelta(nanoseconds=1) + + # Check that the attribute is available on the class, mirroring + # the stdlib timedelta behavior + result = Timedelta.resolution + assert result == Timedelta(nanoseconds=1) + + +@pytest.mark.parametrize( + "value, expected", + [ + (Timedelta("10s"), True), + (Timedelta("-10s"), True), + (Timedelta(10, unit="ns"), True), + (Timedelta(0, unit="ns"), False), + (Timedelta(-10, unit="ns"), True), + (Timedelta(None), True), + (NaT, True), + ], +) +def test_truthiness(value, expected): + # https://github.com/pandas-dev/pandas/issues/21484 + assert bool(value) is expected + + +def test_timedelta_attribute_precision(): + # GH 31354 + td = Timedelta(1552211999999999872, unit="ns") + result = td.days * 86400 + result += td.seconds + result *= 1000000 + result += td.microseconds + result *= 1000 + result += td.nanoseconds + expected = td._value + assert result == expected diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timestamp/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timestamp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timestamp/test_arithmetic.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timestamp/test_arithmetic.py new file mode 100644 index 0000000000000000000000000000000000000000..2d58513989a66a84791ed3b9169bd4f2c9b87c20 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timestamp/test_arithmetic.py @@ -0,0 +1,334 @@ +from datetime import ( + datetime, + timedelta, + timezone, +) + +from dateutil.tz import gettz +import numpy as np +import pytest +import pytz + +from pandas._libs.tslibs import ( + OutOfBoundsDatetime, + OutOfBoundsTimedelta, + Timedelta, + Timestamp, + offsets, + to_offset, +) + +import pandas._testing as tm + + +class TestTimestampArithmetic: + def test_overflow_offset(self): + # no overflow expected + + stamp = Timestamp("2000/1/1") + offset_no_overflow = to_offset("D") * 100 + + expected = Timestamp("2000/04/10") + assert stamp + offset_no_overflow == expected + + assert offset_no_overflow + stamp == expected + + expected = Timestamp("1999/09/23") + assert stamp - offset_no_overflow == expected + + def test_overflow_offset_raises(self): + # xref https://github.com/statsmodels/statsmodels/issues/3374 + # ends up multiplying really large numbers which overflow + + stamp = Timestamp("2017-01-13 00:00:00").as_unit("ns") + offset_overflow = 20169940 * offsets.Day(1) + lmsg2 = r"Cannot cast -?20169940 days \+?00:00:00 to unit='ns' without overflow" + + with pytest.raises(OutOfBoundsTimedelta, match=lmsg2): + stamp + offset_overflow + + with pytest.raises(OutOfBoundsTimedelta, match=lmsg2): + offset_overflow + stamp + + with pytest.raises(OutOfBoundsTimedelta, match=lmsg2): + stamp - offset_overflow + + # xref https://github.com/pandas-dev/pandas/issues/14080 + # used to crash, so check for proper overflow exception + + stamp = Timestamp("2000/1/1").as_unit("ns") + offset_overflow = to_offset("D") * 100**5 + + lmsg3 = ( + r"Cannot cast -?10000000000 days \+?00:00:00 to unit='ns' without overflow" + ) + with pytest.raises(OutOfBoundsTimedelta, match=lmsg3): + stamp + offset_overflow + + with pytest.raises(OutOfBoundsTimedelta, match=lmsg3): + offset_overflow + stamp + + with pytest.raises(OutOfBoundsTimedelta, match=lmsg3): + stamp - offset_overflow + + def test_overflow_timestamp_raises(self): + # https://github.com/pandas-dev/pandas/issues/31774 + msg = "Result is too large" + a = Timestamp("2101-01-01 00:00:00").as_unit("ns") + b = Timestamp("1688-01-01 00:00:00").as_unit("ns") + + with pytest.raises(OutOfBoundsDatetime, match=msg): + a - b + + # but we're OK for timestamp and datetime.datetime + assert (a - b.to_pydatetime()) == (a.to_pydatetime() - b) + + def test_delta_preserve_nanos(self): + val = Timestamp(1337299200000000123) + result = val + timedelta(1) + assert result.nanosecond == val.nanosecond + + def test_rsub_dtscalars(self, tz_naive_fixture): + # In particular, check that datetime64 - Timestamp works GH#28286 + td = Timedelta(1235345642000) + ts = Timestamp("2021-01-01", tz=tz_naive_fixture) + other = ts + td + + assert other - ts == td + assert other.to_pydatetime() - ts == td + if tz_naive_fixture is None: + assert other.to_datetime64() - ts == td + else: + msg = "Cannot subtract tz-naive and tz-aware datetime-like objects" + with pytest.raises(TypeError, match=msg): + other.to_datetime64() - ts + + def test_timestamp_sub_datetime(self): + dt = datetime(2013, 10, 12) + ts = Timestamp(datetime(2013, 10, 13)) + assert (ts - dt).days == 1 + assert (dt - ts).days == -1 + + def test_subtract_tzaware_datetime(self): + t1 = Timestamp("2020-10-22T22:00:00+00:00") + t2 = datetime(2020, 10, 22, 22, tzinfo=timezone.utc) + + result = t1 - t2 + + assert isinstance(result, Timedelta) + assert result == Timedelta("0 days") + + def test_subtract_timestamp_from_different_timezone(self): + t1 = Timestamp("20130101").tz_localize("US/Eastern") + t2 = Timestamp("20130101").tz_localize("CET") + + result = t1 - t2 + + assert isinstance(result, Timedelta) + assert result == Timedelta("0 days 06:00:00") + + def test_subtracting_involving_datetime_with_different_tz(self): + t1 = datetime(2013, 1, 1, tzinfo=timezone(timedelta(hours=-5))) + t2 = Timestamp("20130101").tz_localize("CET") + + result = t1 - t2 + + assert isinstance(result, Timedelta) + assert result == Timedelta("0 days 06:00:00") + + result = t2 - t1 + assert isinstance(result, Timedelta) + assert result == Timedelta("-1 days +18:00:00") + + def test_subtracting_different_timezones(self, tz_aware_fixture): + t_raw = Timestamp("20130101") + t_UTC = t_raw.tz_localize("UTC") + t_diff = t_UTC.tz_convert(tz_aware_fixture) + Timedelta("0 days 05:00:00") + + result = t_diff - t_UTC + + assert isinstance(result, Timedelta) + assert result == Timedelta("0 days 05:00:00") + + def test_addition_subtraction_types(self): + # Assert on the types resulting from Timestamp +/- various date/time + # objects + dt = datetime(2014, 3, 4) + td = timedelta(seconds=1) + ts = Timestamp(dt) + + msg = "Addition/subtraction of integers" + with pytest.raises(TypeError, match=msg): + # GH#22535 add/sub with integers is deprecated + ts + 1 + with pytest.raises(TypeError, match=msg): + ts - 1 + + # Timestamp + datetime not supported, though subtraction is supported + # and yields timedelta more tests in tseries/base/tests/test_base.py + assert type(ts - dt) == Timedelta + assert type(ts + td) == Timestamp + assert type(ts - td) == Timestamp + + # Timestamp +/- datetime64 not supported, so not tested (could possibly + # assert error raised?) + td64 = np.timedelta64(1, "D") + assert type(ts + td64) == Timestamp + assert type(ts - td64) == Timestamp + + @pytest.mark.parametrize( + "td", [Timedelta(hours=3), np.timedelta64(3, "h"), timedelta(hours=3)] + ) + def test_radd_tdscalar(self, td, fixed_now_ts): + # GH#24775 timedelta64+Timestamp should not raise + ts = fixed_now_ts + assert td + ts == ts + td + + @pytest.mark.parametrize( + "other,expected_difference", + [ + (np.timedelta64(-123, "ns"), -123), + (np.timedelta64(1234567898, "ns"), 1234567898), + (np.timedelta64(-123, "us"), -123000), + (np.timedelta64(-123, "ms"), -123000000), + ], + ) + def test_timestamp_add_timedelta64_unit(self, other, expected_difference): + now = datetime.now(timezone.utc) + ts = Timestamp(now).as_unit("ns") + result = ts + other + valdiff = result._value - ts._value + assert valdiff == expected_difference + + ts2 = Timestamp(now) + assert ts2 + other == result + + @pytest.mark.parametrize( + "ts", + [ + Timestamp("1776-07-04"), + Timestamp("1776-07-04", tz="UTC"), + ], + ) + @pytest.mark.parametrize( + "other", + [ + 1, + np.int64(1), + np.array([1, 2], dtype=np.int32), + np.array([3, 4], dtype=np.uint64), + ], + ) + def test_add_int_with_freq(self, ts, other): + msg = "Addition/subtraction of integers and integer-arrays" + with pytest.raises(TypeError, match=msg): + ts + other + with pytest.raises(TypeError, match=msg): + other + ts + + with pytest.raises(TypeError, match=msg): + ts - other + + msg = "unsupported operand type" + with pytest.raises(TypeError, match=msg): + other - ts + + @pytest.mark.parametrize("shape", [(6,), (2, 3)]) + def test_addsub_m8ndarray(self, shape): + # GH#33296 + ts = Timestamp("2020-04-04 15:45").as_unit("ns") + other = np.arange(6).astype("m8[h]").reshape(shape) + + result = ts + other + + ex_stamps = [ts + Timedelta(hours=n) for n in range(6)] + expected = np.array([x.asm8 for x in ex_stamps], dtype="M8[ns]").reshape(shape) + tm.assert_numpy_array_equal(result, expected) + + result = other + ts + tm.assert_numpy_array_equal(result, expected) + + result = ts - other + ex_stamps = [ts - Timedelta(hours=n) for n in range(6)] + expected = np.array([x.asm8 for x in ex_stamps], dtype="M8[ns]").reshape(shape) + tm.assert_numpy_array_equal(result, expected) + + msg = r"unsupported operand type\(s\) for -: 'numpy.ndarray' and 'Timestamp'" + with pytest.raises(TypeError, match=msg): + other - ts + + @pytest.mark.parametrize("shape", [(6,), (2, 3)]) + def test_addsub_m8ndarray_tzaware(self, shape): + # GH#33296 + ts = Timestamp("2020-04-04 15:45", tz="US/Pacific") + + other = np.arange(6).astype("m8[h]").reshape(shape) + + result = ts + other + + ex_stamps = [ts + Timedelta(hours=n) for n in range(6)] + expected = np.array(ex_stamps).reshape(shape) + tm.assert_numpy_array_equal(result, expected) + + result = other + ts + tm.assert_numpy_array_equal(result, expected) + + result = ts - other + ex_stamps = [ts - Timedelta(hours=n) for n in range(6)] + expected = np.array(ex_stamps).reshape(shape) + tm.assert_numpy_array_equal(result, expected) + + msg = r"unsupported operand type\(s\) for -: 'numpy.ndarray' and 'Timestamp'" + with pytest.raises(TypeError, match=msg): + other - ts + + def test_subtract_different_utc_objects(self, utc_fixture, utc_fixture2): + # GH 32619 + dt = datetime(2021, 1, 1) + ts1 = Timestamp(dt, tz=utc_fixture) + ts2 = Timestamp(dt, tz=utc_fixture2) + result = ts1 - ts2 + expected = Timedelta(0) + assert result == expected + + @pytest.mark.parametrize( + "tz", + [ + pytz.timezone("US/Eastern"), + gettz("US/Eastern"), + "US/Eastern", + "dateutil/US/Eastern", + ], + ) + def test_timestamp_add_timedelta_push_over_dst_boundary(self, tz): + # GH#1389 + + # 4 hours before DST transition + stamp = Timestamp("3/10/2012 22:00", tz=tz) + + result = stamp + timedelta(hours=6) + + # spring forward, + "7" hours + expected = Timestamp("3/11/2012 05:00", tz=tz) + + assert result == expected + + +class SubDatetime(datetime): + pass + + +@pytest.mark.parametrize( + "lh,rh", + [ + (SubDatetime(2000, 1, 1), Timedelta(hours=1)), + (Timedelta(hours=1), SubDatetime(2000, 1, 1)), + ], +) +def test_dt_subclass_add_timedelta(lh, rh): + # GH#25851 + # ensure that subclassed datetime works for + # Timedelta operations + result = lh + rh + expected = SubDatetime(2000, 1, 1, 1) + assert result == expected diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timestamp/test_formats.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timestamp/test_formats.py new file mode 100644 index 0000000000000000000000000000000000000000..e7ebcccef1c86ee34cb8889d8840b77f5cfc2616 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/scalar/timestamp/test_formats.py @@ -0,0 +1,201 @@ +from datetime import datetime +import pprint + +import dateutil.tz +import pytest +import pytz # a test below uses pytz but only inside a `eval` call + +from pandas import Timestamp + +ts_no_ns = Timestamp( + year=2019, + month=5, + day=18, + hour=15, + minute=17, + second=8, + microsecond=132263, +) +ts_no_ns_year1 = Timestamp( + year=1, + month=5, + day=18, + hour=15, + minute=17, + second=8, + microsecond=132263, +) +ts_ns = Timestamp( + year=2019, + month=5, + day=18, + hour=15, + minute=17, + second=8, + microsecond=132263, + nanosecond=123, +) +ts_ns_tz = Timestamp( + year=2019, + month=5, + day=18, + hour=15, + minute=17, + second=8, + microsecond=132263, + nanosecond=123, + tz="UTC", +) +ts_no_us = Timestamp( + year=2019, + month=5, + day=18, + hour=15, + minute=17, + second=8, + microsecond=0, + nanosecond=123, +) + + +@pytest.mark.parametrize( + "ts, timespec, expected_iso", + [ + (ts_no_ns, "auto", "2019-05-18T15:17:08.132263"), + (ts_no_ns, "seconds", "2019-05-18T15:17:08"), + (ts_no_ns, "nanoseconds", "2019-05-18T15:17:08.132263000"), + (ts_no_ns_year1, "seconds", "0001-05-18T15:17:08"), + (ts_no_ns_year1, "nanoseconds", "0001-05-18T15:17:08.132263000"), + (ts_ns, "auto", "2019-05-18T15:17:08.132263123"), + (ts_ns, "hours", "2019-05-18T15"), + (ts_ns, "minutes", "2019-05-18T15:17"), + (ts_ns, "seconds", "2019-05-18T15:17:08"), + (ts_ns, "milliseconds", "2019-05-18T15:17:08.132"), + (ts_ns, "microseconds", "2019-05-18T15:17:08.132263"), + (ts_ns, "nanoseconds", "2019-05-18T15:17:08.132263123"), + (ts_ns_tz, "auto", "2019-05-18T15:17:08.132263123+00:00"), + (ts_ns_tz, "hours", "2019-05-18T15+00:00"), + (ts_ns_tz, "minutes", "2019-05-18T15:17+00:00"), + (ts_ns_tz, "seconds", "2019-05-18T15:17:08+00:00"), + (ts_ns_tz, "milliseconds", "2019-05-18T15:17:08.132+00:00"), + (ts_ns_tz, "microseconds", "2019-05-18T15:17:08.132263+00:00"), + (ts_ns_tz, "nanoseconds", "2019-05-18T15:17:08.132263123+00:00"), + (ts_no_us, "auto", "2019-05-18T15:17:08.000000123"), + ], +) +def test_isoformat(ts, timespec, expected_iso): + assert ts.isoformat(timespec=timespec) == expected_iso + + +class TestTimestampRendering: + timezones = ["UTC", "Asia/Tokyo", "US/Eastern", "dateutil/America/Los_Angeles"] + + @pytest.mark.parametrize("tz", timezones) + @pytest.mark.parametrize("freq", ["D", "M", "S", "N"]) + @pytest.mark.parametrize( + "date", ["2014-03-07", "2014-01-01 09:00", "2014-01-01 00:00:00.000000001"] + ) + def test_repr(self, date, freq, tz): + # avoid to match with timezone name + freq_repr = f"'{freq}'" + if tz.startswith("dateutil"): + tz_repr = tz.replace("dateutil", "") + else: + tz_repr = tz + + date_only = Timestamp(date) + assert date in repr(date_only) + assert tz_repr not in repr(date_only) + assert freq_repr not in repr(date_only) + assert date_only == eval(repr(date_only)) + + date_tz = Timestamp(date, tz=tz) + assert date in repr(date_tz) + assert tz_repr in repr(date_tz) + assert freq_repr not in repr(date_tz) + assert date_tz == eval(repr(date_tz)) + + def test_repr_utcoffset(self): + # This can cause the tz field to be populated, but it's redundant to + # include this information in the date-string. + date_with_utc_offset = Timestamp("2014-03-13 00:00:00-0400", tz=None) + assert "2014-03-13 00:00:00-0400" in repr(date_with_utc_offset) + assert "tzoffset" not in repr(date_with_utc_offset) + assert "UTC-04:00" in repr(date_with_utc_offset) + expr = repr(date_with_utc_offset) + assert date_with_utc_offset == eval(expr) + + def test_timestamp_repr_pre1900(self): + # pre-1900 + stamp = Timestamp("1850-01-01", tz="US/Eastern") + repr(stamp) + + iso8601 = "1850-01-01 01:23:45.012345" + stamp = Timestamp(iso8601, tz="US/Eastern") + result = repr(stamp) + assert iso8601 in result + + def test_pprint(self): + # GH#12622 + nested_obj = {"foo": 1, "bar": [{"w": {"a": Timestamp("2011-01-01")}}] * 10} + result = pprint.pformat(nested_obj, width=50) + expected = r"""{'bar': [{'w': {'a': Timestamp('2011-01-01 00:00:00')}}, + {'w': {'a': Timestamp('2011-01-01 00:00:00')}}, + {'w': {'a': Timestamp('2011-01-01 00:00:00')}}, + {'w': {'a': Timestamp('2011-01-01 00:00:00')}}, + {'w': {'a': Timestamp('2011-01-01 00:00:00')}}, + {'w': {'a': Timestamp('2011-01-01 00:00:00')}}, + {'w': {'a': Timestamp('2011-01-01 00:00:00')}}, + {'w': {'a': Timestamp('2011-01-01 00:00:00')}}, + {'w': {'a': Timestamp('2011-01-01 00:00:00')}}, + {'w': {'a': Timestamp('2011-01-01 00:00:00')}}], + 'foo': 1}""" + assert result == expected + + def test_to_timestamp_repr_is_code(self): + zs = [ + Timestamp("99-04-17 00:00:00", tz="UTC"), + Timestamp("2001-04-17 00:00:00", tz="UTC"), + Timestamp("2001-04-17 00:00:00", tz="America/Los_Angeles"), + Timestamp("2001-04-17 00:00:00", tz=None), + ] + for z in zs: + assert eval(repr(z)) == z + + def test_repr_matches_pydatetime_no_tz(self): + dt_date = datetime(2013, 1, 2) + assert str(dt_date) == str(Timestamp(dt_date)) + + dt_datetime = datetime(2013, 1, 2, 12, 1, 3) + assert str(dt_datetime) == str(Timestamp(dt_datetime)) + + dt_datetime_us = datetime(2013, 1, 2, 12, 1, 3, 45) + assert str(dt_datetime_us) == str(Timestamp(dt_datetime_us)) + + ts_nanos_only = Timestamp(200) + assert str(ts_nanos_only) == "1970-01-01 00:00:00.000000200" + + ts_nanos_micros = Timestamp(1200) + assert str(ts_nanos_micros) == "1970-01-01 00:00:00.000001200" + + def test_repr_matches_pydatetime_tz_pytz(self): + dt_date = datetime(2013, 1, 2, tzinfo=pytz.utc) + assert str(dt_date) == str(Timestamp(dt_date)) + + dt_datetime = datetime(2013, 1, 2, 12, 1, 3, tzinfo=pytz.utc) + assert str(dt_datetime) == str(Timestamp(dt_datetime)) + + dt_datetime_us = datetime(2013, 1, 2, 12, 1, 3, 45, tzinfo=pytz.utc) + assert str(dt_datetime_us) == str(Timestamp(dt_datetime_us)) + + def test_repr_matches_pydatetime_tz_dateutil(self): + utc = dateutil.tz.tzutc() + + dt_date = datetime(2013, 1, 2, tzinfo=utc) + assert str(dt_date) == str(Timestamp(dt_date)) + + dt_datetime = datetime(2013, 1, 2, 12, 1, 3, tzinfo=utc) + assert str(dt_datetime) == str(Timestamp(dt_datetime)) + + dt_datetime_us = datetime(2013, 1, 2, 12, 1, 3, 45, tzinfo=utc) + assert str(dt_datetime_us) == str(Timestamp(dt_datetime_us)) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/accessors/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/accessors/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/accessors/test_cat_accessor.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/accessors/test_cat_accessor.py new file mode 100644 index 0000000000000000000000000000000000000000..2d50b0f36904a94e7e16aac426e1d098fe320c94 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/accessors/test_cat_accessor.py @@ -0,0 +1,258 @@ +import numpy as np +import pytest + +from pandas import ( + Categorical, + DataFrame, + Index, + Series, + Timestamp, + date_range, + period_range, + timedelta_range, +) +import pandas._testing as tm +from pandas.core.arrays.categorical import CategoricalAccessor +from pandas.core.indexes.accessors import Properties + + +class TestCatAccessor: + @pytest.mark.parametrize( + "method", + [ + lambda x: x.cat.set_categories([1, 2, 3]), + lambda x: x.cat.reorder_categories([2, 3, 1], ordered=True), + lambda x: x.cat.rename_categories([1, 2, 3]), + lambda x: x.cat.remove_unused_categories(), + lambda x: x.cat.remove_categories([2]), + lambda x: x.cat.add_categories([4]), + lambda x: x.cat.as_ordered(), + lambda x: x.cat.as_unordered(), + ], + ) + def test_getname_categorical_accessor(self, method): + # GH#17509 + ser = Series([1, 2, 3], name="A").astype("category") + expected = "A" + result = method(ser).name + assert result == expected + + def test_cat_accessor(self): + ser = Series(Categorical(["a", "b", np.nan, "a"])) + tm.assert_index_equal(ser.cat.categories, Index(["a", "b"])) + assert not ser.cat.ordered, False + + exp = Categorical(["a", "b", np.nan, "a"], categories=["b", "a"]) + + res = ser.cat.set_categories(["b", "a"]) + tm.assert_categorical_equal(res.values, exp) + + ser[:] = "a" + ser = ser.cat.remove_unused_categories() + tm.assert_index_equal(ser.cat.categories, Index(["a"])) + + def test_cat_accessor_api(self): + # GH#9322 + + assert Series.cat is CategoricalAccessor + ser = Series(list("aabbcde")).astype("category") + assert isinstance(ser.cat, CategoricalAccessor) + + invalid = Series([1]) + with pytest.raises(AttributeError, match="only use .cat accessor"): + invalid.cat + assert not hasattr(invalid, "cat") + + def test_cat_accessor_no_new_attributes(self): + # https://github.com/pandas-dev/pandas/issues/10673 + cat = Series(list("aabbcde")).astype("category") + with pytest.raises(AttributeError, match="You cannot add any new attribute"): + cat.cat.xlabel = "a" + + def test_categorical_delegations(self): + # invalid accessor + msg = r"Can only use \.cat accessor with a 'category' dtype" + with pytest.raises(AttributeError, match=msg): + Series([1, 2, 3]).cat + with pytest.raises(AttributeError, match=msg): + Series([1, 2, 3]).cat() + with pytest.raises(AttributeError, match=msg): + Series(["a", "b", "c"]).cat + with pytest.raises(AttributeError, match=msg): + Series(np.arange(5.0)).cat + with pytest.raises(AttributeError, match=msg): + Series([Timestamp("20130101")]).cat + + # Series should delegate calls to '.categories', '.codes', '.ordered' + # and the methods '.set_categories()' 'drop_unused_categories()' to the + # categorical + ser = Series(Categorical(["a", "b", "c", "a"], ordered=True)) + exp_categories = Index(["a", "b", "c"]) + tm.assert_index_equal(ser.cat.categories, exp_categories) + ser = ser.cat.rename_categories([1, 2, 3]) + exp_categories = Index([1, 2, 3]) + tm.assert_index_equal(ser.cat.categories, exp_categories) + + exp_codes = Series([0, 1, 2, 0], dtype="int8") + tm.assert_series_equal(ser.cat.codes, exp_codes) + + assert ser.cat.ordered + ser = ser.cat.as_unordered() + assert not ser.cat.ordered + + ser = ser.cat.as_ordered() + assert ser.cat.ordered + + # reorder + ser = Series(Categorical(["a", "b", "c", "a"], ordered=True)) + exp_categories = Index(["c", "b", "a"]) + exp_values = np.array(["a", "b", "c", "a"], dtype=np.object_) + ser = ser.cat.set_categories(["c", "b", "a"]) + tm.assert_index_equal(ser.cat.categories, exp_categories) + tm.assert_numpy_array_equal(ser.values.__array__(), exp_values) + tm.assert_numpy_array_equal(ser.__array__(), exp_values) + + # remove unused categories + ser = Series(Categorical(["a", "b", "b", "a"], categories=["a", "b", "c"])) + exp_categories = Index(["a", "b"]) + exp_values = np.array(["a", "b", "b", "a"], dtype=np.object_) + ser = ser.cat.remove_unused_categories() + tm.assert_index_equal(ser.cat.categories, exp_categories) + tm.assert_numpy_array_equal(ser.values.__array__(), exp_values) + tm.assert_numpy_array_equal(ser.__array__(), exp_values) + + # This method is likely to be confused, so test that it raises an error + # on wrong inputs: + msg = "'Series' object has no attribute 'set_categories'" + with pytest.raises(AttributeError, match=msg): + ser.set_categories([4, 3, 2, 1]) + + # right: ser.cat.set_categories([4,3,2,1]) + + # GH#18862 (let Series.cat.rename_categories take callables) + ser = Series(Categorical(["a", "b", "c", "a"], ordered=True)) + result = ser.cat.rename_categories(lambda x: x.upper()) + expected = Series( + Categorical(["A", "B", "C", "A"], categories=["A", "B", "C"], ordered=True) + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "idx", + [ + date_range("1/1/2015", periods=5), + date_range("1/1/2015", periods=5, tz="MET"), + period_range("1/1/2015", freq="D", periods=5), + timedelta_range("1 days", "10 days"), + ], + ) + def test_dt_accessor_api_for_categorical(self, idx): + # https://github.com/pandas-dev/pandas/issues/10661 + + ser = Series(idx) + cat = ser.astype("category") + + # only testing field (like .day) + # and bool (is_month_start) + attr_names = type(ser._values)._datetimelike_ops + + assert isinstance(cat.dt, Properties) + + special_func_defs = [ + ("strftime", ("%Y-%m-%d",), {}), + ("round", ("D",), {}), + ("floor", ("D",), {}), + ("ceil", ("D",), {}), + ("asfreq", ("D",), {}), + ("as_unit", ("s"), {}), + ] + if idx.dtype == "M8[ns]": + # exclude dt64tz since that is already localized and would raise + tup = ("tz_localize", ("UTC",), {}) + special_func_defs.append(tup) + elif idx.dtype.kind == "M": + # exclude dt64 since that is not localized so would raise + tup = ("tz_convert", ("EST",), {}) + special_func_defs.append(tup) + + _special_func_names = [f[0] for f in special_func_defs] + + _ignore_names = ["components", "tz_localize", "tz_convert"] + + func_names = [ + fname + for fname in dir(ser.dt) + if not ( + fname.startswith("_") + or fname in attr_names + or fname in _special_func_names + or fname in _ignore_names + ) + ] + + func_defs = [(fname, (), {}) for fname in func_names] + func_defs.extend( + f_def for f_def in special_func_defs if f_def[0] in dir(ser.dt) + ) + + for func, args, kwargs in func_defs: + warn_cls = [] + if func == "to_period" and getattr(idx, "tz", None) is not None: + # dropping TZ + warn_cls.append(UserWarning) + if func == "to_pydatetime": + # deprecated to return Index[object] + warn_cls.append(FutureWarning) + if warn_cls: + warn_cls = tuple(warn_cls) + else: + warn_cls = None + with tm.assert_produces_warning(warn_cls): + res = getattr(cat.dt, func)(*args, **kwargs) + exp = getattr(ser.dt, func)(*args, **kwargs) + + tm.assert_equal(res, exp) + + for attr in attr_names: + res = getattr(cat.dt, attr) + exp = getattr(ser.dt, attr) + + tm.assert_equal(res, exp) + + def test_dt_accessor_api_for_categorical_invalid(self): + invalid = Series([1, 2, 3]).astype("category") + msg = "Can only use .dt accessor with datetimelike" + + with pytest.raises(AttributeError, match=msg): + invalid.dt + assert not hasattr(invalid, "str") + + def test_set_categories_setitem(self): + # GH#43334 + + df = DataFrame({"Survived": [1, 0, 1], "Sex": [0, 1, 1]}, dtype="category") + + df["Survived"] = df["Survived"].cat.rename_categories(["No", "Yes"]) + df["Sex"] = df["Sex"].cat.rename_categories(["female", "male"]) + + # values should not be coerced to NaN + assert list(df["Sex"]) == ["female", "male", "male"] + assert list(df["Survived"]) == ["Yes", "No", "Yes"] + + df["Sex"] = Categorical(df["Sex"], categories=["female", "male"], ordered=False) + df["Survived"] = Categorical( + df["Survived"], categories=["No", "Yes"], ordered=False + ) + + # values should not be coerced to NaN + assert list(df["Sex"]) == ["female", "male", "male"] + assert list(df["Survived"]) == ["Yes", "No", "Yes"] + + def test_categorical_of_booleans_is_boolean(self): + # https://github.com/pandas-dev/pandas/issues/46313 + df = DataFrame( + {"int_cat": [1, 2, 3], "bool_cat": [True, False, False]}, dtype="category" + ) + value = df["bool_cat"].cat.categories.dtype + expected = np.dtype(np.bool_) + assert value is expected diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/accessors/test_dt_accessor.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/accessors/test_dt_accessor.py new file mode 100644 index 0000000000000000000000000000000000000000..a06a3a0d4067582ca71b7fb5a5c550fa58c1a509 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/accessors/test_dt_accessor.py @@ -0,0 +1,843 @@ +import calendar +from datetime import ( + date, + datetime, + time, +) +import locale +import unicodedata + +import numpy as np +import pytest +import pytz + +from pandas._libs.tslibs.timezones import maybe_get_tz +from pandas.errors import SettingWithCopyError + +from pandas.core.dtypes.common import ( + is_integer_dtype, + is_list_like, +) + +import pandas as pd +from pandas import ( + DataFrame, + DatetimeIndex, + Index, + Period, + PeriodIndex, + Series, + StringDtype, + TimedeltaIndex, + date_range, + period_range, + timedelta_range, +) +import pandas._testing as tm +from pandas.core.arrays import ( + DatetimeArray, + PeriodArray, + TimedeltaArray, +) + +ok_for_period = PeriodArray._datetimelike_ops +ok_for_period_methods = ["strftime", "to_timestamp", "asfreq"] +ok_for_dt = DatetimeArray._datetimelike_ops +ok_for_dt_methods = [ + "to_period", + "to_pydatetime", + "tz_localize", + "tz_convert", + "normalize", + "strftime", + "round", + "floor", + "ceil", + "day_name", + "month_name", + "isocalendar", + "as_unit", +] +ok_for_td = TimedeltaArray._datetimelike_ops +ok_for_td_methods = [ + "components", + "to_pytimedelta", + "total_seconds", + "round", + "floor", + "ceil", + "as_unit", +] + + +def get_dir(ser): + # check limited display api + results = [r for r in ser.dt.__dir__() if not r.startswith("_")] + return sorted(set(results)) + + +class TestSeriesDatetimeValues: + def _compare(self, ser, name): + # GH 7207, 11128 + # test .dt namespace accessor + + def get_expected(ser, prop): + result = getattr(Index(ser._values), prop) + if isinstance(result, np.ndarray): + if is_integer_dtype(result): + result = result.astype("int64") + elif not is_list_like(result) or isinstance(result, DataFrame): + return result + return Series(result, index=ser.index, name=ser.name) + + left = getattr(ser.dt, name) + right = get_expected(ser, name) + if not (is_list_like(left) and is_list_like(right)): + assert left == right + elif isinstance(left, DataFrame): + tm.assert_frame_equal(left, right) + else: + tm.assert_series_equal(left, right) + + @pytest.mark.parametrize("freq", ["D", "s", "ms"]) + def test_dt_namespace_accessor_datetime64(self, freq): + # GH#7207, GH#11128 + # test .dt namespace accessor + + # datetimeindex + dti = date_range("20130101", periods=5, freq=freq) + ser = Series(dti, name="xxx") + + for prop in ok_for_dt: + # we test freq below + if prop != "freq": + self._compare(ser, prop) + + for prop in ok_for_dt_methods: + getattr(ser.dt, prop) + + msg = "The behavior of DatetimeProperties.to_pydatetime is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = ser.dt.to_pydatetime() + assert isinstance(result, np.ndarray) + assert result.dtype == object + + result = ser.dt.tz_localize("US/Eastern") + exp_values = DatetimeIndex(ser.values).tz_localize("US/Eastern") + expected = Series(exp_values, index=ser.index, name="xxx") + tm.assert_series_equal(result, expected) + + tz_result = result.dt.tz + assert str(tz_result) == "US/Eastern" + freq_result = ser.dt.freq + assert freq_result == DatetimeIndex(ser.values, freq="infer").freq + + # let's localize, then convert + result = ser.dt.tz_localize("UTC").dt.tz_convert("US/Eastern") + exp_values = ( + DatetimeIndex(ser.values).tz_localize("UTC").tz_convert("US/Eastern") + ) + expected = Series(exp_values, index=ser.index, name="xxx") + tm.assert_series_equal(result, expected) + + def test_dt_namespace_accessor_datetime64tz(self): + # GH#7207, GH#11128 + # test .dt namespace accessor + + # datetimeindex with tz + dti = date_range("20130101", periods=5, tz="US/Eastern") + ser = Series(dti, name="xxx") + for prop in ok_for_dt: + # we test freq below + if prop != "freq": + self._compare(ser, prop) + + for prop in ok_for_dt_methods: + getattr(ser.dt, prop) + + msg = "The behavior of DatetimeProperties.to_pydatetime is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = ser.dt.to_pydatetime() + assert isinstance(result, np.ndarray) + assert result.dtype == object + + result = ser.dt.tz_convert("CET") + expected = Series(ser._values.tz_convert("CET"), index=ser.index, name="xxx") + tm.assert_series_equal(result, expected) + + tz_result = result.dt.tz + assert str(tz_result) == "CET" + freq_result = ser.dt.freq + assert freq_result == DatetimeIndex(ser.values, freq="infer").freq + + def test_dt_namespace_accessor_timedelta(self): + # GH#7207, GH#11128 + # test .dt namespace accessor + + # timedelta index + cases = [ + Series( + timedelta_range("1 day", periods=5), index=list("abcde"), name="xxx" + ), + Series(timedelta_range("1 day 01:23:45", periods=5, freq="s"), name="xxx"), + Series( + timedelta_range("2 days 01:23:45.012345", periods=5, freq="ms"), + name="xxx", + ), + ] + for ser in cases: + for prop in ok_for_td: + # we test freq below + if prop != "freq": + self._compare(ser, prop) + + for prop in ok_for_td_methods: + getattr(ser.dt, prop) + + result = ser.dt.components + assert isinstance(result, DataFrame) + tm.assert_index_equal(result.index, ser.index) + + result = ser.dt.to_pytimedelta() + assert isinstance(result, np.ndarray) + assert result.dtype == object + + result = ser.dt.total_seconds() + assert isinstance(result, Series) + assert result.dtype == "float64" + + freq_result = ser.dt.freq + assert freq_result == TimedeltaIndex(ser.values, freq="infer").freq + + def test_dt_namespace_accessor_period(self): + # GH#7207, GH#11128 + # test .dt namespace accessor + + # periodindex + pi = period_range("20130101", periods=5, freq="D") + ser = Series(pi, name="xxx") + + for prop in ok_for_period: + # we test freq below + if prop != "freq": + self._compare(ser, prop) + + for prop in ok_for_period_methods: + getattr(ser.dt, prop) + + freq_result = ser.dt.freq + assert freq_result == PeriodIndex(ser.values).freq + + def test_dt_namespace_accessor_index_and_values(self): + # both + index = date_range("20130101", periods=3, freq="D") + dti = date_range("20140204", periods=3, freq="s") + ser = Series(dti, index=index, name="xxx") + exp = Series( + np.array([2014, 2014, 2014], dtype="int32"), index=index, name="xxx" + ) + tm.assert_series_equal(ser.dt.year, exp) + + exp = Series(np.array([2, 2, 2], dtype="int32"), index=index, name="xxx") + tm.assert_series_equal(ser.dt.month, exp) + + exp = Series(np.array([0, 1, 2], dtype="int32"), index=index, name="xxx") + tm.assert_series_equal(ser.dt.second, exp) + + exp = Series([ser.iloc[0]] * 3, index=index, name="xxx") + tm.assert_series_equal(ser.dt.normalize(), exp) + + def test_dt_accessor_limited_display_api(self): + # tznaive + ser = Series(date_range("20130101", periods=5, freq="D"), name="xxx") + results = get_dir(ser) + tm.assert_almost_equal(results, sorted(set(ok_for_dt + ok_for_dt_methods))) + + # tzaware + ser = Series(date_range("2015-01-01", "2016-01-01", freq="min"), name="xxx") + ser = ser.dt.tz_localize("UTC").dt.tz_convert("America/Chicago") + results = get_dir(ser) + tm.assert_almost_equal(results, sorted(set(ok_for_dt + ok_for_dt_methods))) + + # Period + idx = period_range("20130101", periods=5, freq="D", name="xxx").astype(object) + with tm.assert_produces_warning(FutureWarning, match="Dtype inference"): + ser = Series(idx) + results = get_dir(ser) + tm.assert_almost_equal( + results, sorted(set(ok_for_period + ok_for_period_methods)) + ) + + def test_dt_accessor_ambiguous_freq_conversions(self): + # GH#11295 + # ambiguous time error on the conversions + ser = Series(date_range("2015-01-01", "2016-01-01", freq="min"), name="xxx") + ser = ser.dt.tz_localize("UTC").dt.tz_convert("America/Chicago") + + exp_values = date_range( + "2015-01-01", "2016-01-01", freq="min", tz="UTC" + ).tz_convert("America/Chicago") + # freq not preserved by tz_localize above + exp_values = exp_values._with_freq(None) + expected = Series(exp_values, name="xxx") + tm.assert_series_equal(ser, expected) + + def test_dt_accessor_not_writeable(self, using_copy_on_write, warn_copy_on_write): + # no setting allowed + ser = Series(date_range("20130101", periods=5, freq="D"), name="xxx") + with pytest.raises(ValueError, match="modifications"): + ser.dt.hour = 5 + + # trying to set a copy + msg = "modifications to a property of a datetimelike.+not supported" + with pd.option_context("chained_assignment", "raise"): + if using_copy_on_write: + with tm.raises_chained_assignment_error(): + ser.dt.hour[0] = 5 + elif warn_copy_on_write: + with tm.assert_produces_warning( + FutureWarning, match="ChainedAssignmentError" + ): + ser.dt.hour[0] = 5 + else: + with pytest.raises(SettingWithCopyError, match=msg): + ser.dt.hour[0] = 5 + + @pytest.mark.parametrize( + "method, dates", + [ + ["round", ["2012-01-02", "2012-01-02", "2012-01-01"]], + ["floor", ["2012-01-01", "2012-01-01", "2012-01-01"]], + ["ceil", ["2012-01-02", "2012-01-02", "2012-01-02"]], + ], + ) + def test_dt_round(self, method, dates): + # round + ser = Series( + pd.to_datetime( + ["2012-01-01 13:00:00", "2012-01-01 12:01:00", "2012-01-01 08:00:00"] + ), + name="xxx", + ) + result = getattr(ser.dt, method)("D") + expected = Series(pd.to_datetime(dates), name="xxx") + tm.assert_series_equal(result, expected) + + def test_dt_round_tz(self): + ser = Series( + pd.to_datetime( + ["2012-01-01 13:00:00", "2012-01-01 12:01:00", "2012-01-01 08:00:00"] + ), + name="xxx", + ) + result = ser.dt.tz_localize("UTC").dt.tz_convert("US/Eastern").dt.round("D") + + exp_values = pd.to_datetime( + ["2012-01-01", "2012-01-01", "2012-01-01"] + ).tz_localize("US/Eastern") + expected = Series(exp_values, name="xxx") + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("method", ["ceil", "round", "floor"]) + def test_dt_round_tz_ambiguous(self, method): + # GH 18946 round near "fall back" DST + df1 = DataFrame( + [ + pd.to_datetime("2017-10-29 02:00:00+02:00", utc=True), + pd.to_datetime("2017-10-29 02:00:00+01:00", utc=True), + pd.to_datetime("2017-10-29 03:00:00+01:00", utc=True), + ], + columns=["date"], + ) + df1["date"] = df1["date"].dt.tz_convert("Europe/Madrid") + # infer + result = getattr(df1.date.dt, method)("h", ambiguous="infer") + expected = df1["date"] + tm.assert_series_equal(result, expected) + + # bool-array + result = getattr(df1.date.dt, method)("h", ambiguous=[True, False, False]) + tm.assert_series_equal(result, expected) + + # NaT + result = getattr(df1.date.dt, method)("h", ambiguous="NaT") + expected = df1["date"].copy() + expected.iloc[0:2] = pd.NaT + tm.assert_series_equal(result, expected) + + # raise + with tm.external_error_raised(pytz.AmbiguousTimeError): + getattr(df1.date.dt, method)("h", ambiguous="raise") + + @pytest.mark.parametrize( + "method, ts_str, freq", + [ + ["ceil", "2018-03-11 01:59:00-0600", "5min"], + ["round", "2018-03-11 01:59:00-0600", "5min"], + ["floor", "2018-03-11 03:01:00-0500", "2h"], + ], + ) + def test_dt_round_tz_nonexistent(self, method, ts_str, freq): + # GH 23324 round near "spring forward" DST + ser = Series([pd.Timestamp(ts_str, tz="America/Chicago")]) + result = getattr(ser.dt, method)(freq, nonexistent="shift_forward") + expected = Series([pd.Timestamp("2018-03-11 03:00:00", tz="America/Chicago")]) + tm.assert_series_equal(result, expected) + + result = getattr(ser.dt, method)(freq, nonexistent="NaT") + expected = Series([pd.NaT]).dt.tz_localize(result.dt.tz) + tm.assert_series_equal(result, expected) + + with pytest.raises(pytz.NonExistentTimeError, match="2018-03-11 02:00:00"): + getattr(ser.dt, method)(freq, nonexistent="raise") + + @pytest.mark.parametrize("freq", ["ns", "us", "1000us"]) + def test_dt_round_nonnano_higher_resolution_no_op(self, freq): + # GH 52761 + ser = Series( + ["2020-05-31 08:00:00", "2000-12-31 04:00:05", "1800-03-14 07:30:20"], + dtype="datetime64[ms]", + ) + expected = ser.copy() + result = ser.dt.round(freq) + tm.assert_series_equal(result, expected) + + assert not np.shares_memory(ser.array._ndarray, result.array._ndarray) + + def test_dt_namespace_accessor_categorical(self): + # GH 19468 + dti = DatetimeIndex(["20171111", "20181212"]).repeat(2) + ser = Series(pd.Categorical(dti), name="foo") + result = ser.dt.year + expected = Series([2017, 2017, 2018, 2018], dtype="int32", name="foo") + tm.assert_series_equal(result, expected) + + def test_dt_tz_localize_categorical(self, tz_aware_fixture): + # GH 27952 + tz = tz_aware_fixture + datetimes = Series( + ["2019-01-01", "2019-01-01", "2019-01-02"], dtype="datetime64[ns]" + ) + categorical = datetimes.astype("category") + result = categorical.dt.tz_localize(tz) + expected = datetimes.dt.tz_localize(tz) + tm.assert_series_equal(result, expected) + + def test_dt_tz_convert_categorical(self, tz_aware_fixture): + # GH 27952 + tz = tz_aware_fixture + datetimes = Series( + ["2019-01-01", "2019-01-01", "2019-01-02"], dtype="datetime64[ns, MET]" + ) + categorical = datetimes.astype("category") + result = categorical.dt.tz_convert(tz) + expected = datetimes.dt.tz_convert(tz) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("accessor", ["year", "month", "day"]) + def test_dt_other_accessors_categorical(self, accessor): + # GH 27952 + datetimes = Series( + ["2018-01-01", "2018-01-01", "2019-01-02"], dtype="datetime64[ns]" + ) + categorical = datetimes.astype("category") + result = getattr(categorical.dt, accessor) + expected = getattr(datetimes.dt, accessor) + tm.assert_series_equal(result, expected) + + def test_dt_accessor_no_new_attributes(self): + # https://github.com/pandas-dev/pandas/issues/10673 + ser = Series(date_range("20130101", periods=5, freq="D")) + with pytest.raises(AttributeError, match="You cannot add any new attribute"): + ser.dt.xlabel = "a" + + # error: Unsupported operand types for + ("List[None]" and "List[str]") + @pytest.mark.parametrize( + "time_locale", [None] + tm.get_locales() # type: ignore[operator] + ) + def test_dt_accessor_datetime_name_accessors(self, time_locale): + # Test Monday -> Sunday and January -> December, in that sequence + if time_locale is None: + # If the time_locale is None, day-name and month_name should + # return the english attributes + expected_days = [ + "Monday", + "Tuesday", + "Wednesday", + "Thursday", + "Friday", + "Saturday", + "Sunday", + ] + expected_months = [ + "January", + "February", + "March", + "April", + "May", + "June", + "July", + "August", + "September", + "October", + "November", + "December", + ] + else: + with tm.set_locale(time_locale, locale.LC_TIME): + expected_days = calendar.day_name[:] + expected_months = calendar.month_name[1:] + + ser = Series(date_range(freq="D", start=datetime(1998, 1, 1), periods=365)) + english_days = [ + "Monday", + "Tuesday", + "Wednesday", + "Thursday", + "Friday", + "Saturday", + "Sunday", + ] + for day, name, eng_name in zip(range(4, 11), expected_days, english_days): + name = name.capitalize() + assert ser.dt.day_name(locale=time_locale)[day] == name + assert ser.dt.day_name(locale=None)[day] == eng_name + ser = pd.concat([ser, Series([pd.NaT])]) + assert np.isnan(ser.dt.day_name(locale=time_locale).iloc[-1]) + + ser = Series(date_range(freq="ME", start="2012", end="2013")) + result = ser.dt.month_name(locale=time_locale) + expected = Series([month.capitalize() for month in expected_months]) + + # work around https://github.com/pandas-dev/pandas/issues/22342 + result = result.str.normalize("NFD") + expected = expected.str.normalize("NFD") + + tm.assert_series_equal(result, expected) + + for s_date, expected in zip(ser, expected_months): + result = s_date.month_name(locale=time_locale) + expected = expected.capitalize() + + result = unicodedata.normalize("NFD", result) + expected = unicodedata.normalize("NFD", expected) + + assert result == expected + + ser = pd.concat([ser, Series([pd.NaT])]) + assert np.isnan(ser.dt.month_name(locale=time_locale).iloc[-1]) + + def test_strftime(self): + # GH 10086 + ser = Series(date_range("20130101", periods=5)) + result = ser.dt.strftime("%Y/%m/%d") + expected = Series( + ["2013/01/01", "2013/01/02", "2013/01/03", "2013/01/04", "2013/01/05"] + ) + tm.assert_series_equal(result, expected) + + ser = Series(date_range("2015-02-03 11:22:33.4567", periods=5)) + result = ser.dt.strftime("%Y/%m/%d %H-%M-%S") + expected = Series( + [ + "2015/02/03 11-22-33", + "2015/02/04 11-22-33", + "2015/02/05 11-22-33", + "2015/02/06 11-22-33", + "2015/02/07 11-22-33", + ] + ) + tm.assert_series_equal(result, expected) + + ser = Series(period_range("20130101", periods=5)) + result = ser.dt.strftime("%Y/%m/%d") + expected = Series( + ["2013/01/01", "2013/01/02", "2013/01/03", "2013/01/04", "2013/01/05"] + ) + tm.assert_series_equal(result, expected) + + ser = Series(period_range("2015-02-03 11:22:33.4567", periods=5, freq="s")) + result = ser.dt.strftime("%Y/%m/%d %H-%M-%S") + expected = Series( + [ + "2015/02/03 11-22-33", + "2015/02/03 11-22-34", + "2015/02/03 11-22-35", + "2015/02/03 11-22-36", + "2015/02/03 11-22-37", + ] + ) + tm.assert_series_equal(result, expected) + + def test_strftime_dt64_days(self): + ser = Series(date_range("20130101", periods=5)) + ser.iloc[0] = pd.NaT + result = ser.dt.strftime("%Y/%m/%d") + expected = Series( + [np.nan, "2013/01/02", "2013/01/03", "2013/01/04", "2013/01/05"] + ) + tm.assert_series_equal(result, expected) + + datetime_index = date_range("20150301", periods=5) + result = datetime_index.strftime("%Y/%m/%d") + + expected = Index( + ["2015/03/01", "2015/03/02", "2015/03/03", "2015/03/04", "2015/03/05"], + ) + # dtype may be S10 or U10 depending on python version + tm.assert_index_equal(result, expected) + + def test_strftime_period_days(self, using_infer_string): + period_index = period_range("20150301", periods=5) + result = period_index.strftime("%Y/%m/%d") + expected = Index( + ["2015/03/01", "2015/03/02", "2015/03/03", "2015/03/04", "2015/03/05"], + dtype="=U10", + ) + if using_infer_string: + expected = expected.astype(StringDtype(na_value=np.nan)) + tm.assert_index_equal(result, expected) + + def test_strftime_dt64_microsecond_resolution(self): + ser = Series([datetime(2013, 1, 1, 2, 32, 59), datetime(2013, 1, 2, 14, 32, 1)]) + result = ser.dt.strftime("%Y-%m-%d %H:%M:%S") + expected = Series(["2013-01-01 02:32:59", "2013-01-02 14:32:01"]) + tm.assert_series_equal(result, expected) + + def test_strftime_period_hours(self): + ser = Series(period_range("20130101", periods=4, freq="h")) + result = ser.dt.strftime("%Y/%m/%d %H:%M:%S") + expected = Series( + [ + "2013/01/01 00:00:00", + "2013/01/01 01:00:00", + "2013/01/01 02:00:00", + "2013/01/01 03:00:00", + ] + ) + tm.assert_series_equal(result, expected) + + def test_strftime_period_minutes(self): + ser = Series(period_range("20130101", periods=4, freq="ms")) + result = ser.dt.strftime("%Y/%m/%d %H:%M:%S.%l") + expected = Series( + [ + "2013/01/01 00:00:00.000", + "2013/01/01 00:00:00.001", + "2013/01/01 00:00:00.002", + "2013/01/01 00:00:00.003", + ] + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "data", + [ + DatetimeIndex(["2019-01-01", pd.NaT]), + PeriodIndex(["2019-01-01", pd.NaT], dtype="period[D]"), + ], + ) + def test_strftime_nat(self, data): + # GH 29578 + ser = Series(data) + result = ser.dt.strftime("%Y-%m-%d") + expected = Series(["2019-01-01", np.nan]) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "data", [DatetimeIndex([pd.NaT]), PeriodIndex([pd.NaT], dtype="period[D]")] + ) + def test_strftime_all_nat(self, data): + # https://github.com/pandas-dev/pandas/issues/45858 + ser = Series(data) + with tm.assert_produces_warning(None): + result = ser.dt.strftime("%Y-%m-%d") + expected = Series([np.nan], dtype="str") + tm.assert_series_equal(result, expected) + + def test_valid_dt_with_missing_values(self): + # GH 8689 + ser = Series(date_range("20130101", periods=5, freq="D")) + ser.iloc[2] = pd.NaT + + for attr in ["microsecond", "nanosecond", "second", "minute", "hour", "day"]: + expected = getattr(ser.dt, attr).copy() + expected.iloc[2] = np.nan + result = getattr(ser.dt, attr) + tm.assert_series_equal(result, expected) + + result = ser.dt.date + expected = Series( + [ + date(2013, 1, 1), + date(2013, 1, 2), + pd.NaT, + date(2013, 1, 4), + date(2013, 1, 5), + ], + dtype="object", + ) + tm.assert_series_equal(result, expected) + + result = ser.dt.time + expected = Series([time(0), time(0), pd.NaT, time(0), time(0)], dtype="object") + tm.assert_series_equal(result, expected) + + def test_dt_accessor_api(self): + # GH 9322 + from pandas.core.indexes.accessors import ( + CombinedDatetimelikeProperties, + DatetimeProperties, + ) + + assert Series.dt is CombinedDatetimelikeProperties + + ser = Series(date_range("2000-01-01", periods=3)) + assert isinstance(ser.dt, DatetimeProperties) + + @pytest.mark.parametrize( + "ser", + [ + Series(np.arange(5)), + Series(list("abcde")), + Series(np.random.default_rng(2).standard_normal(5)), + ], + ) + def test_dt_accessor_invalid(self, ser): + # GH#9322 check that series with incorrect dtypes don't have attr + with pytest.raises(AttributeError, match="only use .dt accessor"): + ser.dt + assert not hasattr(ser, "dt") + + def test_dt_accessor_updates_on_inplace(self): + ser = Series(date_range("2018-01-01", periods=10)) + ser[2] = None + return_value = ser.fillna(pd.Timestamp("2018-01-01"), inplace=True) + assert return_value is None + result = ser.dt.date + assert result[0] == result[2] + + def test_date_tz(self): + # GH11757 + rng = DatetimeIndex( + ["2014-04-04 23:56", "2014-07-18 21:24", "2015-11-22 22:14"], + tz="US/Eastern", + ) + ser = Series(rng) + expected = Series([date(2014, 4, 4), date(2014, 7, 18), date(2015, 11, 22)]) + tm.assert_series_equal(ser.dt.date, expected) + tm.assert_series_equal(ser.apply(lambda x: x.date()), expected) + + def test_dt_timetz_accessor(self, tz_naive_fixture): + # GH21358 + tz = maybe_get_tz(tz_naive_fixture) + + dtindex = DatetimeIndex( + ["2014-04-04 23:56", "2014-07-18 21:24", "2015-11-22 22:14"], tz=tz + ) + ser = Series(dtindex) + expected = Series( + [time(23, 56, tzinfo=tz), time(21, 24, tzinfo=tz), time(22, 14, tzinfo=tz)] + ) + result = ser.dt.timetz + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "input_series, expected_output", + [ + [["2020-01-01"], [[2020, 1, 3]]], + [[pd.NaT], [[np.nan, np.nan, np.nan]]], + [["2019-12-31", "2019-12-29"], [[2020, 1, 2], [2019, 52, 7]]], + [["2010-01-01", pd.NaT], [[2009, 53, 5], [np.nan, np.nan, np.nan]]], + # see GH#36032 + [["2016-01-08", "2016-01-04"], [[2016, 1, 5], [2016, 1, 1]]], + [["2016-01-07", "2016-01-01"], [[2016, 1, 4], [2015, 53, 5]]], + ], + ) + def test_isocalendar(self, input_series, expected_output): + result = pd.to_datetime(Series(input_series)).dt.isocalendar() + expected_frame = DataFrame( + expected_output, columns=["year", "week", "day"], dtype="UInt32" + ) + tm.assert_frame_equal(result, expected_frame) + + def test_hour_index(self): + dt_series = Series( + date_range(start="2021-01-01", periods=5, freq="h"), + index=[2, 6, 7, 8, 11], + dtype="category", + ) + result = dt_series.dt.hour + expected = Series( + [0, 1, 2, 3, 4], + dtype="int32", + index=[2, 6, 7, 8, 11], + ) + tm.assert_series_equal(result, expected) + + +class TestSeriesPeriodValuesDtAccessor: + @pytest.mark.parametrize( + "input_vals", + [ + [Period("2016-01", freq="M"), Period("2016-02", freq="M")], + [Period("2016-01-01", freq="D"), Period("2016-01-02", freq="D")], + [ + Period("2016-01-01 00:00:00", freq="h"), + Period("2016-01-01 01:00:00", freq="h"), + ], + [ + Period("2016-01-01 00:00:00", freq="M"), + Period("2016-01-01 00:01:00", freq="M"), + ], + [ + Period("2016-01-01 00:00:00", freq="s"), + Period("2016-01-01 00:00:01", freq="s"), + ], + ], + ) + def test_end_time_timevalues(self, input_vals): + # GH#17157 + # Check that the time part of the Period is adjusted by end_time + # when using the dt accessor on a Series + input_vals = PeriodArray._from_sequence(np.asarray(input_vals)) + + ser = Series(input_vals) + result = ser.dt.end_time + expected = ser.apply(lambda x: x.end_time) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("input_vals", [("2001"), ("NaT")]) + def test_to_period(self, input_vals): + # GH#21205 + expected = Series([input_vals], dtype="Period[D]") + result = Series([input_vals], dtype="datetime64[ns]").dt.to_period("D") + tm.assert_series_equal(result, expected) + + +def test_normalize_pre_epoch_dates(): + # GH: 36294 + ser = pd.to_datetime(Series(["1969-01-01 09:00:00", "2016-01-01 09:00:00"])) + result = ser.dt.normalize() + expected = pd.to_datetime(Series(["1969-01-01", "2016-01-01"])) + tm.assert_series_equal(result, expected) + + +def test_day_attribute_non_nano_beyond_int32(): + # GH 52386 + data = np.array( + [ + 136457654736252, + 134736784364431, + 245345345545332, + 223432411, + 2343241, + 3634548734, + 23234, + ], + dtype="timedelta64[s]", + ) + ser = Series(data) + result = ser.dt.days + expected = Series([1579371003, 1559453522, 2839645203, 2586, 27, 42066, 0]) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/accessors/test_list_accessor.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/accessors/test_list_accessor.py new file mode 100644 index 0000000000000000000000000000000000000000..1c60567c1a5300dd4118ae729c3c784eabd4d747 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/accessors/test_list_accessor.py @@ -0,0 +1,129 @@ +import re + +import pytest + +from pandas import ( + ArrowDtype, + Series, +) +import pandas._testing as tm + +pa = pytest.importorskip("pyarrow") + +from pandas.compat import pa_version_under11p0 + + +@pytest.mark.parametrize( + "list_dtype", + ( + pa.list_(pa.int64()), + pa.list_(pa.int64(), list_size=3), + pa.large_list(pa.int64()), + ), +) +def test_list_getitem(list_dtype): + ser = Series( + [[1, 2, 3], [4, None, 5], None], + dtype=ArrowDtype(list_dtype), + ) + actual = ser.list[1] + expected = Series([2, None, None], dtype="int64[pyarrow]") + tm.assert_series_equal(actual, expected) + + +def test_list_getitem_slice(): + ser = Series( + [[1, 2, 3], [4, None, 5], None], + dtype=ArrowDtype(pa.list_(pa.int64())), + ) + if pa_version_under11p0: + with pytest.raises( + NotImplementedError, match="List slice not supported by pyarrow " + ): + ser.list[1:None:None] + else: + actual = ser.list[1:None:None] + expected = Series( + [[2, 3], [None, 5], None], dtype=ArrowDtype(pa.list_(pa.int64())) + ) + tm.assert_series_equal(actual, expected) + + +def test_list_len(): + ser = Series( + [[1, 2, 3], [4, None], None], + dtype=ArrowDtype(pa.list_(pa.int64())), + ) + actual = ser.list.len() + expected = Series([3, 2, None], dtype=ArrowDtype(pa.int32())) + tm.assert_series_equal(actual, expected) + + +def test_list_flatten(): + ser = Series( + [[1, 2, 3], [4, None], None], + dtype=ArrowDtype(pa.list_(pa.int64())), + ) + actual = ser.list.flatten() + expected = Series([1, 2, 3, 4, None], dtype=ArrowDtype(pa.int64())) + tm.assert_series_equal(actual, expected) + + +def test_list_getitem_slice_invalid(): + ser = Series( + [[1, 2, 3], [4, None, 5], None], + dtype=ArrowDtype(pa.list_(pa.int64())), + ) + if pa_version_under11p0: + with pytest.raises( + NotImplementedError, match="List slice not supported by pyarrow " + ): + ser.list[1:None:0] + else: + with pytest.raises(pa.lib.ArrowInvalid, match=re.escape("`step` must be >= 1")): + ser.list[1:None:0] + + +def test_list_accessor_non_list_dtype(): + ser = Series( + [1, 2, 4], + dtype=ArrowDtype(pa.int64()), + ) + with pytest.raises( + AttributeError, + match=re.escape( + "Can only use the '.list' accessor with 'list[pyarrow]' dtype, " + "not int64[pyarrow]." + ), + ): + ser.list[1:None:0] + + +@pytest.mark.parametrize( + "list_dtype", + ( + pa.list_(pa.int64()), + pa.list_(pa.int64(), list_size=3), + pa.large_list(pa.int64()), + ), +) +def test_list_getitem_invalid_index(list_dtype): + ser = Series( + [[1, 2, 3], [4, None, 5], None], + dtype=ArrowDtype(list_dtype), + ) + with pytest.raises(pa.lib.ArrowInvalid, match="Index -1 is out of bounds"): + ser.list[-1] + with pytest.raises(pa.lib.ArrowInvalid, match="Index 5 is out of bounds"): + ser.list[5] + with pytest.raises(ValueError, match="key must be an int or slice, got str"): + ser.list["abc"] + + +def test_list_accessor_not_iterable(): + ser = Series( + [[1, 2, 3], [4, None], None], + dtype=ArrowDtype(pa.list_(pa.int64())), + ) + with pytest.raises(TypeError, match="'ListAccessor' object is not iterable"): + iter(ser.list) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/accessors/test_sparse_accessor.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/accessors/test_sparse_accessor.py new file mode 100644 index 0000000000000000000000000000000000000000..118095b5dcdbc4f63ba511feb954673485f34427 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/accessors/test_sparse_accessor.py @@ -0,0 +1,9 @@ +from pandas import Series + + +class TestSparseAccessor: + def test_sparse_accessor_updates_on_inplace(self): + ser = Series([1, 1, 2, 3], dtype="Sparse[int]") + return_value = ser.drop([0, 1], inplace=True) + assert return_value is None + assert ser.sparse.density == 1.0 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/accessors/test_str_accessor.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/accessors/test_str_accessor.py new file mode 100644 index 0000000000000000000000000000000000000000..09d965ef1f32268550bbf35a17f11529545054ed --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/accessors/test_str_accessor.py @@ -0,0 +1,25 @@ +import pytest + +from pandas import Series +import pandas._testing as tm + + +class TestStrAccessor: + def test_str_attribute(self): + # GH#9068 + methods = ["strip", "rstrip", "lstrip"] + ser = Series([" jack", "jill ", " jesse ", "frank"]) + for method in methods: + expected = Series([getattr(str, method)(x) for x in ser.values]) + tm.assert_series_equal(getattr(Series.str, method)(ser.str), expected) + + # str accessor only valid with string values + ser = Series(range(5)) + with pytest.raises(AttributeError, match="only use .str accessor"): + ser.str.repeat(2) + + def test_str_accessor_updates_on_inplace(self): + ser = Series(list("abc")) + return_value = ser.drop([0], inplace=True) + assert return_value is None + assert len(ser.str.lower()) == 2 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/accessors/test_struct_accessor.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/accessors/test_struct_accessor.py new file mode 100644 index 0000000000000000000000000000000000000000..80aea75fda406cf77e0fda73a6d9849c393e7d83 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/accessors/test_struct_accessor.py @@ -0,0 +1,196 @@ +import re + +import pytest + +from pandas.compat.pyarrow import ( + pa_version_under11p0, + pa_version_under13p0, +) + +from pandas import ( + ArrowDtype, + DataFrame, + Index, + Series, +) +import pandas._testing as tm + +pa = pytest.importorskip("pyarrow") +pc = pytest.importorskip("pyarrow.compute") + + +def test_struct_accessor_dtypes(): + ser = Series( + [], + dtype=ArrowDtype( + pa.struct( + [ + ("int_col", pa.int64()), + ("string_col", pa.string()), + ( + "struct_col", + pa.struct( + [ + ("int_col", pa.int64()), + ("float_col", pa.float64()), + ] + ), + ), + ] + ) + ), + ) + actual = ser.struct.dtypes + expected = Series( + [ + ArrowDtype(pa.int64()), + ArrowDtype(pa.string()), + ArrowDtype( + pa.struct( + [ + ("int_col", pa.int64()), + ("float_col", pa.float64()), + ] + ) + ), + ], + index=Index(["int_col", "string_col", "struct_col"]), + ) + tm.assert_series_equal(actual, expected) + + +@pytest.mark.skipif(pa_version_under13p0, reason="pyarrow>=13.0.0 required") +def test_struct_accessor_field(): + index = Index([-100, 42, 123]) + ser = Series( + [ + {"rice": 1.0, "maize": -1, "wheat": "a"}, + {"rice": 2.0, "maize": 0, "wheat": "b"}, + {"rice": 3.0, "maize": 1, "wheat": "c"}, + ], + dtype=ArrowDtype( + pa.struct( + [ + ("rice", pa.float64()), + ("maize", pa.int64()), + ("wheat", pa.string()), + ] + ) + ), + index=index, + ) + by_name = ser.struct.field("maize") + by_name_expected = Series( + [-1, 0, 1], + dtype=ArrowDtype(pa.int64()), + index=index, + name="maize", + ) + tm.assert_series_equal(by_name, by_name_expected) + + by_index = ser.struct.field(2) + by_index_expected = Series( + ["a", "b", "c"], + dtype=ArrowDtype(pa.string()), + index=index, + name="wheat", + ) + tm.assert_series_equal(by_index, by_index_expected) + + +def test_struct_accessor_field_with_invalid_name_or_index(): + ser = Series([], dtype=ArrowDtype(pa.struct([("field", pa.int64())]))) + + with pytest.raises(ValueError, match="name_or_index must be an int, str,"): + ser.struct.field(1.1) + + +@pytest.mark.skipif(pa_version_under11p0, reason="pyarrow>=11.0.0 required") +def test_struct_accessor_explode(): + index = Index([-100, 42, 123]) + ser = Series( + [ + {"painted": 1, "snapping": {"sea": "green"}}, + {"painted": 2, "snapping": {"sea": "leatherback"}}, + {"painted": 3, "snapping": {"sea": "hawksbill"}}, + ], + dtype=ArrowDtype( + pa.struct( + [ + ("painted", pa.int64()), + ("snapping", pa.struct([("sea", pa.string())])), + ] + ) + ), + index=index, + ) + actual = ser.struct.explode() + expected = DataFrame( + { + "painted": Series([1, 2, 3], index=index, dtype=ArrowDtype(pa.int64())), + "snapping": Series( + [{"sea": "green"}, {"sea": "leatherback"}, {"sea": "hawksbill"}], + index=index, + dtype=ArrowDtype(pa.struct([("sea", pa.string())])), + ), + }, + ) + tm.assert_frame_equal(actual, expected) + + +@pytest.mark.parametrize( + "invalid", + [ + pytest.param(Series([1, 2, 3], dtype="int64"), id="int64"), + pytest.param( + Series(["a", "b", "c"], dtype="string[pyarrow]"), id="string-pyarrow" + ), + ], +) +def test_struct_accessor_api_for_invalid(invalid): + with pytest.raises( + AttributeError, + match=re.escape( + "Can only use the '.struct' accessor with 'struct[pyarrow]' dtype, " + f"not {invalid.dtype}." + ), + ): + invalid.struct + + +@pytest.mark.parametrize( + ["indices", "name"], + [ + (0, "int_col"), + ([1, 2], "str_col"), + (pc.field("int_col"), "int_col"), + ("int_col", "int_col"), + (b"string_col", b"string_col"), + ([b"string_col"], "string_col"), + ], +) +@pytest.mark.skipif(pa_version_under13p0, reason="pyarrow>=13.0.0 required") +def test_struct_accessor_field_expanded(indices, name): + arrow_type = pa.struct( + [ + ("int_col", pa.int64()), + ( + "struct_col", + pa.struct( + [ + ("int_col", pa.int64()), + ("float_col", pa.float64()), + ("str_col", pa.string()), + ] + ), + ), + (b"string_col", pa.string()), + ] + ) + + data = pa.array([], type=arrow_type) + ser = Series(data, dtype=ArrowDtype(arrow_type)) + expected = pc.struct_field(data, indices) + result = ser.struct.field(indices) + tm.assert_equal(result.array._pa_array.combine_chunks(), expected) + assert result.name == name diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_datetime.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_datetime.py new file mode 100644 index 0000000000000000000000000000000000000000..fc1c80eb4dec6e1b81114e8de181fc8f6b77d489 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_datetime.py @@ -0,0 +1,499 @@ +""" +Also test support for datetime64[ns] in Series / DataFrame +""" +from datetime import ( + datetime, + timedelta, +) +import re + +from dateutil.tz import ( + gettz, + tzutc, +) +import numpy as np +import pytest +import pytz + +from pandas._libs import index as libindex + +import pandas as pd +from pandas import ( + DataFrame, + Series, + Timestamp, + date_range, + period_range, +) +import pandas._testing as tm + + +def test_fancy_getitem(): + dti = date_range( + freq="WOM-1FRI", start=datetime(2005, 1, 1), end=datetime(2010, 1, 1) + ) + + s = Series(np.arange(len(dti)), index=dti) + + msg = "Series.__getitem__ treating keys as positions is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + assert s[48] == 48 + assert s["1/2/2009"] == 48 + assert s["2009-1-2"] == 48 + assert s[datetime(2009, 1, 2)] == 48 + assert s[Timestamp(datetime(2009, 1, 2))] == 48 + with pytest.raises(KeyError, match=r"^'2009-1-3'$"): + s["2009-1-3"] + tm.assert_series_equal( + s["3/6/2009":"2009-06-05"], s[datetime(2009, 3, 6) : datetime(2009, 6, 5)] + ) + + +def test_fancy_setitem(): + dti = date_range( + freq="WOM-1FRI", start=datetime(2005, 1, 1), end=datetime(2010, 1, 1) + ) + + s = Series(np.arange(len(dti)), index=dti) + + msg = "Series.__setitem__ treating keys as positions is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + s[48] = -1 + assert s.iloc[48] == -1 + s["1/2/2009"] = -2 + assert s.iloc[48] == -2 + s["1/2/2009":"2009-06-05"] = -3 + assert (s[48:54] == -3).all() + + +@pytest.mark.parametrize("tz_source", ["pytz", "dateutil"]) +def test_getitem_setitem_datetime_tz(tz_source): + if tz_source == "pytz": + tzget = pytz.timezone + else: + # handle special case for utc in dateutil + tzget = lambda x: tzutc() if x == "UTC" else gettz(x) + + N = 50 + # testing with timezone, GH #2785 + rng = date_range("1/1/1990", periods=N, freq="h", tz=tzget("US/Eastern")) + ts = Series(np.random.default_rng(2).standard_normal(N), index=rng) + + # also test Timestamp tz handling, GH #2789 + result = ts.copy() + result["1990-01-01 09:00:00+00:00"] = 0 + result["1990-01-01 09:00:00+00:00"] = ts.iloc[4] + tm.assert_series_equal(result, ts) + + result = ts.copy() + result["1990-01-01 03:00:00-06:00"] = 0 + result["1990-01-01 03:00:00-06:00"] = ts.iloc[4] + tm.assert_series_equal(result, ts) + + # repeat with datetimes + result = ts.copy() + result[datetime(1990, 1, 1, 9, tzinfo=tzget("UTC"))] = 0 + result[datetime(1990, 1, 1, 9, tzinfo=tzget("UTC"))] = ts.iloc[4] + tm.assert_series_equal(result, ts) + + result = ts.copy() + dt = Timestamp(1990, 1, 1, 3).tz_localize(tzget("US/Central")) + dt = dt.to_pydatetime() + result[dt] = 0 + result[dt] = ts.iloc[4] + tm.assert_series_equal(result, ts) + + +def test_getitem_setitem_datetimeindex(): + N = 50 + # testing with timezone, GH #2785 + rng = date_range("1/1/1990", periods=N, freq="h", tz="US/Eastern") + ts = Series(np.random.default_rng(2).standard_normal(N), index=rng) + + result = ts["1990-01-01 04:00:00"] + expected = ts.iloc[4] + assert result == expected + + result = ts.copy() + result["1990-01-01 04:00:00"] = 0 + result["1990-01-01 04:00:00"] = ts.iloc[4] + tm.assert_series_equal(result, ts) + + result = ts["1990-01-01 04:00:00":"1990-01-01 07:00:00"] + expected = ts[4:8] + tm.assert_series_equal(result, expected) + + result = ts.copy() + result["1990-01-01 04:00:00":"1990-01-01 07:00:00"] = 0 + result["1990-01-01 04:00:00":"1990-01-01 07:00:00"] = ts[4:8] + tm.assert_series_equal(result, ts) + + lb = "1990-01-01 04:00:00" + rb = "1990-01-01 07:00:00" + # GH#18435 strings get a pass from tzawareness compat + result = ts[(ts.index >= lb) & (ts.index <= rb)] + expected = ts[4:8] + tm.assert_series_equal(result, expected) + + lb = "1990-01-01 04:00:00-0500" + rb = "1990-01-01 07:00:00-0500" + result = ts[(ts.index >= lb) & (ts.index <= rb)] + expected = ts[4:8] + tm.assert_series_equal(result, expected) + + # But we do not give datetimes a pass on tzawareness compat + msg = "Cannot compare tz-naive and tz-aware datetime-like objects" + naive = datetime(1990, 1, 1, 4) + for key in [naive, Timestamp(naive), np.datetime64(naive, "ns")]: + with pytest.raises(KeyError, match=re.escape(repr(key))): + # GH#36148 as of 2.0 we require tzawareness-compat + ts[key] + + result = ts.copy() + # GH#36148 as of 2.0 we do not ignore tzawareness mismatch in indexing, + # so setting it as a new key casts to object rather than matching + # rng[4] + result[naive] = ts.iloc[4] + assert result.index.dtype == object + tm.assert_index_equal(result.index[:-1], rng.astype(object)) + assert result.index[-1] == naive + + msg = "Cannot compare tz-naive and tz-aware datetime-like objects" + with pytest.raises(TypeError, match=msg): + # GH#36148 require tzawareness compat as of 2.0 + ts[naive : datetime(1990, 1, 1, 7)] + + result = ts.copy() + with pytest.raises(TypeError, match=msg): + # GH#36148 require tzawareness compat as of 2.0 + result[naive : datetime(1990, 1, 1, 7)] = 0 + with pytest.raises(TypeError, match=msg): + # GH#36148 require tzawareness compat as of 2.0 + result[naive : datetime(1990, 1, 1, 7)] = 99 + # the __setitems__ here failed, so result should still match ts + tm.assert_series_equal(result, ts) + + lb = naive + rb = datetime(1990, 1, 1, 7) + msg = r"Invalid comparison between dtype=datetime64\[ns, US/Eastern\] and datetime" + with pytest.raises(TypeError, match=msg): + # tznaive vs tzaware comparison is invalid + # see GH#18376, GH#18162 + ts[(ts.index >= lb) & (ts.index <= rb)] + + lb = Timestamp(naive).tz_localize(rng.tzinfo) + rb = Timestamp(datetime(1990, 1, 1, 7)).tz_localize(rng.tzinfo) + result = ts[(ts.index >= lb) & (ts.index <= rb)] + expected = ts[4:8] + tm.assert_series_equal(result, expected) + + result = ts[ts.index[4]] + expected = ts.iloc[4] + assert result == expected + + result = ts[ts.index[4:8]] + expected = ts[4:8] + tm.assert_series_equal(result, expected) + + result = ts.copy() + result[ts.index[4:8]] = 0 + result.iloc[4:8] = ts.iloc[4:8] + tm.assert_series_equal(result, ts) + + # also test partial date slicing + result = ts["1990-01-02"] + expected = ts[24:48] + tm.assert_series_equal(result, expected) + + result = ts.copy() + result["1990-01-02"] = 0 + result["1990-01-02"] = ts[24:48] + tm.assert_series_equal(result, ts) + + +def test_getitem_setitem_periodindex(): + N = 50 + rng = period_range("1/1/1990", periods=N, freq="h") + ts = Series(np.random.default_rng(2).standard_normal(N), index=rng) + + result = ts["1990-01-01 04"] + expected = ts.iloc[4] + assert result == expected + + result = ts.copy() + result["1990-01-01 04"] = 0 + result["1990-01-01 04"] = ts.iloc[4] + tm.assert_series_equal(result, ts) + + result = ts["1990-01-01 04":"1990-01-01 07"] + expected = ts[4:8] + tm.assert_series_equal(result, expected) + + result = ts.copy() + result["1990-01-01 04":"1990-01-01 07"] = 0 + result["1990-01-01 04":"1990-01-01 07"] = ts[4:8] + tm.assert_series_equal(result, ts) + + lb = "1990-01-01 04" + rb = "1990-01-01 07" + result = ts[(ts.index >= lb) & (ts.index <= rb)] + expected = ts[4:8] + tm.assert_series_equal(result, expected) + + # GH 2782 + result = ts[ts.index[4]] + expected = ts.iloc[4] + assert result == expected + + result = ts[ts.index[4:8]] + expected = ts[4:8] + tm.assert_series_equal(result, expected) + + result = ts.copy() + result[ts.index[4:8]] = 0 + result.iloc[4:8] = ts.iloc[4:8] + tm.assert_series_equal(result, ts) + + +def test_datetime_indexing(): + index = date_range("1/1/2000", "1/7/2000") + index = index.repeat(3) + + s = Series(len(index), index=index) + stamp = Timestamp("1/8/2000") + + with pytest.raises(KeyError, match=re.escape(repr(stamp))): + s[stamp] + s[stamp] = 0 + assert s[stamp] == 0 + + # not monotonic + s = Series(len(index), index=index) + s = s[::-1] + + with pytest.raises(KeyError, match=re.escape(repr(stamp))): + s[stamp] + s[stamp] = 0 + assert s[stamp] == 0 + + +# test duplicates in time series + + +def test_indexing_with_duplicate_datetimeindex( + rand_series_with_duplicate_datetimeindex, +): + ts = rand_series_with_duplicate_datetimeindex + + uniques = ts.index.unique() + for date in uniques: + result = ts[date] + + mask = ts.index == date + total = (ts.index == date).sum() + expected = ts[mask] + if total > 1: + tm.assert_series_equal(result, expected) + else: + tm.assert_almost_equal(result, expected.iloc[0]) + + cp = ts.copy() + cp[date] = 0 + expected = Series(np.where(mask, 0, ts), index=ts.index) + tm.assert_series_equal(cp, expected) + + key = datetime(2000, 1, 6) + with pytest.raises(KeyError, match=re.escape(repr(key))): + ts[key] + + # new index + ts[datetime(2000, 1, 6)] = 0 + assert ts[datetime(2000, 1, 6)] == 0 + + +def test_loc_getitem_over_size_cutoff(monkeypatch): + # #1821 + + monkeypatch.setattr(libindex, "_SIZE_CUTOFF", 1000) + + # create large list of non periodic datetime + dates = [] + sec = timedelta(seconds=1) + half_sec = timedelta(microseconds=500000) + d = datetime(2011, 12, 5, 20, 30) + n = 1100 + for i in range(n): + dates.append(d) + dates.append(d + sec) + dates.append(d + sec + half_sec) + dates.append(d + sec + sec + half_sec) + d += 3 * sec + + # duplicate some values in the list + duplicate_positions = np.random.default_rng(2).integers(0, len(dates) - 1, 20) + for p in duplicate_positions: + dates[p + 1] = dates[p] + + df = DataFrame( + np.random.default_rng(2).standard_normal((len(dates), 4)), + index=dates, + columns=list("ABCD"), + ) + + pos = n * 3 + timestamp = df.index[pos] + assert timestamp in df.index + + # it works! + df.loc[timestamp] + assert len(df.loc[[timestamp]]) > 0 + + +def test_indexing_over_size_cutoff_period_index(monkeypatch): + # GH 27136 + + monkeypatch.setattr(libindex, "_SIZE_CUTOFF", 1000) + + n = 1100 + idx = period_range("1/1/2000", freq="min", periods=n) + assert idx._engine.over_size_threshold + + s = Series(np.random.default_rng(2).standard_normal(len(idx)), index=idx) + + pos = n - 1 + timestamp = idx[pos] + assert timestamp in s.index + + # it works! + s[timestamp] + assert len(s.loc[[timestamp]]) > 0 + + +def test_indexing_unordered(): + # GH 2437 + rng = date_range(start="2011-01-01", end="2011-01-15") + ts = Series(np.random.default_rng(2).random(len(rng)), index=rng) + ts2 = pd.concat([ts[0:4], ts[-4:], ts[4:-4]]) + + for t in ts.index: + expected = ts[t] + result = ts2[t] + assert expected == result + + # GH 3448 (ranges) + def compare(slobj): + result = ts2[slobj].copy() + result = result.sort_index() + expected = ts[slobj] + expected.index = expected.index._with_freq(None) + tm.assert_series_equal(result, expected) + + for key in [ + slice("2011-01-01", "2011-01-15"), + slice("2010-12-30", "2011-01-15"), + slice("2011-01-01", "2011-01-16"), + # partial ranges + slice("2011-01-01", "2011-01-6"), + slice("2011-01-06", "2011-01-8"), + slice("2011-01-06", "2011-01-12"), + ]: + with pytest.raises( + KeyError, match="Value based partial slicing on non-monotonic" + ): + compare(key) + + # single values + result = ts2["2011"].sort_index() + expected = ts["2011"] + expected.index = expected.index._with_freq(None) + tm.assert_series_equal(result, expected) + + +def test_indexing_unordered2(): + # diff freq + rng = date_range(datetime(2005, 1, 1), periods=20, freq="ME") + ts = Series(np.arange(len(rng)), index=rng) + ts = ts.take(np.random.default_rng(2).permutation(20)) + + result = ts["2005"] + for t in result.index: + assert t.year == 2005 + + +def test_indexing(): + idx = date_range("2001-1-1", periods=20, freq="ME") + ts = Series(np.random.default_rng(2).random(len(idx)), index=idx) + + # getting + + # GH 3070, make sure semantics work on Series/Frame + result = ts["2001"] + tm.assert_series_equal(result, ts.iloc[:12]) + + df = DataFrame({"A": ts.copy()}) + + # GH#36179 pre-2.0 df["2001"] operated as slicing on rows. in 2.0 it behaves + # like any other key, so raises + with pytest.raises(KeyError, match="2001"): + df["2001"] + + # setting + ts = Series(np.random.default_rng(2).random(len(idx)), index=idx) + expected = ts.copy() + expected.iloc[:12] = 1 + ts["2001"] = 1 + tm.assert_series_equal(ts, expected) + + expected = df.copy() + expected.iloc[:12, 0] = 1 + df.loc["2001", "A"] = 1 + tm.assert_frame_equal(df, expected) + + +def test_getitem_str_month_with_datetimeindex(): + # GH3546 (not including times on the last day) + idx = date_range(start="2013-05-31 00:00", end="2013-05-31 23:00", freq="h") + ts = Series(range(len(idx)), index=idx) + expected = ts["2013-05"] + tm.assert_series_equal(expected, ts) + + idx = date_range(start="2013-05-31 00:00", end="2013-05-31 23:59", freq="s") + ts = Series(range(len(idx)), index=idx) + expected = ts["2013-05"] + tm.assert_series_equal(expected, ts) + + +def test_getitem_str_year_with_datetimeindex(): + idx = [ + Timestamp("2013-05-31 00:00"), + Timestamp(datetime(2013, 5, 31, 23, 59, 59, 999999)), + ] + ts = Series(range(len(idx)), index=idx) + expected = ts["2013"] + tm.assert_series_equal(expected, ts) + + +def test_getitem_str_second_with_datetimeindex(): + # GH14826, indexing with a seconds resolution string / datetime object + df = DataFrame( + np.random.default_rng(2).random((5, 5)), + columns=["open", "high", "low", "close", "volume"], + index=date_range("2012-01-02 18:01:00", periods=5, tz="US/Central", freq="s"), + ) + + # this is a single date, so will raise + with pytest.raises(KeyError, match=r"^'2012-01-02 18:01:02'$"): + df["2012-01-02 18:01:02"] + + msg = r"Timestamp\('2012-01-02 18:01:02-0600', tz='US/Central'\)" + with pytest.raises(KeyError, match=msg): + df[df.index[2]] + + +def test_compare_datetime_with_all_none(): + # GH#54870 + ser = Series(["2020-01-01", "2020-01-02"], dtype="datetime64[ns]") + ser2 = Series([None, None]) + result = ser > ser2 + expected = Series([False, False]) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_delitem.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_delitem.py new file mode 100644 index 0000000000000000000000000000000000000000..7440ef2692c47fbce129f06276a8e8761a23cf2b --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_delitem.py @@ -0,0 +1,69 @@ +import pytest + +from pandas import ( + Index, + Series, + date_range, +) +import pandas._testing as tm + + +class TestSeriesDelItem: + def test_delitem(self): + # GH#5542 + # should delete the item inplace + s = Series(range(5)) + del s[0] + + expected = Series(range(1, 5), index=range(1, 5)) + tm.assert_series_equal(s, expected) + + del s[1] + expected = Series(range(2, 5), index=range(2, 5)) + tm.assert_series_equal(s, expected) + + # only 1 left, del, add, del + s = Series(1) + del s[0] + tm.assert_series_equal(s, Series(dtype="int64", index=Index([], dtype="int64"))) + s[0] = 1 + tm.assert_series_equal(s, Series(1)) + del s[0] + tm.assert_series_equal(s, Series(dtype="int64", index=Index([], dtype="int64"))) + + def test_delitem_object_index(self): + # Index(dtype=object) + s = Series(1, index=Index(["a"], dtype="str")) + del s["a"] + tm.assert_series_equal(s, Series(dtype="int64", index=Index([], dtype="str"))) + s["a"] = 1 + tm.assert_series_equal(s, Series(1, index=Index(["a"], dtype="str"))) + del s["a"] + tm.assert_series_equal(s, Series(dtype="int64", index=Index([], dtype="str"))) + + def test_delitem_missing_key(self): + # empty + s = Series(dtype=object) + + with pytest.raises(KeyError, match=r"^0$"): + del s[0] + + def test_delitem_extension_dtype(self): + # GH#40386 + # DatetimeTZDtype + dti = date_range("2016-01-01", periods=3, tz="US/Pacific") + ser = Series(dti) + + expected = ser[[0, 2]] + del ser[1] + assert ser.dtype == dti.dtype + tm.assert_series_equal(ser, expected) + + # PeriodDtype + pi = dti.tz_localize(None).to_period("D") + ser = Series(pi) + + expected = ser[:2] + del ser[2] + assert ser.dtype == pi.dtype + tm.assert_series_equal(ser, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_get.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_get.py new file mode 100644 index 0000000000000000000000000000000000000000..1f3711ad919036a779c5357e3e6ae921bb795458 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_get.py @@ -0,0 +1,238 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DatetimeIndex, + Index, + Series, + date_range, +) +import pandas._testing as tm + + +def test_get(): + # GH 6383 + s = Series( + np.array( + [ + 43, + 48, + 60, + 48, + 50, + 51, + 50, + 45, + 57, + 48, + 56, + 45, + 51, + 39, + 55, + 43, + 54, + 52, + 51, + 54, + ] + ) + ) + + result = s.get(25, 0) + expected = 0 + assert result == expected + + s = Series( + np.array( + [ + 43, + 48, + 60, + 48, + 50, + 51, + 50, + 45, + 57, + 48, + 56, + 45, + 51, + 39, + 55, + 43, + 54, + 52, + 51, + 54, + ] + ), + index=Index( + [ + 25.0, + 36.0, + 49.0, + 64.0, + 81.0, + 100.0, + 121.0, + 144.0, + 169.0, + 196.0, + 1225.0, + 1296.0, + 1369.0, + 1444.0, + 1521.0, + 1600.0, + 1681.0, + 1764.0, + 1849.0, + 1936.0, + ], + dtype=np.float64, + ), + ) + + result = s.get(25, 0) + expected = 43 + assert result == expected + + # GH 7407 + # with a boolean accessor + df = pd.DataFrame({"i": [0] * 3, "b": [False] * 3}) + vc = df.i.value_counts() + result = vc.get(99, default="Missing") + assert result == "Missing" + + vc = df.b.value_counts() + result = vc.get(False, default="Missing") + assert result == 3 + + result = vc.get(True, default="Missing") + assert result == "Missing" + + +def test_get_nan(float_numpy_dtype): + # GH 8569 + s = Index(range(10), dtype=float_numpy_dtype).to_series() + assert s.get(np.nan) is None + assert s.get(np.nan, default="Missing") == "Missing" + + +def test_get_nan_multiple(float_numpy_dtype): + # GH 8569 + # ensure that fixing "test_get_nan" above hasn't broken get + # with multiple elements + s = Index(range(10), dtype=float_numpy_dtype).to_series() + + idx = [2, 30] + assert s.get(idx) is None + + idx = [2, np.nan] + assert s.get(idx) is None + + # GH 17295 - all missing keys + idx = [20, 30] + assert s.get(idx) is None + + idx = [np.nan, np.nan] + assert s.get(idx) is None + + +def test_get_with_default(): + # GH#7725 + d0 = ["a", "b", "c", "d"] + d1 = np.arange(4, dtype="int64") + + for data, index in ((d0, d1), (d1, d0)): + s = Series(data, index=index) + for i, d in zip(index, data): + assert s.get(i) == d + assert s.get(i, d) == d + assert s.get(i, "z") == d + + assert s.get("e", "z") == "z" + assert s.get("e", "e") == "e" + + msg = "Series.__getitem__ treating keys as positions is deprecated" + warn = None + if index is d0: + warn = FutureWarning + with tm.assert_produces_warning(warn, match=msg): + assert s.get(10, "z") == "z" + assert s.get(10, 10) == 10 + + +@pytest.mark.parametrize( + "arr", + [ + np.random.default_rng(2).standard_normal(10), + DatetimeIndex(date_range("2020-01-01", periods=10), name="a").tz_localize( + tz="US/Eastern" + ), + ], +) +def test_get_with_ea(arr): + # GH#21260 + ser = Series(arr, index=[2 * i for i in range(len(arr))]) + assert ser.get(4) == ser.iloc[2] + + result = ser.get([4, 6]) + expected = ser.iloc[[2, 3]] + tm.assert_series_equal(result, expected) + + result = ser.get(slice(2)) + expected = ser.iloc[[0, 1]] + tm.assert_series_equal(result, expected) + + assert ser.get(-1) is None + assert ser.get(ser.index.max() + 1) is None + + ser = Series(arr[:6], index=list("abcdef")) + assert ser.get("c") == ser.iloc[2] + + result = ser.get(slice("b", "d")) + expected = ser.iloc[[1, 2, 3]] + tm.assert_series_equal(result, expected) + + result = ser.get("Z") + assert result is None + + msg = "Series.__getitem__ treating keys as positions is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + assert ser.get(4) == ser.iloc[4] + with tm.assert_produces_warning(FutureWarning, match=msg): + assert ser.get(-1) == ser.iloc[-1] + with tm.assert_produces_warning(FutureWarning, match=msg): + assert ser.get(len(ser)) is None + + # GH#21257 + ser = Series(arr) + ser2 = ser[::2] + assert ser2.get(1) is None + + +def test_getitem_get(string_series, object_series): + msg = "Series.__getitem__ treating keys as positions is deprecated" + + for obj in [string_series, object_series]: + idx = obj.index[5] + + assert obj[idx] == obj.get(idx) + assert obj[idx] == obj.iloc[5] + + with tm.assert_produces_warning(FutureWarning, match=msg): + assert string_series.get(-1) == string_series.get(string_series.index[-1]) + assert string_series.iloc[5] == string_series.get(string_series.index[5]) + + +def test_get_none(): + # GH#5652 + s1 = Series(dtype=object) + s2 = Series(dtype=object, index=list("abc")) + for s in [s1, s2]: + result = s.get(None) + assert result is None diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_getitem.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_getitem.py new file mode 100644 index 0000000000000000000000000000000000000000..9891684e9597c00ade0c9f2b5c69bb0cf4d6df46 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_getitem.py @@ -0,0 +1,733 @@ +""" +Series.__getitem__ test classes are organized by the type of key passed. +""" +from datetime import ( + date, + datetime, + time, +) + +import numpy as np +import pytest + +from pandas._libs.tslibs import ( + conversion, + timezones, +) + +from pandas.core.dtypes.common import is_scalar + +import pandas as pd +from pandas import ( + Categorical, + DataFrame, + DatetimeIndex, + Index, + Series, + Timestamp, + date_range, + period_range, + timedelta_range, +) +import pandas._testing as tm +from pandas.core.indexing import IndexingError + +from pandas.tseries.offsets import BDay + + +class TestSeriesGetitemScalars: + def test_getitem_object_index_float_string(self): + # GH#17286 + ser = Series([1] * 4, index=Index(["a", "b", "c", 1.0])) + assert ser["a"] == 1 + assert ser[1.0] == 1 + + def test_getitem_float_keys_tuple_values(self): + # see GH#13509 + + # unique Index + ser = Series([(1, 1), (2, 2), (3, 3)], index=[0.0, 0.1, 0.2], name="foo") + result = ser[0.0] + assert result == (1, 1) + + # non-unique Index + expected = Series([(1, 1), (2, 2)], index=[0.0, 0.0], name="foo") + ser = Series([(1, 1), (2, 2), (3, 3)], index=[0.0, 0.0, 0.2], name="foo") + + result = ser[0.0] + tm.assert_series_equal(result, expected) + + def test_getitem_unrecognized_scalar(self): + # GH#32684 a scalar key that is not recognized by lib.is_scalar + + # a series that might be produced via `frame.dtypes` + ser = Series([1, 2], index=[np.dtype("O"), np.dtype("i8")]) + + key = ser.index[1] + + result = ser[key] + assert result == 2 + + def test_getitem_negative_out_of_bounds(self): + ser = Series(["a"] * 10, index=["a"] * 10) + + msg = "index -11 is out of bounds for axis 0 with size 10|index out of bounds" + warn_msg = "Series.__getitem__ treating keys as positions is deprecated" + with pytest.raises(IndexError, match=msg): + with tm.assert_produces_warning(FutureWarning, match=warn_msg): + ser[-11] + + def test_getitem_out_of_bounds_indexerror(self, datetime_series): + # don't segfault, GH#495 + msg = r"index \d+ is out of bounds for axis 0 with size \d+" + warn_msg = "Series.__getitem__ treating keys as positions is deprecated" + with pytest.raises(IndexError, match=msg): + with tm.assert_produces_warning(FutureWarning, match=warn_msg): + datetime_series[len(datetime_series)] + + def test_getitem_out_of_bounds_empty_rangeindex_keyerror(self): + # GH#917 + # With a RangeIndex, an int key gives a KeyError + ser = Series([], dtype=object) + with pytest.raises(KeyError, match="-1"): + ser[-1] + + def test_getitem_keyerror_with_integer_index(self, any_int_numpy_dtype): + dtype = any_int_numpy_dtype + ser = Series( + np.random.default_rng(2).standard_normal(6), + index=Index([0, 0, 1, 1, 2, 2], dtype=dtype), + ) + + with pytest.raises(KeyError, match=r"^5$"): + ser[5] + + with pytest.raises(KeyError, match=r"^'c'$"): + ser["c"] + + # not monotonic + ser = Series( + np.random.default_rng(2).standard_normal(6), index=[2, 2, 0, 0, 1, 1] + ) + + with pytest.raises(KeyError, match=r"^5$"): + ser[5] + + with pytest.raises(KeyError, match=r"^'c'$"): + ser["c"] + + def test_getitem_int64(self, datetime_series): + idx = np.int64(5) + msg = "Series.__getitem__ treating keys as positions is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + res = datetime_series[idx] + assert res == datetime_series.iloc[5] + + def test_getitem_full_range(self): + # github.com/pandas-dev/pandas/commit/4f433773141d2eb384325714a2776bcc5b2e20f7 + ser = Series(range(5), index=list(range(5))) + result = ser[list(range(5))] + tm.assert_series_equal(result, ser) + + # ------------------------------------------------------------------ + # Series with DatetimeIndex + + @pytest.mark.parametrize("tzstr", ["Europe/Berlin", "dateutil/Europe/Berlin"]) + def test_getitem_pydatetime_tz(self, tzstr): + tz = timezones.maybe_get_tz(tzstr) + + index = date_range( + start="2012-12-24 16:00", end="2012-12-24 18:00", freq="h", tz=tzstr + ) + ts = Series(index=index, data=index.hour) + time_pandas = Timestamp("2012-12-24 17:00", tz=tzstr) + + dt = datetime(2012, 12, 24, 17, 0) + time_datetime = conversion.localize_pydatetime(dt, tz) + assert ts[time_pandas] == ts[time_datetime] + + @pytest.mark.parametrize("tz", ["US/Eastern", "dateutil/US/Eastern"]) + def test_string_index_alias_tz_aware(self, tz): + rng = date_range("1/1/2000", periods=10, tz=tz) + ser = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng) + + result = ser["1/3/2000"] + tm.assert_almost_equal(result, ser.iloc[2]) + + def test_getitem_time_object(self): + rng = date_range("1/1/2000", "1/5/2000", freq="5min") + ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng) + + mask = (rng.hour == 9) & (rng.minute == 30) + result = ts[time(9, 30)] + expected = ts[mask] + result.index = result.index._with_freq(None) + tm.assert_series_equal(result, expected) + + # ------------------------------------------------------------------ + # Series with CategoricalIndex + + def test_getitem_scalar_categorical_index(self): + cats = Categorical([Timestamp("12-31-1999"), Timestamp("12-31-2000")]) + + ser = Series([1, 2], index=cats) + + expected = ser.iloc[0] + result = ser[cats[0]] + assert result == expected + + def test_getitem_numeric_categorical_listlike_matches_scalar(self): + # GH#15470 + ser = Series(["a", "b", "c"], index=pd.CategoricalIndex([2, 1, 0])) + + # 0 is treated as a label + assert ser[0] == "c" + + # the listlike analogue should also be treated as labels + res = ser[[0]] + expected = ser.iloc[-1:] + tm.assert_series_equal(res, expected) + + res2 = ser[[0, 1, 2]] + tm.assert_series_equal(res2, ser.iloc[::-1]) + + def test_getitem_integer_categorical_not_positional(self): + # GH#14865 + ser = Series(["a", "b", "c"], index=Index([1, 2, 3], dtype="category")) + assert ser.get(3) == "c" + assert ser[3] == "c" + + def test_getitem_str_with_timedeltaindex(self): + rng = timedelta_range("1 day 10:11:12", freq="h", periods=500) + ser = Series(np.arange(len(rng)), index=rng) + + key = "6 days, 23:11:12" + indexer = rng.get_loc(key) + assert indexer == 133 + + result = ser[key] + assert result == ser.iloc[133] + + msg = r"^Timedelta\('50 days 00:00:00'\)$" + with pytest.raises(KeyError, match=msg): + rng.get_loc("50 days") + with pytest.raises(KeyError, match=msg): + ser["50 days"] + + def test_getitem_bool_index_positional(self): + # GH#48653 + ser = Series({True: 1, False: 0}) + msg = "Series.__getitem__ treating keys as positions is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = ser[0] + assert result == 1 + + +class TestSeriesGetitemSlices: + def test_getitem_partial_str_slice_with_datetimeindex(self): + # GH#34860 + arr = date_range("1/1/2008", "1/1/2009") + ser = arr.to_series() + result = ser["2008"] + + rng = date_range(start="2008-01-01", end="2008-12-31") + expected = Series(rng, index=rng) + + tm.assert_series_equal(result, expected) + + def test_getitem_slice_strings_with_datetimeindex(self): + idx = DatetimeIndex( + ["1/1/2000", "1/2/2000", "1/2/2000", "1/3/2000", "1/4/2000"] + ) + + ts = Series(np.random.default_rng(2).standard_normal(len(idx)), index=idx) + + result = ts["1/2/2000":] + expected = ts[1:] + tm.assert_series_equal(result, expected) + + result = ts["1/2/2000":"1/3/2000"] + expected = ts[1:4] + tm.assert_series_equal(result, expected) + + def test_getitem_partial_str_slice_with_timedeltaindex(self): + rng = timedelta_range("1 day 10:11:12", freq="h", periods=500) + ser = Series(np.arange(len(rng)), index=rng) + + result = ser["5 day":"6 day"] + expected = ser.iloc[86:134] + tm.assert_series_equal(result, expected) + + result = ser["5 day":] + expected = ser.iloc[86:] + tm.assert_series_equal(result, expected) + + result = ser[:"6 day"] + expected = ser.iloc[:134] + tm.assert_series_equal(result, expected) + + def test_getitem_partial_str_slice_high_reso_with_timedeltaindex(self): + # higher reso + rng = timedelta_range("1 day 10:11:12", freq="us", periods=2000) + ser = Series(np.arange(len(rng)), index=rng) + + result = ser["1 day 10:11:12":] + expected = ser.iloc[0:] + tm.assert_series_equal(result, expected) + + result = ser["1 day 10:11:12.001":] + expected = ser.iloc[1000:] + tm.assert_series_equal(result, expected) + + result = ser["1 days, 10:11:12.001001"] + assert result == ser.iloc[1001] + + def test_getitem_slice_2d(self, datetime_series): + # GH#30588 multi-dimensional indexing deprecated + with pytest.raises(ValueError, match="Multi-dimensional indexing"): + datetime_series[:, np.newaxis] + + def test_getitem_median_slice_bug(self): + index = date_range("20090415", "20090519", freq="2B") + ser = Series(np.random.default_rng(2).standard_normal(13), index=index) + + indexer = [slice(6, 7, None)] + msg = "Indexing with a single-item list" + with pytest.raises(ValueError, match=msg): + # GH#31299 + ser[indexer] + # but we're OK with a single-element tuple + result = ser[(indexer[0],)] + expected = ser[indexer[0]] + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "slc, positions", + [ + [slice(date(2018, 1, 1), None), [0, 1, 2]], + [slice(date(2019, 1, 2), None), [2]], + [slice(date(2020, 1, 1), None), []], + [slice(None, date(2020, 1, 1)), [0, 1, 2]], + [slice(None, date(2019, 1, 1)), [0]], + ], + ) + def test_getitem_slice_date(self, slc, positions): + # https://github.com/pandas-dev/pandas/issues/31501 + ser = Series( + [0, 1, 2], + DatetimeIndex(["2019-01-01", "2019-01-01T06:00:00", "2019-01-02"]), + ) + result = ser[slc] + expected = ser.take(positions) + tm.assert_series_equal(result, expected) + + def test_getitem_slice_float_raises(self, datetime_series): + msg = ( + "cannot do slice indexing on DatetimeIndex with these indexers " + r"\[{key}\] of type float" + ) + with pytest.raises(TypeError, match=msg.format(key=r"4\.0")): + datetime_series[4.0:10.0] + + with pytest.raises(TypeError, match=msg.format(key=r"4\.5")): + datetime_series[4.5:10.0] + + def test_getitem_slice_bug(self): + ser = Series(range(10), index=list(range(10))) + result = ser[-12:] + tm.assert_series_equal(result, ser) + + result = ser[-7:] + tm.assert_series_equal(result, ser[3:]) + + result = ser[:-12] + tm.assert_series_equal(result, ser[:0]) + + def test_getitem_slice_integers(self): + ser = Series( + np.random.default_rng(2).standard_normal(8), + index=[2, 4, 6, 8, 10, 12, 14, 16], + ) + + result = ser[:4] + expected = Series(ser.values[:4], index=[2, 4, 6, 8]) + tm.assert_series_equal(result, expected) + + +class TestSeriesGetitemListLike: + @pytest.mark.parametrize("box", [list, np.array, Index, Series]) + def test_getitem_no_matches(self, box): + # GH#33462 we expect the same behavior for list/ndarray/Index/Series + ser = Series(["A", "B"]) + + key = Series(["C"]) + key = box(key) + + msg = r"None of \[Index\(\['C'\], dtype='object|str'\)\] are in the \[index\]" + with pytest.raises(KeyError, match=msg): + ser[key] + + def test_getitem_intlist_intindex_periodvalues(self): + ser = Series(period_range("2000-01-01", periods=10, freq="D")) + + result = ser[[2, 4]] + exp = Series( + [pd.Period("2000-01-03", freq="D"), pd.Period("2000-01-05", freq="D")], + index=[2, 4], + dtype="Period[D]", + ) + tm.assert_series_equal(result, exp) + assert result.dtype == "Period[D]" + + @pytest.mark.parametrize("box", [list, np.array, Index]) + def test_getitem_intlist_intervalindex_non_int(self, box): + # GH#33404 fall back to positional since ints are unambiguous + dti = date_range("2000-01-03", periods=3)._with_freq(None) + ii = pd.IntervalIndex.from_breaks(dti) + ser = Series(range(len(ii)), index=ii) + + expected = ser.iloc[:1] + key = box([0]) + msg = "Series.__getitem__ treating keys as positions is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = ser[key] + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("box", [list, np.array, Index]) + @pytest.mark.parametrize("dtype", [np.int64, np.float64, np.uint64]) + def test_getitem_intlist_multiindex_numeric_level(self, dtype, box): + # GH#33404 do _not_ fall back to positional since ints are ambiguous + idx = Index(range(4)).astype(dtype) + dti = date_range("2000-01-03", periods=3) + mi = pd.MultiIndex.from_product([idx, dti]) + ser = Series(range(len(mi))[::-1], index=mi) + + key = box([5]) + with pytest.raises(KeyError, match="5"): + ser[key] + + def test_getitem_uint_array_key(self, any_unsigned_int_numpy_dtype): + # GH #37218 + ser = Series([1, 2, 3]) + key = np.array([4], dtype=any_unsigned_int_numpy_dtype) + + with pytest.raises(KeyError, match="4"): + ser[key] + with pytest.raises(KeyError, match="4"): + ser.loc[key] + + +class TestGetitemBooleanMask: + def test_getitem_boolean(self, string_series): + ser = string_series + mask = ser > ser.median() + + # passing list is OK + result = ser[list(mask)] + expected = ser[mask] + tm.assert_series_equal(result, expected) + tm.assert_index_equal(result.index, ser.index[mask]) + + def test_getitem_boolean_empty(self): + ser = Series([], dtype=np.int64) + ser.index.name = "index_name" + ser = ser[ser.isna()] + assert ser.index.name == "index_name" + assert ser.dtype == np.int64 + + # GH#5877 + # indexing with empty series + ser = Series(["A", "B"], dtype=object) + expected = Series(dtype=object, index=Index([], dtype="int64")) + result = ser[Series([], dtype=object)] + tm.assert_series_equal(result, expected) + + # invalid because of the boolean indexer + # that's empty or not-aligned + msg = ( + r"Unalignable boolean Series provided as indexer \(index of " + r"the boolean Series and of the indexed object do not match" + ) + with pytest.raises(IndexingError, match=msg): + ser[Series([], dtype=bool)] + + with pytest.raises(IndexingError, match=msg): + ser[Series([True], dtype=bool)] + + def test_getitem_boolean_object(self, string_series): + # using column from DataFrame + + ser = string_series + mask = ser > ser.median() + omask = mask.astype(object) + + # getitem + result = ser[omask] + expected = ser[mask] + tm.assert_series_equal(result, expected) + + # setitem + s2 = ser.copy() + cop = ser.copy() + cop[omask] = 5 + s2[mask] = 5 + tm.assert_series_equal(cop, s2) + + # nans raise exception + omask[5:10] = np.nan + msg = "Cannot mask with non-boolean array containing NA / NaN values" + with pytest.raises(ValueError, match=msg): + ser[omask] + with pytest.raises(ValueError, match=msg): + ser[omask] = 5 + + def test_getitem_boolean_dt64_copies(self): + # GH#36210 + dti = date_range("2016-01-01", periods=4, tz="US/Pacific") + key = np.array([True, True, False, False]) + + ser = Series(dti._data) + + res = ser[key] + assert res._values._ndarray.base is None + + # compare with numeric case for reference + ser2 = Series(range(4)) + res2 = ser2[key] + assert res2._values.base is None + + def test_getitem_boolean_corner(self, datetime_series): + ts = datetime_series + mask_shifted = ts.shift(1, freq=BDay()) > ts.median() + + msg = ( + r"Unalignable boolean Series provided as indexer \(index of " + r"the boolean Series and of the indexed object do not match" + ) + with pytest.raises(IndexingError, match=msg): + ts[mask_shifted] + + with pytest.raises(IndexingError, match=msg): + ts.loc[mask_shifted] + + def test_getitem_boolean_different_order(self, string_series): + ordered = string_series.sort_values() + + sel = string_series[ordered > 0] + exp = string_series[string_series > 0] + tm.assert_series_equal(sel, exp) + + def test_getitem_boolean_contiguous_preserve_freq(self): + rng = date_range("1/1/2000", "3/1/2000", freq="B") + + mask = np.zeros(len(rng), dtype=bool) + mask[10:20] = True + + masked = rng[mask] + expected = rng[10:20] + assert expected.freq == rng.freq + tm.assert_index_equal(masked, expected) + + mask[22] = True + masked = rng[mask] + assert masked.freq is None + + +class TestGetitemCallable: + def test_getitem_callable(self): + # GH#12533 + ser = Series(4, index=list("ABCD")) + result = ser[lambda x: "A"] + assert result == ser.loc["A"] + + result = ser[lambda x: ["A", "B"]] + expected = ser.loc[["A", "B"]] + tm.assert_series_equal(result, expected) + + result = ser[lambda x: [True, False, True, True]] + expected = ser.iloc[[0, 2, 3]] + tm.assert_series_equal(result, expected) + + +def test_getitem_generator(string_series): + gen = (x > 0 for x in string_series) + result = string_series[gen] + result2 = string_series[iter(string_series > 0)] + expected = string_series[string_series > 0] + tm.assert_series_equal(result, expected) + tm.assert_series_equal(result2, expected) + + +@pytest.mark.parametrize( + "series", + [ + Series([0, 1]), + Series(date_range("2012-01-01", periods=2)), + Series(date_range("2012-01-01", periods=2, tz="CET")), + ], +) +def test_getitem_ndim_deprecated(series): + with pytest.raises(ValueError, match="Multi-dimensional indexing"): + series[:, None] + + +def test_getitem_multilevel_scalar_slice_not_implemented( + multiindex_year_month_day_dataframe_random_data, +): + # not implementing this for now + df = multiindex_year_month_day_dataframe_random_data + ser = df["A"] + + msg = r"\(2000, slice\(3, 4, None\)\)" + with pytest.raises(TypeError, match=msg): + ser[2000, 3:4] + + +def test_getitem_dataframe_raises(): + rng = list(range(10)) + ser = Series(10, index=rng) + df = DataFrame(rng, index=rng) + msg = ( + "Indexing a Series with DataFrame is not supported, " + "use the appropriate DataFrame column" + ) + with pytest.raises(TypeError, match=msg): + ser[df > 5] + + +def test_getitem_assignment_series_alignment(): + # https://github.com/pandas-dev/pandas/issues/37427 + # with getitem, when assigning with a Series, it is not first aligned + ser = Series(range(10)) + idx = np.array([2, 4, 9]) + ser[idx] = Series([10, 11, 12]) + expected = Series([0, 1, 10, 3, 11, 5, 6, 7, 8, 12]) + tm.assert_series_equal(ser, expected) + + +def test_getitem_duplicate_index_mistyped_key_raises_keyerror(): + # GH#29189 float_index.get_loc(None) should raise KeyError, not TypeError + ser = Series([2, 5, 6, 8], index=[2.0, 4.0, 4.0, 5.0]) + with pytest.raises(KeyError, match="None"): + ser[None] + + with pytest.raises(KeyError, match="None"): + ser.index.get_loc(None) + + with pytest.raises(KeyError, match="None"): + ser.index._engine.get_loc(None) + + +def test_getitem_1tuple_slice_without_multiindex(): + ser = Series(range(5)) + key = (slice(3),) + + result = ser[key] + expected = ser[key[0]] + tm.assert_series_equal(result, expected) + + +def test_getitem_preserve_name(datetime_series): + result = datetime_series[datetime_series > 0] + assert result.name == datetime_series.name + + msg = "Series.__getitem__ treating keys as positions is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = datetime_series[[0, 2, 4]] + assert result.name == datetime_series.name + + result = datetime_series[5:10] + assert result.name == datetime_series.name + + +def test_getitem_with_integer_labels(): + # integer indexes, be careful + ser = Series( + np.random.default_rng(2).standard_normal(10), index=list(range(0, 20, 2)) + ) + inds = [0, 2, 5, 7, 8] + arr_inds = np.array([0, 2, 5, 7, 8]) + with pytest.raises(KeyError, match="not in index"): + ser[inds] + + with pytest.raises(KeyError, match="not in index"): + ser[arr_inds] + + +def test_getitem_missing(datetime_series): + # missing + d = datetime_series.index[0] - BDay() + msg = r"Timestamp\('1999-12-31 00:00:00'\)" + with pytest.raises(KeyError, match=msg): + datetime_series[d] + + +def test_getitem_fancy(string_series, object_series): + msg = "Series.__getitem__ treating keys as positions is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + slice1 = string_series[[1, 2, 3]] + slice2 = object_series[[1, 2, 3]] + assert string_series.index[2] == slice1.index[1] + assert object_series.index[2] == slice2.index[1] + assert string_series.iloc[2] == slice1.iloc[1] + assert object_series.iloc[2] == slice2.iloc[1] + + +def test_getitem_box_float64(datetime_series): + msg = "Series.__getitem__ treating keys as positions is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + value = datetime_series[5] + assert isinstance(value, np.float64) + + +def test_getitem_unordered_dup(): + obj = Series(range(5), index=["c", "a", "a", "b", "b"]) + assert is_scalar(obj["c"]) + assert obj["c"] == 0 + + +def test_getitem_dups(): + ser = Series(range(5), index=["A", "A", "B", "C", "C"], dtype=np.int64) + expected = Series([3, 4], index=["C", "C"], dtype=np.int64) + result = ser["C"] + tm.assert_series_equal(result, expected) + + +def test_getitem_categorical_str(): + # GH#31765 + ser = Series(range(5), index=Categorical(["a", "b", "c", "a", "b"])) + result = ser["a"] + expected = ser.iloc[[0, 3]] + tm.assert_series_equal(result, expected) + + +def test_slice_can_reorder_not_uniquely_indexed(): + ser = Series(1, index=["a", "a", "b", "b", "c"]) + ser[::-1] # it works! + + +@pytest.mark.parametrize("index_vals", ["aabcd", "aadcb"]) +def test_duplicated_index_getitem_positional_indexer(index_vals): + # GH 11747 + s = Series(range(5), index=list(index_vals)) + + msg = "Series.__getitem__ treating keys as positions is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = s[3] + assert result == 3 + + +class TestGetitemDeprecatedIndexers: + @pytest.mark.parametrize("key", [{1}, {1: 1}]) + def test_getitem_dict_and_set_deprecated(self, key): + # GH#42825 enforced in 2.0 + ser = Series([1, 2, 3]) + with pytest.raises(TypeError, match="as an indexer is not supported"): + ser[key] + + @pytest.mark.parametrize("key", [{1}, {1: 1}]) + def test_setitem_dict_and_set_disallowed(self, key): + # GH#42825 enforced in 2.0 + ser = Series([1, 2, 3]) + with pytest.raises(TypeError, match="as an indexer is not supported"): + ser[key] = 1 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_indexing.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_indexing.py new file mode 100644 index 0000000000000000000000000000000000000000..f153628d32e27ed4cd27dd4823c6ded3a9921352 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_indexing.py @@ -0,0 +1,526 @@ +""" test get/set & misc """ +from datetime import timedelta +import re + +import numpy as np +import pytest + +from pandas.compat import WARNING_CHECK_DISABLED +from pandas.errors import IndexingError + +from pandas import ( + NA, + DataFrame, + Index, + IndexSlice, + MultiIndex, + NaT, + Series, + Timedelta, + Timestamp, + concat, + date_range, + isna, + period_range, + timedelta_range, +) +import pandas._testing as tm + + +def test_basic_indexing(): + s = Series( + np.random.default_rng(2).standard_normal(5), index=["a", "b", "a", "a", "b"] + ) + + warn_msg = "Series.__[sg]etitem__ treating keys as positions is deprecated" + msg = "index 5 is out of bounds for axis 0 with size 5" + with pytest.raises(IndexError, match=msg): + with tm.assert_produces_warning(FutureWarning, match=warn_msg): + s[5] + with pytest.raises(IndexError, match=msg): + with tm.assert_produces_warning(FutureWarning, match=warn_msg): + s[5] = 0 + + with pytest.raises(KeyError, match=r"^'c'$"): + s["c"] + + s = s.sort_index() + + with pytest.raises(IndexError, match=msg): + with tm.assert_produces_warning(FutureWarning, match=warn_msg): + s[5] + msg = r"index 5 is out of bounds for axis (0|1) with size 5|^5$" + with pytest.raises(IndexError, match=msg): + with tm.assert_produces_warning(FutureWarning, match=warn_msg): + s[5] = 0 + + +def test_getitem_numeric_should_not_fallback_to_positional(any_numeric_dtype): + # GH51053 + dtype = any_numeric_dtype + idx = Index([1, 0, 1], dtype=dtype) + ser = Series(range(3), index=idx) + result = ser[1] + expected = Series([0, 2], index=Index([1, 1], dtype=dtype)) + tm.assert_series_equal(result, expected, check_exact=True) + + +def test_setitem_numeric_should_not_fallback_to_positional(any_numeric_dtype): + # GH51053 + dtype = any_numeric_dtype + idx = Index([1, 0, 1], dtype=dtype) + ser = Series(range(3), index=idx) + ser[1] = 10 + expected = Series([10, 1, 10], index=idx) + tm.assert_series_equal(ser, expected, check_exact=True) + + +def test_basic_getitem_with_labels(datetime_series): + indices = datetime_series.index[[5, 10, 15]] + + result = datetime_series[indices] + expected = datetime_series.reindex(indices) + tm.assert_series_equal(result, expected) + + result = datetime_series[indices[0] : indices[2]] + expected = datetime_series.loc[indices[0] : indices[2]] + tm.assert_series_equal(result, expected) + + +def test_basic_getitem_dt64tz_values(): + # GH12089 + # with tz for values + ser = Series( + date_range("2011-01-01", periods=3, tz="US/Eastern"), index=["a", "b", "c"] + ) + expected = Timestamp("2011-01-01", tz="US/Eastern") + result = ser.loc["a"] + assert result == expected + result = ser.iloc[0] + assert result == expected + result = ser["a"] + assert result == expected + + +def test_getitem_setitem_ellipsis(using_copy_on_write, warn_copy_on_write): + s = Series(np.random.default_rng(2).standard_normal(10)) + + result = s[...] + tm.assert_series_equal(result, s) + + with tm.assert_cow_warning(warn_copy_on_write): + s[...] = 5 + if not using_copy_on_write: + assert (result == 5).all() + + +@pytest.mark.parametrize( + "result_1, duplicate_item, expected_1", + [ + [ + Series({1: 12, 2: [1, 2, 2, 3]}), + Series({1: 313}), + Series({1: 12}, dtype=object), + ], + [ + Series({1: [1, 2, 3], 2: [1, 2, 2, 3]}), + Series({1: [1, 2, 3]}), + Series({1: [1, 2, 3]}), + ], + ], +) +def test_getitem_with_duplicates_indices(result_1, duplicate_item, expected_1): + # GH 17610 + result = result_1._append(duplicate_item) + expected = expected_1._append(duplicate_item) + tm.assert_series_equal(result[1], expected) + assert result[2] == result_1[2] + + +def test_getitem_setitem_integers(): + # caused bug without test + s = Series([1, 2, 3], ["a", "b", "c"]) + + assert s.iloc[0] == s["a"] + s.iloc[0] = 5 + tm.assert_almost_equal(s["a"], 5) + + +def test_series_box_timestamp(): + rng = date_range("20090415", "20090519", freq="B") + ser = Series(rng) + assert isinstance(ser[0], Timestamp) + assert isinstance(ser.at[1], Timestamp) + assert isinstance(ser.iat[2], Timestamp) + assert isinstance(ser.loc[3], Timestamp) + assert isinstance(ser.iloc[4], Timestamp) + + ser = Series(rng, index=rng) + msg = "Series.__getitem__ treating keys as positions is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + assert isinstance(ser[0], Timestamp) + assert isinstance(ser.at[rng[1]], Timestamp) + assert isinstance(ser.iat[2], Timestamp) + assert isinstance(ser.loc[rng[3]], Timestamp) + assert isinstance(ser.iloc[4], Timestamp) + + +def test_series_box_timedelta(): + rng = timedelta_range("1 day 1 s", periods=5, freq="h") + ser = Series(rng) + assert isinstance(ser[0], Timedelta) + assert isinstance(ser.at[1], Timedelta) + assert isinstance(ser.iat[2], Timedelta) + assert isinstance(ser.loc[3], Timedelta) + assert isinstance(ser.iloc[4], Timedelta) + + +def test_getitem_ambiguous_keyerror(indexer_sl): + ser = Series(range(10), index=list(range(0, 20, 2))) + with pytest.raises(KeyError, match=r"^1$"): + indexer_sl(ser)[1] + + +def test_getitem_dups_with_missing(indexer_sl): + # breaks reindex, so need to use .loc internally + # GH 4246 + ser = Series([1, 2, 3, 4], ["foo", "bar", "foo", "bah"]) + with pytest.raises(KeyError, match=re.escape("['bam'] not in index")): + indexer_sl(ser)[["foo", "bar", "bah", "bam"]] + + +def test_setitem_ambiguous_keyerror(indexer_sl): + s = Series(range(10), index=list(range(0, 20, 2))) + + # equivalent of an append + s2 = s.copy() + indexer_sl(s2)[1] = 5 + expected = concat([s, Series([5], index=[1])]) + tm.assert_series_equal(s2, expected) + + +def test_setitem(datetime_series): + datetime_series[datetime_series.index[5]] = np.nan + datetime_series.iloc[[1, 2, 17]] = np.nan + datetime_series.iloc[6] = np.nan + assert np.isnan(datetime_series.iloc[6]) + assert np.isnan(datetime_series.iloc[2]) + datetime_series[np.isnan(datetime_series)] = 5 + assert not np.isnan(datetime_series.iloc[2]) + + +def test_setslice(datetime_series): + sl = datetime_series[5:20] + assert len(sl) == len(sl.index) + assert sl.index.is_unique is True + + +def test_basic_getitem_setitem_corner(datetime_series): + # invalid tuples, e.g. td.ts[:, None] vs. td.ts[:, 2] + msg = "key of type tuple not found and not a MultiIndex" + with pytest.raises(KeyError, match=msg): + datetime_series[:, 2] + with pytest.raises(KeyError, match=msg): + datetime_series[:, 2] = 2 + + # weird lists. [slice(0, 5)] raises but not two slices + msg = "Indexing with a single-item list" + with pytest.raises(ValueError, match=msg): + # GH#31299 + datetime_series[[slice(None, 5)]] + + # but we're OK with a single-element tuple + result = datetime_series[(slice(None, 5),)] + expected = datetime_series[:5] + tm.assert_series_equal(result, expected) + + # OK + msg = r"unhashable type(: 'slice')?" + with pytest.raises(TypeError, match=msg): + datetime_series[[5, [None, None]]] + with pytest.raises(TypeError, match=msg): + datetime_series[[5, [None, None]]] = 2 + + +def test_slice(string_series, object_series, using_copy_on_write, warn_copy_on_write): + original = string_series.copy() + numSlice = string_series[10:20] + numSliceEnd = string_series[-10:] + objSlice = object_series[10:20] + + assert string_series.index[9] not in numSlice.index + assert object_series.index[9] not in objSlice.index + + assert len(numSlice) == len(numSlice.index) + assert string_series[numSlice.index[0]] == numSlice[numSlice.index[0]] + + assert numSlice.index[1] == string_series.index[11] + tm.assert_numpy_array_equal(np.array(numSliceEnd), np.array(string_series)[-10:]) + + # Test return view. + sl = string_series[10:20] + with tm.assert_cow_warning(warn_copy_on_write): + sl[:] = 0 + + if using_copy_on_write: + # Doesn't modify parent (CoW) + tm.assert_series_equal(string_series, original) + else: + assert (string_series[10:20] == 0).all() + + +def test_timedelta_assignment(): + # GH 8209 + s = Series([], dtype=object) + s.loc["B"] = timedelta(1) + expected = Series(Timedelta("1 days"), dtype="timedelta64[ns]", index=["B"]) + tm.assert_series_equal(s, expected) + + s = s.reindex(s.index.insert(0, "A")) + expected = Series( + [np.nan, Timedelta("1 days")], dtype="timedelta64[ns]", index=["A", "B"] + ) + tm.assert_series_equal(s, expected) + + s.loc["A"] = timedelta(1) + expected = Series(Timedelta("1 days"), dtype="timedelta64[ns]", index=["A", "B"]) + tm.assert_series_equal(s, expected) + + +def test_underlying_data_conversion(using_copy_on_write): + # GH 4080 + df = DataFrame({c: [1, 2, 3] for c in ["a", "b", "c"]}) + return_value = df.set_index(["a", "b", "c"], inplace=True) + assert return_value is None + s = Series([1], index=[(2, 2, 2)]) + df["val"] = 0 + df_original = df.copy() + df + + if using_copy_on_write: + with tm.raises_chained_assignment_error(): + df["val"].update(s) + expected = df_original + else: + with tm.assert_produces_warning( + FutureWarning if not WARNING_CHECK_DISABLED else None, + match="inplace method", + ): + df["val"].update(s) + expected = DataFrame( + {"a": [1, 2, 3], "b": [1, 2, 3], "c": [1, 2, 3], "val": [0, 1, 0]} + ) + return_value = expected.set_index(["a", "b", "c"], inplace=True) + assert return_value is None + tm.assert_frame_equal(df, expected) + + +def test_preserve_refs(datetime_series): + seq = datetime_series.iloc[[5, 10, 15]] + seq.iloc[1] = np.nan + assert not np.isnan(datetime_series.iloc[10]) + + +def test_multilevel_preserve_name(lexsorted_two_level_string_multiindex, indexer_sl): + index = lexsorted_two_level_string_multiindex + ser = Series( + np.random.default_rng(2).standard_normal(len(index)), index=index, name="sth" + ) + + result = indexer_sl(ser)["foo"] + assert result.name == ser.name + + +# miscellaneous methods + + +@pytest.mark.parametrize( + "index", + [ + date_range("2014-01-01", periods=20, freq="MS"), + period_range("2014-01", periods=20, freq="M"), + timedelta_range("0", periods=20, freq="h"), + ], +) +def test_slice_with_negative_step(index): + keystr1 = str(index[9]) + keystr2 = str(index[13]) + + ser = Series(np.arange(20), index) + SLC = IndexSlice + + for key in [keystr1, index[9]]: + tm.assert_indexing_slices_equivalent(ser, SLC[key::-1], SLC[9::-1]) + tm.assert_indexing_slices_equivalent(ser, SLC[:key:-1], SLC[:8:-1]) + + for key2 in [keystr2, index[13]]: + tm.assert_indexing_slices_equivalent(ser, SLC[key2:key:-1], SLC[13:8:-1]) + tm.assert_indexing_slices_equivalent(ser, SLC[key:key2:-1], SLC[0:0:-1]) + + +def test_tuple_index(): + # GH 35534 - Selecting values when a Series has an Index of tuples + s = Series([1, 2], index=[("a",), ("b",)]) + assert s[("a",)] == 1 + assert s[("b",)] == 2 + s[("b",)] = 3 + assert s[("b",)] == 3 + + +def test_frozenset_index(): + # GH35747 - Selecting values when a Series has an Index of frozenset + idx0, idx1 = frozenset("a"), frozenset("b") + s = Series([1, 2], index=[idx0, idx1]) + assert s[idx0] == 1 + assert s[idx1] == 2 + s[idx1] = 3 + assert s[idx1] == 3 + + +def test_loc_setitem_all_false_indexer(): + # GH#45778 + ser = Series([1, 2], index=["a", "b"]) + expected = ser.copy() + rhs = Series([6, 7], index=["a", "b"]) + ser.loc[ser > 100] = rhs + tm.assert_series_equal(ser, expected) + + +def test_loc_boolean_indexer_non_matching_index(): + # GH#46551 + ser = Series([1]) + result = ser.loc[Series([NA, False], dtype="boolean")] + expected = Series([], dtype="int64") + tm.assert_series_equal(result, expected) + + +def test_loc_boolean_indexer_miss_matching_index(): + # GH#46551 + ser = Series([1]) + indexer = Series([NA, False], dtype="boolean", index=[1, 2]) + with pytest.raises(IndexingError, match="Unalignable"): + ser.loc[indexer] + + +def test_loc_setitem_nested_data_enlargement(): + # GH#48614 + df = DataFrame({"a": [1]}) + ser = Series({"label": df}) + ser.loc["new_label"] = df + expected = Series({"label": df, "new_label": df}) + tm.assert_series_equal(ser, expected) + + +def test_loc_ea_numeric_index_oob_slice_end(): + # GH#50161 + ser = Series(1, index=Index([0, 1, 2], dtype="Int64")) + result = ser.loc[2:3] + expected = Series(1, index=Index([2], dtype="Int64")) + tm.assert_series_equal(result, expected) + + +def test_getitem_bool_int_key(): + # GH#48653 + ser = Series({True: 1, False: 0}) + with pytest.raises(KeyError, match="0"): + ser.loc[0] + + +@pytest.mark.parametrize("val", [{}, {"b": "x"}]) +@pytest.mark.parametrize("indexer", [[], [False, False], slice(0, -1), np.array([])]) +def test_setitem_empty_indexer(indexer, val): + # GH#45981 + df = DataFrame({"a": [1, 2], **val}) + expected = df.copy() + df.loc[indexer] = 1.5 + tm.assert_frame_equal(df, expected) + + +class TestDeprecatedIndexers: + @pytest.mark.parametrize("key", [{1}, {1: 1}]) + def test_getitem_dict_and_set_deprecated(self, key): + # GH#42825 enforced in 2.0 + ser = Series([1, 2]) + with pytest.raises(TypeError, match="as an indexer is not supported"): + ser.loc[key] + + @pytest.mark.parametrize("key", [{1}, {1: 1}, ({1}, 2), ({1: 1}, 2)]) + def test_getitem_dict_and_set_deprecated_multiindex(self, key): + # GH#42825 enforced in 2.0 + ser = Series([1, 2], index=MultiIndex.from_tuples([(1, 2), (3, 4)])) + with pytest.raises(TypeError, match="as an indexer is not supported"): + ser.loc[key] + + @pytest.mark.parametrize("key", [{1}, {1: 1}]) + def test_setitem_dict_and_set_disallowed(self, key): + # GH#42825 enforced in 2.0 + ser = Series([1, 2]) + with pytest.raises(TypeError, match="as an indexer is not supported"): + ser.loc[key] = 1 + + @pytest.mark.parametrize("key", [{1}, {1: 1}, ({1}, 2), ({1: 1}, 2)]) + def test_setitem_dict_and_set_disallowed_multiindex(self, key): + # GH#42825 enforced in 2.0 + ser = Series([1, 2], index=MultiIndex.from_tuples([(1, 2), (3, 4)])) + with pytest.raises(TypeError, match="as an indexer is not supported"): + ser.loc[key] = 1 + + +class TestSetitemValidation: + # This is adapted from pandas/tests/arrays/masked/test_indexing.py + # but checks for warnings instead of errors. + def _check_setitem_invalid(self, ser, invalid, indexer, warn): + msg = "Setting an item of incompatible dtype is deprecated" + msg = re.escape(msg) + + orig_ser = ser.copy() + + with tm.assert_produces_warning(warn, match=msg): + ser[indexer] = invalid + ser = orig_ser.copy() + + with tm.assert_produces_warning(warn, match=msg): + ser.iloc[indexer] = invalid + ser = orig_ser.copy() + + with tm.assert_produces_warning(warn, match=msg): + ser.loc[indexer] = invalid + ser = orig_ser.copy() + + with tm.assert_produces_warning(warn, match=msg): + ser[:] = invalid + + _invalid_scalars = [ + 1 + 2j, + "True", + "1", + "1.0", + NaT, + np.datetime64("NaT"), + np.timedelta64("NaT"), + ] + _indexers = [0, [0], slice(0, 1), [True, False, False], slice(None, None, None)] + + @pytest.mark.parametrize( + "invalid", _invalid_scalars + [1, 1.0, np.int64(1), np.float64(1)] + ) + @pytest.mark.parametrize("indexer", _indexers) + def test_setitem_validation_scalar_bool(self, invalid, indexer): + ser = Series([True, False, False], dtype="bool") + self._check_setitem_invalid(ser, invalid, indexer, FutureWarning) + + @pytest.mark.parametrize("invalid", _invalid_scalars + [True, 1.5, np.float64(1.5)]) + @pytest.mark.parametrize("indexer", _indexers) + def test_setitem_validation_scalar_int(self, invalid, any_int_numpy_dtype, indexer): + ser = Series([1, 2, 3], dtype=any_int_numpy_dtype) + if isna(invalid) and invalid is not NaT and not np.isnat(invalid): + warn = None + else: + warn = FutureWarning + self._check_setitem_invalid(ser, invalid, indexer, warn) + + @pytest.mark.parametrize("invalid", _invalid_scalars + [True]) + @pytest.mark.parametrize("indexer", _indexers) + def test_setitem_validation_scalar_float(self, invalid, float_numpy_dtype, indexer): + ser = Series([1, 2, None], dtype=float_numpy_dtype) + self._check_setitem_invalid(ser, invalid, indexer, FutureWarning) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_mask.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_mask.py new file mode 100644 index 0000000000000000000000000000000000000000..3c21cd0d5ca648dcf0f1ac412dd232221c031c6f --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_mask.py @@ -0,0 +1,69 @@ +import numpy as np +import pytest + +from pandas import Series +import pandas._testing as tm + + +def test_mask(): + # compare with tested results in test_where + s = Series(np.random.default_rng(2).standard_normal(5)) + cond = s > 0 + + rs = s.where(~cond, np.nan) + tm.assert_series_equal(rs, s.mask(cond)) + + rs = s.where(~cond) + rs2 = s.mask(cond) + tm.assert_series_equal(rs, rs2) + + rs = s.where(~cond, -s) + rs2 = s.mask(cond, -s) + tm.assert_series_equal(rs, rs2) + + cond = Series([True, False, False, True, False], index=s.index) + s2 = -(s.abs()) + rs = s2.where(~cond[:3]) + rs2 = s2.mask(cond[:3]) + tm.assert_series_equal(rs, rs2) + + rs = s2.where(~cond[:3], -s2) + rs2 = s2.mask(cond[:3], -s2) + tm.assert_series_equal(rs, rs2) + + msg = "Array conditional must be same shape as self" + with pytest.raises(ValueError, match=msg): + s.mask(1) + with pytest.raises(ValueError, match=msg): + s.mask(cond[:3].values, -s) + + +def test_mask_casts(): + # dtype changes + ser = Series([1, 2, 3, 4]) + result = ser.mask(ser > 2, np.nan) + expected = Series([1, 2, np.nan, np.nan]) + tm.assert_series_equal(result, expected) + + +def test_mask_casts2(): + # see gh-21891 + ser = Series([1, 2]) + res = ser.mask([True, False]) + + exp = Series([np.nan, 2]) + tm.assert_series_equal(res, exp) + + +def test_mask_inplace(): + s = Series(np.random.default_rng(2).standard_normal(5)) + cond = s > 0 + + rs = s.copy() + rs.mask(cond, inplace=True) + tm.assert_series_equal(rs.dropna(), s[~cond]) + tm.assert_series_equal(rs, s.mask(cond)) + + rs = s.copy() + rs.mask(cond, -s, inplace=True) + tm.assert_series_equal(rs, s.mask(cond, -s)) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_set_value.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_set_value.py new file mode 100644 index 0000000000000000000000000000000000000000..cbe1a8bf296c8106c2cfa3ad519b0a7d44401493 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_set_value.py @@ -0,0 +1,45 @@ +from datetime import datetime + +import numpy as np + +from pandas import ( + DatetimeIndex, + Series, +) +import pandas._testing as tm + + +def test_series_set_value(): + # GH#1561 + + dates = [datetime(2001, 1, 1), datetime(2001, 1, 2)] + index = DatetimeIndex(dates) + + s = Series(dtype=object) + s._set_value(dates[0], 1.0) + s._set_value(dates[1], np.nan) + + expected = Series([1.0, np.nan], index=index) + + tm.assert_series_equal(s, expected) + + +def test_set_value_dt64(datetime_series): + idx = datetime_series.index[10] + res = datetime_series._set_value(idx, 0) + assert res is None + assert datetime_series[idx] == 0 + + +def test_set_value_str_index(string_series): + # equiv + ser = string_series.copy() + res = ser._set_value("foobar", 0) + assert res is None + assert ser.index[-1] == "foobar" + assert ser["foobar"] == 0 + + ser2 = string_series.copy() + ser2.loc["foobar"] = 0 + assert ser2.index[-1] == "foobar" + assert ser2["foobar"] == 0 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_setitem.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_setitem.py new file mode 100644 index 0000000000000000000000000000000000000000..ad2c5a27bea9e44037a3708d7bd5ba2fa1a4d456 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_setitem.py @@ -0,0 +1,1847 @@ +from datetime import ( + date, + datetime, +) +from decimal import Decimal +import os + +import numpy as np +import pytest + +from pandas.compat.numpy import ( + np_version_gt2, + np_version_gte1p24, +) +from pandas.errors import IndexingError + +from pandas.core.dtypes.common import is_list_like + +from pandas import ( + NA, + Categorical, + DataFrame, + DatetimeIndex, + Index, + Interval, + IntervalIndex, + MultiIndex, + NaT, + Period, + Series, + Timedelta, + Timestamp, + array, + concat, + date_range, + interval_range, + period_range, + timedelta_range, +) +import pandas._testing as tm + +from pandas.tseries.offsets import BDay + + +class TestSetitemDT64Values: + def test_setitem_none_nan(self): + series = Series(date_range("1/1/2000", periods=10)) + series[3] = None + assert series[3] is NaT + + series[3:5] = None + assert series[4] is NaT + + series[5] = np.nan + assert series[5] is NaT + + series[5:7] = np.nan + assert series[6] is NaT + + def test_setitem_multiindex_empty_slice(self): + # https://github.com/pandas-dev/pandas/issues/35878 + idx = MultiIndex.from_tuples([("a", 1), ("b", 2)]) + result = Series([1, 2], index=idx) + expected = result.copy() + result.loc[[]] = 0 + tm.assert_series_equal(result, expected) + + def test_setitem_with_string_index(self): + # GH#23451 + # Set object dtype to avoid upcast when setting date.today() + ser = Series([1, 2, 3], index=["Date", "b", "other"], dtype=object) + ser["Date"] = date.today() + assert ser.Date == date.today() + assert ser["Date"] == date.today() + + def test_setitem_tuple_with_datetimetz_values(self): + # GH#20441 + arr = date_range("2017", periods=4, tz="US/Eastern") + index = [(0, 1), (0, 2), (0, 3), (0, 4)] + result = Series(arr, index=index) + expected = result.copy() + result[(0, 1)] = np.nan + expected.iloc[0] = np.nan + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("tz", ["US/Eastern", "UTC", "Asia/Tokyo"]) + def test_setitem_with_tz(self, tz, indexer_sli): + orig = Series(date_range("2016-01-01", freq="h", periods=3, tz=tz)) + assert orig.dtype == f"datetime64[ns, {tz}]" + + exp = Series( + [ + Timestamp("2016-01-01 00:00", tz=tz), + Timestamp("2011-01-01 00:00", tz=tz), + Timestamp("2016-01-01 02:00", tz=tz), + ], + dtype=orig.dtype, + ) + + # scalar + ser = orig.copy() + indexer_sli(ser)[1] = Timestamp("2011-01-01", tz=tz) + tm.assert_series_equal(ser, exp) + + # vector + vals = Series( + [Timestamp("2011-01-01", tz=tz), Timestamp("2012-01-01", tz=tz)], + index=[1, 2], + dtype=orig.dtype, + ) + assert vals.dtype == f"datetime64[ns, {tz}]" + + exp = Series( + [ + Timestamp("2016-01-01 00:00", tz=tz), + Timestamp("2011-01-01 00:00", tz=tz), + Timestamp("2012-01-01 00:00", tz=tz), + ], + dtype=orig.dtype, + ) + + ser = orig.copy() + indexer_sli(ser)[[1, 2]] = vals + tm.assert_series_equal(ser, exp) + + def test_setitem_with_tz_dst(self, indexer_sli): + # GH#14146 trouble setting values near DST boundary + tz = "US/Eastern" + orig = Series(date_range("2016-11-06", freq="h", periods=3, tz=tz)) + assert orig.dtype == f"datetime64[ns, {tz}]" + + exp = Series( + [ + Timestamp("2016-11-06 00:00-04:00", tz=tz), + Timestamp("2011-01-01 00:00-05:00", tz=tz), + Timestamp("2016-11-06 01:00-05:00", tz=tz), + ], + dtype=orig.dtype, + ) + + # scalar + ser = orig.copy() + indexer_sli(ser)[1] = Timestamp("2011-01-01", tz=tz) + tm.assert_series_equal(ser, exp) + + # vector + vals = Series( + [Timestamp("2011-01-01", tz=tz), Timestamp("2012-01-01", tz=tz)], + index=[1, 2], + dtype=orig.dtype, + ) + assert vals.dtype == f"datetime64[ns, {tz}]" + + exp = Series( + [ + Timestamp("2016-11-06 00:00", tz=tz), + Timestamp("2011-01-01 00:00", tz=tz), + Timestamp("2012-01-01 00:00", tz=tz), + ], + dtype=orig.dtype, + ) + + ser = orig.copy() + indexer_sli(ser)[[1, 2]] = vals + tm.assert_series_equal(ser, exp) + + def test_object_series_setitem_dt64array_exact_match(self): + # make sure the dt64 isn't cast by numpy to integers + # https://github.com/numpy/numpy/issues/12550 + + ser = Series({"X": np.nan}, dtype=object) + + indexer = [True] + + # "exact_match" -> size of array being set matches size of ser + value = np.array([4], dtype="M8[ns]") + + ser.iloc[indexer] = value + + expected = Series([value[0]], index=["X"], dtype=object) + assert all(isinstance(x, np.datetime64) for x in expected.values) + + tm.assert_series_equal(ser, expected) + + +class TestSetitemScalarIndexer: + def test_setitem_negative_out_of_bounds(self): + ser = Series(["a"] * 10, index=["a"] * 10) + + # string index falls back to positional + msg = "index -11|-1 is out of bounds for axis 0 with size 10" + warn_msg = "Series.__setitem__ treating keys as positions is deprecated" + with pytest.raises(IndexError, match=msg): + with tm.assert_produces_warning(FutureWarning, match=warn_msg): + ser[-11] = "foo" + + @pytest.mark.parametrize("indexer", [tm.loc, tm.at]) + @pytest.mark.parametrize("ser_index", [0, 1]) + def test_setitem_series_object_dtype(self, indexer, ser_index): + # GH#38303 + ser = Series([0, 0], dtype="object") + idxr = indexer(ser) + idxr[0] = Series([42], index=[ser_index]) + expected = Series([Series([42], index=[ser_index]), 0], dtype="object") + tm.assert_series_equal(ser, expected) + + @pytest.mark.parametrize("index, exp_value", [(0, 42), (1, np.nan)]) + def test_setitem_series(self, index, exp_value): + # GH#38303 + ser = Series([0, 0]) + ser.loc[0] = Series([42], index=[index]) + expected = Series([exp_value, 0]) + tm.assert_series_equal(ser, expected) + + +class TestSetitemSlices: + def test_setitem_slice_float_raises(self, datetime_series): + msg = ( + "cannot do slice indexing on DatetimeIndex with these indexers " + r"\[{key}\] of type float" + ) + with pytest.raises(TypeError, match=msg.format(key=r"4\.0")): + datetime_series[4.0:10.0] = 0 + + with pytest.raises(TypeError, match=msg.format(key=r"4\.5")): + datetime_series[4.5:10.0] = 0 + + def test_setitem_slice(self): + ser = Series(range(10), index=list(range(10))) + ser[-12:] = 0 + assert (ser == 0).all() + + ser[:-12] = 5 + assert (ser == 0).all() + + def test_setitem_slice_integers(self): + ser = Series( + np.random.default_rng(2).standard_normal(8), + index=[2, 4, 6, 8, 10, 12, 14, 16], + ) + + ser[:4] = 0 + assert (ser[:4] == 0).all() + assert not (ser[4:] == 0).any() + + def test_setitem_slicestep(self): + # caught this bug when writing tests + series = Series( + np.arange(20, dtype=np.float64), index=np.arange(20, dtype=np.int64) + ) + + series[::2] = 0 + assert (series[::2] == 0).all() + + def test_setitem_multiindex_slice(self, indexer_sli): + # GH 8856 + mi = MultiIndex.from_product(([0, 1], list("abcde"))) + result = Series(np.arange(10, dtype=np.int64), mi) + indexer_sli(result)[::4] = 100 + expected = Series([100, 1, 2, 3, 100, 5, 6, 7, 100, 9], mi) + tm.assert_series_equal(result, expected) + + +class TestSetitemBooleanMask: + def test_setitem_mask_cast(self): + # GH#2746 + # need to upcast + ser = Series([1, 2], index=[1, 2], dtype="int64") + ser[[True, False]] = Series([0], index=[1], dtype="int64") + expected = Series([0, 2], index=[1, 2], dtype="int64") + + tm.assert_series_equal(ser, expected) + + def test_setitem_mask_align_and_promote(self): + # GH#8387: test that changing types does not break alignment + ts = Series( + np.random.default_rng(2).standard_normal(100), index=np.arange(100, 0, -1) + ).round(5) + mask = ts > 0 + left = ts.copy() + right = ts[mask].copy().map(str) + with tm.assert_produces_warning( + FutureWarning, match="item of incompatible dtype" + ): + left[mask] = right + expected = ts.map(lambda t: str(t) if t > 0 else t) + tm.assert_series_equal(left, expected) + + def test_setitem_mask_promote_strs(self): + ser = Series([0, 1, 2, 0]) + mask = ser > 0 + ser2 = ser[mask].map(str) + with tm.assert_produces_warning( + FutureWarning, match="item of incompatible dtype" + ): + ser[mask] = ser2 + + expected = Series([0, "1", "2", 0]) + tm.assert_series_equal(ser, expected) + + def test_setitem_mask_promote(self): + ser = Series([0, "foo", "bar", 0]) + mask = Series([False, True, True, False]) + ser2 = ser[mask] + ser[mask] = ser2 + + expected = Series([0, "foo", "bar", 0]) + tm.assert_series_equal(ser, expected) + + def test_setitem_boolean(self, string_series): + mask = string_series > string_series.median() + + # similar indexed series + result = string_series.copy() + result[mask] = string_series * 2 + expected = string_series * 2 + tm.assert_series_equal(result[mask], expected[mask]) + + # needs alignment + result = string_series.copy() + result[mask] = (string_series * 2)[0:5] + expected = (string_series * 2)[0:5].reindex_like(string_series) + expected[-mask] = string_series[mask] + tm.assert_series_equal(result[mask], expected[mask]) + + def test_setitem_boolean_corner(self, datetime_series): + ts = datetime_series + mask_shifted = ts.shift(1, freq=BDay()) > ts.median() + + msg = ( + r"Unalignable boolean Series provided as indexer \(index of " + r"the boolean Series and of the indexed object do not match" + ) + with pytest.raises(IndexingError, match=msg): + ts[mask_shifted] = 1 + + with pytest.raises(IndexingError, match=msg): + ts.loc[mask_shifted] = 1 + + def test_setitem_boolean_different_order(self, string_series): + ordered = string_series.sort_values() + + copy = string_series.copy() + copy[ordered > 0] = 0 + + expected = string_series.copy() + expected[expected > 0] = 0 + + tm.assert_series_equal(copy, expected) + + @pytest.mark.parametrize("func", [list, np.array, Series]) + def test_setitem_boolean_python_list(self, func): + # GH19406 + ser = Series([None, "b", None]) + mask = func([True, False, True]) + ser[mask] = ["a", "c"] + expected = Series(["a", "b", "c"]) + tm.assert_series_equal(ser, expected) + + def test_setitem_boolean_nullable_int_types(self, any_numeric_ea_dtype): + # GH: 26468 + ser = Series([5, 6, 7, 8], dtype=any_numeric_ea_dtype) + ser[ser > 6] = Series(range(4), dtype=any_numeric_ea_dtype) + expected = Series([5, 6, 2, 3], dtype=any_numeric_ea_dtype) + tm.assert_series_equal(ser, expected) + + ser = Series([5, 6, 7, 8], dtype=any_numeric_ea_dtype) + ser.loc[ser > 6] = Series(range(4), dtype=any_numeric_ea_dtype) + tm.assert_series_equal(ser, expected) + + ser = Series([5, 6, 7, 8], dtype=any_numeric_ea_dtype) + loc_ser = Series(range(4), dtype=any_numeric_ea_dtype) + ser.loc[ser > 6] = loc_ser.loc[loc_ser > 1] + tm.assert_series_equal(ser, expected) + + def test_setitem_with_bool_mask_and_values_matching_n_trues_in_length(self): + # GH#30567 + ser = Series([None] * 10) + mask = [False] * 3 + [True] * 5 + [False] * 2 + ser[mask] = range(5) + result = ser + expected = Series([None] * 3 + list(range(5)) + [None] * 2, dtype=object) + tm.assert_series_equal(result, expected) + + def test_setitem_nan_with_bool(self): + # GH 13034 + result = Series([True, False, True]) + with tm.assert_produces_warning( + FutureWarning, match="item of incompatible dtype" + ): + result[0] = np.nan + expected = Series([np.nan, False, True], dtype=object) + tm.assert_series_equal(result, expected) + + def test_setitem_mask_smallint_upcast(self): + orig = Series([1, 2, 3], dtype="int8") + alt = np.array([999, 1000, 1001], dtype=np.int64) + + mask = np.array([True, False, True]) + + ser = orig.copy() + with tm.assert_produces_warning( + FutureWarning, match="item of incompatible dtype" + ): + ser[mask] = Series(alt) + expected = Series([999, 2, 1001]) + tm.assert_series_equal(ser, expected) + + ser2 = orig.copy() + with tm.assert_produces_warning( + FutureWarning, match="item of incompatible dtype" + ): + ser2.mask(mask, alt, inplace=True) + tm.assert_series_equal(ser2, expected) + + ser3 = orig.copy() + res = ser3.where(~mask, Series(alt)) + tm.assert_series_equal(res, expected) + + def test_setitem_mask_smallint_no_upcast(self): + # like test_setitem_mask_smallint_upcast, but while we can't hold 'alt', + # we *can* hold alt[mask] without casting + orig = Series([1, 2, 3], dtype="uint8") + alt = Series([245, 1000, 246], dtype=np.int64) + + mask = np.array([True, False, True]) + + ser = orig.copy() + ser[mask] = alt + expected = Series([245, 2, 246], dtype="uint8") + tm.assert_series_equal(ser, expected) + + ser2 = orig.copy() + ser2.mask(mask, alt, inplace=True) + tm.assert_series_equal(ser2, expected) + + # TODO: ser.where(~mask, alt) unnecessarily upcasts to int64 + ser3 = orig.copy() + res = ser3.where(~mask, alt) + tm.assert_series_equal(res, expected, check_dtype=False) + + +class TestSetitemViewCopySemantics: + def test_setitem_invalidates_datetime_index_freq(self, using_copy_on_write): + # GH#24096 altering a datetime64tz Series inplace invalidates the + # `freq` attribute on the underlying DatetimeIndex + + dti = date_range("20130101", periods=3, tz="US/Eastern") + ts = dti[1] + ser = Series(dti) + assert ser._values is not dti + if using_copy_on_write: + assert ser._values._ndarray.base is dti._data._ndarray.base + else: + assert ser._values._ndarray.base is not dti._data._ndarray.base + assert dti.freq == "D" + ser.iloc[1] = NaT + assert ser._values.freq is None + + # check that the DatetimeIndex was not altered in place + assert ser._values is not dti + assert ser._values._ndarray.base is not dti._data._ndarray.base + assert dti[1] == ts + assert dti.freq == "D" + + def test_dt64tz_setitem_does_not_mutate_dti(self, using_copy_on_write): + # GH#21907, GH#24096 + dti = date_range("2016-01-01", periods=10, tz="US/Pacific") + ts = dti[0] + ser = Series(dti) + assert ser._values is not dti + if using_copy_on_write: + assert ser._values._ndarray.base is dti._data._ndarray.base + assert ser._mgr.arrays[0]._ndarray.base is dti._data._ndarray.base + else: + assert ser._values._ndarray.base is not dti._data._ndarray.base + assert ser._mgr.arrays[0]._ndarray.base is not dti._data._ndarray.base + + assert ser._mgr.arrays[0] is not dti + + ser[::3] = NaT + assert ser[0] is NaT + assert dti[0] == ts + + +class TestSetitemCallable: + def test_setitem_callable_key(self): + # GH#12533 + ser = Series([1, 2, 3, 4], index=list("ABCD")) + ser[lambda x: "A"] = -1 + + expected = Series([-1, 2, 3, 4], index=list("ABCD")) + tm.assert_series_equal(ser, expected) + + def test_setitem_callable_other(self): + # GH#13299 + inc = lambda x: x + 1 + + # set object dtype to avoid upcast when setting inc + ser = Series([1, 2, -1, 4], dtype=object) + ser[ser < 0] = inc + + expected = Series([1, 2, inc, 4]) + tm.assert_series_equal(ser, expected) + + +class TestSetitemWithExpansion: + def test_setitem_empty_series(self): + # GH#10193 + key = Timestamp("2012-01-01") + series = Series(dtype=object) + series[key] = 47 + expected = Series(47, [key]) + tm.assert_series_equal(series, expected) + + def test_setitem_empty_series_datetimeindex_preserves_freq(self): + # GH#33573 our index should retain its freq + dti = DatetimeIndex([], freq="D", dtype="M8[ns]") + series = Series([], index=dti, dtype=object) + key = Timestamp("2012-01-01") + series[key] = 47 + expected = Series(47, DatetimeIndex([key], freq="D").as_unit("ns")) + tm.assert_series_equal(series, expected) + assert series.index.freq == expected.index.freq + + def test_setitem_empty_series_timestamp_preserves_dtype(self): + # GH 21881 + timestamp = Timestamp(1412526600000000000) + series = Series([timestamp], index=["timestamp"], dtype=object) + expected = series["timestamp"] + + series = Series([], dtype=object) + series["anything"] = 300.0 + series["timestamp"] = timestamp + result = series["timestamp"] + assert result == expected + + @pytest.mark.parametrize( + "td", + [ + Timedelta("9 days"), + Timedelta("9 days").to_timedelta64(), + Timedelta("9 days").to_pytimedelta(), + ], + ) + def test_append_timedelta_does_not_cast(self, td, using_infer_string, request): + # GH#22717 inserting a Timedelta should _not_ cast to int64 + if using_infer_string and not isinstance(td, Timedelta): + # TODO: GH#56010 + request.applymarker(pytest.mark.xfail(reason="inferred as string")) + + expected = Series(["x", td], index=[0, "td"], dtype=object) + + ser = Series(["x"]) + ser["td"] = td + tm.assert_series_equal(ser, expected) + assert isinstance(ser["td"], Timedelta) + + ser = Series(["x"]) + ser.loc["td"] = Timedelta("9 days") + tm.assert_series_equal(ser, expected) + assert isinstance(ser["td"], Timedelta) + + def test_setitem_with_expansion_type_promotion(self): + # GH#12599 + ser = Series(dtype=object) + ser["a"] = Timestamp("2016-01-01") + ser["b"] = 3.0 + ser["c"] = "foo" + expected = Series([Timestamp("2016-01-01"), 3.0, "foo"], index=["a", "b", "c"]) + tm.assert_series_equal(ser, expected) + + def test_setitem_not_contained(self, string_series): + # set item that's not contained + ser = string_series.copy() + assert "foobar" not in ser.index + ser["foobar"] = 1 + + app = Series([1], index=["foobar"], name="series") + expected = concat([string_series, app]) + tm.assert_series_equal(ser, expected) + + def test_setitem_keep_precision(self, any_numeric_ea_dtype): + # GH#32346 + ser = Series([1, 2], dtype=any_numeric_ea_dtype) + ser[2] = 10 + expected = Series([1, 2, 10], dtype=any_numeric_ea_dtype) + tm.assert_series_equal(ser, expected) + + @pytest.mark.parametrize( + "na, target_na, dtype, target_dtype, indexer, warn", + [ + (NA, NA, "Int64", "Int64", 1, None), + (NA, NA, "Int64", "Int64", 2, None), + (NA, np.nan, "int64", "float64", 1, None), + (NA, np.nan, "int64", "float64", 2, None), + (NaT, NaT, "int64", "object", 1, FutureWarning), + (NaT, NaT, "int64", "object", 2, None), + (np.nan, NA, "Int64", "Int64", 1, None), + (np.nan, NA, "Int64", "Int64", 2, None), + (np.nan, NA, "Float64", "Float64", 1, None), + (np.nan, NA, "Float64", "Float64", 2, None), + (np.nan, np.nan, "int64", "float64", 1, None), + (np.nan, np.nan, "int64", "float64", 2, None), + ], + ) + def test_setitem_enlarge_with_na( + self, na, target_na, dtype, target_dtype, indexer, warn + ): + # GH#32346 + ser = Series([1, 2], dtype=dtype) + with tm.assert_produces_warning(warn, match="incompatible dtype"): + ser[indexer] = na + expected_values = [1, target_na] if indexer == 1 else [1, 2, target_na] + expected = Series(expected_values, dtype=target_dtype) + tm.assert_series_equal(ser, expected) + + def test_setitem_enlargement_object_none(self, nulls_fixture, using_infer_string): + # GH#48665 + ser = Series(["a", "b"]) + ser[3] = nulls_fixture + dtype = ( + "str" + if using_infer_string and not isinstance(nulls_fixture, Decimal) + else object + ) + expected = Series(["a", "b", nulls_fixture], index=[0, 1, 3], dtype=dtype) + tm.assert_series_equal(ser, expected) + if using_infer_string: + ser[3] is np.nan + else: + assert ser[3] is nulls_fixture + + +def test_setitem_scalar_into_readonly_backing_data(): + # GH#14359: test that you cannot mutate a read only buffer + + array = np.zeros(5) + array.flags.writeable = False # make the array immutable + series = Series(array, copy=False) + + for n in series.index: + msg = "assignment destination is read-only" + with pytest.raises(ValueError, match=msg): + series[n] = 1 + + assert array[n] == 0 + + +def test_setitem_slice_into_readonly_backing_data(): + # GH#14359: test that you cannot mutate a read only buffer + + array = np.zeros(5) + array.flags.writeable = False # make the array immutable + series = Series(array, copy=False) + + msg = "assignment destination is read-only" + with pytest.raises(ValueError, match=msg): + series[1:3] = 1 + + assert not array.any() + + +def test_setitem_categorical_assigning_ops(): + orig = Series(Categorical(["b", "b"], categories=["a", "b"])) + ser = orig.copy() + ser[:] = "a" + exp = Series(Categorical(["a", "a"], categories=["a", "b"])) + tm.assert_series_equal(ser, exp) + + ser = orig.copy() + ser[1] = "a" + exp = Series(Categorical(["b", "a"], categories=["a", "b"])) + tm.assert_series_equal(ser, exp) + + ser = orig.copy() + ser[ser.index > 0] = "a" + exp = Series(Categorical(["b", "a"], categories=["a", "b"])) + tm.assert_series_equal(ser, exp) + + ser = orig.copy() + ser[[False, True]] = "a" + exp = Series(Categorical(["b", "a"], categories=["a", "b"])) + tm.assert_series_equal(ser, exp) + + ser = orig.copy() + ser.index = ["x", "y"] + ser["y"] = "a" + exp = Series(Categorical(["b", "a"], categories=["a", "b"]), index=["x", "y"]) + tm.assert_series_equal(ser, exp) + + +def test_setitem_nan_into_categorical(): + # ensure that one can set something to np.nan + ser = Series(Categorical([1, 2, 3])) + exp = Series(Categorical([1, np.nan, 3], categories=[1, 2, 3])) + ser[1] = np.nan + tm.assert_series_equal(ser, exp) + + +class TestSetitemCasting: + @pytest.mark.parametrize("unique", [True, False]) + @pytest.mark.parametrize("val", [3, 3.0, "3"], ids=type) + def test_setitem_non_bool_into_bool(self, val, indexer_sli, unique): + # dont cast these 3-like values to bool + ser = Series([True, False]) + if not unique: + ser.index = [1, 1] + + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + indexer_sli(ser)[1] = val + assert type(ser.iloc[1]) == type(val) + + expected = Series([True, val], dtype=object, index=ser.index) + if not unique and indexer_sli is not tm.iloc: + expected = Series([val, val], dtype=object, index=[1, 1]) + tm.assert_series_equal(ser, expected) + + def test_setitem_boolean_array_into_npbool(self): + # GH#45462 + ser = Series([True, False, True]) + values = ser._values + arr = array([True, False, None]) + + ser[:2] = arr[:2] # no NAs -> can set inplace + assert ser._values is values + + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + ser[1:] = arr[1:] # has an NA -> cast to boolean dtype + expected = Series(arr) + tm.assert_series_equal(ser, expected) + + +class SetitemCastingEquivalents: + """ + Check each of several methods that _should_ be equivalent to `obj[key] = val` + + We assume that + - obj.index is the default Index(range(len(obj))) + - the setitem does not expand the obj + """ + + @pytest.fixture + def is_inplace(self, obj, expected): + """ + Whether we expect the setting to be in-place or not. + """ + return expected.dtype == obj.dtype + + def check_indexer(self, obj, key, expected, val, indexer, is_inplace): + orig = obj + obj = obj.copy() + arr = obj._values + + indexer(obj)[key] = val + tm.assert_series_equal(obj, expected) + + self._check_inplace(is_inplace, orig, arr, obj) + + def _check_inplace(self, is_inplace, orig, arr, obj): + if is_inplace is None: + # We are not (yet) checking whether setting is inplace or not + pass + elif is_inplace: + if arr.dtype.kind in ["m", "M"]: + # We may not have the same DTA/TDA, but will have the same + # underlying data + assert arr._ndarray is obj._values._ndarray + else: + assert obj._values is arr + else: + # otherwise original array should be unchanged + tm.assert_equal(arr, orig._values) + + def test_int_key(self, obj, key, expected, warn, val, indexer_sli, is_inplace): + if not isinstance(key, int): + pytest.skip("Not relevant for int key") + + with tm.assert_produces_warning(warn, match="incompatible dtype"): + self.check_indexer(obj, key, expected, val, indexer_sli, is_inplace) + + if indexer_sli is tm.loc: + with tm.assert_produces_warning(warn, match="incompatible dtype"): + self.check_indexer(obj, key, expected, val, tm.at, is_inplace) + elif indexer_sli is tm.iloc: + with tm.assert_produces_warning(warn, match="incompatible dtype"): + self.check_indexer(obj, key, expected, val, tm.iat, is_inplace) + + rng = range(key, key + 1) + with tm.assert_produces_warning(warn, match="incompatible dtype"): + self.check_indexer(obj, rng, expected, val, indexer_sli, is_inplace) + + if indexer_sli is not tm.loc: + # Note: no .loc because that handles slice edges differently + slc = slice(key, key + 1) + with tm.assert_produces_warning(warn, match="incompatible dtype"): + self.check_indexer(obj, slc, expected, val, indexer_sli, is_inplace) + + ilkey = [key] + with tm.assert_produces_warning(warn, match="incompatible dtype"): + self.check_indexer(obj, ilkey, expected, val, indexer_sli, is_inplace) + + indkey = np.array(ilkey) + with tm.assert_produces_warning(warn, match="incompatible dtype"): + self.check_indexer(obj, indkey, expected, val, indexer_sli, is_inplace) + + genkey = (x for x in [key]) + with tm.assert_produces_warning(warn, match="incompatible dtype"): + self.check_indexer(obj, genkey, expected, val, indexer_sli, is_inplace) + + def test_slice_key(self, obj, key, expected, warn, val, indexer_sli, is_inplace): + if not isinstance(key, slice): + pytest.skip("Not relevant for slice key") + + if indexer_sli is not tm.loc: + # Note: no .loc because that handles slice edges differently + with tm.assert_produces_warning(warn, match="incompatible dtype"): + self.check_indexer(obj, key, expected, val, indexer_sli, is_inplace) + + ilkey = list(range(len(obj)))[key] + with tm.assert_produces_warning(warn, match="incompatible dtype"): + self.check_indexer(obj, ilkey, expected, val, indexer_sli, is_inplace) + + indkey = np.array(ilkey) + with tm.assert_produces_warning(warn, match="incompatible dtype"): + self.check_indexer(obj, indkey, expected, val, indexer_sli, is_inplace) + + genkey = (x for x in indkey) + with tm.assert_produces_warning(warn, match="incompatible dtype"): + self.check_indexer(obj, genkey, expected, val, indexer_sli, is_inplace) + + def test_mask_key(self, obj, key, expected, warn, val, indexer_sli): + # setitem with boolean mask + mask = np.zeros(obj.shape, dtype=bool) + mask[key] = True + + obj = obj.copy() + + if is_list_like(val) and len(val) < mask.sum(): + msg = "boolean index did not match indexed array along dimension" + with pytest.raises(IndexError, match=msg): + indexer_sli(obj)[mask] = val + return + + with tm.assert_produces_warning(warn, match="incompatible dtype"): + indexer_sli(obj)[mask] = val + tm.assert_series_equal(obj, expected) + + def test_series_where(self, obj, key, expected, warn, val, is_inplace): + mask = np.zeros(obj.shape, dtype=bool) + mask[key] = True + + if is_list_like(val) and len(val) < len(obj): + # Series.where is not valid here + msg = "operands could not be broadcast together with shapes" + with pytest.raises(ValueError, match=msg): + obj.where(~mask, val) + return + + orig = obj + obj = obj.copy() + arr = obj._values + + res = obj.where(~mask, val) + + if val is NA and res.dtype == object: + expected = expected.fillna(NA) + elif val is None and res.dtype == object: + assert expected.dtype == object + expected = expected.copy() + expected[expected.isna()] = None + tm.assert_series_equal(res, expected) + + self._check_inplace(is_inplace, orig, arr, obj) + + def test_index_where(self, obj, key, expected, warn, val): + mask = np.zeros(obj.shape, dtype=bool) + mask[key] = True + + res = Index(obj, dtype=obj.dtype).where(~mask, val) + expected_idx = Index(expected, dtype=expected.dtype) + tm.assert_index_equal(res, expected_idx) + + def test_index_putmask(self, obj, key, expected, warn, val): + mask = np.zeros(obj.shape, dtype=bool) + mask[key] = True + + res = Index(obj, dtype=obj.dtype).putmask(mask, val) + tm.assert_index_equal(res, Index(expected, dtype=expected.dtype)) + + +@pytest.mark.parametrize( + "obj,expected,key,warn", + [ + pytest.param( + # GH#45568 setting a valid NA value into IntervalDtype[int] should + # cast to IntervalDtype[float] + Series(interval_range(1, 5)), + Series( + [Interval(1, 2), np.nan, Interval(3, 4), Interval(4, 5)], + dtype="interval[float64]", + ), + 1, + FutureWarning, + id="interval_int_na_value", + ), + pytest.param( + # these induce dtype changes + Series([2, 3, 4, 5, 6, 7, 8, 9, 10]), + Series([np.nan, 3, np.nan, 5, np.nan, 7, np.nan, 9, np.nan]), + slice(None, None, 2), + None, + id="int_series_slice_key_step", + ), + pytest.param( + Series([True, True, False, False]), + Series([np.nan, True, np.nan, False], dtype=object), + slice(None, None, 2), + FutureWarning, + id="bool_series_slice_key_step", + ), + pytest.param( + # these induce dtype changes + Series(np.arange(10)), + Series([np.nan, np.nan, np.nan, np.nan, np.nan, 5, 6, 7, 8, 9]), + slice(None, 5), + None, + id="int_series_slice_key", + ), + pytest.param( + # changes dtype GH#4463 + Series([1, 2, 3]), + Series([np.nan, 2, 3]), + 0, + None, + id="int_series_int_key", + ), + pytest.param( + # changes dtype GH#4463 + Series([False]), + Series([np.nan], dtype=object), + # TODO: maybe go to float64 since we are changing the _whole_ Series? + 0, + FutureWarning, + id="bool_series_int_key_change_all", + ), + pytest.param( + # changes dtype GH#4463 + Series([False, True]), + Series([np.nan, True], dtype=object), + 0, + FutureWarning, + id="bool_series_int_key", + ), + ], +) +class TestSetitemCastingEquivalents(SetitemCastingEquivalents): + @pytest.fixture(params=[np.nan, np.float64("NaN"), None, NA]) + def val(self, request): + """ + NA values that should generally be valid_na for *all* dtypes. + + Include both python float NaN and np.float64; only np.float64 has a + `dtype` attribute. + """ + return request.param + + +class TestSetitemTimedelta64IntoNumeric(SetitemCastingEquivalents): + # timedelta64 should not be treated as integers when setting into + # numeric Series + + @pytest.fixture + def val(self): + td = np.timedelta64(4, "ns") + return td + # TODO: could also try np.full((1,), td) + + @pytest.fixture(params=[complex, int, float]) + def dtype(self, request): + return request.param + + @pytest.fixture + def obj(self, dtype): + arr = np.arange(5).astype(dtype) + ser = Series(arr) + return ser + + @pytest.fixture + def expected(self, dtype): + arr = np.arange(5).astype(dtype) + ser = Series(arr) + ser = ser.astype(object) + ser.iloc[0] = np.timedelta64(4, "ns") + return ser + + @pytest.fixture + def key(self): + return 0 + + @pytest.fixture + def warn(self): + return FutureWarning + + +class TestSetitemDT64IntoInt(SetitemCastingEquivalents): + # GH#39619 dont cast dt64 to int when doing this setitem + + @pytest.fixture(params=["M8[ns]", "m8[ns]"]) + def dtype(self, request): + return request.param + + @pytest.fixture + def scalar(self, dtype): + val = np.datetime64("2021-01-18 13:25:00", "ns") + if dtype == "m8[ns]": + val = val - val + return val + + @pytest.fixture + def expected(self, scalar): + expected = Series([scalar, scalar, 3], dtype=object) + assert isinstance(expected[0], type(scalar)) + return expected + + @pytest.fixture + def obj(self): + return Series([1, 2, 3]) + + @pytest.fixture + def key(self): + return slice(None, -1) + + @pytest.fixture(params=[None, list, np.array]) + def val(self, scalar, request): + box = request.param + if box is None: + return scalar + return box([scalar, scalar]) + + @pytest.fixture + def warn(self): + return FutureWarning + + +class TestSetitemNAPeriodDtype(SetitemCastingEquivalents): + # Setting compatible NA values into Series with PeriodDtype + + @pytest.fixture + def expected(self, key): + exp = Series(period_range("2000-01-01", periods=10, freq="D")) + exp._values.view("i8")[key] = NaT._value + assert exp[key] is NaT or all(x is NaT for x in exp[key]) + return exp + + @pytest.fixture + def obj(self): + return Series(period_range("2000-01-01", periods=10, freq="D")) + + @pytest.fixture(params=[3, slice(3, 5)]) + def key(self, request): + return request.param + + @pytest.fixture(params=[None, np.nan]) + def val(self, request): + return request.param + + @pytest.fixture + def warn(self): + return None + + +class TestSetitemNADatetimeLikeDtype(SetitemCastingEquivalents): + # some nat-like values should be cast to datetime64/timedelta64 when + # inserting into a datetime64/timedelta64 series. Others should coerce + # to object and retain their dtypes. + # GH#18586 for td64 and boolean mask case + + @pytest.fixture( + params=["m8[ns]", "M8[ns]", "datetime64[ns, UTC]", "datetime64[ns, US/Central]"] + ) + def dtype(self, request): + return request.param + + @pytest.fixture + def obj(self, dtype): + i8vals = date_range("2016-01-01", periods=3).asi8 + idx = Index(i8vals, dtype=dtype) + assert idx.dtype == dtype + return Series(idx) + + @pytest.fixture( + params=[ + None, + np.nan, + NaT, + np.timedelta64("NaT", "ns"), + np.datetime64("NaT", "ns"), + ] + ) + def val(self, request): + return request.param + + @pytest.fixture + def is_inplace(self, val, obj): + # td64 -> cast to object iff val is datetime64("NaT") + # dt64 -> cast to object iff val is timedelta64("NaT") + # dt64tz -> cast to object with anything _but_ NaT + return val is NaT or val is None or val is np.nan or obj.dtype == val.dtype + + @pytest.fixture + def expected(self, obj, val, is_inplace): + dtype = obj.dtype if is_inplace else object + expected = Series([val] + list(obj[1:]), dtype=dtype) + return expected + + @pytest.fixture + def key(self): + return 0 + + @pytest.fixture + def warn(self, is_inplace): + return None if is_inplace else FutureWarning + + +class TestSetitemMismatchedTZCastsToObject(SetitemCastingEquivalents): + # GH#24024 + @pytest.fixture + def obj(self): + return Series(date_range("2000", periods=2, tz="US/Central")) + + @pytest.fixture + def val(self): + return Timestamp("2000", tz="US/Eastern") + + @pytest.fixture + def key(self): + return 0 + + @pytest.fixture + def expected(self, obj, val): + # pre-2.0 this would cast to object, in 2.0 we cast the val to + # the target tz + expected = Series( + [ + val.tz_convert("US/Central"), + Timestamp("2000-01-02 00:00:00-06:00", tz="US/Central"), + ], + dtype=obj.dtype, + ) + return expected + + @pytest.fixture + def warn(self): + return None + + +@pytest.mark.parametrize( + "obj,expected,warn", + [ + # For numeric series, we should coerce to NaN. + (Series([1, 2, 3]), Series([np.nan, 2, 3]), None), + (Series([1.0, 2.0, 3.0]), Series([np.nan, 2.0, 3.0]), None), + # For datetime series, we should coerce to NaT. + ( + Series([datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)]), + Series([NaT, datetime(2000, 1, 2), datetime(2000, 1, 3)]), + None, + ), + # For objects, we should preserve the None value. + (Series(["foo", "bar", "baz"]), Series([None, "bar", "baz"]), None), + ], +) +class TestSeriesNoneCoercion(SetitemCastingEquivalents): + @pytest.fixture + def key(self): + return 0 + + @pytest.fixture + def val(self): + return None + + +class TestSetitemFloatIntervalWithIntIntervalValues(SetitemCastingEquivalents): + # GH#44201 Cast to shared IntervalDtype rather than object + + def test_setitem_example(self): + # Just a case here to make obvious what this test class is aimed at + idx = IntervalIndex.from_breaks(range(4)) + obj = Series(idx) + val = Interval(0.5, 1.5) + + with tm.assert_produces_warning( + FutureWarning, match="Setting an item of incompatible dtype" + ): + obj[0] = val + assert obj.dtype == "Interval[float64, right]" + + @pytest.fixture + def obj(self): + idx = IntervalIndex.from_breaks(range(4)) + return Series(idx) + + @pytest.fixture + def val(self): + return Interval(0.5, 1.5) + + @pytest.fixture + def key(self): + return 0 + + @pytest.fixture + def expected(self, obj, val): + data = [val] + list(obj[1:]) + idx = IntervalIndex(data, dtype="Interval[float64]") + return Series(idx) + + @pytest.fixture + def warn(self): + return FutureWarning + + +class TestSetitemRangeIntoIntegerSeries(SetitemCastingEquivalents): + # GH#44261 Setting a range with sufficiently-small integers into + # small-itemsize integer dtypes should not need to upcast + + @pytest.fixture + def obj(self, any_int_numpy_dtype): + dtype = np.dtype(any_int_numpy_dtype) + ser = Series(range(5), dtype=dtype) + return ser + + @pytest.fixture + def val(self): + return range(2, 4) + + @pytest.fixture + def key(self): + return slice(0, 2) + + @pytest.fixture + def expected(self, any_int_numpy_dtype): + dtype = np.dtype(any_int_numpy_dtype) + exp = Series([2, 3, 2, 3, 4], dtype=dtype) + return exp + + @pytest.fixture + def warn(self): + return None + + +@pytest.mark.parametrize( + "val, warn", + [ + (np.array([2.0, 3.0]), None), + (np.array([2.5, 3.5]), FutureWarning), + ( + np.array([2**65, 2**65 + 1], dtype=np.float64), + FutureWarning, + ), # all ints, but can't cast + ], +) +class TestSetitemFloatNDarrayIntoIntegerSeries(SetitemCastingEquivalents): + @pytest.fixture + def obj(self): + return Series(range(5), dtype=np.int64) + + @pytest.fixture + def key(self): + return slice(0, 2) + + @pytest.fixture + def expected(self, val): + if val[0] == 2: + # NB: this condition is based on currently-hardcoded "val" cases + dtype = np.int64 + else: + dtype = np.float64 + res_values = np.array(range(5), dtype=dtype) + res_values[:2] = val + return Series(res_values) + + +@pytest.mark.parametrize("val", [512, np.int16(512)]) +class TestSetitemIntoIntegerSeriesNeedsUpcast(SetitemCastingEquivalents): + @pytest.fixture + def obj(self): + return Series([1, 2, 3], dtype=np.int8) + + @pytest.fixture + def key(self): + return 1 + + @pytest.fixture + def expected(self): + return Series([1, 512, 3], dtype=np.int16) + + @pytest.fixture + def warn(self): + return FutureWarning + + +@pytest.mark.parametrize("val", [2**33 + 1.0, 2**33 + 1.1, 2**62]) +class TestSmallIntegerSetitemUpcast(SetitemCastingEquivalents): + # https://github.com/pandas-dev/pandas/issues/39584#issuecomment-941212124 + @pytest.fixture + def obj(self): + return Series([1, 2, 3], dtype="i4") + + @pytest.fixture + def key(self): + return 0 + + @pytest.fixture + def expected(self, val): + if val % 1 != 0: + dtype = "f8" + else: + dtype = "i8" + return Series([val, 2, 3], dtype=dtype) + + @pytest.fixture + def warn(self): + return FutureWarning + + +class CoercionTest(SetitemCastingEquivalents): + # Tests ported from tests.indexing.test_coercion + + @pytest.fixture + def key(self): + return 1 + + @pytest.fixture + def expected(self, obj, key, val, exp_dtype): + vals = list(obj) + vals[key] = val + return Series(vals, dtype=exp_dtype) + + +@pytest.mark.parametrize( + "val,exp_dtype,warn", + [(np.int32(1), np.int8, None), (np.int16(2**9), np.int16, FutureWarning)], +) +class TestCoercionInt8(CoercionTest): + # previously test_setitem_series_int8 in tests.indexing.test_coercion + @pytest.fixture + def obj(self): + return Series([1, 2, 3, 4], dtype=np.int8) + + +@pytest.mark.parametrize("val", [1, 1.1, 1 + 1j, True]) +@pytest.mark.parametrize("exp_dtype", [object]) +class TestCoercionObject(CoercionTest): + # previously test_setitem_series_object in tests.indexing.test_coercion + @pytest.fixture + def obj(self): + return Series(["a", "b", "c", "d"], dtype=object) + + @pytest.fixture + def warn(self): + return None + + +@pytest.mark.parametrize( + "val,exp_dtype,warn", + [ + (1, np.complex128, None), + (1.1, np.complex128, None), + (1 + 1j, np.complex128, None), + (True, object, FutureWarning), + ], +) +class TestCoercionComplex(CoercionTest): + # previously test_setitem_series_complex128 in tests.indexing.test_coercion + @pytest.fixture + def obj(self): + return Series([1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j]) + + +@pytest.mark.parametrize( + "val,exp_dtype,warn", + [ + (1, object, FutureWarning), + ("3", object, FutureWarning), + (3, object, FutureWarning), + (1.1, object, FutureWarning), + (1 + 1j, object, FutureWarning), + (True, bool, None), + ], +) +class TestCoercionBool(CoercionTest): + # previously test_setitem_series_bool in tests.indexing.test_coercion + @pytest.fixture + def obj(self): + return Series([True, False, True, False], dtype=bool) + + +@pytest.mark.parametrize( + "val,exp_dtype,warn", + [ + (1, np.int64, None), + (1.1, np.float64, FutureWarning), + (1 + 1j, np.complex128, FutureWarning), + (True, object, FutureWarning), + ], +) +class TestCoercionInt64(CoercionTest): + # previously test_setitem_series_int64 in tests.indexing.test_coercion + @pytest.fixture + def obj(self): + return Series([1, 2, 3, 4]) + + +@pytest.mark.parametrize( + "val,exp_dtype,warn", + [ + (1, np.float64, None), + (1.1, np.float64, None), + (1 + 1j, np.complex128, FutureWarning), + (True, object, FutureWarning), + ], +) +class TestCoercionFloat64(CoercionTest): + # previously test_setitem_series_float64 in tests.indexing.test_coercion + @pytest.fixture + def obj(self): + return Series([1.1, 2.2, 3.3, 4.4]) + + +@pytest.mark.parametrize( + "val,exp_dtype,warn", + [ + (1, np.float32, None), + pytest.param( + 1.1, + np.float32, + None, + marks=pytest.mark.xfail( + ( + not np_version_gte1p24 + or ( + np_version_gte1p24 + and not np_version_gt2 + and os.environ.get("NPY_PROMOTION_STATE", "legacy") != "weak" + ) + ), + reason="np.float32(1.1) ends up as 1.100000023841858, so " + "np_can_hold_element raises and we cast to float64", + ), + ), + (1 + 1j, np.complex128, FutureWarning), + (True, object, FutureWarning), + (np.uint8(2), np.float32, None), + (np.uint32(2), np.float32, None), + # float32 cannot hold np.iinfo(np.uint32).max exactly + # (closest it can hold is 4294967300.0 which off by 5.0), so + # we cast to float64 + (np.uint32(np.iinfo(np.uint32).max), np.float64, FutureWarning), + (np.uint64(2), np.float32, None), + (np.int64(2), np.float32, None), + ], +) +class TestCoercionFloat32(CoercionTest): + @pytest.fixture + def obj(self): + return Series([1.1, 2.2, 3.3, 4.4], dtype=np.float32) + + def test_slice_key(self, obj, key, expected, warn, val, indexer_sli, is_inplace): + super().test_slice_key(obj, key, expected, warn, val, indexer_sli, is_inplace) + + if isinstance(val, float): + # the xfail would xpass bc test_slice_key short-circuits + raise AssertionError("xfail not relevant for this test.") + + +@pytest.mark.parametrize( + "val,exp_dtype,warn", + [ + (Timestamp("2012-01-01"), "datetime64[ns]", None), + (1, object, FutureWarning), + ("x", object, FutureWarning), + ], +) +class TestCoercionDatetime64(CoercionTest): + # previously test_setitem_series_datetime64 in tests.indexing.test_coercion + + @pytest.fixture + def obj(self): + return Series(date_range("2011-01-01", freq="D", periods=4)) + + @pytest.fixture + def warn(self): + return None + + +@pytest.mark.parametrize( + "val,exp_dtype,warn", + [ + (Timestamp("2012-01-01", tz="US/Eastern"), "datetime64[ns, US/Eastern]", None), + # pre-2.0, a mis-matched tz would end up casting to object + (Timestamp("2012-01-01", tz="US/Pacific"), "datetime64[ns, US/Eastern]", None), + (Timestamp("2012-01-01"), object, FutureWarning), + (1, object, FutureWarning), + ], +) +class TestCoercionDatetime64TZ(CoercionTest): + # previously test_setitem_series_datetime64tz in tests.indexing.test_coercion + @pytest.fixture + def obj(self): + tz = "US/Eastern" + return Series(date_range("2011-01-01", freq="D", periods=4, tz=tz)) + + @pytest.fixture + def warn(self): + return None + + +@pytest.mark.parametrize( + "val,exp_dtype,warn", + [ + (Timedelta("12 day"), "timedelta64[ns]", None), + (1, object, FutureWarning), + ("x", object, FutureWarning), + ], +) +class TestCoercionTimedelta64(CoercionTest): + # previously test_setitem_series_timedelta64 in tests.indexing.test_coercion + @pytest.fixture + def obj(self): + return Series(timedelta_range("1 day", periods=4)) + + @pytest.fixture + def warn(self): + return None + + +@pytest.mark.parametrize( + "val", ["foo", Period("2016", freq="Y"), Interval(1, 2, closed="both")] +) +@pytest.mark.parametrize("exp_dtype", [object]) +class TestPeriodIntervalCoercion(CoercionTest): + # GH#45768 + @pytest.fixture( + params=[ + period_range("2016-01-01", periods=3, freq="D"), + interval_range(1, 5), + ] + ) + def obj(self, request): + return Series(request.param) + + @pytest.fixture + def warn(self): + return FutureWarning + + +def test_20643(): + # closed by GH#45121 + orig = Series([0, 1, 2], index=["a", "b", "c"]) + + expected = Series([0, 2.7, 2], index=["a", "b", "c"]) + + ser = orig.copy() + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + ser.at["b"] = 2.7 + tm.assert_series_equal(ser, expected) + + ser = orig.copy() + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + ser.loc["b"] = 2.7 + tm.assert_series_equal(ser, expected) + + ser = orig.copy() + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + ser["b"] = 2.7 + tm.assert_series_equal(ser, expected) + + ser = orig.copy() + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + ser.iat[1] = 2.7 + tm.assert_series_equal(ser, expected) + + ser = orig.copy() + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + ser.iloc[1] = 2.7 + tm.assert_series_equal(ser, expected) + + orig_df = orig.to_frame("A") + expected_df = expected.to_frame("A") + + df = orig_df.copy() + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + df.at["b", "A"] = 2.7 + tm.assert_frame_equal(df, expected_df) + + df = orig_df.copy() + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + df.loc["b", "A"] = 2.7 + tm.assert_frame_equal(df, expected_df) + + df = orig_df.copy() + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + df.iloc[1, 0] = 2.7 + tm.assert_frame_equal(df, expected_df) + + df = orig_df.copy() + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + df.iat[1, 0] = 2.7 + tm.assert_frame_equal(df, expected_df) + + +def test_20643_comment(): + # https://github.com/pandas-dev/pandas/issues/20643#issuecomment-431244590 + # fixed sometime prior to GH#45121 + orig = Series([0, 1, 2], index=["a", "b", "c"]) + expected = Series([np.nan, 1, 2], index=["a", "b", "c"]) + + ser = orig.copy() + ser.iat[0] = None + tm.assert_series_equal(ser, expected) + + ser = orig.copy() + ser.iloc[0] = None + tm.assert_series_equal(ser, expected) + + +def test_15413(): + # fixed by GH#45121 + ser = Series([1, 2, 3]) + + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + ser[ser == 2] += 0.5 + expected = Series([1, 2.5, 3]) + tm.assert_series_equal(ser, expected) + + ser = Series([1, 2, 3]) + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + ser[1] += 0.5 + tm.assert_series_equal(ser, expected) + + ser = Series([1, 2, 3]) + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + ser.loc[1] += 0.5 + tm.assert_series_equal(ser, expected) + + ser = Series([1, 2, 3]) + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + ser.iloc[1] += 0.5 + tm.assert_series_equal(ser, expected) + + ser = Series([1, 2, 3]) + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + ser.iat[1] += 0.5 + tm.assert_series_equal(ser, expected) + + ser = Series([1, 2, 3]) + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + ser.at[1] += 0.5 + tm.assert_series_equal(ser, expected) + + +def test_32878_int_itemsize(): + # Fixed by GH#45121 + arr = np.arange(5).astype("i4") + ser = Series(arr) + val = np.int64(np.iinfo(np.int64).max) + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + ser[0] = val + expected = Series([val, 1, 2, 3, 4], dtype=np.int64) + tm.assert_series_equal(ser, expected) + + +def test_32878_complex_itemsize(): + arr = np.arange(5).astype("c8") + ser = Series(arr) + val = np.finfo(np.float64).max + val = val.astype("c16") + + # GH#32878 used to coerce val to inf+0.000000e+00j + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + ser[0] = val + assert ser[0] == val + expected = Series([val, 1, 2, 3, 4], dtype="c16") + tm.assert_series_equal(ser, expected) + + +def test_37692(indexer_al): + # GH#37692 + ser = Series([1, 2, 3], index=["a", "b", "c"]) + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + indexer_al(ser)["b"] = "test" + expected = Series([1, "test", 3], index=["a", "b", "c"], dtype=object) + tm.assert_series_equal(ser, expected) + + +def test_setitem_bool_int_float_consistency(indexer_sli): + # GH#21513 + # bool-with-int and bool-with-float both upcast to object + # int-with-float and float-with-int are both non-casting so long + # as the setitem can be done losslessly + for dtype in [np.float64, np.int64]: + ser = Series(0, index=range(3), dtype=dtype) + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + indexer_sli(ser)[0] = True + assert ser.dtype == object + + ser = Series(0, index=range(3), dtype=bool) + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + ser[0] = dtype(1) + assert ser.dtype == object + + # 1.0 can be held losslessly, so no casting + ser = Series(0, index=range(3), dtype=np.int64) + indexer_sli(ser)[0] = np.float64(1.0) + assert ser.dtype == np.int64 + + # 1 can be held losslessly, so no casting + ser = Series(0, index=range(3), dtype=np.float64) + indexer_sli(ser)[0] = np.int64(1) + + +def test_setitem_positional_with_casting(): + # GH#45070 case where in __setitem__ we get a KeyError, then when + # we fallback we *also* get a ValueError if we try to set inplace. + ser = Series([1, 2, 3], index=["a", "b", "c"]) + + warn_msg = "Series.__setitem__ treating keys as positions is deprecated" + with tm.assert_produces_warning(FutureWarning, match=warn_msg): + ser[0] = "X" + expected = Series(["X", 2, 3], index=["a", "b", "c"], dtype=object) + tm.assert_series_equal(ser, expected) + + +def test_setitem_positional_float_into_int_coerces(): + # Case where we hit a KeyError and then trying to set in-place incorrectly + # casts a float to an int + ser = Series([1, 2, 3], index=["a", "b", "c"]) + + warn_msg = "Series.__setitem__ treating keys as positions is deprecated" + with tm.assert_produces_warning(FutureWarning, match=warn_msg): + ser[0] = 1.5 + expected = Series([1.5, 2, 3], index=["a", "b", "c"]) + tm.assert_series_equal(ser, expected) + + +def test_setitem_int_not_positional(): + # GH#42215 deprecated falling back to positional on __setitem__ with an + # int not contained in the index; enforced in 2.0 + ser = Series([1, 2, 3, 4], index=[1.1, 2.1, 3.0, 4.1]) + assert not ser.index._should_fallback_to_positional + # assert not ser.index.astype(object)._should_fallback_to_positional + + # 3.0 is in our index, so post-enforcement behavior is unchanged + ser[3] = 10 + expected = Series([1, 2, 10, 4], index=ser.index) + tm.assert_series_equal(ser, expected) + + # pre-enforcement `ser[5] = 5` raised IndexError + ser[5] = 5 + expected = Series([1, 2, 10, 4, 5], index=[1.1, 2.1, 3.0, 4.1, 5.0]) + tm.assert_series_equal(ser, expected) + + ii = IntervalIndex.from_breaks(range(10))[::2] + ser2 = Series(range(len(ii)), index=ii) + exp_index = ii.astype(object).append(Index([4])) + expected2 = Series([0, 1, 2, 3, 4, 9], index=exp_index) + # pre-enforcement `ser2[4] = 9` interpreted 4 as positional + ser2[4] = 9 + tm.assert_series_equal(ser2, expected2) + + mi = MultiIndex.from_product([ser.index, ["A", "B"]]) + ser3 = Series(range(len(mi)), index=mi) + expected3 = ser3.copy() + expected3.loc[4] = 99 + # pre-enforcement `ser3[4] = 99` interpreted 4 as positional + ser3[4] = 99 + tm.assert_series_equal(ser3, expected3) + + +def test_setitem_with_bool_indexer(): + # GH#42530 + + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + result = df.pop("b").copy() + result[[True, False, False]] = 9 + expected = Series(data=[9, 5, 6], name="b") + tm.assert_series_equal(result, expected) + + df.loc[[True, False, False], "a"] = 10 + expected = DataFrame({"a": [10, 2, 3]}) + tm.assert_frame_equal(df, expected) + + +@pytest.mark.parametrize("size", range(2, 6)) +@pytest.mark.parametrize( + "mask", [[True, False, False, False, False], [True, False], [False]] +) +@pytest.mark.parametrize( + "item", [2.0, np.nan, np.finfo(float).max, np.finfo(float).min] +) +# Test numpy arrays, lists and tuples as the input to be +# broadcast +@pytest.mark.parametrize( + "box", [lambda x: np.array([x]), lambda x: [x], lambda x: (x,)] +) +def test_setitem_bool_indexer_dont_broadcast_length1_values(size, mask, item, box): + # GH#44265 + # see also tests.series.indexing.test_where.test_broadcast + + selection = np.resize(mask, size) + + data = np.arange(size, dtype=float) + + ser = Series(data) + + if selection.sum() != 1: + msg = ( + "cannot set using a list-like indexer with a different " + "length than the value" + ) + with pytest.raises(ValueError, match=msg): + # GH#44265 + ser[selection] = box(item) + else: + # In this corner case setting is equivalent to setting with the unboxed + # item + ser[selection] = box(item) + + expected = Series(np.arange(size, dtype=float)) + expected[selection] = item + tm.assert_series_equal(ser, expected) + + +def test_setitem_empty_mask_dont_upcast_dt64(): + dti = date_range("2016-01-01", periods=3) + ser = Series(dti) + orig = ser.copy() + mask = np.zeros(3, dtype=bool) + + ser[mask] = "foo" + assert ser.dtype == dti.dtype # no-op -> dont upcast + tm.assert_series_equal(ser, orig) + + ser.mask(mask, "foo", inplace=True) + assert ser.dtype == dti.dtype # no-op -> dont upcast + tm.assert_series_equal(ser, orig) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_take.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_take.py new file mode 100644 index 0000000000000000000000000000000000000000..43fbae89089665fb2bf589d35db1b3f3041dd018 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_take.py @@ -0,0 +1,50 @@ +import pytest + +import pandas as pd +from pandas import Series +import pandas._testing as tm + + +def test_take_validate_axis(): + # GH#51022 + ser = Series([-1, 5, 6, 2, 4]) + + msg = "No axis named foo for object type Series" + with pytest.raises(ValueError, match=msg): + ser.take([1, 2], axis="foo") + + +def test_take(): + ser = Series([-1, 5, 6, 2, 4]) + + actual = ser.take([1, 3, 4]) + expected = Series([5, 2, 4], index=[1, 3, 4]) + tm.assert_series_equal(actual, expected) + + actual = ser.take([-1, 3, 4]) + expected = Series([4, 2, 4], index=[4, 3, 4]) + tm.assert_series_equal(actual, expected) + + msg = "indices are out-of-bounds" + with pytest.raises(IndexError, match=msg): + ser.take([1, 10]) + with pytest.raises(IndexError, match=msg): + ser.take([2, 5]) + + +def test_take_categorical(): + # https://github.com/pandas-dev/pandas/issues/20664 + ser = Series(pd.Categorical(["a", "b", "c"])) + result = ser.take([-2, -2, 0]) + expected = Series( + pd.Categorical(["b", "b", "a"], categories=["a", "b", "c"]), index=[1, 1, 0] + ) + tm.assert_series_equal(result, expected) + + +def test_take_slice_raises(): + ser = Series([-1, 5, 6, 2, 4]) + + msg = "Series.take requires a sequence of integers, not slice" + with pytest.raises(TypeError, match=msg): + ser.take(slice(0, 3, 1)) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_where.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_where.py new file mode 100644 index 0000000000000000000000000000000000000000..0fa2f63e5fb3666a8910088391f97971ac944873 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_where.py @@ -0,0 +1,478 @@ +import numpy as np +import pytest + +from pandas.core.dtypes.common import is_integer + +import pandas as pd +from pandas import ( + Series, + Timestamp, + date_range, + isna, +) +import pandas._testing as tm + + +def test_where_unsafe_int(any_signed_int_numpy_dtype): + s = Series(np.arange(10), dtype=any_signed_int_numpy_dtype) + mask = s < 5 + + s[mask] = range(2, 7) + expected = Series( + list(range(2, 7)) + list(range(5, 10)), + dtype=any_signed_int_numpy_dtype, + ) + + tm.assert_series_equal(s, expected) + + +def test_where_unsafe_float(float_numpy_dtype): + s = Series(np.arange(10), dtype=float_numpy_dtype) + mask = s < 5 + + s[mask] = range(2, 7) + data = list(range(2, 7)) + list(range(5, 10)) + expected = Series(data, dtype=float_numpy_dtype) + + tm.assert_series_equal(s, expected) + + +@pytest.mark.parametrize( + "dtype,expected_dtype", + [ + (np.int8, np.float64), + (np.int16, np.float64), + (np.int32, np.float64), + (np.int64, np.float64), + (np.float32, np.float32), + (np.float64, np.float64), + ], +) +def test_where_unsafe_upcast(dtype, expected_dtype): + # see gh-9743 + s = Series(np.arange(10), dtype=dtype) + values = [2.5, 3.5, 4.5, 5.5, 6.5] + mask = s < 5 + expected = Series(values + list(range(5, 10)), dtype=expected_dtype) + warn = ( + None + if np.dtype(dtype).kind == np.dtype(expected_dtype).kind == "f" + else FutureWarning + ) + with tm.assert_produces_warning(warn, match="incompatible dtype"): + s[mask] = values + tm.assert_series_equal(s, expected) + + +def test_where_unsafe(): + # see gh-9731 + s = Series(np.arange(10), dtype="int64") + values = [2.5, 3.5, 4.5, 5.5] + + mask = s > 5 + expected = Series(list(range(6)) + values, dtype="float64") + + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + s[mask] = values + tm.assert_series_equal(s, expected) + + # see gh-3235 + s = Series(np.arange(10), dtype="int64") + mask = s < 5 + s[mask] = range(2, 7) + expected = Series(list(range(2, 7)) + list(range(5, 10)), dtype="int64") + tm.assert_series_equal(s, expected) + assert s.dtype == expected.dtype + + s = Series(np.arange(10), dtype="int64") + mask = s > 5 + s[mask] = [0] * 4 + expected = Series([0, 1, 2, 3, 4, 5] + [0] * 4, dtype="int64") + tm.assert_series_equal(s, expected) + + s = Series(np.arange(10)) + mask = s > 5 + + msg = "cannot set using a list-like indexer with a different length than the value" + with pytest.raises(ValueError, match=msg): + s[mask] = [5, 4, 3, 2, 1] + + with pytest.raises(ValueError, match=msg): + s[mask] = [0] * 5 + + # dtype changes + s = Series([1, 2, 3, 4]) + result = s.where(s > 2, np.nan) + expected = Series([np.nan, np.nan, 3, 4]) + tm.assert_series_equal(result, expected) + + # GH 4667 + # setting with None changes dtype + s = Series(range(10)).astype(float) + s[8] = None + result = s[8] + assert isna(result) + + s = Series(range(10)).astype(float) + s[s > 8] = None + result = s[isna(s)] + expected = Series(np.nan, index=[9]) + tm.assert_series_equal(result, expected) + + +def test_where(): + s = Series(np.random.default_rng(2).standard_normal(5)) + cond = s > 0 + + rs = s.where(cond).dropna() + rs2 = s[cond] + tm.assert_series_equal(rs, rs2) + + rs = s.where(cond, -s) + tm.assert_series_equal(rs, s.abs()) + + rs = s.where(cond) + assert s.shape == rs.shape + assert rs is not s + + # test alignment + cond = Series([True, False, False, True, False], index=s.index) + s2 = -(s.abs()) + + expected = s2[cond].reindex(s2.index[:3]).reindex(s2.index) + rs = s2.where(cond[:3]) + tm.assert_series_equal(rs, expected) + + expected = s2.abs() + expected.iloc[0] = s2[0] + rs = s2.where(cond[:3], -s2) + tm.assert_series_equal(rs, expected) + + +def test_where_error(): + s = Series(np.random.default_rng(2).standard_normal(5)) + cond = s > 0 + + msg = "Array conditional must be same shape as self" + with pytest.raises(ValueError, match=msg): + s.where(1) + with pytest.raises(ValueError, match=msg): + s.where(cond[:3].values, -s) + + # GH 2745 + s = Series([1, 2]) + s[[True, False]] = [0, 1] + expected = Series([0, 2]) + tm.assert_series_equal(s, expected) + + # failures + msg = "cannot set using a list-like indexer with a different length than the value" + with pytest.raises(ValueError, match=msg): + s[[True, False]] = [0, 2, 3] + + with pytest.raises(ValueError, match=msg): + s[[True, False]] = [] + + +@pytest.mark.parametrize("klass", [list, tuple, np.array, Series]) +def test_where_array_like(klass): + # see gh-15414 + s = Series([1, 2, 3]) + cond = [False, True, True] + expected = Series([np.nan, 2, 3]) + + result = s.where(klass(cond)) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "cond", + [ + [1, 0, 1], + Series([2, 5, 7]), + ["True", "False", "True"], + [Timestamp("2017-01-01"), pd.NaT, Timestamp("2017-01-02")], + ], +) +def test_where_invalid_input(cond): + # see gh-15414: only boolean arrays accepted + s = Series([1, 2, 3]) + msg = "Boolean array expected for the condition" + + with pytest.raises(ValueError, match=msg): + s.where(cond) + + msg = "Array conditional must be same shape as self" + with pytest.raises(ValueError, match=msg): + s.where([True]) + + +def test_where_ndframe_align(): + msg = "Array conditional must be same shape as self" + s = Series([1, 2, 3]) + + cond = [True] + with pytest.raises(ValueError, match=msg): + s.where(cond) + + expected = Series([1, np.nan, np.nan]) + + out = s.where(Series(cond)) + tm.assert_series_equal(out, expected) + + cond = np.array([False, True, False, True]) + with pytest.raises(ValueError, match=msg): + s.where(cond) + + expected = Series([np.nan, 2, np.nan]) + + out = s.where(Series(cond)) + tm.assert_series_equal(out, expected) + + +def test_where_setitem_invalid(): + # GH 2702 + # make sure correct exceptions are raised on invalid list assignment + + msg = ( + lambda x: f"cannot set using a {x} indexer with a " + "different length than the value" + ) + # slice + s = Series(list("abc"), dtype=object) + + with pytest.raises(ValueError, match=msg("slice")): + s[0:3] = list(range(27)) + + s[0:3] = list(range(3)) + expected = Series([0, 1, 2]) + tm.assert_series_equal(s.astype(np.int64), expected) + + # slice with step + s = Series(list("abcdef"), dtype=object) + + with pytest.raises(ValueError, match=msg("slice")): + s[0:4:2] = list(range(27)) + + s = Series(list("abcdef"), dtype=object) + s[0:4:2] = list(range(2)) + expected = Series([0, "b", 1, "d", "e", "f"]) + tm.assert_series_equal(s, expected) + + # neg slices + s = Series(list("abcdef"), dtype=object) + + with pytest.raises(ValueError, match=msg("slice")): + s[:-1] = list(range(27)) + + s[-3:-1] = list(range(2)) + expected = Series(["a", "b", "c", 0, 1, "f"]) + tm.assert_series_equal(s, expected) + + # list + s = Series(list("abc"), dtype=object) + + with pytest.raises(ValueError, match=msg("list-like")): + s[[0, 1, 2]] = list(range(27)) + + s = Series(list("abc"), dtype=object) + + with pytest.raises(ValueError, match=msg("list-like")): + s[[0, 1, 2]] = list(range(2)) + + # scalar + s = Series(list("abc"), dtype=object) + s[0] = list(range(10)) + expected = Series([list(range(10)), "b", "c"]) + tm.assert_series_equal(s, expected) + + +@pytest.mark.parametrize("size", range(2, 6)) +@pytest.mark.parametrize( + "mask", [[True, False, False, False, False], [True, False], [False]] +) +@pytest.mark.parametrize( + "item", [2.0, np.nan, np.finfo(float).max, np.finfo(float).min] +) +# Test numpy arrays, lists and tuples as the input to be +# broadcast +@pytest.mark.parametrize( + "box", [lambda x: np.array([x]), lambda x: [x], lambda x: (x,)] +) +def test_broadcast(size, mask, item, box): + # GH#8801, GH#4195 + selection = np.resize(mask, size) + + data = np.arange(size, dtype=float) + + # Construct the expected series by taking the source + # data or item based on the selection + expected = Series( + [item if use_item else data[i] for i, use_item in enumerate(selection)] + ) + + s = Series(data) + + s[selection] = item + tm.assert_series_equal(s, expected) + + s = Series(data) + result = s.where(~selection, box(item)) + tm.assert_series_equal(result, expected) + + s = Series(data) + result = s.mask(selection, box(item)) + tm.assert_series_equal(result, expected) + + +def test_where_inplace(): + s = Series(np.random.default_rng(2).standard_normal(5)) + cond = s > 0 + + rs = s.copy() + + rs.where(cond, inplace=True) + tm.assert_series_equal(rs.dropna(), s[cond]) + tm.assert_series_equal(rs, s.where(cond)) + + rs = s.copy() + rs.where(cond, -s, inplace=True) + tm.assert_series_equal(rs, s.where(cond, -s)) + + +def test_where_dups(): + # GH 4550 + # where crashes with dups in index + s1 = Series(list(range(3))) + s2 = Series(list(range(3))) + comb = pd.concat([s1, s2]) + result = comb.where(comb < 2) + expected = Series([0, 1, np.nan, 0, 1, np.nan], index=[0, 1, 2, 0, 1, 2]) + tm.assert_series_equal(result, expected) + + # GH 4548 + # inplace updating not working with dups + comb[comb < 1] = 5 + expected = Series([5, 1, 2, 5, 1, 2], index=[0, 1, 2, 0, 1, 2]) + tm.assert_series_equal(comb, expected) + + comb[comb < 2] += 10 + expected = Series([5, 11, 2, 5, 11, 2], index=[0, 1, 2, 0, 1, 2]) + tm.assert_series_equal(comb, expected) + + +def test_where_numeric_with_string(): + # GH 9280 + s = Series([1, 2, 3]) + w = s.where(s > 1, "X") + + assert not is_integer(w[0]) + assert is_integer(w[1]) + assert is_integer(w[2]) + assert isinstance(w[0], str) + assert w.dtype == "object" + + w = s.where(s > 1, ["X", "Y", "Z"]) + assert not is_integer(w[0]) + assert is_integer(w[1]) + assert is_integer(w[2]) + assert isinstance(w[0], str) + assert w.dtype == "object" + + w = s.where(s > 1, np.array(["X", "Y", "Z"])) + assert not is_integer(w[0]) + assert is_integer(w[1]) + assert is_integer(w[2]) + assert isinstance(w[0], str) + assert w.dtype == "object" + + +@pytest.mark.parametrize("dtype", ["timedelta64[ns]", "datetime64[ns]"]) +def test_where_datetimelike_coerce(dtype): + ser = Series([1, 2], dtype=dtype) + expected = Series([10, 10]) + mask = np.array([False, False]) + + msg = "Downcasting behavior in Series and DataFrame methods 'where'" + with tm.assert_produces_warning(FutureWarning, match=msg): + rs = ser.where(mask, [10, 10]) + tm.assert_series_equal(rs, expected) + + with tm.assert_produces_warning(FutureWarning, match=msg): + rs = ser.where(mask, 10) + tm.assert_series_equal(rs, expected) + + with tm.assert_produces_warning(FutureWarning, match=msg): + rs = ser.where(mask, 10.0) + tm.assert_series_equal(rs, expected) + + with tm.assert_produces_warning(FutureWarning, match=msg): + rs = ser.where(mask, [10.0, 10.0]) + tm.assert_series_equal(rs, expected) + + rs = ser.where(mask, [10.0, np.nan]) + expected = Series([10, np.nan], dtype="object") + tm.assert_series_equal(rs, expected) + + +def test_where_datetimetz(): + # GH 15701 + timestamps = ["2016-12-31 12:00:04+00:00", "2016-12-31 12:00:04.010000+00:00"] + ser = Series([Timestamp(t) for t in timestamps], dtype="datetime64[ns, UTC]") + rs = ser.where(Series([False, True])) + expected = Series([pd.NaT, ser[1]], dtype="datetime64[ns, UTC]") + tm.assert_series_equal(rs, expected) + + +def test_where_sparse(): + # GH#17198 make sure we dont get an AttributeError for sp_index + ser = Series(pd.arrays.SparseArray([1, 2])) + result = ser.where(ser >= 2, 0) + expected = Series(pd.arrays.SparseArray([0, 2])) + tm.assert_series_equal(result, expected) + + +def test_where_empty_series_and_empty_cond_having_non_bool_dtypes(): + # https://github.com/pandas-dev/pandas/issues/34592 + ser = Series([], dtype=float) + result = ser.where([]) + tm.assert_series_equal(result, ser) + + +def test_where_categorical(frame_or_series): + # https://github.com/pandas-dev/pandas/issues/18888 + exp = frame_or_series( + pd.Categorical(["A", "A", "B", "B", np.nan], categories=["A", "B", "C"]), + dtype="category", + ) + df = frame_or_series(["A", "A", "B", "B", "C"], dtype="category") + res = df.where(df != "C") + tm.assert_equal(exp, res) + + +def test_where_datetimelike_categorical(tz_naive_fixture): + # GH#37682 + tz = tz_naive_fixture + + dr = date_range("2001-01-01", periods=3, tz=tz)._with_freq(None) + lvals = pd.DatetimeIndex([dr[0], dr[1], pd.NaT]) + rvals = pd.Categorical([dr[0], pd.NaT, dr[2]]) + + mask = np.array([True, True, False]) + + # DatetimeIndex.where + res = lvals.where(mask, rvals) + tm.assert_index_equal(res, dr) + + # DatetimeArray.where + res = lvals._data._where(mask, rvals) + tm.assert_datetime_array_equal(res, dr._data) + + # Series.where + res = Series(lvals).where(mask, rvals) + tm.assert_series_equal(res, Series(dr)) + + # DataFrame.where + res = pd.DataFrame(lvals).where(mask[:, None], pd.DataFrame(rvals)) + + tm.assert_frame_equal(res, pd.DataFrame(dr)) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_xs.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_xs.py new file mode 100644 index 0000000000000000000000000000000000000000..a67f3ec708f24a97dab544bb5cf859dc54659215 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/indexing/test_xs.py @@ -0,0 +1,82 @@ +import numpy as np +import pytest + +from pandas import ( + MultiIndex, + Series, + date_range, +) +import pandas._testing as tm + + +def test_xs_datetimelike_wrapping(): + # GH#31630 a case where we shouldn't wrap datetime64 in Timestamp + arr = date_range("2016-01-01", periods=3)._data._ndarray + + ser = Series(arr, dtype=object) + for i in range(len(ser)): + ser.iloc[i] = arr[i] + assert ser.dtype == object + assert isinstance(ser[0], np.datetime64) + + result = ser.xs(0) + assert isinstance(result, np.datetime64) + + +class TestXSWithMultiIndex: + def test_xs_level_series(self, multiindex_dataframe_random_data): + df = multiindex_dataframe_random_data + ser = df["A"] + expected = ser[:, "two"] + result = df.xs("two", level=1)["A"] + tm.assert_series_equal(result, expected) + + def test_series_getitem_multiindex_xs_by_label(self): + # GH#5684 + idx = MultiIndex.from_tuples( + [("a", "one"), ("a", "two"), ("b", "one"), ("b", "two")] + ) + ser = Series([1, 2, 3, 4], index=idx) + return_value = ser.index.set_names(["L1", "L2"], inplace=True) + assert return_value is None + expected = Series([1, 3], index=["a", "b"]) + return_value = expected.index.set_names(["L1"], inplace=True) + assert return_value is None + + result = ser.xs("one", level="L2") + tm.assert_series_equal(result, expected) + + def test_series_getitem_multiindex_xs(self): + # GH#6258 + dt = list(date_range("20130903", periods=3)) + idx = MultiIndex.from_product([list("AB"), dt]) + ser = Series([1, 3, 4, 1, 3, 4], index=idx) + expected = Series([1, 1], index=list("AB")) + + result = ser.xs("20130903", level=1) + tm.assert_series_equal(result, expected) + + def test_series_xs_droplevel_false(self): + # GH: 19056 + mi = MultiIndex.from_tuples( + [("a", "x"), ("a", "y"), ("b", "x")], names=["level1", "level2"] + ) + ser = Series([1, 1, 1], index=mi) + result = ser.xs("a", axis=0, drop_level=False) + expected = Series( + [1, 1], + index=MultiIndex.from_tuples( + [("a", "x"), ("a", "y")], names=["level1", "level2"] + ), + ) + tm.assert_series_equal(result, expected) + + def test_xs_key_as_list(self): + # GH#41760 + mi = MultiIndex.from_tuples([("a", "x")], names=["level1", "level2"]) + ser = Series([1], index=mi) + with pytest.raises(TypeError, match="list keys are not supported"): + ser.xs(["a", "x"], axis=0, drop_level=False) + + with pytest.raises(TypeError, match="list keys are not supported"): + ser.xs(["a"], axis=0, drop_level=False) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bcb0d30f405e2079a1ff21d838f52cd5f659fd93 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/__init__.py @@ -0,0 +1,7 @@ +""" +Test files dedicated to individual (stand-alone) Series methods + +Ideally these files/tests should correspond 1-to-1 with tests.frame.methods + +These may also present opportunities for sharing/de-duplicating test code. +""" diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_add_prefix_suffix.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_add_prefix_suffix.py new file mode 100644 index 0000000000000000000000000000000000000000..289a56b98b7e123fb1c1edf5cc9ff41369d51122 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_add_prefix_suffix.py @@ -0,0 +1,41 @@ +import pytest + +from pandas import Index +import pandas._testing as tm + + +def test_add_prefix_suffix(string_series): + with_prefix = string_series.add_prefix("foo#") + expected = Index([f"foo#{c}" for c in string_series.index]) + tm.assert_index_equal(with_prefix.index, expected) + + with_suffix = string_series.add_suffix("#foo") + expected = Index([f"{c}#foo" for c in string_series.index]) + tm.assert_index_equal(with_suffix.index, expected) + + with_pct_prefix = string_series.add_prefix("%") + expected = Index([f"%{c}" for c in string_series.index]) + tm.assert_index_equal(with_pct_prefix.index, expected) + + with_pct_suffix = string_series.add_suffix("%") + expected = Index([f"{c}%" for c in string_series.index]) + tm.assert_index_equal(with_pct_suffix.index, expected) + + +def test_add_prefix_suffix_axis(string_series): + # GH 47819 + with_prefix = string_series.add_prefix("foo#", axis=0) + expected = Index([f"foo#{c}" for c in string_series.index]) + tm.assert_index_equal(with_prefix.index, expected) + + with_pct_suffix = string_series.add_suffix("#foo", axis=0) + expected = Index([f"{c}#foo" for c in string_series.index]) + tm.assert_index_equal(with_pct_suffix.index, expected) + + +def test_add_prefix_suffix_invalid_axis(string_series): + with pytest.raises(ValueError, match="No axis named 1 for object type Series"): + string_series.add_prefix("foo#", axis=1) + + with pytest.raises(ValueError, match="No axis named 1 for object type Series"): + string_series.add_suffix("foo#", axis=1) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_align.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_align.py new file mode 100644 index 0000000000000000000000000000000000000000..f332aad0c05f909cb7f2a386fff451f3e7edcdb4 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_align.py @@ -0,0 +1,262 @@ +from datetime import timezone + +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Series, + date_range, + period_range, +) +import pandas._testing as tm + + +@pytest.mark.parametrize( + "first_slice,second_slice", + [ + [[2, None], [None, -5]], + [[None, 0], [None, -5]], + [[None, -5], [None, 0]], + [[None, 0], [None, 0]], + ], +) +@pytest.mark.parametrize("fill", [None, -1]) +def test_align(datetime_series, first_slice, second_slice, join_type, fill): + a = datetime_series[slice(*first_slice)] + b = datetime_series[slice(*second_slice)] + + aa, ab = a.align(b, join=join_type, fill_value=fill) + + join_index = a.index.join(b.index, how=join_type) + if fill is not None: + diff_a = aa.index.difference(join_index) + diff_b = ab.index.difference(join_index) + if len(diff_a) > 0: + assert (aa.reindex(diff_a) == fill).all() + if len(diff_b) > 0: + assert (ab.reindex(diff_b) == fill).all() + + ea = a.reindex(join_index) + eb = b.reindex(join_index) + + if fill is not None: + ea = ea.fillna(fill) + eb = eb.fillna(fill) + + tm.assert_series_equal(aa, ea) + tm.assert_series_equal(ab, eb) + assert aa.name == "ts" + assert ea.name == "ts" + assert ab.name == "ts" + assert eb.name == "ts" + + +@pytest.mark.parametrize( + "first_slice,second_slice", + [ + [[2, None], [None, -5]], + [[None, 0], [None, -5]], + [[None, -5], [None, 0]], + [[None, 0], [None, 0]], + ], +) +@pytest.mark.parametrize("method", ["pad", "bfill"]) +@pytest.mark.parametrize("limit", [None, 1]) +def test_align_fill_method( + datetime_series, first_slice, second_slice, join_type, method, limit +): + a = datetime_series[slice(*first_slice)] + b = datetime_series[slice(*second_slice)] + + msg = ( + "The 'method', 'limit', and 'fill_axis' keywords in Series.align " + "are deprecated" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + aa, ab = a.align(b, join=join_type, method=method, limit=limit) + + join_index = a.index.join(b.index, how=join_type) + ea = a.reindex(join_index) + eb = b.reindex(join_index) + + msg2 = "Series.fillna with 'method' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg2): + ea = ea.fillna(method=method, limit=limit) + eb = eb.fillna(method=method, limit=limit) + + tm.assert_series_equal(aa, ea) + tm.assert_series_equal(ab, eb) + + +def test_align_nocopy(datetime_series, using_copy_on_write): + b = datetime_series[:5].copy() + + # do copy + a = datetime_series.copy() + ra, _ = a.align(b, join="left") + ra[:5] = 5 + assert not (a[:5] == 5).any() + + # do not copy + a = datetime_series.copy() + ra, _ = a.align(b, join="left", copy=False) + ra[:5] = 5 + if using_copy_on_write: + assert not (a[:5] == 5).any() + else: + assert (a[:5] == 5).all() + + # do copy + a = datetime_series.copy() + b = datetime_series[:5].copy() + _, rb = a.align(b, join="right") + rb[:3] = 5 + assert not (b[:3] == 5).any() + + # do not copy + a = datetime_series.copy() + b = datetime_series[:5].copy() + _, rb = a.align(b, join="right", copy=False) + rb[:2] = 5 + if using_copy_on_write: + assert not (b[:2] == 5).any() + else: + assert (b[:2] == 5).all() + + +def test_align_same_index(datetime_series, using_copy_on_write): + a, b = datetime_series.align(datetime_series, copy=False) + if not using_copy_on_write: + assert a.index is datetime_series.index + assert b.index is datetime_series.index + else: + assert a.index.is_(datetime_series.index) + assert b.index.is_(datetime_series.index) + + a, b = datetime_series.align(datetime_series, copy=True) + assert a.index is not datetime_series.index + assert b.index is not datetime_series.index + assert a.index.is_(datetime_series.index) + assert b.index.is_(datetime_series.index) + + +def test_align_multiindex(): + # GH 10665 + + midx = pd.MultiIndex.from_product( + [range(2), range(3), range(2)], names=("a", "b", "c") + ) + idx = pd.Index(range(2), name="b") + s1 = Series(np.arange(12, dtype="int64"), index=midx) + s2 = Series(np.arange(2, dtype="int64"), index=idx) + + # these must be the same results (but flipped) + res1l, res1r = s1.align(s2, join="left") + res2l, res2r = s2.align(s1, join="right") + + expl = s1 + tm.assert_series_equal(expl, res1l) + tm.assert_series_equal(expl, res2r) + expr = Series([0, 0, 1, 1, np.nan, np.nan] * 2, index=midx) + tm.assert_series_equal(expr, res1r) + tm.assert_series_equal(expr, res2l) + + res1l, res1r = s1.align(s2, join="right") + res2l, res2r = s2.align(s1, join="left") + + exp_idx = pd.MultiIndex.from_product( + [range(2), range(2), range(2)], names=("a", "b", "c") + ) + expl = Series([0, 1, 2, 3, 6, 7, 8, 9], index=exp_idx) + tm.assert_series_equal(expl, res1l) + tm.assert_series_equal(expl, res2r) + expr = Series([0, 0, 1, 1] * 2, index=exp_idx) + tm.assert_series_equal(expr, res1r) + tm.assert_series_equal(expr, res2l) + + +@pytest.mark.parametrize("method", ["backfill", "bfill", "pad", "ffill", None]) +def test_align_with_dataframe_method(method): + # GH31788 + ser = Series(range(3), index=range(3)) + df = pd.DataFrame(0.0, index=range(3), columns=range(3)) + + msg = ( + "The 'method', 'limit', and 'fill_axis' keywords in Series.align " + "are deprecated" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + result_ser, result_df = ser.align(df, method=method) + tm.assert_series_equal(result_ser, ser) + tm.assert_frame_equal(result_df, df) + + +def test_align_dt64tzindex_mismatched_tzs(): + idx1 = date_range("2001", periods=5, freq="h", tz="US/Eastern") + ser = Series(np.random.default_rng(2).standard_normal(len(idx1)), index=idx1) + ser_central = ser.tz_convert("US/Central") + # different timezones convert to UTC + + new1, new2 = ser.align(ser_central) + assert new1.index.tz is timezone.utc + assert new2.index.tz is timezone.utc + + +def test_align_periodindex(join_type): + rng = period_range("1/1/2000", "1/1/2010", freq="Y") + ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng) + + # TODO: assert something? + ts.align(ts[::2], join=join_type) + + +def test_align_stringindex(any_string_dtype): + left = Series(range(3), index=pd.Index(["a", "b", "d"], dtype=any_string_dtype)) + right = Series(range(3), index=pd.Index(["a", "b", "c"], dtype=any_string_dtype)) + result_left, result_right = left.align(right) + + expected_idx = pd.Index(["a", "b", "c", "d"], dtype=any_string_dtype) + expected_left = Series([0, 1, np.nan, 2], index=expected_idx) + expected_right = Series([0, 1, 2, np.nan], index=expected_idx) + + tm.assert_series_equal(result_left, expected_left) + tm.assert_series_equal(result_right, expected_right) + + +def test_align_left_fewer_levels(): + # GH#45224 + left = Series([2], index=pd.MultiIndex.from_tuples([(1, 3)], names=["a", "c"])) + right = Series( + [1], index=pd.MultiIndex.from_tuples([(1, 2, 3)], names=["a", "b", "c"]) + ) + result_left, result_right = left.align(right) + + expected_right = Series( + [1], index=pd.MultiIndex.from_tuples([(1, 3, 2)], names=["a", "c", "b"]) + ) + expected_left = Series( + [2], index=pd.MultiIndex.from_tuples([(1, 3, 2)], names=["a", "c", "b"]) + ) + tm.assert_series_equal(result_left, expected_left) + tm.assert_series_equal(result_right, expected_right) + + +def test_align_left_different_named_levels(): + # GH#45224 + left = Series( + [2], index=pd.MultiIndex.from_tuples([(1, 4, 3)], names=["a", "d", "c"]) + ) + right = Series( + [1], index=pd.MultiIndex.from_tuples([(1, 2, 3)], names=["a", "b", "c"]) + ) + result_left, result_right = left.align(right) + + expected_left = Series( + [2], index=pd.MultiIndex.from_tuples([(1, 4, 3, 2)], names=["a", "d", "c", "b"]) + ) + expected_right = Series( + [1], index=pd.MultiIndex.from_tuples([(1, 4, 3, 2)], names=["a", "d", "c", "b"]) + ) + tm.assert_series_equal(result_left, expected_left) + tm.assert_series_equal(result_right, expected_right) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_argsort.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_argsort.py new file mode 100644 index 0000000000000000000000000000000000000000..432c0eceee01107cdcd23056a39e0ef4bd55545b --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_argsort.py @@ -0,0 +1,84 @@ +import numpy as np +import pytest + +from pandas import ( + Series, + Timestamp, + isna, +) +import pandas._testing as tm + + +class TestSeriesArgsort: + def test_argsort_axis(self): + # GH#54257 + ser = Series(range(3)) + + msg = "No axis named 2 for object type Series" + with pytest.raises(ValueError, match=msg): + ser.argsort(axis=2) + + def test_argsort_numpy(self, datetime_series): + ser = datetime_series + + res = np.argsort(ser).values + expected = np.argsort(np.array(ser)) + tm.assert_numpy_array_equal(res, expected) + + # with missing values + ts = ser.copy() + ts[::2] = np.nan + + msg = "The behavior of Series.argsort in the presence of NA values" + with tm.assert_produces_warning( + FutureWarning, match=msg, check_stacklevel=False + ): + result = np.argsort(ts)[1::2] + expected = np.argsort(np.array(ts.dropna())) + + tm.assert_numpy_array_equal(result.values, expected) + + def test_argsort(self, datetime_series): + argsorted = datetime_series.argsort() + assert issubclass(argsorted.dtype.type, np.integer) + + def test_argsort_dt64(self, unit): + # GH#2967 (introduced bug in 0.11-dev I think) + ser = Series( + [Timestamp(f"201301{i:02d}") for i in range(1, 6)], dtype=f"M8[{unit}]" + ) + assert ser.dtype == f"datetime64[{unit}]" + shifted = ser.shift(-1) + assert shifted.dtype == f"datetime64[{unit}]" + assert isna(shifted[4]) + + result = ser.argsort() + expected = Series(range(5), dtype=np.intp) + tm.assert_series_equal(result, expected) + + msg = "The behavior of Series.argsort in the presence of NA values" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = shifted.argsort() + expected = Series(list(range(4)) + [-1], dtype=np.intp) + tm.assert_series_equal(result, expected) + + def test_argsort_stable(self): + ser = Series(np.random.default_rng(2).integers(0, 100, size=10000)) + mindexer = ser.argsort(kind="mergesort") + qindexer = ser.argsort() + + mexpected = np.argsort(ser.values, kind="mergesort") + qexpected = np.argsort(ser.values, kind="quicksort") + + tm.assert_series_equal(mindexer.astype(np.intp), Series(mexpected)) + tm.assert_series_equal(qindexer.astype(np.intp), Series(qexpected)) + msg = ( + r"ndarray Expected type , " + r"found instead" + ) + with pytest.raises(AssertionError, match=msg): + tm.assert_numpy_array_equal(qindexer, mindexer) + + def test_argsort_preserve_name(self, datetime_series): + result = datetime_series.argsort() + assert result.name == datetime_series.name diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_asof.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_asof.py new file mode 100644 index 0000000000000000000000000000000000000000..2acc2921e5efc089b1dd4ed7aa9a6cebc1a3d0fe --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_asof.py @@ -0,0 +1,205 @@ +import numpy as np +import pytest + +from pandas._libs.tslibs import IncompatibleFrequency + +from pandas import ( + DatetimeIndex, + PeriodIndex, + Series, + Timestamp, + date_range, + isna, + notna, + offsets, + period_range, +) +import pandas._testing as tm + + +class TestSeriesAsof: + def test_asof_nanosecond_index_access(self): + ts = Timestamp("20130101").as_unit("ns")._value + dti = DatetimeIndex([ts + 50 + i for i in range(100)]) + ser = Series(np.random.default_rng(2).standard_normal(100), index=dti) + + first_value = ser.asof(ser.index[0]) + + # GH#46903 previously incorrectly was "day" + assert dti.resolution == "nanosecond" + + # this used to not work bc parsing was done by dateutil that didn't + # handle nanoseconds + assert first_value == ser["2013-01-01 00:00:00.000000050"] + + expected_ts = np.datetime64("2013-01-01 00:00:00.000000050", "ns") + assert first_value == ser[Timestamp(expected_ts)] + + def test_basic(self): + # array or list or dates + N = 50 + rng = date_range("1/1/1990", periods=N, freq="53s") + ts = Series(np.random.default_rng(2).standard_normal(N), index=rng) + ts.iloc[15:30] = np.nan + dates = date_range("1/1/1990", periods=N * 3, freq="25s") + + result = ts.asof(dates) + assert notna(result).all() + lb = ts.index[14] + ub = ts.index[30] + + result = ts.asof(list(dates)) + assert notna(result).all() + lb = ts.index[14] + ub = ts.index[30] + + mask = (result.index >= lb) & (result.index < ub) + rs = result[mask] + assert (rs == ts[lb]).all() + + val = result[result.index[result.index >= ub][0]] + assert ts[ub] == val + + def test_scalar(self): + N = 30 + rng = date_range("1/1/1990", periods=N, freq="53s") + # Explicit cast to float avoid implicit cast when setting nan + ts = Series(np.arange(N), index=rng, dtype="float") + ts.iloc[5:10] = np.nan + ts.iloc[15:20] = np.nan + + val1 = ts.asof(ts.index[7]) + val2 = ts.asof(ts.index[19]) + + assert val1 == ts.iloc[4] + assert val2 == ts.iloc[14] + + # accepts strings + val1 = ts.asof(str(ts.index[7])) + assert val1 == ts.iloc[4] + + # in there + result = ts.asof(ts.index[3]) + assert result == ts.iloc[3] + + # no as of value + d = ts.index[0] - offsets.BDay() + assert np.isnan(ts.asof(d)) + + def test_with_nan(self): + # basic asof test + rng = date_range("1/1/2000", "1/2/2000", freq="4h") + s = Series(np.arange(len(rng)), index=rng) + r = s.resample("2h").mean() + + result = r.asof(r.index) + expected = Series( + [0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6.0], + index=date_range("1/1/2000", "1/2/2000", freq="2h"), + ) + tm.assert_series_equal(result, expected) + + r.iloc[3:5] = np.nan + result = r.asof(r.index) + expected = Series( + [0, 0, 1, 1, 1, 1, 3, 3, 4, 4, 5, 5, 6.0], + index=date_range("1/1/2000", "1/2/2000", freq="2h"), + ) + tm.assert_series_equal(result, expected) + + r.iloc[-3:] = np.nan + result = r.asof(r.index) + expected = Series( + [0, 0, 1, 1, 1, 1, 3, 3, 4, 4, 4, 4, 4.0], + index=date_range("1/1/2000", "1/2/2000", freq="2h"), + ) + tm.assert_series_equal(result, expected) + + def test_periodindex(self): + # array or list or dates + N = 50 + rng = period_range("1/1/1990", periods=N, freq="h") + ts = Series(np.random.default_rng(2).standard_normal(N), index=rng) + ts.iloc[15:30] = np.nan + dates = date_range("1/1/1990", periods=N * 3, freq="37min") + + result = ts.asof(dates) + assert notna(result).all() + lb = ts.index[14] + ub = ts.index[30] + + result = ts.asof(list(dates)) + assert notna(result).all() + lb = ts.index[14] + ub = ts.index[30] + + pix = PeriodIndex(result.index.values, freq="h") + mask = (pix >= lb) & (pix < ub) + rs = result[mask] + assert (rs == ts[lb]).all() + + ts.iloc[5:10] = np.nan + ts.iloc[15:20] = np.nan + + val1 = ts.asof(ts.index[7]) + val2 = ts.asof(ts.index[19]) + + assert val1 == ts.iloc[4] + assert val2 == ts.iloc[14] + + # accepts strings + val1 = ts.asof(str(ts.index[7])) + assert val1 == ts.iloc[4] + + # in there + assert ts.asof(ts.index[3]) == ts.iloc[3] + + # no as of value + d = ts.index[0].to_timestamp() - offsets.BDay() + assert isna(ts.asof(d)) + + # Mismatched freq + msg = "Input has different freq" + with pytest.raises(IncompatibleFrequency, match=msg): + ts.asof(rng.asfreq("D")) + + def test_errors(self): + s = Series( + [1, 2, 3], + index=[Timestamp("20130101"), Timestamp("20130103"), Timestamp("20130102")], + ) + + # non-monotonic + assert not s.index.is_monotonic_increasing + with pytest.raises(ValueError, match="requires a sorted index"): + s.asof(s.index[0]) + + # subset with Series + N = 10 + rng = date_range("1/1/1990", periods=N, freq="53s") + s = Series(np.random.default_rng(2).standard_normal(N), index=rng) + with pytest.raises(ValueError, match="not valid for Series"): + s.asof(s.index[0], subset="foo") + + def test_all_nans(self): + # GH 15713 + # series is all nans + + # testing non-default indexes + N = 50 + rng = date_range("1/1/1990", periods=N, freq="53s") + + dates = date_range("1/1/1990", periods=N * 3, freq="25s") + result = Series(np.nan, index=rng).asof(dates) + expected = Series(np.nan, index=dates) + tm.assert_series_equal(result, expected) + + # testing scalar input + date = date_range("1/1/1990", periods=N * 3, freq="25s")[0] + result = Series(np.nan, index=rng).asof(date) + assert isna(result) + + # test name is propagated + result = Series(np.nan, index=[1, 2, 3, 4], name="test").asof([4, 5]) + expected = Series(np.nan, index=[4, 5], name="test") + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_astype.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_astype.py new file mode 100644 index 0000000000000000000000000000000000000000..b9ba03d1e9f417252d33f76195f1117fa29dd238 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_astype.py @@ -0,0 +1,689 @@ +from datetime import ( + datetime, + timedelta, +) +from importlib import reload +import string +import sys + +import numpy as np +import pytest + +from pandas._libs.tslibs import iNaT +import pandas.util._test_decorators as td + +from pandas import ( + NA, + Categorical, + CategoricalDtype, + DatetimeTZDtype, + Index, + Interval, + NaT, + Series, + Timedelta, + Timestamp, + cut, + date_range, + to_datetime, +) +import pandas._testing as tm + + +def rand_str(nchars: int) -> str: + """ + Generate one random byte string. + """ + RANDS_CHARS = np.array( + list(string.ascii_letters + string.digits), dtype=(np.str_, 1) + ) + return "".join(np.random.default_rng(2).choice(RANDS_CHARS, nchars)) + + +class TestAstypeAPI: + def test_astype_unitless_dt64_raises(self): + # GH#47844 + ser = Series(["1970-01-01", "1970-01-01", "1970-01-01"], dtype="datetime64[ns]") + df = ser.to_frame() + + msg = "Casting to unit-less dtype 'datetime64' is not supported" + with pytest.raises(TypeError, match=msg): + ser.astype(np.datetime64) + with pytest.raises(TypeError, match=msg): + df.astype(np.datetime64) + with pytest.raises(TypeError, match=msg): + ser.astype("datetime64") + with pytest.raises(TypeError, match=msg): + df.astype("datetime64") + + def test_arg_for_errors_in_astype(self): + # see GH#14878 + ser = Series([1, 2, 3]) + + msg = ( + r"Expected value of kwarg 'errors' to be one of \['raise', " + r"'ignore'\]\. Supplied value is 'False'" + ) + with pytest.raises(ValueError, match=msg): + ser.astype(np.float64, errors=False) + + ser.astype(np.int8, errors="raise") + + @pytest.mark.parametrize("dtype_class", [dict, Series]) + def test_astype_dict_like(self, dtype_class): + # see GH#7271 + ser = Series(range(0, 10, 2), name="abc") + + dt1 = dtype_class({"abc": str}) + result = ser.astype(dt1) + expected = Series(["0", "2", "4", "6", "8"], name="abc", dtype="str") + tm.assert_series_equal(result, expected) + + dt2 = dtype_class({"abc": "float64"}) + result = ser.astype(dt2) + expected = Series([0.0, 2.0, 4.0, 6.0, 8.0], dtype="float64", name="abc") + tm.assert_series_equal(result, expected) + + dt3 = dtype_class({"abc": str, "def": str}) + msg = ( + "Only the Series name can be used for the key in Series dtype " + r"mappings\." + ) + with pytest.raises(KeyError, match=msg): + ser.astype(dt3) + + dt4 = dtype_class({0: str}) + with pytest.raises(KeyError, match=msg): + ser.astype(dt4) + + # GH#16717 + # if dtypes provided is empty, it should error + if dtype_class is Series: + dt5 = dtype_class({}, dtype=object) + else: + dt5 = dtype_class({}) + + with pytest.raises(KeyError, match=msg): + ser.astype(dt5) + + +class TestAstype: + @pytest.mark.parametrize("tz", [None, "UTC", "US/Pacific"]) + def test_astype_object_to_dt64_non_nano(self, tz): + # GH#55756, GH#54620 + ts = Timestamp("2999-01-01") + dtype = "M8[us]" + if tz is not None: + dtype = f"M8[us, {tz}]" + vals = [ts, "2999-01-02 03:04:05.678910", 2500] + ser = Series(vals, dtype=object) + result = ser.astype(dtype) + + # The 2500 is interpreted as microseconds, consistent with what + # we would get if we created DatetimeIndexes from vals[:2] and vals[2:] + # and concated the results. + pointwise = [ + vals[0].tz_localize(tz), + Timestamp(vals[1], tz=tz), + to_datetime(vals[2], unit="us", utc=True).tz_convert(tz), + ] + exp_vals = [x.as_unit("us").asm8 for x in pointwise] + exp_arr = np.array(exp_vals, dtype="M8[us]") + expected = Series(exp_arr, dtype="M8[us]") + if tz is not None: + expected = expected.dt.tz_localize("UTC").dt.tz_convert(tz) + tm.assert_series_equal(result, expected) + + def test_astype_mixed_object_to_dt64tz(self): + # pre-2.0 this raised ValueError bc of tz mismatch + # xref GH#32581 + ts = Timestamp("2016-01-04 05:06:07", tz="US/Pacific") + ts2 = ts.tz_convert("Asia/Tokyo") + + ser = Series([ts, ts2], dtype=object) + res = ser.astype("datetime64[ns, Europe/Brussels]") + expected = Series( + [ts.tz_convert("Europe/Brussels"), ts2.tz_convert("Europe/Brussels")], + dtype="datetime64[ns, Europe/Brussels]", + ) + tm.assert_series_equal(res, expected) + + @pytest.mark.parametrize("dtype", np.typecodes["All"]) + def test_astype_empty_constructor_equality(self, dtype): + # see GH#15524 + + if dtype not in ( + "S", + "V", # poor support (if any) currently + "M", + "m", # Generic timestamps raise a ValueError. Already tested. + ): + init_empty = Series([], dtype=dtype) + as_type_empty = Series([]).astype(dtype) + tm.assert_series_equal(init_empty, as_type_empty) + + @pytest.mark.parametrize("dtype", [str, np.str_]) + @pytest.mark.parametrize( + "series", + [ + Series([string.digits * 10, rand_str(63), rand_str(64), rand_str(1000)]), + Series([string.digits * 10, rand_str(63), rand_str(64), np.nan, 1.0]), + ], + ) + def test_astype_str_map(self, dtype, series, using_infer_string): + # see GH#4405 + using_string_dtype = using_infer_string and dtype is str + result = series.astype(dtype) + if using_string_dtype: + expected = series.map(lambda val: str(val) if val is not np.nan else np.nan) + else: + expected = series.map(str) + if using_infer_string: + expected = expected.astype(object) + tm.assert_series_equal(result, expected) + + def test_astype_float_to_period(self): + result = Series([np.nan]).astype("period[D]") + expected = Series([NaT], dtype="period[D]") + tm.assert_series_equal(result, expected) + + def test_astype_no_pandas_dtype(self): + # https://github.com/pandas-dev/pandas/pull/24866 + ser = Series([1, 2], dtype="int64") + # Don't have NumpyEADtype in the public API, so we use `.array.dtype`, + # which is a NumpyEADtype. + result = ser.astype(ser.array.dtype) + tm.assert_series_equal(result, ser) + + @pytest.mark.parametrize("dtype", [np.datetime64, np.timedelta64]) + def test_astype_generic_timestamp_no_frequency(self, dtype, request): + # see GH#15524, GH#15987 + data = [1] + ser = Series(data) + + if np.dtype(dtype).name not in ["timedelta64", "datetime64"]: + mark = pytest.mark.xfail(reason="GH#33890 Is assigned ns unit") + request.applymarker(mark) + + msg = ( + rf"The '{dtype.__name__}' dtype has no unit\. " + rf"Please pass in '{dtype.__name__}\[ns\]' instead." + ) + with pytest.raises(ValueError, match=msg): + ser.astype(dtype) + + def test_astype_dt64_to_str(self): + # GH#10442 : testing astype(str) is correct for Series/DatetimeIndex + dti = date_range("2012-01-01", periods=3) + result = Series(dti).astype(str) + expected = Series(["2012-01-01", "2012-01-02", "2012-01-03"], dtype="str") + tm.assert_series_equal(result, expected) + + def test_astype_dt64tz_to_str(self): + # GH#10442 : testing astype(str) is correct for Series/DatetimeIndex + dti_tz = date_range("2012-01-01", periods=3, tz="US/Eastern") + result = Series(dti_tz).astype(str) + expected = Series( + [ + "2012-01-01 00:00:00-05:00", + "2012-01-02 00:00:00-05:00", + "2012-01-03 00:00:00-05:00", + ], + dtype="str", + ) + tm.assert_series_equal(result, expected) + + def test_astype_datetime(self, unit): + ser = Series(iNaT, dtype=f"M8[{unit}]", index=range(5)) + + ser = ser.astype("O") + assert ser.dtype == np.object_ + + ser = Series([datetime(2001, 1, 2, 0, 0)]) + + ser = ser.astype("O") + assert ser.dtype == np.object_ + + ser = Series( + [datetime(2001, 1, 2, 0, 0) for i in range(3)], dtype=f"M8[{unit}]" + ) + + ser[1] = np.nan + assert ser.dtype == f"M8[{unit}]" + + ser = ser.astype("O") + assert ser.dtype == np.object_ + + def test_astype_datetime64tz(self): + ser = Series(date_range("20130101", periods=3, tz="US/Eastern")) + + # astype + result = ser.astype(object) + expected = Series(ser.astype(object), dtype=object) + tm.assert_series_equal(result, expected) + + result = Series(ser.values).dt.tz_localize("UTC").dt.tz_convert(ser.dt.tz) + tm.assert_series_equal(result, ser) + + # astype - object, preserves on construction + result = Series(ser.astype(object)) + expected = ser.astype(object) + tm.assert_series_equal(result, expected) + + # astype - datetime64[ns, tz] + msg = "Cannot use .astype to convert from timezone-naive" + with pytest.raises(TypeError, match=msg): + # dt64->dt64tz astype deprecated + Series(ser.values).astype("datetime64[ns, US/Eastern]") + + with pytest.raises(TypeError, match=msg): + # dt64->dt64tz astype deprecated + Series(ser.values).astype(ser.dtype) + + result = ser.astype("datetime64[ns, CET]") + expected = Series(date_range("20130101 06:00:00", periods=3, tz="CET")) + tm.assert_series_equal(result, expected) + + def test_astype_str_cast_dt64(self): + # see GH#9757 + ts = Series([Timestamp("2010-01-04 00:00:00")]) + res = ts.astype(str) + + expected = Series(["2010-01-04"], dtype="str") + tm.assert_series_equal(res, expected) + + ts = Series([Timestamp("2010-01-04 00:00:00", tz="US/Eastern")]) + res = ts.astype(str) + + expected = Series(["2010-01-04 00:00:00-05:00"], dtype="str") + tm.assert_series_equal(res, expected) + + def test_astype_str_cast_td64(self): + # see GH#9757 + + td = Series([Timedelta(1, unit="d")]) + ser = td.astype(str) + + expected = Series(["1 days"], dtype="str") + tm.assert_series_equal(ser, expected) + + def test_dt64_series_astype_object(self): + dt64ser = Series(date_range("20130101", periods=3)) + result = dt64ser.astype(object) + assert isinstance(result.iloc[0], datetime) + assert result.dtype == np.object_ + + def test_td64_series_astype_object(self): + tdser = Series(["59 Days", "59 Days", "NaT"], dtype="timedelta64[ns]") + result = tdser.astype(object) + assert isinstance(result.iloc[0], timedelta) + assert result.dtype == np.object_ + + @pytest.mark.parametrize( + "data, dtype", + [ + (["x", "y", "z"], "string[python]"), + pytest.param( + ["x", "y", "z"], + "string[pyarrow]", + marks=td.skip_if_no("pyarrow"), + ), + (["x", "y", "z"], "category"), + (3 * [Timestamp("2020-01-01", tz="UTC")], None), + (3 * [Interval(0, 1)], None), + ], + ) + @pytest.mark.parametrize("errors", ["raise", "ignore"]) + def test_astype_ignores_errors_for_extension_dtypes(self, data, dtype, errors): + # https://github.com/pandas-dev/pandas/issues/35471 + ser = Series(data, dtype=dtype) + if errors == "ignore": + expected = ser + result = ser.astype(float, errors="ignore") + tm.assert_series_equal(result, expected) + else: + msg = "(Cannot cast)|(could not convert)" + with pytest.raises((ValueError, TypeError), match=msg): + ser.astype(float, errors=errors) + + @pytest.mark.parametrize("dtype", [np.float16, np.float32, np.float64]) + def test_astype_from_float_to_str(self, dtype): + # https://github.com/pandas-dev/pandas/issues/36451 + ser = Series([0.1], dtype=dtype) + result = ser.astype(str) + expected = Series(["0.1"], dtype="str") + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "value, string_value", + [ + (None, "None"), + (np.nan, "nan"), + (NA, ""), + ], + ) + def test_astype_to_str_preserves_na(self, value, string_value, using_infer_string): + # https://github.com/pandas-dev/pandas/issues/36904 + ser = Series(["a", "b", value], dtype=object) + result = ser.astype(str) + expected = Series( + ["a", "b", None if using_infer_string else string_value], dtype="str" + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("dtype", ["float32", "float64", "int64", "int32"]) + def test_astype(self, dtype): + ser = Series(np.random.default_rng(2).standard_normal(5), name="foo") + as_typed = ser.astype(dtype) + + assert as_typed.dtype == dtype + assert as_typed.name == ser.name + + @pytest.mark.parametrize("value", [np.nan, np.inf]) + @pytest.mark.parametrize("dtype", [np.int32, np.int64]) + def test_astype_cast_nan_inf_int(self, dtype, value): + # gh-14265: check NaN and inf raise error when converting to int + msg = "Cannot convert non-finite values \\(NA or inf\\) to integer" + ser = Series([value]) + + with pytest.raises(ValueError, match=msg): + ser.astype(dtype) + + @pytest.mark.parametrize("dtype", [int, np.int8, np.int64]) + def test_astype_cast_object_int_fail(self, dtype): + arr = Series(["car", "house", "tree", "1"]) + msg = r"invalid literal for int\(\) with base 10: 'car'" + with pytest.raises(ValueError, match=msg): + arr.astype(dtype) + + def test_astype_float_to_uint_negatives_raise( + self, float_numpy_dtype, any_unsigned_int_numpy_dtype + ): + # GH#45151 We don't cast negative numbers to nonsense values + # TODO: same for EA float/uint dtypes, signed integers? + arr = np.arange(5).astype(float_numpy_dtype) - 3 # includes negatives + ser = Series(arr) + + msg = "Cannot losslessly cast from .* to .*" + with pytest.raises(ValueError, match=msg): + ser.astype(any_unsigned_int_numpy_dtype) + + with pytest.raises(ValueError, match=msg): + ser.to_frame().astype(any_unsigned_int_numpy_dtype) + + with pytest.raises(ValueError, match=msg): + # We currently catch and re-raise in Index.astype + Index(ser).astype(any_unsigned_int_numpy_dtype) + + with pytest.raises(ValueError, match=msg): + ser.array.astype(any_unsigned_int_numpy_dtype) + + def test_astype_cast_object_int(self): + arr = Series(["1", "2", "3", "4"], dtype=object) + result = arr.astype(int) + + tm.assert_series_equal(result, Series(np.arange(1, 5))) + + def test_astype_unicode(self, using_infer_string): + # see GH#7758: A bit of magic is required to set + # default encoding to utf-8 + digits = string.digits + test_series = [ + Series([digits * 10, rand_str(63), rand_str(64), rand_str(1000)]), + Series(["データーサイエンス、お前はもう死んでいる"]), + ] + + former_encoding = None + + if sys.getdefaultencoding() == "utf-8": + # GH#45326 as of 2.0 Series.astype matches Index.astype by handling + # bytes with obj.decode() instead of str(obj) + item = "野菜食べないとやばい" + ser = Series([item.encode()]) + result = ser.astype(np.str_) + expected = Series([item], dtype=object) + tm.assert_series_equal(result, expected) + + for ser in test_series: + res = ser.astype(np.str_) + expec = ser.map(str) + if using_infer_string: + expec = expec.astype(object) + tm.assert_series_equal(res, expec) + + # Restore the former encoding + if former_encoding is not None and former_encoding != "utf-8": + reload(sys) + sys.setdefaultencoding(former_encoding) + + def test_astype_bytes(self): + # GH#39474 + result = Series(["foo", "bar", "baz"]).astype(bytes) + assert result.dtypes == np.dtype("S3") + + def test_astype_nan_to_bool(self): + # GH#43018 + ser = Series(np.nan, dtype="object") + result = ser.astype("bool") + expected = Series(True, dtype="bool") + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "dtype", + tm.ALL_INT_EA_DTYPES + tm.FLOAT_EA_DTYPES, + ) + def test_astype_ea_to_datetimetzdtype(self, dtype): + # GH37553 + ser = Series([4, 0, 9], dtype=dtype) + result = ser.astype(DatetimeTZDtype(tz="US/Pacific")) + + expected = Series( + { + 0: Timestamp("1969-12-31 16:00:00.000000004-08:00", tz="US/Pacific"), + 1: Timestamp("1969-12-31 16:00:00.000000000-08:00", tz="US/Pacific"), + 2: Timestamp("1969-12-31 16:00:00.000000009-08:00", tz="US/Pacific"), + } + ) + + tm.assert_series_equal(result, expected) + + def test_astype_retain_attrs(self, any_numpy_dtype): + # GH#44414 + ser = Series([0, 1, 2, 3]) + ser.attrs["Location"] = "Michigan" + + result = ser.astype(any_numpy_dtype).attrs + expected = ser.attrs + + tm.assert_dict_equal(expected, result) + + +class TestAstypeString: + @pytest.mark.parametrize( + "data, dtype", + [ + ([True, NA], "boolean"), + (["A", NA], "category"), + (["2020-10-10", "2020-10-10"], "datetime64[ns]"), + (["2020-10-10", "2020-10-10", NaT], "datetime64[ns]"), + ( + ["2012-01-01 00:00:00-05:00", NaT], + "datetime64[ns, US/Eastern]", + ), + ([1, None], "UInt16"), + (["1/1/2021", "2/1/2021"], "period[M]"), + (["1/1/2021", "2/1/2021", NaT], "period[M]"), + (["1 Day", "59 Days", NaT], "timedelta64[ns]"), + # currently no way to parse IntervalArray from a list of strings + ], + ) + def test_astype_string_to_extension_dtype_roundtrip( + self, data, dtype, request, nullable_string_dtype + ): + if dtype == "boolean": + mark = pytest.mark.xfail( + reason="TODO StringArray.astype() with missing values #GH40566" + ) + request.applymarker(mark) + # GH-40351 + ser = Series(data, dtype=dtype) + + # Note: just passing .astype(dtype) fails for dtype="category" + # with bc ser.dtype.categories will be object dtype whereas + # result.dtype.categories will have string dtype + result = ser.astype(nullable_string_dtype).astype(ser.dtype) + tm.assert_series_equal(result, ser) + + +class TestAstypeCategorical: + def test_astype_categorical_to_other(self): + cat = Categorical([f"{i} - {i + 499}" for i in range(0, 10000, 500)]) + ser = Series(np.random.default_rng(2).integers(0, 10000, 100)).sort_values() + ser = cut(ser, range(0, 10500, 500), right=False, labels=cat) + + expected = ser + tm.assert_series_equal(ser.astype("category"), expected) + tm.assert_series_equal(ser.astype(CategoricalDtype()), expected) + msg = r"Cannot cast object|str dtype to float64" + with pytest.raises(ValueError, match=msg): + ser.astype("float64") + + cat = Series(Categorical(["a", "b", "b", "a", "a", "c", "c", "c"])) + exp = Series(["a", "b", "b", "a", "a", "c", "c", "c"], dtype="str") + tm.assert_series_equal(cat.astype("str"), exp) + s2 = Series(Categorical(["1", "2", "3", "4"])) + exp2 = Series([1, 2, 3, 4]).astype("int") + tm.assert_series_equal(s2.astype("int"), exp2) + + # object don't sort correctly, so just compare that we have the same + # values + def cmp(a, b): + tm.assert_almost_equal(np.sort(np.unique(a)), np.sort(np.unique(b))) + + expected = Series(np.array(ser.values), name="value_group") + cmp(ser.astype("object"), expected) + cmp(ser.astype(np.object_), expected) + + # array conversion + tm.assert_almost_equal(np.array(ser), np.array(ser.values)) + + tm.assert_series_equal(ser.astype("category"), ser) + tm.assert_series_equal(ser.astype(CategoricalDtype()), ser) + + roundtrip_expected = ser.cat.set_categories( + ser.cat.categories.sort_values() + ).cat.remove_unused_categories() + result = ser.astype("object").astype("category") + tm.assert_series_equal(result, roundtrip_expected) + result = ser.astype("object").astype(CategoricalDtype()) + tm.assert_series_equal(result, roundtrip_expected) + + def test_astype_categorical_invalid_conversions(self): + # invalid conversion (these are NOT a dtype) + cat = Categorical([f"{i} - {i + 499}" for i in range(0, 10000, 500)]) + ser = Series(np.random.default_rng(2).integers(0, 10000, 100)).sort_values() + ser = cut(ser, range(0, 10500, 500), right=False, labels=cat) + + msg = ( + "dtype '' " + "not understood" + ) + with pytest.raises(TypeError, match=msg): + ser.astype(Categorical) + with pytest.raises(TypeError, match=msg): + ser.astype("object").astype(Categorical) + + def test_astype_categoricaldtype(self): + ser = Series(["a", "b", "a"]) + result = ser.astype(CategoricalDtype(["a", "b"], ordered=True)) + expected = Series(Categorical(["a", "b", "a"], ordered=True)) + tm.assert_series_equal(result, expected) + + result = ser.astype(CategoricalDtype(["a", "b"], ordered=False)) + expected = Series(Categorical(["a", "b", "a"], ordered=False)) + tm.assert_series_equal(result, expected) + + result = ser.astype(CategoricalDtype(["a", "b", "c"], ordered=False)) + expected = Series( + Categorical(["a", "b", "a"], categories=["a", "b", "c"], ordered=False) + ) + tm.assert_series_equal(result, expected) + tm.assert_index_equal(result.cat.categories, Index(["a", "b", "c"])) + + @pytest.mark.parametrize("name", [None, "foo"]) + @pytest.mark.parametrize("dtype_ordered", [True, False]) + @pytest.mark.parametrize("series_ordered", [True, False]) + def test_astype_categorical_to_categorical( + self, name, dtype_ordered, series_ordered + ): + # GH#10696, GH#18593 + s_data = list("abcaacbab") + s_dtype = CategoricalDtype(list("bac"), ordered=series_ordered) + ser = Series(s_data, dtype=s_dtype, name=name) + + # unspecified categories + dtype = CategoricalDtype(ordered=dtype_ordered) + result = ser.astype(dtype) + exp_dtype = CategoricalDtype(s_dtype.categories, dtype_ordered) + expected = Series(s_data, name=name, dtype=exp_dtype) + tm.assert_series_equal(result, expected) + + # different categories + dtype = CategoricalDtype(list("adc"), dtype_ordered) + result = ser.astype(dtype) + expected = Series(s_data, name=name, dtype=dtype) + tm.assert_series_equal(result, expected) + + if dtype_ordered is False: + # not specifying ordered, so only test once + expected = ser + result = ser.astype("category") + tm.assert_series_equal(result, expected) + + def test_astype_bool_missing_to_categorical(self): + # GH-19182 + ser = Series([True, False, np.nan]) + assert ser.dtypes == np.object_ + + result = ser.astype(CategoricalDtype(categories=[True, False])) + expected = Series(Categorical([True, False, np.nan], categories=[True, False])) + tm.assert_series_equal(result, expected) + + def test_astype_categories_raises(self): + # deprecated GH#17636, removed in GH#27141 + ser = Series(["a", "b", "a"]) + with pytest.raises(TypeError, match="got an unexpected"): + ser.astype("category", categories=["a", "b"], ordered=True) + + @pytest.mark.parametrize("items", [["a", "b", "c", "a"], [1, 2, 3, 1]]) + def test_astype_from_categorical(self, items): + ser = Series(items) + exp = Series(Categorical(items)) + res = ser.astype("category") + tm.assert_series_equal(res, exp) + + def test_astype_from_categorical_with_keywords(self): + # with keywords + lst = ["a", "b", "c", "a"] + ser = Series(lst) + exp = Series(Categorical(lst, ordered=True)) + res = ser.astype(CategoricalDtype(None, ordered=True)) + tm.assert_series_equal(res, exp) + + exp = Series(Categorical(lst, categories=list("abcdef"), ordered=True)) + res = ser.astype(CategoricalDtype(list("abcdef"), ordered=True)) + tm.assert_series_equal(res, exp) + + def test_astype_timedelta64_with_np_nan(self): + # GH45798 + result = Series([Timedelta(1), np.nan], dtype="timedelta64[ns]") + expected = Series([Timedelta(1), NaT], dtype="timedelta64[ns]") + tm.assert_series_equal(result, expected) + + @td.skip_if_no("pyarrow") + def test_astype_int_na_string(self): + # GH#57418 + ser = Series([12, NA], dtype="Int64[pyarrow]") + result = ser.astype("string[pyarrow]") + expected = Series(["12", NA], dtype="string[pyarrow]") + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_autocorr.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_autocorr.py new file mode 100644 index 0000000000000000000000000000000000000000..c1d768cf02f37baceb01b9ef39b91e419cef1e0f --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_autocorr.py @@ -0,0 +1,30 @@ +import numpy as np + + +class TestAutoCorr: + def test_autocorr(self, datetime_series): + # Just run the function + corr1 = datetime_series.autocorr() + + # Now run it with the lag parameter + corr2 = datetime_series.autocorr(lag=1) + + # corr() with lag needs Series of at least length 2 + if len(datetime_series) <= 2: + assert np.isnan(corr1) + assert np.isnan(corr2) + else: + assert corr1 == corr2 + + # Choose a random lag between 1 and length of Series - 2 + # and compare the result with the Series corr() function + n = 1 + np.random.default_rng(2).integers(max(1, len(datetime_series) - 2)) + corr1 = datetime_series.corr(datetime_series.shift(n)) + corr2 = datetime_series.autocorr(lag=n) + + # corr() with lag needs Series of at least length 2 + if len(datetime_series) <= 2: + assert np.isnan(corr1) + assert np.isnan(corr2) + else: + assert corr1 == corr2 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_between.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_between.py new file mode 100644 index 0000000000000000000000000000000000000000..3913419038876938c9538ef435a7eb02c37f62a6 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_between.py @@ -0,0 +1,75 @@ +import numpy as np +import pytest + +from pandas import ( + Series, + bdate_range, + date_range, + period_range, +) +import pandas._testing as tm + + +class TestBetween: + def test_between(self): + series = Series(date_range("1/1/2000", periods=10)) + left, right = series[[2, 7]] + + result = series.between(left, right) + expected = (series >= left) & (series <= right) + tm.assert_series_equal(result, expected) + + def test_between_datetime_object_dtype(self): + ser = Series(bdate_range("1/1/2000", periods=20), dtype=object) + ser[::2] = np.nan + + result = ser[ser.between(ser[3], ser[17])] + expected = ser[3:18].dropna() + tm.assert_series_equal(result, expected) + + result = ser[ser.between(ser[3], ser[17], inclusive="neither")] + expected = ser[5:16].dropna() + tm.assert_series_equal(result, expected) + + def test_between_period_values(self): + ser = Series(period_range("2000-01-01", periods=10, freq="D")) + left, right = ser[[2, 7]] + result = ser.between(left, right) + expected = (ser >= left) & (ser <= right) + tm.assert_series_equal(result, expected) + + def test_between_inclusive_string(self): + # GH 40628 + series = Series(date_range("1/1/2000", periods=10)) + left, right = series[[2, 7]] + + result = series.between(left, right, inclusive="both") + expected = (series >= left) & (series <= right) + tm.assert_series_equal(result, expected) + + result = series.between(left, right, inclusive="left") + expected = (series >= left) & (series < right) + tm.assert_series_equal(result, expected) + + result = series.between(left, right, inclusive="right") + expected = (series > left) & (series <= right) + tm.assert_series_equal(result, expected) + + result = series.between(left, right, inclusive="neither") + expected = (series > left) & (series < right) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("inclusive", ["yes", True, False]) + def test_between_error_args(self, inclusive): + # GH 40628 + series = Series(date_range("1/1/2000", periods=10)) + left, right = series[[2, 7]] + + value_error_msg = ( + "Inclusive has to be either string of 'both'," + "'left', 'right', or 'neither'." + ) + + with pytest.raises(ValueError, match=value_error_msg): + series = Series(date_range("1/1/2000", periods=10)) + series.between(left, right, inclusive=inclusive) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_case_when.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_case_when.py new file mode 100644 index 0000000000000000000000000000000000000000..7cb60a11644a357811136c991143554c4477d485 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_case_when.py @@ -0,0 +1,148 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Series, + array as pd_array, + date_range, +) +import pandas._testing as tm + + +@pytest.fixture +def df(): + """ + base dataframe for testing + """ + return DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + + +def test_case_when_caselist_is_not_a_list(df): + """ + Raise ValueError if caselist is not a list. + """ + msg = "The caselist argument should be a list; " + msg += "instead got.+" + with pytest.raises(TypeError, match=msg): # GH39154 + df["a"].case_when(caselist=()) + + +def test_case_when_no_caselist(df): + """ + Raise ValueError if no caselist is provided. + """ + msg = "provide at least one boolean condition, " + msg += "with a corresponding replacement." + with pytest.raises(ValueError, match=msg): # GH39154 + df["a"].case_when([]) + + +def test_case_when_odd_caselist(df): + """ + Raise ValueError if no of caselist is odd. + """ + msg = "Argument 0 must have length 2; " + msg += "a condition and replacement; instead got length 3." + + with pytest.raises(ValueError, match=msg): + df["a"].case_when([(df["a"].eq(1), 1, df.a.gt(1))]) + + +def test_case_when_raise_error_from_mask(df): + """ + Raise Error from within Series.mask + """ + msg = "Failed to apply condition0 and replacement0." + with pytest.raises(ValueError, match=msg): + df["a"].case_when([(df["a"].eq(1), [1, 2])]) + + +def test_case_when_single_condition(df): + """ + Test output on a single condition. + """ + result = Series([np.nan, np.nan, np.nan]).case_when([(df.a.eq(1), 1)]) + expected = Series([1, np.nan, np.nan]) + tm.assert_series_equal(result, expected) + + +def test_case_when_multiple_conditions(df): + """ + Test output when booleans are derived from a computation + """ + result = Series([np.nan, np.nan, np.nan]).case_when( + [(df.a.eq(1), 1), (Series([False, True, False]), 2)] + ) + expected = Series([1, 2, np.nan]) + tm.assert_series_equal(result, expected) + + +def test_case_when_multiple_conditions_replacement_list(df): + """ + Test output when replacement is a list + """ + result = Series([np.nan, np.nan, np.nan]).case_when( + [([True, False, False], 1), (df["a"].gt(1) & df["b"].eq(5), [1, 2, 3])] + ) + expected = Series([1, 2, np.nan]) + tm.assert_series_equal(result, expected) + + +def test_case_when_multiple_conditions_replacement_extension_dtype(df): + """ + Test output when replacement has an extension dtype + """ + result = Series([np.nan, np.nan, np.nan]).case_when( + [ + ([True, False, False], 1), + (df["a"].gt(1) & df["b"].eq(5), pd_array([1, 2, 3], dtype="Int64")), + ], + ) + expected = Series([1, 2, np.nan], dtype="Float64") + tm.assert_series_equal(result, expected) + + +def test_case_when_multiple_conditions_replacement_series(df): + """ + Test output when replacement is a Series + """ + result = Series([np.nan, np.nan, np.nan]).case_when( + [ + (np.array([True, False, False]), 1), + (df["a"].gt(1) & df["b"].eq(5), Series([1, 2, 3])), + ], + ) + expected = Series([1, 2, np.nan]) + tm.assert_series_equal(result, expected) + + +def test_case_when_non_range_index(): + """ + Test output if index is not RangeIndex + """ + rng = np.random.default_rng(seed=123) + dates = date_range("1/1/2000", periods=8) + df = DataFrame( + rng.standard_normal(size=(8, 4)), index=dates, columns=["A", "B", "C", "D"] + ) + result = Series(5, index=df.index, name="A").case_when([(df.A.gt(0), df.B)]) + expected = df.A.mask(df.A.gt(0), df.B).where(df.A.gt(0), 5) + tm.assert_series_equal(result, expected) + + +def test_case_when_callable(): + """ + Test output on a callable + """ + # https://numpy.org/doc/stable/reference/generated/numpy.piecewise.html + x = np.linspace(-2.5, 2.5, 6) + ser = Series(x) + result = ser.case_when( + caselist=[ + (lambda df: df < 0, lambda df: -df), + (lambda df: df >= 0, lambda df: df), + ] + ) + expected = np.piecewise(x, [x < 0, x >= 0], [lambda x: -x, lambda x: x]) + tm.assert_series_equal(result, Series(expected)) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_clip.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_clip.py new file mode 100644 index 0000000000000000000000000000000000000000..5bbf35f6e39bb237ef1a3853403d47f1fed5b3de --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_clip.py @@ -0,0 +1,146 @@ +from datetime import datetime + +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Series, + Timestamp, + isna, + notna, +) +import pandas._testing as tm + + +class TestSeriesClip: + def test_clip(self, datetime_series): + val = datetime_series.median() + + assert datetime_series.clip(lower=val).min() == val + assert datetime_series.clip(upper=val).max() == val + + result = datetime_series.clip(-0.5, 0.5) + expected = np.clip(datetime_series, -0.5, 0.5) + tm.assert_series_equal(result, expected) + assert isinstance(expected, Series) + + def test_clip_types_and_nulls(self): + sers = [ + Series([np.nan, 1.0, 2.0, 3.0]), + Series([None, "a", "b", "c"]), + Series(pd.to_datetime([np.nan, 1, 2, 3], unit="D")), + ] + + for s in sers: + thresh = s[2] + lower = s.clip(lower=thresh) + upper = s.clip(upper=thresh) + assert lower[notna(lower)].min() == thresh + assert upper[notna(upper)].max() == thresh + assert list(isna(s)) == list(isna(lower)) + assert list(isna(s)) == list(isna(upper)) + + def test_series_clipping_with_na_values(self, any_numeric_ea_dtype, nulls_fixture): + # Ensure that clipping method can handle NA values with out failing + # GH#40581 + + if nulls_fixture is pd.NaT: + # constructor will raise, see + # test_constructor_mismatched_null_nullable_dtype + pytest.skip("See test_constructor_mismatched_null_nullable_dtype") + + ser = Series([nulls_fixture, 1.0, 3.0], dtype=any_numeric_ea_dtype) + s_clipped_upper = ser.clip(upper=2.0) + s_clipped_lower = ser.clip(lower=2.0) + + expected_upper = Series([nulls_fixture, 1.0, 2.0], dtype=any_numeric_ea_dtype) + expected_lower = Series([nulls_fixture, 2.0, 3.0], dtype=any_numeric_ea_dtype) + + tm.assert_series_equal(s_clipped_upper, expected_upper) + tm.assert_series_equal(s_clipped_lower, expected_lower) + + def test_clip_with_na_args(self): + """Should process np.nan argument as None""" + # GH#17276 + s = Series([1, 2, 3]) + + tm.assert_series_equal(s.clip(np.nan), Series([1, 2, 3])) + tm.assert_series_equal(s.clip(upper=np.nan, lower=np.nan), Series([1, 2, 3])) + + # GH#19992 + msg = "Downcasting behavior in Series and DataFrame methods 'where'" + # TODO: avoid this warning here? seems like we should never be upcasting + # in the first place? + with tm.assert_produces_warning(FutureWarning, match=msg): + res = s.clip(lower=[0, 4, np.nan]) + tm.assert_series_equal(res, Series([1, 4, 3])) + with tm.assert_produces_warning(FutureWarning, match=msg): + res = s.clip(upper=[1, np.nan, 1]) + tm.assert_series_equal(res, Series([1, 2, 1])) + + # GH#40420 + s = Series([1, 2, 3]) + result = s.clip(0, [np.nan, np.nan, np.nan]) + tm.assert_series_equal(s, result) + + def test_clip_against_series(self): + # GH#6966 + + s = Series([1.0, 1.0, 4.0]) + + lower = Series([1.0, 2.0, 3.0]) + upper = Series([1.5, 2.5, 3.5]) + + tm.assert_series_equal(s.clip(lower, upper), Series([1.0, 2.0, 3.5])) + tm.assert_series_equal(s.clip(1.5, upper), Series([1.5, 1.5, 3.5])) + + @pytest.mark.parametrize("inplace", [True, False]) + @pytest.mark.parametrize("upper", [[1, 2, 3], np.asarray([1, 2, 3])]) + def test_clip_against_list_like(self, inplace, upper): + # GH#15390 + original = Series([5, 6, 7]) + result = original.clip(upper=upper, inplace=inplace) + expected = Series([1, 2, 3]) + + if inplace: + result = original + tm.assert_series_equal(result, expected, check_exact=True) + + def test_clip_with_datetimes(self): + # GH#11838 + # naive and tz-aware datetimes + + t = Timestamp("2015-12-01 09:30:30") + s = Series([Timestamp("2015-12-01 09:30:00"), Timestamp("2015-12-01 09:31:00")]) + result = s.clip(upper=t) + expected = Series( + [Timestamp("2015-12-01 09:30:00"), Timestamp("2015-12-01 09:30:30")] + ) + tm.assert_series_equal(result, expected) + + t = Timestamp("2015-12-01 09:30:30", tz="US/Eastern") + s = Series( + [ + Timestamp("2015-12-01 09:30:00", tz="US/Eastern"), + Timestamp("2015-12-01 09:31:00", tz="US/Eastern"), + ] + ) + result = s.clip(upper=t) + expected = Series( + [ + Timestamp("2015-12-01 09:30:00", tz="US/Eastern"), + Timestamp("2015-12-01 09:30:30", tz="US/Eastern"), + ] + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("dtype", [object, "M8[us]"]) + def test_clip_with_timestamps_and_oob_datetimes(self, dtype): + # GH-42794 + ser = Series([datetime(1, 1, 1), datetime(9999, 9, 9)], dtype=dtype) + + result = ser.clip(lower=Timestamp.min, upper=Timestamp.max) + expected = Series([Timestamp.min, Timestamp.max], dtype=dtype) + + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_combine.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_combine.py new file mode 100644 index 0000000000000000000000000000000000000000..75d47e3daa10339f4c4cc7b35c52f24bbb20277a --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_combine.py @@ -0,0 +1,17 @@ +from pandas import Series +import pandas._testing as tm + + +class TestCombine: + def test_combine_scalar(self): + # GH#21248 + # Note - combine() with another Series is tested elsewhere because + # it is used when testing operators + ser = Series([i * 10 for i in range(5)]) + result = ser.combine(3, lambda x, y: x + y) + expected = Series([i * 10 + 3 for i in range(5)]) + tm.assert_series_equal(result, expected) + + result = ser.combine(22, lambda x, y: min(x, y)) + expected = Series([min(i * 10, 22) for i in range(5)]) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_combine_first.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_combine_first.py new file mode 100644 index 0000000000000000000000000000000000000000..9ad83b7570290cdba887fba298f49649ea8ba51b --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_combine_first.py @@ -0,0 +1,150 @@ +from datetime import datetime + +import numpy as np + +import pandas as pd +from pandas import ( + Period, + Series, + date_range, + period_range, + to_datetime, +) +import pandas._testing as tm + + +class TestCombineFirst: + def test_combine_first_period_datetime(self): + # GH#3367 + didx = date_range(start="1950-01-31", end="1950-07-31", freq="ME") + pidx = period_range(start=Period("1950-1"), end=Period("1950-7"), freq="M") + # check to be consistent with DatetimeIndex + for idx in [didx, pidx]: + a = Series([1, np.nan, np.nan, 4, 5, np.nan, 7], index=idx) + b = Series([9, 9, 9, 9, 9, 9, 9], index=idx) + + result = a.combine_first(b) + expected = Series([1, 9, 9, 4, 5, 9, 7], index=idx, dtype=np.float64) + tm.assert_series_equal(result, expected) + + def test_combine_first_name(self, datetime_series): + result = datetime_series.combine_first(datetime_series[:5]) + assert result.name == datetime_series.name + + def test_combine_first(self, using_infer_string): + values = np.arange(20, dtype=np.float64) + series = Series(values, index=np.arange(20, dtype=np.int64)) + + series_copy = series * 2 + series_copy[::2] = np.nan + + # nothing used from the input + combined = series.combine_first(series_copy) + + tm.assert_series_equal(combined, series) + + # Holes filled from input + combined = series_copy.combine_first(series) + assert np.isfinite(combined).all() + + tm.assert_series_equal(combined[::2], series[::2]) + tm.assert_series_equal(combined[1::2], series_copy[1::2]) + + # mixed types + index = pd.Index([str(i) for i in range(20)]) + floats = Series(np.random.default_rng(2).standard_normal(20), index=index) + strings = Series([str(i) for i in range(10)], index=index[::2], dtype=object) + + combined = strings.combine_first(floats) + + tm.assert_series_equal(strings, combined.loc[index[::2]]) + tm.assert_series_equal(floats[1::2].astype(object), combined.loc[index[1::2]]) + + # corner case + ser = Series([1.0, 2, 3], index=[0, 1, 2]) + empty = Series([], index=[], dtype=object) + msg = "The behavior of array concatenation with empty entries is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = ser.combine_first(empty) + if not using_infer_string: + ser.index = ser.index.astype("O") + tm.assert_series_equal(ser, result) + + def test_combine_first_dt64(self, unit): + s0 = to_datetime(Series(["2010", np.nan])).dt.as_unit(unit) + s1 = to_datetime(Series([np.nan, "2011"])).dt.as_unit(unit) + rs = s0.combine_first(s1) + xp = to_datetime(Series(["2010", "2011"])).dt.as_unit(unit) + tm.assert_series_equal(rs, xp) + + s0 = to_datetime(Series(["2010", np.nan])).dt.as_unit(unit) + s1 = Series([np.nan, "2011"]) + rs = s0.combine_first(s1) + + xp = Series([datetime(2010, 1, 1), "2011"], dtype="datetime64[ns]") + + tm.assert_series_equal(rs, xp) + + def test_combine_first_dt_tz_values(self, tz_naive_fixture): + ser1 = Series( + pd.DatetimeIndex(["20150101", "20150102", "20150103"], tz=tz_naive_fixture), + name="ser1", + ) + ser2 = Series( + pd.DatetimeIndex(["20160514", "20160515", "20160516"], tz=tz_naive_fixture), + index=[2, 3, 4], + name="ser2", + ) + result = ser1.combine_first(ser2) + exp_vals = pd.DatetimeIndex( + ["20150101", "20150102", "20150103", "20160515", "20160516"], + tz=tz_naive_fixture, + ) + exp = Series(exp_vals, name="ser1") + tm.assert_series_equal(exp, result) + + def test_combine_first_timezone_series_with_empty_series(self): + # GH 41800 + time_index = date_range( + datetime(2021, 1, 1, 1), + datetime(2021, 1, 1, 10), + freq="h", + tz="Europe/Rome", + ) + s1 = Series(range(10), index=time_index) + s2 = Series(index=time_index) + msg = "The behavior of array concatenation with empty entries is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = s1.combine_first(s2) + tm.assert_series_equal(result, s1) + + def test_combine_first_preserves_dtype(self): + # GH51764 + s1 = Series([1666880195890293744, 1666880195890293837]) + s2 = Series([1, 2, 3]) + result = s1.combine_first(s2) + expected = Series([1666880195890293744, 1666880195890293837, 3]) + tm.assert_series_equal(result, expected) + + def test_combine_mixed_timezone(self): + # GH 26283 + uniform_tz = Series({pd.Timestamp("2019-05-01", tz="UTC"): 1.0}) + multi_tz = Series( + { + pd.Timestamp("2019-05-01 01:00:00+0100", tz="Europe/London"): 2.0, + pd.Timestamp("2019-05-02", tz="UTC"): 3.0, + } + ) + + result = uniform_tz.combine_first(multi_tz) + expected = Series( + [1.0, 3.0], + index=pd.Index( + [ + pd.Timestamp("2019-05-01 00:00:00+00:00", tz="UTC"), + pd.Timestamp("2019-05-02 00:00:00+00:00", tz="UTC"), + ], + dtype="object", + ), + ) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_compare.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_compare.py new file mode 100644 index 0000000000000000000000000000000000000000..fe2016a245ec7c1373c72f40c7b6e7d899cf4f96 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_compare.py @@ -0,0 +1,141 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm + + +@pytest.mark.parametrize("align_axis", [0, 1, "index", "columns"]) +def test_compare_axis(align_axis): + # GH#30429 + s1 = pd.Series(["a", "b", "c"]) + s2 = pd.Series(["x", "b", "z"]) + + result = s1.compare(s2, align_axis=align_axis) + + if align_axis in (1, "columns"): + indices = pd.Index([0, 2]) + columns = pd.Index(["self", "other"]) + expected = pd.DataFrame( + [["a", "x"], ["c", "z"]], index=indices, columns=columns + ) + tm.assert_frame_equal(result, expected) + else: + indices = pd.MultiIndex.from_product([[0, 2], ["self", "other"]]) + expected = pd.Series(["a", "x", "c", "z"], index=indices) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "keep_shape, keep_equal", + [ + (True, False), + (False, True), + (True, True), + # False, False case is already covered in test_compare_axis + ], +) +def test_compare_various_formats(keep_shape, keep_equal): + s1 = pd.Series(["a", "b", "c"]) + s2 = pd.Series(["x", "b", "z"]) + + result = s1.compare(s2, keep_shape=keep_shape, keep_equal=keep_equal) + + if keep_shape: + indices = pd.Index([0, 1, 2]) + columns = pd.Index(["self", "other"]) + if keep_equal: + expected = pd.DataFrame( + [["a", "x"], ["b", "b"], ["c", "z"]], index=indices, columns=columns + ) + else: + expected = pd.DataFrame( + [["a", "x"], [np.nan, np.nan], ["c", "z"]], + index=indices, + columns=columns, + ) + else: + indices = pd.Index([0, 2]) + columns = pd.Index(["self", "other"]) + expected = pd.DataFrame( + [["a", "x"], ["c", "z"]], index=indices, columns=columns + ) + tm.assert_frame_equal(result, expected) + + +def test_compare_with_equal_nulls(): + # We want to make sure two NaNs are considered the same + # and dropped where applicable + s1 = pd.Series(["a", "b", np.nan]) + s2 = pd.Series(["x", "b", np.nan]) + + result = s1.compare(s2) + expected = pd.DataFrame([["a", "x"]], columns=["self", "other"]) + tm.assert_frame_equal(result, expected) + + +def test_compare_with_non_equal_nulls(): + # We want to make sure the relevant NaNs do not get dropped + s1 = pd.Series(["a", "b", "c"]) + s2 = pd.Series(["x", "b", np.nan]) + + result = s1.compare(s2, align_axis=0) + + indices = pd.MultiIndex.from_product([[0, 2], ["self", "other"]]) + expected = pd.Series(["a", "x", "c", np.nan], index=indices) + tm.assert_series_equal(result, expected) + + +def test_compare_multi_index(): + index = pd.MultiIndex.from_arrays([[0, 0, 1], [0, 1, 2]]) + s1 = pd.Series(["a", "b", "c"], index=index) + s2 = pd.Series(["x", "b", "z"], index=index) + + result = s1.compare(s2, align_axis=0) + + indices = pd.MultiIndex.from_arrays( + [[0, 0, 1, 1], [0, 0, 2, 2], ["self", "other", "self", "other"]] + ) + expected = pd.Series(["a", "x", "c", "z"], index=indices) + tm.assert_series_equal(result, expected) + + +def test_compare_unaligned_objects(): + # test Series with different indices + msg = "Can only compare identically-labeled Series objects" + with pytest.raises(ValueError, match=msg): + ser1 = pd.Series([1, 2, 3], index=["a", "b", "c"]) + ser2 = pd.Series([1, 2, 3], index=["a", "b", "d"]) + ser1.compare(ser2) + + # test Series with different lengths + msg = "Can only compare identically-labeled Series objects" + with pytest.raises(ValueError, match=msg): + ser1 = pd.Series([1, 2, 3]) + ser2 = pd.Series([1, 2, 3, 4]) + ser1.compare(ser2) + + +def test_compare_datetime64_and_string(): + # Issue https://github.com/pandas-dev/pandas/issues/45506 + # Catch OverflowError when comparing datetime64 and string + data = [ + {"a": "2015-07-01", "b": "08335394550"}, + {"a": "2015-07-02", "b": "+49 (0) 0345 300033"}, + {"a": "2015-07-03", "b": "+49(0)2598 04457"}, + {"a": "2015-07-04", "b": "0741470003"}, + {"a": "2015-07-05", "b": "04181 83668"}, + ] + dtypes = {"a": "datetime64[ns]", "b": "string"} + df = pd.DataFrame(data=data).astype(dtypes) + + result_eq1 = df["a"].eq(df["b"]) + result_eq2 = df["a"] == df["b"] + result_neq = df["a"] != df["b"] + + expected_eq = pd.Series([False] * 5) # For .eq and == + expected_neq = pd.Series([True] * 5) # For != + + tm.assert_series_equal(result_eq1, expected_eq) + tm.assert_series_equal(result_eq2, expected_eq) + tm.assert_series_equal(result_neq, expected_neq) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_convert_dtypes.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_convert_dtypes.py new file mode 100644 index 0000000000000000000000000000000000000000..c2cc838619790c848f9b707dae2f46785972ec49 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_convert_dtypes.py @@ -0,0 +1,309 @@ +from itertools import product + +import numpy as np +import pytest + +from pandas._config import using_string_dtype + +from pandas._libs import lib + +import pandas as pd +import pandas._testing as tm + +# Each test case consists of a tuple with the data and dtype to create the +# test Series, the default dtype for the expected result (which is valid +# for most cases), and the specific cases where the result deviates from +# this default. Those overrides are defined as a dict with (keyword, val) as +# dictionary key. In case of multiple items, the last override takes precedence. + + +@pytest.fixture( + params=[ + ( + # data + [1, 2, 3], + # original dtype + np.dtype("int32"), + # default expected dtype + "Int32", + # exceptions on expected dtype + {("convert_integer", False): np.dtype("int32")}, + ), + ( + [1, 2, 3], + np.dtype("int64"), + "Int64", + {("convert_integer", False): np.dtype("int64")}, + ), + ( + ["x", "y", "z"], + np.dtype("O"), + pd.StringDtype(), + {("convert_string", False): np.dtype("O")}, + ), + ( + [True, False, np.nan], + np.dtype("O"), + pd.BooleanDtype(), + {("convert_boolean", False): np.dtype("O")}, + ), + ( + ["h", "i", np.nan], + np.dtype("O"), + pd.StringDtype(), + {("convert_string", False): np.dtype("O")}, + ), + ( # GH32117 + ["h", "i", 1], + np.dtype("O"), + np.dtype("O"), + {}, + ), + ( + [10, np.nan, 20], + np.dtype("float"), + "Int64", + { + ("convert_integer", False, "convert_floating", True): "Float64", + ("convert_integer", False, "convert_floating", False): np.dtype( + "float" + ), + }, + ), + ( + [np.nan, 100.5, 200], + np.dtype("float"), + "Float64", + {("convert_floating", False): np.dtype("float")}, + ), + ( + [3, 4, 5], + "Int8", + "Int8", + {}, + ), + ( + [[1, 2], [3, 4], [5]], + None, + np.dtype("O"), + {}, + ), + ( + [4, 5, 6], + np.dtype("uint32"), + "UInt32", + {("convert_integer", False): np.dtype("uint32")}, + ), + ( + [-10, 12, 13], + np.dtype("i1"), + "Int8", + {("convert_integer", False): np.dtype("i1")}, + ), + ( + [1.2, 1.3], + np.dtype("float32"), + "Float32", + {("convert_floating", False): np.dtype("float32")}, + ), + ( + [1, 2.0], + object, + "Int64", + { + ("convert_integer", False): "Float64", + ("convert_integer", False, "convert_floating", False): np.dtype( + "float" + ), + ("infer_objects", False): np.dtype("object"), + }, + ), + ( + [1, 2.5], + object, + "Float64", + { + ("convert_floating", False): np.dtype("float"), + ("infer_objects", False): np.dtype("object"), + }, + ), + (["a", "b"], pd.CategoricalDtype(), pd.CategoricalDtype(), {}), + ( + pd.to_datetime(["2020-01-14 10:00", "2020-01-15 11:11"]).as_unit("s"), + pd.DatetimeTZDtype(tz="UTC"), + pd.DatetimeTZDtype(tz="UTC"), + {}, + ), + ( + pd.to_datetime(["2020-01-14 10:00", "2020-01-15 11:11"]).as_unit("ms"), + pd.DatetimeTZDtype(tz="UTC"), + pd.DatetimeTZDtype(tz="UTC"), + {}, + ), + ( + pd.to_datetime(["2020-01-14 10:00", "2020-01-15 11:11"]).as_unit("us"), + pd.DatetimeTZDtype(tz="UTC"), + pd.DatetimeTZDtype(tz="UTC"), + {}, + ), + ( + pd.to_datetime(["2020-01-14 10:00", "2020-01-15 11:11"]).as_unit("ns"), + pd.DatetimeTZDtype(tz="UTC"), + pd.DatetimeTZDtype(tz="UTC"), + {}, + ), + ( + pd.to_datetime(["2020-01-14 10:00", "2020-01-15 11:11"]).as_unit("ns"), + "datetime64[ns]", + np.dtype("datetime64[ns]"), + {}, + ), + ( + pd.to_datetime(["2020-01-14 10:00", "2020-01-15 11:11"]).as_unit("ns"), + object, + np.dtype("datetime64[ns]"), + {("infer_objects", False): np.dtype("object")}, + ), + ( + pd.period_range("1/1/2011", freq="M", periods=3), + None, + pd.PeriodDtype("M"), + {}, + ), + ( + pd.arrays.IntervalArray([pd.Interval(0, 1), pd.Interval(1, 5)]), + None, + pd.IntervalDtype("int64", "right"), + {}, + ), + ] +) +def test_cases(request): + return request.param + + +class TestSeriesConvertDtypes: + @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)", strict=False) + @pytest.mark.parametrize("params", product(*[(True, False)] * 5)) + def test_convert_dtypes( + self, + test_cases, + params, + using_infer_string, + ): + data, maindtype, expected_default, expected_other = test_cases + if ( + hasattr(data, "dtype") + and lib.is_np_dtype(data.dtype, "M") + and isinstance(maindtype, pd.DatetimeTZDtype) + ): + # this astype is deprecated in favor of tz_localize + msg = "Cannot use .astype to convert from timezone-naive dtype" + with pytest.raises(TypeError, match=msg): + pd.Series(data, dtype=maindtype) + return + + if maindtype is not None: + series = pd.Series(data, dtype=maindtype) + else: + series = pd.Series(data) + + result = series.convert_dtypes(*params) + + param_names = [ + "infer_objects", + "convert_string", + "convert_integer", + "convert_boolean", + "convert_floating", + ] + params_dict = dict(zip(param_names, params)) + + expected_dtype = expected_default + for spec, dtype in expected_other.items(): + if all(params_dict[key] is val for key, val in zip(spec[::2], spec[1::2])): + expected_dtype = dtype + if ( + using_infer_string + and expected_default == "string" + and expected_dtype == object + and params[0] + and not params[1] + ): + # If convert_string=False and infer_objects=True, we end up with the + # default string dtype instead of preserving object for string data + expected_dtype = pd.StringDtype(na_value=np.nan) + + expected = pd.Series(data, dtype=expected_dtype) + tm.assert_series_equal(result, expected) + + # Test that it is a copy + copy = series.copy(deep=True) + + if result.notna().sum() > 0 and result.dtype in ["interval[int64, right]"]: + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + result[result.notna()] = np.nan + else: + result[result.notna()] = np.nan + + # Make sure original not changed + tm.assert_series_equal(series, copy) + + def test_convert_string_dtype(self, nullable_string_dtype): + # https://github.com/pandas-dev/pandas/issues/31731 -> converting columns + # that are already string dtype + df = pd.DataFrame( + {"A": ["a", "b", pd.NA], "B": ["ä", "ö", "ü"]}, dtype=nullable_string_dtype + ) + result = df.convert_dtypes() + tm.assert_frame_equal(df, result) + + def test_convert_bool_dtype(self): + # GH32287 + df = pd.DataFrame({"A": pd.array([True])}) + tm.assert_frame_equal(df, df.convert_dtypes()) + + def test_convert_byte_string_dtype(self): + # GH-43183 + byte_str = b"binary-string" + + df = pd.DataFrame(data={"A": byte_str}, index=[0]) + result = df.convert_dtypes() + expected = df + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "infer_objects, dtype", [(True, "Int64"), (False, "object")] + ) + def test_convert_dtype_object_with_na(self, infer_objects, dtype): + # GH#48791 + ser = pd.Series([1, pd.NA]) + result = ser.convert_dtypes(infer_objects=infer_objects) + expected = pd.Series([1, pd.NA], dtype=dtype) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "infer_objects, dtype", [(True, "Float64"), (False, "object")] + ) + def test_convert_dtype_object_with_na_float(self, infer_objects, dtype): + # GH#48791 + ser = pd.Series([1.5, pd.NA]) + result = ser.convert_dtypes(infer_objects=infer_objects) + expected = pd.Series([1.5, pd.NA], dtype=dtype) + tm.assert_series_equal(result, expected) + + def test_convert_dtypes_pyarrow_to_np_nullable(self): + # GH 53648 + pytest.importorskip("pyarrow") + ser = pd.Series(range(2), dtype="int32[pyarrow]") + result = ser.convert_dtypes(dtype_backend="numpy_nullable") + expected = pd.Series(range(2), dtype="Int32") + tm.assert_series_equal(result, expected) + + def test_convert_dtypes_pyarrow_null(self): + # GH#55346 + pa = pytest.importorskip("pyarrow") + ser = pd.Series([None, None]) + result = ser.convert_dtypes(dtype_backend="pyarrow") + expected = pd.Series([None, None], dtype=pd.ArrowDtype(pa.null())) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_copy.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_copy.py new file mode 100644 index 0000000000000000000000000000000000000000..23dbe85075916dbb901afdcf8267c8877db3b3f8 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_copy.py @@ -0,0 +1,91 @@ +import numpy as np +import pytest + +from pandas import ( + Series, + Timestamp, +) +import pandas._testing as tm + + +class TestCopy: + @pytest.mark.parametrize("deep", ["default", None, False, True]) + def test_copy(self, deep, using_copy_on_write, warn_copy_on_write): + ser = Series(np.arange(10), dtype="float64") + + # default deep is True + if deep == "default": + ser2 = ser.copy() + else: + ser2 = ser.copy(deep=deep) + + if using_copy_on_write: + # INFO(CoW) a shallow copy doesn't yet copy the data + # but parent will not be modified (CoW) + if deep is None or deep is False: + assert np.may_share_memory(ser.values, ser2.values) + else: + assert not np.may_share_memory(ser.values, ser2.values) + + with tm.assert_cow_warning(warn_copy_on_write and deep is False): + ser2[::2] = np.nan + + if deep is not False or using_copy_on_write: + # Did not modify original Series + assert np.isnan(ser2[0]) + assert not np.isnan(ser[0]) + else: + # we DID modify the original Series + assert np.isnan(ser2[0]) + assert np.isnan(ser[0]) + + @pytest.mark.filterwarnings("ignore:Setting a value on a view:FutureWarning") + @pytest.mark.parametrize("deep", ["default", None, False, True]) + def test_copy_tzaware(self, deep, using_copy_on_write): + # GH#11794 + # copy of tz-aware + expected = Series([Timestamp("2012/01/01", tz="UTC")]) + expected2 = Series([Timestamp("1999/01/01", tz="UTC")]) + + ser = Series([Timestamp("2012/01/01", tz="UTC")]) + + if deep == "default": + ser2 = ser.copy() + else: + ser2 = ser.copy(deep=deep) + + if using_copy_on_write: + # INFO(CoW) a shallow copy doesn't yet copy the data + # but parent will not be modified (CoW) + if deep is None or deep is False: + assert np.may_share_memory(ser.values, ser2.values) + else: + assert not np.may_share_memory(ser.values, ser2.values) + + ser2[0] = Timestamp("1999/01/01", tz="UTC") + + # default deep is True + if deep is not False or using_copy_on_write: + # Did not modify original Series + tm.assert_series_equal(ser2, expected2) + tm.assert_series_equal(ser, expected) + else: + # we DID modify the original Series + tm.assert_series_equal(ser2, expected2) + tm.assert_series_equal(ser, expected2) + + def test_copy_name(self, datetime_series): + result = datetime_series.copy() + assert result.name == datetime_series.name + + def test_copy_index_name_checking(self, datetime_series): + # don't want to be able to modify the index stored elsewhere after + # making a copy + + datetime_series.index.name = None + assert datetime_series.index.name is None + assert datetime_series is datetime_series + + cp = datetime_series.copy() + cp.index.name = "foo" + assert datetime_series.index.name is None diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_count.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_count.py new file mode 100644 index 0000000000000000000000000000000000000000..9ba163f347198a5533c67fdeffeb4012a804066f --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_count.py @@ -0,0 +1,34 @@ +import numpy as np + +import pandas as pd +from pandas import ( + Categorical, + Series, +) +import pandas._testing as tm + + +class TestSeriesCount: + def test_count(self, datetime_series): + assert datetime_series.count() == len(datetime_series) + + datetime_series[::2] = np.nan + + assert datetime_series.count() == np.isfinite(datetime_series).sum() + + def test_count_inf_as_na(self): + # GH#29478 + ser = Series([pd.Timestamp("1990/1/1")]) + msg = "use_inf_as_na option is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + with pd.option_context("use_inf_as_na", True): + assert ser.count() == 1 + + def test_count_categorical(self): + ser = Series( + Categorical( + [np.nan, 1, 2, np.nan], categories=[5, 4, 3, 2, 1], ordered=True + ) + ) + result = ser.count() + assert result == 2 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_cov_corr.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_cov_corr.py new file mode 100644 index 0000000000000000000000000000000000000000..a369145b4e884d740af39b236edbf2ce6e088cd0 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_cov_corr.py @@ -0,0 +1,185 @@ +import math + +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Series, + date_range, + isna, +) +import pandas._testing as tm + + +class TestSeriesCov: + def test_cov(self, datetime_series): + # full overlap + tm.assert_almost_equal( + datetime_series.cov(datetime_series), datetime_series.std() ** 2 + ) + + # partial overlap + tm.assert_almost_equal( + datetime_series[:15].cov(datetime_series[5:]), + datetime_series[5:15].std() ** 2, + ) + + # No overlap + assert np.isnan(datetime_series[::2].cov(datetime_series[1::2])) + + # all NA + cp = datetime_series[:10].copy() + cp[:] = np.nan + assert isna(cp.cov(cp)) + + # min_periods + assert isna(datetime_series[:15].cov(datetime_series[5:], min_periods=12)) + + ts1 = datetime_series[:15].reindex(datetime_series.index) + ts2 = datetime_series[5:].reindex(datetime_series.index) + assert isna(ts1.cov(ts2, min_periods=12)) + + @pytest.mark.parametrize("test_ddof", [None, 0, 1, 2, 3]) + @pytest.mark.parametrize("dtype", ["float64", "Float64"]) + def test_cov_ddof(self, test_ddof, dtype): + # GH#34611 + np_array1 = np.random.default_rng(2).random(10) + np_array2 = np.random.default_rng(2).random(10) + + s1 = Series(np_array1, dtype=dtype) + s2 = Series(np_array2, dtype=dtype) + + result = s1.cov(s2, ddof=test_ddof) + expected = np.cov(np_array1, np_array2, ddof=test_ddof)[0][1] + assert math.isclose(expected, result) + + +class TestSeriesCorr: + @pytest.mark.parametrize("dtype", ["float64", "Float64"]) + def test_corr(self, datetime_series, dtype): + stats = pytest.importorskip("scipy.stats") + + datetime_series = datetime_series.astype(dtype) + + # full overlap + tm.assert_almost_equal(datetime_series.corr(datetime_series), 1) + + # partial overlap + tm.assert_almost_equal(datetime_series[:15].corr(datetime_series[5:]), 1) + + assert isna(datetime_series[:15].corr(datetime_series[5:], min_periods=12)) + + ts1 = datetime_series[:15].reindex(datetime_series.index) + ts2 = datetime_series[5:].reindex(datetime_series.index) + assert isna(ts1.corr(ts2, min_periods=12)) + + # No overlap + assert np.isnan(datetime_series[::2].corr(datetime_series[1::2])) + + # all NA + cp = datetime_series[:10].copy() + cp[:] = np.nan + assert isna(cp.corr(cp)) + + A = Series( + np.arange(10, dtype=np.float64), + index=date_range("2020-01-01", periods=10), + name="ts", + ) + B = A.copy() + result = A.corr(B) + expected, _ = stats.pearsonr(A, B) + tm.assert_almost_equal(result, expected) + + def test_corr_rank(self): + stats = pytest.importorskip("scipy.stats") + + # kendall and spearman + A = Series( + np.arange(10, dtype=np.float64), + index=date_range("2020-01-01", periods=10), + name="ts", + ) + B = A.copy() + A[-5:] = A[:5].copy() + result = A.corr(B, method="kendall") + expected = stats.kendalltau(A, B)[0] + tm.assert_almost_equal(result, expected) + + result = A.corr(B, method="spearman") + expected = stats.spearmanr(A, B)[0] + tm.assert_almost_equal(result, expected) + + # results from R + A = Series( + [ + -0.89926396, + 0.94209606, + -1.03289164, + -0.95445587, + 0.76910310, + -0.06430576, + -2.09704447, + 0.40660407, + -0.89926396, + 0.94209606, + ] + ) + B = Series( + [ + -1.01270225, + -0.62210117, + -1.56895827, + 0.59592943, + -0.01680292, + 1.17258718, + -1.06009347, + -0.10222060, + -0.89076239, + 0.89372375, + ] + ) + kexp = 0.4319297 + sexp = 0.5853767 + tm.assert_almost_equal(A.corr(B, method="kendall"), kexp) + tm.assert_almost_equal(A.corr(B, method="spearman"), sexp) + + def test_corr_invalid_method(self): + # GH PR #22298 + s1 = Series(np.random.default_rng(2).standard_normal(10)) + s2 = Series(np.random.default_rng(2).standard_normal(10)) + msg = "method must be either 'pearson', 'spearman', 'kendall', or a callable, " + with pytest.raises(ValueError, match=msg): + s1.corr(s2, method="____") + + def test_corr_callable_method(self, datetime_series): + # simple correlation example + # returns 1 if exact equality, 0 otherwise + my_corr = lambda a, b: 1.0 if (a == b).all() else 0.0 + + # simple example + s1 = Series([1, 2, 3, 4, 5]) + s2 = Series([5, 4, 3, 2, 1]) + expected = 0 + tm.assert_almost_equal(s1.corr(s2, method=my_corr), expected) + + # full overlap + tm.assert_almost_equal( + datetime_series.corr(datetime_series, method=my_corr), 1.0 + ) + + # partial overlap + tm.assert_almost_equal( + datetime_series[:15].corr(datetime_series[5:], method=my_corr), 1.0 + ) + + # No overlap + assert np.isnan( + datetime_series[::2].corr(datetime_series[1::2], method=my_corr) + ) + + # dataframe example + df = pd.DataFrame([s1, s2]) + expected = pd.DataFrame([{0: 1.0, 1: 0}, {0: 0, 1: 1.0}]) + tm.assert_almost_equal(df.transpose().corr(method=my_corr), expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_describe.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_describe.py new file mode 100644 index 0000000000000000000000000000000000000000..79ec11feb530817e735cf1d45cd7985839cd4d05 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_describe.py @@ -0,0 +1,203 @@ +import numpy as np +import pytest + +from pandas.compat.numpy import np_version_gte1p25 + +from pandas.core.dtypes.common import ( + is_complex_dtype, + is_extension_array_dtype, +) + +from pandas import ( + NA, + Period, + Series, + Timedelta, + Timestamp, + date_range, +) +import pandas._testing as tm + + +class TestSeriesDescribe: + def test_describe_ints(self): + ser = Series([0, 1, 2, 3, 4], name="int_data") + result = ser.describe() + expected = Series( + [5, 2, ser.std(), 0, 1, 2, 3, 4], + name="int_data", + index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"], + ) + tm.assert_series_equal(result, expected) + + def test_describe_bools(self): + ser = Series([True, True, False, False, False], name="bool_data") + result = ser.describe() + expected = Series( + [5, 2, False, 3], name="bool_data", index=["count", "unique", "top", "freq"] + ) + tm.assert_series_equal(result, expected) + + def test_describe_strs(self): + ser = Series(["a", "a", "b", "c", "d"], name="str_data") + result = ser.describe() + expected = Series( + [5, 4, "a", 2], name="str_data", index=["count", "unique", "top", "freq"] + ) + tm.assert_series_equal(result, expected) + + def test_describe_timedelta64(self): + ser = Series( + [ + Timedelta("1 days"), + Timedelta("2 days"), + Timedelta("3 days"), + Timedelta("4 days"), + Timedelta("5 days"), + ], + name="timedelta_data", + ) + result = ser.describe() + expected = Series( + [5, ser[2], ser.std(), ser[0], ser[1], ser[2], ser[3], ser[4]], + name="timedelta_data", + index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"], + ) + tm.assert_series_equal(result, expected) + + def test_describe_period(self): + ser = Series( + [Period("2020-01", "M"), Period("2020-01", "M"), Period("2019-12", "M")], + name="period_data", + ) + result = ser.describe() + expected = Series( + [3, 2, ser[0], 2], + name="period_data", + index=["count", "unique", "top", "freq"], + ) + tm.assert_series_equal(result, expected) + + def test_describe_empty_object(self): + # https://github.com/pandas-dev/pandas/issues/27183 + s = Series([None, None], dtype=object) + result = s.describe() + expected = Series( + [0, 0, np.nan, np.nan], + dtype=object, + index=["count", "unique", "top", "freq"], + ) + tm.assert_series_equal(result, expected) + + result = s[:0].describe() + tm.assert_series_equal(result, expected) + # ensure NaN, not None + assert np.isnan(result.iloc[2]) + assert np.isnan(result.iloc[3]) + + def test_describe_with_tz(self, tz_naive_fixture): + # GH 21332 + tz = tz_naive_fixture + name = str(tz_naive_fixture) + start = Timestamp(2018, 1, 1) + end = Timestamp(2018, 1, 5) + s = Series(date_range(start, end, tz=tz), name=name) + result = s.describe() + expected = Series( + [ + 5, + Timestamp(2018, 1, 3).tz_localize(tz), + start.tz_localize(tz), + s[1], + s[2], + s[3], + end.tz_localize(tz), + ], + name=name, + index=["count", "mean", "min", "25%", "50%", "75%", "max"], + ) + tm.assert_series_equal(result, expected) + + def test_describe_with_tz_numeric(self): + name = tz = "CET" + start = Timestamp(2018, 1, 1) + end = Timestamp(2018, 1, 5) + s = Series(date_range(start, end, tz=tz), name=name) + + result = s.describe() + + expected = Series( + [ + 5, + Timestamp("2018-01-03 00:00:00", tz=tz), + Timestamp("2018-01-01 00:00:00", tz=tz), + Timestamp("2018-01-02 00:00:00", tz=tz), + Timestamp("2018-01-03 00:00:00", tz=tz), + Timestamp("2018-01-04 00:00:00", tz=tz), + Timestamp("2018-01-05 00:00:00", tz=tz), + ], + name=name, + index=["count", "mean", "min", "25%", "50%", "75%", "max"], + ) + tm.assert_series_equal(result, expected) + + def test_datetime_is_numeric_includes_datetime(self): + s = Series(date_range("2012", periods=3)) + result = s.describe() + expected = Series( + [ + 3, + Timestamp("2012-01-02"), + Timestamp("2012-01-01"), + Timestamp("2012-01-01T12:00:00"), + Timestamp("2012-01-02"), + Timestamp("2012-01-02T12:00:00"), + Timestamp("2012-01-03"), + ], + index=["count", "mean", "min", "25%", "50%", "75%", "max"], + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.filterwarnings("ignore:Casting complex values to real discards") + def test_numeric_result_dtype(self, any_numeric_dtype): + # GH#48340 - describe should always return float on non-complex numeric input + if is_extension_array_dtype(any_numeric_dtype): + dtype = "Float64" + else: + dtype = "complex128" if is_complex_dtype(any_numeric_dtype) else None + + ser = Series([0, 1], dtype=any_numeric_dtype) + if dtype == "complex128" and np_version_gte1p25: + with pytest.raises( + TypeError, match=r"^a must be an array of real numbers$" + ): + ser.describe() + return + result = ser.describe() + expected = Series( + [ + 2.0, + 0.5, + ser.std(), + 0, + 0.25, + 0.5, + 0.75, + 1.0, + ], + index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"], + dtype=dtype, + ) + tm.assert_series_equal(result, expected) + + def test_describe_one_element_ea(self): + # GH#52515 + ser = Series([0.0], dtype="Float64") + with tm.assert_produces_warning(None): + result = ser.describe() + expected = Series( + [1, 0, NA, 0, 0, 0, 0, 0], + dtype="Float64", + index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"], + ) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_diff.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_diff.py new file mode 100644 index 0000000000000000000000000000000000000000..18de81a927c3a7697ee67290c8a6c73336de5643 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_diff.py @@ -0,0 +1,88 @@ +import numpy as np +import pytest + +from pandas import ( + Series, + TimedeltaIndex, + date_range, +) +import pandas._testing as tm + + +class TestSeriesDiff: + def test_diff_np(self): + # TODO(__array_function__): could make np.diff return a Series + # matching ser.diff() + + ser = Series(np.arange(5)) + + res = np.diff(ser) + expected = np.array([1, 1, 1, 1]) + tm.assert_numpy_array_equal(res, expected) + + def test_diff_int(self): + # int dtype + a = 10000000000000000 + b = a + 1 + ser = Series([a, b]) + + result = ser.diff() + assert result[1] == 1 + + def test_diff_tz(self): + # Combined datetime diff, normal diff and boolean diff test + ts = Series( + np.arange(10, dtype=np.float64), + index=date_range("2020-01-01", periods=10), + name="ts", + ) + ts.diff() + + # neg n + result = ts.diff(-1) + expected = ts - ts.shift(-1) + tm.assert_series_equal(result, expected) + + # 0 + result = ts.diff(0) + expected = ts - ts + tm.assert_series_equal(result, expected) + + def test_diff_dt64(self): + # datetime diff (GH#3100) + ser = Series(date_range("20130102", periods=5)) + result = ser.diff() + expected = ser - ser.shift(1) + tm.assert_series_equal(result, expected) + + # timedelta diff + result = result - result.shift(1) # previous result + expected = expected.diff() # previously expected + tm.assert_series_equal(result, expected) + + def test_diff_dt64tz(self): + # with tz + ser = Series( + date_range("2000-01-01 09:00:00", periods=5, tz="US/Eastern"), name="foo" + ) + result = ser.diff() + expected = Series(TimedeltaIndex(["NaT"] + ["1 days"] * 4), name="foo") + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "input,output,diff", + [([False, True, True, False, False], [np.nan, True, False, True, False], 1)], + ) + def test_diff_bool(self, input, output, diff): + # boolean series (test for fixing #17294) + ser = Series(input) + result = ser.diff() + expected = Series(output) + tm.assert_series_equal(result, expected) + + def test_diff_object_dtype(self): + # object series + ser = Series([False, True, 5.0, np.nan, True, False]) + result = ser.diff() + expected = ser - ser.shift(1) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_drop.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_drop.py new file mode 100644 index 0000000000000000000000000000000000000000..5d9a469915cfb718aba9020b82105c66d93b429f --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_drop.py @@ -0,0 +1,99 @@ +import pytest + +from pandas import ( + Index, + Series, +) +import pandas._testing as tm +from pandas.api.types import is_bool_dtype + + +@pytest.mark.parametrize( + "data, index, drop_labels, axis, expected_data, expected_index", + [ + # Unique Index + ([1, 2], ["one", "two"], ["two"], 0, [1], ["one"]), + ([1, 2], ["one", "two"], ["two"], "rows", [1], ["one"]), + ([1, 1, 2], ["one", "two", "one"], ["two"], 0, [1, 2], ["one", "one"]), + # GH 5248 Non-Unique Index + ([1, 1, 2], ["one", "two", "one"], "two", 0, [1, 2], ["one", "one"]), + ([1, 1, 2], ["one", "two", "one"], ["one"], 0, [1], ["two"]), + ([1, 1, 2], ["one", "two", "one"], "one", 0, [1], ["two"]), + ], +) +def test_drop_unique_and_non_unique_index( + data, index, axis, drop_labels, expected_data, expected_index +): + ser = Series(data=data, index=index) + result = ser.drop(drop_labels, axis=axis) + expected = Series(data=expected_data, index=expected_index) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "data, index, drop_labels, axis, error_type, error_desc", + [ + # single string/tuple-like + (range(3), list("abc"), "bc", 0, KeyError, "not found in axis"), + # bad axis + (range(3), list("abc"), ("a",), 0, KeyError, "not found in axis"), + (range(3), list("abc"), "one", "columns", ValueError, "No axis named columns"), + ], +) +def test_drop_exception_raised(data, index, drop_labels, axis, error_type, error_desc): + ser = Series(data, index=index) + with pytest.raises(error_type, match=error_desc): + ser.drop(drop_labels, axis=axis) + + +def test_drop_with_ignore_errors(): + # errors='ignore' + ser = Series(range(3), index=list("abc")) + result = ser.drop("bc", errors="ignore") + tm.assert_series_equal(result, ser) + result = ser.drop(["a", "d"], errors="ignore") + expected = ser.iloc[1:] + tm.assert_series_equal(result, expected) + + # GH 8522 + ser = Series([2, 3], index=[True, False]) + assert is_bool_dtype(ser.index) + assert ser.index.dtype == bool + result = ser.drop(True) + expected = Series([3], index=[False]) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("index", [[1, 2, 3], [1, 1, 3]]) +@pytest.mark.parametrize("drop_labels", [[], [1], [3]]) +def test_drop_empty_list(index, drop_labels): + # GH 21494 + expected_index = [i for i in index if i not in drop_labels] + series = Series(index=index, dtype=object).drop(drop_labels) + expected = Series(index=expected_index, dtype=object) + tm.assert_series_equal(series, expected) + + +@pytest.mark.parametrize( + "data, index, drop_labels", + [ + (None, [1, 2, 3], [1, 4]), + (None, [1, 2, 2], [1, 4]), + ([2, 3], [0, 1], [False, True]), + ], +) +def test_drop_non_empty_list(data, index, drop_labels): + # GH 21494 and GH 16877 + dtype = object if data is None else None + ser = Series(data=data, index=index, dtype=dtype) + with pytest.raises(KeyError, match="not found in axis"): + ser.drop(drop_labels) + + +def test_drop_index_ea_dtype(any_numeric_ea_dtype): + # GH#45860 + df = Series(100, index=Index([1, 2, 2], dtype=any_numeric_ea_dtype)) + idx = Index([df.index[1]]) + result = df.drop(idx) + expected = Series(100, index=Index([1], dtype=any_numeric_ea_dtype)) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_drop_duplicates.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_drop_duplicates.py new file mode 100644 index 0000000000000000000000000000000000000000..10b2e98586365929e4ff05df0d93660d55cf8850 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_drop_duplicates.py @@ -0,0 +1,267 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Categorical, + Series, +) +import pandas._testing as tm + + +@pytest.mark.parametrize( + "keep, expected", + [ + ("first", Series([False, False, False, False, True, True, False])), + ("last", Series([False, True, True, False, False, False, False])), + (False, Series([False, True, True, False, True, True, False])), + ], +) +def test_drop_duplicates(any_numpy_dtype, keep, expected): + tc = Series([1, 0, 3, 5, 3, 0, 4], dtype=np.dtype(any_numpy_dtype)) + + if tc.dtype == "bool": + pytest.skip("tested separately in test_drop_duplicates_bool") + + tm.assert_series_equal(tc.duplicated(keep=keep), expected) + tm.assert_series_equal(tc.drop_duplicates(keep=keep), tc[~expected]) + sc = tc.copy() + return_value = sc.drop_duplicates(keep=keep, inplace=True) + assert return_value is None + tm.assert_series_equal(sc, tc[~expected]) + + +@pytest.mark.parametrize( + "keep, expected", + [ + ("first", Series([False, False, True, True])), + ("last", Series([True, True, False, False])), + (False, Series([True, True, True, True])), + ], +) +def test_drop_duplicates_bool(keep, expected): + tc = Series([True, False, True, False]) + + tm.assert_series_equal(tc.duplicated(keep=keep), expected) + tm.assert_series_equal(tc.drop_duplicates(keep=keep), tc[~expected]) + sc = tc.copy() + return_value = sc.drop_duplicates(keep=keep, inplace=True) + tm.assert_series_equal(sc, tc[~expected]) + assert return_value is None + + +@pytest.mark.parametrize("values", [[], list(range(5))]) +def test_drop_duplicates_no_duplicates(any_numpy_dtype, keep, values): + tc = Series(values, dtype=np.dtype(any_numpy_dtype)) + expected = Series([False] * len(tc), dtype="bool") + + if tc.dtype == "bool": + # 0 -> False and 1-> True + # any other value would be duplicated + tc = tc[:2] + expected = expected[:2] + + tm.assert_series_equal(tc.duplicated(keep=keep), expected) + + result_dropped = tc.drop_duplicates(keep=keep) + tm.assert_series_equal(result_dropped, tc) + + # validate shallow copy + assert result_dropped is not tc + + +class TestSeriesDropDuplicates: + @pytest.fixture( + params=["int_", "uint", "float64", "str_", "timedelta64[h]", "datetime64[D]"] + ) + def dtype(self, request): + return request.param + + @pytest.fixture + def cat_series_unused_category(self, dtype, ordered): + # Test case 1 + cat_array = np.array([1, 2, 3, 4, 5], dtype=np.dtype(dtype)) + + input1 = np.array([1, 2, 3, 3], dtype=np.dtype(dtype)) + cat = Categorical(input1, categories=cat_array, ordered=ordered) + tc1 = Series(cat) + return tc1 + + def test_drop_duplicates_categorical_non_bool(self, cat_series_unused_category): + tc1 = cat_series_unused_category + + expected = Series([False, False, False, True]) + + result = tc1.duplicated() + tm.assert_series_equal(result, expected) + + result = tc1.drop_duplicates() + tm.assert_series_equal(result, tc1[~expected]) + + sc = tc1.copy() + return_value = sc.drop_duplicates(inplace=True) + assert return_value is None + tm.assert_series_equal(sc, tc1[~expected]) + + def test_drop_duplicates_categorical_non_bool_keeplast( + self, cat_series_unused_category + ): + tc1 = cat_series_unused_category + + expected = Series([False, False, True, False]) + + result = tc1.duplicated(keep="last") + tm.assert_series_equal(result, expected) + + result = tc1.drop_duplicates(keep="last") + tm.assert_series_equal(result, tc1[~expected]) + + sc = tc1.copy() + return_value = sc.drop_duplicates(keep="last", inplace=True) + assert return_value is None + tm.assert_series_equal(sc, tc1[~expected]) + + def test_drop_duplicates_categorical_non_bool_keepfalse( + self, cat_series_unused_category + ): + tc1 = cat_series_unused_category + + expected = Series([False, False, True, True]) + + result = tc1.duplicated(keep=False) + tm.assert_series_equal(result, expected) + + result = tc1.drop_duplicates(keep=False) + tm.assert_series_equal(result, tc1[~expected]) + + sc = tc1.copy() + return_value = sc.drop_duplicates(keep=False, inplace=True) + assert return_value is None + tm.assert_series_equal(sc, tc1[~expected]) + + @pytest.fixture + def cat_series(self, dtype, ordered): + # no unused categories, unlike cat_series_unused_category + cat_array = np.array([1, 2, 3, 4, 5], dtype=np.dtype(dtype)) + + input2 = np.array([1, 2, 3, 5, 3, 2, 4], dtype=np.dtype(dtype)) + cat = Categorical(input2, categories=cat_array, ordered=ordered) + tc2 = Series(cat) + return tc2 + + def test_drop_duplicates_categorical_non_bool2(self, cat_series): + tc2 = cat_series + + expected = Series([False, False, False, False, True, True, False]) + + result = tc2.duplicated() + tm.assert_series_equal(result, expected) + + result = tc2.drop_duplicates() + tm.assert_series_equal(result, tc2[~expected]) + + sc = tc2.copy() + return_value = sc.drop_duplicates(inplace=True) + assert return_value is None + tm.assert_series_equal(sc, tc2[~expected]) + + def test_drop_duplicates_categorical_non_bool2_keeplast(self, cat_series): + tc2 = cat_series + + expected = Series([False, True, True, False, False, False, False]) + + result = tc2.duplicated(keep="last") + tm.assert_series_equal(result, expected) + + result = tc2.drop_duplicates(keep="last") + tm.assert_series_equal(result, tc2[~expected]) + + sc = tc2.copy() + return_value = sc.drop_duplicates(keep="last", inplace=True) + assert return_value is None + tm.assert_series_equal(sc, tc2[~expected]) + + def test_drop_duplicates_categorical_non_bool2_keepfalse(self, cat_series): + tc2 = cat_series + + expected = Series([False, True, True, False, True, True, False]) + + result = tc2.duplicated(keep=False) + tm.assert_series_equal(result, expected) + + result = tc2.drop_duplicates(keep=False) + tm.assert_series_equal(result, tc2[~expected]) + + sc = tc2.copy() + return_value = sc.drop_duplicates(keep=False, inplace=True) + assert return_value is None + tm.assert_series_equal(sc, tc2[~expected]) + + def test_drop_duplicates_categorical_bool(self, ordered): + tc = Series( + Categorical( + [True, False, True, False], categories=[True, False], ordered=ordered + ) + ) + + expected = Series([False, False, True, True]) + tm.assert_series_equal(tc.duplicated(), expected) + tm.assert_series_equal(tc.drop_duplicates(), tc[~expected]) + sc = tc.copy() + return_value = sc.drop_duplicates(inplace=True) + assert return_value is None + tm.assert_series_equal(sc, tc[~expected]) + + expected = Series([True, True, False, False]) + tm.assert_series_equal(tc.duplicated(keep="last"), expected) + tm.assert_series_equal(tc.drop_duplicates(keep="last"), tc[~expected]) + sc = tc.copy() + return_value = sc.drop_duplicates(keep="last", inplace=True) + assert return_value is None + tm.assert_series_equal(sc, tc[~expected]) + + expected = Series([True, True, True, True]) + tm.assert_series_equal(tc.duplicated(keep=False), expected) + tm.assert_series_equal(tc.drop_duplicates(keep=False), tc[~expected]) + sc = tc.copy() + return_value = sc.drop_duplicates(keep=False, inplace=True) + assert return_value is None + tm.assert_series_equal(sc, tc[~expected]) + + def test_drop_duplicates_categorical_bool_na(self, nulls_fixture): + # GH#44351 + ser = Series( + Categorical( + [True, False, True, False, nulls_fixture], + categories=[True, False], + ordered=True, + ) + ) + result = ser.drop_duplicates() + expected = Series( + Categorical([True, False, np.nan], categories=[True, False], ordered=True), + index=[0, 1, 4], + ) + tm.assert_series_equal(result, expected) + + def test_drop_duplicates_ignore_index(self): + # GH#48304 + ser = Series([1, 2, 2, 3]) + result = ser.drop_duplicates(ignore_index=True) + expected = Series([1, 2, 3]) + tm.assert_series_equal(result, expected) + + def test_duplicated_arrow_dtype(self): + pytest.importorskip("pyarrow") + ser = Series([True, False, None, False], dtype="bool[pyarrow]") + result = ser.drop_duplicates() + expected = Series([True, False, None], dtype="bool[pyarrow]") + tm.assert_series_equal(result, expected) + + def test_drop_duplicates_arrow_strings(self): + # GH#54904 + pa = pytest.importorskip("pyarrow") + ser = Series(["a", "a"], dtype=pd.ArrowDtype(pa.string())) + result = ser.drop_duplicates() + expecetd = Series(["a"], dtype=pd.ArrowDtype(pa.string())) + tm.assert_series_equal(result, expecetd) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_dropna.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_dropna.py new file mode 100644 index 0000000000000000000000000000000000000000..d03fcac24003e99b724a2e7ac43b66d3d9b51bcf --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_dropna.py @@ -0,0 +1,117 @@ +import numpy as np +import pytest + +from pandas import ( + DatetimeIndex, + IntervalIndex, + NaT, + Period, + Series, + Timestamp, +) +import pandas._testing as tm + + +class TestDropna: + def test_dropna_empty(self): + ser = Series([], dtype=object) + + assert len(ser.dropna()) == 0 + return_value = ser.dropna(inplace=True) + assert return_value is None + assert len(ser) == 0 + + # invalid axis + msg = "No axis named 1 for object type Series" + with pytest.raises(ValueError, match=msg): + ser.dropna(axis=1) + + def test_dropna_preserve_name(self, datetime_series): + datetime_series[:5] = np.nan + result = datetime_series.dropna() + assert result.name == datetime_series.name + name = datetime_series.name + ts = datetime_series.copy() + return_value = ts.dropna(inplace=True) + assert return_value is None + assert ts.name == name + + def test_dropna_no_nan(self): + for ser in [ + Series([1, 2, 3], name="x"), + Series([False, True, False], name="x"), + ]: + result = ser.dropna() + tm.assert_series_equal(result, ser) + assert result is not ser + + s2 = ser.copy() + return_value = s2.dropna(inplace=True) + assert return_value is None + tm.assert_series_equal(s2, ser) + + def test_dropna_intervals(self): + ser = Series( + [np.nan, 1, 2, 3], + IntervalIndex.from_arrays([np.nan, 0, 1, 2], [np.nan, 1, 2, 3]), + ) + + result = ser.dropna() + expected = ser.iloc[1:] + tm.assert_series_equal(result, expected) + + def test_dropna_period_dtype(self): + # GH#13737 + ser = Series([Period("2011-01", freq="M"), Period("NaT", freq="M")]) + result = ser.dropna() + expected = Series([Period("2011-01", freq="M")]) + + tm.assert_series_equal(result, expected) + + def test_datetime64_tz_dropna(self, unit): + # DatetimeLikeBlock + ser = Series( + [ + Timestamp("2011-01-01 10:00"), + NaT, + Timestamp("2011-01-03 10:00"), + NaT, + ], + dtype=f"M8[{unit}]", + ) + result = ser.dropna() + expected = Series( + [Timestamp("2011-01-01 10:00"), Timestamp("2011-01-03 10:00")], + index=[0, 2], + dtype=f"M8[{unit}]", + ) + tm.assert_series_equal(result, expected) + + # DatetimeTZBlock + idx = DatetimeIndex( + ["2011-01-01 10:00", NaT, "2011-01-03 10:00", NaT], tz="Asia/Tokyo" + ).as_unit(unit) + ser = Series(idx) + assert ser.dtype == f"datetime64[{unit}, Asia/Tokyo]" + result = ser.dropna() + expected = Series( + [ + Timestamp("2011-01-01 10:00", tz="Asia/Tokyo"), + Timestamp("2011-01-03 10:00", tz="Asia/Tokyo"), + ], + index=[0, 2], + dtype=f"datetime64[{unit}, Asia/Tokyo]", + ) + assert result.dtype == f"datetime64[{unit}, Asia/Tokyo]" + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("val", [1, 1.5]) + def test_dropna_ignore_index(self, val): + # GH#31725 + ser = Series([1, 2, val], index=[3, 2, 1]) + result = ser.dropna(ignore_index=True) + expected = Series([1, 2, val]) + tm.assert_series_equal(result, expected) + + ser.dropna(ignore_index=True, inplace=True) + tm.assert_series_equal(ser, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_dtypes.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_dtypes.py new file mode 100644 index 0000000000000000000000000000000000000000..82260bc2a65b9f7e387532b380b9ce013d29cbb7 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_dtypes.py @@ -0,0 +1,7 @@ +import numpy as np + + +class TestSeriesDtypes: + def test_dtype(self, datetime_series): + assert datetime_series.dtype == np.dtype("float64") + assert datetime_series.dtypes == np.dtype("float64") diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_duplicated.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_duplicated.py new file mode 100644 index 0000000000000000000000000000000000000000..e177b5275d855fffbede91280d9ee7fb61ece2cd --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_duplicated.py @@ -0,0 +1,77 @@ +import numpy as np +import pytest + +from pandas import ( + NA, + Categorical, + Series, +) +import pandas._testing as tm + + +@pytest.mark.parametrize( + "keep, expected", + [ + ("first", Series([False, False, True, False, True], name="name")), + ("last", Series([True, True, False, False, False], name="name")), + (False, Series([True, True, True, False, True], name="name")), + ], +) +def test_duplicated_keep(keep, expected): + ser = Series(["a", "b", "b", "c", "a"], name="name") + + result = ser.duplicated(keep=keep) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "keep, expected", + [ + ("first", Series([False, False, True, False, True])), + ("last", Series([True, True, False, False, False])), + (False, Series([True, True, True, False, True])), + ], +) +def test_duplicated_nan_none(keep, expected): + ser = Series([np.nan, 3, 3, None, np.nan], dtype=object) + + result = ser.duplicated(keep=keep) + tm.assert_series_equal(result, expected) + + +def test_duplicated_categorical_bool_na(nulls_fixture): + # GH#44351 + ser = Series( + Categorical( + [True, False, True, False, nulls_fixture], + categories=[True, False], + ordered=True, + ) + ) + result = ser.duplicated() + expected = Series([False, False, True, True, False]) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "keep, vals", + [ + ("last", [True, True, False]), + ("first", [False, True, True]), + (False, [True, True, True]), + ], +) +def test_duplicated_mask(keep, vals): + # GH#48150 + ser = Series([1, 2, NA, NA, NA], dtype="Int64") + result = ser.duplicated(keep=keep) + expected = Series([False, False] + vals) + tm.assert_series_equal(result, expected) + + +def test_duplicated_mask_no_duplicated_na(keep): + # GH#48150 + ser = Series([1, 2, NA], dtype="Int64") + result = ser.duplicated(keep=keep) + expected = Series([False, False, False]) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_equals.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_equals.py new file mode 100644 index 0000000000000000000000000000000000000000..875ffdd3fe8514edb4c313c8f558330c787cef08 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_equals.py @@ -0,0 +1,145 @@ +from contextlib import nullcontext +import copy + +import numpy as np +import pytest + +from pandas._libs.missing import is_matching_na +from pandas.compat.numpy import np_version_gte1p25 + +from pandas.core.dtypes.common import is_float + +from pandas import ( + Index, + MultiIndex, + Series, +) +import pandas._testing as tm + + +@pytest.mark.parametrize( + "arr, idx", + [ + ([1, 2, 3, 4], [0, 2, 1, 3]), + ([1, np.nan, 3, np.nan], [0, 2, 1, 3]), + ( + [1, np.nan, 3, np.nan], + MultiIndex.from_tuples([(0, "a"), (1, "b"), (2, "c"), (3, "c")]), + ), + ], +) +def test_equals(arr, idx): + s1 = Series(arr, index=idx) + s2 = s1.copy() + assert s1.equals(s2) + + s1[1] = 9 + assert not s1.equals(s2) + + +@pytest.mark.parametrize( + "val", [1, 1.1, 1 + 1j, True, "abc", [1, 2], (1, 2), {1, 2}, {"a": 1}, None] +) +def test_equals_list_array(val): + # GH20676 Verify equals operator for list of Numpy arrays + arr = np.array([1, 2]) + s1 = Series([arr, arr]) + s2 = s1.copy() + assert s1.equals(s2) + + s1[1] = val + + cm = ( + tm.assert_produces_warning(FutureWarning, check_stacklevel=False) + if isinstance(val, str) and not np_version_gte1p25 + else nullcontext() + ) + with cm: + assert not s1.equals(s2) + + +def test_equals_false_negative(): + # GH8437 Verify false negative behavior of equals function for dtype object + arr = [False, np.nan] + s1 = Series(arr) + s2 = s1.copy() + s3 = Series(index=range(2), dtype=object) + s4 = s3.copy() + s5 = s3.copy() + s6 = s3.copy() + + s3[:-1] = s4[:-1] = s5[0] = s6[0] = False + assert s1.equals(s1) + assert s1.equals(s2) + assert s1.equals(s3) + assert s1.equals(s4) + assert s1.equals(s5) + assert s5.equals(s6) + + +def test_equals_matching_nas(): + # matching but not identical NAs + left = Series([np.datetime64("NaT")], dtype=object) + right = Series([np.datetime64("NaT")], dtype=object) + assert left.equals(right) + with tm.assert_produces_warning(FutureWarning, match="Dtype inference"): + assert Index(left).equals(Index(right)) + assert left.array.equals(right.array) + + left = Series([np.timedelta64("NaT")], dtype=object) + right = Series([np.timedelta64("NaT")], dtype=object) + assert left.equals(right) + with tm.assert_produces_warning(FutureWarning, match="Dtype inference"): + assert Index(left).equals(Index(right)) + assert left.array.equals(right.array) + + left = Series([np.float64("NaN")], dtype=object) + right = Series([np.float64("NaN")], dtype=object) + assert left.equals(right) + assert Index(left, dtype=left.dtype).equals(Index(right, dtype=right.dtype)) + assert left.array.equals(right.array) + + +def test_equals_mismatched_nas(nulls_fixture, nulls_fixture2): + # GH#39650 + left = nulls_fixture + right = nulls_fixture2 + if hasattr(right, "copy"): + right = right.copy() + else: + right = copy.copy(right) + + ser = Series([left], dtype=object) + ser2 = Series([right], dtype=object) + + if is_matching_na(left, right): + assert ser.equals(ser2) + elif (left is None and is_float(right)) or (right is None and is_float(left)): + assert ser.equals(ser2) + else: + assert not ser.equals(ser2) + + +def test_equals_none_vs_nan(): + # GH#39650 + ser = Series([1, None], dtype=object) + ser2 = Series([1, np.nan], dtype=object) + + assert ser.equals(ser2) + assert Index(ser, dtype=ser.dtype).equals(Index(ser2, dtype=ser2.dtype)) + assert ser.array.equals(ser2.array) + + +def test_equals_None_vs_float(): + # GH#44190 + left = Series([-np.inf, np.nan, -1.0, 0.0, 1.0, 10 / 3, np.inf], dtype=object) + right = Series([None] * len(left)) + + # these series were found to be equal due to a bug, check that they are correctly + # found to not equal + assert not left.equals(right) + assert not right.equals(left) + assert not left.to_frame().equals(right.to_frame()) + assert not right.to_frame().equals(left.to_frame()) + assert not Index(left, dtype="object").equals(Index(right, dtype="object")) + assert not Index(right, dtype="object").equals(Index(left, dtype="object")) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_explode.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_explode.py new file mode 100644 index 0000000000000000000000000000000000000000..f14f31384facb644e8814fe0cb34021452ac24be --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_explode.py @@ -0,0 +1,183 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm + + +def test_basic(): + s = pd.Series([[0, 1, 2], np.nan, [], (3, 4)], index=list("abcd"), name="foo") + result = s.explode() + expected = pd.Series( + [0, 1, 2, np.nan, np.nan, 3, 4], index=list("aaabcdd"), dtype=object, name="foo" + ) + tm.assert_series_equal(result, expected) + + +def test_mixed_type(): + s = pd.Series( + [[0, 1, 2], np.nan, None, np.array([]), pd.Series(["a", "b"])], name="foo" + ) + result = s.explode() + expected = pd.Series( + [0, 1, 2, np.nan, None, np.nan, "a", "b"], + index=[0, 0, 0, 1, 2, 3, 4, 4], + dtype=object, + name="foo", + ) + tm.assert_series_equal(result, expected) + + +def test_empty(): + s = pd.Series(dtype=object) + result = s.explode() + expected = s.copy() + tm.assert_series_equal(result, expected) + + +def test_nested_lists(): + s = pd.Series([[[1, 2, 3]], [1, 2], 1]) + result = s.explode() + expected = pd.Series([[1, 2, 3], 1, 2, 1], index=[0, 1, 1, 2]) + tm.assert_series_equal(result, expected) + + +def test_multi_index(): + s = pd.Series( + [[0, 1, 2], np.nan, [], (3, 4)], + name="foo", + index=pd.MultiIndex.from_product([list("ab"), range(2)], names=["foo", "bar"]), + ) + result = s.explode() + index = pd.MultiIndex.from_tuples( + [("a", 0), ("a", 0), ("a", 0), ("a", 1), ("b", 0), ("b", 1), ("b", 1)], + names=["foo", "bar"], + ) + expected = pd.Series( + [0, 1, 2, np.nan, np.nan, 3, 4], index=index, dtype=object, name="foo" + ) + tm.assert_series_equal(result, expected) + + +def test_large(): + s = pd.Series([range(256)]).explode() + result = s.explode() + tm.assert_series_equal(result, s) + + +def test_invert_array(): + df = pd.DataFrame({"a": pd.date_range("20190101", periods=3, tz="UTC")}) + + listify = df.apply(lambda x: x.array, axis=1) + result = listify.explode() + tm.assert_series_equal(result, df["a"].rename()) + + +@pytest.mark.parametrize( + "s", [pd.Series([1, 2, 3]), pd.Series(pd.date_range("2019", periods=3, tz="UTC"))] +) +def test_non_object_dtype(s): + result = s.explode() + tm.assert_series_equal(result, s) + + +def test_typical_usecase(): + df = pd.DataFrame( + [{"var1": "a,b,c", "var2": 1}, {"var1": "d,e,f", "var2": 2}], + columns=["var1", "var2"], + ) + exploded = df.var1.str.split(",").explode() + result = df[["var2"]].join(exploded) + expected = pd.DataFrame( + {"var2": [1, 1, 1, 2, 2, 2], "var1": list("abcdef")}, + columns=["var2", "var1"], + index=[0, 0, 0, 1, 1, 1], + ) + tm.assert_frame_equal(result, expected) + + +def test_nested_EA(): + # a nested EA array + s = pd.Series( + [ + pd.date_range("20170101", periods=3, tz="UTC"), + pd.date_range("20170104", periods=3, tz="UTC"), + ] + ) + result = s.explode() + expected = pd.Series( + pd.date_range("20170101", periods=6, tz="UTC"), index=[0, 0, 0, 1, 1, 1] + ) + tm.assert_series_equal(result, expected) + + +def test_duplicate_index(): + # GH 28005 + s = pd.Series([[1, 2], [3, 4]], index=[0, 0]) + result = s.explode() + expected = pd.Series([1, 2, 3, 4], index=[0, 0, 0, 0], dtype=object) + tm.assert_series_equal(result, expected) + + +def test_ignore_index(): + # GH 34932 + s = pd.Series([[1, 2], [3, 4]]) + result = s.explode(ignore_index=True) + expected = pd.Series([1, 2, 3, 4], index=[0, 1, 2, 3], dtype=object) + tm.assert_series_equal(result, expected) + + +def test_explode_sets(): + # https://github.com/pandas-dev/pandas/issues/35614 + s = pd.Series([{"a", "b", "c"}], index=[1]) + result = s.explode().sort_values() + expected = pd.Series(["a", "b", "c"], index=[1, 1, 1]) + tm.assert_series_equal(result, expected) + + +def test_explode_scalars_can_ignore_index(): + # https://github.com/pandas-dev/pandas/issues/40487 + s = pd.Series([1, 2, 3], index=["a", "b", "c"]) + result = s.explode(ignore_index=True) + expected = pd.Series([1, 2, 3]) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("ignore_index", [True, False]) +def test_explode_pyarrow_list_type(ignore_index): + # GH 53602 + pa = pytest.importorskip("pyarrow") + + data = [ + [None, None], + [1], + [], + [2, 3], + None, + ] + ser = pd.Series(data, dtype=pd.ArrowDtype(pa.list_(pa.int64()))) + result = ser.explode(ignore_index=ignore_index) + expected = pd.Series( + data=[None, None, 1, None, 2, 3, None], + index=None if ignore_index else [0, 0, 1, 2, 3, 3, 4], + dtype=pd.ArrowDtype(pa.int64()), + ) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("ignore_index", [True, False]) +def test_explode_pyarrow_non_list_type(ignore_index): + pa = pytest.importorskip("pyarrow") + data = [1, 2, 3] + ser = pd.Series(data, dtype=pd.ArrowDtype(pa.int64())) + result = ser.explode(ignore_index=ignore_index) + expected = pd.Series([1, 2, 3], dtype="int64[pyarrow]", index=[0, 1, 2]) + tm.assert_series_equal(result, expected) + + +def test_str_dtype(): + # https://github.com/pandas-dev/pandas/pull/61623 + ser = pd.Series(["x", "y"], dtype="str") + result = ser.explode() + assert result is not ser + tm.assert_series_equal(result, ser) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_fillna.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_fillna.py new file mode 100644 index 0000000000000000000000000000000000000000..293259661cd9a107eb4ad8e33c0b73dfb4010a14 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_fillna.py @@ -0,0 +1,1155 @@ +from datetime import ( + datetime, + timedelta, + timezone, +) + +import numpy as np +import pytest +import pytz + +from pandas import ( + Categorical, + DataFrame, + DatetimeIndex, + NaT, + Period, + Series, + Timedelta, + Timestamp, + date_range, + isna, + timedelta_range, +) +import pandas._testing as tm +from pandas.core.arrays import period_array + + +@pytest.mark.filterwarnings( + "ignore:(Series|DataFrame).fillna with 'method' is deprecated:FutureWarning" +) +class TestSeriesFillNA: + def test_fillna_nat(self): + series = Series([0, 1, 2, NaT._value], dtype="M8[ns]") + + filled = series.fillna(method="pad") + filled2 = series.fillna(value=series.values[2]) + + expected = series.copy() + expected.iloc[3] = expected.iloc[2] + + tm.assert_series_equal(filled, expected) + tm.assert_series_equal(filled2, expected) + + df = DataFrame({"A": series}) + filled = df.fillna(method="pad") + filled2 = df.fillna(value=series.values[2]) + expected = DataFrame({"A": expected}) + tm.assert_frame_equal(filled, expected) + tm.assert_frame_equal(filled2, expected) + + series = Series([NaT._value, 0, 1, 2], dtype="M8[ns]") + + filled = series.fillna(method="bfill") + filled2 = series.fillna(value=series[1]) + + expected = series.copy() + expected[0] = expected[1] + + tm.assert_series_equal(filled, expected) + tm.assert_series_equal(filled2, expected) + + df = DataFrame({"A": series}) + filled = df.fillna(method="bfill") + filled2 = df.fillna(value=series[1]) + expected = DataFrame({"A": expected}) + tm.assert_frame_equal(filled, expected) + tm.assert_frame_equal(filled2, expected) + + def test_fillna_value_or_method(self, datetime_series): + msg = "Cannot specify both 'value' and 'method'" + with pytest.raises(ValueError, match=msg): + datetime_series.fillna(value=0, method="ffill") + + def test_fillna(self): + ts = Series( + [0.0, 1.0, 2.0, 3.0, 4.0], index=date_range("2020-01-01", periods=5) + ) + + tm.assert_series_equal(ts, ts.fillna(method="ffill")) + + ts.iloc[2] = np.nan + + exp = Series([0.0, 1.0, 1.0, 3.0, 4.0], index=ts.index) + tm.assert_series_equal(ts.fillna(method="ffill"), exp) + + exp = Series([0.0, 1.0, 3.0, 3.0, 4.0], index=ts.index) + tm.assert_series_equal(ts.fillna(method="backfill"), exp) + + exp = Series([0.0, 1.0, 5.0, 3.0, 4.0], index=ts.index) + tm.assert_series_equal(ts.fillna(value=5), exp) + + msg = "Must specify a fill 'value' or 'method'" + with pytest.raises(ValueError, match=msg): + ts.fillna() + + def test_fillna_nonscalar(self): + # GH#5703 + s1 = Series([np.nan]) + s2 = Series([1]) + result = s1.fillna(s2) + expected = Series([1.0]) + tm.assert_series_equal(result, expected) + result = s1.fillna({}) + tm.assert_series_equal(result, s1) + result = s1.fillna(Series((), dtype=object)) + tm.assert_series_equal(result, s1) + result = s2.fillna(s1) + tm.assert_series_equal(result, s2) + result = s1.fillna({0: 1}) + tm.assert_series_equal(result, expected) + result = s1.fillna({1: 1}) + tm.assert_series_equal(result, Series([np.nan])) + result = s1.fillna({0: 1, 1: 1}) + tm.assert_series_equal(result, expected) + result = s1.fillna(Series({0: 1, 1: 1})) + tm.assert_series_equal(result, expected) + result = s1.fillna(Series({0: 1, 1: 1}, index=[4, 5])) + tm.assert_series_equal(result, s1) + + def test_fillna_aligns(self): + s1 = Series([0, 1, 2], list("abc")) + s2 = Series([0, np.nan, 2], list("bac")) + result = s2.fillna(s1) + expected = Series([0, 0, 2.0], list("bac")) + tm.assert_series_equal(result, expected) + + def test_fillna_limit(self): + ser = Series(np.nan, index=[0, 1, 2]) + result = ser.fillna(999, limit=1) + expected = Series([999, np.nan, np.nan], index=[0, 1, 2]) + tm.assert_series_equal(result, expected) + + result = ser.fillna(999, limit=2) + expected = Series([999, 999, np.nan], index=[0, 1, 2]) + tm.assert_series_equal(result, expected) + + def test_fillna_dont_cast_strings(self): + # GH#9043 + # make sure a string representation of int/float values can be filled + # correctly without raising errors or being converted + vals = ["0", "1.5", "-0.3"] + for val in vals: + ser = Series([0, 1, np.nan, np.nan, 4], dtype="float64") + result = ser.fillna(val) + expected = Series([0, 1, val, val, 4], dtype="object") + tm.assert_series_equal(result, expected) + + def test_fillna_consistency(self): + # GH#16402 + # fillna with a tz aware to a tz-naive, should result in object + + ser = Series([Timestamp("20130101"), NaT]) + + result = ser.fillna(Timestamp("20130101", tz="US/Eastern")) + expected = Series( + [Timestamp("20130101"), Timestamp("2013-01-01", tz="US/Eastern")], + dtype="object", + ) + tm.assert_series_equal(result, expected) + + result = ser.where([True, False], Timestamp("20130101", tz="US/Eastern")) + tm.assert_series_equal(result, expected) + + result = ser.where([True, False], Timestamp("20130101", tz="US/Eastern")) + tm.assert_series_equal(result, expected) + + # with a non-datetime + result = ser.fillna("foo") + expected = Series([Timestamp("20130101"), "foo"]) + tm.assert_series_equal(result, expected) + + # assignment + ser2 = ser.copy() + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + ser2[1] = "foo" + tm.assert_series_equal(ser2, expected) + + def test_fillna_downcast(self): + # GH#15277 + # infer int64 from float64 + ser = Series([1.0, np.nan]) + msg = "The 'downcast' keyword in fillna is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = ser.fillna(0, downcast="infer") + expected = Series([1, 0]) + tm.assert_series_equal(result, expected) + + # infer int64 from float64 when fillna value is a dict + ser = Series([1.0, np.nan]) + with tm.assert_produces_warning(FutureWarning, match=msg): + result = ser.fillna({1: 0}, downcast="infer") + expected = Series([1, 0]) + tm.assert_series_equal(result, expected) + + def test_fillna_downcast_infer_objects_to_numeric(self): + # GH#44241 if we have object-dtype, 'downcast="infer"' should + # _actually_ infer + + arr = np.arange(5).astype(object) + arr[3] = np.nan + + ser = Series(arr) + + msg = "The 'downcast' keyword in fillna is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + res = ser.fillna(3, downcast="infer") + expected = Series(np.arange(5), dtype=np.int64) + tm.assert_series_equal(res, expected) + + msg = "The 'downcast' keyword in ffill is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + res = ser.ffill(downcast="infer") + expected = Series([0, 1, 2, 2, 4], dtype=np.int64) + tm.assert_series_equal(res, expected) + + msg = "The 'downcast' keyword in bfill is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + res = ser.bfill(downcast="infer") + expected = Series([0, 1, 2, 4, 4], dtype=np.int64) + tm.assert_series_equal(res, expected) + + # with a non-round float present, we will downcast to float64 + ser[2] = 2.5 + + expected = Series([0, 1, 2.5, 3, 4], dtype=np.float64) + msg = "The 'downcast' keyword in fillna is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + res = ser.fillna(3, downcast="infer") + tm.assert_series_equal(res, expected) + + msg = "The 'downcast' keyword in ffill is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + res = ser.ffill(downcast="infer") + expected = Series([0, 1, 2.5, 2.5, 4], dtype=np.float64) + tm.assert_series_equal(res, expected) + + msg = "The 'downcast' keyword in bfill is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + res = ser.bfill(downcast="infer") + expected = Series([0, 1, 2.5, 4, 4], dtype=np.float64) + tm.assert_series_equal(res, expected) + + def test_timedelta_fillna(self, frame_or_series, unit): + # GH#3371 + ser = Series( + [ + Timestamp("20130101"), + Timestamp("20130101"), + Timestamp("20130102"), + Timestamp("20130103 9:01:01"), + ], + dtype=f"M8[{unit}]", + ) + td = ser.diff() + obj = frame_or_series(td).copy() + + # reg fillna + result = obj.fillna(Timedelta(seconds=0)) + expected = Series( + [ + timedelta(0), + timedelta(0), + timedelta(1), + timedelta(days=1, seconds=9 * 3600 + 60 + 1), + ], + dtype=f"m8[{unit}]", + ) + expected = frame_or_series(expected) + tm.assert_equal(result, expected) + + # GH#45746 pre-1.? ints were interpreted as seconds. then that was + # deprecated and changed to raise. In 2.0 it casts to common dtype, + # consistent with every other dtype's behavior + res = obj.fillna(1) + expected = obj.astype(object).fillna(1) + tm.assert_equal(res, expected) + + result = obj.fillna(Timedelta(seconds=1)) + expected = Series( + [ + timedelta(seconds=1), + timedelta(0), + timedelta(1), + timedelta(days=1, seconds=9 * 3600 + 60 + 1), + ], + dtype=f"m8[{unit}]", + ) + expected = frame_or_series(expected) + tm.assert_equal(result, expected) + + result = obj.fillna(timedelta(days=1, seconds=1)) + expected = Series( + [ + timedelta(days=1, seconds=1), + timedelta(0), + timedelta(1), + timedelta(days=1, seconds=9 * 3600 + 60 + 1), + ], + dtype=f"m8[{unit}]", + ) + expected = frame_or_series(expected) + tm.assert_equal(result, expected) + + result = obj.fillna(np.timedelta64(10**9)) + expected = Series( + [ + timedelta(seconds=1), + timedelta(0), + timedelta(1), + timedelta(days=1, seconds=9 * 3600 + 60 + 1), + ], + dtype=f"m8[{unit}]", + ) + expected = frame_or_series(expected) + tm.assert_equal(result, expected) + + result = obj.fillna(NaT) + expected = Series( + [ + NaT, + timedelta(0), + timedelta(1), + timedelta(days=1, seconds=9 * 3600 + 60 + 1), + ], + dtype=f"m8[{unit}]", + ) + expected = frame_or_series(expected) + tm.assert_equal(result, expected) + + # ffill + td[2] = np.nan + obj = frame_or_series(td).copy() + result = obj.ffill() + expected = td.fillna(Timedelta(seconds=0)) + expected[0] = np.nan + expected = frame_or_series(expected) + + tm.assert_equal(result, expected) + + # bfill + td[2] = np.nan + obj = frame_or_series(td) + result = obj.bfill() + expected = td.fillna(Timedelta(seconds=0)) + expected[2] = timedelta(days=1, seconds=9 * 3600 + 60 + 1) + expected = frame_or_series(expected) + tm.assert_equal(result, expected) + + def test_datetime64_fillna(self): + ser = Series( + [ + Timestamp("20130101"), + Timestamp("20130101"), + Timestamp("20130102"), + Timestamp("20130103 9:01:01"), + ] + ) + ser[2] = np.nan + + # ffill + result = ser.ffill() + expected = Series( + [ + Timestamp("20130101"), + Timestamp("20130101"), + Timestamp("20130101"), + Timestamp("20130103 9:01:01"), + ] + ) + tm.assert_series_equal(result, expected) + + # bfill + result = ser.bfill() + expected = Series( + [ + Timestamp("20130101"), + Timestamp("20130101"), + Timestamp("20130103 9:01:01"), + Timestamp("20130103 9:01:01"), + ] + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "scalar", + [ + False, + pytest.param( + True, + marks=pytest.mark.xfail( + reason="GH#56410 scalar case not yet addressed" + ), + ), + ], + ) + @pytest.mark.parametrize("tz", [None, "UTC"]) + def test_datetime64_fillna_mismatched_reso_no_rounding(self, tz, scalar): + # GH#56410 + dti = date_range("2016-01-01", periods=3, unit="s", tz=tz) + item = Timestamp("2016-02-03 04:05:06.789", tz=tz) + vec = date_range(item, periods=3, unit="ms") + + exp_dtype = "M8[ms]" if tz is None else "M8[ms, UTC]" + expected = Series([item, dti[1], dti[2]], dtype=exp_dtype) + + ser = Series(dti) + ser[0] = NaT + ser2 = ser.copy() + + res = ser.fillna(item) + res2 = ser2.fillna(Series(vec)) + + if scalar: + tm.assert_series_equal(res, expected) + else: + tm.assert_series_equal(res2, expected) + + @pytest.mark.parametrize( + "scalar", + [ + False, + pytest.param( + True, + marks=pytest.mark.xfail( + reason="GH#56410 scalar case not yet addressed" + ), + ), + ], + ) + def test_timedelta64_fillna_mismatched_reso_no_rounding(self, scalar): + # GH#56410 + tdi = date_range("2016-01-01", periods=3, unit="s") - Timestamp("1970-01-01") + item = Timestamp("2016-02-03 04:05:06.789") - Timestamp("1970-01-01") + vec = timedelta_range(item, periods=3, unit="ms") + + expected = Series([item, tdi[1], tdi[2]], dtype="m8[ms]") + + ser = Series(tdi) + ser[0] = NaT + ser2 = ser.copy() + + res = ser.fillna(item) + res2 = ser2.fillna(Series(vec)) + + if scalar: + tm.assert_series_equal(res, expected) + else: + tm.assert_series_equal(res2, expected) + + def test_datetime64_fillna_backfill(self): + # GH#6587 + # make sure that we are treating as integer when filling + ser = Series([NaT, NaT, "2013-08-05 15:30:00.000001"], dtype="M8[ns]") + + expected = Series( + [ + "2013-08-05 15:30:00.000001", + "2013-08-05 15:30:00.000001", + "2013-08-05 15:30:00.000001", + ], + dtype="M8[ns]", + ) + result = ser.fillna(method="backfill") + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("tz", ["US/Eastern", "Asia/Tokyo"]) + def test_datetime64_tz_fillna(self, tz, unit): + # DatetimeLikeBlock + ser = Series( + [ + Timestamp("2011-01-01 10:00"), + NaT, + Timestamp("2011-01-03 10:00"), + NaT, + ], + dtype=f"M8[{unit}]", + ) + null_loc = Series([False, True, False, True]) + + result = ser.fillna(Timestamp("2011-01-02 10:00")) + expected = Series( + [ + Timestamp("2011-01-01 10:00"), + Timestamp("2011-01-02 10:00"), + Timestamp("2011-01-03 10:00"), + Timestamp("2011-01-02 10:00"), + ], + dtype=f"M8[{unit}]", + ) + tm.assert_series_equal(expected, result) + # check s is not changed + tm.assert_series_equal(isna(ser), null_loc) + + result = ser.fillna(Timestamp("2011-01-02 10:00", tz=tz)) + expected = Series( + [ + Timestamp("2011-01-01 10:00"), + Timestamp("2011-01-02 10:00", tz=tz), + Timestamp("2011-01-03 10:00"), + Timestamp("2011-01-02 10:00", tz=tz), + ] + ) + tm.assert_series_equal(expected, result) + tm.assert_series_equal(isna(ser), null_loc) + + result = ser.fillna("AAA") + expected = Series( + [ + Timestamp("2011-01-01 10:00"), + "AAA", + Timestamp("2011-01-03 10:00"), + "AAA", + ], + dtype=object, + ) + tm.assert_series_equal(expected, result) + tm.assert_series_equal(isna(ser), null_loc) + + result = ser.fillna( + { + 1: Timestamp("2011-01-02 10:00", tz=tz), + 3: Timestamp("2011-01-04 10:00"), + } + ) + expected = Series( + [ + Timestamp("2011-01-01 10:00"), + Timestamp("2011-01-02 10:00", tz=tz), + Timestamp("2011-01-03 10:00"), + Timestamp("2011-01-04 10:00"), + ] + ) + tm.assert_series_equal(expected, result) + tm.assert_series_equal(isna(ser), null_loc) + + result = ser.fillna( + {1: Timestamp("2011-01-02 10:00"), 3: Timestamp("2011-01-04 10:00")} + ) + expected = Series( + [ + Timestamp("2011-01-01 10:00"), + Timestamp("2011-01-02 10:00"), + Timestamp("2011-01-03 10:00"), + Timestamp("2011-01-04 10:00"), + ], + dtype=f"M8[{unit}]", + ) + tm.assert_series_equal(expected, result) + tm.assert_series_equal(isna(ser), null_loc) + + # DatetimeTZBlock + idx = DatetimeIndex( + ["2011-01-01 10:00", NaT, "2011-01-03 10:00", NaT], tz=tz + ).as_unit(unit) + ser = Series(idx) + assert ser.dtype == f"datetime64[{unit}, {tz}]" + tm.assert_series_equal(isna(ser), null_loc) + + result = ser.fillna(Timestamp("2011-01-02 10:00")) + expected = Series( + [ + Timestamp("2011-01-01 10:00", tz=tz), + Timestamp("2011-01-02 10:00"), + Timestamp("2011-01-03 10:00", tz=tz), + Timestamp("2011-01-02 10:00"), + ] + ) + tm.assert_series_equal(expected, result) + tm.assert_series_equal(isna(ser), null_loc) + + result = ser.fillna(Timestamp("2011-01-02 10:00", tz=tz)) + idx = DatetimeIndex( + [ + "2011-01-01 10:00", + "2011-01-02 10:00", + "2011-01-03 10:00", + "2011-01-02 10:00", + ], + tz=tz, + ).as_unit(unit) + expected = Series(idx) + tm.assert_series_equal(expected, result) + tm.assert_series_equal(isna(ser), null_loc) + + result = ser.fillna(Timestamp("2011-01-02 10:00", tz=tz).to_pydatetime()) + idx = DatetimeIndex( + [ + "2011-01-01 10:00", + "2011-01-02 10:00", + "2011-01-03 10:00", + "2011-01-02 10:00", + ], + tz=tz, + ).as_unit(unit) + expected = Series(idx) + tm.assert_series_equal(expected, result) + tm.assert_series_equal(isna(ser), null_loc) + + result = ser.fillna("AAA") + expected = Series( + [ + Timestamp("2011-01-01 10:00", tz=tz), + "AAA", + Timestamp("2011-01-03 10:00", tz=tz), + "AAA", + ], + dtype=object, + ) + tm.assert_series_equal(expected, result) + tm.assert_series_equal(isna(ser), null_loc) + + result = ser.fillna( + { + 1: Timestamp("2011-01-02 10:00", tz=tz), + 3: Timestamp("2011-01-04 10:00"), + } + ) + expected = Series( + [ + Timestamp("2011-01-01 10:00", tz=tz), + Timestamp("2011-01-02 10:00", tz=tz), + Timestamp("2011-01-03 10:00", tz=tz), + Timestamp("2011-01-04 10:00"), + ] + ) + tm.assert_series_equal(expected, result) + tm.assert_series_equal(isna(ser), null_loc) + + result = ser.fillna( + { + 1: Timestamp("2011-01-02 10:00", tz=tz), + 3: Timestamp("2011-01-04 10:00", tz=tz), + } + ) + expected = Series( + [ + Timestamp("2011-01-01 10:00", tz=tz), + Timestamp("2011-01-02 10:00", tz=tz), + Timestamp("2011-01-03 10:00", tz=tz), + Timestamp("2011-01-04 10:00", tz=tz), + ] + ).dt.as_unit(unit) + tm.assert_series_equal(expected, result) + tm.assert_series_equal(isna(ser), null_loc) + + # filling with a naive/other zone, coerce to object + result = ser.fillna(Timestamp("20130101")) + expected = Series( + [ + Timestamp("2011-01-01 10:00", tz=tz), + Timestamp("2013-01-01"), + Timestamp("2011-01-03 10:00", tz=tz), + Timestamp("2013-01-01"), + ] + ) + tm.assert_series_equal(expected, result) + tm.assert_series_equal(isna(ser), null_loc) + + # pre-2.0 fillna with mixed tzs would cast to object, in 2.0 + # it retains dtype. + result = ser.fillna(Timestamp("20130101", tz="US/Pacific")) + expected = Series( + [ + Timestamp("2011-01-01 10:00", tz=tz), + Timestamp("2013-01-01", tz="US/Pacific").tz_convert(tz), + Timestamp("2011-01-03 10:00", tz=tz), + Timestamp("2013-01-01", tz="US/Pacific").tz_convert(tz), + ] + ).dt.as_unit(unit) + tm.assert_series_equal(expected, result) + tm.assert_series_equal(isna(ser), null_loc) + + def test_fillna_dt64tz_with_method(self): + # with timezone + # GH#15855 + ser = Series([Timestamp("2012-11-11 00:00:00+01:00"), NaT]) + exp = Series( + [ + Timestamp("2012-11-11 00:00:00+01:00"), + Timestamp("2012-11-11 00:00:00+01:00"), + ] + ) + tm.assert_series_equal(ser.fillna(method="pad"), exp) + + ser = Series([NaT, Timestamp("2012-11-11 00:00:00+01:00")]) + exp = Series( + [ + Timestamp("2012-11-11 00:00:00+01:00"), + Timestamp("2012-11-11 00:00:00+01:00"), + ] + ) + tm.assert_series_equal(ser.fillna(method="bfill"), exp) + + def test_fillna_pytimedelta(self): + # GH#8209 + ser = Series([np.nan, Timedelta("1 days")], index=["A", "B"]) + + result = ser.fillna(timedelta(1)) + expected = Series(Timedelta("1 days"), index=["A", "B"]) + tm.assert_series_equal(result, expected) + + def test_fillna_period(self): + # GH#13737 + ser = Series([Period("2011-01", freq="M"), Period("NaT", freq="M")]) + + res = ser.fillna(Period("2012-01", freq="M")) + exp = Series([Period("2011-01", freq="M"), Period("2012-01", freq="M")]) + tm.assert_series_equal(res, exp) + assert res.dtype == "Period[M]" + + def test_fillna_dt64_timestamp(self, frame_or_series): + ser = Series( + [ + Timestamp("20130101"), + Timestamp("20130101"), + Timestamp("20130102"), + Timestamp("20130103 9:01:01"), + ] + ) + ser[2] = np.nan + obj = frame_or_series(ser) + + # reg fillna + result = obj.fillna(Timestamp("20130104")) + expected = Series( + [ + Timestamp("20130101"), + Timestamp("20130101"), + Timestamp("20130104"), + Timestamp("20130103 9:01:01"), + ] + ) + expected = frame_or_series(expected) + tm.assert_equal(result, expected) + + result = obj.fillna(NaT) + expected = obj + tm.assert_equal(result, expected) + + def test_fillna_dt64_non_nao(self): + # GH#27419 + ser = Series([Timestamp("2010-01-01"), NaT, Timestamp("2000-01-01")]) + val = np.datetime64("1975-04-05", "ms") + + result = ser.fillna(val) + expected = Series( + [Timestamp("2010-01-01"), Timestamp("1975-04-05"), Timestamp("2000-01-01")] + ) + tm.assert_series_equal(result, expected) + + def test_fillna_numeric_inplace(self): + x = Series([np.nan, 1.0, np.nan, 3.0, np.nan], ["z", "a", "b", "c", "d"]) + y = x.copy() + + return_value = y.fillna(value=0, inplace=True) + assert return_value is None + + expected = x.fillna(value=0) + tm.assert_series_equal(y, expected) + + # --------------------------------------------------------------- + # CategoricalDtype + + @pytest.mark.parametrize( + "fill_value, expected_output", + [ + ("a", ["a", "a", "b", "a", "a"]), + ({1: "a", 3: "b", 4: "b"}, ["a", "a", "b", "b", "b"]), + ({1: "a"}, ["a", "a", "b", np.nan, np.nan]), + ({1: "a", 3: "b"}, ["a", "a", "b", "b", np.nan]), + (Series("a"), ["a", np.nan, "b", np.nan, np.nan]), + (Series("a", index=[1]), ["a", "a", "b", np.nan, np.nan]), + (Series({1: "a", 3: "b"}), ["a", "a", "b", "b", np.nan]), + (Series(["a", "b"], index=[3, 4]), ["a", np.nan, "b", "a", "b"]), + ], + ) + def test_fillna_categorical(self, fill_value, expected_output): + # GH#17033 + # Test fillna for a Categorical series + data = ["a", np.nan, "b", np.nan, np.nan] + ser = Series(Categorical(data, categories=["a", "b"])) + exp = Series(Categorical(expected_output, categories=["a", "b"])) + result = ser.fillna(fill_value) + tm.assert_series_equal(result, exp) + + @pytest.mark.parametrize( + "fill_value, expected_output", + [ + (Series(["a", "b", "c", "d", "e"]), ["a", "b", "b", "d", "e"]), + (Series(["b", "d", "a", "d", "a"]), ["a", "d", "b", "d", "a"]), + ( + Series( + Categorical( + ["b", "d", "a", "d", "a"], categories=["b", "c", "d", "e", "a"] + ) + ), + ["a", "d", "b", "d", "a"], + ), + ], + ) + def test_fillna_categorical_with_new_categories(self, fill_value, expected_output): + # GH#26215 + data = ["a", np.nan, "b", np.nan, np.nan] + ser = Series(Categorical(data, categories=["a", "b", "c", "d", "e"])) + exp = Series(Categorical(expected_output, categories=["a", "b", "c", "d", "e"])) + result = ser.fillna(fill_value) + tm.assert_series_equal(result, exp) + + def test_fillna_categorical_raises(self): + data = ["a", np.nan, "b", np.nan, np.nan] + ser = Series(Categorical(data, categories=["a", "b"])) + cat = ser._values + + msg = "Cannot setitem on a Categorical with a new category" + with pytest.raises(TypeError, match=msg): + ser.fillna("d") + + msg2 = "Length of 'value' does not match." + with pytest.raises(ValueError, match=msg2): + cat.fillna(Series("d")) + + with pytest.raises(TypeError, match=msg): + ser.fillna({1: "d", 3: "a"}) + + msg = '"value" parameter must be a scalar or dict, but you passed a "list"' + with pytest.raises(TypeError, match=msg): + ser.fillna(["a", "b"]) + + msg = '"value" parameter must be a scalar or dict, but you passed a "tuple"' + with pytest.raises(TypeError, match=msg): + ser.fillna(("a", "b")) + + msg = ( + '"value" parameter must be a scalar, dict ' + 'or Series, but you passed a "DataFrame"' + ) + with pytest.raises(TypeError, match=msg): + ser.fillna(DataFrame({1: ["a"], 3: ["b"]})) + + @pytest.mark.parametrize("dtype", [float, "float32", "float64"]) + @pytest.mark.parametrize("fill_type", tm.ALL_REAL_NUMPY_DTYPES) + @pytest.mark.parametrize("scalar", [True, False]) + def test_fillna_float_casting(self, dtype, fill_type, scalar): + # GH-43424 + ser = Series([np.nan, 1.2], dtype=dtype) + fill_values = Series([2, 2], dtype=fill_type) + if scalar: + fill_values = fill_values.dtype.type(2) + + result = ser.fillna(fill_values) + expected = Series([2.0, 1.2], dtype=dtype) + tm.assert_series_equal(result, expected) + + ser = Series([np.nan, 1.2], dtype=dtype) + mask = ser.isna().to_numpy() + ser[mask] = fill_values + tm.assert_series_equal(ser, expected) + + ser = Series([np.nan, 1.2], dtype=dtype) + ser.mask(mask, fill_values, inplace=True) + tm.assert_series_equal(ser, expected) + + ser = Series([np.nan, 1.2], dtype=dtype) + res = ser.where(~mask, fill_values) + tm.assert_series_equal(res, expected) + + def test_fillna_f32_upcast_with_dict(self): + # GH-43424 + ser = Series([np.nan, 1.2], dtype=np.float32) + result = ser.fillna({0: 1}) + expected = Series([1.0, 1.2], dtype=np.float32) + tm.assert_series_equal(result, expected) + + # --------------------------------------------------------------- + # Invalid Usages + + def test_fillna_invalid_method(self, datetime_series): + try: + datetime_series.fillna(method="ffil") + except ValueError as inst: + assert "ffil" in str(inst) + + def test_fillna_listlike_invalid(self): + ser = Series(np.random.default_rng(2).integers(-100, 100, 50)) + msg = '"value" parameter must be a scalar or dict, but you passed a "list"' + with pytest.raises(TypeError, match=msg): + ser.fillna([1, 2]) + + msg = '"value" parameter must be a scalar or dict, but you passed a "tuple"' + with pytest.raises(TypeError, match=msg): + ser.fillna((1, 2)) + + def test_fillna_method_and_limit_invalid(self): + # related GH#9217, make sure limit is an int and greater than 0 + ser = Series([1, 2, 3, None]) + msg = "|".join( + [ + r"Cannot specify both 'value' and 'method'\.", + "Limit must be greater than 0", + "Limit must be an integer", + ] + ) + for limit in [-1, 0, 1.0, 2.0]: + for method in ["backfill", "bfill", "pad", "ffill", None]: + with pytest.raises(ValueError, match=msg): + ser.fillna(1, limit=limit, method=method) + + def test_fillna_datetime64_with_timezone_tzinfo(self): + # https://github.com/pandas-dev/pandas/issues/38851 + # different tzinfos representing UTC treated as equal + ser = Series(date_range("2020", periods=3, tz="UTC")) + expected = ser.copy() + ser[1] = NaT + result = ser.fillna(datetime(2020, 1, 2, tzinfo=timezone.utc)) + tm.assert_series_equal(result, expected) + + # pre-2.0 we cast to object with mixed tzs, in 2.0 we retain dtype + ts = Timestamp("2000-01-01", tz="US/Pacific") + ser2 = Series(ser._values.tz_convert("dateutil/US/Pacific")) + assert ser2.dtype.kind == "M" + result = ser2.fillna(ts) + expected = Series( + [ser2[0], ts.tz_convert(ser2.dtype.tz), ser2[2]], + dtype=ser2.dtype, + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "input, input_fillna, expected_data, expected_categories", + [ + (["A", "B", None, "A"], "B", ["A", "B", "B", "A"], ["A", "B"]), + (["A", "B", np.nan, "A"], "B", ["A", "B", "B", "A"], ["A", "B"]), + ], + ) + def test_fillna_categorical_accept_same_type( + self, input, input_fillna, expected_data, expected_categories + ): + # GH32414 + cat = Categorical(input) + ser = Series(cat).fillna(input_fillna) + filled = cat.fillna(ser) + result = cat.fillna(filled) + expected = Categorical(expected_data, categories=expected_categories) + tm.assert_categorical_equal(result, expected) + + +@pytest.mark.filterwarnings( + "ignore:Series.fillna with 'method' is deprecated:FutureWarning" +) +class TestFillnaPad: + def test_fillna_bug(self): + ser = Series([np.nan, 1.0, np.nan, 3.0, np.nan], ["z", "a", "b", "c", "d"]) + filled = ser.fillna(method="ffill") + expected = Series([np.nan, 1.0, 1.0, 3.0, 3.0], ser.index) + tm.assert_series_equal(filled, expected) + + filled = ser.fillna(method="bfill") + expected = Series([1.0, 1.0, 3.0, 3.0, np.nan], ser.index) + tm.assert_series_equal(filled, expected) + + def test_ffill(self): + ts = Series( + [0.0, 1.0, 2.0, 3.0, 4.0], index=date_range("2020-01-01", periods=5) + ) + ts.iloc[2] = np.nan + tm.assert_series_equal(ts.ffill(), ts.fillna(method="ffill")) + + def test_ffill_mixed_dtypes_without_missing_data(self): + # GH#14956 + series = Series([datetime(2015, 1, 1, tzinfo=pytz.utc), 1]) + result = series.ffill() + tm.assert_series_equal(series, result) + + def test_bfill(self): + ts = Series( + [0.0, 1.0, 2.0, 3.0, 4.0], index=date_range("2020-01-01", periods=5) + ) + ts.iloc[2] = np.nan + tm.assert_series_equal(ts.bfill(), ts.fillna(method="bfill")) + + def test_pad_nan(self): + x = Series( + [np.nan, 1.0, np.nan, 3.0, np.nan], ["z", "a", "b", "c", "d"], dtype=float + ) + + return_value = x.fillna(method="pad", inplace=True) + assert return_value is None + + expected = Series( + [np.nan, 1.0, 1.0, 3.0, 3.0], ["z", "a", "b", "c", "d"], dtype=float + ) + tm.assert_series_equal(x[1:], expected[1:]) + assert np.isnan(x.iloc[0]), np.isnan(expected.iloc[0]) + + def test_series_fillna_limit(self): + index = np.arange(10) + s = Series(np.random.default_rng(2).standard_normal(10), index=index) + + result = s[:2].reindex(index) + result = result.fillna(method="pad", limit=5) + + expected = s[:2].reindex(index).fillna(method="pad") + expected[-3:] = np.nan + tm.assert_series_equal(result, expected) + + result = s[-2:].reindex(index) + result = result.fillna(method="bfill", limit=5) + + expected = s[-2:].reindex(index).fillna(method="backfill") + expected[:3] = np.nan + tm.assert_series_equal(result, expected) + + def test_series_pad_backfill_limit(self): + index = np.arange(10) + s = Series(np.random.default_rng(2).standard_normal(10), index=index) + + result = s[:2].reindex(index, method="pad", limit=5) + + expected = s[:2].reindex(index).fillna(method="pad") + expected[-3:] = np.nan + tm.assert_series_equal(result, expected) + + result = s[-2:].reindex(index, method="backfill", limit=5) + + expected = s[-2:].reindex(index).fillna(method="backfill") + expected[:3] = np.nan + tm.assert_series_equal(result, expected) + + def test_fillna_int(self): + ser = Series(np.random.default_rng(2).integers(-100, 100, 50)) + return_value = ser.fillna(method="ffill", inplace=True) + assert return_value is None + tm.assert_series_equal(ser.fillna(method="ffill", inplace=False), ser) + + def test_datetime64tz_fillna_round_issue(self): + # GH#14872 + + data = Series( + [NaT, NaT, datetime(2016, 12, 12, 22, 24, 6, 100001, tzinfo=pytz.utc)] + ) + + filled = data.bfill() + + expected = Series( + [ + datetime(2016, 12, 12, 22, 24, 6, 100001, tzinfo=pytz.utc), + datetime(2016, 12, 12, 22, 24, 6, 100001, tzinfo=pytz.utc), + datetime(2016, 12, 12, 22, 24, 6, 100001, tzinfo=pytz.utc), + ] + ) + + tm.assert_series_equal(filled, expected) + + def test_fillna_parr(self): + # GH-24537 + dti = date_range( + Timestamp.max - Timedelta(nanoseconds=10), periods=5, freq="ns" + ) + ser = Series(dti.to_period("ns")) + ser[2] = NaT + arr = period_array( + [ + Timestamp("2262-04-11 23:47:16.854775797"), + Timestamp("2262-04-11 23:47:16.854775798"), + Timestamp("2262-04-11 23:47:16.854775798"), + Timestamp("2262-04-11 23:47:16.854775800"), + Timestamp("2262-04-11 23:47:16.854775801"), + ], + freq="ns", + ) + expected = Series(arr) + + filled = ser.ffill() + + tm.assert_series_equal(filled, expected) + + @pytest.mark.parametrize("func", ["pad", "backfill"]) + def test_pad_backfill_deprecated(self, func): + # GH#33396 + ser = Series([1, 2, 3]) + with tm.assert_produces_warning(FutureWarning): + getattr(ser, func)() + + +@pytest.mark.parametrize( + "data, expected_data, method, kwargs", + ( + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [np.nan, np.nan, 3.0, 3.0, 3.0, 3.0, 7.0, np.nan, np.nan], + "ffill", + {"limit_area": "inside"}, + ), + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [np.nan, np.nan, 3.0, 3.0, np.nan, np.nan, 7.0, np.nan, np.nan], + "ffill", + {"limit_area": "inside", "limit": 1}, + ), + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, 7.0], + "ffill", + {"limit_area": "outside"}, + ), + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan], + "ffill", + {"limit_area": "outside", "limit": 1}, + ), + ( + [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan], + "ffill", + {"limit_area": "outside", "limit": 1}, + ), + ( + range(5), + range(5), + "ffill", + {"limit_area": "outside", "limit": 1}, + ), + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [np.nan, np.nan, 3.0, 7.0, 7.0, 7.0, 7.0, np.nan, np.nan], + "bfill", + {"limit_area": "inside"}, + ), + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [np.nan, np.nan, 3.0, np.nan, np.nan, 7.0, 7.0, np.nan, np.nan], + "bfill", + {"limit_area": "inside", "limit": 1}, + ), + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [3.0, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, np.nan, np.nan], + "bfill", + {"limit_area": "outside"}, + ), + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [np.nan, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, np.nan, np.nan], + "bfill", + {"limit_area": "outside", "limit": 1}, + ), + ), +) +def test_ffill_bfill_limit_area(data, expected_data, method, kwargs): + # GH#56492 + s = Series(data) + expected = Series(expected_data) + result = getattr(s, method)(**kwargs) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_get_numeric_data.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_get_numeric_data.py new file mode 100644 index 0000000000000000000000000000000000000000..8325cc884ebcba069c18f457371cfa061893cf81 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_get_numeric_data.py @@ -0,0 +1,38 @@ +from pandas import ( + Index, + Series, + date_range, +) +import pandas._testing as tm + + +class TestGetNumericData: + def test_get_numeric_data_preserve_dtype( + self, using_copy_on_write, warn_copy_on_write + ): + # get the numeric data + obj = Series([1, 2, 3]) + result = obj._get_numeric_data() + tm.assert_series_equal(result, obj) + + # returned object is a shallow copy + with tm.assert_cow_warning(warn_copy_on_write): + result.iloc[0] = 0 + if using_copy_on_write: + assert obj.iloc[0] == 1 + else: + assert obj.iloc[0] == 0 + + obj = Series([1, "2", 3.0]) + result = obj._get_numeric_data() + expected = Series([], dtype=object, index=Index([], dtype=object)) + tm.assert_series_equal(result, expected) + + obj = Series([True, False, True]) + result = obj._get_numeric_data() + tm.assert_series_equal(result, obj) + + obj = Series(date_range("20130101", periods=3)) + result = obj._get_numeric_data() + expected = Series([], dtype="M8[ns]", index=Index([], dtype=object)) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_head_tail.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_head_tail.py new file mode 100644 index 0000000000000000000000000000000000000000..d9f8d85eda350e0627790626d52b7703d23ead02 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_head_tail.py @@ -0,0 +1,8 @@ +import pandas._testing as tm + + +def test_head_tail(string_series): + tm.assert_series_equal(string_series.head(), string_series[:5]) + tm.assert_series_equal(string_series.head(0), string_series[0:0]) + tm.assert_series_equal(string_series.tail(), string_series[-5:]) + tm.assert_series_equal(string_series.tail(0), string_series[0:0]) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_infer_objects.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_infer_objects.py new file mode 100644 index 0000000000000000000000000000000000000000..29abac6b3780ec500ef0c635785c9624e4264934 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_infer_objects.py @@ -0,0 +1,56 @@ +import numpy as np + +from pandas import ( + Series, + interval_range, +) +import pandas._testing as tm + + +class TestInferObjects: + def test_copy(self, index_or_series): + # GH#50096 + # case where we don't need to do inference because it is already non-object + obj = index_or_series(np.array([1, 2, 3], dtype="int64")) + + result = obj.infer_objects(copy=False) + assert tm.shares_memory(result, obj) + + # case where we try to do inference but can't do better than object + obj2 = index_or_series(np.array(["foo", 2], dtype=object)) + result2 = obj2.infer_objects(copy=False) + assert tm.shares_memory(result2, obj2) + + def test_infer_objects_series(self, index_or_series): + # GH#11221 + actual = index_or_series(np.array([1, 2, 3], dtype="O")).infer_objects() + expected = index_or_series([1, 2, 3]) + tm.assert_equal(actual, expected) + + actual = index_or_series(np.array([1, 2, 3, None], dtype="O")).infer_objects() + expected = index_or_series([1.0, 2.0, 3.0, np.nan]) + tm.assert_equal(actual, expected) + + # only soft conversions, unconvertible pass thru unchanged + + obj = index_or_series(np.array([1, 2, 3, None, "a"], dtype="O")) + actual = obj.infer_objects() + expected = index_or_series([1, 2, 3, None, "a"], dtype=object) + + assert actual.dtype == "object" + tm.assert_equal(actual, expected) + + def test_infer_objects_interval(self, index_or_series): + # GH#50090 + ii = interval_range(1, 10) + obj = index_or_series(ii) + + result = obj.astype(object).infer_objects() + tm.assert_equal(result, obj) + + def test_infer_objects_bytes(self): + # GH#49650 + ser = Series([b"a"], dtype="bytes") + expected = ser.copy() + result = ser.infer_objects() + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_info.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_info.py new file mode 100644 index 0000000000000000000000000000000000000000..7defad8a463f3cc987ec204888ff54e2857d58e1 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_info.py @@ -0,0 +1,192 @@ +from io import StringIO +from string import ascii_uppercase +import textwrap + +import numpy as np +import pytest + +from pandas._config import using_string_dtype + +from pandas.compat import ( + HAS_PYARROW, + PYPY, +) + +from pandas import ( + CategoricalIndex, + Index, + MultiIndex, + Series, + date_range, +) + + +def test_info_categorical_column_just_works(): + n = 2500 + data = np.array(list("abcdefghij")).take( + np.random.default_rng(2).integers(0, 10, size=n, dtype=int) + ) + s = Series(data).astype("category") + s.isna() + buf = StringIO() + s.info(buf=buf) + + s2 = s[s == "d"] + buf = StringIO() + s2.info(buf=buf) + + +def test_info_categorical(): + # GH14298 + idx = CategoricalIndex(["a", "b"]) + s = Series(np.zeros(2), index=idx) + buf = StringIO() + s.info(buf=buf) + + +@pytest.mark.parametrize("verbose", [True, False]) +def test_info_series( + lexsorted_two_level_string_multiindex, verbose, using_infer_string +): + index = lexsorted_two_level_string_multiindex + ser = Series(range(len(index)), index=index, name="sth") + buf = StringIO() + ser.info(verbose=verbose, buf=buf) + result = buf.getvalue() + + expected = textwrap.dedent( + """\ + + MultiIndex: 10 entries, ('foo', 'one') to ('qux', 'three') + """ + ) + if verbose: + expected += textwrap.dedent( + """\ + Series name: sth + Non-Null Count Dtype + -------------- ----- + 10 non-null int64 + """ + ) + qualifier = "" if using_infer_string and HAS_PYARROW else "+" + expected += textwrap.dedent( + f"""\ + dtypes: int64(1) + memory usage: {ser.memory_usage()}.0{qualifier} bytes + """ + ) + assert result == expected + + +def test_info_memory(): + s = Series([1, 2], dtype="i8") + buf = StringIO() + s.info(buf=buf) + result = buf.getvalue() + memory_bytes = float(s.memory_usage()) + expected = textwrap.dedent( + f"""\ + + RangeIndex: 2 entries, 0 to 1 + Series name: None + Non-Null Count Dtype + -------------- ----- + 2 non-null int64 + dtypes: int64(1) + memory usage: {memory_bytes} bytes + """ + ) + assert result == expected + + +def test_info_wide(): + s = Series(np.random.default_rng(2).standard_normal(101)) + msg = "Argument `max_cols` can only be passed in DataFrame.info, not Series.info" + with pytest.raises(ValueError, match=msg): + s.info(max_cols=1) + + +def test_info_shows_dtypes(): + dtypes = [ + "int64", + "float64", + "datetime64[ns]", + "timedelta64[ns]", + "complex128", + "object", + "bool", + ] + n = 10 + for dtype in dtypes: + s = Series(np.random.default_rng(2).integers(2, size=n).astype(dtype)) + buf = StringIO() + s.info(buf=buf) + res = buf.getvalue() + name = f"{n:d} non-null {dtype}" + assert name in res + + +@pytest.mark.xfail(PYPY, reason="on PyPy deep=True doesn't change result") +def test_info_memory_usage_deep_not_pypy(): + s_with_object_index = Series({"a": [1]}, index=["foo"]) + assert s_with_object_index.memory_usage( + index=True, deep=True + ) > s_with_object_index.memory_usage(index=True) + + s_object = Series({"a": ["a"]}) + assert s_object.memory_usage(deep=True) > s_object.memory_usage() + + +@pytest.mark.xfail(not PYPY, reason="on PyPy deep=True does not change result") +def test_info_memory_usage_deep_pypy(): + s_with_object_index = Series({"a": [1]}, index=["foo"]) + assert s_with_object_index.memory_usage( + index=True, deep=True + ) == s_with_object_index.memory_usage(index=True) + + s_object = Series({"a": ["a"]}) + assert s_object.memory_usage(deep=True) == s_object.memory_usage() + + +@pytest.mark.parametrize( + "index, plus", + [ + ([1, 2, 3], False), + (Index(list("ABC"), dtype="str"), not (using_string_dtype() and HAS_PYARROW)), + (Index(list("ABC"), dtype=object), True), + (MultiIndex.from_product([range(3), range(3)]), False), + ( + MultiIndex.from_product([range(3), ["foo", "bar"]]), + not (using_string_dtype() and HAS_PYARROW), + ), + ], +) +def test_info_memory_usage_qualified(index, plus): + series = Series(1, index=index) + buf = StringIO() + series.info(buf=buf) + if plus: + assert "+" in buf.getvalue() + else: + assert "+" not in buf.getvalue() + + +def test_info_memory_usage_bug_on_multiindex(): + # GH 14308 + # memory usage introspection should not materialize .values + N = 100 + M = len(ascii_uppercase) + index = MultiIndex.from_product( + [list(ascii_uppercase), date_range("20160101", periods=N)], + names=["id", "date"], + ) + s = Series(np.random.default_rng(2).standard_normal(N * M), index=index) + + unstacked = s.unstack("id") + assert s.values.nbytes == unstacked.values.nbytes + assert s.memory_usage(deep=True) > unstacked.memory_usage(deep=True).sum() + + # high upper bound + diff = unstacked.memory_usage(deep=True).sum() - s.memory_usage(deep=True) + assert diff < 2000 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_interpolate.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_interpolate.py new file mode 100644 index 0000000000000000000000000000000000000000..d854f0b7877595fba5ac0050a281aa3708240b0e --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_interpolate.py @@ -0,0 +1,868 @@ +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +import pandas as pd +from pandas import ( + Index, + MultiIndex, + Series, + date_range, + isna, +) +import pandas._testing as tm + + +@pytest.fixture( + params=[ + "linear", + "index", + "values", + "nearest", + "slinear", + "zero", + "quadratic", + "cubic", + "barycentric", + "krogh", + "polynomial", + "spline", + "piecewise_polynomial", + "from_derivatives", + "pchip", + "akima", + "cubicspline", + ] +) +def nontemporal_method(request): + """Fixture that returns an (method name, required kwargs) pair. + + This fixture does not include method 'time' as a parameterization; that + method requires a Series with a DatetimeIndex, and is generally tested + separately from these non-temporal methods. + """ + method = request.param + kwargs = {"order": 1} if method in ("spline", "polynomial") else {} + return method, kwargs + + +@pytest.fixture( + params=[ + "linear", + "slinear", + "zero", + "quadratic", + "cubic", + "barycentric", + "krogh", + "polynomial", + "spline", + "piecewise_polynomial", + "from_derivatives", + "pchip", + "akima", + "cubicspline", + ] +) +def interp_methods_ind(request): + """Fixture that returns a (method name, required kwargs) pair to + be tested for various Index types. + + This fixture does not include methods - 'time', 'index', 'nearest', + 'values' as a parameterization + """ + method = request.param + kwargs = {"order": 1} if method in ("spline", "polynomial") else {} + return method, kwargs + + +class TestSeriesInterpolateData: + @pytest.mark.xfail(reason="EA.fillna does not handle 'linear' method") + def test_interpolate_period_values(self): + orig = Series(date_range("2012-01-01", periods=5)) + ser = orig.copy() + ser[2] = pd.NaT + + # period cast + ser_per = ser.dt.to_period("D") + res_per = ser_per.interpolate() + expected_per = orig.dt.to_period("D") + tm.assert_series_equal(res_per, expected_per) + + def test_interpolate(self, datetime_series): + ts = Series(np.arange(len(datetime_series), dtype=float), datetime_series.index) + + ts_copy = ts.copy() + ts_copy[5:10] = np.nan + + linear_interp = ts_copy.interpolate(method="linear") + tm.assert_series_equal(linear_interp, ts) + + ord_ts = Series( + [d.toordinal() for d in datetime_series.index], index=datetime_series.index + ).astype(float) + + ord_ts_copy = ord_ts.copy() + ord_ts_copy[5:10] = np.nan + + time_interp = ord_ts_copy.interpolate(method="time") + tm.assert_series_equal(time_interp, ord_ts) + + def test_interpolate_time_raises_for_non_timeseries(self): + # When method='time' is used on a non-TimeSeries that contains a null + # value, a ValueError should be raised. + non_ts = Series([0, 1, 2, np.nan]) + msg = "time-weighted interpolation only works on Series.* with a DatetimeIndex" + with pytest.raises(ValueError, match=msg): + non_ts.interpolate(method="time") + + def test_interpolate_cubicspline(self): + pytest.importorskip("scipy") + ser = Series([10, 11, 12, 13]) + + expected = Series( + [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00], + index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]), + ) + # interpolate at new_index + new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype( + float + ) + result = ser.reindex(new_index).interpolate(method="cubicspline").loc[1:3] + tm.assert_series_equal(result, expected) + + def test_interpolate_pchip(self): + pytest.importorskip("scipy") + ser = Series(np.sort(np.random.default_rng(2).uniform(size=100))) + + # interpolate at new_index + new_index = ser.index.union( + Index([49.25, 49.5, 49.75, 50.25, 50.5, 50.75]) + ).astype(float) + interp_s = ser.reindex(new_index).interpolate(method="pchip") + # does not blow up, GH5977 + interp_s.loc[49:51] + + def test_interpolate_akima(self): + pytest.importorskip("scipy") + ser = Series([10, 11, 12, 13]) + + # interpolate at new_index where `der` is zero + expected = Series( + [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00], + index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]), + ) + new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype( + float + ) + interp_s = ser.reindex(new_index).interpolate(method="akima") + tm.assert_series_equal(interp_s.loc[1:3], expected) + + # interpolate at new_index where `der` is a non-zero int + expected = Series( + [11.0, 1.0, 1.0, 1.0, 12.0, 1.0, 1.0, 1.0, 13.0], + index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]), + ) + new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype( + float + ) + interp_s = ser.reindex(new_index).interpolate(method="akima", der=1) + tm.assert_series_equal(interp_s.loc[1:3], expected) + + def test_interpolate_piecewise_polynomial(self): + pytest.importorskip("scipy") + ser = Series([10, 11, 12, 13]) + + expected = Series( + [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00], + index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]), + ) + # interpolate at new_index + new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype( + float + ) + interp_s = ser.reindex(new_index).interpolate(method="piecewise_polynomial") + tm.assert_series_equal(interp_s.loc[1:3], expected) + + def test_interpolate_from_derivatives(self): + pytest.importorskip("scipy") + ser = Series([10, 11, 12, 13]) + + expected = Series( + [11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00], + index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]), + ) + # interpolate at new_index + new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype( + float + ) + interp_s = ser.reindex(new_index).interpolate(method="from_derivatives") + tm.assert_series_equal(interp_s.loc[1:3], expected) + + @pytest.mark.parametrize( + "kwargs", + [ + {}, + pytest.param( + {"method": "polynomial", "order": 1}, marks=td.skip_if_no("scipy") + ), + ], + ) + def test_interpolate_corners(self, kwargs): + s = Series([np.nan, np.nan]) + tm.assert_series_equal(s.interpolate(**kwargs), s) + + s = Series([], dtype=object).interpolate() + tm.assert_series_equal(s.interpolate(**kwargs), s) + + def test_interpolate_index_values(self): + s = Series(np.nan, index=np.sort(np.random.default_rng(2).random(30))) + s.loc[::3] = np.random.default_rng(2).standard_normal(10) + + vals = s.index.values.astype(float) + + result = s.interpolate(method="index") + + expected = s.copy() + bad = isna(expected.values) + good = ~bad + expected = Series( + np.interp(vals[bad], vals[good], s.values[good]), index=s.index[bad] + ) + + tm.assert_series_equal(result[bad], expected) + + # 'values' is synonymous with 'index' for the method kwarg + other_result = s.interpolate(method="values") + + tm.assert_series_equal(other_result, result) + tm.assert_series_equal(other_result[bad], expected) + + def test_interpolate_non_ts(self): + s = Series([1, 3, np.nan, np.nan, np.nan, 11]) + msg = ( + "time-weighted interpolation only works on Series or DataFrames " + "with a DatetimeIndex" + ) + with pytest.raises(ValueError, match=msg): + s.interpolate(method="time") + + @pytest.mark.parametrize( + "kwargs", + [ + {}, + pytest.param( + {"method": "polynomial", "order": 1}, marks=td.skip_if_no("scipy") + ), + ], + ) + def test_nan_interpolate(self, kwargs): + s = Series([0, 1, np.nan, 3]) + result = s.interpolate(**kwargs) + expected = Series([0.0, 1.0, 2.0, 3.0]) + tm.assert_series_equal(result, expected) + + def test_nan_irregular_index(self): + s = Series([1, 2, np.nan, 4], index=[1, 3, 5, 9]) + result = s.interpolate() + expected = Series([1.0, 2.0, 3.0, 4.0], index=[1, 3, 5, 9]) + tm.assert_series_equal(result, expected) + + def test_nan_str_index(self): + s = Series([0, 1, 2, np.nan], index=list("abcd")) + result = s.interpolate() + expected = Series([0.0, 1.0, 2.0, 2.0], index=list("abcd")) + tm.assert_series_equal(result, expected) + + def test_interp_quad(self): + pytest.importorskip("scipy") + sq = Series([1, 4, np.nan, 16], index=[1, 2, 3, 4]) + result = sq.interpolate(method="quadratic") + expected = Series([1.0, 4.0, 9.0, 16.0], index=[1, 2, 3, 4]) + tm.assert_series_equal(result, expected) + + def test_interp_scipy_basic(self): + pytest.importorskip("scipy") + s = Series([1, 3, np.nan, 12, np.nan, 25]) + # slinear + expected = Series([1.0, 3.0, 7.5, 12.0, 18.5, 25.0]) + result = s.interpolate(method="slinear") + tm.assert_series_equal(result, expected) + + msg = "The 'downcast' keyword in Series.interpolate is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = s.interpolate(method="slinear", downcast="infer") + tm.assert_series_equal(result, expected) + # nearest + expected = Series([1, 3, 3, 12, 12, 25]) + result = s.interpolate(method="nearest") + tm.assert_series_equal(result, expected.astype("float")) + + with tm.assert_produces_warning(FutureWarning, match=msg): + result = s.interpolate(method="nearest", downcast="infer") + tm.assert_series_equal(result, expected) + # zero + expected = Series([1, 3, 3, 12, 12, 25]) + result = s.interpolate(method="zero") + tm.assert_series_equal(result, expected.astype("float")) + + with tm.assert_produces_warning(FutureWarning, match=msg): + result = s.interpolate(method="zero", downcast="infer") + tm.assert_series_equal(result, expected) + # quadratic + # GH #15662. + expected = Series([1, 3.0, 6.823529, 12.0, 18.058824, 25.0]) + result = s.interpolate(method="quadratic") + tm.assert_series_equal(result, expected) + + with tm.assert_produces_warning(FutureWarning, match=msg): + result = s.interpolate(method="quadratic", downcast="infer") + tm.assert_series_equal(result, expected) + # cubic + expected = Series([1.0, 3.0, 6.8, 12.0, 18.2, 25.0]) + result = s.interpolate(method="cubic") + tm.assert_series_equal(result, expected) + + def test_interp_limit(self): + s = Series([1, 3, np.nan, np.nan, np.nan, 11]) + + expected = Series([1.0, 3.0, 5.0, 7.0, np.nan, 11.0]) + result = s.interpolate(method="linear", limit=2) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("limit", [-1, 0]) + def test_interpolate_invalid_nonpositive_limit(self, nontemporal_method, limit): + # GH 9217: make sure limit is greater than zero. + s = Series([1, 2, np.nan, 4]) + method, kwargs = nontemporal_method + with pytest.raises(ValueError, match="Limit must be greater than 0"): + s.interpolate(limit=limit, method=method, **kwargs) + + def test_interpolate_invalid_float_limit(self, nontemporal_method): + # GH 9217: make sure limit is an integer. + s = Series([1, 2, np.nan, 4]) + method, kwargs = nontemporal_method + limit = 2.0 + with pytest.raises(ValueError, match="Limit must be an integer"): + s.interpolate(limit=limit, method=method, **kwargs) + + @pytest.mark.parametrize("invalid_method", [None, "nonexistent_method"]) + def test_interp_invalid_method(self, invalid_method): + s = Series([1, 3, np.nan, 12, np.nan, 25]) + + msg = f"method must be one of.* Got '{invalid_method}' instead" + if invalid_method is None: + msg = "'method' should be a string, not None" + with pytest.raises(ValueError, match=msg): + s.interpolate(method=invalid_method) + + # When an invalid method and invalid limit (such as -1) are + # provided, the error message reflects the invalid method. + with pytest.raises(ValueError, match=msg): + s.interpolate(method=invalid_method, limit=-1) + + def test_interp_invalid_method_and_value(self): + # GH#36624 + ser = Series([1, 3, np.nan, 12, np.nan, 25]) + + msg = "'fill_value' is not a valid keyword for Series.interpolate" + msg2 = "Series.interpolate with method=pad" + with pytest.raises(ValueError, match=msg): + with tm.assert_produces_warning(FutureWarning, match=msg2): + ser.interpolate(fill_value=3, method="pad") + + def test_interp_limit_forward(self): + s = Series([1, 3, np.nan, np.nan, np.nan, 11]) + + # Provide 'forward' (the default) explicitly here. + expected = Series([1.0, 3.0, 5.0, 7.0, np.nan, 11.0]) + + result = s.interpolate(method="linear", limit=2, limit_direction="forward") + tm.assert_series_equal(result, expected) + + result = s.interpolate(method="linear", limit=2, limit_direction="FORWARD") + tm.assert_series_equal(result, expected) + + def test_interp_unlimited(self): + # these test are for issue #16282 default Limit=None is unlimited + s = Series([np.nan, 1.0, 3.0, np.nan, np.nan, np.nan, 11.0, np.nan]) + expected = Series([1.0, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 11.0]) + result = s.interpolate(method="linear", limit_direction="both") + tm.assert_series_equal(result, expected) + + expected = Series([np.nan, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 11.0]) + result = s.interpolate(method="linear", limit_direction="forward") + tm.assert_series_equal(result, expected) + + expected = Series([1.0, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, np.nan]) + result = s.interpolate(method="linear", limit_direction="backward") + tm.assert_series_equal(result, expected) + + def test_interp_limit_bad_direction(self): + s = Series([1, 3, np.nan, np.nan, np.nan, 11]) + + msg = ( + r"Invalid limit_direction: expecting one of \['forward', " + r"'backward', 'both'\], got 'abc'" + ) + with pytest.raises(ValueError, match=msg): + s.interpolate(method="linear", limit=2, limit_direction="abc") + + # raises an error even if no limit is specified. + with pytest.raises(ValueError, match=msg): + s.interpolate(method="linear", limit_direction="abc") + + # limit_area introduced GH #16284 + def test_interp_limit_area(self): + # These tests are for issue #9218 -- fill NaNs in both directions. + s = Series([np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan]) + + expected = Series([np.nan, np.nan, 3.0, 4.0, 5.0, 6.0, 7.0, np.nan, np.nan]) + result = s.interpolate(method="linear", limit_area="inside") + tm.assert_series_equal(result, expected) + + expected = Series( + [np.nan, np.nan, 3.0, 4.0, np.nan, np.nan, 7.0, np.nan, np.nan] + ) + result = s.interpolate(method="linear", limit_area="inside", limit=1) + tm.assert_series_equal(result, expected) + + expected = Series([np.nan, np.nan, 3.0, 4.0, np.nan, 6.0, 7.0, np.nan, np.nan]) + result = s.interpolate( + method="linear", limit_area="inside", limit_direction="both", limit=1 + ) + tm.assert_series_equal(result, expected) + + expected = Series([np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, 7.0]) + result = s.interpolate(method="linear", limit_area="outside") + tm.assert_series_equal(result, expected) + + expected = Series( + [np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan] + ) + result = s.interpolate(method="linear", limit_area="outside", limit=1) + tm.assert_series_equal(result, expected) + + expected = Series([np.nan, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan]) + result = s.interpolate( + method="linear", limit_area="outside", limit_direction="both", limit=1 + ) + tm.assert_series_equal(result, expected) + + expected = Series([3.0, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, np.nan, np.nan]) + result = s.interpolate( + method="linear", limit_area="outside", limit_direction="backward" + ) + tm.assert_series_equal(result, expected) + + # raises an error even if limit type is wrong. + msg = r"Invalid limit_area: expecting one of \['inside', 'outside'\], got abc" + with pytest.raises(ValueError, match=msg): + s.interpolate(method="linear", limit_area="abc") + + @pytest.mark.parametrize( + "method, limit_direction, expected", + [ + ("pad", "backward", "forward"), + ("ffill", "backward", "forward"), + ("backfill", "forward", "backward"), + ("bfill", "forward", "backward"), + ("pad", "both", "forward"), + ("ffill", "both", "forward"), + ("backfill", "both", "backward"), + ("bfill", "both", "backward"), + ], + ) + def test_interp_limit_direction_raises(self, method, limit_direction, expected): + # https://github.com/pandas-dev/pandas/pull/34746 + s = Series([1, 2, 3]) + + msg = f"`limit_direction` must be '{expected}' for method `{method}`" + msg2 = "Series.interpolate with method=" + with pytest.raises(ValueError, match=msg): + with tm.assert_produces_warning(FutureWarning, match=msg2): + s.interpolate(method=method, limit_direction=limit_direction) + + @pytest.mark.parametrize( + "data, expected_data, kwargs", + ( + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [np.nan, np.nan, 3.0, 3.0, 3.0, 3.0, 7.0, np.nan, np.nan], + {"method": "pad", "limit_area": "inside"}, + ), + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [np.nan, np.nan, 3.0, 3.0, np.nan, np.nan, 7.0, np.nan, np.nan], + {"method": "pad", "limit_area": "inside", "limit": 1}, + ), + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, 7.0], + {"method": "pad", "limit_area": "outside"}, + ), + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan], + {"method": "pad", "limit_area": "outside", "limit": 1}, + ), + ( + [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan], + {"method": "pad", "limit_area": "outside", "limit": 1}, + ), + ( + range(5), + range(5), + {"method": "pad", "limit_area": "outside", "limit": 1}, + ), + ), + ) + def test_interp_limit_area_with_pad(self, data, expected_data, kwargs): + # GH26796 + + s = Series(data) + expected = Series(expected_data) + msg = "Series.interpolate with method=pad" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = s.interpolate(**kwargs) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "data, expected_data, kwargs", + ( + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [np.nan, np.nan, 3.0, 7.0, 7.0, 7.0, 7.0, np.nan, np.nan], + {"method": "bfill", "limit_area": "inside"}, + ), + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [np.nan, np.nan, 3.0, np.nan, np.nan, 7.0, 7.0, np.nan, np.nan], + {"method": "bfill", "limit_area": "inside", "limit": 1}, + ), + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [3.0, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, np.nan, np.nan], + {"method": "bfill", "limit_area": "outside"}, + ), + ( + [np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan], + [np.nan, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, np.nan, np.nan], + {"method": "bfill", "limit_area": "outside", "limit": 1}, + ), + ), + ) + def test_interp_limit_area_with_backfill(self, data, expected_data, kwargs): + # GH26796 + + s = Series(data) + expected = Series(expected_data) + msg = "Series.interpolate with method=bfill" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = s.interpolate(**kwargs) + tm.assert_series_equal(result, expected) + + def test_interp_limit_direction(self): + # These tests are for issue #9218 -- fill NaNs in both directions. + s = Series([1, 3, np.nan, np.nan, np.nan, 11]) + + expected = Series([1.0, 3.0, np.nan, 7.0, 9.0, 11.0]) + result = s.interpolate(method="linear", limit=2, limit_direction="backward") + tm.assert_series_equal(result, expected) + + expected = Series([1.0, 3.0, 5.0, np.nan, 9.0, 11.0]) + result = s.interpolate(method="linear", limit=1, limit_direction="both") + tm.assert_series_equal(result, expected) + + # Check that this works on a longer series of nans. + s = Series([1, 3, np.nan, np.nan, np.nan, 7, 9, np.nan, np.nan, 12, np.nan]) + + expected = Series([1.0, 3.0, 4.0, 5.0, 6.0, 7.0, 9.0, 10.0, 11.0, 12.0, 12.0]) + result = s.interpolate(method="linear", limit=2, limit_direction="both") + tm.assert_series_equal(result, expected) + + expected = Series( + [1.0, 3.0, 4.0, np.nan, 6.0, 7.0, 9.0, 10.0, 11.0, 12.0, 12.0] + ) + result = s.interpolate(method="linear", limit=1, limit_direction="both") + tm.assert_series_equal(result, expected) + + def test_interp_limit_to_ends(self): + # These test are for issue #10420 -- flow back to beginning. + s = Series([np.nan, np.nan, 5, 7, 9, np.nan]) + + expected = Series([5.0, 5.0, 5.0, 7.0, 9.0, np.nan]) + result = s.interpolate(method="linear", limit=2, limit_direction="backward") + tm.assert_series_equal(result, expected) + + expected = Series([5.0, 5.0, 5.0, 7.0, 9.0, 9.0]) + result = s.interpolate(method="linear", limit=2, limit_direction="both") + tm.assert_series_equal(result, expected) + + def test_interp_limit_before_ends(self): + # These test are for issue #11115 -- limit ends properly. + s = Series([np.nan, np.nan, 5, 7, np.nan, np.nan]) + + expected = Series([np.nan, np.nan, 5.0, 7.0, 7.0, np.nan]) + result = s.interpolate(method="linear", limit=1, limit_direction="forward") + tm.assert_series_equal(result, expected) + + expected = Series([np.nan, 5.0, 5.0, 7.0, np.nan, np.nan]) + result = s.interpolate(method="linear", limit=1, limit_direction="backward") + tm.assert_series_equal(result, expected) + + expected = Series([np.nan, 5.0, 5.0, 7.0, 7.0, np.nan]) + result = s.interpolate(method="linear", limit=1, limit_direction="both") + tm.assert_series_equal(result, expected) + + def test_interp_all_good(self): + pytest.importorskip("scipy") + s = Series([1, 2, 3]) + result = s.interpolate(method="polynomial", order=1) + tm.assert_series_equal(result, s) + + # non-scipy + result = s.interpolate() + tm.assert_series_equal(result, s) + + @pytest.mark.parametrize( + "check_scipy", [False, pytest.param(True, marks=td.skip_if_no("scipy"))] + ) + def test_interp_multiIndex(self, check_scipy): + idx = MultiIndex.from_tuples([(0, "a"), (1, "b"), (2, "c")]) + s = Series([1, 2, np.nan], index=idx) + + expected = s.copy() + expected.loc[2] = 2 + result = s.interpolate() + tm.assert_series_equal(result, expected) + + msg = "Only `method=linear` interpolation is supported on MultiIndexes" + if check_scipy: + with pytest.raises(ValueError, match=msg): + s.interpolate(method="polynomial", order=1) + + def test_interp_nonmono_raise(self): + pytest.importorskip("scipy") + s = Series([1, np.nan, 3], index=[0, 2, 1]) + msg = "krogh interpolation requires that the index be monotonic" + with pytest.raises(ValueError, match=msg): + s.interpolate(method="krogh") + + @pytest.mark.parametrize("method", ["nearest", "pad"]) + def test_interp_datetime64(self, method, tz_naive_fixture): + pytest.importorskip("scipy") + df = Series( + [1, np.nan, 3], index=date_range("1/1/2000", periods=3, tz=tz_naive_fixture) + ) + warn = None if method == "nearest" else FutureWarning + msg = "Series.interpolate with method=pad is deprecated" + with tm.assert_produces_warning(warn, match=msg): + result = df.interpolate(method=method) + if warn is not None: + # check the "use ffill instead" is equivalent + alt = df.ffill() + tm.assert_series_equal(result, alt) + + expected = Series( + [1.0, 1.0, 3.0], + index=date_range("1/1/2000", periods=3, tz=tz_naive_fixture), + ) + tm.assert_series_equal(result, expected) + + def test_interp_pad_datetime64tz_values(self): + # GH#27628 missing.interpolate_2d should handle datetimetz values + dti = date_range("2015-04-05", periods=3, tz="US/Central") + ser = Series(dti) + ser[1] = pd.NaT + + msg = "Series.interpolate with method=pad is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = ser.interpolate(method="pad") + # check the "use ffill instead" is equivalent + alt = ser.ffill() + tm.assert_series_equal(result, alt) + + expected = Series(dti) + expected[1] = expected[0] + tm.assert_series_equal(result, expected) + + def test_interp_limit_no_nans(self): + # GH 7173 + s = Series([1.0, 2.0, 3.0]) + result = s.interpolate(limit=1) + expected = s + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("method", ["polynomial", "spline"]) + def test_no_order(self, method): + # see GH-10633, GH-24014 + pytest.importorskip("scipy") + s = Series([0, 1, np.nan, 3]) + msg = "You must specify the order of the spline or polynomial" + with pytest.raises(ValueError, match=msg): + s.interpolate(method=method) + + @pytest.mark.parametrize("order", [-1, -1.0, 0, 0.0, np.nan]) + def test_interpolate_spline_invalid_order(self, order): + pytest.importorskip("scipy") + s = Series([0, 1, np.nan, 3]) + msg = "order needs to be specified and greater than 0" + with pytest.raises(ValueError, match=msg): + s.interpolate(method="spline", order=order) + + def test_spline(self): + pytest.importorskip("scipy") + s = Series([1, 2, np.nan, 4, 5, np.nan, 7]) + result = s.interpolate(method="spline", order=1) + expected = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]) + tm.assert_series_equal(result, expected) + + def test_spline_extrapolate(self): + pytest.importorskip("scipy") + s = Series([1, 2, 3, 4, np.nan, 6, np.nan]) + result3 = s.interpolate(method="spline", order=1, ext=3) + expected3 = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 6.0]) + tm.assert_series_equal(result3, expected3) + + result1 = s.interpolate(method="spline", order=1, ext=0) + expected1 = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]) + tm.assert_series_equal(result1, expected1) + + def test_spline_smooth(self): + pytest.importorskip("scipy") + s = Series([1, 2, np.nan, 4, 5.1, np.nan, 7]) + assert ( + s.interpolate(method="spline", order=3, s=0)[5] + != s.interpolate(method="spline", order=3)[5] + ) + + def test_spline_interpolation(self): + # Explicit cast to float to avoid implicit cast when setting np.nan + pytest.importorskip("scipy") + s = Series(np.arange(10) ** 2, dtype="float") + s[np.random.default_rng(2).integers(0, 9, 3)] = np.nan + result1 = s.interpolate(method="spline", order=1) + expected1 = s.interpolate(method="spline", order=1) + tm.assert_series_equal(result1, expected1) + + def test_interp_timedelta64(self): + # GH 6424 + df = Series([1, np.nan, 3], index=pd.to_timedelta([1, 2, 3])) + result = df.interpolate(method="time") + expected = Series([1.0, 2.0, 3.0], index=pd.to_timedelta([1, 2, 3])) + tm.assert_series_equal(result, expected) + + # test for non uniform spacing + df = Series([1, np.nan, 3], index=pd.to_timedelta([1, 2, 4])) + result = df.interpolate(method="time") + expected = Series([1.0, 1.666667, 3.0], index=pd.to_timedelta([1, 2, 4])) + tm.assert_series_equal(result, expected) + + def test_series_interpolate_method_values(self): + # GH#1646 + rng = date_range("1/1/2000", "1/20/2000", freq="D") + ts = Series(np.random.default_rng(2).standard_normal(len(rng)), index=rng) + + ts[::2] = np.nan + + result = ts.interpolate(method="values") + exp = ts.interpolate() + tm.assert_series_equal(result, exp) + + def test_series_interpolate_intraday(self): + # #1698 + index = date_range("1/1/2012", periods=4, freq="12D") + ts = Series([0, 12, 24, 36], index) + new_index = index.append(index + pd.DateOffset(days=1)).sort_values() + + exp = ts.reindex(new_index).interpolate(method="time") + + index = date_range("1/1/2012", periods=4, freq="12h") + ts = Series([0, 12, 24, 36], index) + new_index = index.append(index + pd.DateOffset(hours=1)).sort_values() + result = ts.reindex(new_index).interpolate(method="time") + + tm.assert_numpy_array_equal(result.values, exp.values) + + @pytest.mark.parametrize( + "ind", + [ + ["a", "b", "c", "d"], + pd.period_range(start="2019-01-01", periods=4), + pd.interval_range(start=0, end=4), + ], + ) + def test_interp_non_timedelta_index(self, interp_methods_ind, ind): + # gh 21662 + df = pd.DataFrame([0, 1, np.nan, 3], index=ind) + + method, kwargs = interp_methods_ind + if method == "pchip": + pytest.importorskip("scipy") + + if method == "linear": + result = df[0].interpolate(**kwargs) + expected = Series([0.0, 1.0, 2.0, 3.0], name=0, index=ind) + tm.assert_series_equal(result, expected) + else: + expected_error = ( + "Index column must be numeric or datetime type when " + f"using {method} method other than linear. " + "Try setting a numeric or datetime index column before " + "interpolating." + ) + with pytest.raises(ValueError, match=expected_error): + df[0].interpolate(method=method, **kwargs) + + def test_interpolate_timedelta_index(self, request, interp_methods_ind): + """ + Tests for non numerical index types - object, period, timedelta + Note that all methods except time, index, nearest and values + are tested here. + """ + # gh 21662 + pytest.importorskip("scipy") + ind = pd.timedelta_range(start=1, periods=4) + df = pd.DataFrame([0, 1, np.nan, 3], index=ind) + + method, kwargs = interp_methods_ind + + if method in {"cubic", "zero"}: + request.applymarker( + pytest.mark.xfail( + reason=f"{method} interpolation is not supported for TimedeltaIndex" + ) + ) + result = df[0].interpolate(method=method, **kwargs) + expected = Series([0.0, 1.0, 2.0, 3.0], name=0, index=ind) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "ascending, expected_values", + [(True, [1, 2, 3, 9, 10]), (False, [10, 9, 3, 2, 1])], + ) + def test_interpolate_unsorted_index(self, ascending, expected_values): + # GH 21037 + ts = Series(data=[10, 9, np.nan, 2, 1], index=[10, 9, 3, 2, 1]) + result = ts.sort_index(ascending=ascending).interpolate(method="index") + expected = Series(data=expected_values, index=expected_values, dtype=float) + tm.assert_series_equal(result, expected) + + def test_interpolate_asfreq_raises(self): + ser = Series(["a", None, "b"], dtype=object) + msg2 = "Series.interpolate with object dtype" + msg = "Invalid fill method" + with pytest.raises(ValueError, match=msg): + with tm.assert_produces_warning(FutureWarning, match=msg2): + ser.interpolate(method="asfreq") + + def test_interpolate_fill_value(self): + # GH#54920 + pytest.importorskip("scipy") + ser = Series([np.nan, 0, 1, np.nan, 3, np.nan]) + result = ser.interpolate(method="nearest", fill_value=0) + expected = Series([np.nan, 0, 1, 1, 3, 0]) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_is_monotonic.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_is_monotonic.py new file mode 100644 index 0000000000000000000000000000000000000000..073ec4172aff6b041d29011bc3151f84f3cbeb19 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_is_monotonic.py @@ -0,0 +1,26 @@ +import numpy as np + +from pandas import ( + Series, + date_range, +) + + +class TestIsMonotonic: + def test_is_monotonic_numeric(self): + ser = Series(np.random.default_rng(2).integers(0, 10, size=1000)) + assert not ser.is_monotonic_increasing + ser = Series(np.arange(1000)) + assert ser.is_monotonic_increasing is True + assert ser.is_monotonic_increasing is True + ser = Series(np.arange(1000, 0, -1)) + assert ser.is_monotonic_decreasing is True + + def test_is_monotonic_dt64(self): + ser = Series(date_range("20130101", periods=10)) + assert ser.is_monotonic_increasing is True + assert ser.is_monotonic_increasing is True + + ser = Series(list(reversed(ser))) + assert ser.is_monotonic_increasing is False + assert ser.is_monotonic_decreasing is True diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_is_unique.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_is_unique.py new file mode 100644 index 0000000000000000000000000000000000000000..edf3839c2cebb6f51d0bb21b06ea8a1c47dec0fe --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_is_unique.py @@ -0,0 +1,40 @@ +import numpy as np +import pytest + +from pandas import Series + + +@pytest.mark.parametrize( + "data, expected", + [ + (np.random.default_rng(2).integers(0, 10, size=1000), False), + (np.arange(1000), True), + ([], True), + ([np.nan], True), + (["foo", "bar", np.nan], True), + (["foo", "foo", np.nan], False), + (["foo", "bar", np.nan, np.nan], False), + ], +) +def test_is_unique(data, expected): + # GH#11946 / GH#25180 + ser = Series(data) + assert ser.is_unique is expected + + +def test_is_unique_class_ne(capsys): + # GH#20661 + class Foo: + def __init__(self, val) -> None: + self._value = val + + def __ne__(self, other): + raise Exception("NEQ not supported") + + with capsys.disabled(): + li = [Foo(i) for i in range(5)] + ser = Series(li, index=list(range(5))) + + ser.is_unique + captured = capsys.readouterr() + assert len(captured.err) == 0 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_isin.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_isin.py new file mode 100644 index 0000000000000000000000000000000000000000..f94f67b8cc40a2031cab0791ccaf2fbc207b5023 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_isin.py @@ -0,0 +1,252 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Series, + date_range, +) +import pandas._testing as tm +from pandas.core import algorithms +from pandas.core.arrays import PeriodArray + + +class TestSeriesIsIn: + def test_isin(self): + s = Series(["A", "B", "C", "a", "B", "B", "A", "C"]) + + result = s.isin(["A", "C"]) + expected = Series([True, False, True, False, False, False, True, True]) + tm.assert_series_equal(result, expected) + + # GH#16012 + # This specific issue has to have a series over 1e6 in len, but the + # comparison array (in_list) must be large enough so that numpy doesn't + # do a manual masking trick that will avoid this issue altogether + s = Series(list("abcdefghijk" * 10**5)) + # If numpy doesn't do the manual comparison/mask, these + # unorderable mixed types are what cause the exception in numpy + in_list = [-1, "a", "b", "G", "Y", "Z", "E", "K", "E", "S", "I", "R", "R"] * 6 + + assert s.isin(in_list).sum() == 200000 + + def test_isin_with_string_scalar(self): + # GH#4763 + s = Series(["A", "B", "C", "a", "B", "B", "A", "C"]) + msg = ( + r"only list-like objects are allowed to be passed to isin\(\), " + r"you passed a `str`" + ) + with pytest.raises(TypeError, match=msg): + s.isin("a") + + s = Series(["aaa", "b", "c"]) + with pytest.raises(TypeError, match=msg): + s.isin("aaa") + + def test_isin_datetimelike_mismatched_reso(self): + expected = Series([True, True, False, False, False]) + + ser = Series(date_range("jan-01-2013", "jan-05-2013")) + + # fails on dtype conversion in the first place + day_values = np.asarray(ser[0:2].values).astype("datetime64[D]") + result = ser.isin(day_values) + tm.assert_series_equal(result, expected) + + dta = ser[:2]._values.astype("M8[s]") + result = ser.isin(dta) + tm.assert_series_equal(result, expected) + + def test_isin_datetimelike_mismatched_reso_list(self): + expected = Series([True, True, False, False, False]) + + ser = Series(date_range("jan-01-2013", "jan-05-2013")) + + dta = ser[:2]._values.astype("M8[s]") + result = ser.isin(list(dta)) + tm.assert_series_equal(result, expected) + + def test_isin_with_i8(self): + # GH#5021 + + expected = Series([True, True, False, False, False]) + expected2 = Series([False, True, False, False, False]) + + # datetime64[ns] + s = Series(date_range("jan-01-2013", "jan-05-2013")) + + result = s.isin(s[0:2]) + tm.assert_series_equal(result, expected) + + result = s.isin(s[0:2].values) + tm.assert_series_equal(result, expected) + + result = s.isin([s[1]]) + tm.assert_series_equal(result, expected2) + + result = s.isin([np.datetime64(s[1])]) + tm.assert_series_equal(result, expected2) + + result = s.isin(set(s[0:2])) + tm.assert_series_equal(result, expected) + + # timedelta64[ns] + s = Series(pd.to_timedelta(range(5), unit="d")) + result = s.isin(s[0:2]) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("empty", [[], Series(dtype=object), np.array([])]) + def test_isin_empty(self, empty): + # see GH#16991 + s = Series(["a", "b"]) + expected = Series([False, False]) + + result = s.isin(empty) + tm.assert_series_equal(expected, result) + + def test_isin_read_only(self): + # https://github.com/pandas-dev/pandas/issues/37174 + arr = np.array([1, 2, 3]) + arr.setflags(write=False) + s = Series([1, 2, 3]) + result = s.isin(arr) + expected = Series([True, True, True]) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("dtype", [object, None]) + def test_isin_dt64_values_vs_ints(self, dtype): + # GH#36621 dont cast integers to datetimes for isin + dti = date_range("2013-01-01", "2013-01-05") + ser = Series(dti) + + comps = np.asarray([1356998400000000000], dtype=dtype) + + res = dti.isin(comps) + expected = np.array([False] * len(dti), dtype=bool) + tm.assert_numpy_array_equal(res, expected) + + res = ser.isin(comps) + tm.assert_series_equal(res, Series(expected)) + + res = pd.core.algorithms.isin(ser, comps) + tm.assert_numpy_array_equal(res, expected) + + def test_isin_tzawareness_mismatch(self): + dti = date_range("2013-01-01", "2013-01-05") + ser = Series(dti) + + other = dti.tz_localize("UTC") + + res = dti.isin(other) + expected = np.array([False] * len(dti), dtype=bool) + tm.assert_numpy_array_equal(res, expected) + + res = ser.isin(other) + tm.assert_series_equal(res, Series(expected)) + + res = pd.core.algorithms.isin(ser, other) + tm.assert_numpy_array_equal(res, expected) + + def test_isin_period_freq_mismatch(self): + dti = date_range("2013-01-01", "2013-01-05") + pi = dti.to_period("M") + ser = Series(pi) + + # We construct another PeriodIndex with the same i8 values + # but different dtype + dtype = dti.to_period("Y").dtype + other = PeriodArray._simple_new(pi.asi8, dtype=dtype) + + res = pi.isin(other) + expected = np.array([False] * len(pi), dtype=bool) + tm.assert_numpy_array_equal(res, expected) + + res = ser.isin(other) + tm.assert_series_equal(res, Series(expected)) + + res = pd.core.algorithms.isin(ser, other) + tm.assert_numpy_array_equal(res, expected) + + @pytest.mark.parametrize("values", [[-9.0, 0.0], [-9, 0]]) + def test_isin_float_in_int_series(self, values): + # GH#19356 GH#21804 + ser = Series(values) + result = ser.isin([-9, -0.5]) + expected = Series([True, False]) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("dtype", ["boolean", "Int64", "Float64"]) + @pytest.mark.parametrize( + "data,values,expected", + [ + ([0, 1, 0], [1], [False, True, False]), + ([0, 1, 0], [1, pd.NA], [False, True, False]), + ([0, pd.NA, 0], [1, 0], [True, False, True]), + ([0, 1, pd.NA], [1, pd.NA], [False, True, True]), + ([0, 1, pd.NA], [1, np.nan], [False, True, False]), + ([0, pd.NA, pd.NA], [np.nan, pd.NaT, None], [False, False, False]), + ], + ) + def test_isin_masked_types(self, dtype, data, values, expected): + # GH#42405 + ser = Series(data, dtype=dtype) + + result = ser.isin(values) + expected = Series(expected, dtype="boolean") + + tm.assert_series_equal(result, expected) + + +def test_isin_large_series_mixed_dtypes_and_nan(monkeypatch): + # https://github.com/pandas-dev/pandas/issues/37094 + # combination of object dtype for the values + # and > _MINIMUM_COMP_ARR_LEN elements + min_isin_comp = 5 + ser = Series([1, 2, np.nan] * min_isin_comp) + with monkeypatch.context() as m: + m.setattr(algorithms, "_MINIMUM_COMP_ARR_LEN", min_isin_comp) + result = ser.isin({"foo", "bar"}) + expected = Series([False] * 3 * min_isin_comp) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "array,expected", + [ + ( + [0, 1j, 1j, 1, 1 + 1j, 1 + 2j, 1 + 1j], + Series([False, True, True, False, True, True, True], dtype=bool), + ) + ], +) +def test_isin_complex_numbers(array, expected): + # GH 17927 + result = Series(array).isin([1j, 1 + 1j, 1 + 2j]) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "data,is_in", + [([1, [2]], [1]), (["simple str", [{"values": 3}]], ["simple str"])], +) +def test_isin_filtering_with_mixed_object_types(data, is_in): + # GH 20883 + + ser = Series(data) + result = ser.isin(is_in) + expected = Series([True, False]) + + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("data", [[1, 2, 3], [1.0, 2.0, 3.0]]) +@pytest.mark.parametrize("isin", [[1, 2], [1.0, 2.0]]) +def test_isin_filtering_on_iterable(data, isin): + # GH 50234 + + ser = Series(data) + result = ser.isin(i for i in isin) + expected_result = Series([True, True, False]) + + tm.assert_series_equal(result, expected_result) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_isna.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_isna.py new file mode 100644 index 0000000000000000000000000000000000000000..7e324aa86a052246a074950082e272fee7e505e3 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_isna.py @@ -0,0 +1,35 @@ +""" +We also test Series.notna in this file. +""" +import numpy as np + +from pandas import ( + Period, + Series, +) +import pandas._testing as tm + + +class TestIsna: + def test_isna_period_dtype(self): + # GH#13737 + ser = Series([Period("2011-01", freq="M"), Period("NaT", freq="M")]) + + expected = Series([False, True]) + + result = ser.isna() + tm.assert_series_equal(result, expected) + + result = ser.notna() + tm.assert_series_equal(result, ~expected) + + def test_isna(self): + ser = Series([0, 5.4, 3, np.nan, -0.001]) + expected = Series([False, False, False, True, False]) + tm.assert_series_equal(ser.isna(), expected) + tm.assert_series_equal(ser.notna(), ~expected) + + ser = Series(["hi", "", np.nan]) + expected = Series([False, False, True]) + tm.assert_series_equal(ser.isna(), expected) + tm.assert_series_equal(ser.notna(), ~expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_item.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_item.py new file mode 100644 index 0000000000000000000000000000000000000000..8e8c33619d564ef87d51416748b8fdc9058e5a41 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_item.py @@ -0,0 +1,59 @@ +""" +Series.item method, mainly testing that we get python scalars as opposed to +numpy scalars. +""" +import pytest + +from pandas import ( + Series, + Timedelta, + Timestamp, + date_range, +) + + +class TestItem: + def test_item(self): + # We are testing that we get python scalars as opposed to numpy scalars + ser = Series([1]) + result = ser.item() + assert result == 1 + assert result == ser.iloc[0] + assert isinstance(result, int) # i.e. not np.int64 + + ser = Series([0.5], index=[3]) + result = ser.item() + assert isinstance(result, float) + assert result == 0.5 + + ser = Series([1, 2]) + msg = "can only convert an array of size 1" + with pytest.raises(ValueError, match=msg): + ser.item() + + dti = date_range("2016-01-01", periods=2) + with pytest.raises(ValueError, match=msg): + dti.item() + with pytest.raises(ValueError, match=msg): + Series(dti).item() + + val = dti[:1].item() + assert isinstance(val, Timestamp) + val = Series(dti)[:1].item() + assert isinstance(val, Timestamp) + + tdi = dti - dti + with pytest.raises(ValueError, match=msg): + tdi.item() + with pytest.raises(ValueError, match=msg): + Series(tdi).item() + + val = tdi[:1].item() + assert isinstance(val, Timedelta) + val = Series(tdi)[:1].item() + assert isinstance(val, Timedelta) + + # Case where ser[0] would not work + ser = Series(dti, index=[5, 6]) + val = ser.iloc[:1].item() + assert val == dti[0] diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_map.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_map.py new file mode 100644 index 0000000000000000000000000000000000000000..f33f5edb5ee66d93a2ec9d8135a81217853970b8 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_map.py @@ -0,0 +1,604 @@ +from collections import ( + Counter, + defaultdict, +) +from decimal import Decimal +import math + +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + Index, + MultiIndex, + Series, + bdate_range, + date_range, + isna, + timedelta_range, +) +import pandas._testing as tm + + +def test_series_map_box_timedelta(): + # GH#11349 + ser = Series(timedelta_range("1 day 1 s", periods=5, freq="h")) + + def f(x): + return x.total_seconds() + + ser.map(f) + + +def test_map_callable(datetime_series): + with np.errstate(all="ignore"): + tm.assert_series_equal(datetime_series.map(np.sqrt), np.sqrt(datetime_series)) + + # map function element-wise + tm.assert_series_equal(datetime_series.map(math.exp), np.exp(datetime_series)) + + # empty series + s = Series(dtype=object, name="foo", index=Index([], name="bar")) + rs = s.map(lambda x: x) + tm.assert_series_equal(s, rs) + + # check all metadata (GH 9322) + assert s is not rs + assert s.index is rs.index + assert s.dtype == rs.dtype + assert s.name == rs.name + + # index but no data + s = Series(index=[1, 2, 3], dtype=np.float64) + rs = s.map(lambda x: x) + tm.assert_series_equal(s, rs) + + +def test_map_same_length_inference_bug(): + s = Series([1, 2]) + + def f(x): + return (x, x + 1) + + s = Series([1, 2, 3]) + result = s.map(f) + expected = Series([(1, 2), (2, 3), (3, 4)]) + tm.assert_series_equal(result, expected) + + s = Series(["foo,bar"]) + result = s.map(lambda x: x.split(",")) + expected = Series([("foo", "bar")]) + tm.assert_series_equal(result, expected) + + +def test_series_map_box_timestamps(): + # GH#2689, GH#2627 + ser = Series(date_range("1/1/2000", periods=3)) + + def func(x): + return (x.hour, x.day, x.month) + + result = ser.map(func) + expected = Series([(0, 1, 1), (0, 2, 1), (0, 3, 1)]) + tm.assert_series_equal(result, expected) + + +def test_map_series_stringdtype(any_string_dtype, using_infer_string): + # map test on StringDType, GH#40823 + ser1 = Series( + data=["cat", "dog", "rabbit"], + index=["id1", "id2", "id3"], + dtype=any_string_dtype, + ) + ser2 = Series(["id3", "id2", "id1", "id7000"], dtype=any_string_dtype) + result = ser2.map(ser1) + + item = pd.NA + if ser2.dtype == object: + item = np.nan + + expected = Series(data=["rabbit", "dog", "cat", item], dtype=any_string_dtype) + if using_infer_string and any_string_dtype == "object": + expected = expected.astype("str") + + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "data, expected_dtype", + [(["1-1", "1-1", np.nan], "category"), (["1-1", "1-2", np.nan], "str")], +) +def test_map_categorical_with_nan_values(data, expected_dtype): + # GH 20714 bug fixed in: GH 24275 + def func(val): + return val.split("-")[0] + + s = Series(data, dtype="category") + + result = s.map(func, na_action="ignore") + expected = Series(["1", "1", np.nan], dtype=expected_dtype) + tm.assert_series_equal(result, expected) + + +def test_map_empty_integer_series(): + # GH52384 + s = Series([], dtype=int) + result = s.map(lambda x: x) + tm.assert_series_equal(result, s) + + +def test_map_empty_integer_series_with_datetime_index(): + # GH 21245 + s = Series([], index=date_range(start="2018-01-01", periods=0), dtype=int) + result = s.map(lambda x: x) + tm.assert_series_equal(result, s) + + +@pytest.mark.parametrize("func", [str, lambda x: str(x)]) +def test_map_simple_str_callables_same_as_astype( + string_series, func, using_infer_string +): + # test that we are evaluating row-by-row first + # before vectorized evaluation + result = string_series.map(func) + expected = string_series.astype(str if not using_infer_string else "str") + tm.assert_series_equal(result, expected) + + +def test_list_raises(string_series): + with pytest.raises(TypeError, match="'list' object is not callable"): + string_series.map([lambda x: x]) + + +def test_map(): + data = { + "A": [0.0, 1.0, 2.0, 3.0, 4.0], + "B": [0.0, 1.0, 0.0, 1.0, 0.0], + "C": ["foo1", "foo2", "foo3", "foo4", "foo5"], + "D": bdate_range("1/1/2009", periods=5), + } + + source = Series(data["B"], index=data["C"]) + target = Series(data["C"][:4], index=data["D"][:4]) + + merged = target.map(source) + + for k, v in merged.items(): + assert v == source[target[k]] + + # input could be a dict + merged = target.map(source.to_dict()) + + for k, v in merged.items(): + assert v == source[target[k]] + + +def test_map_datetime(datetime_series): + # function + result = datetime_series.map(lambda x: x * 2) + tm.assert_series_equal(result, datetime_series * 2) + + +def test_map_category(): + # GH 10324 + a = Series([1, 2, 3, 4]) + b = Series(["even", "odd", "even", "odd"], dtype="category") + c = Series(["even", "odd", "even", "odd"]) + + exp = Series(["odd", "even", "odd", np.nan], dtype="category") + tm.assert_series_equal(a.map(b), exp) + exp = Series(["odd", "even", "odd", np.nan]) + tm.assert_series_equal(a.map(c), exp) + + +def test_map_category_numeric(): + a = Series(["a", "b", "c", "d"]) + b = Series([1, 2, 3, 4], index=pd.CategoricalIndex(["b", "c", "d", "e"])) + c = Series([1, 2, 3, 4], index=Index(["b", "c", "d", "e"])) + + exp = Series([np.nan, 1, 2, 3]) + tm.assert_series_equal(a.map(b), exp) + exp = Series([np.nan, 1, 2, 3]) + tm.assert_series_equal(a.map(c), exp) + + +def test_map_category_string(): + a = Series(["a", "b", "c", "d"]) + b = Series( + ["B", "C", "D", "E"], + dtype="category", + index=pd.CategoricalIndex(["b", "c", "d", "e"]), + ) + c = Series(["B", "C", "D", "E"], index=Index(["b", "c", "d", "e"])) + + exp = Series( + pd.Categorical([np.nan, "B", "C", "D"], categories=["B", "C", "D", "E"]) + ) + tm.assert_series_equal(a.map(b), exp) + exp = Series([np.nan, "B", "C", "D"]) + tm.assert_series_equal(a.map(c), exp) + + +@pytest.mark.filterwarnings(r"ignore:Dtype inference:FutureWarning") +def test_map_empty(request, index): + if isinstance(index, MultiIndex): + request.applymarker( + pytest.mark.xfail( + reason="Initializing a Series from a MultiIndex is not supported" + ) + ) + + s = Series(index) + result = s.map({}) + + expected = Series(np.nan, index=s.index) + tm.assert_series_equal(result, expected) + + +def test_map_compat(): + # related GH 8024 + s = Series([True, True, False], index=[1, 2, 3]) + result = s.map({True: "foo", False: "bar"}) + expected = Series(["foo", "foo", "bar"], index=[1, 2, 3]) + tm.assert_series_equal(result, expected) + + +def test_map_int(): + left = Series({"a": 1.0, "b": 2.0, "c": 3.0, "d": 4}) + right = Series({1: 11, 2: 22, 3: 33}) + + assert left.dtype == np.float64 + assert issubclass(right.dtype.type, np.integer) + + merged = left.map(right) + assert merged.dtype == np.float64 + assert isna(merged["d"]) + assert not isna(merged["c"]) + + +def test_map_type_inference(): + s = Series(range(3)) + s2 = s.map(lambda x: np.where(x == 0, 0, 1)) + assert issubclass(s2.dtype.type, np.integer) + + +def test_map_decimal(string_series): + result = string_series.map(lambda x: Decimal(str(x))) + assert result.dtype == np.object_ + assert isinstance(result.iloc[0], Decimal) + + +def test_map_na_exclusion(): + s = Series([1.5, np.nan, 3, np.nan, 5]) + + result = s.map(lambda x: x * 2, na_action="ignore") + exp = s * 2 + tm.assert_series_equal(result, exp) + + +def test_map_dict_with_tuple_keys(): + """ + Due to new MultiIndex-ing behaviour in v0.14.0, + dicts with tuple keys passed to map were being + converted to a multi-index, preventing tuple values + from being mapped properly. + """ + # GH 18496 + df = DataFrame({"a": [(1,), (2,), (3, 4), (5, 6)]}) + label_mappings = {(1,): "A", (2,): "B", (3, 4): "A", (5, 6): "B"} + + df["labels"] = df["a"].map(label_mappings) + df["expected_labels"] = Series(["A", "B", "A", "B"], index=df.index) + # All labels should be filled now + tm.assert_series_equal(df["labels"], df["expected_labels"], check_names=False) + + +def test_map_counter(): + s = Series(["a", "b", "c"], index=[1, 2, 3]) + counter = Counter() + counter["b"] = 5 + counter["c"] += 1 + result = s.map(counter) + expected = Series([0, 5, 1], index=[1, 2, 3]) + tm.assert_series_equal(result, expected) + + +def test_map_defaultdict(): + s = Series([1, 2, 3], index=["a", "b", "c"]) + default_dict = defaultdict(lambda: "blank") + default_dict[1] = "stuff" + result = s.map(default_dict) + expected = Series(["stuff", "blank", "blank"], index=["a", "b", "c"]) + tm.assert_series_equal(result, expected) + + +def test_map_dict_na_key(): + # https://github.com/pandas-dev/pandas/issues/17648 + # Checks that np.nan key is appropriately mapped + s = Series([1, 2, np.nan]) + expected = Series(["a", "b", "c"]) + result = s.map({1: "a", 2: "b", np.nan: "c"}) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("na_action", [None, "ignore"]) +def test_map_defaultdict_na_key(na_action): + # GH 48813 + s = Series([1, 2, np.nan]) + default_map = defaultdict(lambda: "missing", {1: "a", 2: "b", np.nan: "c"}) + result = s.map(default_map, na_action=na_action) + expected = Series({0: "a", 1: "b", 2: "c" if na_action is None else np.nan}) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("na_action", [None, "ignore"]) +def test_map_defaultdict_missing_key(na_action): + # GH 48813 + s = Series([1, 2, np.nan]) + default_map = defaultdict(lambda: "missing", {1: "a", 2: "b", 3: "c"}) + result = s.map(default_map, na_action=na_action) + expected = Series({0: "a", 1: "b", 2: "missing" if na_action is None else np.nan}) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("na_action", [None, "ignore"]) +def test_map_defaultdict_unmutated(na_action): + # GH 48813 + s = Series([1, 2, np.nan]) + default_map = defaultdict(lambda: "missing", {1: "a", 2: "b", np.nan: "c"}) + expected_default_map = default_map.copy() + s.map(default_map, na_action=na_action) + assert default_map == expected_default_map + + +@pytest.mark.parametrize("arg_func", [dict, Series]) +def test_map_dict_ignore_na(arg_func): + # GH#47527 + mapping = arg_func({1: 10, np.nan: 42}) + ser = Series([1, np.nan, 2]) + result = ser.map(mapping, na_action="ignore") + expected = Series([10, np.nan, np.nan]) + tm.assert_series_equal(result, expected) + + +def test_map_defaultdict_ignore_na(): + # GH#47527 + mapping = defaultdict(int, {1: 10, np.nan: 42}) + ser = Series([1, np.nan, 2]) + result = ser.map(mapping) + expected = Series([10, 42, 0]) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "na_action, expected", + [(None, Series([10.0, 42.0, np.nan])), ("ignore", Series([10, np.nan, np.nan]))], +) +def test_map_categorical_na_ignore(na_action, expected): + # GH#47527 + values = pd.Categorical([1, np.nan, 2], categories=[10, 1, 2]) + ser = Series(values) + result = ser.map({1: 10, np.nan: 42}, na_action=na_action) + tm.assert_series_equal(result, expected) + + +def test_map_dict_subclass_with_missing(): + """ + Test Series.map with a dictionary subclass that defines __missing__, + i.e. sets a default value (GH #15999). + """ + + class DictWithMissing(dict): + def __missing__(self, key): + return "missing" + + s = Series([1, 2, 3]) + dictionary = DictWithMissing({3: "three"}) + result = s.map(dictionary) + expected = Series(["missing", "missing", "three"]) + tm.assert_series_equal(result, expected) + + +def test_map_dict_subclass_without_missing(): + class DictWithoutMissing(dict): + pass + + s = Series([1, 2, 3]) + dictionary = DictWithoutMissing({3: "three"}) + result = s.map(dictionary) + expected = Series([np.nan, np.nan, "three"]) + tm.assert_series_equal(result, expected) + + +def test_map_abc_mapping(non_dict_mapping_subclass): + # https://github.com/pandas-dev/pandas/issues/29733 + # Check collections.abc.Mapping support as mapper for Series.map + s = Series([1, 2, 3]) + not_a_dictionary = non_dict_mapping_subclass({3: "three"}) + result = s.map(not_a_dictionary) + expected = Series([np.nan, np.nan, "three"]) + tm.assert_series_equal(result, expected) + + +def test_map_abc_mapping_with_missing(non_dict_mapping_subclass): + # https://github.com/pandas-dev/pandas/issues/29733 + # Check collections.abc.Mapping support as mapper for Series.map + class NonDictMappingWithMissing(non_dict_mapping_subclass): + def __missing__(self, key): + return "missing" + + s = Series([1, 2, 3]) + not_a_dictionary = NonDictMappingWithMissing({3: "three"}) + result = s.map(not_a_dictionary) + # __missing__ is a dict concept, not a Mapping concept, + # so it should not change the result! + expected = Series([np.nan, np.nan, "three"]) + tm.assert_series_equal(result, expected) + + +def test_map_box_dt64(unit): + vals = [pd.Timestamp("2011-01-01"), pd.Timestamp("2011-01-02")] + ser = Series(vals).dt.as_unit(unit) + assert ser.dtype == f"datetime64[{unit}]" + # boxed value must be Timestamp instance + res = ser.map(lambda x: f"{type(x).__name__}_{x.day}_{x.tz}") + exp = Series(["Timestamp_1_None", "Timestamp_2_None"]) + tm.assert_series_equal(res, exp) + + +def test_map_box_dt64tz(unit): + vals = [ + pd.Timestamp("2011-01-01", tz="US/Eastern"), + pd.Timestamp("2011-01-02", tz="US/Eastern"), + ] + ser = Series(vals).dt.as_unit(unit) + assert ser.dtype == f"datetime64[{unit}, US/Eastern]" + res = ser.map(lambda x: f"{type(x).__name__}_{x.day}_{x.tz}") + exp = Series(["Timestamp_1_US/Eastern", "Timestamp_2_US/Eastern"]) + tm.assert_series_equal(res, exp) + + +def test_map_box_td64(unit): + # timedelta + vals = [pd.Timedelta("1 days"), pd.Timedelta("2 days")] + ser = Series(vals).dt.as_unit(unit) + assert ser.dtype == f"timedelta64[{unit}]" + res = ser.map(lambda x: f"{type(x).__name__}_{x.days}") + exp = Series(["Timedelta_1", "Timedelta_2"]) + tm.assert_series_equal(res, exp) + + +def test_map_box_period(): + # period + vals = [pd.Period("2011-01-01", freq="M"), pd.Period("2011-01-02", freq="M")] + ser = Series(vals) + assert ser.dtype == "Period[M]" + res = ser.map(lambda x: f"{type(x).__name__}_{x.freqstr}") + exp = Series(["Period_M", "Period_M"]) + tm.assert_series_equal(res, exp) + + +@pytest.mark.parametrize("na_action", [None, "ignore"]) +def test_map_categorical(na_action, using_infer_string): + values = pd.Categorical(list("ABBABCD"), categories=list("DCBA"), ordered=True) + s = Series(values, name="XX", index=list("abcdefg")) + + result = s.map(lambda x: x.lower(), na_action=na_action) + exp_values = pd.Categorical(list("abbabcd"), categories=list("dcba"), ordered=True) + exp = Series(exp_values, name="XX", index=list("abcdefg")) + tm.assert_series_equal(result, exp) + tm.assert_categorical_equal(result.values, exp_values) + + result = s.map(lambda x: "A", na_action=na_action) + exp = Series(["A"] * 7, name="XX", index=list("abcdefg")) + tm.assert_series_equal(result, exp) + assert result.dtype == object if not using_infer_string else "str" + + +@pytest.mark.parametrize( + "na_action, expected", + ( + [None, Series(["A", "B", "nan"], name="XX")], + [ + "ignore", + Series( + ["A", "B", np.nan], + name="XX", + dtype=pd.CategoricalDtype(list("DCBA"), True), + ), + ], + ), +) +def test_map_categorical_na_action(na_action, expected): + dtype = pd.CategoricalDtype(list("DCBA"), ordered=True) + values = pd.Categorical(list("AB") + [np.nan], dtype=dtype) + s = Series(values, name="XX") + result = s.map(str, na_action=na_action) + tm.assert_series_equal(result, expected) + + +def test_map_datetimetz(): + values = date_range("2011-01-01", "2011-01-02", freq="h").tz_localize("Asia/Tokyo") + s = Series(values, name="XX") + + # keep tz + result = s.map(lambda x: x + pd.offsets.Day()) + exp_values = date_range("2011-01-02", "2011-01-03", freq="h").tz_localize( + "Asia/Tokyo" + ) + exp = Series(exp_values, name="XX") + tm.assert_series_equal(result, exp) + + result = s.map(lambda x: x.hour) + exp = Series(list(range(24)) + [0], name="XX", dtype=np.int64) + tm.assert_series_equal(result, exp) + + # not vectorized + def f(x): + if not isinstance(x, pd.Timestamp): + raise ValueError + return str(x.tz) + + result = s.map(f) + exp = Series(["Asia/Tokyo"] * 25, name="XX") + tm.assert_series_equal(result, exp) + + +@pytest.mark.parametrize( + "vals,mapping,exp", + [ + (list("abc"), {np.nan: "not NaN"}, [np.nan] * 3 + ["not NaN"]), + (list("abc"), {"a": "a letter"}, ["a letter"] + [np.nan] * 3), + (list(range(3)), {0: 42}, [42] + [np.nan] * 3), + ], +) +def test_map_missing_mixed(vals, mapping, exp): + # GH20495 + s = Series(vals + [np.nan]) + result = s.map(mapping) + exp = Series(exp) + tm.assert_series_equal(result, exp) + + +def test_map_scalar_on_date_time_index_aware_series(): + # GH 25959 + # Calling map on a localized time series should not cause an error + series = Series( + np.arange(10, dtype=np.float64), + index=date_range("2020-01-01", periods=10, tz="UTC"), + name="ts", + ) + result = Series(series.index).map(lambda x: 1) + tm.assert_series_equal(result, Series(np.ones(len(series)), dtype="int64")) + + +def test_map_float_to_string_precision(): + # GH 13228 + ser = Series(1 / 3) + result = ser.map(lambda val: str(val)).to_dict() + expected = {0: "0.3333333333333333"} + assert result == expected + + +def test_map_to_timedelta(): + list_of_valid_strings = ["00:00:01", "00:00:02"] + a = pd.to_timedelta(list_of_valid_strings) + b = Series(list_of_valid_strings).map(pd.to_timedelta) + tm.assert_series_equal(Series(a), b) + + list_of_strings = ["00:00:01", np.nan, pd.NaT, pd.NaT] + + a = pd.to_timedelta(list_of_strings) + ser = Series(list_of_strings) + b = ser.map(pd.to_timedelta) + tm.assert_series_equal(Series(a), b) + + +def test_map_type(): + # GH 46719 + s = Series([3, "string", float], index=["a", "b", "c"]) + result = s.map(type) + expected = Series([int, str, type], index=["a", "b", "c"]) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_matmul.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_matmul.py new file mode 100644 index 0000000000000000000000000000000000000000..4ca3ad3f7031e20b8d6cec36caadbcefef8c4196 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_matmul.py @@ -0,0 +1,82 @@ +import operator + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Series, +) +import pandas._testing as tm + + +class TestMatmul: + def test_matmul(self): + # matmul test is for GH#10259 + a = Series( + np.random.default_rng(2).standard_normal(4), index=["p", "q", "r", "s"] + ) + b = DataFrame( + np.random.default_rng(2).standard_normal((3, 4)), + index=["1", "2", "3"], + columns=["p", "q", "r", "s"], + ).T + + # Series @ DataFrame -> Series + result = operator.matmul(a, b) + expected = Series(np.dot(a.values, b.values), index=["1", "2", "3"]) + tm.assert_series_equal(result, expected) + + # DataFrame @ Series -> Series + result = operator.matmul(b.T, a) + expected = Series(np.dot(b.T.values, a.T.values), index=["1", "2", "3"]) + tm.assert_series_equal(result, expected) + + # Series @ Series -> scalar + result = operator.matmul(a, a) + expected = np.dot(a.values, a.values) + tm.assert_almost_equal(result, expected) + + # GH#21530 + # vector (1D np.array) @ Series (__rmatmul__) + result = operator.matmul(a.values, a) + expected = np.dot(a.values, a.values) + tm.assert_almost_equal(result, expected) + + # GH#21530 + # vector (1D list) @ Series (__rmatmul__) + result = operator.matmul(a.values.tolist(), a) + expected = np.dot(a.values, a.values) + tm.assert_almost_equal(result, expected) + + # GH#21530 + # matrix (2D np.array) @ Series (__rmatmul__) + result = operator.matmul(b.T.values, a) + expected = np.dot(b.T.values, a.values) + tm.assert_almost_equal(result, expected) + + # GH#21530 + # matrix (2D nested lists) @ Series (__rmatmul__) + result = operator.matmul(b.T.values.tolist(), a) + expected = np.dot(b.T.values, a.values) + tm.assert_almost_equal(result, expected) + + # mixed dtype DataFrame @ Series + a["p"] = int(a.p) + result = operator.matmul(b.T, a) + expected = Series(np.dot(b.T.values, a.T.values), index=["1", "2", "3"]) + tm.assert_series_equal(result, expected) + + # different dtypes DataFrame @ Series + a = a.astype(int) + result = operator.matmul(b.T, a) + expected = Series(np.dot(b.T.values, a.T.values), index=["1", "2", "3"]) + tm.assert_series_equal(result, expected) + + msg = r"Dot product shape mismatch, \(4,\) vs \(3,\)" + # exception raised is of type Exception + with pytest.raises(Exception, match=msg): + a.dot(a.values[:3]) + msg = "matrices are not aligned" + with pytest.raises(ValueError, match=msg): + a.dot(b.T) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_nlargest.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_nlargest.py new file mode 100644 index 0000000000000000000000000000000000000000..e8de1cd89e3974f2dff0d775f35655b8caa02a08 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_nlargest.py @@ -0,0 +1,248 @@ +""" +Note: for naming purposes, most tests are title with as e.g. "test_nlargest_foo" +but are implicitly also testing nsmallest_foo. +""" +from itertools import product + +import numpy as np +import pytest + +import pandas as pd +from pandas import Series +import pandas._testing as tm + +main_dtypes = [ + "datetime", + "datetimetz", + "timedelta", + "int8", + "int16", + "int32", + "int64", + "float32", + "float64", + "uint8", + "uint16", + "uint32", + "uint64", +] + + +@pytest.fixture +def s_main_dtypes(): + """ + A DataFrame with many dtypes + + * datetime + * datetimetz + * timedelta + * [u]int{8,16,32,64} + * float{32,64} + + The columns are the name of the dtype. + """ + df = pd.DataFrame( + { + "datetime": pd.to_datetime(["2003", "2002", "2001", "2002", "2005"]), + "datetimetz": pd.to_datetime( + ["2003", "2002", "2001", "2002", "2005"] + ).tz_localize("US/Eastern"), + "timedelta": pd.to_timedelta(["3d", "2d", "1d", "2d", "5d"]), + } + ) + + for dtype in [ + "int8", + "int16", + "int32", + "int64", + "float32", + "float64", + "uint8", + "uint16", + "uint32", + "uint64", + ]: + df[dtype] = Series([3, 2, 1, 2, 5], dtype=dtype) + + return df + + +@pytest.fixture(params=main_dtypes) +def s_main_dtypes_split(request, s_main_dtypes): + """Each series in s_main_dtypes.""" + return s_main_dtypes[request.param] + + +def assert_check_nselect_boundary(vals, dtype, method): + # helper function for 'test_boundary_{dtype}' tests + ser = Series(vals, dtype=dtype) + result = getattr(ser, method)(3) + expected_idxr = [0, 1, 2] if method == "nsmallest" else [3, 2, 1] + expected = ser.loc[expected_idxr] + tm.assert_series_equal(result, expected) + + +class TestSeriesNLargestNSmallest: + @pytest.mark.parametrize( + "r", + [ + Series([3.0, 2, 1, 2, "5"], dtype="object"), + Series([3.0, 2, 1, 2, 5], dtype="object"), + # not supported on some archs + # Series([3., 2, 1, 2, 5], dtype='complex256'), + Series([3.0, 2, 1, 2, 5], dtype="complex128"), + Series(list("abcde")), + Series(list("abcde"), dtype="category"), + ], + ) + def test_nlargest_error(self, r): + dt = r.dtype + msg = f"Cannot use method 'n(largest|smallest)' with dtype {dt}" + args = 2, len(r), 0, -1 + methods = r.nlargest, r.nsmallest + for method, arg in product(methods, args): + with pytest.raises(TypeError, match=msg): + method(arg) + + def test_nsmallest_nlargest(self, s_main_dtypes_split): + # float, int, datetime64 (use i8), timedelts64 (same), + # object that are numbers, object that are strings + ser = s_main_dtypes_split + + tm.assert_series_equal(ser.nsmallest(2), ser.iloc[[2, 1]]) + tm.assert_series_equal(ser.nsmallest(2, keep="last"), ser.iloc[[2, 3]]) + + empty = ser.iloc[0:0] + tm.assert_series_equal(ser.nsmallest(0), empty) + tm.assert_series_equal(ser.nsmallest(-1), empty) + tm.assert_series_equal(ser.nlargest(0), empty) + tm.assert_series_equal(ser.nlargest(-1), empty) + + tm.assert_series_equal(ser.nsmallest(len(ser)), ser.sort_values()) + tm.assert_series_equal(ser.nsmallest(len(ser) + 1), ser.sort_values()) + tm.assert_series_equal(ser.nlargest(len(ser)), ser.iloc[[4, 0, 1, 3, 2]]) + tm.assert_series_equal(ser.nlargest(len(ser) + 1), ser.iloc[[4, 0, 1, 3, 2]]) + + def test_nlargest_misc(self): + ser = Series([3.0, np.nan, 1, 2, 5]) + result = ser.nlargest() + expected = ser.iloc[[4, 0, 3, 2, 1]] + tm.assert_series_equal(result, expected) + result = ser.nsmallest() + expected = ser.iloc[[2, 3, 0, 4, 1]] + tm.assert_series_equal(result, expected) + + msg = 'keep must be either "first", "last"' + with pytest.raises(ValueError, match=msg): + ser.nsmallest(keep="invalid") + with pytest.raises(ValueError, match=msg): + ser.nlargest(keep="invalid") + + # GH#15297 + ser = Series([1] * 5, index=[1, 2, 3, 4, 5]) + expected_first = Series([1] * 3, index=[1, 2, 3]) + expected_last = Series([1] * 3, index=[5, 4, 3]) + + result = ser.nsmallest(3) + tm.assert_series_equal(result, expected_first) + + result = ser.nsmallest(3, keep="last") + tm.assert_series_equal(result, expected_last) + + result = ser.nlargest(3) + tm.assert_series_equal(result, expected_first) + + result = ser.nlargest(3, keep="last") + tm.assert_series_equal(result, expected_last) + + @pytest.mark.parametrize("n", range(1, 5)) + def test_nlargest_n(self, n): + # GH 13412 + ser = Series([1, 4, 3, 2], index=[0, 0, 1, 1]) + result = ser.nlargest(n) + expected = ser.sort_values(ascending=False).head(n) + tm.assert_series_equal(result, expected) + + result = ser.nsmallest(n) + expected = ser.sort_values().head(n) + tm.assert_series_equal(result, expected) + + def test_nlargest_boundary_integer(self, nselect_method, any_int_numpy_dtype): + # GH#21426 + dtype_info = np.iinfo(any_int_numpy_dtype) + min_val, max_val = dtype_info.min, dtype_info.max + vals = [min_val, min_val + 1, max_val - 1, max_val] + assert_check_nselect_boundary(vals, any_int_numpy_dtype, nselect_method) + + def test_nlargest_boundary_float(self, nselect_method, float_numpy_dtype): + # GH#21426 + dtype_info = np.finfo(float_numpy_dtype) + min_val, max_val = dtype_info.min, dtype_info.max + min_2nd, max_2nd = np.nextafter([min_val, max_val], 0, dtype=float_numpy_dtype) + vals = [min_val, min_2nd, max_2nd, max_val] + assert_check_nselect_boundary(vals, float_numpy_dtype, nselect_method) + + @pytest.mark.parametrize("dtype", ["datetime64[ns]", "timedelta64[ns]"]) + def test_nlargest_boundary_datetimelike(self, nselect_method, dtype): + # GH#21426 + # use int64 bounds and +1 to min_val since true minimum is NaT + # (include min_val/NaT at end to maintain same expected_idxr) + dtype_info = np.iinfo("int64") + min_val, max_val = dtype_info.min, dtype_info.max + vals = [min_val + 1, min_val + 2, max_val - 1, max_val, min_val] + assert_check_nselect_boundary(vals, dtype, nselect_method) + + def test_nlargest_duplicate_keep_all_ties(self): + # see GH#16818 + ser = Series([10, 9, 8, 7, 7, 7, 7, 6]) + result = ser.nlargest(4, keep="all") + expected = Series([10, 9, 8, 7, 7, 7, 7]) + tm.assert_series_equal(result, expected) + + result = ser.nsmallest(2, keep="all") + expected = Series([6, 7, 7, 7, 7], index=[7, 3, 4, 5, 6]) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "data,expected", [([True, False], [True]), ([True, False, True, True], [True])] + ) + def test_nlargest_boolean(self, data, expected): + # GH#26154 : ensure True > False + ser = Series(data) + result = ser.nlargest(1) + expected = Series(expected) + tm.assert_series_equal(result, expected) + + def test_nlargest_nullable(self, any_numeric_ea_dtype): + # GH#42816 + dtype = any_numeric_ea_dtype + if dtype.startswith("UInt"): + # Can't cast from negative float to uint on some platforms + arr = np.random.default_rng(2).integers(1, 10, 10) + else: + arr = np.random.default_rng(2).standard_normal(10) + arr = arr.astype(dtype.lower(), copy=False) + + ser = Series(arr.copy(), dtype=dtype) + ser[1] = pd.NA + result = ser.nlargest(5) + + expected = ( + Series(np.delete(arr, 1), index=ser.index.delete(1)) + .nlargest(5) + .astype(dtype) + ) + tm.assert_series_equal(result, expected) + + def test_nsmallest_nan_when_keep_is_all(self): + # GH#46589 + s = Series([1, 2, 3, 3, 3, None]) + result = s.nsmallest(3, keep="all") + expected = Series([1.0, 2.0, 3.0, 3.0, 3.0]) + tm.assert_series_equal(result, expected) + + s = Series([1, 2, None, None, None]) + result = s.nsmallest(3, keep="all") + expected = Series([1, 2, None, None, None]) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_nunique.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_nunique.py new file mode 100644 index 0000000000000000000000000000000000000000..826132eb28162603d03635add59c3ea3da569256 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_nunique.py @@ -0,0 +1,24 @@ +import numpy as np + +from pandas import ( + Categorical, + Series, +) + + +def test_nunique(): + # basics.rst doc example + series = Series(np.random.default_rng(2).standard_normal(500)) + series[20:500] = np.nan + series[10:20] = 5000 + result = series.nunique() + assert result == 11 + + +def test_nunique_categorical(): + # GH#18051 + ser = Series(Categorical([])) + assert ser.nunique() == 0 + + ser = Series(Categorical([np.nan])) + assert ser.nunique() == 0 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_pct_change.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_pct_change.py new file mode 100644 index 0000000000000000000000000000000000000000..6c80e711c36846e565014c1d1c001ae2ba3cf929 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_pct_change.py @@ -0,0 +1,128 @@ +import numpy as np +import pytest + +from pandas import ( + Series, + date_range, +) +import pandas._testing as tm + + +class TestSeriesPctChange: + def test_pct_change(self, datetime_series): + msg = ( + "The 'fill_method' keyword being not None and the 'limit' keyword in " + "Series.pct_change are deprecated" + ) + + rs = datetime_series.pct_change(fill_method=None) + tm.assert_series_equal(rs, datetime_series / datetime_series.shift(1) - 1) + + rs = datetime_series.pct_change(2) + filled = datetime_series.ffill() + tm.assert_series_equal(rs, filled / filled.shift(2) - 1) + + with tm.assert_produces_warning(FutureWarning, match=msg): + rs = datetime_series.pct_change(fill_method="bfill", limit=1) + filled = datetime_series.bfill(limit=1) + tm.assert_series_equal(rs, filled / filled.shift(1) - 1) + + rs = datetime_series.pct_change(freq="5D") + filled = datetime_series.ffill() + tm.assert_series_equal( + rs, (filled / filled.shift(freq="5D") - 1).reindex_like(filled) + ) + + def test_pct_change_with_duplicate_axis(self): + # GH#28664 + common_idx = date_range("2019-11-14", periods=5, freq="D") + result = Series(range(5), common_idx).pct_change(freq="B") + + # the reason that the expected should be like this is documented at PR 28681 + expected = Series([np.nan, np.inf, np.nan, np.nan, 3.0], common_idx) + + tm.assert_series_equal(result, expected) + + def test_pct_change_shift_over_nas(self): + s = Series([1.0, 1.5, np.nan, 2.5, 3.0]) + + msg = "The default fill_method='pad' in Series.pct_change is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + chg = s.pct_change() + + expected = Series([np.nan, 0.5, 0.0, 2.5 / 1.5 - 1, 0.2]) + tm.assert_series_equal(chg, expected) + + @pytest.mark.parametrize( + "freq, periods, fill_method, limit", + [ + ("5B", 5, None, None), + ("3B", 3, None, None), + ("3B", 3, "bfill", None), + ("7B", 7, "pad", 1), + ("7B", 7, "bfill", 3), + ("14B", 14, None, None), + ], + ) + def test_pct_change_periods_freq( + self, freq, periods, fill_method, limit, datetime_series + ): + msg = ( + "The 'fill_method' keyword being not None and the 'limit' keyword in " + "Series.pct_change are deprecated" + ) + + # GH#7292 + with tm.assert_produces_warning(FutureWarning, match=msg): + rs_freq = datetime_series.pct_change( + freq=freq, fill_method=fill_method, limit=limit + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + rs_periods = datetime_series.pct_change( + periods, fill_method=fill_method, limit=limit + ) + tm.assert_series_equal(rs_freq, rs_periods) + + empty_ts = Series(index=datetime_series.index, dtype=object) + with tm.assert_produces_warning(FutureWarning, match=msg): + rs_freq = empty_ts.pct_change( + freq=freq, fill_method=fill_method, limit=limit + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + rs_periods = empty_ts.pct_change( + periods, fill_method=fill_method, limit=limit + ) + tm.assert_series_equal(rs_freq, rs_periods) + + +@pytest.mark.parametrize("fill_method", ["pad", "ffill", None]) +def test_pct_change_with_duplicated_indices(fill_method): + # GH30463 + s = Series([np.nan, 1, 2, 3, 9, 18], index=["a", "b"] * 3) + + warn = None if fill_method is None else FutureWarning + msg = ( + "The 'fill_method' keyword being not None and the 'limit' keyword in " + "Series.pct_change are deprecated" + ) + with tm.assert_produces_warning(warn, match=msg): + result = s.pct_change(fill_method=fill_method) + + expected = Series([np.nan, np.nan, 1.0, 0.5, 2.0, 1.0], index=["a", "b"] * 3) + tm.assert_series_equal(result, expected) + + +def test_pct_change_no_warning_na_beginning(): + # GH#54981 + ser = Series([None, None, 1, 2, 3]) + result = ser.pct_change() + expected = Series([np.nan, np.nan, np.nan, 1, 0.5]) + tm.assert_series_equal(result, expected) + + +def test_pct_change_empty(): + # GH 57056 + ser = Series([], dtype="float64") + expected = ser.copy() + result = ser.pct_change(periods=0) + tm.assert_series_equal(expected, result) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_pop.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_pop.py new file mode 100644 index 0000000000000000000000000000000000000000..7453f98ab3735e924dd7601622d23b4bafdd2176 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_pop.py @@ -0,0 +1,13 @@ +from pandas import Series +import pandas._testing as tm + + +def test_pop(): + # GH#6600 + ser = Series([0, 4, 0], index=["A", "B", "C"], name=4) + + result = ser.pop("B") + assert result == 4 + + expected = Series([0, 0], index=["A", "C"], name=4) + tm.assert_series_equal(ser, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_quantile.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_quantile.py new file mode 100644 index 0000000000000000000000000000000000000000..fa0563271d7df7cb6fdb3f2f7ad807057313f4c2 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_quantile.py @@ -0,0 +1,247 @@ +import numpy as np +import pytest + +from pandas.core.dtypes.common import is_integer + +import pandas as pd +from pandas import ( + Index, + Series, +) +import pandas._testing as tm +from pandas.core.indexes.datetimes import Timestamp + + +class TestSeriesQuantile: + def test_quantile(self, datetime_series): + q = datetime_series.quantile(0.1) + assert q == np.percentile(datetime_series.dropna(), 10) + + q = datetime_series.quantile(0.9) + assert q == np.percentile(datetime_series.dropna(), 90) + + # object dtype + q = Series(datetime_series, dtype=object).quantile(0.9) + assert q == np.percentile(datetime_series.dropna(), 90) + + # datetime64[ns] dtype + dts = datetime_series.index.to_series() + q = dts.quantile(0.2) + assert q == Timestamp("2000-01-10 19:12:00") + + # timedelta64[ns] dtype + tds = dts.diff() + q = tds.quantile(0.25) + assert q == pd.to_timedelta("24:00:00") + + # GH7661 + result = Series([np.timedelta64("NaT")]).sum() + assert result == pd.Timedelta(0) + + msg = "percentiles should all be in the interval \\[0, 1\\]" + for invalid in [-1, 2, [0.5, -1], [0.5, 2]]: + with pytest.raises(ValueError, match=msg): + datetime_series.quantile(invalid) + + s = Series(np.random.default_rng(2).standard_normal(100)) + percentile_array = [-0.5, 0.25, 1.5] + with pytest.raises(ValueError, match=msg): + s.quantile(percentile_array) + + def test_quantile_multi(self, datetime_series, unit): + datetime_series.index = datetime_series.index.as_unit(unit) + qs = [0.1, 0.9] + result = datetime_series.quantile(qs) + expected = Series( + [ + np.percentile(datetime_series.dropna(), 10), + np.percentile(datetime_series.dropna(), 90), + ], + index=qs, + name=datetime_series.name, + ) + tm.assert_series_equal(result, expected) + + dts = datetime_series.index.to_series() + dts.name = "xxx" + result = dts.quantile((0.2, 0.2)) + expected = Series( + [Timestamp("2000-01-10 19:12:00"), Timestamp("2000-01-10 19:12:00")], + index=[0.2, 0.2], + name="xxx", + dtype=f"M8[{unit}]", + ) + tm.assert_series_equal(result, expected) + + result = datetime_series.quantile([]) + expected = Series( + [], name=datetime_series.name, index=Index([], dtype=float), dtype="float64" + ) + tm.assert_series_equal(result, expected) + + def test_quantile_interpolation(self, datetime_series): + # see gh-10174 + + # interpolation = linear (default case) + q = datetime_series.quantile(0.1, interpolation="linear") + assert q == np.percentile(datetime_series.dropna(), 10) + q1 = datetime_series.quantile(0.1) + assert q1 == np.percentile(datetime_series.dropna(), 10) + + # test with and without interpolation keyword + assert q == q1 + + def test_quantile_interpolation_dtype(self): + # GH #10174 + + # interpolation = linear (default case) + q = Series([1, 3, 4]).quantile(0.5, interpolation="lower") + assert q == np.percentile(np.array([1, 3, 4]), 50) + assert is_integer(q) + + q = Series([1, 3, 4]).quantile(0.5, interpolation="higher") + assert q == np.percentile(np.array([1, 3, 4]), 50) + assert is_integer(q) + + def test_quantile_nan(self): + # GH 13098 + ser = Series([1, 2, 3, 4, np.nan]) + result = ser.quantile(0.5) + expected = 2.5 + assert result == expected + + # all nan/empty + s1 = Series([], dtype=object) + cases = [s1, Series([np.nan, np.nan])] + + for ser in cases: + res = ser.quantile(0.5) + assert np.isnan(res) + + res = ser.quantile([0.5]) + tm.assert_series_equal(res, Series([np.nan], index=[0.5])) + + res = ser.quantile([0.2, 0.3]) + tm.assert_series_equal(res, Series([np.nan, np.nan], index=[0.2, 0.3])) + + @pytest.mark.parametrize( + "case", + [ + [ + Timestamp("2011-01-01"), + Timestamp("2011-01-02"), + Timestamp("2011-01-03"), + ], + [ + Timestamp("2011-01-01", tz="US/Eastern"), + Timestamp("2011-01-02", tz="US/Eastern"), + Timestamp("2011-01-03", tz="US/Eastern"), + ], + [pd.Timedelta("1 days"), pd.Timedelta("2 days"), pd.Timedelta("3 days")], + # NaT + [ + Timestamp("2011-01-01"), + Timestamp("2011-01-02"), + Timestamp("2011-01-03"), + pd.NaT, + ], + [ + Timestamp("2011-01-01", tz="US/Eastern"), + Timestamp("2011-01-02", tz="US/Eastern"), + Timestamp("2011-01-03", tz="US/Eastern"), + pd.NaT, + ], + [ + pd.Timedelta("1 days"), + pd.Timedelta("2 days"), + pd.Timedelta("3 days"), + pd.NaT, + ], + ], + ) + def test_quantile_box(self, case): + ser = Series(case, name="XXX") + res = ser.quantile(0.5) + assert res == case[1] + + res = ser.quantile([0.5]) + exp = Series([case[1]], index=[0.5], name="XXX") + tm.assert_series_equal(res, exp) + + def test_datetime_timedelta_quantiles(self): + # covers #9694 + assert pd.isna(Series([], dtype="M8[ns]").quantile(0.5)) + assert pd.isna(Series([], dtype="m8[ns]").quantile(0.5)) + + def test_quantile_nat(self): + res = Series([pd.NaT, pd.NaT]).quantile(0.5) + assert res is pd.NaT + + res = Series([pd.NaT, pd.NaT]).quantile([0.5]) + tm.assert_series_equal(res, Series([pd.NaT], index=[0.5])) + + @pytest.mark.parametrize( + "values, dtype", + [([0, 0, 0, 1, 2, 3], "Sparse[int]"), ([0.0, None, 1.0, 2.0], "Sparse[float]")], + ) + def test_quantile_sparse(self, values, dtype): + ser = Series(values, dtype=dtype) + result = ser.quantile([0.5]) + expected = Series(np.asarray(ser)).quantile([0.5]).astype("Sparse[float]") + tm.assert_series_equal(result, expected) + + def test_quantile_empty_float64(self): + # floats + ser = Series([], dtype="float64") + + res = ser.quantile(0.5) + assert np.isnan(res) + + res = ser.quantile([0.5]) + exp = Series([np.nan], index=[0.5]) + tm.assert_series_equal(res, exp) + + def test_quantile_empty_int64(self): + # int + ser = Series([], dtype="int64") + + res = ser.quantile(0.5) + assert np.isnan(res) + + res = ser.quantile([0.5]) + exp = Series([np.nan], index=[0.5]) + tm.assert_series_equal(res, exp) + + def test_quantile_empty_dt64(self): + # datetime + ser = Series([], dtype="datetime64[ns]") + + res = ser.quantile(0.5) + assert res is pd.NaT + + res = ser.quantile([0.5]) + exp = Series([pd.NaT], index=[0.5], dtype=ser.dtype) + tm.assert_series_equal(res, exp) + + @pytest.mark.parametrize("dtype", [int, float, "Int64"]) + def test_quantile_dtypes(self, dtype): + result = Series([1, 2, 3], dtype=dtype).quantile(np.arange(0, 1, 0.25)) + expected = Series(np.arange(1, 3, 0.5), index=np.arange(0, 1, 0.25)) + if dtype == "Int64": + expected = expected.astype("Float64") + tm.assert_series_equal(result, expected) + + def test_quantile_all_na(self, any_int_ea_dtype): + # GH#50681 + ser = Series([pd.NA, pd.NA], dtype=any_int_ea_dtype) + with tm.assert_produces_warning(None): + result = ser.quantile([0.1, 0.5]) + expected = Series([pd.NA, pd.NA], dtype=any_int_ea_dtype, index=[0.1, 0.5]) + tm.assert_series_equal(result, expected) + + def test_quantile_dtype_size(self, any_int_ea_dtype): + # GH#50681 + ser = Series([pd.NA, pd.NA, 1], dtype=any_int_ea_dtype) + result = ser.quantile([0.1, 0.5]) + expected = Series([1, 1], dtype=any_int_ea_dtype, index=[0.1, 0.5]) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_rank.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_rank.py new file mode 100644 index 0000000000000000000000000000000000000000..1c3ebe5653ce3d62f5cf7d13dc1be14f86a7de9a --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_rank.py @@ -0,0 +1,563 @@ +from itertools import chain +import operator + +import numpy as np +import pytest + +from pandas._libs.algos import ( + Infinity, + NegInfinity, +) +import pandas.util._test_decorators as td + +from pandas import ( + NA, + NaT, + Series, + Timestamp, + date_range, +) +import pandas._testing as tm +from pandas.api.types import CategoricalDtype + + +@pytest.fixture +def ser(): + return Series([1, 3, 4, 2, np.nan, 2, 1, 5, np.nan, 3]) + + +@pytest.fixture( + params=[ + ["average", np.array([1.5, 5.5, 7.0, 3.5, np.nan, 3.5, 1.5, 8.0, np.nan, 5.5])], + ["min", np.array([1, 5, 7, 3, np.nan, 3, 1, 8, np.nan, 5])], + ["max", np.array([2, 6, 7, 4, np.nan, 4, 2, 8, np.nan, 6])], + ["first", np.array([1, 5, 7, 3, np.nan, 4, 2, 8, np.nan, 6])], + ["dense", np.array([1, 3, 4, 2, np.nan, 2, 1, 5, np.nan, 3])], + ], + ids=lambda x: x[0], +) +def results(request): + return request.param + + +@pytest.fixture( + params=[ + "object", + "float64", + "int64", + "Float64", + "Int64", + pytest.param("float64[pyarrow]", marks=td.skip_if_no("pyarrow")), + pytest.param("int64[pyarrow]", marks=td.skip_if_no("pyarrow")), + pytest.param("string[pyarrow]", marks=td.skip_if_no("pyarrow")), + "string[python]", + "str", + ] +) +def dtype(request): + return request.param + + +def expected_dtype(dtype, method, pct=False): + exp_dtype = "float64" + # elif dtype in ["Int64", "Float64", "string[pyarrow]", "string[python]"]: + if dtype in ["string[pyarrow]"]: + exp_dtype = "Float64" + elif dtype in ["float64[pyarrow]", "int64[pyarrow]"]: + if method == "average" or pct: + exp_dtype = "double[pyarrow]" + else: + exp_dtype = "uint64[pyarrow]" + + return exp_dtype + + +class TestSeriesRank: + def test_rank(self, datetime_series): + sp_stats = pytest.importorskip("scipy.stats") + + datetime_series[::2] = np.nan + datetime_series[:10:3] = 4.0 + + ranks = datetime_series.rank() + oranks = datetime_series.astype("O").rank() + + tm.assert_series_equal(ranks, oranks) + + mask = np.isnan(datetime_series) + filled = datetime_series.fillna(np.inf) + + # rankdata returns a ndarray + exp = Series(sp_stats.rankdata(filled), index=filled.index, name="ts") + exp[mask] = np.nan + + tm.assert_series_equal(ranks, exp) + + iseries = Series(np.arange(5).repeat(2)) + + iranks = iseries.rank() + exp = iseries.astype(float).rank() + tm.assert_series_equal(iranks, exp) + iseries = Series(np.arange(5)) + 1.0 + exp = iseries / 5.0 + iranks = iseries.rank(pct=True) + + tm.assert_series_equal(iranks, exp) + + iseries = Series(np.repeat(1, 100)) + exp = Series(np.repeat(0.505, 100)) + iranks = iseries.rank(pct=True) + tm.assert_series_equal(iranks, exp) + + # Explicit cast to float to avoid implicit cast when setting nan + iseries = iseries.astype("float") + iseries[1] = np.nan + exp = Series(np.repeat(50.0 / 99.0, 100)) + exp[1] = np.nan + iranks = iseries.rank(pct=True) + tm.assert_series_equal(iranks, exp) + + iseries = Series(np.arange(5)) + 1.0 + iseries[4] = np.nan + exp = iseries / 4.0 + iranks = iseries.rank(pct=True) + tm.assert_series_equal(iranks, exp) + + iseries = Series(np.repeat(np.nan, 100)) + exp = iseries.copy() + iranks = iseries.rank(pct=True) + tm.assert_series_equal(iranks, exp) + + # Explicit cast to float to avoid implicit cast when setting nan + iseries = Series(np.arange(5), dtype="float") + 1 + iseries[4] = np.nan + exp = iseries / 4.0 + iranks = iseries.rank(pct=True) + tm.assert_series_equal(iranks, exp) + + rng = date_range("1/1/1990", periods=5) + # Explicit cast to float to avoid implicit cast when setting nan + iseries = Series(np.arange(5), rng, dtype="float") + 1 + iseries.iloc[4] = np.nan + exp = iseries / 4.0 + iranks = iseries.rank(pct=True) + tm.assert_series_equal(iranks, exp) + + iseries = Series([1e-50, 1e-100, 1e-20, 1e-2, 1e-20 + 1e-30, 1e-1]) + exp = Series([2, 1, 3, 5, 4, 6.0]) + iranks = iseries.rank() + tm.assert_series_equal(iranks, exp) + + # GH 5968 + iseries = Series(["3 day", "1 day 10m", "-2 day", NaT], dtype="m8[ns]") + exp = Series([3, 2, 1, np.nan]) + iranks = iseries.rank() + tm.assert_series_equal(iranks, exp) + + values = np.array( + [-50, -1, -1e-20, -1e-25, -1e-50, 0, 1e-40, 1e-20, 1e-10, 2, 40], + dtype="float64", + ) + random_order = np.random.default_rng(2).permutation(len(values)) + iseries = Series(values[random_order]) + exp = Series(random_order + 1.0, dtype="float64") + iranks = iseries.rank() + tm.assert_series_equal(iranks, exp) + + def test_rank_categorical(self): + # GH issue #15420 rank incorrectly orders ordered categories + + # Test ascending/descending ranking for ordered categoricals + exp = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) + exp_desc = Series([6.0, 5.0, 4.0, 3.0, 2.0, 1.0]) + ordered = Series( + ["first", "second", "third", "fourth", "fifth", "sixth"] + ).astype( + CategoricalDtype( + categories=["first", "second", "third", "fourth", "fifth", "sixth"], + ordered=True, + ) + ) + tm.assert_series_equal(ordered.rank(), exp) + tm.assert_series_equal(ordered.rank(ascending=False), exp_desc) + + # Unordered categoricals should be ranked as objects + unordered = Series( + ["first", "second", "third", "fourth", "fifth", "sixth"] + ).astype( + CategoricalDtype( + categories=["first", "second", "third", "fourth", "fifth", "sixth"], + ordered=False, + ) + ) + exp_unordered = Series([2.0, 4.0, 6.0, 3.0, 1.0, 5.0]) + res = unordered.rank() + tm.assert_series_equal(res, exp_unordered) + + unordered1 = Series([1, 2, 3, 4, 5, 6]).astype( + CategoricalDtype([1, 2, 3, 4, 5, 6], False) + ) + exp_unordered1 = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) + res1 = unordered1.rank() + tm.assert_series_equal(res1, exp_unordered1) + + # Test na_option for rank data + na_ser = Series( + ["first", "second", "third", "fourth", "fifth", "sixth", np.nan] + ).astype( + CategoricalDtype( + ["first", "second", "third", "fourth", "fifth", "sixth", "seventh"], + True, + ) + ) + + exp_top = Series([2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 1.0]) + exp_bot = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0]) + exp_keep = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, np.nan]) + + tm.assert_series_equal(na_ser.rank(na_option="top"), exp_top) + tm.assert_series_equal(na_ser.rank(na_option="bottom"), exp_bot) + tm.assert_series_equal(na_ser.rank(na_option="keep"), exp_keep) + + # Test na_option for rank data with ascending False + exp_top = Series([7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0]) + exp_bot = Series([6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 7.0]) + exp_keep = Series([6.0, 5.0, 4.0, 3.0, 2.0, 1.0, np.nan]) + + tm.assert_series_equal(na_ser.rank(na_option="top", ascending=False), exp_top) + tm.assert_series_equal( + na_ser.rank(na_option="bottom", ascending=False), exp_bot + ) + tm.assert_series_equal(na_ser.rank(na_option="keep", ascending=False), exp_keep) + + # Test invalid values for na_option + msg = "na_option must be one of 'keep', 'top', or 'bottom'" + + with pytest.raises(ValueError, match=msg): + na_ser.rank(na_option="bad", ascending=False) + + # invalid type + with pytest.raises(ValueError, match=msg): + na_ser.rank(na_option=True, ascending=False) + + # Test with pct=True + na_ser = Series(["first", "second", "third", "fourth", np.nan]).astype( + CategoricalDtype(["first", "second", "third", "fourth"], True) + ) + exp_top = Series([0.4, 0.6, 0.8, 1.0, 0.2]) + exp_bot = Series([0.2, 0.4, 0.6, 0.8, 1.0]) + exp_keep = Series([0.25, 0.5, 0.75, 1.0, np.nan]) + + tm.assert_series_equal(na_ser.rank(na_option="top", pct=True), exp_top) + tm.assert_series_equal(na_ser.rank(na_option="bottom", pct=True), exp_bot) + tm.assert_series_equal(na_ser.rank(na_option="keep", pct=True), exp_keep) + + def test_rank_signature(self): + s = Series([0, 1]) + s.rank(method="average") + msg = "No axis named average for object type Series" + with pytest.raises(ValueError, match=msg): + s.rank("average") + + def test_rank_tie_methods(self, ser, results, dtype, using_infer_string): + method, exp = results + if ( + dtype == "int64" + or dtype == "Int64" + or (not using_infer_string and dtype == "str") + ): + pytest.skip("int64/str does not support NaN") + + ser = ser if dtype is None else ser.astype(dtype) + result = ser.rank(method=method) + tm.assert_series_equal(result, Series(exp, dtype=expected_dtype(dtype, method))) + + @pytest.mark.parametrize("ascending", [True, False]) + @pytest.mark.parametrize("method", ["average", "min", "max", "first", "dense"]) + @pytest.mark.parametrize("na_option", ["top", "bottom", "keep"]) + @pytest.mark.parametrize( + "dtype, na_value, pos_inf, neg_inf", + [ + ("object", None, Infinity(), NegInfinity()), + ("float64", np.nan, np.inf, -np.inf), + ("Float64", NA, np.inf, -np.inf), + pytest.param( + "float64[pyarrow]", + NA, + np.inf, + -np.inf, + marks=td.skip_if_no("pyarrow"), + ), + ], + ) + def test_rank_tie_methods_on_infs_nans( + self, method, na_option, ascending, dtype, na_value, pos_inf, neg_inf + ): + pytest.importorskip("scipy") + if dtype == "float64[pyarrow]": + if method == "average": + exp_dtype = "float64[pyarrow]" + else: + exp_dtype = "uint64[pyarrow]" + else: + exp_dtype = "float64" + + chunk = 3 + in_arr = [neg_inf] * chunk + [na_value] * chunk + [pos_inf] * chunk + iseries = Series(in_arr, dtype=dtype) + exp_ranks = { + "average": ([2, 2, 2], [5, 5, 5], [8, 8, 8]), + "min": ([1, 1, 1], [4, 4, 4], [7, 7, 7]), + "max": ([3, 3, 3], [6, 6, 6], [9, 9, 9]), + "first": ([1, 2, 3], [4, 5, 6], [7, 8, 9]), + "dense": ([1, 1, 1], [2, 2, 2], [3, 3, 3]), + } + ranks = exp_ranks[method] + if na_option == "top": + order = [ranks[1], ranks[0], ranks[2]] + elif na_option == "bottom": + order = [ranks[0], ranks[2], ranks[1]] + else: + order = [ranks[0], [np.nan] * chunk, ranks[1]] + expected = order if ascending else order[::-1] + expected = list(chain.from_iterable(expected)) + result = iseries.rank(method=method, na_option=na_option, ascending=ascending) + tm.assert_series_equal(result, Series(expected, dtype=exp_dtype)) + + def test_rank_desc_mix_nans_infs(self): + # GH 19538 + # check descending ranking when mix nans and infs + iseries = Series([1, np.nan, np.inf, -np.inf, 25]) + result = iseries.rank(ascending=False) + exp = Series([3, np.nan, 1, 4, 2], dtype="float64") + tm.assert_series_equal(result, exp) + + @pytest.mark.parametrize("method", ["average", "min", "max", "first", "dense"]) + @pytest.mark.parametrize( + "op, value", + [ + [operator.add, 0], + [operator.add, 1e6], + [operator.mul, 1e-6], + ], + ) + def test_rank_methods_series(self, method, op, value): + sp_stats = pytest.importorskip("scipy.stats") + + xs = np.random.default_rng(2).standard_normal(9) + xs = np.concatenate([xs[i:] for i in range(0, 9, 2)]) # add duplicates + np.random.default_rng(2).shuffle(xs) + + index = [chr(ord("a") + i) for i in range(len(xs))] + vals = op(xs, value) + ts = Series(vals, index=index) + result = ts.rank(method=method) + sprank = sp_stats.rankdata(vals, method if method != "first" else "ordinal") + expected = Series(sprank, index=index).astype("float64") + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "ser, exp", + [ + ([1], [1]), + ([2], [1]), + ([0], [1]), + ([2, 2], [1, 1]), + ([1, 2, 3], [1, 2, 3]), + ([4, 2, 1], [3, 2, 1]), + ([1, 1, 5, 5, 3], [1, 1, 3, 3, 2]), + ([-5, -4, -3, -2, -1], [1, 2, 3, 4, 5]), + ], + ) + def test_rank_dense_method(self, dtype, ser, exp): + if ser[0] < 0 and dtype.startswith("str"): + exp = exp[::-1] + s = Series(ser).astype(dtype) + result = s.rank(method="dense") + expected = Series(exp).astype(expected_dtype(dtype, "dense")) + tm.assert_series_equal(result, expected) + + def test_rank_descending(self, ser, results, dtype, using_infer_string): + method, _ = results + if dtype == "int64" or (not using_infer_string and dtype == "str"): + s = ser.dropna() + else: + s = ser.astype(dtype) + + res = s.rank(ascending=False) + if dtype.startswith("str"): + expected = (s.astype("float64").max() - s.astype("float64")).rank() + else: + expected = (s.max() - s).rank() + tm.assert_series_equal(res, expected.astype(expected_dtype(dtype, "average"))) + + if dtype.startswith("str"): + expected = (s.astype("float64").max() - s.astype("float64")).rank( + method=method + ) + else: + expected = (s.max() - s).rank(method=method) + res2 = s.rank(method=method, ascending=False) + tm.assert_series_equal(res2, expected.astype(expected_dtype(dtype, method))) + + def test_rank_int(self, ser, results): + method, exp = results + s = ser.dropna().astype("i8") + + result = s.rank(method=method) + expected = Series(exp).dropna() + expected.index = result.index + tm.assert_series_equal(result, expected) + + def test_rank_object_bug(self): + # GH 13445 + + # smoke tests + Series([np.nan] * 32).astype(object).rank(ascending=True) + Series([np.nan] * 32).astype(object).rank(ascending=False) + + def test_rank_modify_inplace(self): + # GH 18521 + # Check rank does not mutate series + s = Series([Timestamp("2017-01-05 10:20:27.569000"), NaT]) + expected = s.copy() + + s.rank() + result = s + tm.assert_series_equal(result, expected) + + def test_rank_ea_small_values(self): + # GH#52471 + ser = Series( + [5.4954145e29, -9.791984e-21, 9.3715776e-26, NA, 1.8790257e-28], + dtype="Float64", + ) + result = ser.rank(method="min") + expected = Series([4, 1, 3, np.nan, 2]) + tm.assert_series_equal(result, expected) + + +# GH15630, pct should be on 100% basis when method='dense' + + +@pytest.mark.parametrize( + "ser, exp", + [ + ([1], [1.0]), + ([1, 2], [1.0 / 2, 2.0 / 2]), + ([2, 2], [1.0, 1.0]), + ([1, 2, 3], [1.0 / 3, 2.0 / 3, 3.0 / 3]), + ([1, 2, 2], [1.0 / 2, 2.0 / 2, 2.0 / 2]), + ([4, 2, 1], [3.0 / 3, 2.0 / 3, 1.0 / 3]), + ([1, 1, 5, 5, 3], [1.0 / 3, 1.0 / 3, 3.0 / 3, 3.0 / 3, 2.0 / 3]), + ([1, 1, 3, 3, 5, 5], [1.0 / 3, 1.0 / 3, 2.0 / 3, 2.0 / 3, 3.0 / 3, 3.0 / 3]), + ([-5, -4, -3, -2, -1], [1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 5.0 / 5]), + ], +) +def test_rank_dense_pct(dtype, ser, exp): + if ser[0] < 0 and dtype.startswith("str"): + exp = exp[::-1] + s = Series(ser).astype(dtype) + result = s.rank(method="dense", pct=True) + expected = Series(exp).astype(expected_dtype(dtype, "dense", pct=True)) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "ser, exp", + [ + ([1], [1.0]), + ([1, 2], [1.0 / 2, 2.0 / 2]), + ([2, 2], [1.0 / 2, 1.0 / 2]), + ([1, 2, 3], [1.0 / 3, 2.0 / 3, 3.0 / 3]), + ([1, 2, 2], [1.0 / 3, 2.0 / 3, 2.0 / 3]), + ([4, 2, 1], [3.0 / 3, 2.0 / 3, 1.0 / 3]), + ([1, 1, 5, 5, 3], [1.0 / 5, 1.0 / 5, 4.0 / 5, 4.0 / 5, 3.0 / 5]), + ([1, 1, 3, 3, 5, 5], [1.0 / 6, 1.0 / 6, 3.0 / 6, 3.0 / 6, 5.0 / 6, 5.0 / 6]), + ([-5, -4, -3, -2, -1], [1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 5.0 / 5]), + ], +) +def test_rank_min_pct(dtype, ser, exp): + if ser[0] < 0 and dtype.startswith("str"): + exp = exp[::-1] + s = Series(ser).astype(dtype) + result = s.rank(method="min", pct=True) + expected = Series(exp).astype(expected_dtype(dtype, "min", pct=True)) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "ser, exp", + [ + ([1], [1.0]), + ([1, 2], [1.0 / 2, 2.0 / 2]), + ([2, 2], [1.0, 1.0]), + ([1, 2, 3], [1.0 / 3, 2.0 / 3, 3.0 / 3]), + ([1, 2, 2], [1.0 / 3, 3.0 / 3, 3.0 / 3]), + ([4, 2, 1], [3.0 / 3, 2.0 / 3, 1.0 / 3]), + ([1, 1, 5, 5, 3], [2.0 / 5, 2.0 / 5, 5.0 / 5, 5.0 / 5, 3.0 / 5]), + ([1, 1, 3, 3, 5, 5], [2.0 / 6, 2.0 / 6, 4.0 / 6, 4.0 / 6, 6.0 / 6, 6.0 / 6]), + ([-5, -4, -3, -2, -1], [1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 5.0 / 5]), + ], +) +def test_rank_max_pct(dtype, ser, exp): + if ser[0] < 0 and dtype.startswith("str"): + exp = exp[::-1] + s = Series(ser).astype(dtype) + result = s.rank(method="max", pct=True) + expected = Series(exp).astype(expected_dtype(dtype, "max", pct=True)) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "ser, exp", + [ + ([1], [1.0]), + ([1, 2], [1.0 / 2, 2.0 / 2]), + ([2, 2], [1.5 / 2, 1.5 / 2]), + ([1, 2, 3], [1.0 / 3, 2.0 / 3, 3.0 / 3]), + ([1, 2, 2], [1.0 / 3, 2.5 / 3, 2.5 / 3]), + ([4, 2, 1], [3.0 / 3, 2.0 / 3, 1.0 / 3]), + ([1, 1, 5, 5, 3], [1.5 / 5, 1.5 / 5, 4.5 / 5, 4.5 / 5, 3.0 / 5]), + ([1, 1, 3, 3, 5, 5], [1.5 / 6, 1.5 / 6, 3.5 / 6, 3.5 / 6, 5.5 / 6, 5.5 / 6]), + ([-5, -4, -3, -2, -1], [1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 5.0 / 5]), + ], +) +def test_rank_average_pct(dtype, ser, exp): + if ser[0] < 0 and dtype.startswith("str"): + exp = exp[::-1] + s = Series(ser).astype(dtype) + result = s.rank(method="average", pct=True) + expected = Series(exp).astype(expected_dtype(dtype, "average", pct=True)) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "ser, exp", + [ + ([1], [1.0]), + ([1, 2], [1.0 / 2, 2.0 / 2]), + ([2, 2], [1.0 / 2, 2.0 / 2.0]), + ([1, 2, 3], [1.0 / 3, 2.0 / 3, 3.0 / 3]), + ([1, 2, 2], [1.0 / 3, 2.0 / 3, 3.0 / 3]), + ([4, 2, 1], [3.0 / 3, 2.0 / 3, 1.0 / 3]), + ([1, 1, 5, 5, 3], [1.0 / 5, 2.0 / 5, 4.0 / 5, 5.0 / 5, 3.0 / 5]), + ([1, 1, 3, 3, 5, 5], [1.0 / 6, 2.0 / 6, 3.0 / 6, 4.0 / 6, 5.0 / 6, 6.0 / 6]), + ([-5, -4, -3, -2, -1], [1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 5.0 / 5]), + ], +) +def test_rank_first_pct(dtype, ser, exp): + if ser[0] < 0 and dtype.startswith("str"): + exp = exp[::-1] + s = Series(ser).astype(dtype) + result = s.rank(method="first", pct=True) + expected = Series(exp).astype(expected_dtype(dtype, "first", pct=True)) + tm.assert_series_equal(result, expected) + + +@pytest.mark.single_cpu +def test_pct_max_many_rows(): + # GH 18271 + s = Series(np.arange(2**24 + 1)) + result = s.rank(pct=True).max() + assert result == 1 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_reindex.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_reindex.py new file mode 100644 index 0000000000000000000000000000000000000000..ecfbecf12bdd3aa07789a79e8ba9383a76f33ed5 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_reindex.py @@ -0,0 +1,443 @@ +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +from pandas import ( + NA, + Categorical, + Float64Dtype, + Index, + MultiIndex, + NaT, + Period, + PeriodIndex, + RangeIndex, + Series, + Timedelta, + Timestamp, + date_range, + isna, +) +import pandas._testing as tm + + +def test_reindex(datetime_series, string_series): + identity = string_series.reindex(string_series.index) + + assert tm.shares_memory(string_series.index, identity.index) + + assert identity.index.is_(string_series.index) + assert identity.index.identical(string_series.index) + + subIndex = string_series.index[10:20] + subSeries = string_series.reindex(subIndex) + + for idx, val in subSeries.items(): + assert val == string_series[idx] + + subIndex2 = datetime_series.index[10:20] + subTS = datetime_series.reindex(subIndex2) + + for idx, val in subTS.items(): + assert val == datetime_series[idx] + stuffSeries = datetime_series.reindex(subIndex) + + assert np.isnan(stuffSeries).all() + + # This is extremely important for the Cython code to not screw up + nonContigIndex = datetime_series.index[::2] + subNonContig = datetime_series.reindex(nonContigIndex) + for idx, val in subNonContig.items(): + assert val == datetime_series[idx] + + # return a copy the same index here + result = datetime_series.reindex() + assert result is not datetime_series + + +def test_reindex_nan(): + ts = Series([2, 3, 5, 7], index=[1, 4, np.nan, 8]) + + i, j = [np.nan, 1, np.nan, 8, 4, np.nan], [2, 0, 2, 3, 1, 2] + tm.assert_series_equal(ts.reindex(i), ts.iloc[j]) + + ts.index = ts.index.astype("object") + + # reindex coerces index.dtype to float, loc/iloc doesn't + tm.assert_series_equal(ts.reindex(i), ts.iloc[j], check_index_type=False) + + +def test_reindex_series_add_nat(): + rng = date_range("1/1/2000 00:00:00", periods=10, freq="10s") + series = Series(rng) + + result = series.reindex(range(15)) + assert np.issubdtype(result.dtype, np.dtype("M8[ns]")) + + mask = result.isna() + assert mask[-5:].all() + assert not mask[:-5].any() + + +def test_reindex_with_datetimes(): + rng = date_range("1/1/2000", periods=20) + ts = Series(np.random.default_rng(2).standard_normal(20), index=rng) + + result = ts.reindex(list(ts.index[5:10])) + expected = ts[5:10] + expected.index = expected.index._with_freq(None) + tm.assert_series_equal(result, expected) + + result = ts[list(ts.index[5:10])] + tm.assert_series_equal(result, expected) + + +def test_reindex_corner(datetime_series): + # (don't forget to fix this) I think it's fixed + empty = Series(index=[]) + empty.reindex(datetime_series.index, method="pad") # it works + + # corner case: pad empty series + reindexed = empty.reindex(datetime_series.index, method="pad") + + # pass non-Index + reindexed = datetime_series.reindex(list(datetime_series.index)) + datetime_series.index = datetime_series.index._with_freq(None) + tm.assert_series_equal(datetime_series, reindexed) + + # bad fill method + ts = datetime_series[::2] + msg = ( + r"Invalid fill method\. Expecting pad \(ffill\), backfill " + r"\(bfill\) or nearest\. Got foo" + ) + with pytest.raises(ValueError, match=msg): + ts.reindex(datetime_series.index, method="foo") + + +def test_reindex_pad(): + s = Series(np.arange(10), dtype="int64") + s2 = s[::2] + + reindexed = s2.reindex(s.index, method="pad") + reindexed2 = s2.reindex(s.index, method="ffill") + tm.assert_series_equal(reindexed, reindexed2) + + expected = Series([0, 0, 2, 2, 4, 4, 6, 6, 8, 8]) + tm.assert_series_equal(reindexed, expected) + + +def test_reindex_pad2(): + # GH4604 + s = Series([1, 2, 3, 4, 5], index=["a", "b", "c", "d", "e"]) + new_index = ["a", "g", "c", "f"] + expected = Series([1, 1, 3, 3], index=new_index) + + # this changes dtype because the ffill happens after + result = s.reindex(new_index).ffill() + tm.assert_series_equal(result, expected.astype("float64")) + + msg = "The 'downcast' keyword in ffill is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = s.reindex(new_index).ffill(downcast="infer") + tm.assert_series_equal(result, expected) + + expected = Series([1, 5, 3, 5], index=new_index) + result = s.reindex(new_index, method="ffill") + tm.assert_series_equal(result, expected) + + +def test_reindex_inference(): + # inference of new dtype + s = Series([True, False, False, True], index=list("abcd")) + new_index = "agc" + msg = "Downcasting object dtype arrays on" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = s.reindex(list(new_index)).ffill() + expected = Series([True, True, False], index=list(new_index)) + tm.assert_series_equal(result, expected) + + +def test_reindex_downcasting(): + # GH4618 shifted series downcasting + s = Series(False, index=range(5)) + msg = "Downcasting object dtype arrays on" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = s.shift(1).bfill() + expected = Series(False, index=range(5)) + tm.assert_series_equal(result, expected) + + +def test_reindex_nearest(): + s = Series(np.arange(10, dtype="int64")) + target = [0.1, 0.9, 1.5, 2.0] + result = s.reindex(target, method="nearest") + expected = Series(np.around(target).astype("int64"), target) + tm.assert_series_equal(expected, result) + + result = s.reindex(target, method="nearest", tolerance=0.2) + expected = Series([0, 1, np.nan, 2], target) + tm.assert_series_equal(expected, result) + + result = s.reindex(target, method="nearest", tolerance=[0.3, 0.01, 0.4, 3]) + expected = Series([0, np.nan, np.nan, 2], target) + tm.assert_series_equal(expected, result) + + +def test_reindex_int(datetime_series): + ts = datetime_series[::2] + int_ts = Series(np.zeros(len(ts), dtype=int), index=ts.index) + + # this should work fine + reindexed_int = int_ts.reindex(datetime_series.index) + + # if NaNs introduced + assert reindexed_int.dtype == np.float64 + + # NO NaNs introduced + reindexed_int = int_ts.reindex(int_ts.index[::2]) + assert reindexed_int.dtype == np.dtype(int) + + +def test_reindex_bool(datetime_series): + # A series other than float, int, string, or object + ts = datetime_series[::2] + bool_ts = Series(np.zeros(len(ts), dtype=bool), index=ts.index) + + # this should work fine + reindexed_bool = bool_ts.reindex(datetime_series.index) + + # if NaNs introduced + assert reindexed_bool.dtype == np.object_ + + # NO NaNs introduced + reindexed_bool = bool_ts.reindex(bool_ts.index[::2]) + assert reindexed_bool.dtype == np.bool_ + + +def test_reindex_bool_pad(datetime_series): + # fail + ts = datetime_series[5:] + bool_ts = Series(np.zeros(len(ts), dtype=bool), index=ts.index) + filled_bool = bool_ts.reindex(datetime_series.index, method="pad") + assert isna(filled_bool[:5]).all() + + +def test_reindex_categorical(): + index = date_range("20000101", periods=3) + + # reindexing to an invalid Categorical + s = Series(["a", "b", "c"], dtype="category") + result = s.reindex(index) + expected = Series( + Categorical(values=[np.nan, np.nan, np.nan], categories=["a", "b", "c"]) + ) + expected.index = index + tm.assert_series_equal(result, expected) + + # partial reindexing + expected = Series(Categorical(values=["b", "c"], categories=["a", "b", "c"])) + expected.index = [1, 2] + result = s.reindex([1, 2]) + tm.assert_series_equal(result, expected) + + expected = Series(Categorical(values=["c", np.nan], categories=["a", "b", "c"])) + expected.index = [2, 3] + result = s.reindex([2, 3]) + tm.assert_series_equal(result, expected) + + +def test_reindex_astype_order_consistency(): + # GH#17444 + ser = Series([1, 2, 3], index=[2, 0, 1]) + new_index = [0, 1, 2] + temp_dtype = "category" + new_dtype = str + result = ser.reindex(new_index).astype(temp_dtype).astype(new_dtype) + expected = ser.astype(temp_dtype).reindex(new_index).astype(new_dtype) + tm.assert_series_equal(result, expected) + + +def test_reindex_fill_value(): + # ----------------------------------------------------------- + # floats + floats = Series([1.0, 2.0, 3.0]) + result = floats.reindex([1, 2, 3]) + expected = Series([2.0, 3.0, np.nan], index=[1, 2, 3]) + tm.assert_series_equal(result, expected) + + result = floats.reindex([1, 2, 3], fill_value=0) + expected = Series([2.0, 3.0, 0], index=[1, 2, 3]) + tm.assert_series_equal(result, expected) + + # ----------------------------------------------------------- + # ints + ints = Series([1, 2, 3]) + + result = ints.reindex([1, 2, 3]) + expected = Series([2.0, 3.0, np.nan], index=[1, 2, 3]) + tm.assert_series_equal(result, expected) + + # don't upcast + result = ints.reindex([1, 2, 3], fill_value=0) + expected = Series([2, 3, 0], index=[1, 2, 3]) + assert issubclass(result.dtype.type, np.integer) + tm.assert_series_equal(result, expected) + + # ----------------------------------------------------------- + # objects + objects = Series([1, 2, 3], dtype=object) + + result = objects.reindex([1, 2, 3]) + expected = Series([2, 3, np.nan], index=[1, 2, 3], dtype=object) + tm.assert_series_equal(result, expected) + + result = objects.reindex([1, 2, 3], fill_value="foo") + expected = Series([2, 3, "foo"], index=[1, 2, 3], dtype=object) + tm.assert_series_equal(result, expected) + + # ------------------------------------------------------------ + # bools + bools = Series([True, False, True]) + + result = bools.reindex([1, 2, 3]) + expected = Series([False, True, np.nan], index=[1, 2, 3], dtype=object) + tm.assert_series_equal(result, expected) + + result = bools.reindex([1, 2, 3], fill_value=False) + expected = Series([False, True, False], index=[1, 2, 3]) + tm.assert_series_equal(result, expected) + + +@td.skip_array_manager_not_yet_implemented +@pytest.mark.parametrize("dtype", ["datetime64[ns]", "timedelta64[ns]"]) +@pytest.mark.parametrize("fill_value", ["string", 0, Timedelta(0)]) +def test_reindex_fill_value_datetimelike_upcast(dtype, fill_value, using_array_manager): + # https://github.com/pandas-dev/pandas/issues/42921 + if dtype == "timedelta64[ns]" and fill_value == Timedelta(0): + # use the scalar that is not compatible with the dtype for this test + fill_value = Timestamp(0) + + ser = Series([NaT], dtype=dtype) + + result = ser.reindex([0, 1], fill_value=fill_value) + expected = Series([NaT, fill_value], index=[0, 1], dtype=object) + tm.assert_series_equal(result, expected) + + +def test_reindex_datetimeindexes_tz_naive_and_aware(): + # GH 8306 + idx = date_range("20131101", tz="America/Chicago", periods=7) + newidx = date_range("20131103", periods=10, freq="h") + s = Series(range(7), index=idx) + msg = ( + r"Cannot compare dtypes datetime64\[ns, America/Chicago\] " + r"and datetime64\[ns\]" + ) + with pytest.raises(TypeError, match=msg): + s.reindex(newidx, method="ffill") + + +def test_reindex_empty_series_tz_dtype(): + # GH 20869 + result = Series(dtype="datetime64[ns, UTC]").reindex([0, 1]) + expected = Series([NaT] * 2, dtype="datetime64[ns, UTC]") + tm.assert_equal(result, expected) + + +@pytest.mark.parametrize( + "p_values, o_values, values, expected_values", + [ + ( + [Period("2019Q1", "Q-DEC"), Period("2019Q2", "Q-DEC")], + [Period("2019Q1", "Q-DEC"), Period("2019Q2", "Q-DEC"), "All"], + [1.0, 1.0], + [1.0, 1.0, np.nan], + ), + ( + [Period("2019Q1", "Q-DEC"), Period("2019Q2", "Q-DEC")], + [Period("2019Q1", "Q-DEC"), Period("2019Q2", "Q-DEC")], + [1.0, 1.0], + [1.0, 1.0], + ), + ], +) +def test_reindex_periodindex_with_object(p_values, o_values, values, expected_values): + # GH#28337 + period_index = PeriodIndex(p_values) + object_index = Index(o_values) + + ser = Series(values, index=period_index) + result = ser.reindex(object_index) + expected = Series(expected_values, index=object_index) + tm.assert_series_equal(result, expected) + + +def test_reindex_too_many_args(): + # GH 40980 + ser = Series([1, 2]) + msg = r"reindex\(\) takes from 1 to 2 positional arguments but 3 were given" + with pytest.raises(TypeError, match=msg): + ser.reindex([2, 3], False) + + +def test_reindex_double_index(): + # GH 40980 + ser = Series([1, 2]) + msg = r"reindex\(\) got multiple values for argument 'index'" + with pytest.raises(TypeError, match=msg): + ser.reindex([2, 3], index=[3, 4]) + + +def test_reindex_no_posargs(): + # GH 40980 + ser = Series([1, 2]) + result = ser.reindex(index=[1, 0]) + expected = Series([2, 1], index=[1, 0]) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("values", [[["a"], ["x"]], [[], []]]) +def test_reindex_empty_with_level(values): + # GH41170 + ser = Series( + range(len(values[0])), index=MultiIndex.from_arrays(values), dtype="object" + ) + result = ser.reindex(np.array(["b"]), level=0) + expected = Series( + index=MultiIndex(levels=[["b"], values[1]], codes=[[], []]), dtype="object" + ) + tm.assert_series_equal(result, expected) + + +def test_reindex_missing_category(): + # GH#18185 + ser = Series([1, 2, 3, 1], dtype="category") + msg = r"Cannot setitem on a Categorical with a new category \(-1\)" + with pytest.raises(TypeError, match=msg): + ser.reindex([1, 2, 3, 4, 5], fill_value=-1) + + +def test_reindexing_with_float64_NA_log(): + # GH 47055 + s = Series([1.0, NA], dtype=Float64Dtype()) + s_reindex = s.reindex(range(3)) + result = s_reindex.values._data + expected = np.array([1, np.nan, np.nan]) + tm.assert_numpy_array_equal(result, expected) + with tm.assert_produces_warning(None): + result_log = np.log(s_reindex) + expected_log = Series([0, np.nan, np.nan], dtype=Float64Dtype()) + tm.assert_series_equal(result_log, expected_log) + + +@pytest.mark.parametrize("dtype", ["timedelta64", "datetime64"]) +def test_reindex_expand_nonnano_nat(dtype): + # GH 53497 + ser = Series(np.array([1], dtype=f"{dtype}[s]")) + result = ser.reindex(RangeIndex(2)) + expected = Series( + np.array([1, getattr(np, dtype)("nat", "s")], dtype=f"{dtype}[s]") + ) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_reindex_like.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_reindex_like.py new file mode 100644 index 0000000000000000000000000000000000000000..7f24c778feb1b4556587773f711e21521efc537c --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_reindex_like.py @@ -0,0 +1,41 @@ +from datetime import datetime + +import numpy as np + +from pandas import Series +import pandas._testing as tm + + +def test_reindex_like(datetime_series): + other = datetime_series[::2] + tm.assert_series_equal( + datetime_series.reindex(other.index), datetime_series.reindex_like(other) + ) + + # GH#7179 + day1 = datetime(2013, 3, 5) + day2 = datetime(2013, 5, 5) + day3 = datetime(2014, 3, 5) + + series1 = Series([5, None, None], [day1, day2, day3]) + series2 = Series([None, None], [day1, day3]) + + result = series1.reindex_like(series2, method="pad") + expected = Series([5, np.nan], index=[day1, day3]) + tm.assert_series_equal(result, expected) + + +def test_reindex_like_nearest(): + ser = Series(np.arange(10, dtype="int64")) + + target = [0.1, 0.9, 1.5, 2.0] + other = ser.reindex(target, method="nearest") + expected = Series(np.around(target).astype("int64"), target) + + result = ser.reindex_like(other, method="nearest") + tm.assert_series_equal(expected, result) + + result = ser.reindex_like(other, method="nearest", tolerance=1) + tm.assert_series_equal(expected, result) + result = ser.reindex_like(other, method="nearest", tolerance=[1, 2, 3, 4]) + tm.assert_series_equal(expected, result) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_rename.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_rename.py new file mode 100644 index 0000000000000000000000000000000000000000..a8f3862d39f07f332243a8e952d72a493ff44daa --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_rename.py @@ -0,0 +1,184 @@ +from datetime import datetime +import re + +import numpy as np +import pytest + +from pandas import ( + Index, + MultiIndex, + Series, + array, +) +import pandas._testing as tm + + +class TestRename: + def test_rename(self, datetime_series): + ts = datetime_series + renamer = lambda x: x.strftime("%Y%m%d") + renamed = ts.rename(renamer) + assert renamed.index[0] == renamer(ts.index[0]) + + # dict + rename_dict = dict(zip(ts.index, renamed.index)) + renamed2 = ts.rename(rename_dict) + tm.assert_series_equal(renamed, renamed2) + + def test_rename_partial_dict(self): + # partial dict + ser = Series(np.arange(4), index=["a", "b", "c", "d"], dtype="int64") + renamed = ser.rename({"b": "foo", "d": "bar"}) + tm.assert_index_equal(renamed.index, Index(["a", "foo", "c", "bar"])) + + def test_rename_retain_index_name(self): + # index with name + renamer = Series( + np.arange(4), index=Index(["a", "b", "c", "d"], name="name"), dtype="int64" + ) + renamed = renamer.rename({}) + assert renamed.index.name == renamer.index.name + + def test_rename_by_series(self): + ser = Series(range(5), name="foo") + renamer = Series({1: 10, 2: 20}) + result = ser.rename(renamer) + expected = Series(range(5), index=[0, 10, 20, 3, 4], name="foo") + tm.assert_series_equal(result, expected) + + def test_rename_set_name(self, using_infer_string): + ser = Series(range(4), index=list("abcd")) + for name in ["foo", 123, 123.0, datetime(2001, 11, 11), ("foo",)]: + result = ser.rename(name) + assert result.name == name + if using_infer_string: + tm.assert_extension_array_equal(result.index.values, ser.index.values) + else: + tm.assert_numpy_array_equal(result.index.values, ser.index.values) + assert ser.name is None + + def test_rename_set_name_inplace(self, using_infer_string): + ser = Series(range(3), index=list("abc")) + for name in ["foo", 123, 123.0, datetime(2001, 11, 11), ("foo",)]: + ser.rename(name, inplace=True) + assert ser.name == name + exp = np.array(["a", "b", "c"], dtype=np.object_) + if using_infer_string: + exp = array(exp, dtype="str") + tm.assert_extension_array_equal(ser.index.values, exp) + else: + tm.assert_numpy_array_equal(ser.index.values, exp) + + def test_rename_axis_supported(self): + # Supporting axis for compatibility, detailed in GH-18589 + ser = Series(range(5)) + ser.rename({}, axis=0) + ser.rename({}, axis="index") + + with pytest.raises(ValueError, match="No axis named 5"): + ser.rename({}, axis=5) + + def test_rename_inplace(self, datetime_series): + renamer = lambda x: x.strftime("%Y%m%d") + expected = renamer(datetime_series.index[0]) + + datetime_series.rename(renamer, inplace=True) + assert datetime_series.index[0] == expected + + def test_rename_with_custom_indexer(self): + # GH 27814 + class MyIndexer: + pass + + ix = MyIndexer() + ser = Series([1, 2, 3]).rename(ix) + assert ser.name is ix + + def test_rename_with_custom_indexer_inplace(self): + # GH 27814 + class MyIndexer: + pass + + ix = MyIndexer() + ser = Series([1, 2, 3]) + ser.rename(ix, inplace=True) + assert ser.name is ix + + def test_rename_callable(self): + # GH 17407 + ser = Series(range(1, 6), index=Index(range(2, 7), name="IntIndex")) + result = ser.rename(str) + expected = ser.rename(lambda i: str(i)) + tm.assert_series_equal(result, expected) + + assert result.name == expected.name + + def test_rename_none(self): + # GH 40977 + ser = Series([1, 2], name="foo") + result = ser.rename(None) + expected = Series([1, 2]) + tm.assert_series_equal(result, expected) + + def test_rename_series_with_multiindex(self): + # issue #43659 + arrays = [ + ["bar", "baz", "baz", "foo", "qux"], + ["one", "one", "two", "two", "one"], + ] + + index = MultiIndex.from_arrays(arrays, names=["first", "second"]) + ser = Series(np.ones(5), index=index) + result = ser.rename(index={"one": "yes"}, level="second", errors="raise") + + arrays_expected = [ + ["bar", "baz", "baz", "foo", "qux"], + ["yes", "yes", "two", "two", "yes"], + ] + + index_expected = MultiIndex.from_arrays( + arrays_expected, names=["first", "second"] + ) + series_expected = Series(np.ones(5), index=index_expected) + + tm.assert_series_equal(result, series_expected) + + def test_rename_series_with_multiindex_keeps_ea_dtypes(self): + # GH21055 + arrays = [ + Index([1, 2, 3], dtype="Int64").astype("category"), + Index([1, 2, 3], dtype="Int64"), + ] + mi = MultiIndex.from_arrays(arrays, names=["A", "B"]) + ser = Series(1, index=mi) + result = ser.rename({1: 4}, level=1) + + arrays_expected = [ + Index([1, 2, 3], dtype="Int64").astype("category"), + Index([4, 2, 3], dtype="Int64"), + ] + mi_expected = MultiIndex.from_arrays(arrays_expected, names=["A", "B"]) + expected = Series(1, index=mi_expected) + + tm.assert_series_equal(result, expected) + + def test_rename_error_arg(self): + # GH 46889 + ser = Series(["foo", "bar"]) + match = re.escape("[2] not found in axis") + with pytest.raises(KeyError, match=match): + ser.rename({2: 9}, errors="raise") + + def test_rename_copy_false(self, using_copy_on_write, warn_copy_on_write): + # GH 46889 + ser = Series(["foo", "bar"]) + ser_orig = ser.copy() + shallow_copy = ser.rename({1: 9}, copy=False) + with tm.assert_cow_warning(warn_copy_on_write): + ser[0] = "foobar" + if using_copy_on_write: + assert ser_orig[0] == shallow_copy[0] + assert ser_orig[1] == shallow_copy[9] + else: + assert ser[0] == shallow_copy[0] + assert ser[1] == shallow_copy[9] diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_rename_axis.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_rename_axis.py new file mode 100644 index 0000000000000000000000000000000000000000..58c095d697ede213be5c730e305bf1789810ac4a --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_rename_axis.py @@ -0,0 +1,47 @@ +import pytest + +from pandas import ( + Index, + MultiIndex, + Series, +) +import pandas._testing as tm + + +class TestSeriesRenameAxis: + def test_rename_axis_mapper(self): + # GH 19978 + mi = MultiIndex.from_product([["a", "b", "c"], [1, 2]], names=["ll", "nn"]) + ser = Series(list(range(len(mi))), index=mi) + + result = ser.rename_axis(index={"ll": "foo"}) + assert result.index.names == ["foo", "nn"] + + result = ser.rename_axis(index=str.upper, axis=0) + assert result.index.names == ["LL", "NN"] + + result = ser.rename_axis(index=["foo", "goo"]) + assert result.index.names == ["foo", "goo"] + + with pytest.raises(TypeError, match="unexpected"): + ser.rename_axis(columns="wrong") + + def test_rename_axis_inplace(self, datetime_series): + # GH 15704 + expected = datetime_series.rename_axis("foo") + result = datetime_series + no_return = result.rename_axis("foo", inplace=True) + + assert no_return is None + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("kwargs", [{"mapper": None}, {"index": None}, {}]) + def test_rename_axis_none(self, kwargs): + # GH 25034 + index = Index(list("abc"), name="foo") + ser = Series([1, 2, 3], index=index) + + result = ser.rename_axis(**kwargs) + expected_index = index.rename(None) if kwargs else index + expected = Series([1, 2, 3], index=expected_index) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_repeat.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_repeat.py new file mode 100644 index 0000000000000000000000000000000000000000..8ecc8052ff49c150444cf395b68e6163fb761775 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_repeat.py @@ -0,0 +1,40 @@ +import numpy as np +import pytest + +from pandas import ( + MultiIndex, + Series, +) +import pandas._testing as tm + + +class TestRepeat: + def test_repeat(self): + ser = Series(np.random.default_rng(2).standard_normal(3), index=["a", "b", "c"]) + + reps = ser.repeat(5) + exp = Series(ser.values.repeat(5), index=ser.index.values.repeat(5)) + tm.assert_series_equal(reps, exp) + + to_rep = [2, 3, 4] + reps = ser.repeat(to_rep) + exp = Series(ser.values.repeat(to_rep), index=ser.index.values.repeat(to_rep)) + tm.assert_series_equal(reps, exp) + + def test_numpy_repeat(self): + ser = Series(np.arange(3), name="x") + expected = Series( + ser.values.repeat(2), name="x", index=ser.index.values.repeat(2) + ) + tm.assert_series_equal(np.repeat(ser, 2), expected) + + msg = "the 'axis' parameter is not supported" + with pytest.raises(ValueError, match=msg): + np.repeat(ser, 2, axis=0) + + def test_repeat_with_multiindex(self): + # GH#9361, fixed by GH#7891 + m_idx = MultiIndex.from_tuples([(1, 2), (3, 4), (5, 6), (7, 8)]) + data = ["a", "b", "c", "d"] + m_df = Series(data, index=m_idx) + assert m_df.repeat(3).shape == (3 * len(data),) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_replace.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_replace.py new file mode 100644 index 0000000000000000000000000000000000000000..53b95d685dc994e3afce152b9353255f587a26e2 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_replace.py @@ -0,0 +1,819 @@ +import re + +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm +from pandas.core.arrays import IntervalArray + + +class TestSeriesReplace: + def test_replace_explicit_none(self): + # GH#36984 if the user explicitly passes value=None, give it to them + ser = pd.Series([0, 0, ""], dtype=object) + result = ser.replace("", None) + expected = pd.Series([0, 0, None], dtype=object) + tm.assert_series_equal(result, expected) + + # Cast column 2 to object to avoid implicit cast when setting entry to "" + df = pd.DataFrame(np.zeros((3, 3))).astype({2: object}) + df.iloc[2, 2] = "" + result = df.replace("", None) + expected = pd.DataFrame( + { + 0: np.zeros(3), + 1: np.zeros(3), + 2: np.array([0.0, 0.0, None], dtype=object), + } + ) + assert expected.iloc[2, 2] is None + tm.assert_frame_equal(result, expected) + + # GH#19998 same thing with object dtype + ser = pd.Series([10, 20, 30, "a", "a", "b", "a"]) + result = ser.replace("a", None) + expected = pd.Series([10, 20, 30, None, None, "b", None]) + assert expected.iloc[-1] is None + tm.assert_series_equal(result, expected) + + def test_replace_noop_doesnt_downcast(self): + # GH#44498 + ser = pd.Series([None, None, pd.Timestamp("2021-12-16 17:31")], dtype=object) + res = ser.replace({np.nan: None}) # should be a no-op + tm.assert_series_equal(res, ser) + assert res.dtype == object + + # same thing but different calling convention + res = ser.replace(np.nan, None) + tm.assert_series_equal(res, ser) + assert res.dtype == object + + def test_replace(self): + N = 50 + ser = pd.Series(np.random.default_rng(2).standard_normal(N)) + ser[0:4] = np.nan + ser[6:10] = 0 + + # replace list with a single value + return_value = ser.replace([np.nan], -1, inplace=True) + assert return_value is None + + exp = ser.fillna(-1) + tm.assert_series_equal(ser, exp) + + rs = ser.replace(0.0, np.nan) + ser[ser == 0.0] = np.nan + tm.assert_series_equal(rs, ser) + + ser = pd.Series( + np.fabs(np.random.default_rng(2).standard_normal(N)), + pd.date_range("2020-01-01", periods=N), + dtype=object, + ) + ser[:5] = np.nan + ser[6:10] = "foo" + ser[20:30] = "bar" + + # replace list with a single value + msg = "Downcasting behavior in `replace`" + with tm.assert_produces_warning(FutureWarning, match=msg): + rs = ser.replace([np.nan, "foo", "bar"], -1) + + assert (rs[:5] == -1).all() + assert (rs[6:10] == -1).all() + assert (rs[20:30] == -1).all() + assert (pd.isna(ser[:5])).all() + + # replace with different values + with tm.assert_produces_warning(FutureWarning, match=msg): + rs = ser.replace({np.nan: -1, "foo": -2, "bar": -3}) + + assert (rs[:5] == -1).all() + assert (rs[6:10] == -2).all() + assert (rs[20:30] == -3).all() + assert (pd.isna(ser[:5])).all() + + # replace with different values with 2 lists + with tm.assert_produces_warning(FutureWarning, match=msg): + rs2 = ser.replace([np.nan, "foo", "bar"], [-1, -2, -3]) + tm.assert_series_equal(rs, rs2) + + # replace inplace + with tm.assert_produces_warning(FutureWarning, match=msg): + return_value = ser.replace([np.nan, "foo", "bar"], -1, inplace=True) + assert return_value is None + + assert (ser[:5] == -1).all() + assert (ser[6:10] == -1).all() + assert (ser[20:30] == -1).all() + + def test_replace_nan_with_inf(self): + ser = pd.Series([np.nan, 0, np.inf]) + tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0)) + + ser = pd.Series([np.nan, 0, "foo", "bar", np.inf, None, pd.NaT]) + tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0)) + filled = ser.copy() + filled[4] = 0 + tm.assert_series_equal(ser.replace(np.inf, 0), filled) + + def test_replace_listlike_value_listlike_target(self, datetime_series): + ser = pd.Series(datetime_series.index) + tm.assert_series_equal(ser.replace(np.nan, 0), ser.fillna(0)) + + # malformed + msg = r"Replacement lists must match in length\. Expecting 3 got 2" + with pytest.raises(ValueError, match=msg): + ser.replace([1, 2, 3], [np.nan, 0]) + + # ser is dt64 so can't hold 1 or 2, so this replace is a no-op + result = ser.replace([1, 2], [np.nan, 0]) + tm.assert_series_equal(result, ser) + + ser = pd.Series([0, 1, 2, 3, 4]) + result = ser.replace([0, 1, 2, 3, 4], [4, 3, 2, 1, 0]) + tm.assert_series_equal(result, pd.Series([4, 3, 2, 1, 0])) + + def test_replace_gh5319(self): + # API change from 0.12? + # GH 5319 + ser = pd.Series([0, np.nan, 2, 3, 4]) + expected = ser.ffill() + msg = ( + "Series.replace without 'value' and with non-dict-like " + "'to_replace' is deprecated" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + result = ser.replace([np.nan]) + tm.assert_series_equal(result, expected) + + ser = pd.Series([0, np.nan, 2, 3, 4]) + expected = ser.ffill() + with tm.assert_produces_warning(FutureWarning, match=msg): + result = ser.replace(np.nan) + tm.assert_series_equal(result, expected) + + def test_replace_datetime64(self): + # GH 5797 + ser = pd.Series(pd.date_range("20130101", periods=5)) + expected = ser.copy() + expected.loc[2] = pd.Timestamp("20120101") + result = ser.replace({pd.Timestamp("20130103"): pd.Timestamp("20120101")}) + tm.assert_series_equal(result, expected) + result = ser.replace(pd.Timestamp("20130103"), pd.Timestamp("20120101")) + tm.assert_series_equal(result, expected) + + def test_replace_nat_with_tz(self): + # GH 11792: Test with replacing NaT in a list with tz data + ts = pd.Timestamp("2015/01/01", tz="UTC") + s = pd.Series([pd.NaT, pd.Timestamp("2015/01/01", tz="UTC")]) + result = s.replace([np.nan, pd.NaT], pd.Timestamp.min) + expected = pd.Series([pd.Timestamp.min, ts], dtype=object) + tm.assert_series_equal(expected, result) + + def test_replace_timedelta_td64(self): + tdi = pd.timedelta_range(0, periods=5) + ser = pd.Series(tdi) + + # Using a single dict argument means we go through replace_list + result = ser.replace({ser[1]: ser[3]}) + + expected = pd.Series([ser[0], ser[3], ser[2], ser[3], ser[4]]) + tm.assert_series_equal(result, expected) + + def test_replace_with_single_list(self): + ser = pd.Series([0, 1, 2, 3, 4]) + msg2 = ( + "Series.replace without 'value' and with non-dict-like " + "'to_replace' is deprecated" + ) + with tm.assert_produces_warning(FutureWarning, match=msg2): + result = ser.replace([1, 2, 3]) + tm.assert_series_equal(result, pd.Series([0, 0, 0, 0, 4])) + + s = ser.copy() + with tm.assert_produces_warning(FutureWarning, match=msg2): + return_value = s.replace([1, 2, 3], inplace=True) + assert return_value is None + tm.assert_series_equal(s, pd.Series([0, 0, 0, 0, 4])) + + # make sure things don't get corrupted when fillna call fails + s = ser.copy() + msg = ( + r"Invalid fill method\. Expecting pad \(ffill\) or backfill " + r"\(bfill\)\. Got crash_cymbal" + ) + msg3 = "The 'method' keyword in Series.replace is deprecated" + with pytest.raises(ValueError, match=msg): + with tm.assert_produces_warning(FutureWarning, match=msg3): + return_value = s.replace([1, 2, 3], inplace=True, method="crash_cymbal") + assert return_value is None + tm.assert_series_equal(s, ser) + + def test_replace_mixed_types(self): + ser = pd.Series(np.arange(5), dtype="int64") + + def check_replace(to_rep, val, expected): + sc = ser.copy() + result = ser.replace(to_rep, val) + return_value = sc.replace(to_rep, val, inplace=True) + assert return_value is None + tm.assert_series_equal(expected, result) + tm.assert_series_equal(expected, sc) + + # 3.0 can still be held in our int64 series, so we do not upcast GH#44940 + tr, v = [3], [3.0] + check_replace(tr, v, ser) + # Note this matches what we get with the scalars 3 and 3.0 + check_replace(tr[0], v[0], ser) + + # MUST upcast to float + e = pd.Series([0, 1, 2, 3.5, 4]) + tr, v = [3], [3.5] + check_replace(tr, v, e) + + # casts to object + e = pd.Series([0, 1, 2, 3.5, "a"]) + tr, v = [3, 4], [3.5, "a"] + check_replace(tr, v, e) + + # again casts to object + e = pd.Series([0, 1, 2, 3.5, pd.Timestamp("20130101")]) + tr, v = [3, 4], [3.5, pd.Timestamp("20130101")] + check_replace(tr, v, e) + + # casts to object + e = pd.Series([0, 1, 2, 3.5, True], dtype="object") + tr, v = [3, 4], [3.5, True] + check_replace(tr, v, e) + + # test an object with dates + floats + integers + strings + dr = pd.Series(pd.date_range("1/1/2001", "1/10/2001", freq="D")) + result = dr.astype(object).replace([dr[0], dr[1], dr[2]], [1.0, 2, "a"]) + expected = pd.Series([1.0, 2, "a"] + dr[3:].tolist(), dtype=object) + tm.assert_series_equal(result, expected) + + def test_replace_bool_with_string_no_op(self): + s = pd.Series([True, False, True]) + result = s.replace("fun", "in-the-sun") + tm.assert_series_equal(s, result) + + def test_replace_bool_with_string(self): + # nonexistent elements + s = pd.Series([True, False, True]) + result = s.replace(True, "2u") + expected = pd.Series(["2u", False, "2u"]) + tm.assert_series_equal(expected, result) + + def test_replace_bool_with_bool(self): + s = pd.Series([True, False, True]) + result = s.replace(True, False) + expected = pd.Series([False] * len(s)) + tm.assert_series_equal(expected, result) + + def test_replace_with_dict_with_bool_keys(self): + s = pd.Series([True, False, True]) + result = s.replace({"asdf": "asdb", True: "yes"}) + expected = pd.Series(["yes", False, "yes"]) + tm.assert_series_equal(result, expected) + + def test_replace_Int_with_na(self, any_int_ea_dtype): + # GH 38267 + result = pd.Series([0, None], dtype=any_int_ea_dtype).replace(0, pd.NA) + expected = pd.Series([pd.NA, pd.NA], dtype=any_int_ea_dtype) + tm.assert_series_equal(result, expected) + result = pd.Series([0, 1], dtype=any_int_ea_dtype).replace(0, pd.NA) + result.replace(1, pd.NA, inplace=True) + tm.assert_series_equal(result, expected) + + def test_replace2(self): + N = 50 + ser = pd.Series( + np.fabs(np.random.default_rng(2).standard_normal(N)), + pd.date_range("2020-01-01", periods=N), + dtype=object, + ) + ser[:5] = np.nan + ser[6:10] = "foo" + ser[20:30] = "bar" + + # replace list with a single value + msg = "Downcasting behavior in `replace`" + with tm.assert_produces_warning(FutureWarning, match=msg): + rs = ser.replace([np.nan, "foo", "bar"], -1) + + assert (rs[:5] == -1).all() + assert (rs[6:10] == -1).all() + assert (rs[20:30] == -1).all() + assert (pd.isna(ser[:5])).all() + + # replace with different values + with tm.assert_produces_warning(FutureWarning, match=msg): + rs = ser.replace({np.nan: -1, "foo": -2, "bar": -3}) + + assert (rs[:5] == -1).all() + assert (rs[6:10] == -2).all() + assert (rs[20:30] == -3).all() + assert (pd.isna(ser[:5])).all() + + # replace with different values with 2 lists + with tm.assert_produces_warning(FutureWarning, match=msg): + rs2 = ser.replace([np.nan, "foo", "bar"], [-1, -2, -3]) + tm.assert_series_equal(rs, rs2) + + # replace inplace + with tm.assert_produces_warning(FutureWarning, match=msg): + return_value = ser.replace([np.nan, "foo", "bar"], -1, inplace=True) + assert return_value is None + assert (ser[:5] == -1).all() + assert (ser[6:10] == -1).all() + assert (ser[20:30] == -1).all() + + @pytest.mark.parametrize("inplace", [True, False]) + def test_replace_cascade(self, inplace): + # Test that replaced values are not replaced again + # GH #50778 + ser = pd.Series([1, 2, 3]) + expected = pd.Series([2, 3, 4]) + + res = ser.replace([1, 2, 3], [2, 3, 4], inplace=inplace) + if inplace: + tm.assert_series_equal(ser, expected) + else: + tm.assert_series_equal(res, expected) + + def test_replace_with_dictlike_and_string_dtype(self, nullable_string_dtype): + # GH 32621, GH#44940 + ser = pd.Series(["one", "two", np.nan], dtype=nullable_string_dtype) + expected = pd.Series(["1", "2", np.nan], dtype=nullable_string_dtype) + result = ser.replace({"one": "1", "two": "2"}) + tm.assert_series_equal(expected, result) + + def test_replace_with_empty_dictlike(self): + # GH 15289 + s = pd.Series(list("abcd")) + tm.assert_series_equal(s, s.replace({})) + + empty_series = pd.Series([]) + tm.assert_series_equal(s, s.replace(empty_series)) + + def test_replace_string_with_number(self): + # GH 15743 + s = pd.Series([1, 2, 3]) + result = s.replace("2", np.nan) + expected = pd.Series([1, 2, 3]) + tm.assert_series_equal(expected, result) + + def test_replace_replacer_equals_replacement(self): + # GH 20656 + # make sure all replacers are matching against original values + s = pd.Series(["a", "b"]) + expected = pd.Series(["b", "a"]) + result = s.replace({"a": "b", "b": "a"}) + tm.assert_series_equal(expected, result) + + def test_replace_unicode_with_number(self): + # GH 15743 + s = pd.Series([1, 2, 3]) + result = s.replace("2", np.nan) + expected = pd.Series([1, 2, 3]) + tm.assert_series_equal(expected, result) + + def test_replace_mixed_types_with_string(self): + # Testing mixed + s = pd.Series([1, 2, 3, "4", 4, 5]) + msg = "Downcasting behavior in `replace`" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = s.replace([2, "4"], np.nan) + expected = pd.Series([1, np.nan, 3, np.nan, 4, 5]) + tm.assert_series_equal(expected, result) + + @pytest.mark.parametrize( + "categorical, numeric", + [ + (pd.Categorical(["A"], categories=["A", "B"]), [1]), + (pd.Categorical(["A", "B"], categories=["A", "B"]), [1, 2]), + ], + ) + def test_replace_categorical(self, categorical, numeric, using_infer_string): + # GH 24971, GH#23305 + ser = pd.Series(categorical) + msg = "Downcasting behavior in `replace`" + msg = "with CategoricalDtype is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = ser.replace({"A": 1, "B": 2}) + expected = pd.Series(numeric).astype("category") + if 2 not in expected.cat.categories: + # i.e. categories should be [1, 2] even if there are no "B"s present + # GH#44940 + expected = expected.cat.add_categories(2) + tm.assert_series_equal(expected, result) + + @pytest.mark.parametrize( + "data, data_exp", [(["a", "b", "c"], ["b", "b", "c"]), (["a"], ["b"])] + ) + def test_replace_categorical_inplace(self, data, data_exp): + # GH 53358 + result = pd.Series(data, dtype="category") + msg = "with CategoricalDtype is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result.replace(to_replace="a", value="b", inplace=True) + expected = pd.Series(data_exp, dtype="category") + tm.assert_series_equal(result, expected) + + def test_replace_categorical_single(self): + # GH 26988 + dti = pd.date_range("2016-01-01", periods=3, tz="US/Pacific") + s = pd.Series(dti) + c = s.astype("category") + + expected = c.copy() + expected = expected.cat.add_categories("foo") + expected[2] = "foo" + expected = expected.cat.remove_unused_categories() + assert c[2] != "foo" + + msg = "with CategoricalDtype is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = c.replace(c[2], "foo") + tm.assert_series_equal(expected, result) + assert c[2] != "foo" # ensure non-inplace call does not alter original + + msg = "with CategoricalDtype is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + return_value = c.replace(c[2], "foo", inplace=True) + assert return_value is None + tm.assert_series_equal(expected, c) + + first_value = c[0] + msg = "with CategoricalDtype is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + return_value = c.replace(c[1], c[0], inplace=True) + assert return_value is None + assert c[0] == c[1] == first_value # test replacing with existing value + + def test_replace_with_no_overflowerror(self): + # GH 25616 + # casts to object without Exception from OverflowError + s = pd.Series([0, 1, 2, 3, 4]) + result = s.replace([3], ["100000000000000000000"]) + expected = pd.Series([0, 1, 2, "100000000000000000000", 4]) + tm.assert_series_equal(result, expected) + + s = pd.Series([0, "100000000000000000000", "100000000000000000001"]) + result = s.replace(["100000000000000000000"], [1]) + expected = pd.Series([0, 1, "100000000000000000001"]) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "ser, to_replace, exp", + [ + ([1, 2, 3], {1: 2, 2: 3, 3: 4}, [2, 3, 4]), + (["1", "2", "3"], {"1": "2", "2": "3", "3": "4"}, ["2", "3", "4"]), + ], + ) + def test_replace_commutative(self, ser, to_replace, exp): + # GH 16051 + # DataFrame.replace() overwrites when values are non-numeric + + series = pd.Series(ser) + + expected = pd.Series(exp) + result = series.replace(to_replace) + + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "ser, exp", [([1, 2, 3], [1, True, 3]), (["x", 2, 3], ["x", True, 3])] + ) + def test_replace_no_cast(self, ser, exp): + # GH 9113 + # BUG: replace int64 dtype with bool coerces to int64 + + series = pd.Series(ser) + result = series.replace(2, True) + expected = pd.Series(exp) + + tm.assert_series_equal(result, expected) + + def test_replace_invalid_to_replace(self): + # GH 18634 + # API: replace() should raise an exception if invalid argument is given + series = pd.Series(["a", "b", "c "]) + msg = ( + r"Expecting 'to_replace' to be either a scalar, array-like, " + r"dict or None, got invalid type.*" + ) + msg2 = ( + "Series.replace without 'value' and with non-dict-like " + "'to_replace' is deprecated" + ) + with pytest.raises(TypeError, match=msg): + with tm.assert_produces_warning(FutureWarning, match=msg2): + series.replace(lambda x: x.strip()) + + @pytest.mark.parametrize("frame", [False, True]) + def test_replace_nonbool_regex(self, frame): + obj = pd.Series(["a", "b", "c "]) + if frame: + obj = obj.to_frame() + + msg = "'to_replace' must be 'None' if 'regex' is not a bool" + with pytest.raises(ValueError, match=msg): + obj.replace(to_replace=["a"], regex="foo") + + @pytest.mark.parametrize("frame", [False, True]) + def test_replace_empty_copy(self, frame): + obj = pd.Series([], dtype=np.float64) + if frame: + obj = obj.to_frame() + + res = obj.replace(4, 5, inplace=True) + assert res is None + + res = obj.replace(4, 5, inplace=False) + tm.assert_equal(res, obj) + assert res is not obj + + def test_replace_only_one_dictlike_arg(self, fixed_now_ts): + # GH#33340 + + ser = pd.Series([1, 2, "A", fixed_now_ts, True]) + to_replace = {0: 1, 2: "A"} + value = "foo" + msg = "Series.replace cannot use dict-like to_replace and non-None value" + with pytest.raises(ValueError, match=msg): + ser.replace(to_replace, value) + + to_replace = 1 + value = {0: "foo", 2: "bar"} + msg = "Series.replace cannot use dict-value and non-None to_replace" + with pytest.raises(ValueError, match=msg): + ser.replace(to_replace, value) + + def test_replace_extension_other(self, frame_or_series): + # https://github.com/pandas-dev/pandas/issues/34530 + obj = frame_or_series(pd.array([1, 2, 3], dtype="Int64")) + result = obj.replace("", "") # no exception + # should not have changed dtype + tm.assert_equal(obj, result) + + def _check_replace_with_method(self, ser: pd.Series): + df = ser.to_frame() + + msg1 = "The 'method' keyword in Series.replace is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg1): + res = ser.replace(ser[1], method="pad") + expected = pd.Series([ser[0], ser[0]] + list(ser[2:]), dtype=ser.dtype) + tm.assert_series_equal(res, expected) + + msg2 = "The 'method' keyword in DataFrame.replace is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg2): + res_df = df.replace(ser[1], method="pad") + tm.assert_frame_equal(res_df, expected.to_frame()) + + ser2 = ser.copy() + with tm.assert_produces_warning(FutureWarning, match=msg1): + res2 = ser2.replace(ser[1], method="pad", inplace=True) + assert res2 is None + tm.assert_series_equal(ser2, expected) + + with tm.assert_produces_warning(FutureWarning, match=msg2): + res_df2 = df.replace(ser[1], method="pad", inplace=True) + assert res_df2 is None + tm.assert_frame_equal(df, expected.to_frame()) + + def test_replace_ea_dtype_with_method(self, any_numeric_ea_dtype): + arr = pd.array([1, 2, pd.NA, 4], dtype=any_numeric_ea_dtype) + ser = pd.Series(arr) + + self._check_replace_with_method(ser) + + @pytest.mark.parametrize("as_categorical", [True, False]) + def test_replace_interval_with_method(self, as_categorical): + # in particular interval that can't hold NA + + idx = pd.IntervalIndex.from_breaks(range(4)) + ser = pd.Series(idx) + if as_categorical: + ser = ser.astype("category") + + self._check_replace_with_method(ser) + + @pytest.mark.parametrize("as_period", [True, False]) + @pytest.mark.parametrize("as_categorical", [True, False]) + def test_replace_datetimelike_with_method(self, as_period, as_categorical): + idx = pd.date_range("2016-01-01", periods=5, tz="US/Pacific") + if as_period: + idx = idx.tz_localize(None).to_period("D") + + ser = pd.Series(idx) + ser.iloc[-2] = pd.NaT + if as_categorical: + ser = ser.astype("category") + + self._check_replace_with_method(ser) + + def test_replace_with_compiled_regex(self): + # https://github.com/pandas-dev/pandas/issues/35680 + s = pd.Series(["a", "b", "c"]) + regex = re.compile("^a$") + result = s.replace({regex: "z"}, regex=True) + expected = pd.Series(["z", "b", "c"]) + tm.assert_series_equal(result, expected) + + def test_pandas_replace_na(self): + # GH#43344 + # GH#56599 + ser = pd.Series(["AA", "BB", "CC", "DD", "EE", "", pd.NA, "AA"], dtype="string") + regex_mapping = { + "AA": "CC", + "BB": "CC", + "EE": "CC", + "CC": "CC-REPL", + } + result = ser.replace(regex_mapping, regex=True) + exp = pd.Series( + ["CC", "CC", "CC-REPL", "DD", "CC", "", pd.NA, "CC"], dtype="string" + ) + tm.assert_series_equal(result, exp) + + @pytest.mark.parametrize( + "dtype, input_data, to_replace, expected_data", + [ + ("bool", [True, False], {True: False}, [False, False]), + ("int64", [1, 2], {1: 10, 2: 20}, [10, 20]), + ("Int64", [1, 2], {1: 10, 2: 20}, [10, 20]), + ("float64", [1.1, 2.2], {1.1: 10.1, 2.2: 20.5}, [10.1, 20.5]), + ("Float64", [1.1, 2.2], {1.1: 10.1, 2.2: 20.5}, [10.1, 20.5]), + ("string", ["one", "two"], {"one": "1", "two": "2"}, ["1", "2"]), + ( + pd.IntervalDtype("int64"), + IntervalArray([pd.Interval(1, 2), pd.Interval(2, 3)]), + {pd.Interval(1, 2): pd.Interval(10, 20)}, + IntervalArray([pd.Interval(10, 20), pd.Interval(2, 3)]), + ), + ( + pd.IntervalDtype("float64"), + IntervalArray([pd.Interval(1.0, 2.7), pd.Interval(2.8, 3.1)]), + {pd.Interval(1.0, 2.7): pd.Interval(10.6, 20.8)}, + IntervalArray([pd.Interval(10.6, 20.8), pd.Interval(2.8, 3.1)]), + ), + ( + pd.PeriodDtype("M"), + [pd.Period("2020-05", freq="M")], + {pd.Period("2020-05", freq="M"): pd.Period("2020-06", freq="M")}, + [pd.Period("2020-06", freq="M")], + ), + ], + ) + def test_replace_dtype(self, dtype, input_data, to_replace, expected_data): + # GH#33484 + ser = pd.Series(input_data, dtype=dtype) + result = ser.replace(to_replace) + expected = pd.Series(expected_data, dtype=dtype) + tm.assert_series_equal(result, expected) + + def test_replace_string_dtype(self): + # GH#40732, GH#44940 + ser = pd.Series(["one", "two", np.nan], dtype="string") + res = ser.replace({"one": "1", "two": "2"}) + expected = pd.Series(["1", "2", np.nan], dtype="string") + tm.assert_series_equal(res, expected) + + # GH#31644 + ser2 = pd.Series(["A", np.nan], dtype="string") + res2 = ser2.replace("A", "B") + expected2 = pd.Series(["B", np.nan], dtype="string") + tm.assert_series_equal(res2, expected2) + + ser3 = pd.Series(["A", "B"], dtype="string") + res3 = ser3.replace("A", pd.NA) + expected3 = pd.Series([pd.NA, "B"], dtype="string") + tm.assert_series_equal(res3, expected3) + + def test_replace_string_dtype_list_to_replace(self): + # GH#41215, GH#44940 + ser = pd.Series(["abc", "def"], dtype="string") + res = ser.replace(["abc", "any other string"], "xyz") + expected = pd.Series(["xyz", "def"], dtype="string") + tm.assert_series_equal(res, expected) + + def test_replace_string_dtype_regex(self): + # GH#31644 + ser = pd.Series(["A", "B"], dtype="string") + res = ser.replace(r".", "C", regex=True) + expected = pd.Series(["C", "C"], dtype="string") + tm.assert_series_equal(res, expected) + + def test_replace_nullable_numeric(self): + # GH#40732, GH#44940 + + floats = pd.Series([1.0, 2.0, 3.999, 4.4], dtype=pd.Float64Dtype()) + assert floats.replace({1.0: 9}).dtype == floats.dtype + assert floats.replace(1.0, 9).dtype == floats.dtype + assert floats.replace({1.0: 9.0}).dtype == floats.dtype + assert floats.replace(1.0, 9.0).dtype == floats.dtype + + res = floats.replace(to_replace=[1.0, 2.0], value=[9.0, 10.0]) + assert res.dtype == floats.dtype + + ints = pd.Series([1, 2, 3, 4], dtype=pd.Int64Dtype()) + assert ints.replace({1: 9}).dtype == ints.dtype + assert ints.replace(1, 9).dtype == ints.dtype + assert ints.replace({1: 9.0}).dtype == ints.dtype + assert ints.replace(1, 9.0).dtype == ints.dtype + + # nullable (for now) raises instead of casting + with pytest.raises(TypeError, match="Invalid value"): + ints.replace({1: 9.5}) + with pytest.raises(TypeError, match="Invalid value"): + ints.replace(1, 9.5) + + @pytest.mark.parametrize("regex", [False, True]) + def test_replace_regex_dtype_series(self, regex): + # GH-48644 + series = pd.Series(["0"], dtype=object) + expected = pd.Series([1]) + msg = "Downcasting behavior in `replace`" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = series.replace(to_replace="0", value=1, regex=regex) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("regex", [False, True]) + def test_replace_regex_dtype_series_string(self, regex): + series = pd.Series(["0"], dtype="str") + expected = pd.Series([1], dtype="int64") + msg = "Downcasting behavior in `replace`" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = series.replace(to_replace="0", value=1, regex=regex) + tm.assert_series_equal(result, expected) + + def test_replace_different_int_types(self, any_int_numpy_dtype): + # GH#45311 + labs = pd.Series([1, 1, 1, 0, 0, 2, 2, 2], dtype=any_int_numpy_dtype) + + maps = pd.Series([0, 2, 1], dtype=any_int_numpy_dtype) + map_dict = dict(zip(maps.values, maps.index)) + + result = labs.replace(map_dict) + expected = labs.replace({0: 0, 2: 1, 1: 2}) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("val", [2, np.nan, 2.0]) + def test_replace_value_none_dtype_numeric(self, val): + # GH#48231 + ser = pd.Series([1, val]) + result = ser.replace(val, None) + expected = pd.Series([1, None], dtype=object) + tm.assert_series_equal(result, expected) + + def test_replace_change_dtype_series(self): + # GH#25797 + df = pd.DataFrame({"Test": ["0.5", True, "0.6"]}, dtype=object) + df["Test"] = df["Test"].replace([True], [np.nan]) + expected = pd.DataFrame({"Test": ["0.5", np.nan, "0.6"]}, dtype=object) + tm.assert_frame_equal(df, expected) + + df = pd.DataFrame({"Test": ["0.5", None, "0.6"]}, dtype=object) + df["Test"] = df["Test"].replace([None], [np.nan]) + tm.assert_frame_equal(df, expected) + + df = pd.DataFrame({"Test": ["0.5", None, "0.6"]}, dtype=object) + df["Test"] = df["Test"].fillna(np.nan) + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize("dtype", ["object", "Int64"]) + def test_replace_na_in_obj_column(self, dtype): + # GH#47480 + ser = pd.Series([0, 1, pd.NA], dtype=dtype) + expected = pd.Series([0, 2, pd.NA], dtype=dtype) + result = ser.replace(to_replace=1, value=2) + tm.assert_series_equal(result, expected) + + ser.replace(to_replace=1, value=2, inplace=True) + tm.assert_series_equal(ser, expected) + + @pytest.mark.parametrize("val", [0, 0.5]) + def test_replace_numeric_column_with_na(self, val): + # GH#50758 + ser = pd.Series([val, 1]) + expected = pd.Series([val, pd.NA]) + result = ser.replace(to_replace=1, value=pd.NA) + tm.assert_series_equal(result, expected) + + ser.replace(to_replace=1, value=pd.NA, inplace=True) + tm.assert_series_equal(ser, expected) + + def test_replace_ea_float_with_bool(self): + # GH#55398 + ser = pd.Series([0.0], dtype="Float64") + expected = ser.copy() + result = ser.replace(False, 1.0) + tm.assert_series_equal(result, expected) + + ser = pd.Series([False], dtype="boolean") + expected = ser.copy() + result = ser.replace(0.0, True) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_reset_index.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_reset_index.py new file mode 100644 index 0000000000000000000000000000000000000000..fa571fa126b38282cd5e132472a3824732446354 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_reset_index.py @@ -0,0 +1,225 @@ +from datetime import datetime + +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + Index, + MultiIndex, + RangeIndex, + Series, + date_range, + option_context, +) +import pandas._testing as tm + + +class TestResetIndex: + def test_reset_index_dti_round_trip(self): + dti = date_range(start="1/1/2001", end="6/1/2001", freq="D")._with_freq(None) + d1 = DataFrame({"v": np.random.default_rng(2).random(len(dti))}, index=dti) + d2 = d1.reset_index() + assert d2.dtypes.iloc[0] == np.dtype("M8[ns]") + d3 = d2.set_index("index") + tm.assert_frame_equal(d1, d3, check_names=False) + + # GH#2329 + stamp = datetime(2012, 11, 22) + df = DataFrame([[stamp, 12.1]], columns=["Date", "Value"]) + df = df.set_index("Date") + + assert df.index[0] == stamp + assert df.reset_index()["Date"].iloc[0] == stamp + + def test_reset_index(self): + df = DataFrame( + 1.1 * np.arange(120).reshape((30, 4)), + columns=Index(list("ABCD"), dtype=object), + index=Index([f"i-{i}" for i in range(30)], dtype=object), + )[:5] + ser = df.stack(future_stack=True) + ser.index.names = ["hash", "category"] + + ser.name = "value" + df = ser.reset_index() + assert "value" in df + + df = ser.reset_index(name="value2") + assert "value2" in df + + # check inplace + s = ser.reset_index(drop=True) + s2 = ser + return_value = s2.reset_index(drop=True, inplace=True) + assert return_value is None + tm.assert_series_equal(s, s2) + + # level + index = MultiIndex( + levels=[["bar"], ["one", "two", "three"], [0, 1]], + codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]], + ) + s = Series(np.random.default_rng(2).standard_normal(6), index=index) + rs = s.reset_index(level=1) + assert len(rs.columns) == 2 + + rs = s.reset_index(level=[0, 2], drop=True) + tm.assert_index_equal(rs.index, Index(index.get_level_values(1))) + assert isinstance(rs, Series) + + def test_reset_index_name(self): + s = Series([1, 2, 3], index=Index(range(3), name="x")) + assert s.reset_index().index.name is None + assert s.reset_index(drop=True).index.name is None + + def test_reset_index_level(self): + df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["A", "B", "C"]) + + for levels in ["A", "B"], [0, 1]: + # With MultiIndex + s = df.set_index(["A", "B"])["C"] + + result = s.reset_index(level=levels[0]) + tm.assert_frame_equal(result, df.set_index("B")) + + result = s.reset_index(level=levels[:1]) + tm.assert_frame_equal(result, df.set_index("B")) + + result = s.reset_index(level=levels) + tm.assert_frame_equal(result, df) + + result = df.set_index(["A", "B"]).reset_index(level=levels, drop=True) + tm.assert_frame_equal(result, df[["C"]]) + + with pytest.raises(KeyError, match="Level E "): + s.reset_index(level=["A", "E"]) + + # With single-level Index + s = df.set_index("A")["B"] + + result = s.reset_index(level=levels[0]) + tm.assert_frame_equal(result, df[["A", "B"]]) + + result = s.reset_index(level=levels[:1]) + tm.assert_frame_equal(result, df[["A", "B"]]) + + result = s.reset_index(level=levels[0], drop=True) + tm.assert_series_equal(result, df["B"]) + + with pytest.raises(IndexError, match="Too many levels"): + s.reset_index(level=[0, 1, 2]) + + # Check that .reset_index([],drop=True) doesn't fail + result = Series(range(4)).reset_index([], drop=True) + expected = Series(range(4)) + tm.assert_series_equal(result, expected) + + def test_reset_index_range(self): + # GH 12071 + s = Series(range(2), name="A", dtype="int64") + series_result = s.reset_index() + assert isinstance(series_result.index, RangeIndex) + series_expected = DataFrame( + [[0, 0], [1, 1]], columns=["index", "A"], index=RangeIndex(stop=2) + ) + tm.assert_frame_equal(series_result, series_expected) + + def test_reset_index_drop_errors(self): + # GH 20925 + + # KeyError raised for series index when passed level name is missing + s = Series(range(4)) + with pytest.raises(KeyError, match="does not match index name"): + s.reset_index("wrong", drop=True) + with pytest.raises(KeyError, match="does not match index name"): + s.reset_index("wrong") + + # KeyError raised for series when level to be dropped is missing + s = Series(range(4), index=MultiIndex.from_product([[1, 2]] * 2)) + with pytest.raises(KeyError, match="not found"): + s.reset_index("wrong", drop=True) + + def test_reset_index_with_drop(self): + arrays = [ + ["bar", "bar", "baz", "baz", "qux", "qux", "foo", "foo"], + ["one", "two", "one", "two", "one", "two", "one", "two"], + ] + tuples = zip(*arrays) + index = MultiIndex.from_tuples(tuples) + data = np.random.default_rng(2).standard_normal(8) + ser = Series(data, index=index) + ser.iloc[3] = np.nan + + deleveled = ser.reset_index() + assert isinstance(deleveled, DataFrame) + assert len(deleveled.columns) == len(ser.index.levels) + 1 + assert deleveled.index.name == ser.index.name + + deleveled = ser.reset_index(drop=True) + assert isinstance(deleveled, Series) + assert deleveled.index.name == ser.index.name + + def test_reset_index_inplace_and_drop_ignore_name(self): + # GH#44575 + ser = Series(range(2), name="old") + ser.reset_index(name="new", drop=True, inplace=True) + expected = Series(range(2), name="old") + tm.assert_series_equal(ser, expected) + + def test_reset_index_drop_infer_string(self): + # GH#56160 + pytest.importorskip("pyarrow") + ser = Series(["a", "b", "c"], dtype=object) + with option_context("future.infer_string", True): + result = ser.reset_index(drop=True) + tm.assert_series_equal(result, ser) + + +@pytest.mark.parametrize( + "array, dtype", + [ + (["a", "b"], object), + ( + pd.period_range("12-1-2000", periods=2, freq="Q-DEC"), + pd.PeriodDtype(freq="Q-DEC"), + ), + ], +) +def test_reset_index_dtypes_on_empty_series_with_multiindex( + array, dtype, using_infer_string +): + # GH 19602 - Preserve dtype on empty Series with MultiIndex + idx = MultiIndex.from_product([[0, 1], [0.5, 1.0], array]) + result = Series(dtype=object, index=idx)[:0].reset_index().dtypes + exp = "str" if using_infer_string else object + expected = Series( + { + "level_0": np.int64, + "level_1": np.float64, + "level_2": exp if dtype == object else dtype, + 0: object, + } + ) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "names, expected_names", + [ + (["A", "A"], ["A", "A"]), + (["level_1", None], ["level_1", "level_1"]), + ], +) +@pytest.mark.parametrize("allow_duplicates", [False, True]) +def test_column_name_duplicates(names, expected_names, allow_duplicates): + # GH#44755 reset_index with duplicate column labels + s = Series([1], index=MultiIndex.from_arrays([[1], [1]], names=names)) + if allow_duplicates: + result = s.reset_index(allow_duplicates=True) + expected = DataFrame([[1, 1, 1]], columns=expected_names + [0]) + tm.assert_frame_equal(result, expected) + else: + with pytest.raises(ValueError, match="cannot insert"): + s.reset_index() diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_round.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_round.py new file mode 100644 index 0000000000000000000000000000000000000000..a78f77e990ae13eda068e920bfa8e1f4de72af9a --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_round.py @@ -0,0 +1,81 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import Series +import pandas._testing as tm + + +class TestSeriesRound: + def test_round(self, datetime_series): + datetime_series.index.name = "index_name" + result = datetime_series.round(2) + expected = Series( + np.round(datetime_series.values, 2), index=datetime_series.index, name="ts" + ) + tm.assert_series_equal(result, expected) + assert result.name == datetime_series.name + + def test_round_numpy(self, any_float_dtype): + # See GH#12600 + ser = Series([1.53, 1.36, 0.06], dtype=any_float_dtype) + out = np.round(ser, decimals=0) + expected = Series([2.0, 1.0, 0.0], dtype=any_float_dtype) + tm.assert_series_equal(out, expected) + + msg = "the 'out' parameter is not supported" + with pytest.raises(ValueError, match=msg): + np.round(ser, decimals=0, out=ser) + + def test_round_numpy_with_nan(self, any_float_dtype): + # See GH#14197 + ser = Series([1.53, np.nan, 0.06], dtype=any_float_dtype) + with tm.assert_produces_warning(None): + result = ser.round() + expected = Series([2.0, np.nan, 0.0], dtype=any_float_dtype) + tm.assert_series_equal(result, expected) + + def test_round_builtin(self, any_float_dtype): + ser = Series( + [1.123, 2.123, 3.123], + index=range(3), + dtype=any_float_dtype, + ) + result = round(ser) + expected_rounded0 = Series( + [1.0, 2.0, 3.0], index=range(3), dtype=any_float_dtype + ) + tm.assert_series_equal(result, expected_rounded0) + + decimals = 2 + expected_rounded = Series( + [1.12, 2.12, 3.12], index=range(3), dtype=any_float_dtype + ) + result = round(ser, decimals) + tm.assert_series_equal(result, expected_rounded) + + @pytest.mark.parametrize("method", ["round", "floor", "ceil"]) + @pytest.mark.parametrize("freq", ["s", "5s", "min", "5min", "h", "5h"]) + def test_round_nat(self, method, freq, unit): + # GH14940, GH#56158 + ser = Series([pd.NaT], dtype=f"M8[{unit}]") + expected = Series(pd.NaT, dtype=f"M8[{unit}]") + round_method = getattr(ser.dt, method) + result = round_method(freq) + tm.assert_series_equal(result, expected) + + def test_round_ea_boolean(self): + # GH#55936 + ser = Series([True, False], dtype="boolean") + expected = ser.copy() + result = ser.round(2) + tm.assert_series_equal(result, expected) + result.iloc[0] = False + tm.assert_series_equal(ser, expected) + + def test_round_dtype_object(self): + # GH#61206 + ser = Series([0.2], dtype="object") + msg = "Expected numeric dtype, got object instead." + with pytest.raises(TypeError, match=msg): + ser.round() diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_searchsorted.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_searchsorted.py new file mode 100644 index 0000000000000000000000000000000000000000..239496052b99b42df262262a9ac89b71c93e0a26 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_searchsorted.py @@ -0,0 +1,77 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Series, + Timestamp, + date_range, +) +import pandas._testing as tm +from pandas.api.types import is_scalar + + +class TestSeriesSearchSorted: + def test_searchsorted(self): + ser = Series([1, 2, 3]) + + result = ser.searchsorted(1, side="left") + assert is_scalar(result) + assert result == 0 + + result = ser.searchsorted(1, side="right") + assert is_scalar(result) + assert result == 1 + + def test_searchsorted_numeric_dtypes_scalar(self): + ser = Series([1, 2, 90, 1000, 3e9]) + res = ser.searchsorted(30) + assert is_scalar(res) + assert res == 2 + + res = ser.searchsorted([30]) + exp = np.array([2], dtype=np.intp) + tm.assert_numpy_array_equal(res, exp) + + def test_searchsorted_numeric_dtypes_vector(self): + ser = Series([1, 2, 90, 1000, 3e9]) + res = ser.searchsorted([91, 2e6]) + exp = np.array([3, 4], dtype=np.intp) + tm.assert_numpy_array_equal(res, exp) + + def test_searchsorted_datetime64_scalar(self): + ser = Series(date_range("20120101", periods=10, freq="2D")) + val = Timestamp("20120102") + res = ser.searchsorted(val) + assert is_scalar(res) + assert res == 1 + + def test_searchsorted_datetime64_scalar_mixed_timezones(self): + # GH 30086 + ser = Series(date_range("20120101", periods=10, freq="2D", tz="UTC")) + val = Timestamp("20120102", tz="America/New_York") + res = ser.searchsorted(val) + assert is_scalar(res) + assert res == 1 + + def test_searchsorted_datetime64_list(self): + ser = Series(date_range("20120101", periods=10, freq="2D")) + vals = [Timestamp("20120102"), Timestamp("20120104")] + res = ser.searchsorted(vals) + exp = np.array([1, 2], dtype=np.intp) + tm.assert_numpy_array_equal(res, exp) + + def test_searchsorted_sorter(self): + # GH8490 + ser = Series([3, 1, 2]) + res = ser.searchsorted([0, 3], sorter=np.argsort(ser)) + exp = np.array([0, 2], dtype=np.intp) + tm.assert_numpy_array_equal(res, exp) + + def test_searchsorted_dataframe_fail(self): + # GH#49620 + ser = Series([1, 2, 3, 4, 5]) + vals = pd.DataFrame([[1, 2], [3, 4]]) + msg = "Value must be 1-D array-like or scalar, DataFrame is not supported" + with pytest.raises(ValueError, match=msg): + ser.searchsorted(vals) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_set_name.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_set_name.py new file mode 100644 index 0000000000000000000000000000000000000000..cbc8ebde7a8ab79fabe94781123844c856c9c78b --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_set_name.py @@ -0,0 +1,21 @@ +from datetime import datetime + +from pandas import Series + + +class TestSetName: + def test_set_name(self): + ser = Series([1, 2, 3]) + ser2 = ser._set_name("foo") + assert ser2.name == "foo" + assert ser.name is None + assert ser is not ser2 + + def test_set_name_attribute(self): + ser = Series([1, 2, 3]) + ser2 = Series([1, 2, 3], name="bar") + for name in [7, 7.0, "name", datetime(2001, 1, 1), (1,), "\u05D0"]: + ser.name = name + assert ser.name == name + ser2.name = name + assert ser2.name == name diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_size.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_size.py new file mode 100644 index 0000000000000000000000000000000000000000..20a454996fa4488501d6f623ad3afc6fa38e5634 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_size.py @@ -0,0 +1,22 @@ +import pytest + +from pandas import Series + + +@pytest.mark.parametrize( + "data, index, expected", + [ + ([1, 2, 3], None, 3), + ({"a": 1, "b": 2, "c": 3}, None, 3), + ([1, 2, 3], ["x", "y", "z"], 3), + ([1, 2, 3, 4, 5], ["x", "y", "z", "w", "n"], 5), + ([1, 2, 3], None, 3), + ([1, 2, 3], ["x", "y", "z"], 3), + ([1, 2, 3, 4], ["x", "y", "z", "w"], 4), + ], +) +def test_series(data, index, expected): + # GH#52897 + ser = Series(data, index=index) + assert ser.size == expected + assert isinstance(ser.size, int) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_sort_index.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_sort_index.py new file mode 100644 index 0000000000000000000000000000000000000000..d6817aa179b7bd040e89468c960b2eb3f0259003 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_sort_index.py @@ -0,0 +1,337 @@ +import numpy as np +import pytest + +from pandas import ( + DatetimeIndex, + IntervalIndex, + MultiIndex, + Series, +) +import pandas._testing as tm + + +@pytest.fixture(params=["quicksort", "mergesort", "heapsort", "stable"]) +def sort_kind(request): + return request.param + + +class TestSeriesSortIndex: + def test_sort_index_name(self, datetime_series): + result = datetime_series.sort_index(ascending=False) + assert result.name == datetime_series.name + + def test_sort_index(self, datetime_series): + datetime_series.index = datetime_series.index._with_freq(None) + + rindex = list(datetime_series.index) + np.random.default_rng(2).shuffle(rindex) + + random_order = datetime_series.reindex(rindex) + sorted_series = random_order.sort_index() + tm.assert_series_equal(sorted_series, datetime_series) + + # descending + sorted_series = random_order.sort_index(ascending=False) + tm.assert_series_equal( + sorted_series, datetime_series.reindex(datetime_series.index[::-1]) + ) + + # compat on level + sorted_series = random_order.sort_index(level=0) + tm.assert_series_equal(sorted_series, datetime_series) + + # compat on axis + sorted_series = random_order.sort_index(axis=0) + tm.assert_series_equal(sorted_series, datetime_series) + + msg = "No axis named 1 for object type Series" + with pytest.raises(ValueError, match=msg): + random_order.sort_values(axis=1) + + sorted_series = random_order.sort_index(level=0, axis=0) + tm.assert_series_equal(sorted_series, datetime_series) + + with pytest.raises(ValueError, match=msg): + random_order.sort_index(level=0, axis=1) + + def test_sort_index_inplace(self, datetime_series): + datetime_series.index = datetime_series.index._with_freq(None) + + # For GH#11402 + rindex = list(datetime_series.index) + np.random.default_rng(2).shuffle(rindex) + + # descending + random_order = datetime_series.reindex(rindex) + result = random_order.sort_index(ascending=False, inplace=True) + + assert result is None + expected = datetime_series.reindex(datetime_series.index[::-1]) + expected.index = expected.index._with_freq(None) + tm.assert_series_equal(random_order, expected) + + # ascending + random_order = datetime_series.reindex(rindex) + result = random_order.sort_index(ascending=True, inplace=True) + + assert result is None + expected = datetime_series.copy() + expected.index = expected.index._with_freq(None) + tm.assert_series_equal(random_order, expected) + + def test_sort_index_level(self): + mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC")) + s = Series([1, 2], mi) + backwards = s.iloc[[1, 0]] + + res = s.sort_index(level="A") + tm.assert_series_equal(backwards, res) + + res = s.sort_index(level=["A", "B"]) + tm.assert_series_equal(backwards, res) + + res = s.sort_index(level="A", sort_remaining=False) + tm.assert_series_equal(s, res) + + res = s.sort_index(level=["A", "B"], sort_remaining=False) + tm.assert_series_equal(s, res) + + @pytest.mark.parametrize("level", ["A", 0]) # GH#21052 + def test_sort_index_multiindex(self, level): + mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC")) + s = Series([1, 2], mi) + backwards = s.iloc[[1, 0]] + + # implicit sort_remaining=True + res = s.sort_index(level=level) + tm.assert_series_equal(backwards, res) + + # GH#13496 + # sort has no effect without remaining lvls + res = s.sort_index(level=level, sort_remaining=False) + tm.assert_series_equal(s, res) + + def test_sort_index_kind(self, sort_kind): + # GH#14444 & GH#13589: Add support for sort algo choosing + series = Series(index=[3, 2, 1, 4, 3], dtype=object) + expected_series = Series(index=[1, 2, 3, 3, 4], dtype=object) + + index_sorted_series = series.sort_index(kind=sort_kind) + tm.assert_series_equal(expected_series, index_sorted_series) + + def test_sort_index_na_position(self): + series = Series(index=[3, 2, 1, 4, 3, np.nan], dtype=object) + expected_series_first = Series(index=[np.nan, 1, 2, 3, 3, 4], dtype=object) + + index_sorted_series = series.sort_index(na_position="first") + tm.assert_series_equal(expected_series_first, index_sorted_series) + + expected_series_last = Series(index=[1, 2, 3, 3, 4, np.nan], dtype=object) + + index_sorted_series = series.sort_index(na_position="last") + tm.assert_series_equal(expected_series_last, index_sorted_series) + + def test_sort_index_intervals(self): + s = Series( + [np.nan, 1, 2, 3], IntervalIndex.from_arrays([0, 1, 2, 3], [1, 2, 3, 4]) + ) + + result = s.sort_index() + expected = s + tm.assert_series_equal(result, expected) + + result = s.sort_index(ascending=False) + expected = Series( + [3, 2, 1, np.nan], IntervalIndex.from_arrays([3, 2, 1, 0], [4, 3, 2, 1]) + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("inplace", [True, False]) + @pytest.mark.parametrize( + "original_list, sorted_list, ascending, ignore_index, output_index", + [ + ([2, 3, 6, 1], [2, 3, 6, 1], True, True, [0, 1, 2, 3]), + ([2, 3, 6, 1], [2, 3, 6, 1], True, False, [0, 1, 2, 3]), + ([2, 3, 6, 1], [1, 6, 3, 2], False, True, [0, 1, 2, 3]), + ([2, 3, 6, 1], [1, 6, 3, 2], False, False, [3, 2, 1, 0]), + ], + ) + def test_sort_index_ignore_index( + self, inplace, original_list, sorted_list, ascending, ignore_index, output_index + ): + # GH 30114 + ser = Series(original_list) + expected = Series(sorted_list, index=output_index) + kwargs = { + "ascending": ascending, + "ignore_index": ignore_index, + "inplace": inplace, + } + + if inplace: + result_ser = ser.copy() + result_ser.sort_index(**kwargs) + else: + result_ser = ser.sort_index(**kwargs) + + tm.assert_series_equal(result_ser, expected) + tm.assert_series_equal(ser, Series(original_list)) + + def test_sort_index_ascending_list(self): + # GH#16934 + + # Set up a Series with a three level MultiIndex + arrays = [ + ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"], + ["one", "two", "one", "two", "one", "two", "one", "two"], + [4, 3, 2, 1, 4, 3, 2, 1], + ] + tuples = zip(*arrays) + mi = MultiIndex.from_tuples(tuples, names=["first", "second", "third"]) + ser = Series(range(8), index=mi) + + # Sort with boolean ascending + result = ser.sort_index(level=["third", "first"], ascending=False) + expected = ser.iloc[[4, 0, 5, 1, 6, 2, 7, 3]] + tm.assert_series_equal(result, expected) + + # Sort with list of boolean ascending + result = ser.sort_index(level=["third", "first"], ascending=[False, True]) + expected = ser.iloc[[0, 4, 1, 5, 2, 6, 3, 7]] + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "ascending", + [ + None, + (True, None), + (False, "True"), + ], + ) + def test_sort_index_ascending_bad_value_raises(self, ascending): + ser = Series(range(10), index=[0, 3, 2, 1, 4, 5, 7, 6, 8, 9]) + match = 'For argument "ascending" expected type bool' + with pytest.raises(ValueError, match=match): + ser.sort_index(ascending=ascending) + + +class TestSeriesSortIndexKey: + def test_sort_index_multiindex_key(self): + mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC")) + s = Series([1, 2], mi) + backwards = s.iloc[[1, 0]] + + result = s.sort_index(level="C", key=lambda x: -x) + tm.assert_series_equal(s, result) + + result = s.sort_index(level="C", key=lambda x: x) # nothing happens + tm.assert_series_equal(backwards, result) + + def test_sort_index_multiindex_key_multi_level(self): + mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC")) + s = Series([1, 2], mi) + backwards = s.iloc[[1, 0]] + + result = s.sort_index(level=["A", "C"], key=lambda x: -x) + tm.assert_series_equal(s, result) + + result = s.sort_index(level=["A", "C"], key=lambda x: x) # nothing happens + tm.assert_series_equal(backwards, result) + + def test_sort_index_key(self): + series = Series(np.arange(6, dtype="int64"), index=list("aaBBca")) + + result = series.sort_index() + expected = series.iloc[[2, 3, 0, 1, 5, 4]] + tm.assert_series_equal(result, expected) + + result = series.sort_index(key=lambda x: x.str.lower()) + expected = series.iloc[[0, 1, 5, 2, 3, 4]] + tm.assert_series_equal(result, expected) + + result = series.sort_index(key=lambda x: x.str.lower(), ascending=False) + expected = series.iloc[[4, 2, 3, 0, 1, 5]] + tm.assert_series_equal(result, expected) + + def test_sort_index_key_int(self): + series = Series(np.arange(6, dtype="int64"), index=np.arange(6, dtype="int64")) + + result = series.sort_index() + tm.assert_series_equal(result, series) + + result = series.sort_index(key=lambda x: -x) + expected = series.sort_index(ascending=False) + tm.assert_series_equal(result, expected) + + result = series.sort_index(key=lambda x: 2 * x) + tm.assert_series_equal(result, series) + + def test_sort_index_kind_key(self, sort_kind, sort_by_key): + # GH #14444 & #13589: Add support for sort algo choosing + series = Series(index=[3, 2, 1, 4, 3], dtype=object) + expected_series = Series(index=[1, 2, 3, 3, 4], dtype=object) + + index_sorted_series = series.sort_index(kind=sort_kind, key=sort_by_key) + tm.assert_series_equal(expected_series, index_sorted_series) + + def test_sort_index_kind_neg_key(self, sort_kind): + # GH #14444 & #13589: Add support for sort algo choosing + series = Series(index=[3, 2, 1, 4, 3], dtype=object) + expected_series = Series(index=[4, 3, 3, 2, 1], dtype=object) + + index_sorted_series = series.sort_index(kind=sort_kind, key=lambda x: -x) + tm.assert_series_equal(expected_series, index_sorted_series) + + def test_sort_index_na_position_key(self, sort_by_key): + series = Series(index=[3, 2, 1, 4, 3, np.nan], dtype=object) + expected_series_first = Series(index=[np.nan, 1, 2, 3, 3, 4], dtype=object) + + index_sorted_series = series.sort_index(na_position="first", key=sort_by_key) + tm.assert_series_equal(expected_series_first, index_sorted_series) + + expected_series_last = Series(index=[1, 2, 3, 3, 4, np.nan], dtype=object) + + index_sorted_series = series.sort_index(na_position="last", key=sort_by_key) + tm.assert_series_equal(expected_series_last, index_sorted_series) + + def test_changes_length_raises(self): + s = Series([1, 2, 3]) + with pytest.raises(ValueError, match="change the shape"): + s.sort_index(key=lambda x: x[:1]) + + def test_sort_values_key_type(self): + s = Series([1, 2, 3], DatetimeIndex(["2008-10-24", "2008-11-23", "2007-12-22"])) + + result = s.sort_index(key=lambda x: x.month) + expected = s.iloc[[0, 1, 2]] + tm.assert_series_equal(result, expected) + + result = s.sort_index(key=lambda x: x.day) + expected = s.iloc[[2, 1, 0]] + tm.assert_series_equal(result, expected) + + result = s.sort_index(key=lambda x: x.year) + expected = s.iloc[[2, 0, 1]] + tm.assert_series_equal(result, expected) + + result = s.sort_index(key=lambda x: x.month_name()) + expected = s.iloc[[2, 1, 0]] + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "ascending", + [ + [True, False], + [False, True], + ], + ) + def test_sort_index_multi_already_monotonic(self, ascending): + # GH 56049 + mi = MultiIndex.from_product([[1, 2], [3, 4]]) + ser = Series(range(len(mi)), index=mi) + result = ser.sort_index(ascending=ascending) + if ascending == [True, False]: + expected = ser.take([1, 0, 3, 2]) + elif ascending == [False, True]: + expected = ser.take([2, 3, 0, 1]) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_sort_values.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_sort_values.py new file mode 100644 index 0000000000000000000000000000000000000000..4808272879071e8530c000daddbe7e9518deaefc --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_sort_values.py @@ -0,0 +1,246 @@ +import numpy as np +import pytest + +from pandas import ( + Categorical, + DataFrame, + Series, +) +import pandas._testing as tm + + +class TestSeriesSortValues: + def test_sort_values(self, datetime_series, using_copy_on_write): + # check indexes are reordered corresponding with the values + ser = Series([3, 2, 4, 1], ["A", "B", "C", "D"]) + expected = Series([1, 2, 3, 4], ["D", "B", "A", "C"]) + result = ser.sort_values() + tm.assert_series_equal(expected, result) + + ts = datetime_series.copy() + ts[:5] = np.nan + vals = ts.values + + result = ts.sort_values() + assert np.isnan(result[-5:]).all() + tm.assert_numpy_array_equal(result[:-5].values, np.sort(vals[5:])) + + # na_position + result = ts.sort_values(na_position="first") + assert np.isnan(result[:5]).all() + tm.assert_numpy_array_equal(result[5:].values, np.sort(vals[5:])) + + # something object-type + ser = Series(["A", "B"], [1, 2]) + # no failure + ser.sort_values() + + # ascending=False + ordered = ts.sort_values(ascending=False) + expected = np.sort(ts.dropna().values)[::-1] + tm.assert_almost_equal(expected, ordered.dropna().values) + ordered = ts.sort_values(ascending=False, na_position="first") + tm.assert_almost_equal(expected, ordered.dropna().values) + + # ascending=[False] should behave the same as ascending=False + ordered = ts.sort_values(ascending=[False]) + expected = ts.sort_values(ascending=False) + tm.assert_series_equal(expected, ordered) + ordered = ts.sort_values(ascending=[False], na_position="first") + expected = ts.sort_values(ascending=False, na_position="first") + tm.assert_series_equal(expected, ordered) + + msg = 'For argument "ascending" expected type bool, received type NoneType.' + with pytest.raises(ValueError, match=msg): + ts.sort_values(ascending=None) + msg = r"Length of ascending \(0\) must be 1 for Series" + with pytest.raises(ValueError, match=msg): + ts.sort_values(ascending=[]) + msg = r"Length of ascending \(3\) must be 1 for Series" + with pytest.raises(ValueError, match=msg): + ts.sort_values(ascending=[1, 2, 3]) + msg = r"Length of ascending \(2\) must be 1 for Series" + with pytest.raises(ValueError, match=msg): + ts.sort_values(ascending=[False, False]) + msg = 'For argument "ascending" expected type bool, received type str.' + with pytest.raises(ValueError, match=msg): + ts.sort_values(ascending="foobar") + + # inplace=True + ts = datetime_series.copy() + return_value = ts.sort_values(ascending=False, inplace=True) + assert return_value is None + tm.assert_series_equal(ts, datetime_series.sort_values(ascending=False)) + tm.assert_index_equal( + ts.index, datetime_series.sort_values(ascending=False).index + ) + + # GH#5856/5853 + # Series.sort_values operating on a view + df = DataFrame(np.random.default_rng(2).standard_normal((10, 4))) + s = df.iloc[:, 0] + + msg = ( + "This Series is a view of some other array, to sort in-place " + "you must create a copy" + ) + if using_copy_on_write: + s.sort_values(inplace=True) + tm.assert_series_equal(s, df.iloc[:, 0].sort_values()) + else: + with pytest.raises(ValueError, match=msg): + s.sort_values(inplace=True) + + def test_sort_values_categorical(self): + c = Categorical(["a", "b", "b", "a"], ordered=False) + cat = Series(c.copy()) + + # sort in the categories order + expected = Series( + Categorical(["a", "a", "b", "b"], ordered=False), index=[0, 3, 1, 2] + ) + result = cat.sort_values() + tm.assert_series_equal(result, expected) + + cat = Series(Categorical(["a", "c", "b", "d"], ordered=True)) + res = cat.sort_values() + exp = np.array(["a", "b", "c", "d"], dtype=np.object_) + tm.assert_numpy_array_equal(res.__array__(), exp) + + cat = Series( + Categorical( + ["a", "c", "b", "d"], categories=["a", "b", "c", "d"], ordered=True + ) + ) + res = cat.sort_values() + exp = np.array(["a", "b", "c", "d"], dtype=np.object_) + tm.assert_numpy_array_equal(res.__array__(), exp) + + res = cat.sort_values(ascending=False) + exp = np.array(["d", "c", "b", "a"], dtype=np.object_) + tm.assert_numpy_array_equal(res.__array__(), exp) + + raw_cat1 = Categorical( + ["a", "b", "c", "d"], categories=["a", "b", "c", "d"], ordered=False + ) + raw_cat2 = Categorical( + ["a", "b", "c", "d"], categories=["d", "c", "b", "a"], ordered=True + ) + s = ["a", "b", "c", "d"] + df = DataFrame( + {"unsort": raw_cat1, "sort": raw_cat2, "string": s, "values": [1, 2, 3, 4]} + ) + + # Cats must be sorted in a dataframe + res = df.sort_values(by=["string"], ascending=False) + exp = np.array(["d", "c", "b", "a"], dtype=np.object_) + tm.assert_numpy_array_equal(res["sort"].values.__array__(), exp) + assert res["sort"].dtype == "category" + + res = df.sort_values(by=["sort"], ascending=False) + exp = df.sort_values(by=["string"], ascending=True) + tm.assert_series_equal(res["values"], exp["values"]) + assert res["sort"].dtype == "category" + assert res["unsort"].dtype == "category" + + # unordered cat, but we allow this + df.sort_values(by=["unsort"], ascending=False) + + # multi-columns sort + # GH#7848 + df = DataFrame( + {"id": [6, 5, 4, 3, 2, 1], "raw_grade": ["a", "b", "b", "a", "a", "e"]} + ) + df["grade"] = Categorical(df["raw_grade"], ordered=True) + df["grade"] = df["grade"].cat.set_categories(["b", "e", "a"]) + + # sorts 'grade' according to the order of the categories + result = df.sort_values(by=["grade"]) + expected = df.iloc[[1, 2, 5, 0, 3, 4]] + tm.assert_frame_equal(result, expected) + + # multi + result = df.sort_values(by=["grade", "id"]) + expected = df.iloc[[2, 1, 5, 4, 3, 0]] + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("inplace", [True, False]) + @pytest.mark.parametrize( + "original_list, sorted_list, ignore_index, output_index", + [ + ([2, 3, 6, 1], [6, 3, 2, 1], True, [0, 1, 2, 3]), + ([2, 3, 6, 1], [6, 3, 2, 1], False, [2, 1, 0, 3]), + ], + ) + def test_sort_values_ignore_index( + self, inplace, original_list, sorted_list, ignore_index, output_index + ): + # GH 30114 + ser = Series(original_list) + expected = Series(sorted_list, index=output_index) + kwargs = {"ignore_index": ignore_index, "inplace": inplace} + + if inplace: + result_ser = ser.copy() + result_ser.sort_values(ascending=False, **kwargs) + else: + result_ser = ser.sort_values(ascending=False, **kwargs) + + tm.assert_series_equal(result_ser, expected) + tm.assert_series_equal(ser, Series(original_list)) + + def test_mergesort_descending_stability(self): + # GH 28697 + s = Series([1, 2, 1, 3], ["first", "b", "second", "c"]) + result = s.sort_values(ascending=False, kind="mergesort") + expected = Series([3, 2, 1, 1], ["c", "b", "first", "second"]) + tm.assert_series_equal(result, expected) + + def test_sort_values_validate_ascending_for_value_error(self): + # GH41634 + ser = Series([23, 7, 21]) + + msg = 'For argument "ascending" expected type bool, received type str.' + with pytest.raises(ValueError, match=msg): + ser.sort_values(ascending="False") + + @pytest.mark.parametrize("ascending", [False, 0, 1, True]) + def test_sort_values_validate_ascending_functional(self, ascending): + # GH41634 + ser = Series([23, 7, 21]) + expected = np.sort(ser.values) + + sorted_ser = ser.sort_values(ascending=ascending) + if not ascending: + expected = expected[::-1] + + result = sorted_ser.values + tm.assert_numpy_array_equal(result, expected) + + +class TestSeriesSortingKey: + def test_sort_values_key(self): + series = Series(np.array(["Hello", "goodbye"])) + + result = series.sort_values(axis=0) + expected = series + tm.assert_series_equal(result, expected) + + result = series.sort_values(axis=0, key=lambda x: x.str.lower()) + expected = series[::-1] + tm.assert_series_equal(result, expected) + + def test_sort_values_key_nan(self): + series = Series(np.array([0, 5, np.nan, 3, 2, np.nan])) + + result = series.sort_values(axis=0) + expected = series.iloc[[0, 4, 3, 1, 2, 5]] + tm.assert_series_equal(result, expected) + + result = series.sort_values(axis=0, key=lambda x: x + 5) + expected = series.iloc[[0, 4, 3, 1, 2, 5]] + tm.assert_series_equal(result, expected) + + result = series.sort_values(axis=0, key=lambda x: -x, ascending=False) + expected = series.iloc[[0, 4, 3, 1, 2, 5]] + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_csv.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_csv.py new file mode 100644 index 0000000000000000000000000000000000000000..ba75c7786ef72095c0888b815a778b5bc1446c03 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_csv.py @@ -0,0 +1,179 @@ +from datetime import datetime +from io import StringIO + +import numpy as np +import pytest + +import pandas as pd +from pandas import Series +import pandas._testing as tm + +from pandas.io.common import get_handle + + +class TestSeriesToCSV: + def read_csv(self, path, **kwargs): + params = {"index_col": 0, "header": None} + params.update(**kwargs) + + header = params.get("header") + out = pd.read_csv(path, **params).squeeze("columns") + + if header is None: + out.name = out.index.name = None + + return out + + def test_from_csv(self, datetime_series, string_series): + # freq doesn't round-trip + datetime_series.index = datetime_series.index._with_freq(None) + + with tm.ensure_clean() as path: + datetime_series.to_csv(path, header=False) + ts = self.read_csv(path, parse_dates=True) + tm.assert_series_equal(datetime_series, ts, check_names=False) + + assert ts.name is None + assert ts.index.name is None + + # see gh-10483 + datetime_series.to_csv(path, header=True) + ts_h = self.read_csv(path, header=0) + assert ts_h.name == "ts" + + string_series.to_csv(path, header=False) + series = self.read_csv(path) + tm.assert_series_equal(string_series, series, check_names=False) + + assert series.name is None + assert series.index.name is None + + string_series.to_csv(path, header=True) + series_h = self.read_csv(path, header=0) + assert series_h.name == "series" + + with open(path, "w", encoding="utf-8") as outfile: + outfile.write("1998-01-01|1.0\n1999-01-01|2.0") + + series = self.read_csv(path, sep="|", parse_dates=True) + check_series = Series( + {datetime(1998, 1, 1): 1.0, datetime(1999, 1, 1): 2.0} + ) + tm.assert_series_equal(check_series, series) + + series = self.read_csv(path, sep="|", parse_dates=False) + check_series = Series({"1998-01-01": 1.0, "1999-01-01": 2.0}) + tm.assert_series_equal(check_series, series) + + def test_to_csv(self, datetime_series): + with tm.ensure_clean() as path: + datetime_series.to_csv(path, header=False) + + with open(path, newline=None, encoding="utf-8") as f: + lines = f.readlines() + assert lines[1] != "\n" + + datetime_series.to_csv(path, index=False, header=False) + arr = np.loadtxt(path) + tm.assert_almost_equal(arr, datetime_series.values) + + def test_to_csv_unicode_index(self): + buf = StringIO() + s = Series(["\u05d0", "d2"], index=["\u05d0", "\u05d1"]) + + s.to_csv(buf, encoding="UTF-8", header=False) + buf.seek(0) + + s2 = self.read_csv(buf, index_col=0, encoding="UTF-8") + tm.assert_series_equal(s, s2) + + def test_to_csv_float_format(self): + with tm.ensure_clean() as filename: + ser = Series([0.123456, 0.234567, 0.567567]) + ser.to_csv(filename, float_format="%.2f", header=False) + + rs = self.read_csv(filename) + xp = Series([0.12, 0.23, 0.57]) + tm.assert_series_equal(rs, xp) + + def test_to_csv_list_entries(self): + s = Series(["jack and jill", "jesse and frank"]) + + split = s.str.split(r"\s+and\s+") + + buf = StringIO() + split.to_csv(buf, header=False) + + def test_to_csv_path_is_none(self): + # GH 8215 + # Series.to_csv() was returning None, inconsistent with + # DataFrame.to_csv() which returned string + s = Series([1, 2, 3]) + csv_str = s.to_csv(path_or_buf=None, header=False) + assert isinstance(csv_str, str) + + @pytest.mark.parametrize( + "s,encoding", + [ + ( + Series([0.123456, 0.234567, 0.567567], index=["A", "B", "C"], name="X"), + None, + ), + # GH 21241, 21118 + (Series(["abc", "def", "ghi"], name="X"), "ascii"), + (Series(["123", "你好", "世界"], name="中文"), "gb2312"), + ( + Series(["123", "Γειά σου", "Κόσμε"], name="Ελληνικά"), # noqa: RUF001 + "cp737", + ), + ], + ) + def test_to_csv_compression(self, s, encoding, compression): + with tm.ensure_clean() as filename: + s.to_csv(filename, compression=compression, encoding=encoding, header=True) + # test the round trip - to_csv -> read_csv + result = pd.read_csv( + filename, + compression=compression, + encoding=encoding, + index_col=0, + ).squeeze("columns") + tm.assert_series_equal(s, result) + + # test the round trip using file handle - to_csv -> read_csv + with get_handle( + filename, "w", compression=compression, encoding=encoding + ) as handles: + s.to_csv(handles.handle, encoding=encoding, header=True) + + result = pd.read_csv( + filename, + compression=compression, + encoding=encoding, + index_col=0, + ).squeeze("columns") + tm.assert_series_equal(s, result) + + # explicitly ensure file was compressed + with tm.decompress_file(filename, compression) as fh: + text = fh.read().decode(encoding or "utf8") + assert s.name in text + + with tm.decompress_file(filename, compression) as fh: + tm.assert_series_equal( + s, + pd.read_csv(fh, index_col=0, encoding=encoding).squeeze("columns"), + ) + + def test_to_csv_interval_index(self, using_infer_string): + # GH 28210 + s = Series(["foo", "bar", "baz"], index=pd.interval_range(0, 3)) + + with tm.ensure_clean("__tmp_to_csv_interval_index__.csv") as path: + s.to_csv(path, header=False) + result = self.read_csv(path, index_col=0) + + # can't roundtrip intervalindex via read_csv so check string repr (GH 23595) + expected = s + expected.index = expected.index.astype("str") + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_dict.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_dict.py new file mode 100644 index 0000000000000000000000000000000000000000..41c01f4537f23fdb90110685d2e774427916aee1 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_dict.py @@ -0,0 +1,38 @@ +import collections + +import numpy as np +import pytest + +from pandas import Series +import pandas._testing as tm + + +class TestSeriesToDict: + @pytest.mark.parametrize( + "mapping", (dict, collections.defaultdict(list), collections.OrderedDict) + ) + def test_to_dict(self, mapping, datetime_series): + # GH#16122 + result = Series(datetime_series.to_dict(into=mapping), name="ts") + expected = datetime_series.copy() + expected.index = expected.index._with_freq(None) + tm.assert_series_equal(result, expected) + + from_method = Series(datetime_series.to_dict(into=collections.Counter)) + from_constructor = Series(collections.Counter(datetime_series.items())) + tm.assert_series_equal(from_method, from_constructor) + + @pytest.mark.parametrize( + "input", + ( + {"a": np.int64(64), "b": 10}, + {"a": np.int64(64), "b": 10, "c": "ABC"}, + {"a": np.uint64(64), "b": 10, "c": "ABC"}, + ), + ) + def test_to_dict_return_types(self, input): + # GH25969 + + d = Series(input).to_dict() + assert isinstance(d["a"], int) + assert isinstance(d["b"], int) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_frame.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_frame.py new file mode 100644 index 0000000000000000000000000000000000000000..0eadf696b34cc034d2be76ce5daba2cff679da74 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_frame.py @@ -0,0 +1,63 @@ +import pytest + +from pandas import ( + DataFrame, + Index, + Series, +) +import pandas._testing as tm + + +class TestToFrame: + def test_to_frame_respects_name_none(self): + # GH#44212 if we explicitly pass name=None, then that should be respected, + # not changed to 0 + # GH-45448 this is first deprecated & enforced in 2.0 + ser = Series(range(3)) + result = ser.to_frame(None) + + exp_index = Index([None], dtype=object) + tm.assert_index_equal(result.columns, exp_index) + + result = ser.rename("foo").to_frame(None) + exp_index = Index([None], dtype=object) + tm.assert_index_equal(result.columns, exp_index) + + def test_to_frame(self, datetime_series): + datetime_series.name = None + rs = datetime_series.to_frame() + xp = DataFrame(datetime_series.values, index=datetime_series.index) + tm.assert_frame_equal(rs, xp) + + datetime_series.name = "testname" + rs = datetime_series.to_frame() + xp = DataFrame( + {"testname": datetime_series.values}, index=datetime_series.index + ) + tm.assert_frame_equal(rs, xp) + + rs = datetime_series.to_frame(name="testdifferent") + xp = DataFrame( + {"testdifferent": datetime_series.values}, index=datetime_series.index + ) + tm.assert_frame_equal(rs, xp) + + @pytest.mark.filterwarnings( + "ignore:Passing a BlockManager|Passing a SingleBlockManager:DeprecationWarning" + ) + def test_to_frame_expanddim(self): + # GH#9762 + + class SubclassedSeries(Series): + @property + def _constructor_expanddim(self): + return SubclassedFrame + + class SubclassedFrame(DataFrame): + pass + + ser = SubclassedSeries([1, 2, 3], name="X") + result = ser.to_frame() + assert isinstance(result, SubclassedFrame) + expected = SubclassedFrame({"X": [1, 2, 3]}) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_numpy.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_numpy.py new file mode 100644 index 0000000000000000000000000000000000000000..4bc7631090761e720c61049f9b8fd2a7fadd89af --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_to_numpy.py @@ -0,0 +1,49 @@ +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +from pandas import ( + NA, + Series, + Timedelta, +) +import pandas._testing as tm + + +@pytest.mark.parametrize("dtype", ["int64", "float64"]) +def test_to_numpy_na_value(dtype): + # GH#48951 + ser = Series([1, 2, NA, 4]) + result = ser.to_numpy(dtype=dtype, na_value=0) + expected = np.array([1, 2, 0, 4], dtype=dtype) + tm.assert_numpy_array_equal(result, expected) + + +def test_to_numpy_cast_before_setting_na(): + # GH#50600 + ser = Series([1]) + result = ser.to_numpy(dtype=np.float64, na_value=np.nan) + expected = np.array([1.0]) + tm.assert_numpy_array_equal(result, expected) + + +@td.skip_if_no("pyarrow") +def test_to_numpy_arrow_dtype_given(): + # GH#57121 + ser = Series([1, NA], dtype="int64[pyarrow]") + result = ser.to_numpy(dtype="float64") + expected = np.array([1.0, np.nan]) + tm.assert_numpy_array_equal(result, expected) + + +def test_astype_ea_int_to_td_ts(): + # GH#57093 + ser = Series([1, None], dtype="Int64") + result = ser.astype("m8[ns]") + expected = Series([1, Timedelta("nat")], dtype="m8[ns]") + tm.assert_series_equal(result, expected) + + result = ser.astype("M8[ns]") + expected = Series([1, Timedelta("nat")], dtype="M8[ns]") + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_tolist.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_tolist.py new file mode 100644 index 0000000000000000000000000000000000000000..4af473528e23850794139ac563cc04c6d3c54617 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_tolist.py @@ -0,0 +1,36 @@ +import pytest + +import pandas.util._test_decorators as td + +from pandas import ( + Interval, + Period, + Series, + Timedelta, + Timestamp, +) + + +@pytest.mark.parametrize( + "values, dtype, expected_dtype", + ( + ([1], "int64", int), + ([1], "Int64", int), + ([1.0], "float64", float), + ([1.0], "Float64", float), + (["abc"], "object", str), + (["abc"], "string", str), + ([Interval(1, 3)], "interval", Interval), + ([Period("2000-01-01", "D")], "period[D]", Period), + ([Timedelta(days=1)], "timedelta64[ns]", Timedelta), + ([Timestamp("2000-01-01")], "datetime64[ns]", Timestamp), + pytest.param([1], "int64[pyarrow]", int, marks=td.skip_if_no("pyarrow")), + pytest.param([1.0], "float64[pyarrow]", float, marks=td.skip_if_no("pyarrow")), + pytest.param(["abc"], "string[pyarrow]", str, marks=td.skip_if_no("pyarrow")), + ), +) +def test_tolist_scalar_dtype(values, dtype, expected_dtype): + # GH49890 + ser = Series(values, dtype=dtype) + result_dtype = type(ser.tolist()[0]) + assert result_dtype == expected_dtype diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_truncate.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_truncate.py new file mode 100644 index 0000000000000000000000000000000000000000..33eb5c10ae163862e342b1871669d64d74602e4e --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_truncate.py @@ -0,0 +1,67 @@ +from datetime import datetime + +import pytest + +import pandas as pd +from pandas import ( + Series, + date_range, +) +import pandas._testing as tm + + +class TestTruncate: + def test_truncate_datetimeindex_tz(self): + # GH 9243 + idx = date_range("4/1/2005", "4/30/2005", freq="D", tz="US/Pacific") + s = Series(range(len(idx)), index=idx) + with pytest.raises(TypeError, match="Cannot compare tz-naive"): + # GH#36148 as of 2.0 we require tzawareness compat + s.truncate(datetime(2005, 4, 2), datetime(2005, 4, 4)) + + lb = idx[1] + ub = idx[3] + result = s.truncate(lb.to_pydatetime(), ub.to_pydatetime()) + expected = Series([1, 2, 3], index=idx[1:4]) + tm.assert_series_equal(result, expected) + + def test_truncate_periodindex(self): + # GH 17717 + idx1 = pd.PeriodIndex( + [pd.Period("2017-09-02"), pd.Period("2017-09-02"), pd.Period("2017-09-03")] + ) + series1 = Series([1, 2, 3], index=idx1) + result1 = series1.truncate(after="2017-09-02") + + expected_idx1 = pd.PeriodIndex( + [pd.Period("2017-09-02"), pd.Period("2017-09-02")] + ) + tm.assert_series_equal(result1, Series([1, 2], index=expected_idx1)) + + idx2 = pd.PeriodIndex( + [pd.Period("2017-09-03"), pd.Period("2017-09-02"), pd.Period("2017-09-03")] + ) + series2 = Series([1, 2, 3], index=idx2) + result2 = series2.sort_index().truncate(after="2017-09-02") + + expected_idx2 = pd.PeriodIndex([pd.Period("2017-09-02")]) + tm.assert_series_equal(result2, Series([2], index=expected_idx2)) + + def test_truncate_one_element_series(self): + # GH 35544 + series = Series([0.1], index=pd.DatetimeIndex(["2020-08-04"])) + before = pd.Timestamp("2020-08-02") + after = pd.Timestamp("2020-08-04") + + result = series.truncate(before=before, after=after) + + # the input Series and the expected Series are the same + tm.assert_series_equal(result, series) + + def test_truncate_index_only_one_unique_value(self): + # GH 42365 + obj = Series(0, index=date_range("2021-06-30", "2021-06-30")).repeat(5) + + truncated = obj.truncate("2021-06-28", "2021-07-01") + + tm.assert_series_equal(truncated, obj) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_tz_localize.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_tz_localize.py new file mode 100644 index 0000000000000000000000000000000000000000..45620a721f442ee038569cdd69c1341ac56fd858 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_tz_localize.py @@ -0,0 +1,123 @@ +from datetime import timezone + +import pytest +import pytz + +from pandas._libs.tslibs import timezones + +from pandas import ( + DatetimeIndex, + NaT, + Series, + Timestamp, + date_range, +) +import pandas._testing as tm + + +class TestTZLocalize: + def test_series_tz_localize_ambiguous_bool(self): + # make sure that we are correctly accepting bool values as ambiguous + + # GH#14402 + ts = Timestamp("2015-11-01 01:00:03") + expected0 = Timestamp("2015-11-01 01:00:03-0500", tz="US/Central") + expected1 = Timestamp("2015-11-01 01:00:03-0600", tz="US/Central") + + ser = Series([ts]) + expected0 = Series([expected0]) + expected1 = Series([expected1]) + + with tm.external_error_raised(pytz.AmbiguousTimeError): + ser.dt.tz_localize("US/Central") + + result = ser.dt.tz_localize("US/Central", ambiguous=True) + tm.assert_series_equal(result, expected0) + + result = ser.dt.tz_localize("US/Central", ambiguous=[True]) + tm.assert_series_equal(result, expected0) + + result = ser.dt.tz_localize("US/Central", ambiguous=False) + tm.assert_series_equal(result, expected1) + + result = ser.dt.tz_localize("US/Central", ambiguous=[False]) + tm.assert_series_equal(result, expected1) + + def test_series_tz_localize_matching_index(self): + # Matching the index of the result with that of the original series + # GH 43080 + dt_series = Series( + date_range(start="2021-01-01T02:00:00", periods=5, freq="1D"), + index=[2, 6, 7, 8, 11], + dtype="category", + ) + result = dt_series.dt.tz_localize("Europe/Berlin") + expected = Series( + date_range( + start="2021-01-01T02:00:00", periods=5, freq="1D", tz="Europe/Berlin" + ), + index=[2, 6, 7, 8, 11], + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "method, exp", + [ + ["shift_forward", "2015-03-29 03:00:00"], + ["shift_backward", "2015-03-29 01:59:59.999999999"], + ["NaT", NaT], + ["raise", None], + ["foo", "invalid"], + ], + ) + def test_tz_localize_nonexistent(self, warsaw, method, exp, unit): + # GH 8917 + tz = warsaw + n = 60 + dti = date_range(start="2015-03-29 02:00:00", periods=n, freq="min", unit=unit) + ser = Series(1, index=dti) + df = ser.to_frame() + + if method == "raise": + with tm.external_error_raised(pytz.NonExistentTimeError): + dti.tz_localize(tz, nonexistent=method) + with tm.external_error_raised(pytz.NonExistentTimeError): + ser.tz_localize(tz, nonexistent=method) + with tm.external_error_raised(pytz.NonExistentTimeError): + df.tz_localize(tz, nonexistent=method) + + elif exp == "invalid": + msg = ( + "The nonexistent argument must be one of " + "'raise', 'NaT', 'shift_forward', 'shift_backward' " + "or a timedelta object" + ) + with pytest.raises(ValueError, match=msg): + dti.tz_localize(tz, nonexistent=method) + with pytest.raises(ValueError, match=msg): + ser.tz_localize(tz, nonexistent=method) + with pytest.raises(ValueError, match=msg): + df.tz_localize(tz, nonexistent=method) + + else: + result = ser.tz_localize(tz, nonexistent=method) + expected = Series(1, index=DatetimeIndex([exp] * n, tz=tz).as_unit(unit)) + tm.assert_series_equal(result, expected) + + result = df.tz_localize(tz, nonexistent=method) + expected = expected.to_frame() + tm.assert_frame_equal(result, expected) + + res_index = dti.tz_localize(tz, nonexistent=method) + tm.assert_index_equal(res_index, expected.index) + + @pytest.mark.parametrize("tzstr", ["US/Eastern", "dateutil/US/Eastern"]) + def test_series_tz_localize_empty(self, tzstr): + # GH#2248 + ser = Series(dtype=object) + + ser2 = ser.tz_localize("utc") + assert ser2.index.tz == timezone.utc + + ser2 = ser.tz_localize(tzstr) + timezones.tz_compare(ser2.index.tz, timezones.maybe_get_tz(tzstr)) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_unique.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_unique.py new file mode 100644 index 0000000000000000000000000000000000000000..14e247332503253ede23e94fe1335e712a2675e7 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_unique.py @@ -0,0 +1,76 @@ +import numpy as np + +from pandas import ( + Categorical, + IntervalIndex, + Series, + date_range, +) +import pandas._testing as tm + + +class TestUnique: + def test_unique_uint64(self): + ser = Series([1, 2, 2**63, 2**63], dtype=np.uint64) + res = ser.unique() + exp = np.array([1, 2, 2**63], dtype=np.uint64) + tm.assert_numpy_array_equal(res, exp) + + def test_unique_data_ownership(self): + # it works! GH#1807 + Series(Series(["a", "c", "b"]).unique()).sort_values() + + def test_unique(self): + # GH#714 also, dtype=float + ser = Series([1.2345] * 100) + ser[::2] = np.nan + result = ser.unique() + assert len(result) == 2 + + # explicit f4 dtype + ser = Series([1.2345] * 100, dtype="f4") + ser[::2] = np.nan + result = ser.unique() + assert len(result) == 2 + + def test_unique_nan_object_dtype(self): + # NAs in object arrays GH#714 + ser = Series(["foo"] * 100, dtype="O") + ser[::2] = np.nan + result = ser.unique() + assert len(result) == 2 + + def test_unique_none(self): + # decision about None + ser = Series([1, 2, 3, None, None, None], dtype=object) + result = ser.unique() + expected = np.array([1, 2, 3, None], dtype=object) + tm.assert_numpy_array_equal(result, expected) + + def test_unique_categorical(self): + # GH#18051 + cat = Categorical([]) + ser = Series(cat) + result = ser.unique() + tm.assert_categorical_equal(result, cat) + + cat = Categorical([np.nan]) + ser = Series(cat) + result = ser.unique() + tm.assert_categorical_equal(result, cat) + + def test_tz_unique(self): + # GH 46128 + dti1 = date_range("2016-01-01", periods=3) + ii1 = IntervalIndex.from_breaks(dti1) + ser1 = Series(ii1) + uni1 = ser1.unique() + tm.assert_interval_array_equal(ser1.array, uni1) + + dti2 = date_range("2016-01-01", periods=3, tz="US/Eastern") + ii2 = IntervalIndex.from_breaks(dti2) + ser2 = Series(ii2) + uni2 = ser2.unique() + tm.assert_interval_array_equal(ser2.array, uni2) + + assert uni1.dtype != uni2.dtype diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_unstack.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_unstack.py new file mode 100644 index 0000000000000000000000000000000000000000..11995260dd0beb3325fb6e95847d6cf630bf9e79 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_unstack.py @@ -0,0 +1,169 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + Index, + MultiIndex, + Series, + date_range, +) +import pandas._testing as tm + + +def test_unstack_preserves_object(): + mi = MultiIndex.from_product([["bar", "foo"], ["one", "two"]]) + + ser = Series(np.arange(4.0), index=mi, dtype=object) + + res1 = ser.unstack() + assert (res1.dtypes == object).all() + + res2 = ser.unstack(level=0) + assert (res2.dtypes == object).all() + + +def test_unstack(): + index = MultiIndex( + levels=[["bar", "foo"], ["one", "three", "two"]], + codes=[[1, 1, 0, 0], [0, 1, 0, 2]], + ) + + s = Series(np.arange(4.0), index=index) + unstacked = s.unstack() + + expected = DataFrame( + [[2.0, np.nan, 3.0], [0.0, 1.0, np.nan]], + index=["bar", "foo"], + columns=["one", "three", "two"], + ) + + tm.assert_frame_equal(unstacked, expected) + + unstacked = s.unstack(level=0) + tm.assert_frame_equal(unstacked, expected.T) + + index = MultiIndex( + levels=[["bar"], ["one", "two", "three"], [0, 1]], + codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]], + ) + s = Series(np.random.default_rng(2).standard_normal(6), index=index) + exp_index = MultiIndex( + levels=[["one", "two", "three"], [0, 1]], + codes=[[0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]], + ) + expected = DataFrame({"bar": s.values}, index=exp_index).sort_index(level=0) + unstacked = s.unstack(0).sort_index() + tm.assert_frame_equal(unstacked, expected) + + # GH5873 + idx = MultiIndex.from_arrays([[101, 102], [3.5, np.nan]]) + ts = Series([1, 2], index=idx) + left = ts.unstack() + right = DataFrame( + [[np.nan, 1], [2, np.nan]], index=[101, 102], columns=[np.nan, 3.5] + ) + tm.assert_frame_equal(left, right) + + idx = MultiIndex.from_arrays( + [ + ["cat", "cat", "cat", "dog", "dog"], + ["a", "a", "b", "a", "b"], + [1, 2, 1, 1, np.nan], + ] + ) + ts = Series([1.0, 1.1, 1.2, 1.3, 1.4], index=idx) + right = DataFrame( + [[1.0, 1.3], [1.1, np.nan], [np.nan, 1.4], [1.2, np.nan]], + columns=["cat", "dog"], + ) + tpls = [("a", 1), ("a", 2), ("b", np.nan), ("b", 1)] + right.index = MultiIndex.from_tuples(tpls) + tm.assert_frame_equal(ts.unstack(level=0), right) + + +def test_unstack_tuplename_in_multiindex(): + # GH 19966 + idx = MultiIndex.from_product( + [["a", "b", "c"], [1, 2, 3]], names=[("A", "a"), ("B", "b")] + ) + ser = Series(1, index=idx) + result = ser.unstack(("A", "a")) + + expected = DataFrame( + [[1, 1, 1], [1, 1, 1], [1, 1, 1]], + columns=MultiIndex.from_tuples([("a",), ("b",), ("c",)], names=[("A", "a")]), + index=Index([1, 2, 3], name=("B", "b")), + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "unstack_idx, expected_values, expected_index, expected_columns", + [ + ( + ("A", "a"), + [[1, 1], [1, 1], [1, 1], [1, 1]], + MultiIndex.from_tuples([(1, 3), (1, 4), (2, 3), (2, 4)], names=["B", "C"]), + MultiIndex.from_tuples([("a",), ("b",)], names=[("A", "a")]), + ), + ( + (("A", "a"), "B"), + [[1, 1, 1, 1], [1, 1, 1, 1]], + Index([3, 4], name="C"), + MultiIndex.from_tuples( + [("a", 1), ("a", 2), ("b", 1), ("b", 2)], names=[("A", "a"), "B"] + ), + ), + ], +) +def test_unstack_mixed_type_name_in_multiindex( + unstack_idx, expected_values, expected_index, expected_columns +): + # GH 19966 + idx = MultiIndex.from_product( + [["a", "b"], [1, 2], [3, 4]], names=[("A", "a"), "B", "C"] + ) + ser = Series(1, index=idx) + result = ser.unstack(unstack_idx) + + expected = DataFrame( + expected_values, columns=expected_columns, index=expected_index + ) + tm.assert_frame_equal(result, expected) + + +def test_unstack_multi_index_categorical_values(): + df = DataFrame( + np.random.default_rng(2).standard_normal((10, 4)), + columns=Index(list("ABCD")), + index=date_range("2000-01-01", periods=10, freq="B"), + ) + mi = df.stack(future_stack=True).index.rename(["major", "minor"]) + ser = Series(["foo"] * len(mi), index=mi, name="category", dtype="category") + + result = ser.unstack() + + dti = ser.index.levels[0] + c = pd.Categorical(["foo"] * len(dti)) + expected = DataFrame( + {"A": c.copy(), "B": c.copy(), "C": c.copy(), "D": c.copy()}, + columns=Index(list("ABCD"), name="minor"), + index=dti.rename("major"), + ) + tm.assert_frame_equal(result, expected) + + +def test_unstack_mixed_level_names(): + # GH#48763 + arrays = [["a", "a"], [1, 2], ["red", "blue"]] + idx = MultiIndex.from_arrays(arrays, names=("x", 0, "y")) + ser = Series([1, 2], index=idx) + result = ser.unstack("x") + expected = DataFrame( + [[1], [2]], + columns=Index(["a"], name="x"), + index=MultiIndex.from_tuples([(1, "red"), (2, "blue")], names=[0, "y"]), + ) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_update.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_update.py new file mode 100644 index 0000000000000000000000000000000000000000..b09a58e04947fd5ce236e53b1516ca42ef00ffeb --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_update.py @@ -0,0 +1,143 @@ +import numpy as np +import pytest + +from pandas.compat import WARNING_CHECK_DISABLED +import pandas.util._test_decorators as td + +from pandas import ( + CategoricalDtype, + DataFrame, + NaT, + Series, + Timestamp, +) +import pandas._testing as tm + + +class TestUpdate: + def test_update(self, using_copy_on_write): + s = Series([1.5, np.nan, 3.0, 4.0, np.nan]) + s2 = Series([np.nan, 3.5, np.nan, 5.0]) + s.update(s2) + + expected = Series([1.5, 3.5, 3.0, 5.0, np.nan]) + tm.assert_series_equal(s, expected) + + # GH 3217 + df = DataFrame([{"a": 1}, {"a": 3, "b": 2}]) + df["c"] = np.nan + # Cast to object to avoid upcast when setting "foo" + df["c"] = df["c"].astype(object) + df_orig = df.copy() + + if using_copy_on_write: + with tm.raises_chained_assignment_error(): + df["c"].update(Series(["foo"], index=[0])) + expected = df_orig + else: + with tm.assert_produces_warning( + FutureWarning if not WARNING_CHECK_DISABLED else None, + match="inplace method", + ): + df["c"].update(Series(["foo"], index=[0])) + expected = DataFrame( + [[1, np.nan, "foo"], [3, 2.0, np.nan]], columns=["a", "b", "c"] + ) + expected["c"] = expected["c"].astype(object) + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize( + "other, dtype, expected, warn", + [ + # other is int + ([61, 63], "int32", Series([10, 61, 12], dtype="int32"), None), + ([61, 63], "int64", Series([10, 61, 12]), None), + ([61, 63], float, Series([10.0, 61.0, 12.0]), None), + ([61, 63], object, Series([10, 61, 12], dtype=object), None), + # other is float, but can be cast to int + ([61.0, 63.0], "int32", Series([10, 61, 12], dtype="int32"), None), + ([61.0, 63.0], "int64", Series([10, 61, 12]), None), + ([61.0, 63.0], float, Series([10.0, 61.0, 12.0]), None), + ([61.0, 63.0], object, Series([10, 61.0, 12], dtype=object), None), + # others is float, cannot be cast to int + ([61.1, 63.1], "int32", Series([10.0, 61.1, 12.0]), FutureWarning), + ([61.1, 63.1], "int64", Series([10.0, 61.1, 12.0]), FutureWarning), + ([61.1, 63.1], float, Series([10.0, 61.1, 12.0]), None), + ([61.1, 63.1], object, Series([10, 61.1, 12], dtype=object), None), + # other is object, cannot be cast + ([(61,), (63,)], "int32", Series([10, (61,), 12]), FutureWarning), + ([(61,), (63,)], "int64", Series([10, (61,), 12]), FutureWarning), + ([(61,), (63,)], float, Series([10.0, (61,), 12.0]), FutureWarning), + ([(61,), (63,)], object, Series([10, (61,), 12]), None), + ], + ) + def test_update_dtypes(self, other, dtype, expected, warn): + ser = Series([10, 11, 12], dtype=dtype) + other = Series(other, index=[1, 3]) + with tm.assert_produces_warning(warn, match="item of incompatible dtype"): + ser.update(other) + + tm.assert_series_equal(ser, expected) + + @pytest.mark.parametrize( + "series, other, expected", + [ + # update by key + ( + Series({"a": 1, "b": 2, "c": 3, "d": 4}), + {"b": 5, "c": np.nan}, + Series({"a": 1, "b": 5, "c": 3, "d": 4}), + ), + # update by position + (Series([1, 2, 3, 4]), [np.nan, 5, 1], Series([1, 5, 1, 4])), + ], + ) + def test_update_from_non_series(self, series, other, expected): + # GH 33215 + series.update(other) + tm.assert_series_equal(series, expected) + + @pytest.mark.parametrize( + "data, other, expected, dtype", + [ + (["a", None], [None, "b"], ["a", "b"], "string[python]"), + pytest.param( + ["a", None], + [None, "b"], + ["a", "b"], + "string[pyarrow]", + marks=td.skip_if_no("pyarrow"), + ), + ([1, None], [None, 2], [1, 2], "Int64"), + ([True, None], [None, False], [True, False], "boolean"), + ( + ["a", None], + [None, "b"], + ["a", "b"], + CategoricalDtype(categories=["a", "b"]), + ), + ( + [Timestamp(year=2020, month=1, day=1, tz="Europe/London"), NaT], + [NaT, Timestamp(year=2020, month=1, day=1, tz="Europe/London")], + [Timestamp(year=2020, month=1, day=1, tz="Europe/London")] * 2, + "datetime64[ns, Europe/London]", + ), + ], + ) + def test_update_extension_array_series(self, data, other, expected, dtype): + result = Series(data, dtype=dtype) + other = Series(other, dtype=dtype) + expected = Series(expected, dtype=dtype) + + result.update(other) + tm.assert_series_equal(result, expected) + + def test_update_with_categorical_type(self): + # GH 25744 + dtype = CategoricalDtype(["a", "b", "c", "d"]) + s1 = Series(["a", "b", "c"], index=[1, 2, 3], dtype=dtype) + s2 = Series(["b", "a"], index=[1, 2], dtype=dtype) + s1.update(s2) + result = s1 + expected = Series(["b", "a", "c"], index=[1, 2, 3], dtype=dtype) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_value_counts.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_value_counts.py new file mode 100644 index 0000000000000000000000000000000000000000..859010d9c79c64fbac70f2f5eaa0033bddb4a0c2 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_value_counts.py @@ -0,0 +1,271 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Categorical, + CategoricalIndex, + Index, + Series, +) +import pandas._testing as tm + + +class TestSeriesValueCounts: + def test_value_counts_datetime(self, unit): + # most dtypes are tested in tests/base + values = [ + pd.Timestamp("2011-01-01 09:00"), + pd.Timestamp("2011-01-01 10:00"), + pd.Timestamp("2011-01-01 11:00"), + pd.Timestamp("2011-01-01 09:00"), + pd.Timestamp("2011-01-01 09:00"), + pd.Timestamp("2011-01-01 11:00"), + ] + + exp_idx = pd.DatetimeIndex( + ["2011-01-01 09:00", "2011-01-01 11:00", "2011-01-01 10:00"], + name="xxx", + ).as_unit(unit) + exp = Series([3, 2, 1], index=exp_idx, name="count") + + ser = Series(values, name="xxx").dt.as_unit(unit) + tm.assert_series_equal(ser.value_counts(), exp) + # check DatetimeIndex outputs the same result + idx = pd.DatetimeIndex(values, name="xxx").as_unit(unit) + tm.assert_series_equal(idx.value_counts(), exp) + + # normalize + exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="proportion") + tm.assert_series_equal(ser.value_counts(normalize=True), exp) + tm.assert_series_equal(idx.value_counts(normalize=True), exp) + + def test_value_counts_datetime_tz(self, unit): + values = [ + pd.Timestamp("2011-01-01 09:00", tz="US/Eastern"), + pd.Timestamp("2011-01-01 10:00", tz="US/Eastern"), + pd.Timestamp("2011-01-01 11:00", tz="US/Eastern"), + pd.Timestamp("2011-01-01 09:00", tz="US/Eastern"), + pd.Timestamp("2011-01-01 09:00", tz="US/Eastern"), + pd.Timestamp("2011-01-01 11:00", tz="US/Eastern"), + ] + + exp_idx = pd.DatetimeIndex( + ["2011-01-01 09:00", "2011-01-01 11:00", "2011-01-01 10:00"], + tz="US/Eastern", + name="xxx", + ).as_unit(unit) + exp = Series([3, 2, 1], index=exp_idx, name="count") + + ser = Series(values, name="xxx").dt.as_unit(unit) + tm.assert_series_equal(ser.value_counts(), exp) + idx = pd.DatetimeIndex(values, name="xxx").as_unit(unit) + tm.assert_series_equal(idx.value_counts(), exp) + + exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="proportion") + tm.assert_series_equal(ser.value_counts(normalize=True), exp) + tm.assert_series_equal(idx.value_counts(normalize=True), exp) + + def test_value_counts_period(self): + values = [ + pd.Period("2011-01", freq="M"), + pd.Period("2011-02", freq="M"), + pd.Period("2011-03", freq="M"), + pd.Period("2011-01", freq="M"), + pd.Period("2011-01", freq="M"), + pd.Period("2011-03", freq="M"), + ] + + exp_idx = pd.PeriodIndex( + ["2011-01", "2011-03", "2011-02"], freq="M", name="xxx" + ) + exp = Series([3, 2, 1], index=exp_idx, name="count") + + ser = Series(values, name="xxx") + tm.assert_series_equal(ser.value_counts(), exp) + # check DatetimeIndex outputs the same result + idx = pd.PeriodIndex(values, name="xxx") + tm.assert_series_equal(idx.value_counts(), exp) + + # normalize + exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="proportion") + tm.assert_series_equal(ser.value_counts(normalize=True), exp) + tm.assert_series_equal(idx.value_counts(normalize=True), exp) + + def test_value_counts_categorical_ordered(self): + # most dtypes are tested in tests/base + values = Categorical([1, 2, 3, 1, 1, 3], ordered=True) + + exp_idx = CategoricalIndex( + [1, 3, 2], categories=[1, 2, 3], ordered=True, name="xxx" + ) + exp = Series([3, 2, 1], index=exp_idx, name="count") + + ser = Series(values, name="xxx") + tm.assert_series_equal(ser.value_counts(), exp) + # check CategoricalIndex outputs the same result + idx = CategoricalIndex(values, name="xxx") + tm.assert_series_equal(idx.value_counts(), exp) + + # normalize + exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="proportion") + tm.assert_series_equal(ser.value_counts(normalize=True), exp) + tm.assert_series_equal(idx.value_counts(normalize=True), exp) + + def test_value_counts_categorical_not_ordered(self): + values = Categorical([1, 2, 3, 1, 1, 3], ordered=False) + + exp_idx = CategoricalIndex( + [1, 3, 2], categories=[1, 2, 3], ordered=False, name="xxx" + ) + exp = Series([3, 2, 1], index=exp_idx, name="count") + + ser = Series(values, name="xxx") + tm.assert_series_equal(ser.value_counts(), exp) + # check CategoricalIndex outputs the same result + idx = CategoricalIndex(values, name="xxx") + tm.assert_series_equal(idx.value_counts(), exp) + + # normalize + exp = Series(np.array([3.0, 2.0, 1]) / 6.0, index=exp_idx, name="proportion") + tm.assert_series_equal(ser.value_counts(normalize=True), exp) + tm.assert_series_equal(idx.value_counts(normalize=True), exp) + + def test_value_counts_categorical(self): + # GH#12835 + cats = Categorical(list("abcccb"), categories=list("cabd")) + ser = Series(cats, name="xxx") + res = ser.value_counts(sort=False) + + exp_index = CategoricalIndex( + list("cabd"), categories=cats.categories, name="xxx" + ) + exp = Series([3, 1, 2, 0], name="count", index=exp_index) + tm.assert_series_equal(res, exp) + + res = ser.value_counts(sort=True) + + exp_index = CategoricalIndex( + list("cbad"), categories=cats.categories, name="xxx" + ) + exp = Series([3, 2, 1, 0], name="count", index=exp_index) + tm.assert_series_equal(res, exp) + + # check object dtype handles the Series.name as the same + # (tested in tests/base) + ser = Series(["a", "b", "c", "c", "c", "b"], name="xxx") + res = ser.value_counts() + exp = Series([3, 2, 1], name="count", index=Index(["c", "b", "a"], name="xxx")) + tm.assert_series_equal(res, exp) + + def test_value_counts_categorical_with_nan(self): + # see GH#9443 + + # sanity check + ser = Series(["a", "b", "a"], dtype="category") + exp = Series([2, 1], index=CategoricalIndex(["a", "b"]), name="count") + + res = ser.value_counts(dropna=True) + tm.assert_series_equal(res, exp) + + res = ser.value_counts(dropna=True) + tm.assert_series_equal(res, exp) + + # same Series via two different constructions --> same behaviour + series = [ + Series(["a", "b", None, "a", None, None], dtype="category"), + Series( + Categorical(["a", "b", None, "a", None, None], categories=["a", "b"]) + ), + ] + + for ser in series: + # None is a NaN value, so we exclude its count here + exp = Series([2, 1], index=CategoricalIndex(["a", "b"]), name="count") + res = ser.value_counts(dropna=True) + tm.assert_series_equal(res, exp) + + # we don't exclude the count of None and sort by counts + exp = Series( + [3, 2, 1], index=CategoricalIndex([np.nan, "a", "b"]), name="count" + ) + res = ser.value_counts(dropna=False) + tm.assert_series_equal(res, exp) + + # When we aren't sorting by counts, and np.nan isn't a + # category, it should be last. + exp = Series( + [2, 1, 3], index=CategoricalIndex(["a", "b", np.nan]), name="count" + ) + res = ser.value_counts(dropna=False, sort=False) + tm.assert_series_equal(res, exp) + + @pytest.mark.parametrize( + "ser, dropna, exp", + [ + ( + Series([False, True, True, pd.NA]), + False, + Series([2, 1, 1], index=[True, False, pd.NA], name="count"), + ), + ( + Series([False, True, True, pd.NA]), + True, + Series([2, 1], index=Index([True, False], dtype=object), name="count"), + ), + ( + Series(range(3), index=[True, False, np.nan]).index, + False, + Series([1, 1, 1], index=[True, False, np.nan], name="count"), + ), + ], + ) + def test_value_counts_bool_with_nan(self, ser, dropna, exp): + # GH32146 + out = ser.value_counts(dropna=dropna) + tm.assert_series_equal(out, exp) + + @pytest.mark.parametrize( + "input_array,expected", + [ + ( + [1 + 1j, 1 + 1j, 1, 3j, 3j, 3j], + Series( + [3, 2, 1], + index=Index([3j, 1 + 1j, 1], dtype=np.complex128), + name="count", + ), + ), + ( + np.array([1 + 1j, 1 + 1j, 1, 3j, 3j, 3j], dtype=np.complex64), + Series( + [3, 2, 1], + index=Index([3j, 1 + 1j, 1], dtype=np.complex64), + name="count", + ), + ), + ], + ) + def test_value_counts_complex_numbers(self, input_array, expected): + # GH 17927 + result = Series(input_array).value_counts() + tm.assert_series_equal(result, expected) + + def test_value_counts_masked(self): + # GH#54984 + dtype = "Int64" + ser = Series([1, 2, None, 2, None, 3], dtype=dtype) + result = ser.value_counts(dropna=False) + expected = Series( + [2, 2, 1, 1], + index=Index([2, None, 1, 3], dtype=dtype), + dtype=dtype, + name="count", + ) + tm.assert_series_equal(result, expected) + + result = ser.value_counts(dropna=True) + expected = Series( + [2, 1, 1], index=Index([2, 1, 3], dtype=dtype), dtype=dtype, name="count" + ) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_values.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_values.py new file mode 100644 index 0000000000000000000000000000000000000000..cb1595e68264fbe5f07b014be4975657fa2fa8cf --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_values.py @@ -0,0 +1,29 @@ +import numpy as np +import pytest + +from pandas import ( + IntervalIndex, + Series, + period_range, +) +import pandas._testing as tm + + +class TestValues: + @pytest.mark.parametrize( + "data", + [ + period_range("2000", periods=4), + IntervalIndex.from_breaks([1, 2, 3, 4]), + ], + ) + def test_values_object_extension_dtypes(self, data): + # https://github.com/pandas-dev/pandas/issues/23995 + result = Series(data).values + expected = np.array(data.astype(object)) + tm.assert_numpy_array_equal(result, expected) + + def test_values(self, datetime_series): + tm.assert_almost_equal( + datetime_series.values, list(datetime_series), check_dtype=False + ) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_view.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_view.py new file mode 100644 index 0000000000000000000000000000000000000000..7e0ac372cd443cf9a468b469df1c22818f10aad2 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/methods/test_view.py @@ -0,0 +1,61 @@ +import numpy as np +import pytest + +from pandas import ( + Index, + Series, + array, + date_range, +) +import pandas._testing as tm + +pytestmark = pytest.mark.filterwarnings( + "ignore:Series.view is deprecated and will be removed in a future version.:FutureWarning" # noqa: E501 +) + + +class TestView: + def test_view_i8_to_datetimelike(self): + dti = date_range("2000", periods=4, tz="US/Central") + ser = Series(dti.asi8) + + result = ser.view(dti.dtype) + tm.assert_datetime_array_equal(result._values, dti._data._with_freq(None)) + + pi = dti.tz_localize(None).to_period("D") + ser = Series(pi.asi8) + result = ser.view(pi.dtype) + tm.assert_period_array_equal(result._values, pi._data) + + def test_view_tz(self): + # GH#24024 + ser = Series(date_range("2000", periods=4, tz="US/Central")) + result = ser.view("i8") + expected = Series( + [ + 946706400000000000, + 946792800000000000, + 946879200000000000, + 946965600000000000, + ] + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "first", ["m8[ns]", "M8[ns]", "M8[ns, US/Central]", "period[D]"] + ) + @pytest.mark.parametrize( + "second", ["m8[ns]", "M8[ns]", "M8[ns, US/Central]", "period[D]"] + ) + @pytest.mark.parametrize("box", [Series, Index, array]) + def test_view_between_datetimelike(self, first, second, box): + dti = date_range("2016-01-01", periods=3) + + orig = box(dti) + obj = orig.view(first) + assert obj.dtype == first + tm.assert_numpy_array_equal(np.asarray(obj.view("i8")), dti.asi8) + + res = obj.view(second) + assert res.dtype == second + tm.assert_numpy_array_equal(np.asarray(obj.view("i8")), dti.asi8) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_cumulative.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_cumulative.py new file mode 100644 index 0000000000000000000000000000000000000000..97f5fb4a9f96f976251a15a2487ca6bb20a21850 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_cumulative.py @@ -0,0 +1,212 @@ +""" +Tests for Series cumulative operations. + +See also +-------- +tests.frame.test_cumulative +""" + +import re + +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm + +methods = { + "cumsum": np.cumsum, + "cumprod": np.cumprod, + "cummin": np.minimum.accumulate, + "cummax": np.maximum.accumulate, +} + + +class TestSeriesCumulativeOps: + @pytest.mark.parametrize("func", [np.cumsum, np.cumprod]) + def test_datetime_series(self, datetime_series, func): + tm.assert_numpy_array_equal( + func(datetime_series).values, + func(np.array(datetime_series)), + check_dtype=True, + ) + + # with missing values + ts = datetime_series.copy() + ts[::2] = np.nan + + result = func(ts)[1::2] + expected = func(np.array(ts.dropna())) + + tm.assert_numpy_array_equal(result.values, expected, check_dtype=False) + + @pytest.mark.parametrize("method", ["cummin", "cummax"]) + def test_cummin_cummax(self, datetime_series, method): + ufunc = methods[method] + + result = getattr(datetime_series, method)().values + expected = ufunc(np.array(datetime_series)) + + tm.assert_numpy_array_equal(result, expected) + ts = datetime_series.copy() + ts[::2] = np.nan + result = getattr(ts, method)()[1::2] + expected = ufunc(ts.dropna()) + + result.index = result.index._with_freq(None) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "ts", + [ + pd.Timedelta(0), + pd.Timestamp("1999-12-31"), + pd.Timestamp("1999-12-31").tz_localize("US/Pacific"), + ], + ) + @pytest.mark.parametrize( + "method, skipna, exp_tdi", + [ + ["cummax", True, ["NaT", "2 days", "NaT", "2 days", "NaT", "3 days"]], + ["cummin", True, ["NaT", "2 days", "NaT", "1 days", "NaT", "1 days"]], + [ + "cummax", + False, + ["NaT", "NaT", "NaT", "NaT", "NaT", "NaT"], + ], + [ + "cummin", + False, + ["NaT", "NaT", "NaT", "NaT", "NaT", "NaT"], + ], + ], + ) + def test_cummin_cummax_datetimelike(self, ts, method, skipna, exp_tdi): + # with ts==pd.Timedelta(0), we are testing td64; with naive Timestamp + # we are testing datetime64[ns]; with Timestamp[US/Pacific] + # we are testing dt64tz + tdi = pd.to_timedelta(["NaT", "2 days", "NaT", "1 days", "NaT", "3 days"]) + ser = pd.Series(tdi + ts) + + exp_tdi = pd.to_timedelta(exp_tdi) + expected = pd.Series(exp_tdi + ts) + result = getattr(ser, method)(skipna=skipna) + tm.assert_series_equal(expected, result) + + @pytest.mark.parametrize( + "func, exp", + [ + ("cummin", pd.Period("2012-1-1", freq="D")), + ("cummax", pd.Period("2012-1-2", freq="D")), + ], + ) + def test_cummin_cummax_period(self, func, exp): + # GH#28385 + ser = pd.Series( + [pd.Period("2012-1-1", freq="D"), pd.NaT, pd.Period("2012-1-2", freq="D")] + ) + result = getattr(ser, func)(skipna=False) + expected = pd.Series([pd.Period("2012-1-1", freq="D"), pd.NaT, pd.NaT]) + tm.assert_series_equal(result, expected) + + result = getattr(ser, func)(skipna=True) + expected = pd.Series([pd.Period("2012-1-1", freq="D"), pd.NaT, exp]) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "arg", + [ + [False, False, False, True, True, False, False], + [False, False, False, False, False, False, False], + ], + ) + @pytest.mark.parametrize( + "func", [lambda x: x, lambda x: ~x], ids=["identity", "inverse"] + ) + @pytest.mark.parametrize("method", methods.keys()) + def test_cummethods_bool(self, arg, func, method): + # GH#6270 + # checking Series method vs the ufunc applied to the values + + ser = func(pd.Series(arg)) + ufunc = methods[method] + + exp_vals = ufunc(ser.values) + expected = pd.Series(exp_vals) + + result = getattr(ser, method)() + + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "method, expected", + [ + ["cumsum", pd.Series([0, 1, np.nan, 1], dtype=object)], + ["cumprod", pd.Series([False, 0, np.nan, 0])], + ["cummin", pd.Series([False, False, np.nan, False])], + ["cummax", pd.Series([False, True, np.nan, True])], + ], + ) + def test_cummethods_bool_in_object_dtype(self, method, expected): + ser = pd.Series([False, True, np.nan, False]) + result = getattr(ser, method)() + tm.assert_series_equal(result, expected) + + def test_cumprod_timedelta(self): + # GH#48111 + ser = pd.Series([pd.Timedelta(days=1), pd.Timedelta(days=3)]) + with pytest.raises(TypeError, match="cumprod not supported for Timedelta"): + ser.cumprod() + + @pytest.mark.parametrize( + "data, op, skipna, expected_data", + [ + ([], "cumsum", True, []), + ([], "cumsum", False, []), + (["x", "z", "y"], "cumsum", True, ["x", "xz", "xzy"]), + (["x", "z", "y"], "cumsum", False, ["x", "xz", "xzy"]), + (["x", pd.NA, "y"], "cumsum", True, ["x", pd.NA, "xy"]), + (["x", pd.NA, "y"], "cumsum", False, ["x", pd.NA, pd.NA]), + ([pd.NA, "x", "y"], "cumsum", True, [pd.NA, "x", "xy"]), + ([pd.NA, "x", "y"], "cumsum", False, [pd.NA, pd.NA, pd.NA]), + ([pd.NA, pd.NA, pd.NA], "cumsum", True, [pd.NA, pd.NA, pd.NA]), + ([pd.NA, pd.NA, pd.NA], "cumsum", False, [pd.NA, pd.NA, pd.NA]), + ([], "cummin", True, []), + ([], "cummin", False, []), + (["y", "z", "x"], "cummin", True, ["y", "y", "x"]), + (["y", "z", "x"], "cummin", False, ["y", "y", "x"]), + (["y", pd.NA, "x"], "cummin", True, ["y", pd.NA, "x"]), + (["y", pd.NA, "x"], "cummin", False, ["y", pd.NA, pd.NA]), + ([pd.NA, "y", "x"], "cummin", True, [pd.NA, "y", "x"]), + ([pd.NA, "y", "x"], "cummin", False, [pd.NA, pd.NA, pd.NA]), + ([pd.NA, pd.NA, pd.NA], "cummin", True, [pd.NA, pd.NA, pd.NA]), + ([pd.NA, pd.NA, pd.NA], "cummin", False, [pd.NA, pd.NA, pd.NA]), + ([], "cummax", True, []), + ([], "cummax", False, []), + (["x", "z", "y"], "cummax", True, ["x", "z", "z"]), + (["x", "z", "y"], "cummax", False, ["x", "z", "z"]), + (["x", pd.NA, "y"], "cummax", True, ["x", pd.NA, "y"]), + (["x", pd.NA, "y"], "cummax", False, ["x", pd.NA, pd.NA]), + ([pd.NA, "x", "y"], "cummax", True, [pd.NA, "x", "y"]), + ([pd.NA, "x", "y"], "cummax", False, [pd.NA, pd.NA, pd.NA]), + ([pd.NA, pd.NA, pd.NA], "cummax", True, [pd.NA, pd.NA, pd.NA]), + ([pd.NA, pd.NA, pd.NA], "cummax", False, [pd.NA, pd.NA, pd.NA]), + ], + ) + def test_cum_methods_ea_strings( + self, string_dtype_no_object, data, op, skipna, expected_data + ): + # https://github.com/pandas-dev/pandas/pull/60633 - pyarrow + # https://github.com/pandas-dev/pandas/pull/60938 - Python + ser = pd.Series(data, dtype=string_dtype_no_object) + method = getattr(ser, op) + expected = pd.Series(expected_data, dtype=string_dtype_no_object) + result = method(skipna=skipna) + tm.assert_series_equal(result, expected) + + def test_cumprod_pyarrow_strings(self, pyarrow_string_dtype, skipna): + # https://github.com/pandas-dev/pandas/pull/60633 + ser = pd.Series(list("xyz"), dtype=pyarrow_string_dtype) + msg = re.escape(f"operation 'cumprod' not supported for dtype '{ser.dtype}'") + with pytest.raises(TypeError, match=msg): + ser.cumprod(skipna=skipna) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_formats.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_formats.py new file mode 100644 index 0000000000000000000000000000000000000000..10588c3b744f287451a971dfd17eda7d7421d1f8 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_formats.py @@ -0,0 +1,566 @@ +from datetime import ( + datetime, + timedelta, +) + +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Categorical, + DataFrame, + Index, + Series, + date_range, + option_context, + period_range, + timedelta_range, +) +import pandas._testing as tm + + +class TestSeriesRepr: + def test_multilevel_name_print_0(self): + # GH#55415 None does not get printed, but 0 does + # (matching DataFrame and flat index behavior) + mi = pd.MultiIndex.from_product([range(2, 3), range(3, 4)], names=[0, None]) + ser = Series(1.5, index=mi) + + res = repr(ser) + expected = "0 \n2 3 1.5\ndtype: float64" + assert res == expected + + def test_multilevel_name_print(self, lexsorted_two_level_string_multiindex): + index = lexsorted_two_level_string_multiindex + ser = Series(range(len(index)), index=index, name="sth") + expected = [ + "first second", + "foo one 0", + " two 1", + " three 2", + "bar one 3", + " two 4", + "baz two 5", + " three 6", + "qux one 7", + " two 8", + " three 9", + "Name: sth, dtype: int64", + ] + expected = "\n".join(expected) + assert repr(ser) == expected + + def test_small_name_printing(self): + # Test small Series. + s = Series([0, 1, 2]) + + s.name = "test" + assert "Name: test" in repr(s) + + s.name = None + assert "Name:" not in repr(s) + + def test_big_name_printing(self): + # Test big Series (diff code path). + s = Series(range(1000)) + + s.name = "test" + assert "Name: test" in repr(s) + + s.name = None + assert "Name:" not in repr(s) + + def test_empty_name_printing(self): + s = Series(index=date_range("20010101", "20020101"), name="test", dtype=object) + assert "Name: test" in repr(s) + + @pytest.mark.parametrize("args", [(), (0, -1)]) + def test_float_range(self, args): + str( + Series( + np.random.default_rng(2).standard_normal(1000), + index=np.arange(1000, *args), + ) + ) + + def test_empty_object(self): + # empty + str(Series(dtype=object)) + + def test_string(self, string_series): + str(string_series) + str(string_series.astype(int)) + + # with NaNs + string_series[5:7] = np.nan + str(string_series) + + def test_object(self, object_series): + str(object_series) + + def test_datetime(self, datetime_series): + str(datetime_series) + # with Nones + ots = datetime_series.astype("O") + ots[::2] = None + repr(ots) + + @pytest.mark.parametrize( + "name", + [ + "", + 1, + 1.2, + "foo", + "\u03B1\u03B2\u03B3", + "loooooooooooooooooooooooooooooooooooooooooooooooooooong", + ("foo", "bar", "baz"), + (1, 2), + ("foo", 1, 2.3), + ("\u03B1", "\u03B2", "\u03B3"), + ("\u03B1", "bar"), + ], + ) + def test_various_names(self, name, string_series): + # various names + string_series.name = name + repr(string_series) + + def test_tuple_name(self): + biggie = Series( + np.random.default_rng(2).standard_normal(1000), + index=np.arange(1000), + name=("foo", "bar", "baz"), + ) + repr(biggie) + + @pytest.mark.parametrize("arg", [100, 1001]) + def test_tidy_repr_name_0(self, arg): + # tidy repr + ser = Series(np.random.default_rng(2).standard_normal(arg), name=0) + rep_str = repr(ser) + assert "Name: 0" in rep_str + + def test_newline(self, any_string_dtype): + ser = Series( + ["a\n\r\tb"], + name="a\n\r\td", + index=Index(["a\n\r\tf"], dtype=any_string_dtype), + dtype=any_string_dtype, + ) + assert "\t" not in repr(ser) + assert "\r" not in repr(ser) + assert "a\n" not in repr(ser) + + @pytest.mark.parametrize( + "name, expected", + [ + ["foo", "Series([], Name: foo, dtype: int64)"], + [None, "Series([], dtype: int64)"], + ], + ) + def test_empty_int64(self, name, expected): + # with empty series (#4651) + s = Series([], dtype=np.int64, name=name) + assert repr(s) == expected + + def test_repr_bool_fails(self, capsys): + s = Series( + [ + DataFrame(np.random.default_rng(2).standard_normal((2, 2))) + for i in range(5) + ] + ) + + # It works (with no Cython exception barf)! + repr(s) + + captured = capsys.readouterr() + assert captured.err == "" + + def test_repr_name_iterable_indexable(self): + s = Series([1, 2, 3], name=np.int64(3)) + + # it works! + repr(s) + + s.name = ("\u05d0",) * 2 + repr(s) + + def test_repr_max_rows(self): + # GH 6863 + with option_context("display.max_rows", None): + str(Series(range(1001))) # should not raise exception + + def test_unicode_string_with_unicode(self): + df = Series(["\u05d0"], name="\u05d1") + str(df) + + ser = Series(["\u03c3"] * 10) + repr(ser) + + ser2 = Series(["\u05d0"] * 1000) + ser2.name = "title1" + repr(ser2) + + def test_str_to_bytes_raises(self): + # GH 26447 + df = Series(["abc"], name="abc") + msg = "^'str' object cannot be interpreted as an integer$" + with pytest.raises(TypeError, match=msg): + bytes(df) + + def test_timeseries_repr_object_dtype(self): + index = Index( + [datetime(2000, 1, 1) + timedelta(i) for i in range(1000)], dtype=object + ) + ts = Series(np.random.default_rng(2).standard_normal(len(index)), index) + repr(ts) + + ts = Series( + np.arange(20, dtype=np.float64), index=date_range("2020-01-01", periods=20) + ) + assert repr(ts).splitlines()[-1].startswith("Freq:") + + ts2 = ts.iloc[np.random.default_rng(2).integers(0, len(ts) - 1, 400)] + repr(ts2).splitlines()[-1] + + def test_latex_repr(self): + pytest.importorskip("jinja2") # uses Styler implementation + result = r"""\begin{tabular}{ll} +\toprule + & 0 \\ +\midrule +0 & $\alpha$ \\ +1 & b \\ +2 & c \\ +\bottomrule +\end{tabular} +""" + with option_context( + "styler.format.escape", None, "styler.render.repr", "latex" + ): + s = Series([r"$\alpha$", "b", "c"]) + assert result == s._repr_latex_() + + assert s._repr_latex_() is None + + def test_index_repr_in_frame_with_nan(self): + # see gh-25061 + i = Index([1, np.nan]) + s = Series([1, 2], index=i) + exp = """1.0 1\nNaN 2\ndtype: int64""" + + assert repr(s) == exp + + def test_format_pre_1900_dates(self): + rng = date_range("1/1/1850", "1/1/1950", freq="YE-DEC") + msg = "DatetimeIndex.format is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + rng.format() + ts = Series(1, index=rng) + repr(ts) + + def test_series_repr_nat(self): + series = Series([0, 1000, 2000, pd.NaT._value], dtype="M8[ns]") + + result = repr(series) + expected = ( + "0 1970-01-01 00:00:00.000000\n" + "1 1970-01-01 00:00:00.000001\n" + "2 1970-01-01 00:00:00.000002\n" + "3 NaT\n" + "dtype: datetime64[ns]" + ) + assert result == expected + + def test_float_repr(self): + # GH#35603 + # check float format when cast to object + ser = Series([1.0]).astype(object) + expected = "0 1.0\ndtype: object" + assert repr(ser) == expected + + def test_different_null_objects(self): + # GH#45263 + ser = Series([1, 2, 3, 4], [True, None, np.nan, pd.NaT]) + result = repr(ser) + expected = "True 1\nNone 2\nNaN 3\nNaT 4\ndtype: int64" + assert result == expected + + +class TestCategoricalRepr: + def test_categorical_repr_unicode(self): + # see gh-21002 + + class County: + name = "San Sebastián" + state = "PR" + + def __repr__(self) -> str: + return self.name + ", " + self.state + + cat = Categorical([County() for _ in range(61)]) + idx = Index(cat) + ser = idx.to_series() + + repr(ser) + str(ser) + + def test_categorical_repr(self, using_infer_string): + a = Series(Categorical([1, 2, 3, 4])) + exp = ( + "0 1\n1 2\n2 3\n3 4\n" + "dtype: category\nCategories (4, int64): [1, 2, 3, 4]" + ) + + assert exp == a.__str__() + + a = Series(Categorical(["a", "b"] * 25)) + exp = ( + "0 a\n1 b\n" + " ..\n" + "48 a\n49 b\n" + "Length: 50, dtype: category\nCategories (2, object): ['a', 'b']" + ) + if using_infer_string: + exp = exp.replace("object", "str") + with option_context("display.max_rows", 5): + assert exp == repr(a) + + levs = list("abcdefghijklmnopqrstuvwxyz") + a = Series(Categorical(["a", "b"], categories=levs, ordered=True)) + exp = ( + "0 a\n1 b\n" + "dtype: category\n" + "Categories (26, object): ['a' < 'b' < 'c' < 'd' ... " + "'w' < 'x' < 'y' < 'z']" + ) + if using_infer_string: + exp = exp.replace("object", "str") + assert exp == a.__str__() + + def test_categorical_series_repr(self): + s = Series(Categorical([1, 2, 3])) + exp = """0 1 +1 2 +2 3 +dtype: category +Categories (3, int64): [1, 2, 3]""" + + assert repr(s) == exp + + s = Series(Categorical(np.arange(10))) + exp = f"""0 0 +1 1 +2 2 +3 3 +4 4 +5 5 +6 6 +7 7 +8 8 +9 9 +dtype: category +Categories (10, {np.dtype(int)}): [0, 1, 2, 3, ..., 6, 7, 8, 9]""" + + assert repr(s) == exp + + def test_categorical_series_repr_ordered(self): + s = Series(Categorical([1, 2, 3], ordered=True)) + exp = """0 1 +1 2 +2 3 +dtype: category +Categories (3, int64): [1 < 2 < 3]""" + + assert repr(s) == exp + + s = Series(Categorical(np.arange(10), ordered=True)) + exp = f"""0 0 +1 1 +2 2 +3 3 +4 4 +5 5 +6 6 +7 7 +8 8 +9 9 +dtype: category +Categories (10, {np.dtype(int)}): [0 < 1 < 2 < 3 ... 6 < 7 < 8 < 9]""" + + assert repr(s) == exp + + def test_categorical_series_repr_datetime(self): + idx = date_range("2011-01-01 09:00", freq="h", periods=5) + s = Series(Categorical(idx)) + exp = """0 2011-01-01 09:00:00 +1 2011-01-01 10:00:00 +2 2011-01-01 11:00:00 +3 2011-01-01 12:00:00 +4 2011-01-01 13:00:00 +dtype: category +Categories (5, datetime64[ns]): [2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, + 2011-01-01 12:00:00, 2011-01-01 13:00:00]""" # noqa: E501 + + assert repr(s) == exp + + idx = date_range("2011-01-01 09:00", freq="h", periods=5, tz="US/Eastern") + s = Series(Categorical(idx)) + exp = """0 2011-01-01 09:00:00-05:00 +1 2011-01-01 10:00:00-05:00 +2 2011-01-01 11:00:00-05:00 +3 2011-01-01 12:00:00-05:00 +4 2011-01-01 13:00:00-05:00 +dtype: category +Categories (5, datetime64[ns, US/Eastern]): [2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, + 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, + 2011-01-01 13:00:00-05:00]""" # noqa: E501 + + assert repr(s) == exp + + def test_categorical_series_repr_datetime_ordered(self): + idx = date_range("2011-01-01 09:00", freq="h", periods=5) + s = Series(Categorical(idx, ordered=True)) + exp = """0 2011-01-01 09:00:00 +1 2011-01-01 10:00:00 +2 2011-01-01 11:00:00 +3 2011-01-01 12:00:00 +4 2011-01-01 13:00:00 +dtype: category +Categories (5, datetime64[ns]): [2011-01-01 09:00:00 < 2011-01-01 10:00:00 < 2011-01-01 11:00:00 < + 2011-01-01 12:00:00 < 2011-01-01 13:00:00]""" # noqa: E501 + + assert repr(s) == exp + + idx = date_range("2011-01-01 09:00", freq="h", periods=5, tz="US/Eastern") + s = Series(Categorical(idx, ordered=True)) + exp = """0 2011-01-01 09:00:00-05:00 +1 2011-01-01 10:00:00-05:00 +2 2011-01-01 11:00:00-05:00 +3 2011-01-01 12:00:00-05:00 +4 2011-01-01 13:00:00-05:00 +dtype: category +Categories (5, datetime64[ns, US/Eastern]): [2011-01-01 09:00:00-05:00 < 2011-01-01 10:00:00-05:00 < + 2011-01-01 11:00:00-05:00 < 2011-01-01 12:00:00-05:00 < + 2011-01-01 13:00:00-05:00]""" # noqa: E501 + + assert repr(s) == exp + + def test_categorical_series_repr_period(self): + idx = period_range("2011-01-01 09:00", freq="h", periods=5) + s = Series(Categorical(idx)) + exp = """0 2011-01-01 09:00 +1 2011-01-01 10:00 +2 2011-01-01 11:00 +3 2011-01-01 12:00 +4 2011-01-01 13:00 +dtype: category +Categories (5, period[h]): [2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, + 2011-01-01 13:00]""" # noqa: E501 + + assert repr(s) == exp + + idx = period_range("2011-01", freq="M", periods=5) + s = Series(Categorical(idx)) + exp = """0 2011-01 +1 2011-02 +2 2011-03 +3 2011-04 +4 2011-05 +dtype: category +Categories (5, period[M]): [2011-01, 2011-02, 2011-03, 2011-04, 2011-05]""" + + assert repr(s) == exp + + def test_categorical_series_repr_period_ordered(self): + idx = period_range("2011-01-01 09:00", freq="h", periods=5) + s = Series(Categorical(idx, ordered=True)) + exp = """0 2011-01-01 09:00 +1 2011-01-01 10:00 +2 2011-01-01 11:00 +3 2011-01-01 12:00 +4 2011-01-01 13:00 +dtype: category +Categories (5, period[h]): [2011-01-01 09:00 < 2011-01-01 10:00 < 2011-01-01 11:00 < 2011-01-01 12:00 < + 2011-01-01 13:00]""" # noqa: E501 + + assert repr(s) == exp + + idx = period_range("2011-01", freq="M", periods=5) + s = Series(Categorical(idx, ordered=True)) + exp = """0 2011-01 +1 2011-02 +2 2011-03 +3 2011-04 +4 2011-05 +dtype: category +Categories (5, period[M]): [2011-01 < 2011-02 < 2011-03 < 2011-04 < 2011-05]""" + + assert repr(s) == exp + + def test_categorical_series_repr_timedelta(self): + idx = timedelta_range("1 days", periods=5) + s = Series(Categorical(idx)) + exp = """0 1 days +1 2 days +2 3 days +3 4 days +4 5 days +dtype: category +Categories (5, timedelta64[ns]): [1 days, 2 days, 3 days, 4 days, 5 days]""" + + assert repr(s) == exp + + idx = timedelta_range("1 hours", periods=10) + s = Series(Categorical(idx)) + exp = """0 0 days 01:00:00 +1 1 days 01:00:00 +2 2 days 01:00:00 +3 3 days 01:00:00 +4 4 days 01:00:00 +5 5 days 01:00:00 +6 6 days 01:00:00 +7 7 days 01:00:00 +8 8 days 01:00:00 +9 9 days 01:00:00 +dtype: category +Categories (10, timedelta64[ns]): [0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00, + 3 days 01:00:00, ..., 6 days 01:00:00, 7 days 01:00:00, + 8 days 01:00:00, 9 days 01:00:00]""" # noqa: E501 + + assert repr(s) == exp + + def test_categorical_series_repr_timedelta_ordered(self): + idx = timedelta_range("1 days", periods=5) + s = Series(Categorical(idx, ordered=True)) + exp = """0 1 days +1 2 days +2 3 days +3 4 days +4 5 days +dtype: category +Categories (5, timedelta64[ns]): [1 days < 2 days < 3 days < 4 days < 5 days]""" + + assert repr(s) == exp + + idx = timedelta_range("1 hours", periods=10) + s = Series(Categorical(idx, ordered=True)) + exp = """0 0 days 01:00:00 +1 1 days 01:00:00 +2 2 days 01:00:00 +3 3 days 01:00:00 +4 4 days 01:00:00 +5 5 days 01:00:00 +6 6 days 01:00:00 +7 7 days 01:00:00 +8 8 days 01:00:00 +9 9 days 01:00:00 +dtype: category +Categories (10, timedelta64[ns]): [0 days 01:00:00 < 1 days 01:00:00 < 2 days 01:00:00 < + 3 days 01:00:00 ... 6 days 01:00:00 < 7 days 01:00:00 < + 8 days 01:00:00 < 9 days 01:00:00]""" # noqa: E501 + + assert repr(s) == exp diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_logical_ops.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_logical_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..8d7adc1c1aae644136561a8813b9cd7871d60b36 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_logical_ops.py @@ -0,0 +1,567 @@ +from datetime import datetime +import operator + +import numpy as np +import pytest + +from pandas._config import using_string_dtype + +from pandas import ( + ArrowDtype, + DataFrame, + Index, + Series, + StringDtype, + bdate_range, +) +import pandas._testing as tm +from pandas.core import ops + + +class TestSeriesLogicalOps: + @pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning") + @pytest.mark.parametrize("bool_op", [operator.and_, operator.or_, operator.xor]) + def test_bool_operators_with_nas(self, bool_op): + # boolean &, |, ^ should work with object arrays and propagate NAs + ser = Series(bdate_range("1/1/2000", periods=10), dtype=object) + ser[::2] = np.nan + + mask = ser.isna() + filled = ser.fillna(ser[0]) + + result = bool_op(ser < ser[9], ser > ser[3]) + + expected = bool_op(filled < filled[9], filled > filled[3]) + expected[mask] = False + tm.assert_series_equal(result, expected) + + def test_logical_operators_bool_dtype_with_empty(self): + # GH#9016: support bitwise op for integer types + index = list("bca") + + s_tft = Series([True, False, True], index=index) + s_fff = Series([False, False, False], index=index) + s_empty = Series([], dtype=object) + + res = s_tft & s_empty + expected = s_fff.sort_index() + tm.assert_series_equal(res, expected) + + res = s_tft | s_empty + expected = s_tft.sort_index() + tm.assert_series_equal(res, expected) + + def test_logical_operators_int_dtype_with_int_dtype(self): + # GH#9016: support bitwise op for integer types + + s_0123 = Series(range(4), dtype="int64") + s_3333 = Series([3] * 4) + s_4444 = Series([4] * 4) + + res = s_0123 & s_3333 + expected = Series(range(4), dtype="int64") + tm.assert_series_equal(res, expected) + + res = s_0123 | s_4444 + expected = Series(range(4, 8), dtype="int64") + tm.assert_series_equal(res, expected) + + s_1111 = Series([1] * 4, dtype="int8") + res = s_0123 & s_1111 + expected = Series([0, 1, 0, 1], dtype="int64") + tm.assert_series_equal(res, expected) + + res = s_0123.astype(np.int16) | s_1111.astype(np.int32) + expected = Series([1, 1, 3, 3], dtype="int32") + tm.assert_series_equal(res, expected) + + def test_logical_operators_int_dtype_with_int_scalar(self): + # GH#9016: support bitwise op for integer types + s_0123 = Series(range(4), dtype="int64") + + res = s_0123 & 0 + expected = Series([0] * 4) + tm.assert_series_equal(res, expected) + + res = s_0123 & 1 + expected = Series([0, 1, 0, 1]) + tm.assert_series_equal(res, expected) + + def test_logical_operators_int_dtype_with_float(self): + # GH#9016: support bitwise op for integer types + s_0123 = Series(range(4), dtype="int64") + + warn_msg = ( + r"Logical ops \(and, or, xor\) between Pandas objects and " + "dtype-less sequences" + ) + + msg = "Cannot perform.+with a dtyped.+array and scalar of type" + with pytest.raises(TypeError, match=msg): + s_0123 & np.nan + with pytest.raises(TypeError, match=msg): + s_0123 & 3.14 + msg = "unsupported operand type.+for &:" + with pytest.raises(TypeError, match=msg): + with tm.assert_produces_warning(FutureWarning, match=warn_msg): + s_0123 & [0.1, 4, 3.14, 2] + with pytest.raises(TypeError, match=msg): + s_0123 & np.array([0.1, 4, 3.14, 2]) + with pytest.raises(TypeError, match=msg): + s_0123 & Series([0.1, 4, -3.14, 2]) + + def test_logical_operators_int_dtype_with_str(self): + s_1111 = Series([1] * 4, dtype="int8") + + warn_msg = ( + r"Logical ops \(and, or, xor\) between Pandas objects and " + "dtype-less sequences" + ) + + msg = "Cannot perform 'and_' with a dtyped.+array and scalar of type" + with pytest.raises(TypeError, match=msg): + s_1111 & "a" + with pytest.raises(TypeError, match="unsupported operand.+for &"): + with tm.assert_produces_warning(FutureWarning, match=warn_msg): + s_1111 & ["a", "b", "c", "d"] + + def test_logical_operators_int_dtype_with_bool(self): + # GH#9016: support bitwise op for integer types + s_0123 = Series(range(4), dtype="int64") + + expected = Series([False] * 4) + + result = s_0123 & False + tm.assert_series_equal(result, expected) + + warn_msg = ( + r"Logical ops \(and, or, xor\) between Pandas objects and " + "dtype-less sequences" + ) + with tm.assert_produces_warning(FutureWarning, match=warn_msg): + result = s_0123 & [False] + tm.assert_series_equal(result, expected) + + with tm.assert_produces_warning(FutureWarning, match=warn_msg): + result = s_0123 & (False,) + tm.assert_series_equal(result, expected) + + result = s_0123 ^ False + expected = Series([False, True, True, True]) + tm.assert_series_equal(result, expected) + + def test_logical_operators_int_dtype_with_object(self): + # GH#9016: support bitwise op for integer types + s_0123 = Series(range(4), dtype="int64") + + result = s_0123 & Series([False, np.nan, False, False]) + expected = Series([False] * 4) + tm.assert_series_equal(result, expected) + + s_abNd = Series(["a", "b", np.nan, "d"]) + with pytest.raises( + TypeError, match="unsupported.* 'int' and 'str'|'rand_' not supported" + ): + s_0123 & s_abNd + + def test_logical_operators_bool_dtype_with_int(self): + index = list("bca") + + s_tft = Series([True, False, True], index=index) + s_fff = Series([False, False, False], index=index) + + res = s_tft & 0 + expected = s_fff + tm.assert_series_equal(res, expected) + + res = s_tft & 1 + expected = s_tft + tm.assert_series_equal(res, expected) + + def test_logical_ops_bool_dtype_with_ndarray(self): + # make sure we operate on ndarray the same as Series + left = Series([True, True, True, False, True]) + right = [True, False, None, True, np.nan] + + msg = ( + r"Logical ops \(and, or, xor\) between Pandas objects and " + "dtype-less sequences" + ) + + expected = Series([True, False, False, False, False]) + with tm.assert_produces_warning(FutureWarning, match=msg): + result = left & right + tm.assert_series_equal(result, expected) + result = left & np.array(right) + tm.assert_series_equal(result, expected) + result = left & Index(right) + tm.assert_series_equal(result, expected) + result = left & Series(right) + tm.assert_series_equal(result, expected) + + expected = Series([True, True, True, True, True]) + with tm.assert_produces_warning(FutureWarning, match=msg): + result = left | right + tm.assert_series_equal(result, expected) + result = left | np.array(right) + tm.assert_series_equal(result, expected) + result = left | Index(right) + tm.assert_series_equal(result, expected) + result = left | Series(right) + tm.assert_series_equal(result, expected) + + expected = Series([False, True, True, True, True]) + with tm.assert_produces_warning(FutureWarning, match=msg): + result = left ^ right + tm.assert_series_equal(result, expected) + result = left ^ np.array(right) + tm.assert_series_equal(result, expected) + result = left ^ Index(right) + tm.assert_series_equal(result, expected) + result = left ^ Series(right) + tm.assert_series_equal(result, expected) + + def test_logical_operators_int_dtype_with_bool_dtype_and_reindex(self): + # GH#9016: support bitwise op for integer types + + index = list("bca") + + s_tft = Series([True, False, True], index=index) + s_tft = Series([True, False, True], index=index) + s_tff = Series([True, False, False], index=index) + + s_0123 = Series(range(4), dtype="int64") + + # s_0123 will be all false now because of reindexing like s_tft + expected = Series([False] * 7, index=[0, 1, 2, 3, "a", "b", "c"]) + with tm.assert_produces_warning(FutureWarning): + result = s_tft & s_0123 + tm.assert_series_equal(result, expected) + + # GH 52538: Deprecate casting to object type when reindex is needed; + # matches DataFrame behavior + expected = Series([False] * 7, index=[0, 1, 2, 3, "a", "b", "c"]) + with tm.assert_produces_warning(FutureWarning): + result = s_0123 & s_tft + tm.assert_series_equal(result, expected) + + s_a0b1c0 = Series([1], list("b")) + + with tm.assert_produces_warning(FutureWarning): + res = s_tft & s_a0b1c0 + expected = s_tff.reindex(list("abc")) + tm.assert_series_equal(res, expected) + + with tm.assert_produces_warning(FutureWarning): + res = s_tft | s_a0b1c0 + expected = s_tft.reindex(list("abc")) + tm.assert_series_equal(res, expected) + + def test_scalar_na_logical_ops_corners(self): + s = Series([2, 3, 4, 5, 6, 7, 8, 9, 10]) + + msg = "Cannot perform.+with a dtyped.+array and scalar of type" + with pytest.raises(TypeError, match=msg): + s & datetime(2005, 1, 1) + + s = Series([2, 3, 4, 5, 6, 7, 8, 9, datetime(2005, 1, 1)]) + s[::2] = np.nan + + expected = Series(True, index=s.index) + expected[::2] = False + + msg = ( + r"Logical ops \(and, or, xor\) between Pandas objects and " + "dtype-less sequences" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + result = s & list(s) + tm.assert_series_equal(result, expected) + + def test_scalar_na_logical_ops_corners_aligns(self): + s = Series([2, 3, 4, 5, 6, 7, 8, 9, datetime(2005, 1, 1)]) + s[::2] = np.nan + d = DataFrame({"A": s}) + + expected = DataFrame(False, index=range(9), columns=["A"] + list(range(9))) + + result = s & d + tm.assert_frame_equal(result, expected) + + result = d & s + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("op", [operator.and_, operator.or_, operator.xor]) + def test_logical_ops_with_index(self, op): + # GH#22092, GH#19792 + ser = Series([True, True, False, False]) + idx1 = Index([True, False, True, False]) + idx2 = Index([1, 0, 1, 0]) + + expected = Series([op(ser[n], idx1[n]) for n in range(len(ser))]) + + result = op(ser, idx1) + tm.assert_series_equal(result, expected) + + expected = Series([op(ser[n], idx2[n]) for n in range(len(ser))], dtype=bool) + + result = op(ser, idx2) + tm.assert_series_equal(result, expected) + + def test_reversed_xor_with_index_returns_series(self): + # GH#22092, GH#19792 pre-2.0 these were aliased to setops + ser = Series([True, True, False, False]) + idx1 = Index([True, False, True, False], dtype=bool) + idx2 = Index([1, 0, 1, 0]) + + expected = Series([False, True, True, False]) + result = idx1 ^ ser + tm.assert_series_equal(result, expected) + + result = idx2 ^ ser + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "op", + [ + ops.rand_, + ops.ror_, + ], + ) + def test_reversed_logical_op_with_index_returns_series(self, op): + # GH#22092, GH#19792 + ser = Series([True, True, False, False]) + idx1 = Index([True, False, True, False]) + idx2 = Index([1, 0, 1, 0]) + + expected = Series(op(idx1.values, ser.values)) + result = op(ser, idx1) + tm.assert_series_equal(result, expected) + + expected = op(ser, Series(idx2)) + result = op(ser, idx2) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "op, expected", + [ + (ops.rand_, Series([False, False])), + (ops.ror_, Series([True, True])), + (ops.rxor, Series([True, True])), + ], + ) + def test_reverse_ops_with_index(self, op, expected): + # https://github.com/pandas-dev/pandas/pull/23628 + # multi-set Index ops are buggy, so let's avoid duplicates... + # GH#49503 + ser = Series([True, False]) + idx = Index([False, True]) + + result = op(ser, idx) + tm.assert_series_equal(result, expected) + + @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)") + def test_logical_ops_label_based(self, using_infer_string): + # GH#4947 + # logical ops should be label based + + a = Series([True, False, True], list("bca")) + b = Series([False, True, False], list("abc")) + + expected = Series([False, True, False], list("abc")) + result = a & b + tm.assert_series_equal(result, expected) + + expected = Series([True, True, False], list("abc")) + result = a | b + tm.assert_series_equal(result, expected) + + expected = Series([True, False, False], list("abc")) + result = a ^ b + tm.assert_series_equal(result, expected) + + # rhs is bigger + a = Series([True, False, True], list("bca")) + b = Series([False, True, False, True], list("abcd")) + + expected = Series([False, True, False, False], list("abcd")) + result = a & b + tm.assert_series_equal(result, expected) + + expected = Series([True, True, False, False], list("abcd")) + result = a | b + tm.assert_series_equal(result, expected) + + # filling + + # vs empty + empty = Series([], dtype=object) + + result = a & empty.copy() + expected = Series([False, False, False], list("abc")) + tm.assert_series_equal(result, expected) + + result = a | empty.copy() + expected = Series([True, True, False], list("abc")) + tm.assert_series_equal(result, expected) + + # vs non-matching + with tm.assert_produces_warning(FutureWarning): + result = a & Series([1], ["z"]) + expected = Series([False, False, False, False], list("abcz")) + tm.assert_series_equal(result, expected) + + with tm.assert_produces_warning(FutureWarning): + result = a | Series([1], ["z"]) + expected = Series([True, True, False, False], list("abcz")) + tm.assert_series_equal(result, expected) + + # identity + # we would like s[s|e] == s to hold for any e, whether empty or not + with tm.assert_produces_warning(FutureWarning): + for e in [ + empty.copy(), + Series([1], ["z"]), + Series(np.nan, b.index), + Series(np.nan, a.index), + ]: + result = a[a | e] + tm.assert_series_equal(result, a[a]) + + for e in [Series(["z"])]: + if using_infer_string: + # TODO(infer_string) should this behave differently? + # -> https://github.com/pandas-dev/pandas/issues/60234 + with pytest.raises( + TypeError, match="not supported for dtype|unsupported operand type" + ): + result = a[a | e] + else: + result = a[a | e] + tm.assert_series_equal(result, a[a]) + + # vs scalars + index = list("bca") + t = Series([True, False, True]) + + for v in [True, 1, 2]: + result = Series([True, False, True], index=index) | v + expected = Series([True, True, True], index=index) + tm.assert_series_equal(result, expected) + + msg = "Cannot perform.+with a dtyped.+array and scalar of type" + for v in [np.nan, "foo"]: + with pytest.raises(TypeError, match=msg): + t | v + + for v in [False, 0]: + result = Series([True, False, True], index=index) | v + expected = Series([True, False, True], index=index) + tm.assert_series_equal(result, expected) + + for v in [True, 1]: + result = Series([True, False, True], index=index) & v + expected = Series([True, False, True], index=index) + tm.assert_series_equal(result, expected) + + for v in [False, 0]: + result = Series([True, False, True], index=index) & v + expected = Series([False, False, False], index=index) + tm.assert_series_equal(result, expected) + msg = "Cannot perform.+with a dtyped.+array and scalar of type" + for v in [np.nan]: + with pytest.raises(TypeError, match=msg): + t & v + + def test_logical_ops_df_compat(self): + # GH#1134 + s1 = Series([True, False, True], index=list("ABC"), name="x") + s2 = Series([True, True, False], index=list("ABD"), name="x") + + exp = Series([True, False, False, False], index=list("ABCD"), name="x") + tm.assert_series_equal(s1 & s2, exp) + tm.assert_series_equal(s2 & s1, exp) + + # True | np.nan => True + exp_or1 = Series([True, True, True, False], index=list("ABCD"), name="x") + tm.assert_series_equal(s1 | s2, exp_or1) + # np.nan | True => np.nan, filled with False + exp_or = Series([True, True, False, False], index=list("ABCD"), name="x") + tm.assert_series_equal(s2 | s1, exp_or) + + # DataFrame doesn't fill nan with False + tm.assert_frame_equal(s1.to_frame() & s2.to_frame(), exp.to_frame()) + tm.assert_frame_equal(s2.to_frame() & s1.to_frame(), exp.to_frame()) + + exp = DataFrame({"x": [True, True, np.nan, np.nan]}, index=list("ABCD")) + tm.assert_frame_equal(s1.to_frame() | s2.to_frame(), exp_or1.to_frame()) + tm.assert_frame_equal(s2.to_frame() | s1.to_frame(), exp_or.to_frame()) + + # different length + s3 = Series([True, False, True], index=list("ABC"), name="x") + s4 = Series([True, True, True, True], index=list("ABCD"), name="x") + + exp = Series([True, False, True, False], index=list("ABCD"), name="x") + tm.assert_series_equal(s3 & s4, exp) + tm.assert_series_equal(s4 & s3, exp) + + # np.nan | True => np.nan, filled with False + exp_or1 = Series([True, True, True, False], index=list("ABCD"), name="x") + tm.assert_series_equal(s3 | s4, exp_or1) + # True | np.nan => True + exp_or = Series([True, True, True, True], index=list("ABCD"), name="x") + tm.assert_series_equal(s4 | s3, exp_or) + + tm.assert_frame_equal(s3.to_frame() & s4.to_frame(), exp.to_frame()) + tm.assert_frame_equal(s4.to_frame() & s3.to_frame(), exp.to_frame()) + + tm.assert_frame_equal(s3.to_frame() | s4.to_frame(), exp_or1.to_frame()) + tm.assert_frame_equal(s4.to_frame() | s3.to_frame(), exp_or.to_frame()) + + @pytest.mark.xfail(reason="Will pass once #52839 deprecation is enforced") + def test_int_dtype_different_index_not_bool(self): + # GH 52500 + ser1 = Series([1, 2, 3], index=[10, 11, 23], name="a") + ser2 = Series([10, 20, 30], index=[11, 10, 23], name="a") + result = np.bitwise_xor(ser1, ser2) + expected = Series([21, 8, 29], index=[10, 11, 23], name="a") + tm.assert_series_equal(result, expected) + + result = ser1 ^ ser2 + tm.assert_series_equal(result, expected) + + # TODO: this belongs in comparison tests + def test_pyarrow_numpy_string_invalid(self): + # GH#56008 + pa = pytest.importorskip("pyarrow") + ser = Series([False, True]) + ser2 = Series(["a", "b"], dtype=StringDtype(na_value=np.nan)) + result = ser == ser2 + expected_eq = Series(False, index=ser.index) + tm.assert_series_equal(result, expected_eq) + + result = ser != ser2 + expected_ne = Series(True, index=ser.index) + tm.assert_series_equal(result, expected_ne) + + with pytest.raises(TypeError, match="Invalid comparison"): + ser > ser2 + + # GH#59505 + ser3 = ser2.astype("string[pyarrow]") + result3_eq = ser3 == ser + tm.assert_series_equal(result3_eq, expected_eq.astype("bool[pyarrow]")) + result3_ne = ser3 != ser + tm.assert_series_equal(result3_ne, expected_ne.astype("bool[pyarrow]")) + + with pytest.raises(TypeError, match="Invalid comparison"): + ser > ser3 + + ser4 = ser2.astype(ArrowDtype(pa.string())) + result4_eq = ser4 == ser + tm.assert_series_equal(result4_eq, expected_eq.astype("bool[pyarrow]")) + result4_ne = ser4 != ser + tm.assert_series_equal(result4_ne, expected_ne.astype("bool[pyarrow]")) + + with pytest.raises(TypeError, match="Invalid comparison"): + ser > ser4 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_missing.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_missing.py new file mode 100644 index 0000000000000000000000000000000000000000..cafc69c4d0f20f42dc184366db0ac8933a512f54 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_missing.py @@ -0,0 +1,105 @@ +from datetime import timedelta + +import numpy as np +import pytest + +from pandas._libs import iNaT + +import pandas as pd +from pandas import ( + Categorical, + Index, + NaT, + Series, + isna, +) +import pandas._testing as tm + + +class TestSeriesMissingData: + def test_categorical_nan_handling(self): + # NaNs are represented as -1 in labels + s = Series(Categorical(["a", "b", np.nan, "a"])) + tm.assert_index_equal(s.cat.categories, Index(["a", "b"])) + tm.assert_numpy_array_equal( + s.values.codes, np.array([0, 1, -1, 0], dtype=np.int8) + ) + + def test_isna_for_inf(self): + s = Series(["a", np.inf, np.nan, pd.NA, 1.0]) + msg = "use_inf_as_na option is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + with pd.option_context("mode.use_inf_as_na", True): + r = s.isna() + dr = s.dropna() + e = Series([False, True, True, True, False]) + de = Series(["a", 1.0], index=[0, 4]) + tm.assert_series_equal(r, e) + tm.assert_series_equal(dr, de) + + def test_timedelta64_nan(self): + td = Series([timedelta(days=i) for i in range(10)]) + + # nan ops on timedeltas + td1 = td.copy() + td1[0] = np.nan + assert isna(td1[0]) + assert td1[0]._value == iNaT + td1[0] = td[0] + assert not isna(td1[0]) + + # GH#16674 iNaT is treated as an integer when given by the user + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + td1[1] = iNaT + assert not isna(td1[1]) + assert td1.dtype == np.object_ + assert td1[1] == iNaT + td1[1] = td[1] + assert not isna(td1[1]) + + td1[2] = NaT + assert isna(td1[2]) + assert td1[2]._value == iNaT + td1[2] = td[2] + assert not isna(td1[2]) + + # boolean setting + # GH#2899 boolean setting + td3 = np.timedelta64(timedelta(days=3)) + td7 = np.timedelta64(timedelta(days=7)) + td[(td > td3) & (td < td7)] = np.nan + assert isna(td).sum() == 3 + + @pytest.mark.xfail( + reason="Chained inequality raises when trying to define 'selector'" + ) + def test_logical_range_select(self, datetime_series): + # NumPy limitation =( + # https://github.com/pandas-dev/pandas/commit/9030dc021f07c76809848925cb34828f6c8484f3 + + selector = -0.5 <= datetime_series <= 0.5 + expected = (datetime_series >= -0.5) & (datetime_series <= 0.5) + tm.assert_series_equal(selector, expected) + + def test_valid(self, datetime_series): + ts = datetime_series.copy() + ts.index = ts.index._with_freq(None) + ts[::2] = np.nan + + result = ts.dropna() + assert len(result) == ts.count() + tm.assert_series_equal(result, ts[1::2]) + tm.assert_series_equal(result, ts[pd.notna(ts)]) + + +def test_hasnans_uncached_for_series(): + # GH#19700 + # set float64 dtype to avoid upcast when setting nan + idx = Index([0, 1], dtype="float64") + assert idx.hasnans is False + assert "hasnans" in idx._cache + ser = idx.to_series() + assert ser.hasnans is False + assert not hasattr(ser, "_cache") + ser.iloc[-1] = np.nan + assert ser.hasnans is True diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_npfuncs.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_npfuncs.py new file mode 100644 index 0000000000000000000000000000000000000000..11a51c4700d5c1733e7136909d86d2ea7294dd8e --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_npfuncs.py @@ -0,0 +1,46 @@ +""" +Tests for np.foo applied to Series, not necessarily ufuncs. +""" + +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +from pandas import Series +import pandas._testing as tm + + +class TestPtp: + def test_ptp(self): + # GH#21614 + N = 1000 + arr = np.random.default_rng(2).standard_normal(N) + ser = Series(arr) + assert np.ptp(ser) == np.ptp(arr) + + +def test_numpy_unique(datetime_series): + # it works! + np.unique(datetime_series) + + +@pytest.mark.parametrize("index", [["a", "b", "c", "d", "e"], None]) +def test_numpy_argwhere(index): + # GH#35331 + + s = Series(range(5), index=index, dtype=np.int64) + + result = np.argwhere(s > 2).astype(np.int64) + expected = np.array([[3], [4]], dtype=np.int64) + + tm.assert_numpy_array_equal(result, expected) + + +@td.skip_if_no("pyarrow") +def test_log_arrow_backed_missing_value(): + # GH#56285 + ser = Series([1, 2, None], dtype="float64[pyarrow]") + result = np.log(ser) + expected = np.log(Series([1, 2, None], dtype="float64")) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_reductions.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_reductions.py new file mode 100644 index 0000000000000000000000000000000000000000..5415f220cadd427c244a78c6f5619972f3a12a78 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_reductions.py @@ -0,0 +1,217 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import Series +import pandas._testing as tm + + +@pytest.mark.parametrize("operation, expected", [("min", "a"), ("max", "b")]) +def test_reductions_series_strings(operation, expected): + # GH#31746 + ser = Series(["a", "b"], dtype="string") + res_operation_serie = getattr(ser, operation)() + assert res_operation_serie == expected + + +@pytest.mark.parametrize("as_period", [True, False]) +def test_mode_extension_dtype(as_period): + # GH#41927 preserve dt64tz dtype + ser = Series([pd.Timestamp(1979, 4, n) for n in range(1, 5)]) + + if as_period: + ser = ser.dt.to_period("D") + else: + ser = ser.dt.tz_localize("US/Central") + + res = ser.mode() + assert res.dtype == ser.dtype + tm.assert_series_equal(res, ser) + + +def test_mode_nullable_dtype(any_numeric_ea_dtype): + # GH#55340 + ser = Series([1, 3, 2, pd.NA, 3, 2, pd.NA], dtype=any_numeric_ea_dtype) + result = ser.mode(dropna=False) + expected = Series([2, 3, pd.NA], dtype=any_numeric_ea_dtype) + tm.assert_series_equal(result, expected) + + result = ser.mode(dropna=True) + expected = Series([2, 3], dtype=any_numeric_ea_dtype) + tm.assert_series_equal(result, expected) + + ser[-1] = pd.NA + + result = ser.mode(dropna=True) + expected = Series([2, 3], dtype=any_numeric_ea_dtype) + tm.assert_series_equal(result, expected) + + result = ser.mode(dropna=False) + expected = Series([pd.NA], dtype=any_numeric_ea_dtype) + tm.assert_series_equal(result, expected) + + +def test_mode_infer_string(): + # GH#56183 + pytest.importorskip("pyarrow") + ser = Series(["a", "b"], dtype=object) + with pd.option_context("future.infer_string", True): + result = ser.mode() + expected = Series(["a", "b"], dtype=object) + tm.assert_series_equal(result, expected) + + +def test_reductions_td64_with_nat(): + # GH#8617 + ser = Series([0, pd.NaT], dtype="m8[ns]") + exp = ser[0] + assert ser.median() == exp + assert ser.min() == exp + assert ser.max() == exp + + +@pytest.mark.parametrize("skipna", [True, False]) +def test_td64_sum_empty(skipna): + # GH#37151 + ser = Series([], dtype="timedelta64[ns]") + + result = ser.sum(skipna=skipna) + assert isinstance(result, pd.Timedelta) + assert result == pd.Timedelta(0) + + +def test_td64_summation_overflow(): + # GH#9442 + ser = Series(pd.date_range("20130101", periods=100000, freq="h")) + ser[0] += pd.Timedelta("1s 1ms") + + # mean + result = (ser - ser.min()).mean() + expected = pd.Timedelta((pd.TimedeltaIndex(ser - ser.min()).asi8 / len(ser)).sum()) + + # the computation is converted to float so + # might be some loss of precision + assert np.allclose(result._value / 1000, expected._value / 1000) + + # sum + msg = "overflow in timedelta operation" + with pytest.raises(ValueError, match=msg): + (ser - ser.min()).sum() + + s1 = ser[0:10000] + with pytest.raises(ValueError, match=msg): + (s1 - s1.min()).sum() + s2 = ser[0:1000] + (s2 - s2.min()).sum() + + +def test_prod_numpy16_bug(): + ser = Series([1.0, 1.0, 1.0], index=range(3)) + result = ser.prod() + + assert not isinstance(result, Series) + + +@pytest.mark.parametrize("func", [np.any, np.all]) +@pytest.mark.parametrize("kwargs", [{"keepdims": True}, {"out": object()}]) +def test_validate_any_all_out_keepdims_raises(kwargs, func): + ser = Series([1, 2]) + param = next(iter(kwargs)) + name = func.__name__ + + msg = ( + f"the '{param}' parameter is not " + "supported in the pandas " + rf"implementation of {name}\(\)" + ) + with pytest.raises(ValueError, match=msg): + func(ser, **kwargs) + + +def test_validate_sum_initial(): + ser = Series([1, 2]) + msg = ( + r"the 'initial' parameter is not " + r"supported in the pandas " + r"implementation of sum\(\)" + ) + with pytest.raises(ValueError, match=msg): + np.sum(ser, initial=10) + + +def test_validate_median_initial(): + ser = Series([1, 2]) + msg = ( + r"the 'overwrite_input' parameter is not " + r"supported in the pandas " + r"implementation of median\(\)" + ) + with pytest.raises(ValueError, match=msg): + # It seems like np.median doesn't dispatch, so we use the + # method instead of the ufunc. + ser.median(overwrite_input=True) + + +def test_validate_stat_keepdims(): + ser = Series([1, 2]) + msg = ( + r"the 'keepdims' parameter is not " + r"supported in the pandas " + r"implementation of sum\(\)" + ) + with pytest.raises(ValueError, match=msg): + np.sum(ser, keepdims=True) + + +def test_mean_with_convertible_string_raises(using_array_manager, using_infer_string): + # GH#44008 + ser = Series(["1", "2"]) + assert ser.sum() == "12" + + msg = "Could not convert string '12' to numeric|does not support|Cannot perform" + with pytest.raises(TypeError, match=msg): + ser.mean() + + df = ser.to_frame() + if not using_array_manager: + msg = r"Could not convert \['12'\] to numeric|does not support|Cannot perform" + with pytest.raises(TypeError, match=msg): + df.mean() + + +def test_mean_dont_convert_j_to_complex(using_array_manager): + # GH#36703 + df = pd.DataFrame([{"db": "J", "numeric": 123}]) + if using_array_manager: + msg = "Could not convert string 'J' to numeric" + else: + msg = r"Could not convert \['J'\] to numeric|does not support|Cannot perform" + with pytest.raises(TypeError, match=msg): + df.mean() + + with pytest.raises(TypeError, match=msg): + df.agg("mean") + + msg = "Could not convert string 'J' to numeric|does not support|Cannot perform" + with pytest.raises(TypeError, match=msg): + df["db"].mean() + msg = "Could not convert string 'J' to numeric|ufunc 'divide'|Cannot perform" + with pytest.raises(TypeError, match=msg): + np.mean(df["db"].astype("string").array) + + +def test_median_with_convertible_string_raises(using_array_manager): + # GH#34671 this _could_ return a string "2", but definitely not float 2.0 + msg = r"Cannot convert \['1' '2' '3'\] to numeric|does not support|Cannot perform" + ser = Series(["1", "2", "3"]) + with pytest.raises(TypeError, match=msg): + ser.median() + + if not using_array_manager: + msg = ( + r"Cannot convert \[\['1' '2' '3'\]\] to numeric|does not support" + "|Cannot perform" + ) + df = ser.to_frame() + with pytest.raises(TypeError, match=msg): + df.median() diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_subclass.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_subclass.py new file mode 100644 index 0000000000000000000000000000000000000000..c2d5afcf884b12b3007905061b7c503359e71a5d --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_subclass.py @@ -0,0 +1,82 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm + +pytestmark = pytest.mark.filterwarnings( + "ignore:Passing a BlockManager|Passing a SingleBlockManager:DeprecationWarning" +) + + +class TestSeriesSubclassing: + @pytest.mark.parametrize( + "idx_method, indexer, exp_data, exp_idx", + [ + ["loc", ["a", "b"], [1, 2], "ab"], + ["iloc", [2, 3], [3, 4], "cd"], + ], + ) + def test_indexing_sliced(self, idx_method, indexer, exp_data, exp_idx): + s = tm.SubclassedSeries([1, 2, 3, 4], index=list("abcd")) + res = getattr(s, idx_method)[indexer] + exp = tm.SubclassedSeries(exp_data, index=list(exp_idx)) + tm.assert_series_equal(res, exp) + + def test_to_frame(self): + s = tm.SubclassedSeries([1, 2, 3, 4], index=list("abcd"), name="xxx") + res = s.to_frame() + exp = tm.SubclassedDataFrame({"xxx": [1, 2, 3, 4]}, index=list("abcd")) + tm.assert_frame_equal(res, exp) + + def test_subclass_unstack(self): + # GH 15564 + s = tm.SubclassedSeries([1, 2, 3, 4], index=[list("aabb"), list("xyxy")]) + + res = s.unstack() + exp = tm.SubclassedDataFrame({"x": [1, 3], "y": [2, 4]}, index=["a", "b"]) + + tm.assert_frame_equal(res, exp) + + def test_subclass_empty_repr(self): + sub_series = tm.SubclassedSeries() + assert "SubclassedSeries" in repr(sub_series) + + def test_asof(self): + N = 3 + rng = pd.date_range("1/1/1990", periods=N, freq="53s") + s = tm.SubclassedSeries({"A": [np.nan, np.nan, np.nan]}, index=rng) + + result = s.asof(rng[-2:]) + assert isinstance(result, tm.SubclassedSeries) + + def test_explode(self): + s = tm.SubclassedSeries([[1, 2, 3], "foo", [], [3, 4]]) + result = s.explode() + assert isinstance(result, tm.SubclassedSeries) + + def test_equals(self): + # https://github.com/pandas-dev/pandas/pull/34402 + # allow subclass in both directions + s1 = pd.Series([1, 2, 3]) + s2 = tm.SubclassedSeries([1, 2, 3]) + assert s1.equals(s2) + assert s2.equals(s1) + + +class SubclassedSeries(pd.Series): + @property + def _constructor(self): + def _new(*args, **kwargs): + # some constructor logic that accesses the Series' name + if self.name == "test": + return pd.Series(*args, **kwargs) + return SubclassedSeries(*args, **kwargs) + + return _new + + +def test_constructor_from_dict(): + # https://github.com/pandas-dev/pandas/issues/52445 + result = SubclassedSeries({"a": 1, "b": 2, "c": 3}) + assert isinstance(result, SubclassedSeries) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_ufunc.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_ufunc.py new file mode 100644 index 0000000000000000000000000000000000000000..e03e87a44107f9514af37ee545c51b2117e5cf39 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_ufunc.py @@ -0,0 +1,463 @@ +from collections import deque +import re +import string + +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +import pandas as pd +import pandas._testing as tm +from pandas.arrays import SparseArray + + +@pytest.fixture(params=[np.add, np.logaddexp]) +def ufunc(request): + # dunder op + return request.param + + +@pytest.fixture( + params=[pytest.param(True, marks=pytest.mark.fails_arm_wheels), False], + ids=["sparse", "dense"], +) +def sparse(request): + return request.param + + +@pytest.fixture +def arrays_for_binary_ufunc(): + """ + A pair of random, length-100 integer-dtype arrays, that are mostly 0. + """ + a1 = np.random.default_rng(2).integers(0, 10, 100, dtype="int64") + a2 = np.random.default_rng(2).integers(0, 10, 100, dtype="int64") + a1[::3] = 0 + a2[::4] = 0 + return a1, a2 + + +@pytest.mark.parametrize("ufunc", [np.positive, np.floor, np.exp]) +def test_unary_ufunc(ufunc, sparse): + # Test that ufunc(pd.Series) == pd.Series(ufunc) + arr = np.random.default_rng(2).integers(0, 10, 10, dtype="int64") + arr[::2] = 0 + if sparse: + arr = SparseArray(arr, dtype=pd.SparseDtype("int64", 0)) + + index = list(string.ascii_letters[:10]) + name = "name" + series = pd.Series(arr, index=index, name=name) + + result = ufunc(series) + expected = pd.Series(ufunc(arr), index=index, name=name) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("flip", [True, False], ids=["flipped", "straight"]) +def test_binary_ufunc_with_array(flip, sparse, ufunc, arrays_for_binary_ufunc): + # Test that ufunc(pd.Series(a), array) == pd.Series(ufunc(a, b)) + a1, a2 = arrays_for_binary_ufunc + if sparse: + a1 = SparseArray(a1, dtype=pd.SparseDtype("int64", 0)) + a2 = SparseArray(a2, dtype=pd.SparseDtype("int64", 0)) + + name = "name" # op(pd.Series, array) preserves the name. + series = pd.Series(a1, name=name) + other = a2 + + array_args = (a1, a2) + series_args = (series, other) # ufunc(series, array) + + if flip: + array_args = reversed(array_args) + series_args = reversed(series_args) # ufunc(array, series) + + expected = pd.Series(ufunc(*array_args), name=name) + result = ufunc(*series_args) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("flip", [True, False], ids=["flipped", "straight"]) +def test_binary_ufunc_with_index(flip, sparse, ufunc, arrays_for_binary_ufunc): + # Test that + # * func(pd.Series(a), pd.Series(b)) == pd.Series(ufunc(a, b)) + # * ufunc(Index, pd.Series) dispatches to pd.Series (returns a pd.Series) + a1, a2 = arrays_for_binary_ufunc + if sparse: + a1 = SparseArray(a1, dtype=pd.SparseDtype("int64", 0)) + a2 = SparseArray(a2, dtype=pd.SparseDtype("int64", 0)) + + name = "name" # op(pd.Series, array) preserves the name. + series = pd.Series(a1, name=name) + + other = pd.Index(a2, name=name).astype("int64") + + array_args = (a1, a2) + series_args = (series, other) # ufunc(series, array) + + if flip: + array_args = reversed(array_args) + series_args = reversed(series_args) # ufunc(array, series) + + expected = pd.Series(ufunc(*array_args), name=name) + result = ufunc(*series_args) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("shuffle", [True, False], ids=["unaligned", "aligned"]) +@pytest.mark.parametrize("flip", [True, False], ids=["flipped", "straight"]) +def test_binary_ufunc_with_series( + flip, shuffle, sparse, ufunc, arrays_for_binary_ufunc +): + # Test that + # * func(pd.Series(a), pd.Series(b)) == pd.Series(ufunc(a, b)) + # with alignment between the indices + a1, a2 = arrays_for_binary_ufunc + if sparse: + a1 = SparseArray(a1, dtype=pd.SparseDtype("int64", 0)) + a2 = SparseArray(a2, dtype=pd.SparseDtype("int64", 0)) + + name = "name" # op(pd.Series, array) preserves the name. + series = pd.Series(a1, name=name) + other = pd.Series(a2, name=name) + + idx = np.random.default_rng(2).permutation(len(a1)) + + if shuffle: + other = other.take(idx) + if flip: + index = other.align(series)[0].index + else: + index = series.align(other)[0].index + else: + index = series.index + + array_args = (a1, a2) + series_args = (series, other) # ufunc(series, array) + + if flip: + array_args = tuple(reversed(array_args)) + series_args = tuple(reversed(series_args)) # ufunc(array, series) + + expected = pd.Series(ufunc(*array_args), index=index, name=name) + result = ufunc(*series_args) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("flip", [True, False]) +def test_binary_ufunc_scalar(ufunc, sparse, flip, arrays_for_binary_ufunc): + # Test that + # * ufunc(pd.Series, scalar) == pd.Series(ufunc(array, scalar)) + # * ufunc(pd.Series, scalar) == ufunc(scalar, pd.Series) + arr, _ = arrays_for_binary_ufunc + if sparse: + arr = SparseArray(arr) + other = 2 + series = pd.Series(arr, name="name") + + series_args = (series, other) + array_args = (arr, other) + + if flip: + series_args = tuple(reversed(series_args)) + array_args = tuple(reversed(array_args)) + + expected = pd.Series(ufunc(*array_args), name="name") + result = ufunc(*series_args) + + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("ufunc", [np.divmod]) # TODO: np.modf, np.frexp +@pytest.mark.parametrize("shuffle", [True, False]) +@pytest.mark.filterwarnings("ignore:divide by zero:RuntimeWarning") +def test_multiple_output_binary_ufuncs(ufunc, sparse, shuffle, arrays_for_binary_ufunc): + # Test that + # the same conditions from binary_ufunc_scalar apply to + # ufuncs with multiple outputs. + + a1, a2 = arrays_for_binary_ufunc + # work around https://github.com/pandas-dev/pandas/issues/26987 + a1[a1 == 0] = 1 + a2[a2 == 0] = 1 + + if sparse: + a1 = SparseArray(a1, dtype=pd.SparseDtype("int64", 0)) + a2 = SparseArray(a2, dtype=pd.SparseDtype("int64", 0)) + + s1 = pd.Series(a1) + s2 = pd.Series(a2) + + if shuffle: + # ensure we align before applying the ufunc + s2 = s2.sample(frac=1) + + expected = ufunc(a1, a2) + assert isinstance(expected, tuple) + + result = ufunc(s1, s2) + assert isinstance(result, tuple) + tm.assert_series_equal(result[0], pd.Series(expected[0])) + tm.assert_series_equal(result[1], pd.Series(expected[1])) + + +def test_multiple_output_ufunc(sparse, arrays_for_binary_ufunc): + # Test that the same conditions from unary input apply to multi-output + # ufuncs + arr, _ = arrays_for_binary_ufunc + + if sparse: + arr = SparseArray(arr) + + series = pd.Series(arr, name="name") + result = np.modf(series) + expected = np.modf(arr) + + assert isinstance(result, tuple) + assert isinstance(expected, tuple) + + tm.assert_series_equal(result[0], pd.Series(expected[0], name="name")) + tm.assert_series_equal(result[1], pd.Series(expected[1], name="name")) + + +def test_binary_ufunc_drops_series_name(ufunc, sparse, arrays_for_binary_ufunc): + # Drop the names when they differ. + a1, a2 = arrays_for_binary_ufunc + s1 = pd.Series(a1, name="a") + s2 = pd.Series(a2, name="b") + + result = ufunc(s1, s2) + assert result.name is None + + +def test_object_series_ok(): + class Dummy: + def __init__(self, value) -> None: + self.value = value + + def __add__(self, other): + return self.value + other.value + + arr = np.array([Dummy(0), Dummy(1)]) + ser = pd.Series(arr) + tm.assert_series_equal(np.add(ser, ser), pd.Series(np.add(ser, arr))) + tm.assert_series_equal(np.add(ser, Dummy(1)), pd.Series(np.add(ser, Dummy(1)))) + + +@pytest.fixture( + params=[ + pd.array([1, 3, 2], dtype=np.int64), + pd.array([1, 3, 2], dtype="Int64"), + pd.array([1, 3, 2], dtype="Float32"), + pd.array([1, 10, 2], dtype="Sparse[int]"), + pd.to_datetime(["2000", "2010", "2001"]), + pd.to_datetime(["2000", "2010", "2001"]).tz_localize("CET"), + pd.to_datetime(["2000", "2010", "2001"]).to_period(freq="D"), + pd.to_timedelta(["1 Day", "3 Days", "2 Days"]), + pd.IntervalIndex([pd.Interval(0, 1), pd.Interval(2, 3), pd.Interval(1, 2)]), + ], + ids=lambda x: str(x.dtype), +) +def values_for_np_reduce(request): + # min/max tests assume that these are monotonic increasing + return request.param + + +class TestNumpyReductions: + # TODO: cases with NAs, axis kwarg for DataFrame + + def test_multiply(self, values_for_np_reduce, box_with_array, request): + box = box_with_array + values = values_for_np_reduce + + with tm.assert_produces_warning(None): + obj = box(values) + + if isinstance(values, pd.core.arrays.SparseArray): + mark = pytest.mark.xfail(reason="SparseArray has no 'prod'") + request.applymarker(mark) + + if values.dtype.kind in "iuf": + result = np.multiply.reduce(obj) + if box is pd.DataFrame: + expected = obj.prod(numeric_only=False) + tm.assert_series_equal(result, expected) + elif box is pd.Index: + # Index has no 'prod' + expected = obj._values.prod() + assert result == expected + else: + expected = obj.prod() + assert result == expected + else: + msg = "|".join( + [ + "does not support reduction", + "unsupported operand type", + "ufunc 'multiply' cannot use operands", + ] + ) + with pytest.raises(TypeError, match=msg): + np.multiply.reduce(obj) + + def test_add(self, values_for_np_reduce, box_with_array): + box = box_with_array + values = values_for_np_reduce + + with tm.assert_produces_warning(None): + obj = box(values) + + if values.dtype.kind in "miuf": + result = np.add.reduce(obj) + if box is pd.DataFrame: + expected = obj.sum(numeric_only=False) + tm.assert_series_equal(result, expected) + elif box is pd.Index: + # Index has no 'sum' + expected = obj._values.sum() + assert result == expected + else: + expected = obj.sum() + assert result == expected + else: + msg = "|".join( + [ + "does not support reduction", + "unsupported operand type", + "ufunc 'add' cannot use operands", + ] + ) + with pytest.raises(TypeError, match=msg): + np.add.reduce(obj) + + def test_max(self, values_for_np_reduce, box_with_array): + box = box_with_array + values = values_for_np_reduce + + same_type = True + if box is pd.Index and values.dtype.kind in ["i", "f"]: + # ATM Index casts to object, so we get python ints/floats + same_type = False + + with tm.assert_produces_warning(None): + obj = box(values) + + result = np.maximum.reduce(obj) + if box is pd.DataFrame: + # TODO: cases with axis kwarg + expected = obj.max(numeric_only=False) + tm.assert_series_equal(result, expected) + else: + expected = values[1] + assert result == expected + if same_type: + # check we have e.g. Timestamp instead of dt64 + assert type(result) == type(expected) + + def test_min(self, values_for_np_reduce, box_with_array): + box = box_with_array + values = values_for_np_reduce + + same_type = True + if box is pd.Index and values.dtype.kind in ["i", "f"]: + # ATM Index casts to object, so we get python ints/floats + same_type = False + + with tm.assert_produces_warning(None): + obj = box(values) + + result = np.minimum.reduce(obj) + if box is pd.DataFrame: + expected = obj.min(numeric_only=False) + tm.assert_series_equal(result, expected) + else: + expected = values[0] + assert result == expected + if same_type: + # check we have e.g. Timestamp instead of dt64 + assert type(result) == type(expected) + + +@pytest.mark.parametrize("type_", [list, deque, tuple]) +def test_binary_ufunc_other_types(type_): + a = pd.Series([1, 2, 3], name="name") + b = type_([3, 4, 5]) + + result = np.add(a, b) + expected = pd.Series(np.add(a.to_numpy(), b), name="name") + tm.assert_series_equal(result, expected) + + +def test_object_dtype_ok(): + class Thing: + def __init__(self, value) -> None: + self.value = value + + def __add__(self, other): + other = getattr(other, "value", other) + return type(self)(self.value + other) + + def __eq__(self, other) -> bool: + return type(other) is Thing and self.value == other.value + + def __repr__(self) -> str: + return f"Thing({self.value})" + + s = pd.Series([Thing(1), Thing(2)]) + result = np.add(s, Thing(1)) + expected = pd.Series([Thing(2), Thing(3)]) + tm.assert_series_equal(result, expected) + + +def test_outer(): + # https://github.com/pandas-dev/pandas/issues/27186 + ser = pd.Series([1, 2, 3]) + obj = np.array([1, 2, 3]) + + with pytest.raises(NotImplementedError, match=""): + np.subtract.outer(ser, obj) + + +def test_np_matmul(): + # GH26650 + df1 = pd.DataFrame(data=[[-1, 1, 10]]) + df2 = pd.DataFrame(data=[-1, 1, 10]) + expected = pd.DataFrame(data=[102]) + + result = np.matmul(df1, df2) + tm.assert_frame_equal(expected, result) + + +def test_array_ufuncs_for_many_arguments(): + # GH39853 + def add3(x, y, z): + return x + y + z + + ufunc = np.frompyfunc(add3, 3, 1) + ser = pd.Series([1, 2]) + + result = ufunc(ser, ser, 1) + expected = pd.Series([3, 5], dtype=object) + tm.assert_series_equal(result, expected) + + df = pd.DataFrame([[1, 2]]) + + msg = ( + "Cannot apply ufunc " + "to mixed DataFrame and Series inputs." + ) + with pytest.raises(NotImplementedError, match=re.escape(msg)): + ufunc(ser, ser, df) + + +# TODO(CoW) see https://github.com/pandas-dev/pandas/pull/51082 +@td.skip_copy_on_write_not_yet_implemented +def test_np_fix(): + # np.fix is not a ufunc but is composed of several ufunc calls under the hood + # with `out` and `where` keywords + ser = pd.Series([-1.5, -0.5, 0.5, 1.5]) + result = np.fix(ser) + expected = pd.Series([-1.0, -0.0, 0.0, 1.0]) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_unary.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_unary.py new file mode 100644 index 0000000000000000000000000000000000000000..8f153788e413c5e9198dc35867bc628823555dbf --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_unary.py @@ -0,0 +1,50 @@ +import pytest + +from pandas import Series +import pandas._testing as tm + + +class TestSeriesUnaryOps: + # __neg__, __pos__, __invert__ + + def test_neg(self): + ser = Series(range(5), dtype="float64", name="series") + tm.assert_series_equal(-ser, -1 * ser) + + def test_invert(self): + ser = Series(range(5), dtype="float64", name="series") + tm.assert_series_equal(-(ser < 0), ~(ser < 0)) + + @pytest.mark.parametrize( + "source, neg_target, abs_target", + [ + ([1, 2, 3], [-1, -2, -3], [1, 2, 3]), + ([1, 2, None], [-1, -2, None], [1, 2, None]), + ], + ) + def test_all_numeric_unary_operators( + self, any_numeric_ea_dtype, source, neg_target, abs_target + ): + # GH38794 + dtype = any_numeric_ea_dtype + ser = Series(source, dtype=dtype) + neg_result, pos_result, abs_result = -ser, +ser, abs(ser) + if dtype.startswith("U"): + neg_target = -Series(source, dtype=dtype) + else: + neg_target = Series(neg_target, dtype=dtype) + + abs_target = Series(abs_target, dtype=dtype) + + tm.assert_series_equal(neg_result, neg_target) + tm.assert_series_equal(pos_result, ser) + tm.assert_series_equal(abs_result, abs_target) + + @pytest.mark.parametrize("op", ["__neg__", "__abs__"]) + def test_unary_float_op_mask(self, float_ea_dtype, op): + dtype = float_ea_dtype + ser = Series([1.1, 2.2, 3.3], dtype=dtype) + result = getattr(ser, op)() + target = result.copy(deep=True) + ser[0] = None + tm.assert_series_equal(result, target) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_validate.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_validate.py new file mode 100644 index 0000000000000000000000000000000000000000..3c867f7582b7d3250bf5e009ffbf7545da404712 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/series/test_validate.py @@ -0,0 +1,26 @@ +import pytest + + +@pytest.mark.parametrize( + "func", + [ + "reset_index", + "_set_name", + "sort_values", + "sort_index", + "rename", + "dropna", + "drop_duplicates", + ], +) +@pytest.mark.parametrize("inplace", [1, "True", [1, 2, 3], 5.0]) +def test_validate_bool_args(string_series, func, inplace): + """Tests for error handling related to data types of method arguments.""" + msg = 'For argument "inplace" expected type bool' + kwargs = {"inplace": inplace} + + if func == "_set_name": + kwargs["name"] = "hello" + + with pytest.raises(ValueError, match=msg): + getattr(string_series, func)(**kwargs) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_array_to_datetime.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_array_to_datetime.py new file mode 100644 index 0000000000000000000000000000000000000000..82175c67764f84355dfb6d10a0477e9e0344e296 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_array_to_datetime.py @@ -0,0 +1,337 @@ +from datetime import ( + date, + datetime, + timedelta, + timezone, +) + +from dateutil.tz.tz import tzoffset +import numpy as np +import pytest + +from pandas._libs import ( + NaT, + iNaT, + tslib, +) +from pandas._libs.tslibs.dtypes import NpyDatetimeUnit +from pandas._libs.tslibs.np_datetime import OutOfBoundsDatetime + +from pandas import Timestamp +import pandas._testing as tm + +creso_infer = NpyDatetimeUnit.NPY_FR_GENERIC.value + + +class TestArrayToDatetimeResolutionInference: + # TODO: tests that include tzs, ints + + def test_infer_all_nat(self): + arr = np.array([NaT, np.nan], dtype=object) + result, tz = tslib.array_to_datetime(arr, creso=creso_infer) + assert tz is None + assert result.dtype == "M8[s]" + + def test_infer_homogeoneous_datetimes(self): + dt = datetime(2023, 10, 27, 18, 3, 5, 678000) + arr = np.array([dt, dt, dt], dtype=object) + result, tz = tslib.array_to_datetime(arr, creso=creso_infer) + assert tz is None + expected = np.array([dt, dt, dt], dtype="M8[us]") + tm.assert_numpy_array_equal(result, expected) + + def test_infer_homogeoneous_date_objects(self): + dt = datetime(2023, 10, 27, 18, 3, 5, 678000) + dt2 = dt.date() + arr = np.array([None, dt2, dt2, dt2], dtype=object) + result, tz = tslib.array_to_datetime(arr, creso=creso_infer) + assert tz is None + expected = np.array([np.datetime64("NaT"), dt2, dt2, dt2], dtype="M8[s]") + tm.assert_numpy_array_equal(result, expected) + + def test_infer_homogeoneous_dt64(self): + dt = datetime(2023, 10, 27, 18, 3, 5, 678000) + dt64 = np.datetime64(dt, "ms") + arr = np.array([None, dt64, dt64, dt64], dtype=object) + result, tz = tslib.array_to_datetime(arr, creso=creso_infer) + assert tz is None + expected = np.array([np.datetime64("NaT"), dt64, dt64, dt64], dtype="M8[ms]") + tm.assert_numpy_array_equal(result, expected) + + def test_infer_homogeoneous_timestamps(self): + dt = datetime(2023, 10, 27, 18, 3, 5, 678000) + ts = Timestamp(dt).as_unit("ns") + arr = np.array([None, ts, ts, ts], dtype=object) + result, tz = tslib.array_to_datetime(arr, creso=creso_infer) + assert tz is None + expected = np.array([np.datetime64("NaT")] + [ts.asm8] * 3, dtype="M8[ns]") + tm.assert_numpy_array_equal(result, expected) + + def test_infer_homogeoneous_datetimes_strings(self): + item = "2023-10-27 18:03:05.678000" + arr = np.array([None, item, item, item], dtype=object) + result, tz = tslib.array_to_datetime(arr, creso=creso_infer) + assert tz is None + expected = np.array([np.datetime64("NaT"), item, item, item], dtype="M8[us]") + tm.assert_numpy_array_equal(result, expected) + + def test_infer_heterogeneous(self): + dtstr = "2023-10-27 18:03:05.678000" + + arr = np.array([dtstr, dtstr[:-3], dtstr[:-7], None], dtype=object) + result, tz = tslib.array_to_datetime(arr, creso=creso_infer) + assert tz is None + expected = np.array(arr, dtype="M8[us]") + tm.assert_numpy_array_equal(result, expected) + + result, tz = tslib.array_to_datetime(arr[::-1], creso=creso_infer) + assert tz is None + tm.assert_numpy_array_equal(result, expected[::-1]) + + @pytest.mark.parametrize( + "item", [float("nan"), NaT.value, float(NaT.value), "NaT", ""] + ) + def test_infer_with_nat_int_float_str(self, item): + # floats/ints get inferred to nanos *unless* they are NaN/iNaT, + # similar NaT string gets treated like NaT scalar (ignored for resolution) + dt = datetime(2023, 11, 15, 15, 5, 6) + + arr = np.array([dt, item], dtype=object) + result, tz = tslib.array_to_datetime(arr, creso=creso_infer) + assert tz is None + expected = np.array([dt, np.datetime64("NaT")], dtype="M8[us]") + tm.assert_numpy_array_equal(result, expected) + + result2, tz2 = tslib.array_to_datetime(arr[::-1], creso=creso_infer) + assert tz2 is None + tm.assert_numpy_array_equal(result2, expected[::-1]) + + +class TestArrayToDatetimeWithTZResolutionInference: + def test_array_to_datetime_with_tz_resolution(self): + tz = tzoffset("custom", 3600) + vals = np.array(["2016-01-01 02:03:04.567", NaT], dtype=object) + res = tslib.array_to_datetime_with_tz(vals, tz, False, False, creso_infer) + assert res.dtype == "M8[ms]" + + vals2 = np.array([datetime(2016, 1, 1, 2, 3, 4), NaT], dtype=object) + res2 = tslib.array_to_datetime_with_tz(vals2, tz, False, False, creso_infer) + assert res2.dtype == "M8[us]" + + vals3 = np.array([NaT, np.datetime64(12345, "s")], dtype=object) + res3 = tslib.array_to_datetime_with_tz(vals3, tz, False, False, creso_infer) + assert res3.dtype == "M8[s]" + + def test_array_to_datetime_with_tz_resolution_all_nat(self): + tz = tzoffset("custom", 3600) + vals = np.array(["NaT"], dtype=object) + res = tslib.array_to_datetime_with_tz(vals, tz, False, False, creso_infer) + assert res.dtype == "M8[s]" + + vals2 = np.array([NaT, NaT], dtype=object) + res2 = tslib.array_to_datetime_with_tz(vals2, tz, False, False, creso_infer) + assert res2.dtype == "M8[s]" + + +@pytest.mark.parametrize( + "data,expected", + [ + ( + ["01-01-2013", "01-02-2013"], + [ + "2013-01-01T00:00:00.000000000", + "2013-01-02T00:00:00.000000000", + ], + ), + ( + ["Mon Sep 16 2013", "Tue Sep 17 2013"], + [ + "2013-09-16T00:00:00.000000000", + "2013-09-17T00:00:00.000000000", + ], + ), + ], +) +def test_parsing_valid_dates(data, expected): + arr = np.array(data, dtype=object) + result, _ = tslib.array_to_datetime(arr) + + expected = np.array(expected, dtype="M8[ns]") + tm.assert_numpy_array_equal(result, expected) + + +@pytest.mark.parametrize( + "dt_string, expected_tz", + [ + ["01-01-2013 08:00:00+08:00", 480], + ["2013-01-01T08:00:00.000000000+0800", 480], + ["2012-12-31T16:00:00.000000000-0800", -480], + ["12-31-2012 23:00:00-01:00", -60], + ], +) +def test_parsing_timezone_offsets(dt_string, expected_tz): + # All of these datetime strings with offsets are equivalent + # to the same datetime after the timezone offset is added. + arr = np.array(["01-01-2013 00:00:00"], dtype=object) + expected, _ = tslib.array_to_datetime(arr) + + arr = np.array([dt_string], dtype=object) + result, result_tz = tslib.array_to_datetime(arr) + + tm.assert_numpy_array_equal(result, expected) + assert result_tz == timezone(timedelta(minutes=expected_tz)) + + +def test_parsing_non_iso_timezone_offset(): + dt_string = "01-01-2013T00:00:00.000000000+0000" + arr = np.array([dt_string], dtype=object) + + with tm.assert_produces_warning(None): + # GH#50949 should not get tzlocal-deprecation warning here + result, result_tz = tslib.array_to_datetime(arr) + expected = np.array([np.datetime64("2013-01-01 00:00:00.000000000")]) + + tm.assert_numpy_array_equal(result, expected) + assert result_tz is timezone.utc + + +def test_parsing_different_timezone_offsets(): + # see gh-17697 + data = ["2015-11-18 15:30:00+05:30", "2015-11-18 15:30:00+06:30"] + data = np.array(data, dtype=object) + + msg = "parsing datetimes with mixed time zones will raise an error" + with tm.assert_produces_warning(FutureWarning, match=msg): + result, result_tz = tslib.array_to_datetime(data) + expected = np.array( + [ + datetime(2015, 11, 18, 15, 30, tzinfo=tzoffset(None, 19800)), + datetime(2015, 11, 18, 15, 30, tzinfo=tzoffset(None, 23400)), + ], + dtype=object, + ) + + tm.assert_numpy_array_equal(result, expected) + assert result_tz is None + + +@pytest.mark.parametrize( + "data", [["-352.737091", "183.575577"], ["1", "2", "3", "4", "5"]] +) +def test_number_looking_strings_not_into_datetime(data): + # see gh-4601 + # + # These strings don't look like datetimes, so + # they shouldn't be attempted to be converted. + arr = np.array(data, dtype=object) + result, _ = tslib.array_to_datetime(arr, errors="ignore") + + tm.assert_numpy_array_equal(result, arr) + + +@pytest.mark.parametrize( + "invalid_date", + [ + date(1000, 1, 1), + datetime(1000, 1, 1), + "1000-01-01", + "Jan 1, 1000", + np.datetime64("1000-01-01"), + ], +) +@pytest.mark.parametrize("errors", ["coerce", "raise"]) +def test_coerce_outside_ns_bounds(invalid_date, errors): + arr = np.array([invalid_date], dtype="object") + kwargs = {"values": arr, "errors": errors} + + if errors == "raise": + msg = "^Out of bounds nanosecond timestamp: .*, at position 0$" + + with pytest.raises(OutOfBoundsDatetime, match=msg): + tslib.array_to_datetime(**kwargs) + else: # coerce. + result, _ = tslib.array_to_datetime(**kwargs) + expected = np.array([iNaT], dtype="M8[ns]") + + tm.assert_numpy_array_equal(result, expected) + + +def test_coerce_outside_ns_bounds_one_valid(): + arr = np.array(["1/1/1000", "1/1/2000"], dtype=object) + result, _ = tslib.array_to_datetime(arr, errors="coerce") + + expected = [iNaT, "2000-01-01T00:00:00.000000000"] + expected = np.array(expected, dtype="M8[ns]") + + tm.assert_numpy_array_equal(result, expected) + + +@pytest.mark.parametrize("errors", ["ignore", "coerce"]) +def test_coerce_of_invalid_datetimes(errors): + arr = np.array(["01-01-2013", "not_a_date", "1"], dtype=object) + kwargs = {"values": arr, "errors": errors} + + if errors == "ignore": + # Without coercing, the presence of any invalid + # dates prevents any values from being converted. + result, _ = tslib.array_to_datetime(**kwargs) + tm.assert_numpy_array_equal(result, arr) + else: # coerce. + # With coercing, the invalid dates becomes iNaT + result, _ = tslib.array_to_datetime(arr, errors="coerce") + expected = ["2013-01-01T00:00:00.000000000", iNaT, iNaT] + + tm.assert_numpy_array_equal(result, np.array(expected, dtype="M8[ns]")) + + +def test_to_datetime_barely_out_of_bounds(): + # see gh-19382, gh-19529 + # + # Close enough to bounds that dropping nanos + # would result in an in-bounds datetime. + arr = np.array(["2262-04-11 23:47:16.854775808"], dtype=object) + msg = "^Out of bounds nanosecond timestamp: 2262-04-11 23:47:16, at position 0$" + + with pytest.raises(tslib.OutOfBoundsDatetime, match=msg): + tslib.array_to_datetime(arr) + + +@pytest.mark.parametrize( + "timestamp", + [ + # Close enough to bounds that scaling micros to nanos overflows + # but adding nanos would result in an in-bounds datetime. + "1677-09-21T00:12:43.145224193", + "1677-09-21T00:12:43.145224999", + # this always worked + "1677-09-21T00:12:43.145225000", + ], +) +def test_to_datetime_barely_inside_bounds(timestamp): + # see gh-57150 + result, _ = tslib.array_to_datetime(np.array([timestamp], dtype=object)) + tm.assert_numpy_array_equal(result, np.array([timestamp], dtype="M8[ns]")) + + +class SubDatetime(datetime): + pass + + +@pytest.mark.parametrize( + "data,expected", + [ + ([SubDatetime(2000, 1, 1)], ["2000-01-01T00:00:00.000000000"]), + ([datetime(2000, 1, 1)], ["2000-01-01T00:00:00.000000000"]), + ([Timestamp(2000, 1, 1)], ["2000-01-01T00:00:00.000000000"]), + ], +) +def test_datetime_subclass(data, expected): + # GH 25851 + # ensure that subclassed datetime works with + # array_to_datetime + + arr = np.array(data, dtype=object) + result, _ = tslib.array_to_datetime(arr) + + expected = np.array(expected, dtype="M8[ns]") + tm.assert_numpy_array_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_ccalendar.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_ccalendar.py new file mode 100644 index 0000000000000000000000000000000000000000..8dd1bd47e4728d1b35e84b14f29e0a255178ec9b --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_ccalendar.py @@ -0,0 +1,63 @@ +from datetime import ( + date, + datetime, +) + +from hypothesis import given +import numpy as np +import pytest + +from pandas._libs.tslibs import ccalendar + +from pandas._testing._hypothesis import DATETIME_IN_PD_TIMESTAMP_RANGE_NO_TZ + + +@pytest.mark.parametrize( + "date_tuple,expected", + [ + ((2001, 3, 1), 60), + ((2004, 3, 1), 61), + ((1907, 12, 31), 365), # End-of-year, non-leap year. + ((2004, 12, 31), 366), # End-of-year, leap year. + ], +) +def test_get_day_of_year_numeric(date_tuple, expected): + assert ccalendar.get_day_of_year(*date_tuple) == expected + + +def test_get_day_of_year_dt(): + dt = datetime.fromordinal(1 + np.random.default_rng(2).integers(365 * 4000)) + result = ccalendar.get_day_of_year(dt.year, dt.month, dt.day) + + expected = (dt - dt.replace(month=1, day=1)).days + 1 + assert result == expected + + +@pytest.mark.parametrize( + "input_date_tuple, expected_iso_tuple", + [ + [(2020, 1, 1), (2020, 1, 3)], + [(2019, 12, 31), (2020, 1, 2)], + [(2019, 12, 30), (2020, 1, 1)], + [(2009, 12, 31), (2009, 53, 4)], + [(2010, 1, 1), (2009, 53, 5)], + [(2010, 1, 3), (2009, 53, 7)], + [(2010, 1, 4), (2010, 1, 1)], + [(2006, 1, 1), (2005, 52, 7)], + [(2005, 12, 31), (2005, 52, 6)], + [(2008, 12, 28), (2008, 52, 7)], + [(2008, 12, 29), (2009, 1, 1)], + ], +) +def test_dt_correct_iso_8601_year_week_and_day(input_date_tuple, expected_iso_tuple): + result = ccalendar.get_iso_calendar(*input_date_tuple) + expected_from_date_isocalendar = date(*input_date_tuple).isocalendar() + assert result == expected_from_date_isocalendar + assert result == expected_iso_tuple + + +@given(DATETIME_IN_PD_TIMESTAMP_RANGE_NO_TZ) +def test_isocalendar(dt): + expected = dt.isocalendar() + result = ccalendar.get_iso_calendar(dt.year, dt.month, dt.day) + assert result == expected diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_conversion.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_conversion.py new file mode 100644 index 0000000000000000000000000000000000000000..9d7a5e906c3c3771a7d909f0ded9397e5cee0d64 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_conversion.py @@ -0,0 +1,160 @@ +from datetime import datetime + +import numpy as np +import pytest +from pytz import UTC + +from pandas._libs.tslibs import ( + OutOfBoundsTimedelta, + astype_overflowsafe, + conversion, + iNaT, + timezones, + tz_convert_from_utc, + tzconversion, +) + +from pandas import ( + Timestamp, + date_range, +) +import pandas._testing as tm + + +def _compare_utc_to_local(tz_didx): + def f(x): + return tzconversion.tz_convert_from_utc_single(x, tz_didx.tz) + + result = tz_convert_from_utc(tz_didx.asi8, tz_didx.tz) + expected = np.vectorize(f)(tz_didx.asi8) + + tm.assert_numpy_array_equal(result, expected) + + +def _compare_local_to_utc(tz_didx, naive_didx): + # Check that tz_localize behaves the same vectorized and pointwise. + err1 = err2 = None + try: + result = tzconversion.tz_localize_to_utc(naive_didx.asi8, tz_didx.tz) + err1 = None + except Exception as err: + err1 = err + + try: + expected = naive_didx.map(lambda x: x.tz_localize(tz_didx.tz)).asi8 + except Exception as err: + err2 = err + + if err1 is not None: + assert type(err1) == type(err2) + else: + assert err2 is None + tm.assert_numpy_array_equal(result, expected) + + +def test_tz_localize_to_utc_copies(): + # GH#46460 + arr = np.arange(5, dtype="i8") + result = tz_convert_from_utc(arr, tz=UTC) + tm.assert_numpy_array_equal(result, arr) + assert not np.shares_memory(arr, result) + + result = tz_convert_from_utc(arr, tz=None) + tm.assert_numpy_array_equal(result, arr) + assert not np.shares_memory(arr, result) + + +def test_tz_convert_single_matches_tz_convert_hourly(tz_aware_fixture): + tz = tz_aware_fixture + tz_didx = date_range("2014-03-01", "2015-01-10", freq="h", tz=tz) + naive_didx = date_range("2014-03-01", "2015-01-10", freq="h") + + _compare_utc_to_local(tz_didx) + _compare_local_to_utc(tz_didx, naive_didx) + + +@pytest.mark.parametrize("freq", ["D", "YE"]) +def test_tz_convert_single_matches_tz_convert(tz_aware_fixture, freq): + tz = tz_aware_fixture + tz_didx = date_range("2018-01-01", "2020-01-01", freq=freq, tz=tz) + naive_didx = date_range("2018-01-01", "2020-01-01", freq=freq) + + _compare_utc_to_local(tz_didx) + _compare_local_to_utc(tz_didx, naive_didx) + + +@pytest.mark.parametrize( + "arr", + [ + pytest.param(np.array([], dtype=np.int64), id="empty"), + pytest.param(np.array([iNaT], dtype=np.int64), id="all_nat"), + ], +) +def test_tz_convert_corner(arr): + result = tz_convert_from_utc(arr, timezones.maybe_get_tz("Asia/Tokyo")) + tm.assert_numpy_array_equal(result, arr) + + +def test_tz_convert_readonly(): + # GH#35530 + arr = np.array([0], dtype=np.int64) + arr.setflags(write=False) + result = tz_convert_from_utc(arr, UTC) + tm.assert_numpy_array_equal(result, arr) + + +@pytest.mark.parametrize("copy", [True, False]) +@pytest.mark.parametrize("dtype", ["M8[ns]", "M8[s]"]) +def test_length_zero_copy(dtype, copy): + arr = np.array([], dtype=dtype) + result = astype_overflowsafe(arr, copy=copy, dtype=np.dtype("M8[ns]")) + if copy: + assert not np.shares_memory(result, arr) + elif arr.dtype == result.dtype: + assert result is arr + else: + assert not np.shares_memory(result, arr) + + +def test_ensure_datetime64ns_bigendian(): + # GH#29684 + arr = np.array([np.datetime64(1, "ms")], dtype=">M8[ms]") + result = astype_overflowsafe(arr, dtype=np.dtype("M8[ns]")) + + expected = np.array([np.datetime64(1, "ms")], dtype="M8[ns]") + tm.assert_numpy_array_equal(result, expected) + + +def test_ensure_timedelta64ns_overflows(): + arr = np.arange(10).astype("m8[Y]") * 100 + msg = r"Cannot convert 300 years to timedelta64\[ns\] without overflow" + with pytest.raises(OutOfBoundsTimedelta, match=msg): + astype_overflowsafe(arr, dtype=np.dtype("m8[ns]")) + + +class SubDatetime(datetime): + pass + + +@pytest.mark.parametrize( + "dt, expected", + [ + pytest.param( + Timestamp("2000-01-01"), Timestamp("2000-01-01", tz=UTC), id="timestamp" + ), + pytest.param( + datetime(2000, 1, 1), datetime(2000, 1, 1, tzinfo=UTC), id="datetime" + ), + pytest.param( + SubDatetime(2000, 1, 1), + SubDatetime(2000, 1, 1, tzinfo=UTC), + id="subclassed_datetime", + ), + ], +) +def test_localize_pydatetime_dt_types(dt, expected): + # GH 25851 + # ensure that subclassed datetime works with + # localize_pydatetime + result = conversion.localize_pydatetime(dt, UTC) + assert result == expected diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_liboffsets.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_liboffsets.py new file mode 100644 index 0000000000000000000000000000000000000000..c189a431146a7172862586ea3a015ad4f2676cf2 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_liboffsets.py @@ -0,0 +1,173 @@ +""" +Tests for helper functions in the cython tslibs.offsets +""" +from datetime import datetime + +import pytest + +from pandas._libs.tslibs.ccalendar import ( + get_firstbday, + get_lastbday, +) +import pandas._libs.tslibs.offsets as liboffsets +from pandas._libs.tslibs.offsets import roll_qtrday + +from pandas import Timestamp + + +@pytest.fixture(params=["start", "end", "business_start", "business_end"]) +def day_opt(request): + return request.param + + +@pytest.mark.parametrize( + "dt,exp_week_day,exp_last_day", + [ + (datetime(2017, 11, 30), 3, 30), # Business day. + (datetime(1993, 10, 31), 6, 29), # Non-business day. + ], +) +def test_get_last_bday(dt, exp_week_day, exp_last_day): + assert dt.weekday() == exp_week_day + assert get_lastbday(dt.year, dt.month) == exp_last_day + + +@pytest.mark.parametrize( + "dt,exp_week_day,exp_first_day", + [ + (datetime(2017, 4, 1), 5, 3), # Non-weekday. + (datetime(1993, 10, 1), 4, 1), # Business day. + ], +) +def test_get_first_bday(dt, exp_week_day, exp_first_day): + assert dt.weekday() == exp_week_day + assert get_firstbday(dt.year, dt.month) == exp_first_day + + +@pytest.mark.parametrize( + "months,day_opt,expected", + [ + (0, 15, datetime(2017, 11, 15)), + (0, None, datetime(2017, 11, 30)), + (1, "start", datetime(2017, 12, 1)), + (-145, "end", datetime(2005, 10, 31)), + (0, "business_end", datetime(2017, 11, 30)), + (0, "business_start", datetime(2017, 11, 1)), + ], +) +def test_shift_month_dt(months, day_opt, expected): + dt = datetime(2017, 11, 30) + assert liboffsets.shift_month(dt, months, day_opt=day_opt) == expected + + +@pytest.mark.parametrize( + "months,day_opt,expected", + [ + (1, "start", Timestamp("1929-06-01")), + (-3, "end", Timestamp("1929-02-28")), + (25, None, Timestamp("1931-06-5")), + (-1, 31, Timestamp("1929-04-30")), + ], +) +def test_shift_month_ts(months, day_opt, expected): + ts = Timestamp("1929-05-05") + assert liboffsets.shift_month(ts, months, day_opt=day_opt) == expected + + +def test_shift_month_error(): + dt = datetime(2017, 11, 15) + day_opt = "this should raise" + + with pytest.raises(ValueError, match=day_opt): + liboffsets.shift_month(dt, 3, day_opt=day_opt) + + +@pytest.mark.parametrize( + "other,expected", + [ + # Before March 1. + (datetime(2017, 2, 10), {2: 1, -7: -7, 0: 0}), + # After March 1. + (Timestamp("2014-03-15", tz="US/Eastern"), {2: 2, -7: -6, 0: 1}), + ], +) +@pytest.mark.parametrize("n", [2, -7, 0]) +def test_roll_qtrday_year(other, expected, n): + month = 3 + day_opt = "start" # `other` will be compared to March 1. + + assert roll_qtrday(other, n, month, day_opt, modby=12) == expected[n] + + +@pytest.mark.parametrize( + "other,expected", + [ + # Before June 30. + (datetime(1999, 6, 29), {5: 4, -7: -7, 0: 0}), + # After June 30. + (Timestamp(2072, 8, 24, 6, 17, 18), {5: 5, -7: -6, 0: 1}), + ], +) +@pytest.mark.parametrize("n", [5, -7, 0]) +def test_roll_qtrday_year2(other, expected, n): + month = 6 + day_opt = "end" # `other` will be compared to June 30. + + assert roll_qtrday(other, n, month, day_opt, modby=12) == expected[n] + + +def test_get_day_of_month_error(): + # get_day_of_month is not directly exposed. + # We test it via roll_qtrday. + dt = datetime(2017, 11, 15) + day_opt = "foo" + + with pytest.raises(ValueError, match=day_opt): + # To hit the raising case we need month == dt.month and n > 0. + roll_qtrday(dt, n=3, month=11, day_opt=day_opt, modby=12) + + +@pytest.mark.parametrize( + "month", + [3, 5], # (other.month % 3) < (month % 3) # (other.month % 3) > (month % 3) +) +@pytest.mark.parametrize("n", [4, -3]) +def test_roll_qtr_day_not_mod_unequal(day_opt, month, n): + expected = {3: {-3: -2, 4: 4}, 5: {-3: -3, 4: 3}} + + other = Timestamp(2072, 10, 1, 6, 17, 18) # Saturday. + assert roll_qtrday(other, n, month, day_opt, modby=3) == expected[month][n] + + +@pytest.mark.parametrize( + "other,month,exp_dict", + [ + # Monday. + (datetime(1999, 5, 31), 2, {-1: {"start": 0, "business_start": 0}}), + # Saturday. + ( + Timestamp(2072, 10, 1, 6, 17, 18), + 4, + {2: {"end": 1, "business_end": 1, "business_start": 1}}, + ), + # First business day. + ( + Timestamp(2072, 10, 3, 6, 17, 18), + 4, + {2: {"end": 1, "business_end": 1}, -1: {"start": 0}}, + ), + ], +) +@pytest.mark.parametrize("n", [2, -1]) +def test_roll_qtr_day_mod_equal(other, month, exp_dict, n, day_opt): + # All cases have (other.month % 3) == (month % 3). + expected = exp_dict.get(n, {}).get(day_opt, n) + assert roll_qtrday(other, n, month, day_opt, modby=3) == expected + + +@pytest.mark.parametrize( + "n,expected", [(42, {29: 42, 1: 42, 31: 41}), (-4, {29: -4, 1: -3, 31: -4})] +) +@pytest.mark.parametrize("compare", [29, 1, 31]) +def test_roll_convention(n, expected, compare): + assert liboffsets.roll_convention(29, n, compare) == expected[compare] diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_np_datetime.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_np_datetime.py new file mode 100644 index 0000000000000000000000000000000000000000..02edf1a09387766d71097ea0baedc2640cfb824b --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_np_datetime.py @@ -0,0 +1,222 @@ +import numpy as np +import pytest + +from pandas._libs.tslibs.dtypes import NpyDatetimeUnit +from pandas._libs.tslibs.np_datetime import ( + OutOfBoundsDatetime, + OutOfBoundsTimedelta, + astype_overflowsafe, + is_unitless, + py_get_unit_from_dtype, + py_td64_to_tdstruct, +) + +import pandas._testing as tm + + +def test_is_unitless(): + dtype = np.dtype("M8[ns]") + assert not is_unitless(dtype) + + dtype = np.dtype("datetime64") + assert is_unitless(dtype) + + dtype = np.dtype("m8[ns]") + assert not is_unitless(dtype) + + dtype = np.dtype("timedelta64") + assert is_unitless(dtype) + + msg = "dtype must be datetime64 or timedelta64" + with pytest.raises(ValueError, match=msg): + is_unitless(np.dtype(np.int64)) + + msg = "Argument 'dtype' has incorrect type" + with pytest.raises(TypeError, match=msg): + is_unitless("foo") + + +def test_get_unit_from_dtype(): + # datetime64 + assert py_get_unit_from_dtype(np.dtype("M8[Y]")) == NpyDatetimeUnit.NPY_FR_Y.value + assert py_get_unit_from_dtype(np.dtype("M8[M]")) == NpyDatetimeUnit.NPY_FR_M.value + assert py_get_unit_from_dtype(np.dtype("M8[W]")) == NpyDatetimeUnit.NPY_FR_W.value + # B has been deprecated and removed -> no 3 + assert py_get_unit_from_dtype(np.dtype("M8[D]")) == NpyDatetimeUnit.NPY_FR_D.value + assert py_get_unit_from_dtype(np.dtype("M8[h]")) == NpyDatetimeUnit.NPY_FR_h.value + assert py_get_unit_from_dtype(np.dtype("M8[m]")) == NpyDatetimeUnit.NPY_FR_m.value + assert py_get_unit_from_dtype(np.dtype("M8[s]")) == NpyDatetimeUnit.NPY_FR_s.value + assert py_get_unit_from_dtype(np.dtype("M8[ms]")) == NpyDatetimeUnit.NPY_FR_ms.value + assert py_get_unit_from_dtype(np.dtype("M8[us]")) == NpyDatetimeUnit.NPY_FR_us.value + assert py_get_unit_from_dtype(np.dtype("M8[ns]")) == NpyDatetimeUnit.NPY_FR_ns.value + assert py_get_unit_from_dtype(np.dtype("M8[ps]")) == NpyDatetimeUnit.NPY_FR_ps.value + assert py_get_unit_from_dtype(np.dtype("M8[fs]")) == NpyDatetimeUnit.NPY_FR_fs.value + assert py_get_unit_from_dtype(np.dtype("M8[as]")) == NpyDatetimeUnit.NPY_FR_as.value + + # timedelta64 + assert py_get_unit_from_dtype(np.dtype("m8[Y]")) == NpyDatetimeUnit.NPY_FR_Y.value + assert py_get_unit_from_dtype(np.dtype("m8[M]")) == NpyDatetimeUnit.NPY_FR_M.value + assert py_get_unit_from_dtype(np.dtype("m8[W]")) == NpyDatetimeUnit.NPY_FR_W.value + # B has been deprecated and removed -> no 3 + assert py_get_unit_from_dtype(np.dtype("m8[D]")) == NpyDatetimeUnit.NPY_FR_D.value + assert py_get_unit_from_dtype(np.dtype("m8[h]")) == NpyDatetimeUnit.NPY_FR_h.value + assert py_get_unit_from_dtype(np.dtype("m8[m]")) == NpyDatetimeUnit.NPY_FR_m.value + assert py_get_unit_from_dtype(np.dtype("m8[s]")) == NpyDatetimeUnit.NPY_FR_s.value + assert py_get_unit_from_dtype(np.dtype("m8[ms]")) == NpyDatetimeUnit.NPY_FR_ms.value + assert py_get_unit_from_dtype(np.dtype("m8[us]")) == NpyDatetimeUnit.NPY_FR_us.value + assert py_get_unit_from_dtype(np.dtype("m8[ns]")) == NpyDatetimeUnit.NPY_FR_ns.value + assert py_get_unit_from_dtype(np.dtype("m8[ps]")) == NpyDatetimeUnit.NPY_FR_ps.value + assert py_get_unit_from_dtype(np.dtype("m8[fs]")) == NpyDatetimeUnit.NPY_FR_fs.value + assert py_get_unit_from_dtype(np.dtype("m8[as]")) == NpyDatetimeUnit.NPY_FR_as.value + + +def test_td64_to_tdstruct(): + val = 12454636234 # arbitrary value + + res1 = py_td64_to_tdstruct(val, NpyDatetimeUnit.NPY_FR_ns.value) + exp1 = { + "days": 0, + "hrs": 0, + "min": 0, + "sec": 12, + "ms": 454, + "us": 636, + "ns": 234, + "seconds": 12, + "microseconds": 454636, + "nanoseconds": 234, + } + assert res1 == exp1 + + res2 = py_td64_to_tdstruct(val, NpyDatetimeUnit.NPY_FR_us.value) + exp2 = { + "days": 0, + "hrs": 3, + "min": 27, + "sec": 34, + "ms": 636, + "us": 234, + "ns": 0, + "seconds": 12454, + "microseconds": 636234, + "nanoseconds": 0, + } + assert res2 == exp2 + + res3 = py_td64_to_tdstruct(val, NpyDatetimeUnit.NPY_FR_ms.value) + exp3 = { + "days": 144, + "hrs": 3, + "min": 37, + "sec": 16, + "ms": 234, + "us": 0, + "ns": 0, + "seconds": 13036, + "microseconds": 234000, + "nanoseconds": 0, + } + assert res3 == exp3 + + # Note this out of bounds for nanosecond Timedelta + res4 = py_td64_to_tdstruct(val, NpyDatetimeUnit.NPY_FR_s.value) + exp4 = { + "days": 144150, + "hrs": 21, + "min": 10, + "sec": 34, + "ms": 0, + "us": 0, + "ns": 0, + "seconds": 76234, + "microseconds": 0, + "nanoseconds": 0, + } + assert res4 == exp4 + + +class TestAstypeOverflowSafe: + def test_pass_non_dt64_array(self): + # check that we raise, not segfault + arr = np.arange(5) + dtype = np.dtype("M8[ns]") + + msg = ( + "astype_overflowsafe values.dtype and dtype must be either " + "both-datetime64 or both-timedelta64" + ) + with pytest.raises(TypeError, match=msg): + astype_overflowsafe(arr, dtype, copy=True) + + with pytest.raises(TypeError, match=msg): + astype_overflowsafe(arr, dtype, copy=False) + + def test_pass_non_dt64_dtype(self): + # check that we raise, not segfault + arr = np.arange(5, dtype="i8").view("M8[D]") + dtype = np.dtype("m8[ns]") + + msg = ( + "astype_overflowsafe values.dtype and dtype must be either " + "both-datetime64 or both-timedelta64" + ) + with pytest.raises(TypeError, match=msg): + astype_overflowsafe(arr, dtype, copy=True) + + with pytest.raises(TypeError, match=msg): + astype_overflowsafe(arr, dtype, copy=False) + + def test_astype_overflowsafe_dt64(self): + dtype = np.dtype("M8[ns]") + + dt = np.datetime64("2262-04-05", "D") + arr = dt + np.arange(10, dtype="m8[D]") + + # arr.astype silently overflows, so this + wrong = arr.astype(dtype) + roundtrip = wrong.astype(arr.dtype) + assert not (wrong == roundtrip).all() + + msg = "Out of bounds nanosecond timestamp" + with pytest.raises(OutOfBoundsDatetime, match=msg): + astype_overflowsafe(arr, dtype) + + # But converting to microseconds is fine, and we match numpy's results. + dtype2 = np.dtype("M8[us]") + result = astype_overflowsafe(arr, dtype2) + expected = arr.astype(dtype2) + tm.assert_numpy_array_equal(result, expected) + + def test_astype_overflowsafe_td64(self): + dtype = np.dtype("m8[ns]") + + dt = np.datetime64("2262-04-05", "D") + arr = dt + np.arange(10, dtype="m8[D]") + arr = arr.view("m8[D]") + + # arr.astype silently overflows, so this + wrong = arr.astype(dtype) + roundtrip = wrong.astype(arr.dtype) + assert not (wrong == roundtrip).all() + + msg = r"Cannot convert 106752 days to timedelta64\[ns\] without overflow" + with pytest.raises(OutOfBoundsTimedelta, match=msg): + astype_overflowsafe(arr, dtype) + + # But converting to microseconds is fine, and we match numpy's results. + dtype2 = np.dtype("m8[us]") + result = astype_overflowsafe(arr, dtype2) + expected = arr.astype(dtype2) + tm.assert_numpy_array_equal(result, expected) + + def test_astype_overflowsafe_disallow_rounding(self): + arr = np.array([-1500, 1500], dtype="M8[ns]") + dtype = np.dtype("M8[us]") + + msg = "Cannot losslessly cast '-1500 ns' to us" + with pytest.raises(ValueError, match=msg): + astype_overflowsafe(arr, dtype, round_ok=False) + + result = astype_overflowsafe(arr, dtype, round_ok=True) + expected = arr.astype(dtype) + tm.assert_numpy_array_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_npy_units.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_npy_units.py new file mode 100644 index 0000000000000000000000000000000000000000..6d05dc79fbb2cf52688547b672365802463ce6f2 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_npy_units.py @@ -0,0 +1,27 @@ +import numpy as np + +from pandas._libs.tslibs.dtypes import abbrev_to_npy_unit +from pandas._libs.tslibs.vectorized import is_date_array_normalized + +# a datetime64 ndarray which *is* normalized +day_arr = np.arange(10, dtype="i8").view("M8[D]") + + +class TestIsDateArrayNormalized: + def test_is_date_array_normalized_day(self): + arr = day_arr + abbrev = "D" + unit = abbrev_to_npy_unit(abbrev) + result = is_date_array_normalized(arr.view("i8"), None, unit) + assert result is True + + def test_is_date_array_normalized_seconds(self): + abbrev = "s" + arr = day_arr.astype(f"M8[{abbrev}]") + unit = abbrev_to_npy_unit(abbrev) + result = is_date_array_normalized(arr.view("i8"), None, unit) + assert result is True + + arr[0] += np.timedelta64(1, abbrev) + result2 = is_date_array_normalized(arr.view("i8"), None, unit) + assert result2 is False diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_parse_iso8601.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_parse_iso8601.py new file mode 100644 index 0000000000000000000000000000000000000000..1992faae2ea6a687f8bd74b4e1e10ba53bb9e901 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_parse_iso8601.py @@ -0,0 +1,119 @@ +from datetime import datetime + +import pytest + +from pandas._libs import tslib + +from pandas import Timestamp + + +@pytest.mark.parametrize( + "date_str, exp", + [ + ("2011-01-02", datetime(2011, 1, 2)), + ("2011-1-2", datetime(2011, 1, 2)), + ("2011-01", datetime(2011, 1, 1)), + ("2011-1", datetime(2011, 1, 1)), + ("2011 01 02", datetime(2011, 1, 2)), + ("2011.01.02", datetime(2011, 1, 2)), + ("2011/01/02", datetime(2011, 1, 2)), + ("2011\\01\\02", datetime(2011, 1, 2)), + ("2013-01-01 05:30:00", datetime(2013, 1, 1, 5, 30)), + ("2013-1-1 5:30:00", datetime(2013, 1, 1, 5, 30)), + ("2013-1-1 5:30:00+01:00", Timestamp(2013, 1, 1, 5, 30, tz="UTC+01:00")), + ], +) +def test_parsers_iso8601(date_str, exp): + # see gh-12060 + # + # Test only the ISO parser - flexibility to + # different separators and leading zero's. + actual = tslib._test_parse_iso8601(date_str) + assert actual == exp + + +@pytest.mark.parametrize( + "date_str", + [ + "2011-01/02", + "2011=11=11", + "201401", + "201111", + "200101", + # Mixed separated and unseparated. + "2005-0101", + "200501-01", + "20010101 12:3456", + "20010101 1234:56", + # HHMMSS must have two digits in + # each component if unseparated. + "20010101 1", + "20010101 123", + "20010101 12345", + "20010101 12345Z", + ], +) +def test_parsers_iso8601_invalid(date_str): + msg = f'Error parsing datetime string "{date_str}"' + + with pytest.raises(ValueError, match=msg): + tslib._test_parse_iso8601(date_str) + + +def test_parsers_iso8601_invalid_offset_invalid(): + date_str = "2001-01-01 12-34-56" + msg = f'Timezone hours offset out of range in datetime string "{date_str}"' + + with pytest.raises(ValueError, match=msg): + tslib._test_parse_iso8601(date_str) + + +def test_parsers_iso8601_leading_space(): + # GH#25895 make sure isoparser doesn't overflow with long input + date_str, expected = ("2013-1-1 5:30:00", datetime(2013, 1, 1, 5, 30)) + actual = tslib._test_parse_iso8601(" " * 200 + date_str) + assert actual == expected + + +@pytest.mark.parametrize( + "date_str, timespec, exp", + [ + ("2023-01-01 00:00:00", "auto", "2023-01-01T00:00:00"), + ("2023-01-01 00:00:00", "seconds", "2023-01-01T00:00:00"), + ("2023-01-01 00:00:00", "milliseconds", "2023-01-01T00:00:00.000"), + ("2023-01-01 00:00:00", "microseconds", "2023-01-01T00:00:00.000000"), + ("2023-01-01 00:00:00", "nanoseconds", "2023-01-01T00:00:00.000000000"), + ("2023-01-01 00:00:00.001", "auto", "2023-01-01T00:00:00.001000"), + ("2023-01-01 00:00:00.001", "seconds", "2023-01-01T00:00:00"), + ("2023-01-01 00:00:00.001", "milliseconds", "2023-01-01T00:00:00.001"), + ("2023-01-01 00:00:00.001", "microseconds", "2023-01-01T00:00:00.001000"), + ("2023-01-01 00:00:00.001", "nanoseconds", "2023-01-01T00:00:00.001000000"), + ("2023-01-01 00:00:00.000001", "auto", "2023-01-01T00:00:00.000001"), + ("2023-01-01 00:00:00.000001", "seconds", "2023-01-01T00:00:00"), + ("2023-01-01 00:00:00.000001", "milliseconds", "2023-01-01T00:00:00.000"), + ("2023-01-01 00:00:00.000001", "microseconds", "2023-01-01T00:00:00.000001"), + ("2023-01-01 00:00:00.000001", "nanoseconds", "2023-01-01T00:00:00.000001000"), + ("2023-01-01 00:00:00.000000001", "auto", "2023-01-01T00:00:00.000000001"), + ("2023-01-01 00:00:00.000000001", "seconds", "2023-01-01T00:00:00"), + ("2023-01-01 00:00:00.000000001", "milliseconds", "2023-01-01T00:00:00.000"), + ("2023-01-01 00:00:00.000000001", "microseconds", "2023-01-01T00:00:00.000000"), + ( + "2023-01-01 00:00:00.000000001", + "nanoseconds", + "2023-01-01T00:00:00.000000001", + ), + ("2023-01-01 00:00:00.000001001", "auto", "2023-01-01T00:00:00.000001001"), + ("2023-01-01 00:00:00.000001001", "seconds", "2023-01-01T00:00:00"), + ("2023-01-01 00:00:00.000001001", "milliseconds", "2023-01-01T00:00:00.000"), + ("2023-01-01 00:00:00.000001001", "microseconds", "2023-01-01T00:00:00.000001"), + ( + "2023-01-01 00:00:00.000001001", + "nanoseconds", + "2023-01-01T00:00:00.000001001", + ), + ], +) +def test_iso8601_formatter(date_str: str, timespec: str, exp: str): + # GH#53020 + ts = Timestamp(date_str) + assert ts.isoformat(timespec=timespec) == exp diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_parsing.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_parsing.py new file mode 100644 index 0000000000000000000000000000000000000000..2a3cc623e48acc4fddc435f5c46ed0bdcbaa16d1 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_parsing.py @@ -0,0 +1,416 @@ +""" +Tests for Timestamp parsing, aimed at pandas/_libs/tslibs/parsing.pyx +""" +from datetime import datetime +import re + +from dateutil.parser import parse as du_parse +from dateutil.tz import tzlocal +from hypothesis import given +import numpy as np +import pytest + +from pandas._libs.tslibs import ( + parsing, + strptime, +) +from pandas._libs.tslibs.parsing import parse_datetime_string_with_reso +from pandas.compat import ( + ISMUSL, + is_platform_arm, + is_platform_windows, +) +import pandas.util._test_decorators as td + +import pandas._testing as tm +from pandas._testing._hypothesis import DATETIME_NO_TZ + + +@pytest.mark.skipif( + is_platform_windows() or ISMUSL or is_platform_arm(), + reason="TZ setting incorrect on Windows and MUSL Linux", +) +def test_parsing_tzlocal_deprecated(): + # GH#50791 + msg = ( + "Parsing 'EST' as tzlocal.*" + "Pass the 'tz' keyword or call tz_localize after construction instead" + ) + dtstr = "Jan 15 2004 03:00 EST" + + with tm.set_timezone("US/Eastern"): + with tm.assert_produces_warning(FutureWarning, match=msg): + res, _ = parse_datetime_string_with_reso(dtstr) + + assert isinstance(res.tzinfo, tzlocal) + + with tm.assert_produces_warning(FutureWarning, match=msg): + res = parsing.py_parse_datetime_string(dtstr) + assert isinstance(res.tzinfo, tzlocal) + + +def test_parse_datetime_string_with_reso(): + (parsed, reso) = parse_datetime_string_with_reso("4Q1984") + (parsed_lower, reso_lower) = parse_datetime_string_with_reso("4q1984") + + assert reso == reso_lower + assert parsed == parsed_lower + + +def test_parse_datetime_string_with_reso_nanosecond_reso(): + # GH#46811 + parsed, reso = parse_datetime_string_with_reso("2022-04-20 09:19:19.123456789") + assert reso == "nanosecond" + + +def test_parse_datetime_string_with_reso_invalid_type(): + # Raise on invalid input, don't just return it + msg = "Argument 'date_string' has incorrect type (expected str, got tuple)" + with pytest.raises(TypeError, match=re.escape(msg)): + parse_datetime_string_with_reso((4, 5)) + + +@pytest.mark.parametrize( + "dashed,normal", [("1988-Q2", "1988Q2"), ("2Q-1988", "2Q1988")] +) +def test_parse_time_quarter_with_dash(dashed, normal): + # see gh-9688 + (parsed_dash, reso_dash) = parse_datetime_string_with_reso(dashed) + (parsed, reso) = parse_datetime_string_with_reso(normal) + + assert parsed_dash == parsed + assert reso_dash == reso + + +@pytest.mark.parametrize("dashed", ["-2Q1992", "2-Q1992", "4-4Q1992"]) +def test_parse_time_quarter_with_dash_error(dashed): + msg = f"Unknown datetime string format, unable to parse: {dashed}" + + with pytest.raises(parsing.DateParseError, match=msg): + parse_datetime_string_with_reso(dashed) + + +@pytest.mark.parametrize( + "date_string,expected", + [ + ("123.1234", False), + ("-50000", False), + ("999", False), + ("m", False), + ("T", False), + ("Mon Sep 16, 2013", True), + ("2012-01-01", True), + ("01/01/2012", True), + ("01012012", True), + ("0101", True), + ("1-1", True), + ], +) +def test_does_not_convert_mixed_integer(date_string, expected): + assert parsing._does_string_look_like_datetime(date_string) is expected + + +@pytest.mark.parametrize( + "date_str,kwargs,msg", + [ + ( + "2013Q5", + {}, + ( + "Incorrect quarterly string is given, " + "quarter must be between 1 and 4: 2013Q5" + ), + ), + # see gh-5418 + ( + "2013Q1", + {"freq": "INVLD-L-DEC-SAT"}, + ( + "Unable to retrieve month information " + "from given freq: INVLD-L-DEC-SAT" + ), + ), + ], +) +def test_parsers_quarterly_with_freq_error(date_str, kwargs, msg): + with pytest.raises(parsing.DateParseError, match=msg): + parsing.parse_datetime_string_with_reso(date_str, **kwargs) + + +@pytest.mark.parametrize( + "date_str,freq,expected", + [ + ("2013Q2", None, datetime(2013, 4, 1)), + ("2013Q2", "Y-APR", datetime(2012, 8, 1)), + ("2013-Q2", "Y-DEC", datetime(2013, 4, 1)), + ], +) +def test_parsers_quarterly_with_freq(date_str, freq, expected): + result, _ = parsing.parse_datetime_string_with_reso(date_str, freq=freq) + assert result == expected + + +@pytest.mark.parametrize( + "date_str", ["2Q 2005", "2Q-200Y", "2Q-200", "22Q2005", "2Q200.", "6Q-20"] +) +def test_parsers_quarter_invalid(date_str): + if date_str == "6Q-20": + msg = ( + "Incorrect quarterly string is given, quarter " + f"must be between 1 and 4: {date_str}" + ) + else: + msg = f"Unknown datetime string format, unable to parse: {date_str}" + + with pytest.raises(ValueError, match=msg): + parsing.parse_datetime_string_with_reso(date_str) + + +@pytest.mark.parametrize( + "date_str,expected", + [("201101", datetime(2011, 1, 1, 0, 0)), ("200005", datetime(2000, 5, 1, 0, 0))], +) +def test_parsers_month_freq(date_str, expected): + result, _ = parsing.parse_datetime_string_with_reso(date_str, freq="ME") + assert result == expected + + +@td.skip_if_not_us_locale +@pytest.mark.parametrize( + "string,fmt", + [ + ("20111230", "%Y%m%d"), + ("201112300000", "%Y%m%d%H%M"), + ("20111230000000", "%Y%m%d%H%M%S"), + ("20111230T00", "%Y%m%dT%H"), + ("20111230T0000", "%Y%m%dT%H%M"), + ("20111230T000000", "%Y%m%dT%H%M%S"), + ("2011-12-30", "%Y-%m-%d"), + ("2011", "%Y"), + ("2011-01", "%Y-%m"), + ("30-12-2011", "%d-%m-%Y"), + ("2011-12-30 00:00:00", "%Y-%m-%d %H:%M:%S"), + ("2011-12-30T00:00:00", "%Y-%m-%dT%H:%M:%S"), + ("2011-12-30T00:00:00UTC", "%Y-%m-%dT%H:%M:%S%Z"), + ("2011-12-30T00:00:00Z", "%Y-%m-%dT%H:%M:%S%z"), + ("2011-12-30T00:00:00+9", "%Y-%m-%dT%H:%M:%S%z"), + ("2011-12-30T00:00:00+09", "%Y-%m-%dT%H:%M:%S%z"), + ("2011-12-30T00:00:00+090", None), + ("2011-12-30T00:00:00+0900", "%Y-%m-%dT%H:%M:%S%z"), + ("2011-12-30T00:00:00-0900", "%Y-%m-%dT%H:%M:%S%z"), + ("2011-12-30T00:00:00+09:00", "%Y-%m-%dT%H:%M:%S%z"), + ("2011-12-30T00:00:00+09:000", None), + ("2011-12-30T00:00:00+9:0", "%Y-%m-%dT%H:%M:%S%z"), + ("2011-12-30T00:00:00+09:", None), + ("2011-12-30T00:00:00.000000UTC", "%Y-%m-%dT%H:%M:%S.%f%Z"), + ("2011-12-30T00:00:00.000000Z", "%Y-%m-%dT%H:%M:%S.%f%z"), + ("2011-12-30T00:00:00.000000+9", "%Y-%m-%dT%H:%M:%S.%f%z"), + ("2011-12-30T00:00:00.000000+09", "%Y-%m-%dT%H:%M:%S.%f%z"), + ("2011-12-30T00:00:00.000000+090", None), + ("2011-12-30T00:00:00.000000+0900", "%Y-%m-%dT%H:%M:%S.%f%z"), + ("2011-12-30T00:00:00.000000-0900", "%Y-%m-%dT%H:%M:%S.%f%z"), + ("2011-12-30T00:00:00.000000+09:00", "%Y-%m-%dT%H:%M:%S.%f%z"), + ("2011-12-30T00:00:00.000000+09:000", None), + ("2011-12-30T00:00:00.000000+9:0", "%Y-%m-%dT%H:%M:%S.%f%z"), + ("2011-12-30T00:00:00.000000+09:", None), + ("2011-12-30 00:00:00.000000", "%Y-%m-%d %H:%M:%S.%f"), + ("Tue 24 Aug 2021 01:30:48", "%a %d %b %Y %H:%M:%S"), + ("Tuesday 24 Aug 2021 01:30:48", "%A %d %b %Y %H:%M:%S"), + ("Tue 24 Aug 2021 01:30:48 AM", "%a %d %b %Y %I:%M:%S %p"), + ("Tuesday 24 Aug 2021 01:30:48 AM", "%A %d %b %Y %I:%M:%S %p"), + ("27.03.2003 14:55:00.000", "%d.%m.%Y %H:%M:%S.%f"), # GH50317 + ], +) +def test_guess_datetime_format_with_parseable_formats(string, fmt): + with tm.maybe_produces_warning( + UserWarning, fmt is not None and re.search(r"%d.*%m", fmt) + ): + result = parsing.guess_datetime_format(string) + assert result == fmt + + +@pytest.mark.parametrize("dayfirst,expected", [(True, "%d/%m/%Y"), (False, "%m/%d/%Y")]) +def test_guess_datetime_format_with_dayfirst(dayfirst, expected): + ambiguous_string = "01/01/2011" + result = parsing.guess_datetime_format(ambiguous_string, dayfirst=dayfirst) + assert result == expected + + +@td.skip_if_not_us_locale +@pytest.mark.parametrize( + "string,fmt", + [ + ("30/Dec/2011", "%d/%b/%Y"), + ("30/December/2011", "%d/%B/%Y"), + ("30/Dec/2011 00:00:00", "%d/%b/%Y %H:%M:%S"), + ], +) +def test_guess_datetime_format_with_locale_specific_formats(string, fmt): + result = parsing.guess_datetime_format(string) + assert result == fmt + + +@pytest.mark.parametrize( + "invalid_dt", + [ + "01/2013", + "12:00:00", + "1/1/1/1", + "this_is_not_a_datetime", + "51a", + "13/2019", + "202001", # YYYYMM isn't ISO8601 + "2020/01", # YYYY/MM isn't ISO8601 either + "87156549591102612381000001219H5", + ], +) +def test_guess_datetime_format_invalid_inputs(invalid_dt): + # A datetime string must include a year, month and a day for it to be + # guessable, in addition to being a string that looks like a datetime. + assert parsing.guess_datetime_format(invalid_dt) is None + + +@pytest.mark.parametrize("invalid_type_dt", [9, datetime(2011, 1, 1)]) +def test_guess_datetime_format_wrong_type_inputs(invalid_type_dt): + # A datetime string must include a year, month and a day for it to be + # guessable, in addition to being a string that looks like a datetime. + with pytest.raises( + TypeError, + match=r"^Argument 'dt_str' has incorrect type \(expected str, got .*\)$", + ): + parsing.guess_datetime_format(invalid_type_dt) + + +@pytest.mark.parametrize( + "string,fmt,dayfirst,warning", + [ + ("2011-1-1", "%Y-%m-%d", False, None), + ("2011-1-1", "%Y-%d-%m", True, None), + ("1/1/2011", "%m/%d/%Y", False, None), + ("1/1/2011", "%d/%m/%Y", True, None), + ("30-1-2011", "%d-%m-%Y", False, UserWarning), + ("30-1-2011", "%d-%m-%Y", True, None), + ("2011-1-1 0:0:0", "%Y-%m-%d %H:%M:%S", False, None), + ("2011-1-1 0:0:0", "%Y-%d-%m %H:%M:%S", True, None), + ("2011-1-3T00:00:0", "%Y-%m-%dT%H:%M:%S", False, None), + ("2011-1-3T00:00:0", "%Y-%d-%mT%H:%M:%S", True, None), + ("2011-1-1 00:00:00", "%Y-%m-%d %H:%M:%S", False, None), + ("2011-1-1 00:00:00", "%Y-%d-%m %H:%M:%S", True, None), + ], +) +def test_guess_datetime_format_no_padding(string, fmt, dayfirst, warning): + # see gh-11142 + msg = ( + rf"Parsing dates in {fmt} format when dayfirst=False \(the default\) " + "was specified. " + "Pass `dayfirst=True` or specify a format to silence this warning." + ) + with tm.assert_produces_warning(warning, match=msg): + result = parsing.guess_datetime_format(string, dayfirst=dayfirst) + assert result == fmt + + +def test_try_parse_dates(): + arr = np.array(["5/1/2000", "6/1/2000", "7/1/2000"], dtype=object) + result = parsing.try_parse_dates(arr, parser=lambda x: du_parse(x, dayfirst=True)) + + expected = np.array([du_parse(d, dayfirst=True) for d in arr]) + tm.assert_numpy_array_equal(result, expected) + + +def test_parse_datetime_string_with_reso_check_instance_type_raise_exception(): + # issue 20684 + msg = "Argument 'date_string' has incorrect type (expected str, got tuple)" + with pytest.raises(TypeError, match=re.escape(msg)): + parse_datetime_string_with_reso((1, 2, 3)) + + result = parse_datetime_string_with_reso("2019") + expected = (datetime(2019, 1, 1), "year") + assert result == expected + + +@pytest.mark.parametrize( + "fmt,expected", + [ + ("%Y %m %d %H:%M:%S", True), + ("%Y/%m/%d %H:%M:%S", True), + (r"%Y\%m\%d %H:%M:%S", True), + ("%Y-%m-%d %H:%M:%S", True), + ("%Y.%m.%d %H:%M:%S", True), + ("%Y%m%d %H:%M:%S", True), + ("%Y-%m-%dT%H:%M:%S", True), + ("%Y-%m-%dT%H:%M:%S%z", True), + ("%Y-%m-%dT%H:%M:%S%Z", False), + ("%Y-%m-%dT%H:%M:%S.%f", True), + ("%Y-%m-%dT%H:%M:%S.%f%z", True), + ("%Y-%m-%dT%H:%M:%S.%f%Z", False), + ("%Y%m%d", True), + ("%Y%m", False), + ("%Y", True), + ("%Y-%m-%d", True), + ("%Y-%m", True), + ], +) +def test_is_iso_format(fmt, expected): + # see gh-41047 + result = strptime._test_format_is_iso(fmt) + assert result == expected + + +@pytest.mark.parametrize( + "input", + [ + "2018-01-01T00:00:00.123456789", + "2018-01-01T00:00:00.123456", + "2018-01-01T00:00:00.123", + ], +) +def test_guess_datetime_format_f(input): + # https://github.com/pandas-dev/pandas/issues/49043 + result = parsing.guess_datetime_format(input) + expected = "%Y-%m-%dT%H:%M:%S.%f" + assert result == expected + + +def _helper_hypothesis_delimited_date(call, date_string, **kwargs): + msg, result = None, None + try: + result = call(date_string, **kwargs) + except ValueError as err: + msg = str(err) + return msg, result + + +@given(DATETIME_NO_TZ) +@pytest.mark.parametrize("delimiter", list(" -./")) +@pytest.mark.parametrize("dayfirst", [True, False]) +@pytest.mark.parametrize( + "date_format", + ["%d %m %Y", "%m %d %Y", "%m %Y", "%Y %m %d", "%y %m %d", "%Y%m%d", "%y%m%d"], +) +def test_hypothesis_delimited_date( + request, date_format, dayfirst, delimiter, test_datetime +): + if date_format == "%m %Y" and delimiter == ".": + request.applymarker( + pytest.mark.xfail( + reason="parse_datetime_string cannot reliably tell whether " + "e.g. %m.%Y is a float or a date", + strict=False, + ) + ) + date_string = test_datetime.strftime(date_format.replace(" ", delimiter)) + + except_out_dateutil, result = _helper_hypothesis_delimited_date( + parsing.py_parse_datetime_string, date_string, dayfirst=dayfirst + ) + except_in_dateutil, expected = _helper_hypothesis_delimited_date( + du_parse, + date_string, + default=datetime(1, 1, 1), + dayfirst=dayfirst, + yearfirst=False, + ) + + assert except_out_dateutil == except_in_dateutil + assert result == expected diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_period.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_period.py new file mode 100644 index 0000000000000000000000000000000000000000..715e2d3da88dbc69fd6a376f21b7bba78a46ca9f --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_period.py @@ -0,0 +1,123 @@ +import numpy as np +import pytest + +from pandas._libs.tslibs import ( + iNaT, + to_offset, +) +from pandas._libs.tslibs.period import ( + extract_ordinals, + get_period_field_arr, + period_asfreq, + period_ordinal, +) + +import pandas._testing as tm + + +def get_freq_code(freqstr: str) -> int: + off = to_offset(freqstr, is_period=True) + # error: "BaseOffset" has no attribute "_period_dtype_code" + code = off._period_dtype_code # type: ignore[attr-defined] + return code + + +@pytest.mark.parametrize( + "freq1,freq2,expected", + [ + ("D", "h", 24), + ("D", "min", 1440), + ("D", "s", 86400), + ("D", "ms", 86400000), + ("D", "us", 86400000000), + ("D", "ns", 86400000000000), + ("h", "min", 60), + ("h", "s", 3600), + ("h", "ms", 3600000), + ("h", "us", 3600000000), + ("h", "ns", 3600000000000), + ("min", "s", 60), + ("min", "ms", 60000), + ("min", "us", 60000000), + ("min", "ns", 60000000000), + ("s", "ms", 1000), + ("s", "us", 1000000), + ("s", "ns", 1000000000), + ("ms", "us", 1000), + ("ms", "ns", 1000000), + ("us", "ns", 1000), + ], +) +def test_intra_day_conversion_factors(freq1, freq2, expected): + assert ( + period_asfreq(1, get_freq_code(freq1), get_freq_code(freq2), False) == expected + ) + + +@pytest.mark.parametrize( + "freq,expected", [("Y", 0), ("M", 0), ("W", 1), ("D", 0), ("B", 0)] +) +def test_period_ordinal_start_values(freq, expected): + # information for Jan. 1, 1970. + assert period_ordinal(1970, 1, 1, 0, 0, 0, 0, 0, get_freq_code(freq)) == expected + + +@pytest.mark.parametrize( + "dt,expected", + [ + ((1970, 1, 4, 0, 0, 0, 0, 0), 1), + ((1970, 1, 5, 0, 0, 0, 0, 0), 2), + ((2013, 10, 6, 0, 0, 0, 0, 0), 2284), + ((2013, 10, 7, 0, 0, 0, 0, 0), 2285), + ], +) +def test_period_ordinal_week(dt, expected): + args = dt + (get_freq_code("W"),) + assert period_ordinal(*args) == expected + + +@pytest.mark.parametrize( + "day,expected", + [ + # Thursday (Oct. 3, 2013). + (3, 11415), + # Friday (Oct. 4, 2013). + (4, 11416), + # Saturday (Oct. 5, 2013). + (5, 11417), + # Sunday (Oct. 6, 2013). + (6, 11417), + # Monday (Oct. 7, 2013). + (7, 11417), + # Tuesday (Oct. 8, 2013). + (8, 11418), + ], +) +def test_period_ordinal_business_day(day, expected): + # 5000 is PeriodDtypeCode for BusinessDay + args = (2013, 10, day, 0, 0, 0, 0, 0, 5000) + assert period_ordinal(*args) == expected + + +class TestExtractOrdinals: + def test_extract_ordinals_raises(self): + # with non-object, make sure we raise TypeError, not segfault + arr = np.arange(5) + freq = to_offset("D") + with pytest.raises(TypeError, match="values must be object-dtype"): + extract_ordinals(arr, freq) + + def test_extract_ordinals_2d(self): + freq = to_offset("D") + arr = np.empty(10, dtype=object) + arr[:] = iNaT + + res = extract_ordinals(arr, freq) + res2 = extract_ordinals(arr.reshape(5, 2), freq) + tm.assert_numpy_array_equal(res, res2.reshape(-1)) + + +def test_get_period_field_array_raises_on_out_of_range(): + msg = "Buffer dtype mismatch, expected 'const int64_t' but got 'double'" + with pytest.raises(ValueError, match=msg): + get_period_field_arr(-1, np.empty(1), 0) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_resolution.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_resolution.py new file mode 100644 index 0000000000000000000000000000000000000000..690962f1daa5eebd047d11297914eb36b494e0dc --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_resolution.py @@ -0,0 +1,57 @@ +import numpy as np +import pytest +import pytz + +from pandas._libs.tslibs import ( + Resolution, + get_resolution, +) +from pandas._libs.tslibs.dtypes import NpyDatetimeUnit + +import pandas._testing as tm + + +def test_get_resolution_nano(): + # don't return the fallback RESO_DAY + arr = np.array([1], dtype=np.int64) + res = get_resolution(arr) + assert res == Resolution.RESO_NS + + +def test_get_resolution_non_nano_data(): + arr = np.array([1], dtype=np.int64) + res = get_resolution(arr, None, NpyDatetimeUnit.NPY_FR_us.value) + assert res == Resolution.RESO_US + + res = get_resolution(arr, pytz.UTC, NpyDatetimeUnit.NPY_FR_us.value) + assert res == Resolution.RESO_US + + +@pytest.mark.parametrize( + "freqstr,expected", + [ + ("Y", "year"), + ("Q", "quarter"), + ("M", "month"), + ("D", "day"), + ("h", "hour"), + ("min", "minute"), + ("s", "second"), + ("ms", "millisecond"), + ("us", "microsecond"), + ("ns", "nanosecond"), + ], +) +def test_get_attrname_from_abbrev(freqstr, expected): + reso = Resolution.get_reso_from_freqstr(freqstr) + assert reso.attr_abbrev == freqstr + assert reso.attrname == expected + + +@pytest.mark.parametrize("freq", ["A", "H", "T", "S", "L", "U", "N"]) +def test_units_A_H_T_S_L_U_N_deprecated_from_attrname_to_abbrevs(freq): + # GH#52536 + msg = f"'{freq}' is deprecated and will be removed in a future version." + + with tm.assert_produces_warning(FutureWarning, match=msg): + Resolution.get_reso_from_freqstr(freq) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_strptime.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_strptime.py new file mode 100644 index 0000000000000000000000000000000000000000..d726006b03f6d43cb94a91518daabb2b29b757e0 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_strptime.py @@ -0,0 +1,110 @@ +from datetime import ( + datetime, + timezone, +) + +import numpy as np +import pytest + +from pandas._libs.tslibs.dtypes import NpyDatetimeUnit +from pandas._libs.tslibs.strptime import array_strptime + +from pandas import ( + NaT, + Timestamp, +) +import pandas._testing as tm + +creso_infer = NpyDatetimeUnit.NPY_FR_GENERIC.value + + +class TestArrayStrptimeResolutionInference: + def test_array_strptime_resolution_all_nat(self): + arr = np.array([NaT, np.nan], dtype=object) + + fmt = "%Y-%m-%d %H:%M:%S" + res, _ = array_strptime(arr, fmt=fmt, utc=False, creso=creso_infer) + assert res.dtype == "M8[s]" + + res, _ = array_strptime(arr, fmt=fmt, utc=True, creso=creso_infer) + assert res.dtype == "M8[s]" + + @pytest.mark.parametrize("tz", [None, timezone.utc]) + def test_array_strptime_resolution_inference_homogeneous_strings(self, tz): + dt = datetime(2016, 1, 2, 3, 4, 5, 678900, tzinfo=tz) + + fmt = "%Y-%m-%d %H:%M:%S" + dtstr = dt.strftime(fmt) + arr = np.array([dtstr] * 3, dtype=object) + expected = np.array([dt.replace(tzinfo=None)] * 3, dtype="M8[s]") + + res, _ = array_strptime(arr, fmt=fmt, utc=False, creso=creso_infer) + tm.assert_numpy_array_equal(res, expected) + + fmt = "%Y-%m-%d %H:%M:%S.%f" + dtstr = dt.strftime(fmt) + arr = np.array([dtstr] * 3, dtype=object) + expected = np.array([dt.replace(tzinfo=None)] * 3, dtype="M8[us]") + + res, _ = array_strptime(arr, fmt=fmt, utc=False, creso=creso_infer) + tm.assert_numpy_array_equal(res, expected) + + fmt = "ISO8601" + res, _ = array_strptime(arr, fmt=fmt, utc=False, creso=creso_infer) + tm.assert_numpy_array_equal(res, expected) + + @pytest.mark.parametrize("tz", [None, timezone.utc]) + def test_array_strptime_resolution_mixed(self, tz): + dt = datetime(2016, 1, 2, 3, 4, 5, 678900, tzinfo=tz) + + ts = Timestamp(dt).as_unit("ns") + + arr = np.array([dt, ts], dtype=object) + expected = np.array( + [Timestamp(dt).as_unit("ns").asm8, ts.asm8], + dtype="M8[ns]", + ) + + fmt = "%Y-%m-%d %H:%M:%S" + res, _ = array_strptime(arr, fmt=fmt, utc=False, creso=creso_infer) + tm.assert_numpy_array_equal(res, expected) + + fmt = "ISO8601" + res, _ = array_strptime(arr, fmt=fmt, utc=False, creso=creso_infer) + tm.assert_numpy_array_equal(res, expected) + + def test_array_strptime_resolution_todaynow(self): + # specifically case where today/now is the *first* item + vals = np.array(["today", np.datetime64("2017-01-01", "us")], dtype=object) + + now = Timestamp("now").asm8 + res, _ = array_strptime(vals, fmt="%Y-%m-%d", utc=False, creso=creso_infer) + res2, _ = array_strptime( + vals[::-1], fmt="%Y-%m-%d", utc=False, creso=creso_infer + ) + + # 1s is an arbitrary cutoff for call overhead; in local testing the + # actual difference is about 250us + tolerance = np.timedelta64(1, "s") + + assert res.dtype == "M8[us]" + assert abs(res[0] - now) < tolerance + assert res[1] == vals[1] + + assert res2.dtype == "M8[us]" + assert abs(res2[1] - now) < tolerance * 2 + assert res2[0] == vals[1] + + def test_array_strptime_str_outside_nano_range(self): + vals = np.array(["2401-09-15"], dtype=object) + expected = np.array(["2401-09-15"], dtype="M8[s]") + fmt = "ISO8601" + res, _ = array_strptime(vals, fmt=fmt, creso=creso_infer) + tm.assert_numpy_array_equal(res, expected) + + # non-iso -> different path + vals2 = np.array(["Sep 15, 2401"], dtype=object) + expected2 = np.array(["2401-09-15"], dtype="M8[s]") + fmt2 = "%b %d, %Y" + res2, _ = array_strptime(vals2, fmt=fmt2, creso=creso_infer) + tm.assert_numpy_array_equal(res2, expected2) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_timedeltas.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_timedeltas.py new file mode 100644 index 0000000000000000000000000000000000000000..4784a6d0d600dcc77e359fb3d7d56301f78270d2 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_timedeltas.py @@ -0,0 +1,149 @@ +import re + +import numpy as np +import pytest + +from pandas._libs.tslibs.timedeltas import ( + array_to_timedelta64, + delta_to_nanoseconds, + ints_to_pytimedelta, +) + +from pandas import ( + Timedelta, + offsets, +) +import pandas._testing as tm + + +@pytest.mark.parametrize( + "obj,expected", + [ + (np.timedelta64(14, "D"), 14 * 24 * 3600 * 1e9), + (Timedelta(minutes=-7), -7 * 60 * 1e9), + (Timedelta(minutes=-7).to_pytimedelta(), -7 * 60 * 1e9), + (Timedelta(seconds=1234e-9), 1234), # GH43764, GH40946 + ( + Timedelta(seconds=1e-9, milliseconds=1e-5, microseconds=1e-1), + 111, + ), # GH43764 + ( + Timedelta(days=1, seconds=1e-9, milliseconds=1e-5, microseconds=1e-1), + 24 * 3600e9 + 111, + ), # GH43764 + (offsets.Nano(125), 125), + ], +) +def test_delta_to_nanoseconds(obj, expected): + result = delta_to_nanoseconds(obj) + assert result == expected + + +def test_delta_to_nanoseconds_error(): + obj = np.array([123456789], dtype="m8[ns]") + + with pytest.raises(TypeError, match=""): + delta_to_nanoseconds(obj) + + with pytest.raises(TypeError, match="float"): + delta_to_nanoseconds(1.5) + with pytest.raises(TypeError, match="int"): + delta_to_nanoseconds(1) + with pytest.raises(TypeError, match="int"): + delta_to_nanoseconds(np.int64(2)) + with pytest.raises(TypeError, match="int"): + delta_to_nanoseconds(np.int32(3)) + + +def test_delta_to_nanoseconds_td64_MY_raises(): + msg = ( + "delta_to_nanoseconds does not support Y or M units, " + "as their duration in nanoseconds is ambiguous" + ) + + td = np.timedelta64(1234, "Y") + + with pytest.raises(ValueError, match=msg): + delta_to_nanoseconds(td) + + td = np.timedelta64(1234, "M") + + with pytest.raises(ValueError, match=msg): + delta_to_nanoseconds(td) + + +@pytest.mark.parametrize("unit", ["Y", "M"]) +def test_unsupported_td64_unit_raises(unit): + # GH 52806 + with pytest.raises( + ValueError, + match=f"Unit {unit} is not supported. " + "Only unambiguous timedelta values durations are supported. " + "Allowed units are 'W', 'D', 'h', 'm', 's', 'ms', 'us', 'ns'", + ): + Timedelta(np.timedelta64(1, unit)) + + +def test_huge_nanoseconds_overflow(): + # GH 32402 + assert delta_to_nanoseconds(Timedelta(1e10)) == 1e10 + assert delta_to_nanoseconds(Timedelta(nanoseconds=1e10)) == 1e10 + + +@pytest.mark.parametrize( + "kwargs", [{"Seconds": 1}, {"seconds": 1, "Nanoseconds": 1}, {"Foo": 2}] +) +def test_kwarg_assertion(kwargs): + err_message = ( + "cannot construct a Timedelta from the passed arguments, " + "allowed keywords are " + "[weeks, days, hours, minutes, seconds, " + "milliseconds, microseconds, nanoseconds]" + ) + + with pytest.raises(ValueError, match=re.escape(err_message)): + Timedelta(**kwargs) + + +class TestArrayToTimedelta64: + def test_array_to_timedelta64_string_with_unit_2d_raises(self): + # check the 'unit is not None and errors != "coerce"' path + # in array_to_timedelta64 raises correctly with 2D values + values = np.array([["1", 2], [3, "4"]], dtype=object) + with pytest.raises(ValueError, match="unit must not be specified"): + array_to_timedelta64(values, unit="s") + + def test_array_to_timedelta64_non_object_raises(self): + # check we raise, not segfault + values = np.arange(5) + + msg = "'values' must have object dtype" + with pytest.raises(TypeError, match=msg): + array_to_timedelta64(values) + + +@pytest.mark.parametrize("unit", ["s", "ms", "us"]) +def test_ints_to_pytimedelta(unit): + # tests for non-nanosecond cases + arr = np.arange(6, dtype=np.int64).view(f"m8[{unit}]") + + res = ints_to_pytimedelta(arr, box=False) + # For non-nanosecond, .astype(object) gives pytimedelta objects + # instead of integers + expected = arr.astype(object) + tm.assert_numpy_array_equal(res, expected) + + res = ints_to_pytimedelta(arr, box=True) + expected = np.array([Timedelta(x) for x in arr], dtype=object) + tm.assert_numpy_array_equal(res, expected) + + +@pytest.mark.parametrize("unit", ["Y", "M", "ps", "fs", "as"]) +def test_ints_to_pytimedelta_unsupported(unit): + arr = np.arange(6, dtype=np.int64).view(f"m8[{unit}]") + + with pytest.raises(NotImplementedError, match=r"\d{1,2}"): + ints_to_pytimedelta(arr, box=False) + msg = "Only resolutions 's', 'ms', 'us', 'ns' are supported" + with pytest.raises(NotImplementedError, match=msg): + ints_to_pytimedelta(arr, box=True) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_timezones.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_timezones.py new file mode 100644 index 0000000000000000000000000000000000000000..28e4889983fb964167dd74623c8e4c4585c99a96 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_timezones.py @@ -0,0 +1,168 @@ +from datetime import ( + datetime, + timedelta, + timezone, +) + +import dateutil.tz +import pytest +import pytz + +from pandas._libs.tslibs import ( + conversion, + timezones, +) +from pandas.compat import is_platform_windows + +from pandas import Timestamp + + +def test_is_utc(utc_fixture): + tz = timezones.maybe_get_tz(utc_fixture) + assert timezones.is_utc(tz) + + +@pytest.mark.parametrize("tz_name", list(pytz.common_timezones)) +def test_cache_keys_are_distinct_for_pytz_vs_dateutil(tz_name): + tz_p = timezones.maybe_get_tz(tz_name) + tz_d = timezones.maybe_get_tz("dateutil/" + tz_name) + + if tz_d is None: + pytest.skip(tz_name + ": dateutil does not know about this one") + + if not (tz_name == "UTC" and is_platform_windows()): + # they both end up as tzwin("UTC") on windows + assert timezones._p_tz_cache_key(tz_p) != timezones._p_tz_cache_key(tz_d) + + +def test_tzlocal_repr(): + # see gh-13583 + ts = Timestamp("2011-01-01", tz=dateutil.tz.tzlocal()) + assert ts.tz == dateutil.tz.tzlocal() + assert "tz='tzlocal()')" in repr(ts) + + +def test_tzlocal_maybe_get_tz(): + # see gh-13583 + tz = timezones.maybe_get_tz("tzlocal()") + assert tz == dateutil.tz.tzlocal() + + +def test_tzlocal_offset(): + # see gh-13583 + # + # Get offset using normal datetime for test. + ts = Timestamp("2011-01-01", tz=dateutil.tz.tzlocal()) + + offset = dateutil.tz.tzlocal().utcoffset(datetime(2011, 1, 1)) + offset = offset.total_seconds() + + assert ts._value + offset == Timestamp("2011-01-01")._value + + +def test_tzlocal_is_not_utc(): + # even if the machine running the test is localized to UTC + tz = dateutil.tz.tzlocal() + assert not timezones.is_utc(tz) + + assert not timezones.tz_compare(tz, dateutil.tz.tzutc()) + + +def test_tz_compare_utc(utc_fixture, utc_fixture2): + tz = timezones.maybe_get_tz(utc_fixture) + tz2 = timezones.maybe_get_tz(utc_fixture2) + assert timezones.tz_compare(tz, tz2) + + +@pytest.fixture( + params=[ + (pytz.timezone("US/Eastern"), lambda tz, x: tz.localize(x)), + (dateutil.tz.gettz("US/Eastern"), lambda tz, x: x.replace(tzinfo=tz)), + ] +) +def infer_setup(request): + eastern, localize = request.param + + start_naive = datetime(2001, 1, 1) + end_naive = datetime(2009, 1, 1) + + start = localize(eastern, start_naive) + end = localize(eastern, end_naive) + + return eastern, localize, start, end, start_naive, end_naive + + +def test_infer_tz_compat(infer_setup): + eastern, _, start, end, start_naive, end_naive = infer_setup + + assert ( + timezones.infer_tzinfo(start, end) + is conversion.localize_pydatetime(start_naive, eastern).tzinfo + ) + assert ( + timezones.infer_tzinfo(start, None) + is conversion.localize_pydatetime(start_naive, eastern).tzinfo + ) + assert ( + timezones.infer_tzinfo(None, end) + is conversion.localize_pydatetime(end_naive, eastern).tzinfo + ) + + +def test_infer_tz_utc_localize(infer_setup): + _, _, start, end, start_naive, end_naive = infer_setup + utc = pytz.utc + + start = utc.localize(start_naive) + end = utc.localize(end_naive) + + assert timezones.infer_tzinfo(start, end) is utc + + +@pytest.mark.parametrize("ordered", [True, False]) +def test_infer_tz_mismatch(infer_setup, ordered): + eastern, _, _, _, start_naive, end_naive = infer_setup + msg = "Inputs must both have the same timezone" + + utc = pytz.utc + start = utc.localize(start_naive) + end = conversion.localize_pydatetime(end_naive, eastern) + + args = (start, end) if ordered else (end, start) + + with pytest.raises(AssertionError, match=msg): + timezones.infer_tzinfo(*args) + + +def test_maybe_get_tz_invalid_types(): + with pytest.raises(TypeError, match=""): + timezones.maybe_get_tz(44.0) + + with pytest.raises(TypeError, match=""): + timezones.maybe_get_tz(pytz) + + msg = "" + with pytest.raises(TypeError, match=msg): + timezones.maybe_get_tz(Timestamp("2021-01-01", tz="UTC")) + + +def test_maybe_get_tz_offset_only(): + # see gh-36004 + + # timezone.utc + tz = timezones.maybe_get_tz(timezone.utc) + assert tz == timezone(timedelta(hours=0, minutes=0)) + + # without UTC+- prefix + tz = timezones.maybe_get_tz("+01:15") + assert tz == timezone(timedelta(hours=1, minutes=15)) + + tz = timezones.maybe_get_tz("-01:15") + assert tz == timezone(-timedelta(hours=1, minutes=15)) + + # with UTC+- prefix + tz = timezones.maybe_get_tz("UTC+02:45") + assert tz == timezone(timedelta(hours=2, minutes=45)) + + tz = timezones.maybe_get_tz("UTC-02:45") + assert tz == timezone(-timedelta(hours=2, minutes=45)) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_to_offset.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_to_offset.py new file mode 100644 index 0000000000000000000000000000000000000000..8ca55648f3780e6f31621f7b5cfdcd1435b1a231 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_to_offset.py @@ -0,0 +1,219 @@ +import re + +import pytest + +from pandas._libs.tslibs import ( + Timedelta, + offsets, + to_offset, +) + + +@pytest.mark.parametrize( + "freq_input,expected", + [ + (to_offset("10us"), offsets.Micro(10)), + (offsets.Hour(), offsets.Hour()), + ("2h30min", offsets.Minute(150)), + ("2h 30min", offsets.Minute(150)), + ("2h30min15s", offsets.Second(150 * 60 + 15)), + ("2h 60min", offsets.Hour(3)), + ("2h 20.5min", offsets.Second(8430)), + ("1.5min", offsets.Second(90)), + ("0.5s", offsets.Milli(500)), + ("15ms500us", offsets.Micro(15500)), + ("10s75ms", offsets.Milli(10075)), + ("1s0.25ms", offsets.Micro(1000250)), + ("1s0.25ms", offsets.Micro(1000250)), + ("2800ns", offsets.Nano(2800)), + ("2SME", offsets.SemiMonthEnd(2)), + ("2SME-16", offsets.SemiMonthEnd(2, day_of_month=16)), + ("2SMS-14", offsets.SemiMonthBegin(2, day_of_month=14)), + ("2SMS-15", offsets.SemiMonthBegin(2)), + ], +) +def test_to_offset(freq_input, expected): + result = to_offset(freq_input) + assert result == expected + + +@pytest.mark.parametrize( + "freqstr,expected", [("-1s", -1), ("-2SME", -2), ("-1SMS", -1), ("-5min10s", -310)] +) +def test_to_offset_negative(freqstr, expected): + result = to_offset(freqstr) + assert result.n == expected + + +@pytest.mark.filterwarnings("ignore:.*'m' is deprecated.*:FutureWarning") +@pytest.mark.parametrize( + "freqstr", + [ + "2h20m", + "us1", + "-us", + "3us1", + "-2-3us", + "-2D:3h", + "1.5.0s", + "2SMS-15-15", + "2SMS-15D", + "100foo", + # Invalid leading +/- signs. + "+-1d", + "-+1h", + "+1", + "-7", + "+d", + "-m", + # Invalid shortcut anchors. + "SME-0", + "SME-28", + "SME-29", + "SME-FOO", + "BSM", + "SME--1", + "SMS-1", + "SMS-28", + "SMS-30", + "SMS-BAR", + "SMS-BYR", + "BSMS", + "SMS--2", + ], +) +def test_to_offset_invalid(freqstr): + # see gh-13930 + + # We escape string because some of our + # inputs contain regex special characters. + msg = re.escape(f"Invalid frequency: {freqstr}") + with pytest.raises(ValueError, match=msg): + to_offset(freqstr) + + +def test_to_offset_no_evaluate(): + msg = str(("", "")) + with pytest.raises(TypeError, match=msg): + to_offset(("", "")) + + +def test_to_offset_tuple_unsupported(): + with pytest.raises(TypeError, match="pass as a string instead"): + to_offset((5, "T")) + + +@pytest.mark.parametrize( + "freqstr,expected", + [ + ("2D 3h", offsets.Hour(51)), + ("2 D3 h", offsets.Hour(51)), + ("2 D 3 h", offsets.Hour(51)), + (" 2 D 3 h ", offsets.Hour(51)), + (" h ", offsets.Hour()), + (" 3 h ", offsets.Hour(3)), + ], +) +def test_to_offset_whitespace(freqstr, expected): + result = to_offset(freqstr) + assert result == expected + + +@pytest.mark.parametrize( + "freqstr,expected", [("00h 00min 01s", 1), ("-00h 03min 14s", -194)] +) +def test_to_offset_leading_zero(freqstr, expected): + result = to_offset(freqstr) + assert result.n == expected + + +@pytest.mark.parametrize("freqstr,expected", [("+1d", 1), ("+2h30min", 150)]) +def test_to_offset_leading_plus(freqstr, expected): + result = to_offset(freqstr) + assert result.n == expected + + +@pytest.mark.parametrize( + "kwargs,expected", + [ + ({"days": 1, "seconds": 1}, offsets.Second(86401)), + ({"days": -1, "seconds": 1}, offsets.Second(-86399)), + ({"hours": 1, "minutes": 10}, offsets.Minute(70)), + ({"hours": 1, "minutes": -10}, offsets.Minute(50)), + ({"weeks": 1}, offsets.Day(7)), + ({"hours": 1}, offsets.Hour(1)), + ({"hours": 1}, to_offset("60min")), + ({"microseconds": 1}, offsets.Micro(1)), + ({"microseconds": 0}, offsets.Nano(0)), + ], +) +def test_to_offset_pd_timedelta(kwargs, expected): + # see gh-9064 + td = Timedelta(**kwargs) + result = to_offset(td) + assert result == expected + + +@pytest.mark.parametrize( + "shortcut,expected", + [ + ("W", offsets.Week(weekday=6)), + ("W-SUN", offsets.Week(weekday=6)), + ("QE", offsets.QuarterEnd(startingMonth=12)), + ("QE-DEC", offsets.QuarterEnd(startingMonth=12)), + ("QE-MAY", offsets.QuarterEnd(startingMonth=5)), + ("SME", offsets.SemiMonthEnd(day_of_month=15)), + ("SME-15", offsets.SemiMonthEnd(day_of_month=15)), + ("SME-1", offsets.SemiMonthEnd(day_of_month=1)), + ("SME-27", offsets.SemiMonthEnd(day_of_month=27)), + ("SMS-2", offsets.SemiMonthBegin(day_of_month=2)), + ("SMS-27", offsets.SemiMonthBegin(day_of_month=27)), + ], +) +def test_anchored_shortcuts(shortcut, expected): + result = to_offset(shortcut) + assert result == expected + + +@pytest.mark.parametrize( + "freq_depr", + [ + "2ye-mar", + "2ys", + "2qe", + "2qs-feb", + "2bqs", + "2sms", + "2bms", + "2cbme", + "2me", + "2w", + ], +) +def test_to_offset_lowercase_frequency_deprecated(freq_depr): + # GH#54939 + depr_msg = f"'{freq_depr[1:]}' is deprecated and will be removed in a " + f"future version, please use '{freq_depr.upper()[1:]}' instead." + + with pytest.raises(FutureWarning, match=depr_msg): + to_offset(freq_depr) + + +@pytest.mark.parametrize( + "freq_depr", + [ + "2H", + "2BH", + "2MIN", + "2S", + "2Us", + "2NS", + ], +) +def test_to_offset_uppercase_frequency_deprecated(freq_depr): + # GH#54939 + depr_msg = f"'{freq_depr[1:]}' is deprecated and will be removed in a " + f"future version, please use '{freq_depr.lower()[1:]}' instead." + + with pytest.raises(FutureWarning, match=depr_msg): + to_offset(freq_depr) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_tzconversion.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_tzconversion.py new file mode 100644 index 0000000000000000000000000000000000000000..c1a56ffb71b020df338721e44d56d7e03479fef6 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/tslibs/test_tzconversion.py @@ -0,0 +1,23 @@ +import numpy as np +import pytest +import pytz + +from pandas._libs.tslibs.tzconversion import tz_localize_to_utc + + +class TestTZLocalizeToUTC: + def test_tz_localize_to_utc_ambiguous_infer(self): + # val is a timestamp that is ambiguous when localized to US/Eastern + val = 1_320_541_200_000_000_000 + vals = np.array([val, val - 1, val], dtype=np.int64) + + with pytest.raises(pytz.AmbiguousTimeError, match="2011-11-06 01:00:00"): + tz_localize_to_utc(vals, pytz.timezone("US/Eastern"), ambiguous="infer") + + with pytest.raises(pytz.AmbiguousTimeError, match="are no repeated times"): + tz_localize_to_utc(vals[:1], pytz.timezone("US/Eastern"), ambiguous="infer") + + vals[1] += 1 + msg = "There are 2 dst switches when there should only be 1" + with pytest.raises(pytz.AmbiguousTimeError, match=msg): + tz_localize_to_utc(vals, pytz.timezone("US/Eastern"), ambiguous="infer") diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/conftest.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..b68bcc93431d015a5b9bdc47bdd7e46dd531b703 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/conftest.py @@ -0,0 +1,26 @@ +import pytest + + +@pytest.fixture(params=[True, False]) +def check_dtype(request): + return request.param + + +@pytest.fixture(params=[True, False]) +def check_exact(request): + return request.param + + +@pytest.fixture(params=[True, False]) +def check_index_type(request): + return request.param + + +@pytest.fixture(params=[0.5e-3, 0.5e-5]) +def rtol(request): + return request.param + + +@pytest.fixture(params=[True, False]) +def check_categorical(request): + return request.param diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_assert_almost_equal.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_assert_almost_equal.py new file mode 100644 index 0000000000000000000000000000000000000000..4e692084f7352f873b8c7354e7651b432058a1a5 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_assert_almost_equal.py @@ -0,0 +1,586 @@ +import numpy as np +import pytest + +from pandas import ( + NA, + DataFrame, + Index, + NaT, + Series, + Timestamp, +) +import pandas._testing as tm + + +def _assert_almost_equal_both(a, b, **kwargs): + """ + Check that two objects are approximately equal. + + This check is performed commutatively. + + Parameters + ---------- + a : object + The first object to compare. + b : object + The second object to compare. + **kwargs + The arguments passed to `tm.assert_almost_equal`. + """ + tm.assert_almost_equal(a, b, **kwargs) + tm.assert_almost_equal(b, a, **kwargs) + + +def _assert_not_almost_equal(a, b, **kwargs): + """ + Check that two objects are not approximately equal. + + Parameters + ---------- + a : object + The first object to compare. + b : object + The second object to compare. + **kwargs + The arguments passed to `tm.assert_almost_equal`. + """ + try: + tm.assert_almost_equal(a, b, **kwargs) + msg = f"{a} and {b} were approximately equal when they shouldn't have been" + pytest.fail(reason=msg) + except AssertionError: + pass + + +def _assert_not_almost_equal_both(a, b, **kwargs): + """ + Check that two objects are not approximately equal. + + This check is performed commutatively. + + Parameters + ---------- + a : object + The first object to compare. + b : object + The second object to compare. + **kwargs + The arguments passed to `tm.assert_almost_equal`. + """ + _assert_not_almost_equal(a, b, **kwargs) + _assert_not_almost_equal(b, a, **kwargs) + + +@pytest.mark.parametrize( + "a,b", + [ + (1.1, 1.1), + (1.1, 1.100001), + (np.int16(1), 1.000001), + (np.float64(1.1), 1.1), + (np.uint32(5), 5), + ], +) +def test_assert_almost_equal_numbers(a, b): + _assert_almost_equal_both(a, b) + + +@pytest.mark.parametrize( + "a,b", + [ + (1.1, 1), + (1.1, True), + (1, 2), + (1.0001, np.int16(1)), + # The following two examples are not "almost equal" due to tol. + (0.1, 0.1001), + (0.0011, 0.0012), + ], +) +def test_assert_not_almost_equal_numbers(a, b): + _assert_not_almost_equal_both(a, b) + + +@pytest.mark.parametrize( + "a,b", + [ + (1.1, 1.1), + (1.1, 1.100001), + (1.1, 1.1001), + (0.000001, 0.000005), + (1000.0, 1000.0005), + # Testing this example, as per #13357 + (0.000011, 0.000012), + ], +) +def test_assert_almost_equal_numbers_atol(a, b): + # Equivalent to the deprecated check_less_precise=True, enforced in 2.0 + _assert_almost_equal_both(a, b, rtol=0.5e-3, atol=0.5e-3) + + +@pytest.mark.parametrize("a,b", [(1.1, 1.11), (0.1, 0.101), (0.000011, 0.001012)]) +def test_assert_not_almost_equal_numbers_atol(a, b): + _assert_not_almost_equal_both(a, b, atol=1e-3) + + +@pytest.mark.parametrize( + "a,b", + [ + (1.1, 1.1), + (1.1, 1.100001), + (1.1, 1.1001), + (1000.0, 1000.0005), + (1.1, 1.11), + (0.1, 0.101), + ], +) +def test_assert_almost_equal_numbers_rtol(a, b): + _assert_almost_equal_both(a, b, rtol=0.05) + + +@pytest.mark.parametrize("a,b", [(0.000011, 0.000012), (0.000001, 0.000005)]) +def test_assert_not_almost_equal_numbers_rtol(a, b): + _assert_not_almost_equal_both(a, b, rtol=0.05) + + +@pytest.mark.parametrize( + "a,b,rtol", + [ + (1.00001, 1.00005, 0.001), + (-0.908356 + 0.2j, -0.908358 + 0.2j, 1e-3), + (0.1 + 1.009j, 0.1 + 1.006j, 0.1), + (0.1001 + 2.0j, 0.1 + 2.001j, 0.01), + ], +) +def test_assert_almost_equal_complex_numbers(a, b, rtol): + _assert_almost_equal_both(a, b, rtol=rtol) + _assert_almost_equal_both(np.complex64(a), np.complex64(b), rtol=rtol) + _assert_almost_equal_both(np.complex128(a), np.complex128(b), rtol=rtol) + + +@pytest.mark.parametrize( + "a,b,rtol", + [ + (0.58310768, 0.58330768, 1e-7), + (-0.908 + 0.2j, -0.978 + 0.2j, 0.001), + (0.1 + 1j, 0.1 + 2j, 0.01), + (-0.132 + 1.001j, -0.132 + 1.005j, 1e-5), + (0.58310768j, 0.58330768j, 1e-9), + ], +) +def test_assert_not_almost_equal_complex_numbers(a, b, rtol): + _assert_not_almost_equal_both(a, b, rtol=rtol) + _assert_not_almost_equal_both(np.complex64(a), np.complex64(b), rtol=rtol) + _assert_not_almost_equal_both(np.complex128(a), np.complex128(b), rtol=rtol) + + +@pytest.mark.parametrize("a,b", [(0, 0), (0, 0.0), (0, np.float64(0)), (0.00000001, 0)]) +def test_assert_almost_equal_numbers_with_zeros(a, b): + _assert_almost_equal_both(a, b) + + +@pytest.mark.parametrize("a,b", [(0.001, 0), (1, 0)]) +def test_assert_not_almost_equal_numbers_with_zeros(a, b): + _assert_not_almost_equal_both(a, b) + + +@pytest.mark.parametrize("a,b", [(1, "abc"), (1, [1]), (1, object())]) +def test_assert_not_almost_equal_numbers_with_mixed(a, b): + _assert_not_almost_equal_both(a, b) + + +@pytest.mark.parametrize( + "left_dtype", ["M8[ns]", "m8[ns]", "float64", "int64", "object"] +) +@pytest.mark.parametrize( + "right_dtype", ["M8[ns]", "m8[ns]", "float64", "int64", "object"] +) +def test_assert_almost_equal_edge_case_ndarrays(left_dtype, right_dtype): + # Empty compare. + _assert_almost_equal_both( + np.array([], dtype=left_dtype), + np.array([], dtype=right_dtype), + check_dtype=False, + ) + + +def test_assert_almost_equal_sets(): + # GH#51727 + _assert_almost_equal_both({1, 2, 3}, {1, 2, 3}) + + +def test_assert_almost_not_equal_sets(): + # GH#51727 + msg = r"{1, 2, 3} != {1, 2, 4}" + with pytest.raises(AssertionError, match=msg): + _assert_almost_equal_both({1, 2, 3}, {1, 2, 4}) + + +def test_assert_almost_equal_dicts(): + _assert_almost_equal_both({"a": 1, "b": 2}, {"a": 1, "b": 2}) + + +@pytest.mark.parametrize( + "a,b", + [ + ({"a": 1, "b": 2}, {"a": 1, "b": 3}), + ({"a": 1, "b": 2}, {"a": 1, "b": 2, "c": 3}), + ({"a": 1}, 1), + ({"a": 1}, "abc"), + ({"a": 1}, [1]), + ], +) +def test_assert_not_almost_equal_dicts(a, b): + _assert_not_almost_equal_both(a, b) + + +@pytest.mark.parametrize("val", [1, 2]) +def test_assert_almost_equal_dict_like_object(val): + dict_val = 1 + real_dict = {"a": val} + + class DictLikeObj: + def keys(self): + return ("a",) + + def __getitem__(self, item): + if item == "a": + return dict_val + + func = ( + _assert_almost_equal_both if val == dict_val else _assert_not_almost_equal_both + ) + func(real_dict, DictLikeObj(), check_dtype=False) + + +def test_assert_almost_equal_strings(): + _assert_almost_equal_both("abc", "abc") + + +@pytest.mark.parametrize( + "a,b", [("abc", "abcd"), ("abc", "abd"), ("abc", 1), ("abc", [1])] +) +def test_assert_not_almost_equal_strings(a, b): + _assert_not_almost_equal_both(a, b) + + +@pytest.mark.parametrize( + "a,b", [([1, 2, 3], [1, 2, 3]), (np.array([1, 2, 3]), np.array([1, 2, 3]))] +) +def test_assert_almost_equal_iterables(a, b): + _assert_almost_equal_both(a, b) + + +@pytest.mark.parametrize( + "a,b", + [ + # Class is different. + (np.array([1, 2, 3]), [1, 2, 3]), + # Dtype is different. + (np.array([1, 2, 3]), np.array([1.0, 2.0, 3.0])), + # Can't compare generators. + (iter([1, 2, 3]), [1, 2, 3]), + ([1, 2, 3], [1, 2, 4]), + ([1, 2, 3], [1, 2, 3, 4]), + ([1, 2, 3], 1), + ], +) +def test_assert_not_almost_equal_iterables(a, b): + _assert_not_almost_equal(a, b) + + +def test_assert_almost_equal_null(): + _assert_almost_equal_both(None, None) + + +@pytest.mark.parametrize("a,b", [(None, np.nan), (None, 0), (np.nan, 0)]) +def test_assert_not_almost_equal_null(a, b): + _assert_not_almost_equal(a, b) + + +@pytest.mark.parametrize( + "a,b", + [ + (np.inf, np.inf), + (np.inf, float("inf")), + (np.array([np.inf, np.nan, -np.inf]), np.array([np.inf, np.nan, -np.inf])), + ], +) +def test_assert_almost_equal_inf(a, b): + _assert_almost_equal_both(a, b) + + +objs = [NA, np.nan, NaT, None, np.datetime64("NaT"), np.timedelta64("NaT")] + + +@pytest.mark.parametrize("left", objs) +@pytest.mark.parametrize("right", objs) +def test_mismatched_na_assert_almost_equal_deprecation(left, right): + left_arr = np.array([left], dtype=object) + right_arr = np.array([right], dtype=object) + + msg = "Mismatched null-like values" + + if left is right: + _assert_almost_equal_both(left, right, check_dtype=False) + tm.assert_numpy_array_equal(left_arr, right_arr) + tm.assert_index_equal( + Index(left_arr, dtype=object), Index(right_arr, dtype=object) + ) + tm.assert_series_equal( + Series(left_arr, dtype=object), Series(right_arr, dtype=object) + ) + tm.assert_frame_equal( + DataFrame(left_arr, dtype=object), DataFrame(right_arr, dtype=object) + ) + + else: + with tm.assert_produces_warning(FutureWarning, match=msg): + _assert_almost_equal_both(left, right, check_dtype=False) + + # TODO: to get the same deprecation in assert_numpy_array_equal we need + # to change/deprecate the default for strict_nan to become True + # TODO: to get the same deprecation in assert_index_equal we need to + # change/deprecate array_equivalent_object to be stricter, as + # assert_index_equal uses Index.equal which uses array_equivalent. + with tm.assert_produces_warning(FutureWarning, match=msg): + tm.assert_series_equal( + Series(left_arr, dtype=object), Series(right_arr, dtype=object) + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + tm.assert_frame_equal( + DataFrame(left_arr, dtype=object), DataFrame(right_arr, dtype=object) + ) + + +def test_assert_not_almost_equal_inf(): + _assert_not_almost_equal_both(np.inf, 0) + + +@pytest.mark.parametrize( + "a,b", + [ + (Index([1.0, 1.1]), Index([1.0, 1.100001])), + (Series([1.0, 1.1]), Series([1.0, 1.100001])), + (np.array([1.1, 2.000001]), np.array([1.1, 2.0])), + (DataFrame({"a": [1.0, 1.1]}), DataFrame({"a": [1.0, 1.100001]})), + ], +) +def test_assert_almost_equal_pandas(a, b): + _assert_almost_equal_both(a, b) + + +def test_assert_almost_equal_object(): + a = [Timestamp("2011-01-01"), Timestamp("2011-01-01")] + b = [Timestamp("2011-01-01"), Timestamp("2011-01-01")] + _assert_almost_equal_both(a, b) + + +def test_assert_almost_equal_value_mismatch(): + msg = "expected 2\\.00000 but got 1\\.00000, with rtol=1e-05, atol=1e-08" + + with pytest.raises(AssertionError, match=msg): + tm.assert_almost_equal(1, 2) + + +@pytest.mark.parametrize( + "a,b,klass1,klass2", + [(np.array([1]), 1, "ndarray", "int"), (1, np.array([1]), "int", "ndarray")], +) +def test_assert_almost_equal_class_mismatch(a, b, klass1, klass2): + msg = f"""numpy array are different + +numpy array classes are different +\\[left\\]: {klass1} +\\[right\\]: {klass2}""" + + with pytest.raises(AssertionError, match=msg): + tm.assert_almost_equal(a, b) + + +def test_assert_almost_equal_value_mismatch1(): + msg = """numpy array are different + +numpy array values are different \\(66\\.66667 %\\) +\\[left\\]: \\[nan, 2\\.0, 3\\.0\\] +\\[right\\]: \\[1\\.0, nan, 3\\.0\\]""" + + with pytest.raises(AssertionError, match=msg): + tm.assert_almost_equal(np.array([np.nan, 2, 3]), np.array([1, np.nan, 3])) + + +def test_assert_almost_equal_value_mismatch2(): + msg = """numpy array are different + +numpy array values are different \\(50\\.0 %\\) +\\[left\\]: \\[1, 2\\] +\\[right\\]: \\[1, 3\\]""" + + with pytest.raises(AssertionError, match=msg): + tm.assert_almost_equal(np.array([1, 2]), np.array([1, 3])) + + +def test_assert_almost_equal_value_mismatch3(): + msg = """numpy array are different + +numpy array values are different \\(16\\.66667 %\\) +\\[left\\]: \\[\\[1, 2\\], \\[3, 4\\], \\[5, 6\\]\\] +\\[right\\]: \\[\\[1, 3\\], \\[3, 4\\], \\[5, 6\\]\\]""" + + with pytest.raises(AssertionError, match=msg): + tm.assert_almost_equal( + np.array([[1, 2], [3, 4], [5, 6]]), np.array([[1, 3], [3, 4], [5, 6]]) + ) + + +def test_assert_almost_equal_value_mismatch4(): + msg = """numpy array are different + +numpy array values are different \\(25\\.0 %\\) +\\[left\\]: \\[\\[1, 2\\], \\[3, 4\\]\\] +\\[right\\]: \\[\\[1, 3\\], \\[3, 4\\]\\]""" + + with pytest.raises(AssertionError, match=msg): + tm.assert_almost_equal(np.array([[1, 2], [3, 4]]), np.array([[1, 3], [3, 4]])) + + +def test_assert_almost_equal_shape_mismatch_override(): + msg = """Index are different + +Index shapes are different +\\[left\\]: \\(2L*,\\) +\\[right\\]: \\(3L*,\\)""" + with pytest.raises(AssertionError, match=msg): + tm.assert_almost_equal(np.array([1, 2]), np.array([3, 4, 5]), obj="Index") + + +def test_assert_almost_equal_unicode(): + # see gh-20503 + msg = """numpy array are different + +numpy array values are different \\(33\\.33333 %\\) +\\[left\\]: \\[á, à, ä\\] +\\[right\\]: \\[á, à, å\\]""" + + with pytest.raises(AssertionError, match=msg): + tm.assert_almost_equal(np.array(["á", "à", "ä"]), np.array(["á", "à", "å"])) + + +def test_assert_almost_equal_timestamp(): + a = np.array([Timestamp("2011-01-01"), Timestamp("2011-01-01")]) + b = np.array([Timestamp("2011-01-01"), Timestamp("2011-01-02")]) + + msg = """numpy array are different + +numpy array values are different \\(50\\.0 %\\) +\\[left\\]: \\[2011-01-01 00:00:00, 2011-01-01 00:00:00\\] +\\[right\\]: \\[2011-01-01 00:00:00, 2011-01-02 00:00:00\\]""" + + with pytest.raises(AssertionError, match=msg): + tm.assert_almost_equal(a, b) + + +def test_assert_almost_equal_iterable_length_mismatch(): + msg = """Iterable are different + +Iterable length are different +\\[left\\]: 2 +\\[right\\]: 3""" + + with pytest.raises(AssertionError, match=msg): + tm.assert_almost_equal([1, 2], [3, 4, 5]) + + +def test_assert_almost_equal_iterable_values_mismatch(): + msg = """Iterable are different + +Iterable values are different \\(50\\.0 %\\) +\\[left\\]: \\[1, 2\\] +\\[right\\]: \\[1, 3\\]""" + + with pytest.raises(AssertionError, match=msg): + tm.assert_almost_equal([1, 2], [1, 3]) + + +subarr = np.empty(2, dtype=object) +subarr[:] = [np.array([None, "b"], dtype=object), np.array(["c", "d"], dtype=object)] + +NESTED_CASES = [ + # nested array + ( + np.array([np.array([50, 70, 90]), np.array([20, 30])], dtype=object), + np.array([np.array([50, 70, 90]), np.array([20, 30])], dtype=object), + ), + # >1 level of nesting + ( + np.array( + [ + np.array([np.array([50, 70]), np.array([90])], dtype=object), + np.array([np.array([20, 30])], dtype=object), + ], + dtype=object, + ), + np.array( + [ + np.array([np.array([50, 70]), np.array([90])], dtype=object), + np.array([np.array([20, 30])], dtype=object), + ], + dtype=object, + ), + ), + # lists + ( + np.array([[50, 70, 90], [20, 30]], dtype=object), + np.array([[50, 70, 90], [20, 30]], dtype=object), + ), + # mixed array/list + ( + np.array([np.array([1, 2, 3]), np.array([4, 5])], dtype=object), + np.array([[1, 2, 3], [4, 5]], dtype=object), + ), + ( + np.array( + [ + np.array([np.array([1, 2, 3]), np.array([4, 5])], dtype=object), + np.array( + [np.array([6]), np.array([7, 8]), np.array([9])], dtype=object + ), + ], + dtype=object, + ), + np.array([[[1, 2, 3], [4, 5]], [[6], [7, 8], [9]]], dtype=object), + ), + # same-length lists + ( + np.array([subarr, None], dtype=object), + np.array([[[None, "b"], ["c", "d"]], None], dtype=object), + ), + # dicts + ( + np.array([{"f1": 1, "f2": np.array(["a", "b"], dtype=object)}], dtype=object), + np.array([{"f1": 1, "f2": np.array(["a", "b"], dtype=object)}], dtype=object), + ), + ( + np.array([{"f1": 1, "f2": np.array(["a", "b"], dtype=object)}], dtype=object), + np.array([{"f1": 1, "f2": ["a", "b"]}], dtype=object), + ), + # array/list of dicts + ( + np.array( + [ + np.array( + [{"f1": 1, "f2": np.array(["a", "b"], dtype=object)}], dtype=object + ), + np.array([], dtype=object), + ], + dtype=object, + ), + np.array([[{"f1": 1, "f2": ["a", "b"]}], []], dtype=object), + ), +] + + +@pytest.mark.filterwarnings("ignore:elementwise comparison failed:DeprecationWarning") +@pytest.mark.parametrize("a,b", NESTED_CASES) +def test_assert_almost_equal_array_nested(a, b): + _assert_almost_equal_both(a, b) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_assert_attr_equal.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_assert_attr_equal.py new file mode 100644 index 0000000000000000000000000000000000000000..bbbb0bf2172b12f93c9f0f6a97751854d1566a99 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_assert_attr_equal.py @@ -0,0 +1,33 @@ +from types import SimpleNamespace + +import pytest + +from pandas.core.dtypes.common import is_float + +import pandas._testing as tm + + +def test_assert_attr_equal(nulls_fixture): + obj = SimpleNamespace() + obj.na_value = nulls_fixture + tm.assert_attr_equal("na_value", obj, obj) + + +def test_assert_attr_equal_different_nulls(nulls_fixture, nulls_fixture2): + obj = SimpleNamespace() + obj.na_value = nulls_fixture + + obj2 = SimpleNamespace() + obj2.na_value = nulls_fixture2 + + if nulls_fixture is nulls_fixture2: + tm.assert_attr_equal("na_value", obj, obj2) + elif is_float(nulls_fixture) and is_float(nulls_fixture2): + # we consider float("nan") and np.float64("nan") to be equivalent + tm.assert_attr_equal("na_value", obj, obj2) + elif type(nulls_fixture) is type(nulls_fixture2): + # e.g. Decimal("NaN") + tm.assert_attr_equal("na_value", obj, obj2) + else: + with pytest.raises(AssertionError, match='"na_value" are different'): + tm.assert_attr_equal("na_value", obj, obj2) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_assert_categorical_equal.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_assert_categorical_equal.py new file mode 100644 index 0000000000000000000000000000000000000000..d07bbcbc460a19ec943c1f8727e25835803cf0e4 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_assert_categorical_equal.py @@ -0,0 +1,90 @@ +import pytest + +from pandas import Categorical +import pandas._testing as tm + + +@pytest.mark.parametrize( + "c", + [Categorical([1, 2, 3, 4]), Categorical([1, 2, 3, 4], categories=[1, 2, 3, 4, 5])], +) +def test_categorical_equal(c): + tm.assert_categorical_equal(c, c) + + +@pytest.mark.parametrize("check_category_order", [True, False]) +def test_categorical_equal_order_mismatch(check_category_order): + c1 = Categorical([1, 2, 3, 4], categories=[1, 2, 3, 4]) + c2 = Categorical([1, 2, 3, 4], categories=[4, 3, 2, 1]) + kwargs = {"check_category_order": check_category_order} + + if check_category_order: + msg = """Categorical\\.categories are different + +Categorical\\.categories values are different \\(100\\.0 %\\) +\\[left\\]: Index\\(\\[1, 2, 3, 4\\], dtype='int64'\\) +\\[right\\]: Index\\(\\[4, 3, 2, 1\\], dtype='int64'\\)""" + with pytest.raises(AssertionError, match=msg): + tm.assert_categorical_equal(c1, c2, **kwargs) + else: + tm.assert_categorical_equal(c1, c2, **kwargs) + + +def test_categorical_equal_categories_mismatch(): + msg = """Categorical\\.categories are different + +Categorical\\.categories values are different \\(25\\.0 %\\) +\\[left\\]: Index\\(\\[1, 2, 3, 4\\], dtype='int64'\\) +\\[right\\]: Index\\(\\[1, 2, 3, 5\\], dtype='int64'\\)""" + + c1 = Categorical([1, 2, 3, 4]) + c2 = Categorical([1, 2, 3, 5]) + + with pytest.raises(AssertionError, match=msg): + tm.assert_categorical_equal(c1, c2) + + +def test_categorical_equal_codes_mismatch(): + categories = [1, 2, 3, 4] + msg = """Categorical\\.codes are different + +Categorical\\.codes values are different \\(50\\.0 %\\) +\\[left\\]: \\[0, 1, 3, 2\\] +\\[right\\]: \\[0, 1, 2, 3\\]""" + + c1 = Categorical([1, 2, 4, 3], categories=categories) + c2 = Categorical([1, 2, 3, 4], categories=categories) + + with pytest.raises(AssertionError, match=msg): + tm.assert_categorical_equal(c1, c2) + + +def test_categorical_equal_ordered_mismatch(): + data = [1, 2, 3, 4] + msg = """Categorical are different + +Attribute "ordered" are different +\\[left\\]: False +\\[right\\]: True""" + + c1 = Categorical(data, ordered=False) + c2 = Categorical(data, ordered=True) + + with pytest.raises(AssertionError, match=msg): + tm.assert_categorical_equal(c1, c2) + + +@pytest.mark.parametrize("obj", ["index", "foo", "pandas"]) +def test_categorical_equal_object_override(obj): + data = [1, 2, 3, 4] + msg = f"""{obj} are different + +Attribute "ordered" are different +\\[left\\]: False +\\[right\\]: True""" + + c1 = Categorical(data, ordered=False) + c2 = Categorical(data, ordered=True) + + with pytest.raises(AssertionError, match=msg): + tm.assert_categorical_equal(c1, c2, obj=obj) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_assert_extension_array_equal.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_assert_extension_array_equal.py new file mode 100644 index 0000000000000000000000000000000000000000..674e9307d8bb982826f7c92da798ba8d1eee9fde --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_assert_extension_array_equal.py @@ -0,0 +1,126 @@ +import numpy as np +import pytest + +from pandas import ( + Timestamp, + array, +) +import pandas._testing as tm +from pandas.core.arrays.sparse import SparseArray + + +@pytest.mark.parametrize( + "kwargs", + [ + {}, # Default is check_exact=False + {"check_exact": False}, + {"check_exact": True}, + ], +) +def test_assert_extension_array_equal_not_exact(kwargs): + # see gh-23709 + arr1 = SparseArray([-0.17387645482451206, 0.3414148016424936]) + arr2 = SparseArray([-0.17387645482451206, 0.3414148016424937]) + + if kwargs.get("check_exact", False): + msg = """\ +ExtensionArray are different + +ExtensionArray values are different \\(50\\.0 %\\) +\\[left\\]: \\[-0\\.17387645482.*, 0\\.341414801642.*\\] +\\[right\\]: \\[-0\\.17387645482.*, 0\\.341414801642.*\\]""" + + with pytest.raises(AssertionError, match=msg): + tm.assert_extension_array_equal(arr1, arr2, **kwargs) + else: + tm.assert_extension_array_equal(arr1, arr2, **kwargs) + + +@pytest.mark.parametrize("decimals", range(10)) +def test_assert_extension_array_equal_less_precise(decimals): + rtol = 0.5 * 10**-decimals + arr1 = SparseArray([0.5, 0.123456]) + arr2 = SparseArray([0.5, 0.123457]) + + if decimals >= 5: + msg = """\ +ExtensionArray are different + +ExtensionArray values are different \\(50\\.0 %\\) +\\[left\\]: \\[0\\.5, 0\\.123456\\] +\\[right\\]: \\[0\\.5, 0\\.123457\\]""" + + with pytest.raises(AssertionError, match=msg): + tm.assert_extension_array_equal(arr1, arr2, rtol=rtol) + else: + tm.assert_extension_array_equal(arr1, arr2, rtol=rtol) + + +def test_assert_extension_array_equal_dtype_mismatch(check_dtype): + end = 5 + kwargs = {"check_dtype": check_dtype} + + arr1 = SparseArray(np.arange(end, dtype="int64")) + arr2 = SparseArray(np.arange(end, dtype="int32")) + + if check_dtype: + msg = """\ +ExtensionArray are different + +Attribute "dtype" are different +\\[left\\]: Sparse\\[int64, 0\\] +\\[right\\]: Sparse\\[int32, 0\\]""" + + with pytest.raises(AssertionError, match=msg): + tm.assert_extension_array_equal(arr1, arr2, **kwargs) + else: + tm.assert_extension_array_equal(arr1, arr2, **kwargs) + + +def test_assert_extension_array_equal_missing_values(): + arr1 = SparseArray([np.nan, 1, 2, np.nan]) + arr2 = SparseArray([np.nan, 1, 2, 3]) + + msg = """\ +ExtensionArray NA mask are different + +ExtensionArray NA mask values are different \\(25\\.0 %\\) +\\[left\\]: \\[True, False, False, True\\] +\\[right\\]: \\[True, False, False, False\\]""" + + with pytest.raises(AssertionError, match=msg): + tm.assert_extension_array_equal(arr1, arr2) + + +@pytest.mark.parametrize("side", ["left", "right"]) +def test_assert_extension_array_equal_non_extension_array(side): + numpy_array = np.arange(5) + extension_array = SparseArray(numpy_array) + + msg = f"{side} is not an ExtensionArray" + args = ( + (numpy_array, extension_array) + if side == "left" + else (extension_array, numpy_array) + ) + + with pytest.raises(AssertionError, match=msg): + tm.assert_extension_array_equal(*args) + + +@pytest.mark.parametrize("right_dtype", ["Int32", "int64"]) +def test_assert_extension_array_equal_ignore_dtype_mismatch(right_dtype): + # https://github.com/pandas-dev/pandas/issues/35715 + left = array([1, 2, 3], dtype="Int64") + right = array([1, 2, 3], dtype=right_dtype) + tm.assert_extension_array_equal(left, right, check_dtype=False) + + +def test_assert_extension_array_equal_time_units(): + # https://github.com/pandas-dev/pandas/issues/55730 + timestamp = Timestamp("2023-11-04T12") + naive = array([timestamp], dtype="datetime64[ns]") + utc = array([timestamp], dtype="datetime64[ns, UTC]") + + tm.assert_extension_array_equal(naive, utc, check_dtype=False) + tm.assert_extension_array_equal(utc, naive, check_dtype=False) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_assert_frame_equal.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_assert_frame_equal.py new file mode 100644 index 0000000000000000000000000000000000000000..dd5218ab9404f35b009cf5f81c09f9d58c78bb18 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_assert_frame_equal.py @@ -0,0 +1,393 @@ +import pytest + +import pandas as pd +from pandas import DataFrame +import pandas._testing as tm + + +@pytest.fixture(params=[True, False]) +def by_blocks_fixture(request): + return request.param + + +@pytest.fixture(params=["DataFrame", "Series"]) +def obj_fixture(request): + return request.param + + +def _assert_frame_equal_both(a, b, **kwargs): + """ + Check that two DataFrame equal. + + This check is performed commutatively. + + Parameters + ---------- + a : DataFrame + The first DataFrame to compare. + b : DataFrame + The second DataFrame to compare. + kwargs : dict + The arguments passed to `tm.assert_frame_equal`. + """ + tm.assert_frame_equal(a, b, **kwargs) + tm.assert_frame_equal(b, a, **kwargs) + + +@pytest.mark.parametrize("check_like", [True, False]) +def test_frame_equal_row_order_mismatch(check_like, obj_fixture): + df1 = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}, index=["a", "b", "c"]) + df2 = DataFrame({"A": [3, 2, 1], "B": [6, 5, 4]}, index=["c", "b", "a"]) + + if not check_like: # Do not ignore row-column orderings. + msg = f"{obj_fixture}.index are different" + with pytest.raises(AssertionError, match=msg): + tm.assert_frame_equal(df1, df2, check_like=check_like, obj=obj_fixture) + else: + _assert_frame_equal_both(df1, df2, check_like=check_like, obj=obj_fixture) + + +@pytest.mark.parametrize( + "df1,df2", + [ + (DataFrame({"A": [1, 2, 3]}), DataFrame({"A": [1, 2, 3, 4]})), + (DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}), DataFrame({"A": [1, 2, 3]})), + ], +) +def test_frame_equal_shape_mismatch(df1, df2, obj_fixture): + msg = f"{obj_fixture} are different" + + with pytest.raises(AssertionError, match=msg): + tm.assert_frame_equal(df1, df2, obj=obj_fixture) + + +@pytest.mark.parametrize( + "df1,df2,msg", + [ + # Index + ( + DataFrame.from_records({"a": [1, 2], "c": ["l1", "l2"]}, index=["a"]), + DataFrame.from_records({"a": [1.0, 2.0], "c": ["l1", "l2"]}, index=["a"]), + "DataFrame\\.index are different", + ), + # MultiIndex + ( + DataFrame.from_records( + {"a": [1, 2], "b": [2.1, 1.5], "c": ["l1", "l2"]}, index=["a", "b"] + ), + DataFrame.from_records( + {"a": [1.0, 2.0], "b": [2.1, 1.5], "c": ["l1", "l2"]}, index=["a", "b"] + ), + "MultiIndex level \\[0\\] are different", + ), + ], +) +def test_frame_equal_index_dtype_mismatch(df1, df2, msg, check_index_type): + kwargs = {"check_index_type": check_index_type} + + if check_index_type: + with pytest.raises(AssertionError, match=msg): + tm.assert_frame_equal(df1, df2, **kwargs) + else: + tm.assert_frame_equal(df1, df2, **kwargs) + + +def test_empty_dtypes(check_dtype): + columns = ["col1", "col2"] + df1 = DataFrame(columns=columns) + df2 = DataFrame(columns=columns) + + kwargs = {"check_dtype": check_dtype} + df1["col1"] = df1["col1"].astype("int64") + + if check_dtype: + msg = r"Attributes of DataFrame\..* are different" + with pytest.raises(AssertionError, match=msg): + tm.assert_frame_equal(df1, df2, **kwargs) + else: + tm.assert_frame_equal(df1, df2, **kwargs) + + +@pytest.mark.parametrize("check_like", [True, False]) +def test_frame_equal_index_mismatch(check_like, obj_fixture, using_infer_string): + if using_infer_string: + dtype = "str" + else: + dtype = "object" + msg = f"""{obj_fixture}\\.index are different + +{obj_fixture}\\.index values are different \\(33\\.33333 %\\) +\\[left\\]: Index\\(\\['a', 'b', 'c'\\], dtype='{dtype}'\\) +\\[right\\]: Index\\(\\['a', 'b', 'd'\\], dtype='{dtype}'\\) +At positional index 2, first diff: c != d""" + + df1 = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}, index=["a", "b", "c"]) + df2 = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}, index=["a", "b", "d"]) + + with pytest.raises(AssertionError, match=msg): + tm.assert_frame_equal(df1, df2, check_like=check_like, obj=obj_fixture) + + +@pytest.mark.parametrize("check_like", [True, False]) +def test_frame_equal_columns_mismatch(check_like, obj_fixture, using_infer_string): + if using_infer_string: + dtype = "str" + else: + dtype = "object" + msg = f"""{obj_fixture}\\.columns are different + +{obj_fixture}\\.columns values are different \\(50\\.0 %\\) +\\[left\\]: Index\\(\\['A', 'B'\\], dtype='{dtype}'\\) +\\[right\\]: Index\\(\\['A', 'b'\\], dtype='{dtype}'\\)""" + + df1 = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}, index=["a", "b", "c"]) + df2 = DataFrame({"A": [1, 2, 3], "b": [4, 5, 6]}, index=["a", "b", "c"]) + + with pytest.raises(AssertionError, match=msg): + tm.assert_frame_equal(df1, df2, check_like=check_like, obj=obj_fixture) + + +def test_frame_equal_block_mismatch(by_blocks_fixture, obj_fixture): + obj = obj_fixture + msg = f"""{obj}\\.iloc\\[:, 1\\] \\(column name="B"\\) are different + +{obj}\\.iloc\\[:, 1\\] \\(column name="B"\\) values are different \\(33\\.33333 %\\) +\\[index\\]: \\[0, 1, 2\\] +\\[left\\]: \\[4, 5, 6\\] +\\[right\\]: \\[4, 5, 7\\]""" + + df1 = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) + df2 = DataFrame({"A": [1, 2, 3], "B": [4, 5, 7]}) + + with pytest.raises(AssertionError, match=msg): + tm.assert_frame_equal(df1, df2, by_blocks=by_blocks_fixture, obj=obj_fixture) + + +@pytest.mark.parametrize( + "df1,df2,msg", + [ + ( + DataFrame({"A": ["á", "à", "ä"], "E": ["é", "è", "ë"]}), + DataFrame({"A": ["á", "à", "ä"], "E": ["é", "è", "e̊"]}), + """{obj}\\.iloc\\[:, 1\\] \\(column name="E"\\) are different + +{obj}\\.iloc\\[:, 1\\] \\(column name="E"\\) values are different \\(33\\.33333 %\\) +\\[index\\]: \\[0, 1, 2\\] +\\[left\\]: \\[é, è, ë\\] +\\[right\\]: \\[é, è, e̊\\]""", + ), + ( + DataFrame({"A": ["á", "à", "ä"], "E": ["é", "è", "ë"]}), + DataFrame({"A": ["a", "a", "a"], "E": ["e", "e", "e"]}), + """{obj}\\.iloc\\[:, 0\\] \\(column name="A"\\) are different + +{obj}\\.iloc\\[:, 0\\] \\(column name="A"\\) values are different \\(100\\.0 %\\) +\\[index\\]: \\[0, 1, 2\\] +\\[left\\]: \\[á, à, ä\\] +\\[right\\]: \\[a, a, a\\]""", + ), + ], +) +def test_frame_equal_unicode(df1, df2, msg, by_blocks_fixture, obj_fixture): + # see gh-20503 + # + # Test ensures that `tm.assert_frame_equals` raises the right exception + # when comparing DataFrames containing differing unicode objects. + msg = msg.format(obj=obj_fixture) + with pytest.raises(AssertionError, match=msg): + tm.assert_frame_equal(df1, df2, by_blocks=by_blocks_fixture, obj=obj_fixture) + + +def test_assert_frame_equal_extension_dtype_mismatch(): + # https://github.com/pandas-dev/pandas/issues/32747 + left = DataFrame({"a": [1, 2, 3]}, dtype="Int64") + right = left.astype(int) + + msg = ( + "Attributes of DataFrame\\.iloc\\[:, 0\\] " + '\\(column name="a"\\) are different\n\n' + 'Attribute "dtype" are different\n' + "\\[left\\]: Int64\n" + "\\[right\\]: int[32|64]" + ) + + tm.assert_frame_equal(left, right, check_dtype=False) + + with pytest.raises(AssertionError, match=msg): + tm.assert_frame_equal(left, right, check_dtype=True) + + +def test_assert_frame_equal_interval_dtype_mismatch(): + # https://github.com/pandas-dev/pandas/issues/32747 + left = DataFrame({"a": [pd.Interval(0, 1)]}, dtype="interval") + right = left.astype(object) + + msg = ( + "Attributes of DataFrame\\.iloc\\[:, 0\\] " + '\\(column name="a"\\) are different\n\n' + 'Attribute "dtype" are different\n' + "\\[left\\]: interval\\[int64, right\\]\n" + "\\[right\\]: object" + ) + + tm.assert_frame_equal(left, right, check_dtype=False) + + with pytest.raises(AssertionError, match=msg): + tm.assert_frame_equal(left, right, check_dtype=True) + + +def test_assert_frame_equal_ignore_extension_dtype_mismatch(): + # https://github.com/pandas-dev/pandas/issues/35715 + left = DataFrame({"a": [1, 2, 3]}, dtype="Int64") + right = DataFrame({"a": [1, 2, 3]}, dtype="Int32") + tm.assert_frame_equal(left, right, check_dtype=False) + + +def test_assert_frame_equal_ignore_extension_dtype_mismatch_cross_class(): + # https://github.com/pandas-dev/pandas/issues/35715 + left = DataFrame({"a": [1, 2, 3]}, dtype="Int64") + right = DataFrame({"a": [1, 2, 3]}, dtype="int64") + tm.assert_frame_equal(left, right, check_dtype=False) + + +@pytest.mark.parametrize( + "dtype", + [ + ("timedelta64[ns]"), + ("datetime64[ns, UTC]"), + ("Period[D]"), + ], +) +def test_assert_frame_equal_datetime_like_dtype_mismatch(dtype): + df1 = DataFrame({"a": []}, dtype=dtype) + df2 = DataFrame({"a": []}) + tm.assert_frame_equal(df1, df2, check_dtype=False) + + +def test_allows_duplicate_labels(): + left = DataFrame() + right = DataFrame().set_flags(allows_duplicate_labels=False) + tm.assert_frame_equal(left, left) + tm.assert_frame_equal(right, right) + tm.assert_frame_equal(left, right, check_flags=False) + tm.assert_frame_equal(right, left, check_flags=False) + + with pytest.raises(AssertionError, match="\\]""" + + with pytest.raises(AssertionError, match=msg): + tm.assert_numpy_array_equal(a, b) + + +def test_numpy_array_equal_identical_na(nulls_fixture): + a = np.array([nulls_fixture], dtype=object) + + tm.assert_numpy_array_equal(a, a) + + # matching but not the identical object + if hasattr(nulls_fixture, "copy"): + other = nulls_fixture.copy() + else: + other = copy.copy(nulls_fixture) + b = np.array([other], dtype=object) + tm.assert_numpy_array_equal(a, b) + + +def test_numpy_array_equal_different_na(): + a = np.array([np.nan], dtype=object) + b = np.array([pd.NA], dtype=object) + + msg = """numpy array are different + +numpy array values are different \\(100.0 %\\) +\\[left\\]: \\[nan\\] +\\[right\\]: \\[\\]""" + + with pytest.raises(AssertionError, match=msg): + tm.assert_numpy_array_equal(a, b) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_assert_produces_warning.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_assert_produces_warning.py new file mode 100644 index 0000000000000000000000000000000000000000..5c27a3ee79d4a82bce83eec56ab9d88e10dc06cd --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_assert_produces_warning.py @@ -0,0 +1,241 @@ +"""" +Test module for testing ``pandas._testing.assert_produces_warning``. +""" +import warnings + +import pytest + +from pandas.errors import ( + DtypeWarning, + PerformanceWarning, +) + +import pandas._testing as tm + + +@pytest.fixture( + params=[ + RuntimeWarning, + ResourceWarning, + UserWarning, + FutureWarning, + DeprecationWarning, + PerformanceWarning, + DtypeWarning, + ], +) +def category(request): + """ + Return unique warning. + + Useful for testing behavior of tm.assert_produces_warning with various categories. + """ + return request.param + + +@pytest.fixture( + params=[ + (RuntimeWarning, UserWarning), + (UserWarning, FutureWarning), + (FutureWarning, RuntimeWarning), + (DeprecationWarning, PerformanceWarning), + (PerformanceWarning, FutureWarning), + (DtypeWarning, DeprecationWarning), + (ResourceWarning, DeprecationWarning), + (FutureWarning, DeprecationWarning), + ], + ids=lambda x: type(x).__name__, +) +def pair_different_warnings(request): + """ + Return pair or different warnings. + + Useful for testing how several different warnings are handled + in tm.assert_produces_warning. + """ + return request.param + + +def f(): + warnings.warn("f1", FutureWarning) + warnings.warn("f2", RuntimeWarning) + + +@pytest.mark.filterwarnings("ignore:f1:FutureWarning") +def test_assert_produces_warning_honors_filter(): + # Raise by default. + msg = r"Caused unexpected warning\(s\)" + with pytest.raises(AssertionError, match=msg): + with tm.assert_produces_warning(RuntimeWarning): + f() + + with tm.assert_produces_warning(RuntimeWarning, raise_on_extra_warnings=False): + f() + + +@pytest.mark.parametrize( + "message, match", + [ + ("", None), + ("", ""), + ("Warning message", r".*"), + ("Warning message", "War"), + ("Warning message", r"[Ww]arning"), + ("Warning message", "age"), + ("Warning message", r"age$"), + ("Message 12-234 with numbers", r"\d{2}-\d{3}"), + ("Message 12-234 with numbers", r"^Mes.*\d{2}-\d{3}"), + ("Message 12-234 with numbers", r"\d{2}-\d{3}\s\S+"), + ("Message, which we do not match", None), + ], +) +def test_catch_warning_category_and_match(category, message, match): + with tm.assert_produces_warning(category, match=match): + warnings.warn(message, category) + + +def test_fail_to_match_runtime_warning(): + category = RuntimeWarning + match = "Did not see this warning" + unmatched = ( + r"Did not see warning 'RuntimeWarning' matching 'Did not see this warning'. " + r"The emitted warning messages are " + r"\[RuntimeWarning\('This is not a match.'\), " + r"RuntimeWarning\('Another unmatched warning.'\)\]" + ) + with pytest.raises(AssertionError, match=unmatched): + with tm.assert_produces_warning(category, match=match): + warnings.warn("This is not a match.", category) + warnings.warn("Another unmatched warning.", category) + + +def test_fail_to_match_future_warning(): + category = FutureWarning + match = "Warning" + unmatched = ( + r"Did not see warning 'FutureWarning' matching 'Warning'. " + r"The emitted warning messages are " + r"\[FutureWarning\('This is not a match.'\), " + r"FutureWarning\('Another unmatched warning.'\)\]" + ) + with pytest.raises(AssertionError, match=unmatched): + with tm.assert_produces_warning(category, match=match): + warnings.warn("This is not a match.", category) + warnings.warn("Another unmatched warning.", category) + + +def test_fail_to_match_resource_warning(): + category = ResourceWarning + match = r"\d+" + unmatched = ( + r"Did not see warning 'ResourceWarning' matching '\\d\+'. " + r"The emitted warning messages are " + r"\[ResourceWarning\('This is not a match.'\), " + r"ResourceWarning\('Another unmatched warning.'\)\]" + ) + with pytest.raises(AssertionError, match=unmatched): + with tm.assert_produces_warning(category, match=match): + warnings.warn("This is not a match.", category) + warnings.warn("Another unmatched warning.", category) + + +def test_fail_to_catch_actual_warning(pair_different_warnings): + expected_category, actual_category = pair_different_warnings + match = "Did not see expected warning of class" + with pytest.raises(AssertionError, match=match): + with tm.assert_produces_warning(expected_category): + warnings.warn("warning message", actual_category) + + +def test_ignore_extra_warning(pair_different_warnings): + expected_category, extra_category = pair_different_warnings + with tm.assert_produces_warning(expected_category, raise_on_extra_warnings=False): + warnings.warn("Expected warning", expected_category) + warnings.warn("Unexpected warning OK", extra_category) + + +def test_raise_on_extra_warning(pair_different_warnings): + expected_category, extra_category = pair_different_warnings + match = r"Caused unexpected warning\(s\)" + with pytest.raises(AssertionError, match=match): + with tm.assert_produces_warning(expected_category): + warnings.warn("Expected warning", expected_category) + warnings.warn("Unexpected warning NOT OK", extra_category) + + +def test_same_category_different_messages_first_match(): + category = UserWarning + with tm.assert_produces_warning(category, match=r"^Match this"): + warnings.warn("Match this", category) + warnings.warn("Do not match that", category) + warnings.warn("Do not match that either", category) + + +def test_same_category_different_messages_last_match(): + category = DeprecationWarning + with tm.assert_produces_warning(category, match=r"^Match this"): + warnings.warn("Do not match that", category) + warnings.warn("Do not match that either", category) + warnings.warn("Match this", category) + + +def test_match_multiple_warnings(): + # https://github.com/pandas-dev/pandas/issues/47829 + category = (FutureWarning, UserWarning) + with tm.assert_produces_warning(category, match=r"^Match this"): + warnings.warn("Match this", FutureWarning) + warnings.warn("Match this too", UserWarning) + + +def test_right_category_wrong_match_raises(pair_different_warnings): + target_category, other_category = pair_different_warnings + with pytest.raises(AssertionError, match="Did not see warning.*matching"): + with tm.assert_produces_warning(target_category, match=r"^Match this"): + warnings.warn("Do not match it", target_category) + warnings.warn("Match this", other_category) + + +@pytest.mark.parametrize("false_or_none", [False, None]) +class TestFalseOrNoneExpectedWarning: + def test_raise_on_warning(self, false_or_none): + msg = r"Caused unexpected warning\(s\)" + with pytest.raises(AssertionError, match=msg): + with tm.assert_produces_warning(false_or_none): + f() + + def test_no_raise_without_warning(self, false_or_none): + with tm.assert_produces_warning(false_or_none): + pass + + def test_no_raise_with_false_raise_on_extra(self, false_or_none): + with tm.assert_produces_warning(false_or_none, raise_on_extra_warnings=False): + f() + + +def test_raises_during_exception(): + msg = "Did not see expected warning of class 'UserWarning'" + with pytest.raises(AssertionError, match=msg): + with tm.assert_produces_warning(UserWarning): + raise ValueError + + with pytest.raises(AssertionError, match=msg): + with tm.assert_produces_warning(UserWarning): + warnings.warn("FutureWarning", FutureWarning) + raise IndexError + + msg = "Caused unexpected warning" + with pytest.raises(AssertionError, match=msg): + with tm.assert_produces_warning(None): + warnings.warn("FutureWarning", FutureWarning) + raise SystemError + + +def test_passes_during_exception(): + with pytest.raises(SyntaxError, match="Error"): + with tm.assert_produces_warning(None): + raise SyntaxError("Error") + + with pytest.raises(ValueError, match="Error"): + with tm.assert_produces_warning(FutureWarning, match="FutureWarning"): + warnings.warn("FutureWarning", FutureWarning) + raise ValueError("Error") diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_assert_series_equal.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_assert_series_equal.py new file mode 100644 index 0000000000000000000000000000000000000000..9cc9fb924ed67b5f4e9a3ac26acda3f346681d23 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_assert_series_equal.py @@ -0,0 +1,475 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Categorical, + DataFrame, + Series, +) +import pandas._testing as tm + + +def _assert_series_equal_both(a, b, **kwargs): + """ + Check that two Series equal. + + This check is performed commutatively. + + Parameters + ---------- + a : Series + The first Series to compare. + b : Series + The second Series to compare. + kwargs : dict + The arguments passed to `tm.assert_series_equal`. + """ + tm.assert_series_equal(a, b, **kwargs) + tm.assert_series_equal(b, a, **kwargs) + + +def _assert_not_series_equal(a, b, **kwargs): + """ + Check that two Series are not equal. + + Parameters + ---------- + a : Series + The first Series to compare. + b : Series + The second Series to compare. + kwargs : dict + The arguments passed to `tm.assert_series_equal`. + """ + try: + tm.assert_series_equal(a, b, **kwargs) + msg = "The two Series were equal when they shouldn't have been" + + pytest.fail(msg=msg) + except AssertionError: + pass + + +def _assert_not_series_equal_both(a, b, **kwargs): + """ + Check that two Series are not equal. + + This check is performed commutatively. + + Parameters + ---------- + a : Series + The first Series to compare. + b : Series + The second Series to compare. + kwargs : dict + The arguments passed to `tm.assert_series_equal`. + """ + _assert_not_series_equal(a, b, **kwargs) + _assert_not_series_equal(b, a, **kwargs) + + +@pytest.mark.parametrize("data", [range(3), list("abc"), list("áàä")]) +def test_series_equal(data): + _assert_series_equal_both(Series(data), Series(data)) + + +@pytest.mark.parametrize( + "data1,data2", + [ + (range(3), range(1, 4)), + (list("abc"), list("xyz")), + (list("áàä"), list("éèë")), + (list("áàä"), list(b"aaa")), + (range(3), range(4)), + ], +) +def test_series_not_equal_value_mismatch(data1, data2): + _assert_not_series_equal_both(Series(data1), Series(data2)) + + +@pytest.mark.parametrize( + "kwargs", + [ + {"dtype": "float64"}, # dtype mismatch + {"index": [1, 2, 4]}, # index mismatch + {"name": "foo"}, # name mismatch + ], +) +def test_series_not_equal_metadata_mismatch(kwargs): + data = range(3) + s1 = Series(data) + + s2 = Series(data, **kwargs) + _assert_not_series_equal_both(s1, s2) + + +@pytest.mark.parametrize("data1,data2", [(0.12345, 0.12346), (0.1235, 0.1236)]) +@pytest.mark.parametrize("dtype", ["float32", "float64", "Float32"]) +@pytest.mark.parametrize("decimals", [0, 1, 2, 3, 5, 10]) +def test_less_precise(data1, data2, dtype, decimals): + rtol = 10**-decimals + s1 = Series([data1], dtype=dtype) + s2 = Series([data2], dtype=dtype) + + if decimals in (5, 10) or (decimals >= 3 and abs(data1 - data2) >= 0.0005): + msg = "Series values are different" + with pytest.raises(AssertionError, match=msg): + tm.assert_series_equal(s1, s2, rtol=rtol) + else: + _assert_series_equal_both(s1, s2, rtol=rtol) + + +@pytest.mark.parametrize( + "s1,s2,msg", + [ + # Index + ( + Series(["l1", "l2"], index=[1, 2]), + Series(["l1", "l2"], index=[1.0, 2.0]), + "Series\\.index are different", + ), + # MultiIndex + ( + DataFrame.from_records( + {"a": [1, 2], "b": [2.1, 1.5], "c": ["l1", "l2"]}, index=["a", "b"] + ).c, + DataFrame.from_records( + {"a": [1.0, 2.0], "b": [2.1, 1.5], "c": ["l1", "l2"]}, index=["a", "b"] + ).c, + "MultiIndex level \\[0\\] are different", + ), + ], +) +def test_series_equal_index_dtype(s1, s2, msg, check_index_type): + kwargs = {"check_index_type": check_index_type} + + if check_index_type: + with pytest.raises(AssertionError, match=msg): + tm.assert_series_equal(s1, s2, **kwargs) + else: + tm.assert_series_equal(s1, s2, **kwargs) + + +@pytest.mark.parametrize("check_like", [True, False]) +def test_series_equal_order_mismatch(check_like): + s1 = Series([1, 2, 3], index=["a", "b", "c"]) + s2 = Series([3, 2, 1], index=["c", "b", "a"]) + + if not check_like: # Do not ignore index ordering. + with pytest.raises(AssertionError, match="Series.index are different"): + tm.assert_series_equal(s1, s2, check_like=check_like) + else: + _assert_series_equal_both(s1, s2, check_like=check_like) + + +@pytest.mark.parametrize("check_index", [True, False]) +def test_series_equal_index_mismatch(check_index): + s1 = Series([1, 2, 3], index=["a", "b", "c"]) + s2 = Series([1, 2, 3], index=["c", "b", "a"]) + + if check_index: # Do not ignore index. + with pytest.raises(AssertionError, match="Series.index are different"): + tm.assert_series_equal(s1, s2, check_index=check_index) + else: + _assert_series_equal_both(s1, s2, check_index=check_index) + + +def test_series_invalid_param_combination(): + left = Series(dtype=object) + right = Series(dtype=object) + with pytest.raises( + ValueError, match="check_like must be False if check_index is False" + ): + tm.assert_series_equal(left, right, check_index=False, check_like=True) + + +def test_series_equal_length_mismatch(rtol): + msg = """Series are different + +Series length are different +\\[left\\]: 3, RangeIndex\\(start=0, stop=3, step=1\\) +\\[right\\]: 4, RangeIndex\\(start=0, stop=4, step=1\\)""" + + s1 = Series([1, 2, 3]) + s2 = Series([1, 2, 3, 4]) + + with pytest.raises(AssertionError, match=msg): + tm.assert_series_equal(s1, s2, rtol=rtol) + + +def test_series_equal_numeric_values_mismatch(rtol): + msg = """Series are different + +Series values are different \\(33\\.33333 %\\) +\\[index\\]: \\[0, 1, 2\\] +\\[left\\]: \\[1, 2, 3\\] +\\[right\\]: \\[1, 2, 4\\]""" + + s1 = Series([1, 2, 3]) + s2 = Series([1, 2, 4]) + + with pytest.raises(AssertionError, match=msg): + tm.assert_series_equal(s1, s2, rtol=rtol) + + +def test_series_equal_categorical_values_mismatch(rtol, using_infer_string): + dtype = "str" if using_infer_string else "object" + msg = f"""Series are different + +Series values are different \\(66\\.66667 %\\) +\\[index\\]: \\[0, 1, 2\\] +\\[left\\]: \\['a', 'b', 'c'\\] +Categories \\(3, {dtype}\\): \\['a', 'b', 'c'\\] +\\[right\\]: \\['a', 'c', 'b'\\] +Categories \\(3, {dtype}\\): \\['a', 'b', 'c'\\]""" + + s1 = Series(Categorical(["a", "b", "c"])) + s2 = Series(Categorical(["a", "c", "b"])) + + with pytest.raises(AssertionError, match=msg): + tm.assert_series_equal(s1, s2, rtol=rtol) + + +def test_series_equal_datetime_values_mismatch(rtol): + msg = """Series are different + +Series values are different \\(100.0 %\\) +\\[index\\]: \\[0, 1, 2\\] +\\[left\\]: \\[1514764800000000000, 1514851200000000000, 1514937600000000000\\] +\\[right\\]: \\[1549065600000000000, 1549152000000000000, 1549238400000000000\\]""" + + s1 = Series(pd.date_range("2018-01-01", periods=3, freq="D")) + s2 = Series(pd.date_range("2019-02-02", periods=3, freq="D")) + + with pytest.raises(AssertionError, match=msg): + tm.assert_series_equal(s1, s2, rtol=rtol) + + +def test_series_equal_categorical_mismatch(check_categorical, using_infer_string): + if using_infer_string: + dtype = "str" + else: + dtype = "object" + msg = f"""Attributes of Series are different + +Attribute "dtype" are different +\\[left\\]: CategoricalDtype\\(categories=\\['a', 'b'\\], ordered=False, \ +categories_dtype={dtype}\\) +\\[right\\]: CategoricalDtype\\(categories=\\['a', 'b', 'c'\\], \ +ordered=False, categories_dtype={dtype}\\)""" + + s1 = Series(Categorical(["a", "b"])) + s2 = Series(Categorical(["a", "b"], categories=list("abc"))) + + if check_categorical: + with pytest.raises(AssertionError, match=msg): + tm.assert_series_equal(s1, s2, check_categorical=check_categorical) + else: + _assert_series_equal_both(s1, s2, check_categorical=check_categorical) + + +def test_assert_series_equal_extension_dtype_mismatch(): + # https://github.com/pandas-dev/pandas/issues/32747 + left = Series(pd.array([1, 2, 3], dtype="Int64")) + right = left.astype(int) + + msg = """Attributes of Series are different + +Attribute "dtype" are different +\\[left\\]: Int64 +\\[right\\]: int[32|64]""" + + tm.assert_series_equal(left, right, check_dtype=False) + + with pytest.raises(AssertionError, match=msg): + tm.assert_series_equal(left, right, check_dtype=True) + + +def test_assert_series_equal_interval_dtype_mismatch(): + # https://github.com/pandas-dev/pandas/issues/32747 + left = Series([pd.Interval(0, 1)], dtype="interval") + right = left.astype(object) + + msg = """Attributes of Series are different + +Attribute "dtype" are different +\\[left\\]: interval\\[int64, right\\] +\\[right\\]: object""" + + tm.assert_series_equal(left, right, check_dtype=False) + + with pytest.raises(AssertionError, match=msg): + tm.assert_series_equal(left, right, check_dtype=True) + + +def test_series_equal_series_type(): + class MySeries(Series): + pass + + s1 = Series([1, 2]) + s2 = Series([1, 2]) + s3 = MySeries([1, 2]) + + tm.assert_series_equal(s1, s2, check_series_type=False) + tm.assert_series_equal(s1, s2, check_series_type=True) + + tm.assert_series_equal(s1, s3, check_series_type=False) + tm.assert_series_equal(s3, s1, check_series_type=False) + + with pytest.raises(AssertionError, match="Series classes are different"): + tm.assert_series_equal(s1, s3, check_series_type=True) + + with pytest.raises(AssertionError, match="Series classes are different"): + tm.assert_series_equal(s3, s1, check_series_type=True) + + +def test_series_equal_exact_for_nonnumeric(): + # https://github.com/pandas-dev/pandas/issues/35446 + s1 = Series(["a", "b"]) + s2 = Series(["a", "b"]) + s3 = Series(["b", "a"]) + + tm.assert_series_equal(s1, s2, check_exact=True) + tm.assert_series_equal(s2, s1, check_exact=True) + + msg = """Series are different + +Series values are different \\(100\\.0 %\\) +\\[index\\]: \\[0, 1\\] +\\[left\\]: \\[a, b\\] +\\[right\\]: \\[b, a\\]""" + with pytest.raises(AssertionError, match=msg): + tm.assert_series_equal(s1, s3, check_exact=True) + + msg = """Series are different + +Series values are different \\(100\\.0 %\\) +\\[index\\]: \\[0, 1\\] +\\[left\\]: \\[b, a\\] +\\[right\\]: \\[a, b\\]""" + with pytest.raises(AssertionError, match=msg): + tm.assert_series_equal(s3, s1, check_exact=True) + + +def test_assert_series_equal_ignore_extension_dtype_mismatch(): + # https://github.com/pandas-dev/pandas/issues/35715 + left = Series([1, 2, 3], dtype="Int64") + right = Series([1, 2, 3], dtype="Int32") + tm.assert_series_equal(left, right, check_dtype=False) + + +def test_assert_series_equal_ignore_extension_dtype_mismatch_cross_class(): + # https://github.com/pandas-dev/pandas/issues/35715 + left = Series([1, 2, 3], dtype="Int64") + right = Series([1, 2, 3], dtype="int64") + tm.assert_series_equal(left, right, check_dtype=False) + + +def test_allows_duplicate_labels(): + left = Series([1]) + right = Series([1]).set_flags(allows_duplicate_labels=False) + tm.assert_series_equal(left, left) + tm.assert_series_equal(right, right) + tm.assert_series_equal(left, right, check_flags=False) + tm.assert_series_equal(right, left, check_flags=False) + + with pytest.raises(AssertionError, match=">> cumavg([1, 2, 3]) + 2 + """ + ), + method="cumavg", + operation="average", +) +def cumavg(whatever): + pass + + +@doc(cumsum, method="cummax", operation="maximum") +def cummax(whatever): + pass + + +@doc(cummax, method="cummin", operation="minimum") +def cummin(whatever): + pass + + +def test_docstring_formatting(): + docstr = dedent( + """ + This is the cumsum method. + + It computes the cumulative sum. + """ + ) + assert cumsum.__doc__ == docstr + + +def test_docstring_appending(): + docstr = dedent( + """ + This is the cumavg method. + + It computes the cumulative average. + + Examples + -------- + + >>> cumavg([1, 2, 3]) + 2 + """ + ) + assert cumavg.__doc__ == docstr + + +def test_doc_template_from_func(): + docstr = dedent( + """ + This is the cummax method. + + It computes the cumulative maximum. + """ + ) + assert cummax.__doc__ == docstr + + +def test_inherit_doc_template(): + docstr = dedent( + """ + This is the cummin method. + + It computes the cumulative minimum. + """ + ) + assert cummin.__doc__ == docstr diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_hashing.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_hashing.py new file mode 100644 index 0000000000000000000000000000000000000000..1e7fdd920e365cd49abf22732c573ca696d3b3d7 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_hashing.py @@ -0,0 +1,417 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + Index, + MultiIndex, + Series, + period_range, + timedelta_range, +) +import pandas._testing as tm +from pandas.core.util.hashing import hash_tuples +from pandas.util import ( + hash_array, + hash_pandas_object, +) + + +@pytest.fixture( + params=[ + Series([1, 2, 3] * 3, dtype="int32"), + Series([None, 2.5, 3.5] * 3, dtype="float32"), + Series(["a", "b", "c"] * 3, dtype="category"), + Series(["d", "e", "f"] * 3), + Series([True, False, True] * 3), + Series(pd.date_range("20130101", periods=9)), + Series(pd.date_range("20130101", periods=9, tz="US/Eastern")), + Series(timedelta_range("2000", periods=9)), + ] +) +def series(request): + return request.param + + +@pytest.fixture(params=[True, False]) +def index(request): + return request.param + + +def test_consistency(): + # Check that our hash doesn't change because of a mistake + # in the actual code; this is the ground truth. + result = hash_pandas_object(Index(["foo", "bar", "baz"])) + expected = Series( + np.array( + [3600424527151052760, 1374399572096150070, 477881037637427054], + dtype="uint64", + ), + index=["foo", "bar", "baz"], + ) + tm.assert_series_equal(result, expected) + + +def test_hash_array(series): + arr = series.values + tm.assert_numpy_array_equal(hash_array(arr), hash_array(arr)) + + +@pytest.mark.parametrize("dtype", ["U", object]) +def test_hash_array_mixed(dtype): + result1 = hash_array(np.array(["3", "4", "All"])) + result2 = hash_array(np.array([3, 4, "All"], dtype=dtype)) + + tm.assert_numpy_array_equal(result1, result2) + + +@pytest.mark.parametrize("val", [5, "foo", pd.Timestamp("20130101")]) +def test_hash_array_errors(val): + msg = "must pass a ndarray-like" + with pytest.raises(TypeError, match=msg): + hash_array(val) + + +def test_hash_array_index_exception(): + # GH42003 TypeError instead of AttributeError + obj = pd.DatetimeIndex(["2018-10-28 01:20:00"], tz="Europe/Berlin") + + msg = "Use hash_pandas_object instead" + with pytest.raises(TypeError, match=msg): + hash_array(obj) + + +def test_hash_tuples(): + tuples = [(1, "one"), (1, "two"), (2, "one")] + result = hash_tuples(tuples) + + expected = hash_pandas_object(MultiIndex.from_tuples(tuples)).values + tm.assert_numpy_array_equal(result, expected) + + # We only need to support MultiIndex and list-of-tuples + msg = "|".join(["object is not iterable", "zip argument #1 must support iteration"]) + with pytest.raises(TypeError, match=msg): + hash_tuples(tuples[0]) + + +@pytest.mark.parametrize("val", [5, "foo", pd.Timestamp("20130101")]) +def test_hash_tuples_err(val): + msg = "must be convertible to a list-of-tuples" + with pytest.raises(TypeError, match=msg): + hash_tuples(val) + + +def test_multiindex_unique(): + mi = MultiIndex.from_tuples([(118, 472), (236, 118), (51, 204), (102, 51)]) + assert mi.is_unique is True + + result = hash_pandas_object(mi) + assert result.is_unique is True + + +def test_multiindex_objects(): + mi = MultiIndex( + levels=[["b", "d", "a"], [1, 2, 3]], + codes=[[0, 1, 0, 2], [2, 0, 0, 1]], + names=["col1", "col2"], + ) + recons = mi._sort_levels_monotonic() + + # These are equal. + assert mi.equals(recons) + assert Index(mi.values).equals(Index(recons.values)) + + +@pytest.mark.parametrize( + "obj", + [ + Series([1, 2, 3]), + Series([1.0, 1.5, 3.2]), + Series([1.0, 1.5, np.nan]), + Series([1.0, 1.5, 3.2], index=[1.5, 1.1, 3.3]), + Series(["a", "b", "c"]), + Series(["a", np.nan, "c"]), + Series(["a", None, "c"]), + Series([True, False, True]), + Series(dtype=object), + DataFrame({"x": ["a", "b", "c"], "y": [1, 2, 3]}), + DataFrame(), + DataFrame(np.full((10, 4), np.nan)), + DataFrame( + { + "A": [0.0, 1.0, 2.0, 3.0, 4.0], + "B": [0.0, 1.0, 0.0, 1.0, 0.0], + "C": Index(["foo1", "foo2", "foo3", "foo4", "foo5"], dtype=object), + "D": pd.date_range("20130101", periods=5), + } + ), + DataFrame(range(5), index=pd.date_range("2020-01-01", periods=5)), + Series(range(5), index=pd.date_range("2020-01-01", periods=5)), + Series(period_range("2020-01-01", periods=10, freq="D")), + Series(pd.date_range("20130101", periods=3, tz="US/Eastern")), + ], +) +def test_hash_pandas_object(obj, index): + a = hash_pandas_object(obj, index=index) + b = hash_pandas_object(obj, index=index) + tm.assert_series_equal(a, b) + + +@pytest.mark.parametrize( + "obj", + [ + Series([1, 2, 3]), + Series([1.0, 1.5, 3.2]), + Series([1.0, 1.5, np.nan]), + Series([1.0, 1.5, 3.2], index=[1.5, 1.1, 3.3]), + Series(["a", "b", "c"]), + Series(["a", np.nan, "c"]), + Series(["a", None, "c"]), + Series([True, False, True]), + DataFrame({"x": ["a", "b", "c"], "y": [1, 2, 3]}), + DataFrame(np.full((10, 4), np.nan)), + DataFrame( + { + "A": [0.0, 1.0, 2.0, 3.0, 4.0], + "B": [0.0, 1.0, 0.0, 1.0, 0.0], + "C": Index(["foo1", "foo2", "foo3", "foo4", "foo5"], dtype=object), + "D": pd.date_range("20130101", periods=5), + } + ), + DataFrame(range(5), index=pd.date_range("2020-01-01", periods=5)), + Series(range(5), index=pd.date_range("2020-01-01", periods=5)), + Series(period_range("2020-01-01", periods=10, freq="D")), + Series(pd.date_range("20130101", periods=3, tz="US/Eastern")), + ], +) +def test_hash_pandas_object_diff_index_non_empty(obj): + a = hash_pandas_object(obj, index=True) + b = hash_pandas_object(obj, index=False) + assert not (a == b).all() + + +@pytest.mark.parametrize( + "obj", + [ + Index([1, 2, 3]), + Index([True, False, True]), + timedelta_range("1 day", periods=2), + period_range("2020-01-01", freq="D", periods=2), + MultiIndex.from_product( + [range(5), ["foo", "bar", "baz"], pd.date_range("20130101", periods=2)] + ), + MultiIndex.from_product([pd.CategoricalIndex(list("aabc")), range(3)]), + ], +) +def test_hash_pandas_index(obj, index): + a = hash_pandas_object(obj, index=index) + b = hash_pandas_object(obj, index=index) + tm.assert_series_equal(a, b) + + +def test_hash_pandas_series(series, index): + a = hash_pandas_object(series, index=index) + b = hash_pandas_object(series, index=index) + tm.assert_series_equal(a, b) + + +def test_hash_pandas_series_diff_index(series): + a = hash_pandas_object(series, index=True) + b = hash_pandas_object(series, index=False) + assert not (a == b).all() + + +@pytest.mark.parametrize( + "obj", [Series([], dtype="float64"), Series([], dtype="object"), Index([])] +) +def test_hash_pandas_empty_object(obj, index): + # These are by-definition the same with + # or without the index as the data is empty. + a = hash_pandas_object(obj, index=index) + b = hash_pandas_object(obj, index=index) + tm.assert_series_equal(a, b) + + +@pytest.mark.parametrize( + "s1", + [ + Series(["a", "b", "c", "d"]), + Series([1000, 2000, 3000, 4000]), + Series(pd.date_range(0, periods=4)), + ], +) +@pytest.mark.parametrize("categorize", [True, False]) +def test_categorical_consistency(s1, categorize): + # see gh-15143 + # + # Check that categoricals hash consistent with their values, + # not codes. This should work for categoricals of any dtype. + s2 = s1.astype("category").cat.set_categories(s1) + s3 = s2.cat.set_categories(list(reversed(s1))) + + # These should all hash identically. + h1 = hash_pandas_object(s1, categorize=categorize) + h2 = hash_pandas_object(s2, categorize=categorize) + h3 = hash_pandas_object(s3, categorize=categorize) + + tm.assert_series_equal(h1, h2) + tm.assert_series_equal(h1, h3) + + +def test_categorical_with_nan_consistency(): + c = pd.Categorical.from_codes( + [-1, 0, 1, 2, 3, 4], categories=pd.date_range("2012-01-01", periods=5, name="B") + ) + expected = hash_array(c, categorize=False) + + c = pd.Categorical.from_codes([-1, 0], categories=[pd.Timestamp("2012-01-01")]) + result = hash_array(c, categorize=False) + + assert result[0] in expected + assert result[1] in expected + + +def test_pandas_errors(): + msg = "Unexpected type for hashing" + with pytest.raises(TypeError, match=msg): + hash_pandas_object(pd.Timestamp("20130101")) + + +def test_hash_keys(): + # Using different hash keys, should have + # different hashes for the same data. + # + # This only matters for object dtypes. + obj = Series(list("abc")) + + a = hash_pandas_object(obj, hash_key="9876543210123456") + b = hash_pandas_object(obj, hash_key="9876543210123465") + + assert (a != b).all() + + +def test_df_hash_keys(): + # DataFrame version of the test_hash_keys. + # https://github.com/pandas-dev/pandas/issues/41404 + obj = DataFrame({"x": np.arange(3), "y": list("abc")}) + + a = hash_pandas_object(obj, hash_key="9876543210123456") + b = hash_pandas_object(obj, hash_key="9876543210123465") + + assert (a != b).all() + + +def test_df_encoding(): + # Check that DataFrame recognizes optional encoding. + # https://github.com/pandas-dev/pandas/issues/41404 + # https://github.com/pandas-dev/pandas/pull/42049 + obj = DataFrame({"x": np.arange(3), "y": list("a+c")}) + + a = hash_pandas_object(obj, encoding="utf8") + b = hash_pandas_object(obj, encoding="utf7") + + # Note that the "+" is encoded as "+-" in utf-7. + assert a[0] == b[0] + assert a[1] != b[1] + assert a[2] == b[2] + + +def test_invalid_key(): + # This only matters for object dtypes. + msg = "key should be a 16-byte string encoded" + + with pytest.raises(ValueError, match=msg): + hash_pandas_object(Series(list("abc")), hash_key="foo") + + +def test_already_encoded(index): + # If already encoded, then ok. + obj = Series(list("abc")).str.encode("utf8") + a = hash_pandas_object(obj, index=index) + b = hash_pandas_object(obj, index=index) + tm.assert_series_equal(a, b) + + +def test_alternate_encoding(index): + obj = Series(list("abc")) + a = hash_pandas_object(obj, index=index) + b = hash_pandas_object(obj, index=index) + tm.assert_series_equal(a, b) + + +@pytest.mark.parametrize("l_exp", range(8)) +@pytest.mark.parametrize("l_add", [0, 1]) +def test_same_len_hash_collisions(l_exp, l_add): + length = 2 ** (l_exp + 8) + l_add + idx = np.array([str(i) for i in range(length)], dtype=object) + + result = hash_array(idx, "utf8") + assert not result[0] == result[1] + + +def test_hash_collisions(): + # Hash collisions are bad. + # + # https://github.com/pandas-dev/pandas/issues/14711#issuecomment-264885726 + hashes = [ + "Ingrid-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", + "Tim-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", + ] + + # These should be different. + result1 = hash_array(np.asarray(hashes[0:1], dtype=object), "utf8") + expected1 = np.array([14963968704024874985], dtype=np.uint64) + tm.assert_numpy_array_equal(result1, expected1) + + result2 = hash_array(np.asarray(hashes[1:2], dtype=object), "utf8") + expected2 = np.array([16428432627716348016], dtype=np.uint64) + tm.assert_numpy_array_equal(result2, expected2) + + result = hash_array(np.asarray(hashes, dtype=object), "utf8") + tm.assert_numpy_array_equal(result, np.concatenate([expected1, expected2], axis=0)) + + +@pytest.mark.parametrize( + "data, result_data", + [ + [[tuple("1"), tuple("2")], [10345501319357378243, 8331063931016360761]], + [[(1,), (2,)], [9408946347443669104, 3278256261030523334]], + ], +) +def test_hash_with_tuple(data, result_data): + # GH#28969 array containing a tuple raises on call to arr.astype(str) + # apparently a numpy bug github.com/numpy/numpy/issues/9441 + + df = DataFrame({"data": data}) + result = hash_pandas_object(df) + expected = Series(result_data, dtype=np.uint64) + tm.assert_series_equal(result, expected) + + +def test_hashable_tuple_args(): + # require that the elements of such tuples are themselves hashable + + df3 = DataFrame( + { + "data": [ + ( + 1, + [], + ), + ( + 2, + {}, + ), + ] + } + ) + with pytest.raises(TypeError, match="unhashable type: 'list'"): + hash_pandas_object(df3) + + +def test_hash_object_none_key(): + # https://github.com/pandas-dev/pandas/issues/30887 + result = pd.util.hash_pandas_object(Series(["a", "b"]), hash_key=None) + expected = Series([4578374827886788867, 17338122309987883691], dtype="uint64") + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_numba.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_numba.py new file mode 100644 index 0000000000000000000000000000000000000000..27b68ff0f60447e6695d786de9a72ecbb59f7884 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_numba.py @@ -0,0 +1,12 @@ +import pytest + +import pandas.util._test_decorators as td + +from pandas import option_context + + +@td.skip_if_installed("numba") +def test_numba_not_installed_option_context(): + with pytest.raises(ImportError, match="Missing optional"): + with option_context("compute.use_numba", True): + pass diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_rewrite_warning.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_rewrite_warning.py new file mode 100644 index 0000000000000000000000000000000000000000..f847a06d8ea8d7fa75aac1de9025a5bd29bedf37 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_rewrite_warning.py @@ -0,0 +1,39 @@ +import warnings + +import pytest + +from pandas.util._exceptions import rewrite_warning + +import pandas._testing as tm + + +@pytest.mark.parametrize( + "target_category, target_message, hit", + [ + (FutureWarning, "Target message", True), + (FutureWarning, "Target", True), + (FutureWarning, "get mess", True), + (FutureWarning, "Missed message", False), + (DeprecationWarning, "Target message", False), + ], +) +@pytest.mark.parametrize( + "new_category", + [ + None, + DeprecationWarning, + ], +) +def test_rewrite_warning(target_category, target_message, hit, new_category): + new_message = "Rewritten message" + if hit: + expected_category = new_category if new_category else target_category + expected_message = new_message + else: + expected_category = FutureWarning + expected_message = "Target message" + with tm.assert_produces_warning(expected_category, match=expected_message): + with rewrite_warning( + target_message, target_category, new_message, new_category + ): + warnings.warn(message="Target message", category=FutureWarning) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_shares_memory.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_shares_memory.py new file mode 100644 index 0000000000000000000000000000000000000000..8f1ac93b40247050418a5c488a2035f41e6eb0a4 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_shares_memory.py @@ -0,0 +1,32 @@ +import numpy as np + +import pandas.util._test_decorators as td + +import pandas as pd +import pandas._testing as tm + + +def test_shares_memory_interval(): + obj = pd.interval_range(1, 5) + + assert tm.shares_memory(obj, obj) + assert tm.shares_memory(obj, obj._data) + assert tm.shares_memory(obj, obj[::-1]) + assert tm.shares_memory(obj, obj[:2]) + + assert not tm.shares_memory(obj, obj._data.copy()) + + +@td.skip_if_no("pyarrow") +def test_shares_memory_string(): + # GH#55823 + import pyarrow as pa + + obj = pd.array(["a", "b"], dtype=pd.StringDtype("pyarrow", na_value=pd.NA)) + assert tm.shares_memory(obj, obj) + + obj = pd.array(["a", "b"], dtype=pd.StringDtype("pyarrow", na_value=np.nan)) + assert tm.shares_memory(obj, obj) + + obj = pd.array(["a", "b"], dtype=pd.ArrowDtype(pa.string())) + assert tm.shares_memory(obj, obj) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_show_versions.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_show_versions.py new file mode 100644 index 0000000000000000000000000000000000000000..72c9db23b210880793f37227c99e99e804800f08 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_show_versions.py @@ -0,0 +1,81 @@ +import json +import os +import re + +from pandas.util._print_versions import ( + _get_dependency_info, + _get_sys_info, +) + +import pandas as pd + + +def test_show_versions(tmpdir): + # GH39701 + as_json = os.path.join(tmpdir, "test_output.json") + + pd.show_versions(as_json=as_json) + + with open(as_json, encoding="utf-8") as fd: + # check if file output is valid JSON, will raise an exception if not + result = json.load(fd) + + # Basic check that each version element is found in output + expected = { + "system": _get_sys_info(), + "dependencies": _get_dependency_info(), + } + + assert result == expected + + +def test_show_versions_console_json(capsys): + # GH39701 + pd.show_versions(as_json=True) + stdout = capsys.readouterr().out + + # check valid json is printed to the console if as_json is True + result = json.loads(stdout) + + # Basic check that each version element is found in output + expected = { + "system": _get_sys_info(), + "dependencies": _get_dependency_info(), + } + + assert result == expected + + +def test_show_versions_console(capsys): + # gh-32041 + # gh-32041 + pd.show_versions(as_json=False) + result = capsys.readouterr().out + + # check header + assert "INSTALLED VERSIONS" in result + + # check full commit hash + assert re.search(r"commit\s*:\s[0-9a-f]{40}\n", result) + + # check required dependency + # 2020-12-09 npdev has "dirty" in the tag + # 2022-05-25 npdev released with RC wo/ "dirty". + # Just ensure we match [0-9]+\..* since npdev version is variable + assert re.search(r"numpy\s*:\s[0-9]+\..*\n", result) + + # check optional dependency + assert re.search(r"pyarrow\s*:\s([0-9]+.*|None)\n", result) + + +def test_json_output_match(capsys, tmpdir): + # GH39701 + pd.show_versions(as_json=True) + result_console = capsys.readouterr().out + + out_path = os.path.join(tmpdir, "test_json.json") + pd.show_versions(as_json=out_path) + with open(out_path, encoding="utf-8") as out_fd: + result_file = out_fd.read() + + assert result_console == result_file diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_util.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_util.py new file mode 100644 index 0000000000000000000000000000000000000000..dfb8587d3924e1441ac9da0aeeaa5585c6b4fe6c --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_util.py @@ -0,0 +1,58 @@ +import os + +import pytest + +from pandas import ( + array, + compat, +) +import pandas._testing as tm + + +def test_numpy_err_state_is_default(): + expected = {"over": "warn", "divide": "warn", "invalid": "warn", "under": "ignore"} + import numpy as np + + # The error state should be unchanged after that import. + assert np.geterr() == expected + + +def test_convert_rows_list_to_csv_str(): + rows_list = ["aaa", "bbb", "ccc"] + ret = tm.convert_rows_list_to_csv_str(rows_list) + + if compat.is_platform_windows(): + expected = "aaa\r\nbbb\r\nccc\r\n" + else: + expected = "aaa\nbbb\nccc\n" + + assert ret == expected + + +@pytest.mark.parametrize("strict_data_files", [True, False]) +def test_datapath_missing(datapath): + with pytest.raises(ValueError, match="Could not find file"): + datapath("not_a_file") + + +def test_datapath(datapath): + args = ("io", "data", "csv", "iris.csv") + + result = datapath(*args) + expected = os.path.join(os.path.dirname(os.path.dirname(__file__)), *args) + + assert result == expected + + +def test_external_error_raised(): + with tm.external_error_raised(TypeError): + raise TypeError("Should not check this error message, so it will pass") + + +def test_is_sorted(): + arr = array([1, 2, 3], dtype="Int64") + tm.assert_is_sorted(arr) + + arr = array([4, 2, 3], dtype="Int64") + with pytest.raises(AssertionError, match="ExtensionArray are different"): + tm.assert_is_sorted(arr) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_validate_args.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_validate_args.py new file mode 100644 index 0000000000000000000000000000000000000000..eef0931ec28efd02e3db7a85b0b3260742c1ff2d --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_validate_args.py @@ -0,0 +1,70 @@ +import pytest + +from pandas.util._validators import validate_args + + +@pytest.fixture +def _fname(): + return "func" + + +def test_bad_min_fname_arg_count(_fname): + msg = "'max_fname_arg_count' must be non-negative" + + with pytest.raises(ValueError, match=msg): + validate_args(_fname, (None,), -1, "foo") + + +def test_bad_arg_length_max_value_single(_fname): + args = (None, None) + compat_args = ("foo",) + + min_fname_arg_count = 0 + max_length = len(compat_args) + min_fname_arg_count + actual_length = len(args) + min_fname_arg_count + msg = ( + rf"{_fname}\(\) takes at most {max_length} " + rf"argument \({actual_length} given\)" + ) + + with pytest.raises(TypeError, match=msg): + validate_args(_fname, args, min_fname_arg_count, compat_args) + + +def test_bad_arg_length_max_value_multiple(_fname): + args = (None, None) + compat_args = {"foo": None} + + min_fname_arg_count = 2 + max_length = len(compat_args) + min_fname_arg_count + actual_length = len(args) + min_fname_arg_count + msg = ( + rf"{_fname}\(\) takes at most {max_length} " + rf"arguments \({actual_length} given\)" + ) + + with pytest.raises(TypeError, match=msg): + validate_args(_fname, args, min_fname_arg_count, compat_args) + + +@pytest.mark.parametrize("i", range(1, 3)) +def test_not_all_defaults(i, _fname): + bad_arg = "foo" + msg = ( + f"the '{bad_arg}' parameter is not supported " + rf"in the pandas implementation of {_fname}\(\)" + ) + + compat_args = {"foo": 2, "bar": -1, "baz": 3} + arg_vals = (1, -1, 3) + + with pytest.raises(ValueError, match=msg): + validate_args(_fname, arg_vals[:i], 2, compat_args) + + +def test_validation(_fname): + # No exceptions should be raised. + validate_args(_fname, (None,), 2, {"out": None}) + + compat_args = {"axis": 1, "out": None} + validate_args(_fname, (1, None), 2, compat_args) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_validate_args_and_kwargs.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_validate_args_and_kwargs.py new file mode 100644 index 0000000000000000000000000000000000000000..215026d648471c04cb8751506c03626fda73fc68 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_validate_args_and_kwargs.py @@ -0,0 +1,84 @@ +import pytest + +from pandas.util._validators import validate_args_and_kwargs + + +@pytest.fixture +def _fname(): + return "func" + + +def test_invalid_total_length_max_length_one(_fname): + compat_args = ("foo",) + kwargs = {"foo": "FOO"} + args = ("FoO", "BaZ") + + min_fname_arg_count = 0 + max_length = len(compat_args) + min_fname_arg_count + actual_length = len(kwargs) + len(args) + min_fname_arg_count + + msg = ( + rf"{_fname}\(\) takes at most {max_length} " + rf"argument \({actual_length} given\)" + ) + + with pytest.raises(TypeError, match=msg): + validate_args_and_kwargs(_fname, args, kwargs, min_fname_arg_count, compat_args) + + +def test_invalid_total_length_max_length_multiple(_fname): + compat_args = ("foo", "bar", "baz") + kwargs = {"foo": "FOO", "bar": "BAR"} + args = ("FoO", "BaZ") + + min_fname_arg_count = 2 + max_length = len(compat_args) + min_fname_arg_count + actual_length = len(kwargs) + len(args) + min_fname_arg_count + + msg = ( + rf"{_fname}\(\) takes at most {max_length} " + rf"arguments \({actual_length} given\)" + ) + + with pytest.raises(TypeError, match=msg): + validate_args_and_kwargs(_fname, args, kwargs, min_fname_arg_count, compat_args) + + +@pytest.mark.parametrize("args,kwargs", [((), {"foo": -5, "bar": 2}), ((-5, 2), {})]) +def test_missing_args_or_kwargs(args, kwargs, _fname): + bad_arg = "bar" + min_fname_arg_count = 2 + + compat_args = {"foo": -5, bad_arg: 1} + + msg = ( + rf"the '{bad_arg}' parameter is not supported " + rf"in the pandas implementation of {_fname}\(\)" + ) + + with pytest.raises(ValueError, match=msg): + validate_args_and_kwargs(_fname, args, kwargs, min_fname_arg_count, compat_args) + + +def test_duplicate_argument(_fname): + min_fname_arg_count = 2 + + compat_args = {"foo": None, "bar": None, "baz": None} + kwargs = {"foo": None, "bar": None} + args = (None,) # duplicate value for "foo" + + msg = rf"{_fname}\(\) got multiple values for keyword argument 'foo'" + + with pytest.raises(TypeError, match=msg): + validate_args_and_kwargs(_fname, args, kwargs, min_fname_arg_count, compat_args) + + +def test_validation(_fname): + # No exceptions should be raised. + compat_args = {"foo": 1, "bar": None, "baz": -2} + kwargs = {"baz": -2} + + args = (1, None) + min_fname_arg_count = 2 + + validate_args_and_kwargs(_fname, args, kwargs, min_fname_arg_count, compat_args) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_validate_inclusive.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_validate_inclusive.py new file mode 100644 index 0000000000000000000000000000000000000000..c1254c614ab305c447090b148ea6a036569f76e6 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_validate_inclusive.py @@ -0,0 +1,40 @@ +import numpy as np +import pytest + +from pandas.util._validators import validate_inclusive + +import pandas as pd + + +@pytest.mark.parametrize( + "invalid_inclusive", + ( + "ccc", + 2, + object(), + None, + np.nan, + pd.NA, + pd.DataFrame(), + ), +) +def test_invalid_inclusive(invalid_inclusive): + with pytest.raises( + ValueError, + match="Inclusive has to be either 'both', 'neither', 'left' or 'right'", + ): + validate_inclusive(invalid_inclusive) + + +@pytest.mark.parametrize( + "valid_inclusive, expected_tuple", + ( + ("left", (True, False)), + ("right", (False, True)), + ("both", (True, True)), + ("neither", (False, False)), + ), +) +def test_valid_inclusive(valid_inclusive, expected_tuple): + resultant_tuple = validate_inclusive(valid_inclusive) + assert expected_tuple == resultant_tuple diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_validate_kwargs.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_validate_kwargs.py new file mode 100644 index 0000000000000000000000000000000000000000..dba447e30cf579c9f2f5c0bd917a4e0837143ed3 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/util/test_validate_kwargs.py @@ -0,0 +1,69 @@ +import pytest + +from pandas.util._validators import ( + validate_bool_kwarg, + validate_kwargs, +) + + +@pytest.fixture +def _fname(): + return "func" + + +def test_bad_kwarg(_fname): + good_arg = "f" + bad_arg = good_arg + "o" + + compat_args = {good_arg: "foo", bad_arg + "o": "bar"} + kwargs = {good_arg: "foo", bad_arg: "bar"} + + msg = rf"{_fname}\(\) got an unexpected keyword argument '{bad_arg}'" + + with pytest.raises(TypeError, match=msg): + validate_kwargs(_fname, kwargs, compat_args) + + +@pytest.mark.parametrize("i", range(1, 3)) +def test_not_all_none(i, _fname): + bad_arg = "foo" + msg = ( + rf"the '{bad_arg}' parameter is not supported " + rf"in the pandas implementation of {_fname}\(\)" + ) + + compat_args = {"foo": 1, "bar": "s", "baz": None} + + kwarg_keys = ("foo", "bar", "baz") + kwarg_vals = (2, "s", None) + + kwargs = dict(zip(kwarg_keys[:i], kwarg_vals[:i])) + + with pytest.raises(ValueError, match=msg): + validate_kwargs(_fname, kwargs, compat_args) + + +def test_validation(_fname): + # No exceptions should be raised. + compat_args = {"f": None, "b": 1, "ba": "s"} + + kwargs = {"f": None, "b": 1} + validate_kwargs(_fname, kwargs, compat_args) + + +@pytest.mark.parametrize("name", ["inplace", "copy"]) +@pytest.mark.parametrize("value", [1, "True", [1, 2, 3], 5.0]) +def test_validate_bool_kwarg_fail(name, value): + msg = ( + f'For argument "{name}" expected type bool, ' + f"received type {type(value).__name__}" + ) + + with pytest.raises(ValueError, match=msg): + validate_bool_kwarg(value, name) + + +@pytest.mark.parametrize("name", ["inplace", "copy"]) +@pytest.mark.parametrize("value", [True, False, None]) +def test_validate_bool_kwarg(name, value): + assert validate_bool_kwarg(value, name) == value diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/conftest.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..73ab470ab97a77a65001d62aa05be7d372fbc1f5 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/conftest.py @@ -0,0 +1,146 @@ +from datetime import ( + datetime, + timedelta, +) + +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +from pandas import ( + DataFrame, + Series, + bdate_range, +) + + +@pytest.fixture(params=[True, False]) +def raw(request): + """raw keyword argument for rolling.apply""" + return request.param + + +@pytest.fixture( + params=[ + "sum", + "mean", + "median", + "max", + "min", + "var", + "std", + "kurt", + "skew", + "count", + "sem", + ] +) +def arithmetic_win_operators(request): + return request.param + + +@pytest.fixture(params=[True, False]) +def center(request): + return request.param + + +@pytest.fixture(params=[None, 1]) +def min_periods(request): + return request.param + + +@pytest.fixture(params=[True, False]) +def parallel(request): + """parallel keyword argument for numba.jit""" + return request.param + + +# Can parameterize nogil & nopython over True | False, but limiting per +# https://github.com/pandas-dev/pandas/pull/41971#issuecomment-860607472 + + +@pytest.fixture(params=[False]) +def nogil(request): + """nogil keyword argument for numba.jit""" + return request.param + + +@pytest.fixture(params=[True]) +def nopython(request): + """nopython keyword argument for numba.jit""" + return request.param + + +@pytest.fixture(params=[True, False]) +def adjust(request): + """adjust keyword argument for ewm""" + return request.param + + +@pytest.fixture(params=[True, False]) +def ignore_na(request): + """ignore_na keyword argument for ewm""" + return request.param + + +@pytest.fixture(params=[True, False]) +def numeric_only(request): + """numeric_only keyword argument""" + return request.param + + +@pytest.fixture( + params=[ + pytest.param("numba", marks=[td.skip_if_no("numba"), pytest.mark.single_cpu]), + "cython", + ] +) +def engine(request): + """engine keyword argument for rolling.apply""" + return request.param + + +@pytest.fixture( + params=[ + pytest.param( + ("numba", True), marks=[td.skip_if_no("numba"), pytest.mark.single_cpu] + ), + ("cython", True), + ("cython", False), + ] +) +def engine_and_raw(request): + """engine and raw keyword arguments for rolling.apply""" + return request.param + + +@pytest.fixture(params=["1 day", timedelta(days=1), np.timedelta64(1, "D")]) +def halflife_with_times(request): + """Halflife argument for EWM when times is specified.""" + return request.param + + +@pytest.fixture +def series(): + """Make mocked series as fixture.""" + arr = np.random.default_rng(2).standard_normal(100) + locs = np.arange(20, 40) + arr[locs] = np.nan + series = Series(arr, index=bdate_range(datetime(2009, 1, 1), periods=100)) + return series + + +@pytest.fixture +def frame(): + """Make mocked frame as fixture.""" + return DataFrame( + np.random.default_rng(2).standard_normal((100, 10)), + index=bdate_range(datetime(2009, 1, 1), periods=100), + ) + + +@pytest.fixture(params=[None, 1, 2, 5, 10]) +def step(request): + """step keyword argument for rolling window operations.""" + return request.param diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/moments/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/moments/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/moments/conftest.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/moments/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..fccf80c3c7a58d691b818709e51a9f6642956a33 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/moments/conftest.py @@ -0,0 +1,72 @@ +import itertools + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Series, + notna, +) + + +def create_series(): + return [ + Series(dtype=np.float64, name="a"), + Series([np.nan] * 5), + Series([1.0] * 5), + Series(range(5, 0, -1)), + Series(range(5)), + Series([np.nan, 1.0, np.nan, 1.0, 1.0]), + Series([np.nan, 1.0, np.nan, 2.0, 3.0]), + Series([np.nan, 1.0, np.nan, 3.0, 2.0]), + ] + + +def create_dataframes(): + return [ + DataFrame(columns=["a", "a"]), + DataFrame(np.arange(15).reshape((5, 3)), columns=["a", "a", 99]), + ] + [DataFrame(s) for s in create_series()] + + +def is_constant(x): + values = x.values.ravel("K") + return len(set(values[notna(values)])) == 1 + + +@pytest.fixture( + params=( + obj + for obj in itertools.chain(create_series(), create_dataframes()) + if is_constant(obj) + ), +) +def consistent_data(request): + return request.param + + +@pytest.fixture(params=create_series()) +def series_data(request): + return request.param + + +@pytest.fixture(params=itertools.chain(create_series(), create_dataframes())) +def all_data(request): + """ + Test: + - Empty Series / DataFrame + - All NaN + - All consistent value + - Monotonically decreasing + - Monotonically increasing + - Monotonically consistent with NaNs + - Monotonically increasing with NaNs + - Monotonically decreasing with NaNs + """ + return request.param + + +@pytest.fixture(params=[0, 2]) +def min_periods(request): + return request.param diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/moments/test_moments_consistency_ewm.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/moments/test_moments_consistency_ewm.py new file mode 100644 index 0000000000000000000000000000000000000000..49dee50954f4f42365d1ee4525fa48a3e18877fe --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/moments/test_moments_consistency_ewm.py @@ -0,0 +1,243 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Series, + concat, +) +import pandas._testing as tm + + +def create_mock_weights(obj, com, adjust, ignore_na): + if isinstance(obj, DataFrame): + if not len(obj.columns): + return DataFrame(index=obj.index, columns=obj.columns) + w = concat( + [ + create_mock_series_weights( + obj.iloc[:, i], com=com, adjust=adjust, ignore_na=ignore_na + ) + for i in range(len(obj.columns)) + ], + axis=1, + ) + w.index = obj.index + w.columns = obj.columns + return w + else: + return create_mock_series_weights(obj, com, adjust, ignore_na) + + +def create_mock_series_weights(s, com, adjust, ignore_na): + w = Series(np.nan, index=s.index, name=s.name) + alpha = 1.0 / (1.0 + com) + if adjust: + count = 0 + for i in range(len(s)): + if s.iat[i] == s.iat[i]: + w.iat[i] = pow(1.0 / (1.0 - alpha), count) + count += 1 + elif not ignore_na: + count += 1 + else: + sum_wts = 0.0 + prev_i = -1 + count = 0 + for i in range(len(s)): + if s.iat[i] == s.iat[i]: + if prev_i == -1: + w.iat[i] = 1.0 + else: + w.iat[i] = alpha * sum_wts / pow(1.0 - alpha, count - prev_i) + sum_wts += w.iat[i] + prev_i = count + count += 1 + elif not ignore_na: + count += 1 + return w + + +def test_ewm_consistency_mean(all_data, adjust, ignore_na, min_periods): + com = 3.0 + + result = all_data.ewm( + com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na + ).mean() + weights = create_mock_weights(all_data, com=com, adjust=adjust, ignore_na=ignore_na) + expected = all_data.multiply(weights).cumsum().divide(weights.cumsum()).ffill() + expected[ + all_data.expanding().count() < (max(min_periods, 1) if min_periods else 1) + ] = np.nan + tm.assert_equal(result, expected.astype("float64")) + + +def test_ewm_consistency_consistent(consistent_data, adjust, ignore_na, min_periods): + com = 3.0 + + count_x = consistent_data.expanding().count() + mean_x = consistent_data.ewm( + com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na + ).mean() + # check that correlation of a series with itself is either 1 or NaN + corr_x_x = consistent_data.ewm( + com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na + ).corr(consistent_data) + exp = ( + consistent_data.max() + if isinstance(consistent_data, Series) + else consistent_data.max().max() + ) + + # check mean of constant series + expected = consistent_data * np.nan + expected[count_x >= max(min_periods, 1)] = exp + tm.assert_equal(mean_x, expected) + + # check correlation of constant series with itself is NaN + expected[:] = np.nan + tm.assert_equal(corr_x_x, expected) + + +def test_ewm_consistency_var_debiasing_factors( + all_data, adjust, ignore_na, min_periods +): + com = 3.0 + + # check variance debiasing factors + var_unbiased_x = all_data.ewm( + com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na + ).var(bias=False) + var_biased_x = all_data.ewm( + com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na + ).var(bias=True) + + weights = create_mock_weights(all_data, com=com, adjust=adjust, ignore_na=ignore_na) + cum_sum = weights.cumsum().ffill() + cum_sum_sq = (weights * weights).cumsum().ffill() + numerator = cum_sum * cum_sum + denominator = numerator - cum_sum_sq + denominator[denominator <= 0.0] = np.nan + var_debiasing_factors_x = numerator / denominator + + tm.assert_equal(var_unbiased_x, var_biased_x * var_debiasing_factors_x) + + +@pytest.mark.parametrize("bias", [True, False]) +def test_moments_consistency_var(all_data, adjust, ignore_na, min_periods, bias): + com = 3.0 + + mean_x = all_data.ewm( + com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na + ).mean() + var_x = all_data.ewm( + com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na + ).var(bias=bias) + assert not (var_x < 0).any().any() + + if bias: + # check that biased var(x) == mean(x^2) - mean(x)^2 + mean_x2 = ( + (all_data * all_data) + .ewm(com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na) + .mean() + ) + tm.assert_equal(var_x, mean_x2 - (mean_x * mean_x)) + + +@pytest.mark.parametrize("bias", [True, False]) +def test_moments_consistency_var_constant( + consistent_data, adjust, ignore_na, min_periods, bias +): + com = 3.0 + count_x = consistent_data.expanding(min_periods=min_periods).count() + var_x = consistent_data.ewm( + com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na + ).var(bias=bias) + + # check that variance of constant series is identically 0 + assert not (var_x > 0).any().any() + expected = consistent_data * np.nan + expected[count_x >= max(min_periods, 1)] = 0.0 + if not bias: + expected[count_x < 2] = np.nan + tm.assert_equal(var_x, expected) + + +@pytest.mark.parametrize("bias", [True, False]) +def test_ewm_consistency_std(all_data, adjust, ignore_na, min_periods, bias): + com = 3.0 + var_x = all_data.ewm( + com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na + ).var(bias=bias) + assert not (var_x < 0).any().any() + + std_x = all_data.ewm( + com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na + ).std(bias=bias) + assert not (std_x < 0).any().any() + + # check that var(x) == std(x)^2 + tm.assert_equal(var_x, std_x * std_x) + + cov_x_x = all_data.ewm( + com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na + ).cov(all_data, bias=bias) + assert not (cov_x_x < 0).any().any() + + # check that var(x) == cov(x, x) + tm.assert_equal(var_x, cov_x_x) + + +@pytest.mark.parametrize("bias", [True, False]) +def test_ewm_consistency_series_cov_corr( + series_data, adjust, ignore_na, min_periods, bias +): + com = 3.0 + + var_x_plus_y = ( + (series_data + series_data) + .ewm(com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na) + .var(bias=bias) + ) + var_x = series_data.ewm( + com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na + ).var(bias=bias) + var_y = series_data.ewm( + com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na + ).var(bias=bias) + cov_x_y = series_data.ewm( + com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na + ).cov(series_data, bias=bias) + # check that cov(x, y) == (var(x+y) - var(x) - + # var(y)) / 2 + tm.assert_equal(cov_x_y, 0.5 * (var_x_plus_y - var_x - var_y)) + + # check that corr(x, y) == cov(x, y) / (std(x) * + # std(y)) + corr_x_y = series_data.ewm( + com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na + ).corr(series_data) + std_x = series_data.ewm( + com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na + ).std(bias=bias) + std_y = series_data.ewm( + com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na + ).std(bias=bias) + tm.assert_equal(corr_x_y, cov_x_y / (std_x * std_y)) + + if bias: + # check that biased cov(x, y) == mean(x*y) - + # mean(x)*mean(y) + mean_x = series_data.ewm( + com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na + ).mean() + mean_y = series_data.ewm( + com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na + ).mean() + mean_x_times_y = ( + (series_data * series_data) + .ewm(com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na) + .mean() + ) + tm.assert_equal(cov_x_y, mean_x_times_y - (mean_x * mean_y)) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/moments/test_moments_consistency_expanding.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/moments/test_moments_consistency_expanding.py new file mode 100644 index 0000000000000000000000000000000000000000..7d2fa1ad5d21175dcfafe9a57dd8169fc4413360 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/moments/test_moments_consistency_expanding.py @@ -0,0 +1,144 @@ +import numpy as np +import pytest + +from pandas import Series +import pandas._testing as tm + + +def no_nans(x): + return x.notna().all().all() + + +def all_na(x): + return x.isnull().all().all() + + +@pytest.mark.parametrize("f", [lambda v: Series(v).sum(), np.nansum, np.sum]) +def test_expanding_apply_consistency_sum_nans(request, all_data, min_periods, f): + if f is np.sum: + if not no_nans(all_data) and not ( + all_na(all_data) and not all_data.empty and min_periods > 0 + ): + request.applymarker( + pytest.mark.xfail(reason="np.sum has different behavior with NaNs") + ) + expanding_f_result = all_data.expanding(min_periods=min_periods).sum() + expanding_apply_f_result = all_data.expanding(min_periods=min_periods).apply( + func=f, raw=True + ) + tm.assert_equal(expanding_f_result, expanding_apply_f_result) + + +@pytest.mark.parametrize("ddof", [0, 1]) +def test_moments_consistency_var(all_data, min_periods, ddof): + var_x = all_data.expanding(min_periods=min_periods).var(ddof=ddof) + assert not (var_x < 0).any().any() + + if ddof == 0: + # check that biased var(x) == mean(x^2) - mean(x)^2 + mean_x2 = (all_data * all_data).expanding(min_periods=min_periods).mean() + mean_x = all_data.expanding(min_periods=min_periods).mean() + tm.assert_equal(var_x, mean_x2 - (mean_x * mean_x)) + + +@pytest.mark.parametrize("ddof", [0, 1]) +def test_moments_consistency_var_constant(consistent_data, min_periods, ddof): + count_x = consistent_data.expanding(min_periods=min_periods).count() + var_x = consistent_data.expanding(min_periods=min_periods).var(ddof=ddof) + + # check that variance of constant series is identically 0 + assert not (var_x > 0).any().any() + expected = consistent_data * np.nan + expected[count_x >= max(min_periods, 1)] = 0.0 + if ddof == 1: + expected[count_x < 2] = np.nan + tm.assert_equal(var_x, expected) + + +@pytest.mark.parametrize("ddof", [0, 1]) +def test_expanding_consistency_var_std_cov(all_data, min_periods, ddof): + var_x = all_data.expanding(min_periods=min_periods).var(ddof=ddof) + assert not (var_x < 0).any().any() + + std_x = all_data.expanding(min_periods=min_periods).std(ddof=ddof) + assert not (std_x < 0).any().any() + + # check that var(x) == std(x)^2 + tm.assert_equal(var_x, std_x * std_x) + + cov_x_x = all_data.expanding(min_periods=min_periods).cov(all_data, ddof=ddof) + assert not (cov_x_x < 0).any().any() + + # check that var(x) == cov(x, x) + tm.assert_equal(var_x, cov_x_x) + + +@pytest.mark.parametrize("ddof", [0, 1]) +def test_expanding_consistency_series_cov_corr(series_data, min_periods, ddof): + var_x_plus_y = ( + (series_data + series_data).expanding(min_periods=min_periods).var(ddof=ddof) + ) + var_x = series_data.expanding(min_periods=min_periods).var(ddof=ddof) + var_y = series_data.expanding(min_periods=min_periods).var(ddof=ddof) + cov_x_y = series_data.expanding(min_periods=min_periods).cov(series_data, ddof=ddof) + # check that cov(x, y) == (var(x+y) - var(x) - + # var(y)) / 2 + tm.assert_equal(cov_x_y, 0.5 * (var_x_plus_y - var_x - var_y)) + + # check that corr(x, y) == cov(x, y) / (std(x) * + # std(y)) + corr_x_y = series_data.expanding(min_periods=min_periods).corr(series_data) + std_x = series_data.expanding(min_periods=min_periods).std(ddof=ddof) + std_y = series_data.expanding(min_periods=min_periods).std(ddof=ddof) + tm.assert_equal(corr_x_y, cov_x_y / (std_x * std_y)) + + if ddof == 0: + # check that biased cov(x, y) == mean(x*y) - + # mean(x)*mean(y) + mean_x = series_data.expanding(min_periods=min_periods).mean() + mean_y = series_data.expanding(min_periods=min_periods).mean() + mean_x_times_y = ( + (series_data * series_data).expanding(min_periods=min_periods).mean() + ) + tm.assert_equal(cov_x_y, mean_x_times_y - (mean_x * mean_y)) + + +def test_expanding_consistency_mean(all_data, min_periods): + result = all_data.expanding(min_periods=min_periods).mean() + expected = ( + all_data.expanding(min_periods=min_periods).sum() + / all_data.expanding(min_periods=min_periods).count() + ) + tm.assert_equal(result, expected.astype("float64")) + + +def test_expanding_consistency_constant(consistent_data, min_periods): + count_x = consistent_data.expanding().count() + mean_x = consistent_data.expanding(min_periods=min_periods).mean() + # check that correlation of a series with itself is either 1 or NaN + corr_x_x = consistent_data.expanding(min_periods=min_periods).corr(consistent_data) + + exp = ( + consistent_data.max() + if isinstance(consistent_data, Series) + else consistent_data.max().max() + ) + + # check mean of constant series + expected = consistent_data * np.nan + expected[count_x >= max(min_periods, 1)] = exp + tm.assert_equal(mean_x, expected) + + # check correlation of constant series with itself is NaN + expected[:] = np.nan + tm.assert_equal(corr_x_x, expected) + + +def test_expanding_consistency_var_debiasing_factors(all_data, min_periods): + # check variance debiasing factors + var_unbiased_x = all_data.expanding(min_periods=min_periods).var() + var_biased_x = all_data.expanding(min_periods=min_periods).var(ddof=0) + var_debiasing_factors_x = all_data.expanding().count() / ( + all_data.expanding().count() - 1.0 + ).replace(0.0, np.nan) + tm.assert_equal(var_unbiased_x, var_biased_x * var_debiasing_factors_x) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/moments/test_moments_consistency_rolling.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/moments/test_moments_consistency_rolling.py new file mode 100644 index 0000000000000000000000000000000000000000..be22338c00cb28fb4fbd1bfe7f4b6163e239a432 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/moments/test_moments_consistency_rolling.py @@ -0,0 +1,244 @@ +import numpy as np +import pytest + +from pandas import Series +import pandas._testing as tm + + +def no_nans(x): + return x.notna().all().all() + + +def all_na(x): + return x.isnull().all().all() + + +@pytest.fixture(params=[(1, 0), (5, 1)]) +def rolling_consistency_cases(request): + """window, min_periods""" + return request.param + + +@pytest.mark.parametrize("f", [lambda v: Series(v).sum(), np.nansum, np.sum]) +def test_rolling_apply_consistency_sum( + request, all_data, rolling_consistency_cases, center, f +): + window, min_periods = rolling_consistency_cases + + if f is np.sum: + if not no_nans(all_data) and not ( + all_na(all_data) and not all_data.empty and min_periods > 0 + ): + request.applymarker( + pytest.mark.xfail(reason="np.sum has different behavior with NaNs") + ) + rolling_f_result = all_data.rolling( + window=window, min_periods=min_periods, center=center + ).sum() + rolling_apply_f_result = all_data.rolling( + window=window, min_periods=min_periods, center=center + ).apply(func=f, raw=True) + tm.assert_equal(rolling_f_result, rolling_apply_f_result) + + +@pytest.mark.parametrize("ddof", [0, 1]) +def test_moments_consistency_var(all_data, rolling_consistency_cases, center, ddof): + window, min_periods = rolling_consistency_cases + + var_x = all_data.rolling(window=window, min_periods=min_periods, center=center).var( + ddof=ddof + ) + assert not (var_x < 0).any().any() + + if ddof == 0: + # check that biased var(x) == mean(x^2) - mean(x)^2 + mean_x = all_data.rolling( + window=window, min_periods=min_periods, center=center + ).mean() + mean_x2 = ( + (all_data * all_data) + .rolling(window=window, min_periods=min_periods, center=center) + .mean() + ) + tm.assert_equal(var_x, mean_x2 - (mean_x * mean_x)) + + +@pytest.mark.parametrize("ddof", [0, 1]) +def test_moments_consistency_var_constant( + consistent_data, rolling_consistency_cases, center, ddof +): + window, min_periods = rolling_consistency_cases + + count_x = consistent_data.rolling( + window=window, min_periods=min_periods, center=center + ).count() + var_x = consistent_data.rolling( + window=window, min_periods=min_periods, center=center + ).var(ddof=ddof) + + # check that variance of constant series is identically 0 + assert not (var_x > 0).any().any() + expected = consistent_data * np.nan + expected[count_x >= max(min_periods, 1)] = 0.0 + if ddof == 1: + expected[count_x < 2] = np.nan + tm.assert_equal(var_x, expected) + + +@pytest.mark.parametrize("ddof", [0, 1]) +def test_rolling_consistency_var_std_cov( + all_data, rolling_consistency_cases, center, ddof +): + window, min_periods = rolling_consistency_cases + + var_x = all_data.rolling(window=window, min_periods=min_periods, center=center).var( + ddof=ddof + ) + assert not (var_x < 0).any().any() + + std_x = all_data.rolling(window=window, min_periods=min_periods, center=center).std( + ddof=ddof + ) + assert not (std_x < 0).any().any() + + # check that var(x) == std(x)^2 + tm.assert_equal(var_x, std_x * std_x) + + cov_x_x = all_data.rolling( + window=window, min_periods=min_periods, center=center + ).cov(all_data, ddof=ddof) + assert not (cov_x_x < 0).any().any() + + # check that var(x) == cov(x, x) + tm.assert_equal(var_x, cov_x_x) + + +@pytest.mark.parametrize("ddof", [0, 1]) +def test_rolling_consistency_series_cov_corr( + series_data, rolling_consistency_cases, center, ddof +): + window, min_periods = rolling_consistency_cases + + var_x_plus_y = ( + (series_data + series_data) + .rolling(window=window, min_periods=min_periods, center=center) + .var(ddof=ddof) + ) + var_x = series_data.rolling( + window=window, min_periods=min_periods, center=center + ).var(ddof=ddof) + var_y = series_data.rolling( + window=window, min_periods=min_periods, center=center + ).var(ddof=ddof) + cov_x_y = series_data.rolling( + window=window, min_periods=min_periods, center=center + ).cov(series_data, ddof=ddof) + # check that cov(x, y) == (var(x+y) - var(x) - + # var(y)) / 2 + tm.assert_equal(cov_x_y, 0.5 * (var_x_plus_y - var_x - var_y)) + + # check that corr(x, y) == cov(x, y) / (std(x) * + # std(y)) + corr_x_y = series_data.rolling( + window=window, min_periods=min_periods, center=center + ).corr(series_data) + std_x = series_data.rolling( + window=window, min_periods=min_periods, center=center + ).std(ddof=ddof) + std_y = series_data.rolling( + window=window, min_periods=min_periods, center=center + ).std(ddof=ddof) + tm.assert_equal(corr_x_y, cov_x_y / (std_x * std_y)) + + if ddof == 0: + # check that biased cov(x, y) == mean(x*y) - + # mean(x)*mean(y) + mean_x = series_data.rolling( + window=window, min_periods=min_periods, center=center + ).mean() + mean_y = series_data.rolling( + window=window, min_periods=min_periods, center=center + ).mean() + mean_x_times_y = ( + (series_data * series_data) + .rolling(window=window, min_periods=min_periods, center=center) + .mean() + ) + tm.assert_equal(cov_x_y, mean_x_times_y - (mean_x * mean_y)) + + +def test_rolling_consistency_mean(all_data, rolling_consistency_cases, center): + window, min_periods = rolling_consistency_cases + + result = all_data.rolling( + window=window, min_periods=min_periods, center=center + ).mean() + expected = ( + all_data.rolling(window=window, min_periods=min_periods, center=center) + .sum() + .divide( + all_data.rolling( + window=window, min_periods=min_periods, center=center + ).count() + ) + ) + tm.assert_equal(result, expected.astype("float64")) + + +def test_rolling_consistency_constant( + consistent_data, rolling_consistency_cases, center +): + window, min_periods = rolling_consistency_cases + + count_x = consistent_data.rolling( + window=window, min_periods=min_periods, center=center + ).count() + mean_x = consistent_data.rolling( + window=window, min_periods=min_periods, center=center + ).mean() + # check that correlation of a series with itself is either 1 or NaN + corr_x_x = consistent_data.rolling( + window=window, min_periods=min_periods, center=center + ).corr(consistent_data) + + exp = ( + consistent_data.max() + if isinstance(consistent_data, Series) + else consistent_data.max().max() + ) + + # check mean of constant series + expected = consistent_data * np.nan + expected[count_x >= max(min_periods, 1)] = exp + tm.assert_equal(mean_x, expected) + + # check correlation of constant series with itself is NaN + expected[:] = np.nan + tm.assert_equal(corr_x_x, expected) + + +def test_rolling_consistency_var_debiasing_factors( + all_data, rolling_consistency_cases, center +): + window, min_periods = rolling_consistency_cases + + # check variance debiasing factors + var_unbiased_x = all_data.rolling( + window=window, min_periods=min_periods, center=center + ).var() + var_biased_x = all_data.rolling( + window=window, min_periods=min_periods, center=center + ).var(ddof=0) + var_debiasing_factors_x = ( + all_data.rolling(window=window, min_periods=min_periods, center=center) + .count() + .divide( + ( + all_data.rolling( + window=window, min_periods=min_periods, center=center + ).count() + - 1.0 + ).replace(0.0, np.nan) + ) + ) + tm.assert_equal(var_unbiased_x, var_biased_x * var_debiasing_factors_x) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_api.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_api.py new file mode 100644 index 0000000000000000000000000000000000000000..948565be36b5be4e729eba034367b91b4b5fc64e --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_api.py @@ -0,0 +1,398 @@ +import numpy as np +import pytest + +from pandas.errors import ( + DataError, + SpecificationError, +) + +from pandas import ( + DataFrame, + Index, + MultiIndex, + Period, + Series, + Timestamp, + concat, + date_range, + timedelta_range, +) +import pandas._testing as tm + + +def test_getitem(step): + frame = DataFrame(np.random.default_rng(2).standard_normal((5, 5))) + r = frame.rolling(window=5, step=step) + tm.assert_index_equal(r._selected_obj.columns, frame[::step].columns) + + r = frame.rolling(window=5, step=step)[1] + assert r._selected_obj.name == frame[::step].columns[1] + + # technically this is allowed + r = frame.rolling(window=5, step=step)[1, 3] + tm.assert_index_equal(r._selected_obj.columns, frame[::step].columns[[1, 3]]) + + r = frame.rolling(window=5, step=step)[[1, 3]] + tm.assert_index_equal(r._selected_obj.columns, frame[::step].columns[[1, 3]]) + + +def test_select_bad_cols(): + df = DataFrame([[1, 2]], columns=["A", "B"]) + g = df.rolling(window=5) + with pytest.raises(KeyError, match="Columns not found: 'C'"): + g[["C"]] + with pytest.raises(KeyError, match="^[^A]+$"): + # A should not be referenced as a bad column... + # will have to rethink regex if you change message! + g[["A", "C"]] + + +def test_attribute_access(): + df = DataFrame([[1, 2]], columns=["A", "B"]) + r = df.rolling(window=5) + tm.assert_series_equal(r.A.sum(), r["A"].sum()) + msg = "'Rolling' object has no attribute 'F'" + with pytest.raises(AttributeError, match=msg): + r.F + + +def tests_skip_nuisance(step): + df = DataFrame({"A": range(5), "B": range(5, 10), "C": "foo"}) + r = df.rolling(window=3, step=step) + result = r[["A", "B"]].sum() + expected = DataFrame( + {"A": [np.nan, np.nan, 3, 6, 9], "B": [np.nan, np.nan, 18, 21, 24]}, + columns=list("AB"), + )[::step] + tm.assert_frame_equal(result, expected) + + +def test_sum_object_str_raises(step): + df = DataFrame({"A": range(5), "B": range(5, 10), "C": "foo"}) + r = df.rolling(window=3, step=step) + with pytest.raises( + DataError, match="Cannot aggregate non-numeric type: object|str" + ): + # GH#42738, enforced in 2.0 + r.sum() + + +def test_agg(step): + df = DataFrame({"A": range(5), "B": range(0, 10, 2)}) + + r = df.rolling(window=3, step=step) + a_mean = r["A"].mean() + a_std = r["A"].std() + a_sum = r["A"].sum() + b_mean = r["B"].mean() + b_std = r["B"].std() + + with tm.assert_produces_warning(FutureWarning, match="using Rolling.[mean|std]"): + result = r.aggregate([np.mean, np.std]) + expected = concat([a_mean, a_std, b_mean, b_std], axis=1) + expected.columns = MultiIndex.from_product([["A", "B"], ["mean", "std"]]) + tm.assert_frame_equal(result, expected) + + with tm.assert_produces_warning(FutureWarning, match="using Rolling.[mean|std]"): + result = r.aggregate({"A": np.mean, "B": np.std}) + + expected = concat([a_mean, b_std], axis=1) + tm.assert_frame_equal(result, expected, check_like=True) + + result = r.aggregate({"A": ["mean", "std"]}) + expected = concat([a_mean, a_std], axis=1) + expected.columns = MultiIndex.from_tuples([("A", "mean"), ("A", "std")]) + tm.assert_frame_equal(result, expected) + + result = r["A"].aggregate(["mean", "sum"]) + expected = concat([a_mean, a_sum], axis=1) + expected.columns = ["mean", "sum"] + tm.assert_frame_equal(result, expected) + + msg = "nested renamer is not supported" + with pytest.raises(SpecificationError, match=msg): + # using a dict with renaming + r.aggregate({"A": {"mean": "mean", "sum": "sum"}}) + + with pytest.raises(SpecificationError, match=msg): + r.aggregate( + {"A": {"mean": "mean", "sum": "sum"}, "B": {"mean2": "mean", "sum2": "sum"}} + ) + + result = r.aggregate({"A": ["mean", "std"], "B": ["mean", "std"]}) + expected = concat([a_mean, a_std, b_mean, b_std], axis=1) + + exp_cols = [("A", "mean"), ("A", "std"), ("B", "mean"), ("B", "std")] + expected.columns = MultiIndex.from_tuples(exp_cols) + tm.assert_frame_equal(result, expected, check_like=True) + + +@pytest.mark.parametrize( + "func", [["min"], ["mean", "max"], {"b": "sum"}, {"b": "prod", "c": "median"}] +) +def test_multi_axis_1_raises(func): + # GH#46904 + df = DataFrame({"a": [1, 1, 2], "b": [3, 4, 5], "c": [6, 7, 8]}) + msg = "Support for axis=1 in DataFrame.rolling is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + r = df.rolling(window=3, axis=1) + with pytest.raises(NotImplementedError, match="axis other than 0 is not supported"): + r.agg(func) + + +def test_agg_apply(raw): + # passed lambda + df = DataFrame({"A": range(5), "B": range(0, 10, 2)}) + + r = df.rolling(window=3) + a_sum = r["A"].sum() + + with tm.assert_produces_warning(FutureWarning, match="using Rolling.[sum|std]"): + result = r.agg({"A": np.sum, "B": lambda x: np.std(x, ddof=1)}) + rcustom = r["B"].apply(lambda x: np.std(x, ddof=1), raw=raw) + expected = concat([a_sum, rcustom], axis=1) + tm.assert_frame_equal(result, expected, check_like=True) + + +def test_agg_consistency(step): + df = DataFrame({"A": range(5), "B": range(0, 10, 2)}) + r = df.rolling(window=3, step=step) + + with tm.assert_produces_warning(FutureWarning, match="using Rolling.[sum|mean]"): + result = r.agg([np.sum, np.mean]).columns + expected = MultiIndex.from_product([list("AB"), ["sum", "mean"]]) + tm.assert_index_equal(result, expected) + + with tm.assert_produces_warning(FutureWarning, match="using Rolling.[sum|mean]"): + result = r["A"].agg([np.sum, np.mean]).columns + expected = Index(["sum", "mean"]) + tm.assert_index_equal(result, expected) + + with tm.assert_produces_warning(FutureWarning, match="using Rolling.[sum|mean]"): + result = r.agg({"A": [np.sum, np.mean]}).columns + expected = MultiIndex.from_tuples([("A", "sum"), ("A", "mean")]) + tm.assert_index_equal(result, expected) + + +def test_agg_nested_dicts(): + # API change for disallowing these types of nested dicts + df = DataFrame({"A": range(5), "B": range(0, 10, 2)}) + r = df.rolling(window=3) + + msg = "nested renamer is not supported" + with pytest.raises(SpecificationError, match=msg): + r.aggregate({"r1": {"A": ["mean", "sum"]}, "r2": {"B": ["mean", "sum"]}}) + + expected = concat( + [r["A"].mean(), r["A"].std(), r["B"].mean(), r["B"].std()], axis=1 + ) + expected.columns = MultiIndex.from_tuples( + [("ra", "mean"), ("ra", "std"), ("rb", "mean"), ("rb", "std")] + ) + with pytest.raises(SpecificationError, match=msg): + r[["A", "B"]].agg({"A": {"ra": ["mean", "std"]}, "B": {"rb": ["mean", "std"]}}) + + with pytest.raises(SpecificationError, match=msg): + r.agg({"A": {"ra": ["mean", "std"]}, "B": {"rb": ["mean", "std"]}}) + + +def test_count_nonnumeric_types(step): + # GH12541 + cols = [ + "int", + "float", + "string", + "datetime", + "timedelta", + "periods", + "fl_inf", + "fl_nan", + "str_nan", + "dt_nat", + "periods_nat", + ] + dt_nat_col = [Timestamp("20170101"), Timestamp("20170203"), Timestamp(None)] + + df = DataFrame( + { + "int": [1, 2, 3], + "float": [4.0, 5.0, 6.0], + "string": list("abc"), + "datetime": date_range("20170101", periods=3), + "timedelta": timedelta_range("1 s", periods=3, freq="s"), + "periods": [ + Period("2012-01"), + Period("2012-02"), + Period("2012-03"), + ], + "fl_inf": [1.0, 2.0, np.inf], + "fl_nan": [1.0, 2.0, np.nan], + "str_nan": ["aa", "bb", np.nan], + "dt_nat": dt_nat_col, + "periods_nat": [ + Period("2012-01"), + Period("2012-02"), + Period(None), + ], + }, + columns=cols, + ) + + expected = DataFrame( + { + "int": [1.0, 2.0, 2.0], + "float": [1.0, 2.0, 2.0], + "string": [1.0, 2.0, 2.0], + "datetime": [1.0, 2.0, 2.0], + "timedelta": [1.0, 2.0, 2.0], + "periods": [1.0, 2.0, 2.0], + "fl_inf": [1.0, 2.0, 2.0], + "fl_nan": [1.0, 2.0, 1.0], + "str_nan": [1.0, 2.0, 1.0], + "dt_nat": [1.0, 2.0, 1.0], + "periods_nat": [1.0, 2.0, 1.0], + }, + columns=cols, + )[::step] + + result = df.rolling(window=2, min_periods=0, step=step).count() + tm.assert_frame_equal(result, expected) + + result = df.rolling(1, min_periods=0, step=step).count() + expected = df.notna().astype(float)[::step] + tm.assert_frame_equal(result, expected) + + +def test_preserve_metadata(): + # GH 10565 + s = Series(np.arange(100), name="foo") + + s2 = s.rolling(30).sum() + s3 = s.rolling(20).sum() + assert s2.name == "foo" + assert s3.name == "foo" + + +@pytest.mark.parametrize( + "func,window_size,expected_vals", + [ + ( + "rolling", + 2, + [ + [np.nan, np.nan, np.nan, np.nan], + [15.0, 20.0, 25.0, 20.0], + [25.0, 30.0, 35.0, 30.0], + [np.nan, np.nan, np.nan, np.nan], + [20.0, 30.0, 35.0, 30.0], + [35.0, 40.0, 60.0, 40.0], + [60.0, 80.0, 85.0, 80], + ], + ), + ( + "expanding", + None, + [ + [10.0, 10.0, 20.0, 20.0], + [15.0, 20.0, 25.0, 20.0], + [20.0, 30.0, 30.0, 20.0], + [10.0, 10.0, 30.0, 30.0], + [20.0, 30.0, 35.0, 30.0], + [26.666667, 40.0, 50.0, 30.0], + [40.0, 80.0, 60.0, 30.0], + ], + ), + ], +) +def test_multiple_agg_funcs(func, window_size, expected_vals): + # GH 15072 + df = DataFrame( + [ + ["A", 10, 20], + ["A", 20, 30], + ["A", 30, 40], + ["B", 10, 30], + ["B", 30, 40], + ["B", 40, 80], + ["B", 80, 90], + ], + columns=["stock", "low", "high"], + ) + + f = getattr(df.groupby("stock"), func) + if window_size: + window = f(window_size) + else: + window = f() + + index = MultiIndex.from_tuples( + [("A", 0), ("A", 1), ("A", 2), ("B", 3), ("B", 4), ("B", 5), ("B", 6)], + names=["stock", None], + ) + columns = MultiIndex.from_tuples( + [("low", "mean"), ("low", "max"), ("high", "mean"), ("high", "min")] + ) + expected = DataFrame(expected_vals, index=index, columns=columns) + + result = window.agg({"low": ["mean", "max"], "high": ["mean", "min"]}) + + tm.assert_frame_equal(result, expected) + + +def test_dont_modify_attributes_after_methods( + arithmetic_win_operators, closed, center, min_periods, step +): + # GH 39554 + roll_obj = Series(range(1)).rolling( + 1, center=center, closed=closed, min_periods=min_periods, step=step + ) + expected = {attr: getattr(roll_obj, attr) for attr in roll_obj._attributes} + getattr(roll_obj, arithmetic_win_operators)() + result = {attr: getattr(roll_obj, attr) for attr in roll_obj._attributes} + assert result == expected + + +def test_centered_axis_validation(step): + # ok + msg = "The 'axis' keyword in Series.rolling is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + Series(np.ones(10)).rolling(window=3, center=True, axis=0, step=step).mean() + + # bad axis + msg = "No axis named 1 for object type Series" + with pytest.raises(ValueError, match=msg): + Series(np.ones(10)).rolling(window=3, center=True, axis=1, step=step).mean() + + # ok ok + df = DataFrame(np.ones((10, 10))) + msg = "The 'axis' keyword in DataFrame.rolling is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + df.rolling(window=3, center=True, axis=0, step=step).mean() + msg = "Support for axis=1 in DataFrame.rolling is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + df.rolling(window=3, center=True, axis=1, step=step).mean() + + # bad axis + msg = "No axis named 2 for object type DataFrame" + with pytest.raises(ValueError, match=msg): + (df.rolling(window=3, center=True, axis=2, step=step).mean()) + + +def test_rolling_min_min_periods(step): + a = Series([1, 2, 3, 4, 5]) + result = a.rolling(window=100, min_periods=1, step=step).min() + expected = Series(np.ones(len(a)))[::step] + tm.assert_series_equal(result, expected) + msg = "min_periods 5 must be <= window 3" + with pytest.raises(ValueError, match=msg): + Series([1, 2, 3]).rolling(window=3, min_periods=5, step=step).min() + + +def test_rolling_max_min_periods(step): + a = Series([1, 2, 3, 4, 5], dtype=np.float64) + result = a.rolling(window=100, min_periods=1, step=step).max() + expected = a[::step] + tm.assert_almost_equal(result, expected) + msg = "min_periods 5 must be <= window 3" + with pytest.raises(ValueError, match=msg): + Series([1, 2, 3]).rolling(window=3, min_periods=5, step=step).max() diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_apply.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_apply.py new file mode 100644 index 0000000000000000000000000000000000000000..136f81632cb0ad1f8847379e656ff5e3bf028cd3 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_apply.py @@ -0,0 +1,328 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Index, + MultiIndex, + Series, + Timestamp, + concat, + date_range, + isna, + notna, +) +import pandas._testing as tm + +from pandas.tseries import offsets + +# suppress warnings about empty slices, as we are deliberately testing +# with a 0-length Series +pytestmark = pytest.mark.filterwarnings( + "ignore:.*(empty slice|0 for slice).*:RuntimeWarning" +) + + +def f(x): + return x[np.isfinite(x)].mean() + + +@pytest.mark.parametrize("bad_raw", [None, 1, 0]) +def test_rolling_apply_invalid_raw(bad_raw): + with pytest.raises(ValueError, match="raw parameter must be `True` or `False`"): + Series(range(3)).rolling(1).apply(len, raw=bad_raw) + + +def test_rolling_apply_out_of_bounds(engine_and_raw): + # gh-1850 + engine, raw = engine_and_raw + + vals = Series([1, 2, 3, 4]) + + result = vals.rolling(10).apply(np.sum, engine=engine, raw=raw) + assert result.isna().all() + + result = vals.rolling(10, min_periods=1).apply(np.sum, engine=engine, raw=raw) + expected = Series([1, 3, 6, 10], dtype=float) + tm.assert_almost_equal(result, expected) + + +@pytest.mark.parametrize("window", [2, "2s"]) +def test_rolling_apply_with_pandas_objects(window): + # 5071 + df = DataFrame( + { + "A": np.random.default_rng(2).standard_normal(5), + "B": np.random.default_rng(2).integers(0, 10, size=5), + }, + index=date_range("20130101", periods=5, freq="s"), + ) + + # we have an equal spaced timeseries index + # so simulate removing the first period + def f(x): + if x.index[0] == df.index[0]: + return np.nan + return x.iloc[-1] + + result = df.rolling(window).apply(f, raw=False) + expected = df.iloc[2:].reindex_like(df) + tm.assert_frame_equal(result, expected) + + with tm.external_error_raised(AttributeError): + df.rolling(window).apply(f, raw=True) + + +def test_rolling_apply(engine_and_raw, step): + engine, raw = engine_and_raw + + expected = Series([], dtype="float64") + result = expected.rolling(10, step=step).apply( + lambda x: x.mean(), engine=engine, raw=raw + ) + tm.assert_series_equal(result, expected) + + # gh-8080 + s = Series([None, None, None]) + result = s.rolling(2, min_periods=0, step=step).apply( + lambda x: len(x), engine=engine, raw=raw + ) + expected = Series([1.0, 2.0, 2.0])[::step] + tm.assert_series_equal(result, expected) + + result = s.rolling(2, min_periods=0, step=step).apply(len, engine=engine, raw=raw) + tm.assert_series_equal(result, expected) + + +def test_all_apply(engine_and_raw): + engine, raw = engine_and_raw + + df = ( + DataFrame( + {"A": date_range("20130101", periods=5, freq="s"), "B": range(5)} + ).set_index("A") + * 2 + ) + er = df.rolling(window=1) + r = df.rolling(window="1s") + + result = r.apply(lambda x: 1, engine=engine, raw=raw) + expected = er.apply(lambda x: 1, engine=engine, raw=raw) + tm.assert_frame_equal(result, expected) + + +def test_ragged_apply(engine_and_raw): + engine, raw = engine_and_raw + + df = DataFrame({"B": range(5)}) + df.index = [ + Timestamp("20130101 09:00:00"), + Timestamp("20130101 09:00:02"), + Timestamp("20130101 09:00:03"), + Timestamp("20130101 09:00:05"), + Timestamp("20130101 09:00:06"), + ] + + f = lambda x: 1 + result = df.rolling(window="1s", min_periods=1).apply(f, engine=engine, raw=raw) + expected = df.copy() + expected["B"] = 1.0 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="2s", min_periods=1).apply(f, engine=engine, raw=raw) + expected = df.copy() + expected["B"] = 1.0 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="5s", min_periods=1).apply(f, engine=engine, raw=raw) + expected = df.copy() + expected["B"] = 1.0 + tm.assert_frame_equal(result, expected) + + +def test_invalid_engine(): + with pytest.raises(ValueError, match="engine must be either 'numba' or 'cython'"): + Series(range(1)).rolling(1).apply(lambda x: x, engine="foo") + + +def test_invalid_engine_kwargs_cython(): + with pytest.raises(ValueError, match="cython engine does not accept engine_kwargs"): + Series(range(1)).rolling(1).apply( + lambda x: x, engine="cython", engine_kwargs={"nopython": False} + ) + + +def test_invalid_raw_numba(): + with pytest.raises( + ValueError, match="raw must be `True` when using the numba engine" + ): + Series(range(1)).rolling(1).apply(lambda x: x, raw=False, engine="numba") + + +@pytest.mark.parametrize("args_kwargs", [[None, {"par": 10}], [(10,), None]]) +def test_rolling_apply_args_kwargs(args_kwargs): + # GH 33433 + def numpysum(x, par): + return np.sum(x + par) + + df = DataFrame({"gr": [1, 1], "a": [1, 2]}) + + idx = Index(["gr", "a"]) + expected = DataFrame([[11.0, 11.0], [11.0, 12.0]], columns=idx) + + result = df.rolling(1).apply(numpysum, args=args_kwargs[0], kwargs=args_kwargs[1]) + tm.assert_frame_equal(result, expected) + + midx = MultiIndex.from_tuples([(1, 0), (1, 1)], names=["gr", None]) + expected = Series([11.0, 12.0], index=midx, name="a") + + gb_rolling = df.groupby("gr")["a"].rolling(1) + + result = gb_rolling.apply(numpysum, args=args_kwargs[0], kwargs=args_kwargs[1]) + tm.assert_series_equal(result, expected) + + +def test_nans(raw): + obj = Series(np.random.default_rng(2).standard_normal(50)) + obj[:10] = np.nan + obj[-10:] = np.nan + + result = obj.rolling(50, min_periods=30).apply(f, raw=raw) + tm.assert_almost_equal(result.iloc[-1], np.mean(obj[10:-10])) + + # min_periods is working correctly + result = obj.rolling(20, min_periods=15).apply(f, raw=raw) + assert isna(result.iloc[23]) + assert not isna(result.iloc[24]) + + assert not isna(result.iloc[-6]) + assert isna(result.iloc[-5]) + + obj2 = Series(np.random.default_rng(2).standard_normal(20)) + result = obj2.rolling(10, min_periods=5).apply(f, raw=raw) + assert isna(result.iloc[3]) + assert notna(result.iloc[4]) + + result0 = obj.rolling(20, min_periods=0).apply(f, raw=raw) + result1 = obj.rolling(20, min_periods=1).apply(f, raw=raw) + tm.assert_almost_equal(result0, result1) + + +def test_center(raw): + obj = Series(np.random.default_rng(2).standard_normal(50)) + obj[:10] = np.nan + obj[-10:] = np.nan + + result = obj.rolling(20, min_periods=15, center=True).apply(f, raw=raw) + expected = ( + concat([obj, Series([np.nan] * 9)]) + .rolling(20, min_periods=15) + .apply(f, raw=raw) + .iloc[9:] + .reset_index(drop=True) + ) + tm.assert_series_equal(result, expected) + + +def test_series(raw, series): + result = series.rolling(50).apply(f, raw=raw) + assert isinstance(result, Series) + tm.assert_almost_equal(result.iloc[-1], np.mean(series[-50:])) + + +def test_frame(raw, frame): + result = frame.rolling(50).apply(f, raw=raw) + assert isinstance(result, DataFrame) + tm.assert_series_equal( + result.iloc[-1, :], + frame.iloc[-50:, :].apply(np.mean, axis=0, raw=raw), + check_names=False, + ) + + +def test_time_rule_series(raw, series): + win = 25 + minp = 10 + ser = series[::2].resample("B").mean() + series_result = ser.rolling(window=win, min_periods=minp).apply(f, raw=raw) + last_date = series_result.index[-1] + prev_date = last_date - 24 * offsets.BDay() + + trunc_series = series[::2].truncate(prev_date, last_date) + tm.assert_almost_equal(series_result.iloc[-1], np.mean(trunc_series)) + + +def test_time_rule_frame(raw, frame): + win = 25 + minp = 10 + frm = frame[::2].resample("B").mean() + frame_result = frm.rolling(window=win, min_periods=minp).apply(f, raw=raw) + last_date = frame_result.index[-1] + prev_date = last_date - 24 * offsets.BDay() + + trunc_frame = frame[::2].truncate(prev_date, last_date) + tm.assert_series_equal( + frame_result.xs(last_date), + trunc_frame.apply(np.mean, raw=raw), + check_names=False, + ) + + +@pytest.mark.parametrize("minp", [0, 99, 100]) +def test_min_periods(raw, series, minp, step): + result = series.rolling(len(series) + 1, min_periods=minp, step=step).apply( + f, raw=raw + ) + expected = series.rolling(len(series), min_periods=minp, step=step).apply( + f, raw=raw + ) + nan_mask = isna(result) + tm.assert_series_equal(nan_mask, isna(expected)) + + nan_mask = ~nan_mask + tm.assert_almost_equal(result[nan_mask], expected[nan_mask]) + + +def test_center_reindex_series(raw, series): + # shifter index + s = [f"x{x:d}" for x in range(12)] + minp = 10 + + series_xp = ( + series.reindex(list(series.index) + s) + .rolling(window=25, min_periods=minp) + .apply(f, raw=raw) + .shift(-12) + .reindex(series.index) + ) + series_rs = series.rolling(window=25, min_periods=minp, center=True).apply( + f, raw=raw + ) + tm.assert_series_equal(series_xp, series_rs) + + +def test_center_reindex_frame(raw): + # shifter index + frame = DataFrame(range(100), index=date_range("2020-01-01", freq="D", periods=100)) + s = [f"x{x:d}" for x in range(12)] + minp = 10 + + frame_xp = ( + frame.reindex(list(frame.index) + s) + .rolling(window=25, min_periods=minp) + .apply(f, raw=raw) + .shift(-12) + .reindex(frame.index) + ) + frame_rs = frame.rolling(window=25, min_periods=minp, center=True).apply(f, raw=raw) + tm.assert_frame_equal(frame_xp, frame_rs) + + +def test_axis1(raw): + # GH 45912 + df = DataFrame([1, 2]) + msg = "Support for axis=1 in DataFrame.rolling is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.rolling(window=1, axis=1).apply(np.sum, raw=raw) + expected = DataFrame([1.0, 2.0]) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_base_indexer.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_base_indexer.py new file mode 100644 index 0000000000000000000000000000000000000000..104acc1d527cb8dbd92b20211fb760dd413a0757 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_base_indexer.py @@ -0,0 +1,519 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + MultiIndex, + Series, + concat, + date_range, +) +import pandas._testing as tm +from pandas.api.indexers import ( + BaseIndexer, + FixedForwardWindowIndexer, +) +from pandas.core.indexers.objects import ( + ExpandingIndexer, + FixedWindowIndexer, + VariableOffsetWindowIndexer, +) + +from pandas.tseries.offsets import BusinessDay + + +def test_bad_get_window_bounds_signature(): + class BadIndexer(BaseIndexer): + def get_window_bounds(self): + return None + + indexer = BadIndexer() + with pytest.raises(ValueError, match="BadIndexer does not implement"): + Series(range(5)).rolling(indexer) + + +def test_expanding_indexer(): + s = Series(range(10)) + indexer = ExpandingIndexer() + result = s.rolling(indexer).mean() + expected = s.expanding().mean() + tm.assert_series_equal(result, expected) + + +def test_indexer_constructor_arg(): + # Example found in computation.rst + use_expanding = [True, False, True, False, True] + df = DataFrame({"values": range(5)}) + + class CustomIndexer(BaseIndexer): + def get_window_bounds(self, num_values, min_periods, center, closed, step): + start = np.empty(num_values, dtype=np.int64) + end = np.empty(num_values, dtype=np.int64) + for i in range(num_values): + if self.use_expanding[i]: + start[i] = 0 + end[i] = i + 1 + else: + start[i] = i + end[i] = i + self.window_size + return start, end + + indexer = CustomIndexer(window_size=1, use_expanding=use_expanding) + result = df.rolling(indexer).sum() + expected = DataFrame({"values": [0.0, 1.0, 3.0, 3.0, 10.0]}) + tm.assert_frame_equal(result, expected) + + +def test_indexer_accepts_rolling_args(): + df = DataFrame({"values": range(5)}) + + class CustomIndexer(BaseIndexer): + def get_window_bounds(self, num_values, min_periods, center, closed, step): + start = np.empty(num_values, dtype=np.int64) + end = np.empty(num_values, dtype=np.int64) + for i in range(num_values): + if ( + center + and min_periods == 1 + and closed == "both" + and step == 1 + and i == 2 + ): + start[i] = 0 + end[i] = num_values + else: + start[i] = i + end[i] = i + self.window_size + return start, end + + indexer = CustomIndexer(window_size=1) + result = df.rolling( + indexer, center=True, min_periods=1, closed="both", step=1 + ).sum() + expected = DataFrame({"values": [0.0, 1.0, 10.0, 3.0, 4.0]}) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "func,np_func,expected,np_kwargs", + [ + ("count", len, [3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 2.0, np.nan], {}), + ("min", np.min, [0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 6.0, 7.0, 8.0, np.nan], {}), + ( + "max", + np.max, + [2.0, 3.0, 4.0, 100.0, 100.0, 100.0, 8.0, 9.0, 9.0, np.nan], + {}, + ), + ( + "std", + np.std, + [ + 1.0, + 1.0, + 1.0, + 55.71654452, + 54.85739087, + 53.9845657, + 1.0, + 1.0, + 0.70710678, + np.nan, + ], + {"ddof": 1}, + ), + ( + "var", + np.var, + [ + 1.0, + 1.0, + 1.0, + 3104.333333, + 3009.333333, + 2914.333333, + 1.0, + 1.0, + 0.500000, + np.nan, + ], + {"ddof": 1}, + ), + ( + "median", + np.median, + [1.0, 2.0, 3.0, 4.0, 6.0, 7.0, 7.0, 8.0, 8.5, np.nan], + {}, + ), + ], +) +def test_rolling_forward_window( + frame_or_series, func, np_func, expected, np_kwargs, step +): + # GH 32865 + values = np.arange(10.0) + values[5] = 100.0 + + indexer = FixedForwardWindowIndexer(window_size=3) + + match = "Forward-looking windows can't have center=True" + with pytest.raises(ValueError, match=match): + rolling = frame_or_series(values).rolling(window=indexer, center=True) + getattr(rolling, func)() + + match = "Forward-looking windows don't support setting the closed argument" + with pytest.raises(ValueError, match=match): + rolling = frame_or_series(values).rolling(window=indexer, closed="right") + getattr(rolling, func)() + + rolling = frame_or_series(values).rolling(window=indexer, min_periods=2, step=step) + result = getattr(rolling, func)() + + # Check that the function output matches the explicitly provided array + expected = frame_or_series(expected)[::step] + tm.assert_equal(result, expected) + + # Check that the rolling function output matches applying an alternative + # function to the rolling window object + expected2 = frame_or_series(rolling.apply(lambda x: np_func(x, **np_kwargs))) + tm.assert_equal(result, expected2) + + # Check that the function output matches applying an alternative function + # if min_periods isn't specified + # GH 39604: After count-min_periods deprecation, apply(lambda x: len(x)) + # is equivalent to count after setting min_periods=0 + min_periods = 0 if func == "count" else None + rolling3 = frame_or_series(values).rolling(window=indexer, min_periods=min_periods) + result3 = getattr(rolling3, func)() + expected3 = frame_or_series(rolling3.apply(lambda x: np_func(x, **np_kwargs))) + tm.assert_equal(result3, expected3) + + +def test_rolling_forward_skewness(frame_or_series, step): + values = np.arange(10.0) + values[5] = 100.0 + + indexer = FixedForwardWindowIndexer(window_size=5) + rolling = frame_or_series(values).rolling(window=indexer, min_periods=3, step=step) + result = rolling.skew() + + expected = frame_or_series( + [ + 0.0, + 2.232396, + 2.229508, + 2.228340, + 2.229091, + 2.231989, + 0.0, + 0.0, + np.nan, + np.nan, + ] + )[::step] + tm.assert_equal(result, expected) + + +@pytest.mark.parametrize( + "func,expected", + [ + ("cov", [2.0, 2.0, 2.0, 97.0, 2.0, -93.0, 2.0, 2.0, np.nan, np.nan]), + ( + "corr", + [ + 1.0, + 1.0, + 1.0, + 0.8704775290207161, + 0.018229084250926637, + -0.861357304646493, + 1.0, + 1.0, + np.nan, + np.nan, + ], + ), + ], +) +def test_rolling_forward_cov_corr(func, expected): + values1 = np.arange(10).reshape(-1, 1) + values2 = values1 * 2 + values1[5, 0] = 100 + values = np.concatenate([values1, values2], axis=1) + + indexer = FixedForwardWindowIndexer(window_size=3) + rolling = DataFrame(values).rolling(window=indexer, min_periods=3) + # We are interested in checking only pairwise covariance / correlation + result = getattr(rolling, func)().loc[(slice(None), 1), 0] + result = result.reset_index(drop=True) + expected = Series(expected).reset_index(drop=True) + expected.name = result.name + tm.assert_equal(result, expected) + + +@pytest.mark.parametrize( + "closed,expected_data", + [ + ["right", [0.0, 1.0, 2.0, 3.0, 7.0, 12.0, 6.0, 7.0, 8.0, 9.0]], + ["left", [0.0, 0.0, 1.0, 2.0, 5.0, 9.0, 5.0, 6.0, 7.0, 8.0]], + ], +) +def test_non_fixed_variable_window_indexer(closed, expected_data): + index = date_range("2020", periods=10) + df = DataFrame(range(10), index=index) + offset = BusinessDay(1) + indexer = VariableOffsetWindowIndexer(index=index, offset=offset) + result = df.rolling(indexer, closed=closed).sum() + expected = DataFrame(expected_data, index=index) + tm.assert_frame_equal(result, expected) + + +def test_variableoffsetwindowindexer_not_dti(): + # GH 54379 + with pytest.raises(ValueError, match="index must be a DatetimeIndex."): + VariableOffsetWindowIndexer(index="foo", offset=BusinessDay(1)) + + +def test_variableoffsetwindowindexer_not_offset(): + # GH 54379 + idx = date_range("2020", periods=10) + with pytest.raises(ValueError, match="offset must be a DateOffset-like object."): + VariableOffsetWindowIndexer(index=idx, offset="foo") + + +def test_fixed_forward_indexer_count(step): + # GH: 35579 + df = DataFrame({"b": [None, None, None, 7]}) + indexer = FixedForwardWindowIndexer(window_size=2) + result = df.rolling(window=indexer, min_periods=0, step=step).count() + expected = DataFrame({"b": [0.0, 0.0, 1.0, 1.0]})[::step] + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + ("end_value", "values"), [(1, [0.0, 1, 1, 3, 2]), (-1, [0.0, 1, 0, 3, 1])] +) +@pytest.mark.parametrize(("func", "args"), [("median", []), ("quantile", [0.5])]) +def test_indexer_quantile_sum(end_value, values, func, args): + # GH 37153 + class CustomIndexer(BaseIndexer): + def get_window_bounds(self, num_values, min_periods, center, closed, step): + start = np.empty(num_values, dtype=np.int64) + end = np.empty(num_values, dtype=np.int64) + for i in range(num_values): + if self.use_expanding[i]: + start[i] = 0 + end[i] = max(i + end_value, 1) + else: + start[i] = i + end[i] = i + self.window_size + return start, end + + use_expanding = [True, False, True, False, True] + df = DataFrame({"values": range(5)}) + + indexer = CustomIndexer(window_size=1, use_expanding=use_expanding) + result = getattr(df.rolling(indexer), func)(*args) + expected = DataFrame({"values": values}) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "indexer_class", [FixedWindowIndexer, FixedForwardWindowIndexer, ExpandingIndexer] +) +@pytest.mark.parametrize("window_size", [1, 2, 12]) +@pytest.mark.parametrize( + "df_data", + [ + {"a": [1, 1], "b": [0, 1]}, + {"a": [1, 2], "b": [0, 1]}, + {"a": [1] * 16, "b": [np.nan, 1, 2, np.nan] + list(range(4, 16))}, + ], +) +def test_indexers_are_reusable_after_groupby_rolling( + indexer_class, window_size, df_data +): + # GH 43267 + df = DataFrame(df_data) + num_trials = 3 + indexer = indexer_class(window_size=window_size) + original_window_size = indexer.window_size + for i in range(num_trials): + df.groupby("a")["b"].rolling(window=indexer, min_periods=1).mean() + assert indexer.window_size == original_window_size + + +@pytest.mark.parametrize( + "window_size, num_values, expected_start, expected_end", + [ + (1, 1, [0], [1]), + (1, 2, [0, 1], [1, 2]), + (2, 1, [0], [1]), + (2, 2, [0, 1], [2, 2]), + (5, 12, range(12), list(range(5, 12)) + [12] * 5), + (12, 5, range(5), [5] * 5), + (0, 0, np.array([]), np.array([])), + (1, 0, np.array([]), np.array([])), + (0, 1, [0], [0]), + ], +) +def test_fixed_forward_indexer_bounds( + window_size, num_values, expected_start, expected_end, step +): + # GH 43267 + indexer = FixedForwardWindowIndexer(window_size=window_size) + start, end = indexer.get_window_bounds(num_values=num_values, step=step) + + tm.assert_numpy_array_equal( + start, np.array(expected_start[::step]), check_dtype=False + ) + tm.assert_numpy_array_equal(end, np.array(expected_end[::step]), check_dtype=False) + assert len(start) == len(end) + + +@pytest.mark.parametrize( + "df, window_size, expected", + [ + ( + DataFrame({"b": [0, 1, 2], "a": [1, 2, 2]}), + 2, + Series( + [0, 1.5, 2.0], + index=MultiIndex.from_arrays([[1, 2, 2], range(3)], names=["a", None]), + name="b", + dtype=np.float64, + ), + ), + ( + DataFrame( + { + "b": [np.nan, 1, 2, np.nan] + list(range(4, 18)), + "a": [1] * 7 + [2] * 11, + "c": range(18), + } + ), + 12, + Series( + [ + 3.6, + 3.6, + 4.25, + 5.0, + 5.0, + 5.5, + 6.0, + 12.0, + 12.5, + 13.0, + 13.5, + 14.0, + 14.5, + 15.0, + 15.5, + 16.0, + 16.5, + 17.0, + ], + index=MultiIndex.from_arrays( + [[1] * 7 + [2] * 11, range(18)], names=["a", None] + ), + name="b", + dtype=np.float64, + ), + ), + ], +) +def test_rolling_groupby_with_fixed_forward_specific(df, window_size, expected): + # GH 43267 + indexer = FixedForwardWindowIndexer(window_size=window_size) + result = df.groupby("a")["b"].rolling(window=indexer, min_periods=1).mean() + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "group_keys", + [ + (1,), + (1, 2), + (2, 1), + (1, 1, 2), + (1, 2, 1), + (1, 1, 2, 2), + (1, 2, 3, 2, 3), + (1, 1, 2) * 4, + (1, 2, 3) * 5, + ], +) +@pytest.mark.parametrize("window_size", [1, 2, 3, 4, 5, 8, 20]) +def test_rolling_groupby_with_fixed_forward_many(group_keys, window_size): + # GH 43267 + df = DataFrame( + { + "a": np.array(list(group_keys)), + "b": np.arange(len(group_keys), dtype=np.float64) + 17, + "c": np.arange(len(group_keys), dtype=np.int64), + } + ) + + indexer = FixedForwardWindowIndexer(window_size=window_size) + result = df.groupby("a")["b"].rolling(window=indexer, min_periods=1).sum() + result.index.names = ["a", "c"] + + groups = df.groupby("a")[["a", "b", "c"]] + manual = concat( + [ + g.assign( + b=[ + g["b"].iloc[i : i + window_size].sum(min_count=1) + for i in range(len(g)) + ] + ) + for _, g in groups + ] + ) + manual = manual.set_index(["a", "c"])["b"] + + tm.assert_series_equal(result, manual) + + +def test_unequal_start_end_bounds(): + class CustomIndexer(BaseIndexer): + def get_window_bounds(self, num_values, min_periods, center, closed, step): + return np.array([1]), np.array([1, 2]) + + indexer = CustomIndexer() + roll = Series(1).rolling(indexer) + match = "start" + with pytest.raises(ValueError, match=match): + roll.mean() + + with pytest.raises(ValueError, match=match): + next(iter(roll)) + + with pytest.raises(ValueError, match=match): + roll.corr(pairwise=True) + + with pytest.raises(ValueError, match=match): + roll.cov(pairwise=True) + + +def test_unequal_bounds_to_object(): + # GH 44470 + class CustomIndexer(BaseIndexer): + def get_window_bounds(self, num_values, min_periods, center, closed, step): + return np.array([1]), np.array([2]) + + indexer = CustomIndexer() + roll = Series([1, 1]).rolling(indexer) + match = "start and end" + with pytest.raises(ValueError, match=match): + roll.mean() + + with pytest.raises(ValueError, match=match): + next(iter(roll)) + + with pytest.raises(ValueError, match=match): + roll.corr(pairwise=True) + + with pytest.raises(ValueError, match=match): + roll.cov(pairwise=True) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_cython_aggregations.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_cython_aggregations.py new file mode 100644 index 0000000000000000000000000000000000000000..c60cb6ea74ec0aa90cf089841c853c657e1b4c00 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_cython_aggregations.py @@ -0,0 +1,111 @@ +from functools import partial +import sys + +import numpy as np +import pytest + +import pandas._libs.window.aggregations as window_aggregations + +from pandas import Series +import pandas._testing as tm + + +def _get_rolling_aggregations(): + # list pairs of name and function + # each function has this signature: + # (const float64_t[:] values, ndarray[int64_t] start, + # ndarray[int64_t] end, int64_t minp) -> np.ndarray + named_roll_aggs = ( + [ + ("roll_sum", window_aggregations.roll_sum), + ("roll_mean", window_aggregations.roll_mean), + ] + + [ + (f"roll_var({ddof})", partial(window_aggregations.roll_var, ddof=ddof)) + for ddof in [0, 1] + ] + + [ + ("roll_skew", window_aggregations.roll_skew), + ("roll_kurt", window_aggregations.roll_kurt), + ("roll_median_c", window_aggregations.roll_median_c), + ("roll_max", window_aggregations.roll_max), + ("roll_min", window_aggregations.roll_min), + ] + + [ + ( + f"roll_quantile({quantile},{interpolation})", + partial( + window_aggregations.roll_quantile, + quantile=quantile, + interpolation=interpolation, + ), + ) + for quantile in [0.0001, 0.5, 0.9999] + for interpolation in window_aggregations.interpolation_types + ] + + [ + ( + f"roll_rank({percentile},{method},{ascending})", + partial( + window_aggregations.roll_rank, + percentile=percentile, + method=method, + ascending=ascending, + ), + ) + for percentile in [True, False] + for method in window_aggregations.rolling_rank_tiebreakers.keys() + for ascending in [True, False] + ] + ) + # unzip to a list of 2 tuples, names and functions + unzipped = list(zip(*named_roll_aggs)) + return {"ids": unzipped[0], "params": unzipped[1]} + + +_rolling_aggregations = _get_rolling_aggregations() + + +@pytest.fixture( + params=_rolling_aggregations["params"], ids=_rolling_aggregations["ids"] +) +def rolling_aggregation(request): + """Make a rolling aggregation function as fixture.""" + return request.param + + +def test_rolling_aggregation_boundary_consistency(rolling_aggregation): + # GH-45647 + minp, step, width, size, selection = 0, 1, 3, 11, [2, 7] + values = np.arange(1, 1 + size, dtype=np.float64) + end = np.arange(width, size, step, dtype=np.int64) + start = end - width + selarr = np.array(selection, dtype=np.int32) + result = Series(rolling_aggregation(values, start[selarr], end[selarr], minp)) + expected = Series(rolling_aggregation(values, start, end, minp)[selarr]) + tm.assert_equal(expected, result) + + +def test_rolling_aggregation_with_unused_elements(rolling_aggregation): + # GH-45647 + minp, width = 0, 5 # width at least 4 for kurt + size = 2 * width + 5 + values = np.arange(1, size + 1, dtype=np.float64) + values[width : width + 2] = sys.float_info.min + values[width + 2] = np.nan + values[width + 3 : width + 5] = sys.float_info.max + start = np.array([0, size - width], dtype=np.int64) + end = np.array([width, size], dtype=np.int64) + loc = np.array( + [j for i in range(len(start)) for j in range(start[i], end[i])], + dtype=np.int32, + ) + result = Series(rolling_aggregation(values, start, end, minp)) + compact_values = np.array(values[loc], dtype=np.float64) + compact_start = np.arange(0, len(start) * width, width, dtype=np.int64) + compact_end = compact_start + width + expected = Series( + rolling_aggregation(compact_values, compact_start, compact_end, minp) + ) + assert np.isfinite(expected.values).all(), "Not all expected values are finite" + tm.assert_equal(expected, result) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_dtypes.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_dtypes.py new file mode 100644 index 0000000000000000000000000000000000000000..4007320b5de332ee4aef40b1ad1be9092eeb3347 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_dtypes.py @@ -0,0 +1,173 @@ +import numpy as np +import pytest + +from pandas.errors import DataError + +from pandas.core.dtypes.common import pandas_dtype + +from pandas import ( + NA, + DataFrame, + Series, +) +import pandas._testing as tm + +# gh-12373 : rolling functions error on float32 data +# make sure rolling functions works for different dtypes +# +# further note that we are only checking rolling for fully dtype +# compliance (though both expanding and ewm inherit) + + +def get_dtype(dtype, coerce_int=None): + if coerce_int is False and "int" in dtype: + return None + return pandas_dtype(dtype) + + +@pytest.fixture( + params=[ + "object", + "category", + "int8", + "int16", + "int32", + "int64", + "uint8", + "uint16", + "uint32", + "uint64", + "float16", + "float32", + "float64", + "m8[ns]", + "M8[ns]", + "datetime64[ns, UTC]", + ] +) +def dtypes(request): + """Dtypes for window tests""" + return request.param + + +@pytest.mark.parametrize( + "method, data, expected_data, coerce_int, min_periods", + [ + ("count", np.arange(5), [1, 2, 2, 2, 2], True, 0), + ("count", np.arange(10, 0, -2), [1, 2, 2, 2, 2], True, 0), + ("count", [0, 1, 2, np.nan, 4], [1, 2, 2, 1, 1], False, 0), + ("max", np.arange(5), [np.nan, 1, 2, 3, 4], True, None), + ("max", np.arange(10, 0, -2), [np.nan, 10, 8, 6, 4], True, None), + ("max", [0, 1, 2, np.nan, 4], [np.nan, 1, 2, np.nan, np.nan], False, None), + ("min", np.arange(5), [np.nan, 0, 1, 2, 3], True, None), + ("min", np.arange(10, 0, -2), [np.nan, 8, 6, 4, 2], True, None), + ("min", [0, 1, 2, np.nan, 4], [np.nan, 0, 1, np.nan, np.nan], False, None), + ("sum", np.arange(5), [np.nan, 1, 3, 5, 7], True, None), + ("sum", np.arange(10, 0, -2), [np.nan, 18, 14, 10, 6], True, None), + ("sum", [0, 1, 2, np.nan, 4], [np.nan, 1, 3, np.nan, np.nan], False, None), + ("mean", np.arange(5), [np.nan, 0.5, 1.5, 2.5, 3.5], True, None), + ("mean", np.arange(10, 0, -2), [np.nan, 9, 7, 5, 3], True, None), + ("mean", [0, 1, 2, np.nan, 4], [np.nan, 0.5, 1.5, np.nan, np.nan], False, None), + ("std", np.arange(5), [np.nan] + [np.sqrt(0.5)] * 4, True, None), + ("std", np.arange(10, 0, -2), [np.nan] + [np.sqrt(2)] * 4, True, None), + ( + "std", + [0, 1, 2, np.nan, 4], + [np.nan] + [np.sqrt(0.5)] * 2 + [np.nan] * 2, + False, + None, + ), + ("var", np.arange(5), [np.nan, 0.5, 0.5, 0.5, 0.5], True, None), + ("var", np.arange(10, 0, -2), [np.nan, 2, 2, 2, 2], True, None), + ("var", [0, 1, 2, np.nan, 4], [np.nan, 0.5, 0.5, np.nan, np.nan], False, None), + ("median", np.arange(5), [np.nan, 0.5, 1.5, 2.5, 3.5], True, None), + ("median", np.arange(10, 0, -2), [np.nan, 9, 7, 5, 3], True, None), + ( + "median", + [0, 1, 2, np.nan, 4], + [np.nan, 0.5, 1.5, np.nan, np.nan], + False, + None, + ), + ], +) +def test_series_dtypes( + method, data, expected_data, coerce_int, dtypes, min_periods, step +): + ser = Series(data, dtype=get_dtype(dtypes, coerce_int=coerce_int)) + rolled = ser.rolling(2, min_periods=min_periods, step=step) + + if dtypes in ("m8[ns]", "M8[ns]", "datetime64[ns, UTC]") and method != "count": + msg = "No numeric types to aggregate" + with pytest.raises(DataError, match=msg): + getattr(rolled, method)() + else: + result = getattr(rolled, method)() + expected = Series(expected_data, dtype="float64")[::step] + tm.assert_almost_equal(result, expected) + + +def test_series_nullable_int(any_signed_int_ea_dtype, step): + # GH 43016 + ser = Series([0, 1, NA], dtype=any_signed_int_ea_dtype) + result = ser.rolling(2, step=step).mean() + expected = Series([np.nan, 0.5, np.nan])[::step] + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "method, expected_data, min_periods", + [ + ("count", {0: Series([1, 2, 2, 2, 2]), 1: Series([1, 2, 2, 2, 2])}, 0), + ( + "max", + {0: Series([np.nan, 2, 4, 6, 8]), 1: Series([np.nan, 3, 5, 7, 9])}, + None, + ), + ( + "min", + {0: Series([np.nan, 0, 2, 4, 6]), 1: Series([np.nan, 1, 3, 5, 7])}, + None, + ), + ( + "sum", + {0: Series([np.nan, 2, 6, 10, 14]), 1: Series([np.nan, 4, 8, 12, 16])}, + None, + ), + ( + "mean", + {0: Series([np.nan, 1, 3, 5, 7]), 1: Series([np.nan, 2, 4, 6, 8])}, + None, + ), + ( + "std", + { + 0: Series([np.nan] + [np.sqrt(2)] * 4), + 1: Series([np.nan] + [np.sqrt(2)] * 4), + }, + None, + ), + ( + "var", + {0: Series([np.nan, 2, 2, 2, 2]), 1: Series([np.nan, 2, 2, 2, 2])}, + None, + ), + ( + "median", + {0: Series([np.nan, 1, 3, 5, 7]), 1: Series([np.nan, 2, 4, 6, 8])}, + None, + ), + ], +) +def test_dataframe_dtypes(method, expected_data, dtypes, min_periods, step): + df = DataFrame(np.arange(10).reshape((5, 2)), dtype=get_dtype(dtypes)) + rolled = df.rolling(2, min_periods=min_periods, step=step) + + if dtypes in ("m8[ns]", "M8[ns]", "datetime64[ns, UTC]") and method != "count": + msg = "Cannot aggregate non-numeric type" + with pytest.raises(DataError, match=msg): + getattr(rolled, method)() + else: + result = getattr(rolled, method)() + expected = DataFrame(expected_data, dtype="float64")[::step] + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_ewm.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_ewm.py new file mode 100644 index 0000000000000000000000000000000000000000..058e5ce36e53e8fc43355ffc86a2336316ab09a4 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_ewm.py @@ -0,0 +1,727 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + DatetimeIndex, + Series, + date_range, +) +import pandas._testing as tm + + +def test_doc_string(): + df = DataFrame({"B": [0, 1, 2, np.nan, 4]}) + df + df.ewm(com=0.5).mean() + + +def test_constructor(frame_or_series): + c = frame_or_series(range(5)).ewm + + # valid + c(com=0.5) + c(span=1.5) + c(alpha=0.5) + c(halflife=0.75) + c(com=0.5, span=None) + c(alpha=0.5, com=None) + c(halflife=0.75, alpha=None) + + # not valid: mutually exclusive + msg = "comass, span, halflife, and alpha are mutually exclusive" + with pytest.raises(ValueError, match=msg): + c(com=0.5, alpha=0.5) + with pytest.raises(ValueError, match=msg): + c(span=1.5, halflife=0.75) + with pytest.raises(ValueError, match=msg): + c(alpha=0.5, span=1.5) + + # not valid: com < 0 + msg = "comass must satisfy: comass >= 0" + with pytest.raises(ValueError, match=msg): + c(com=-0.5) + + # not valid: span < 1 + msg = "span must satisfy: span >= 1" + with pytest.raises(ValueError, match=msg): + c(span=0.5) + + # not valid: halflife <= 0 + msg = "halflife must satisfy: halflife > 0" + with pytest.raises(ValueError, match=msg): + c(halflife=0) + + # not valid: alpha <= 0 or alpha > 1 + msg = "alpha must satisfy: 0 < alpha <= 1" + for alpha in (-0.5, 1.5): + with pytest.raises(ValueError, match=msg): + c(alpha=alpha) + + +def test_ewma_times_not_datetime_type(): + msg = r"times must be datetime64 dtype." + with pytest.raises(ValueError, match=msg): + Series(range(5)).ewm(times=np.arange(5)) + + +def test_ewma_times_not_same_length(): + msg = "times must be the same length as the object." + with pytest.raises(ValueError, match=msg): + Series(range(5)).ewm(times=np.arange(4).astype("datetime64[ns]")) + + +def test_ewma_halflife_not_correct_type(): + msg = "halflife must be a timedelta convertible object" + with pytest.raises(ValueError, match=msg): + Series(range(5)).ewm(halflife=1, times=np.arange(5).astype("datetime64[ns]")) + + +def test_ewma_halflife_without_times(halflife_with_times): + msg = "halflife can only be a timedelta convertible argument if times is not None." + with pytest.raises(ValueError, match=msg): + Series(range(5)).ewm(halflife=halflife_with_times) + + +@pytest.mark.parametrize( + "times", + [ + np.arange(10).astype("datetime64[D]").astype("datetime64[ns]"), + date_range("2000", freq="D", periods=10), + date_range("2000", freq="D", periods=10).tz_localize("UTC"), + ], +) +@pytest.mark.parametrize("min_periods", [0, 2]) +def test_ewma_with_times_equal_spacing(halflife_with_times, times, min_periods): + halflife = halflife_with_times + data = np.arange(10.0) + data[::2] = np.nan + df = DataFrame({"A": data}) + result = df.ewm(halflife=halflife, min_periods=min_periods, times=times).mean() + expected = df.ewm(halflife=1.0, min_periods=min_periods).mean() + tm.assert_frame_equal(result, expected) + + +def test_ewma_with_times_variable_spacing(tz_aware_fixture, unit): + tz = tz_aware_fixture + halflife = "23 days" + times = ( + DatetimeIndex(["2020-01-01", "2020-01-10T00:04:05", "2020-02-23T05:00:23"]) + .tz_localize(tz) + .as_unit(unit) + ) + data = np.arange(3) + df = DataFrame(data) + result = df.ewm(halflife=halflife, times=times).mean() + expected = DataFrame([0.0, 0.5674161888241773, 1.545239952073459]) + tm.assert_frame_equal(result, expected) + + +def test_ewm_with_nat_raises(halflife_with_times): + # GH#38535 + ser = Series(range(1)) + times = DatetimeIndex(["NaT"]) + with pytest.raises(ValueError, match="Cannot convert NaT values to integer"): + ser.ewm(com=0.1, halflife=halflife_with_times, times=times) + + +def test_ewm_with_times_getitem(halflife_with_times): + # GH 40164 + halflife = halflife_with_times + data = np.arange(10.0) + data[::2] = np.nan + times = date_range("2000", freq="D", periods=10) + df = DataFrame({"A": data, "B": data}) + result = df.ewm(halflife=halflife, times=times)["A"].mean() + expected = df.ewm(halflife=1.0)["A"].mean() + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("arg", ["com", "halflife", "span", "alpha"]) +def test_ewm_getitem_attributes_retained(arg, adjust, ignore_na): + # GH 40164 + kwargs = {arg: 1, "adjust": adjust, "ignore_na": ignore_na} + ewm = DataFrame({"A": range(1), "B": range(1)}).ewm(**kwargs) + expected = {attr: getattr(ewm, attr) for attr in ewm._attributes} + ewm_slice = ewm["A"] + result = {attr: getattr(ewm, attr) for attr in ewm_slice._attributes} + assert result == expected + + +def test_ewma_times_adjust_false_raises(): + # GH 40098 + with pytest.raises( + NotImplementedError, match="times is not supported with adjust=False." + ): + Series(range(1)).ewm( + 0.1, adjust=False, times=date_range("2000", freq="D", periods=1) + ) + + +@pytest.mark.parametrize( + "func, expected", + [ + [ + "mean", + DataFrame( + { + 0: range(5), + 1: range(4, 9), + 2: [7.428571, 9, 10.571429, 12.142857, 13.714286], + }, + dtype=float, + ), + ], + [ + "std", + DataFrame( + { + 0: [np.nan] * 5, + 1: [4.242641] * 5, + 2: [4.6291, 5.196152, 5.781745, 6.380775, 6.989788], + } + ), + ], + [ + "var", + DataFrame( + { + 0: [np.nan] * 5, + 1: [18.0] * 5, + 2: [21.428571, 27, 33.428571, 40.714286, 48.857143], + } + ), + ], + ], +) +def test_float_dtype_ewma(func, expected, float_numpy_dtype): + # GH#42452 + + df = DataFrame( + {0: range(5), 1: range(6, 11), 2: range(10, 20, 2)}, dtype=float_numpy_dtype + ) + msg = "Support for axis=1 in DataFrame.ewm is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + e = df.ewm(alpha=0.5, axis=1) + result = getattr(e, func)() + + tm.assert_frame_equal(result, expected) + + +def test_times_string_col_raises(): + # GH 43265 + df = DataFrame( + {"A": np.arange(10.0), "time_col": date_range("2000", freq="D", periods=10)} + ) + with pytest.raises(ValueError, match="times must be datetime64"): + df.ewm(halflife="1 day", min_periods=0, times="time_col") + + +def test_ewm_sum_adjust_false_notimplemented(): + data = Series(range(1)).ewm(com=1, adjust=False) + with pytest.raises(NotImplementedError, match="sum is not"): + data.sum() + + +@pytest.mark.parametrize( + "expected_data, ignore", + [[[10.0, 5.0, 2.5, 11.25], False], [[10.0, 5.0, 5.0, 12.5], True]], +) +def test_ewm_sum(expected_data, ignore): + # xref from Numbagg tests + # https://github.com/numbagg/numbagg/blob/v0.2.1/numbagg/test/test_moving.py#L50 + data = Series([10, 0, np.nan, 10]) + result = data.ewm(alpha=0.5, ignore_na=ignore).sum() + expected = Series(expected_data) + tm.assert_series_equal(result, expected) + + +def test_ewma_adjust(): + vals = Series(np.zeros(1000)) + vals[5] = 1 + result = vals.ewm(span=100, adjust=False).mean().sum() + assert np.abs(result - 1) < 1e-2 + + +def test_ewma_cases(adjust, ignore_na): + # try adjust/ignore_na args matrix + + s = Series([1.0, 2.0, 4.0, 8.0]) + + if adjust: + expected = Series([1.0, 1.6, 2.736842, 4.923077]) + else: + expected = Series([1.0, 1.333333, 2.222222, 4.148148]) + + result = s.ewm(com=2.0, adjust=adjust, ignore_na=ignore_na).mean() + tm.assert_series_equal(result, expected) + + +def test_ewma_nan_handling(): + s = Series([1.0] + [np.nan] * 5 + [1.0]) + result = s.ewm(com=5).mean() + tm.assert_series_equal(result, Series([1.0] * len(s))) + + s = Series([np.nan] * 2 + [1.0] + [np.nan] * 2 + [1.0]) + result = s.ewm(com=5).mean() + tm.assert_series_equal(result, Series([np.nan] * 2 + [1.0] * 4)) + + +@pytest.mark.parametrize( + "s, adjust, ignore_na, w", + [ + ( + Series([np.nan, 1.0, 101.0]), + True, + False, + [np.nan, (1.0 - (1.0 / (1.0 + 2.0))), 1.0], + ), + ( + Series([np.nan, 1.0, 101.0]), + True, + True, + [np.nan, (1.0 - (1.0 / (1.0 + 2.0))), 1.0], + ), + ( + Series([np.nan, 1.0, 101.0]), + False, + False, + [np.nan, (1.0 - (1.0 / (1.0 + 2.0))), (1.0 / (1.0 + 2.0))], + ), + ( + Series([np.nan, 1.0, 101.0]), + False, + True, + [np.nan, (1.0 - (1.0 / (1.0 + 2.0))), (1.0 / (1.0 + 2.0))], + ), + ( + Series([1.0, np.nan, 101.0]), + True, + False, + [(1.0 - (1.0 / (1.0 + 2.0))) ** 2, np.nan, 1.0], + ), + ( + Series([1.0, np.nan, 101.0]), + True, + True, + [(1.0 - (1.0 / (1.0 + 2.0))), np.nan, 1.0], + ), + ( + Series([1.0, np.nan, 101.0]), + False, + False, + [(1.0 - (1.0 / (1.0 + 2.0))) ** 2, np.nan, (1.0 / (1.0 + 2.0))], + ), + ( + Series([1.0, np.nan, 101.0]), + False, + True, + [(1.0 - (1.0 / (1.0 + 2.0))), np.nan, (1.0 / (1.0 + 2.0))], + ), + ( + Series([np.nan, 1.0, np.nan, np.nan, 101.0, np.nan]), + True, + False, + [np.nan, (1.0 - (1.0 / (1.0 + 2.0))) ** 3, np.nan, np.nan, 1.0, np.nan], + ), + ( + Series([np.nan, 1.0, np.nan, np.nan, 101.0, np.nan]), + True, + True, + [np.nan, (1.0 - (1.0 / (1.0 + 2.0))), np.nan, np.nan, 1.0, np.nan], + ), + ( + Series([np.nan, 1.0, np.nan, np.nan, 101.0, np.nan]), + False, + False, + [ + np.nan, + (1.0 - (1.0 / (1.0 + 2.0))) ** 3, + np.nan, + np.nan, + (1.0 / (1.0 + 2.0)), + np.nan, + ], + ), + ( + Series([np.nan, 1.0, np.nan, np.nan, 101.0, np.nan]), + False, + True, + [ + np.nan, + (1.0 - (1.0 / (1.0 + 2.0))), + np.nan, + np.nan, + (1.0 / (1.0 + 2.0)), + np.nan, + ], + ), + ( + Series([1.0, np.nan, 101.0, 50.0]), + True, + False, + [ + (1.0 - (1.0 / (1.0 + 2.0))) ** 3, + np.nan, + (1.0 - (1.0 / (1.0 + 2.0))), + 1.0, + ], + ), + ( + Series([1.0, np.nan, 101.0, 50.0]), + True, + True, + [ + (1.0 - (1.0 / (1.0 + 2.0))) ** 2, + np.nan, + (1.0 - (1.0 / (1.0 + 2.0))), + 1.0, + ], + ), + ( + Series([1.0, np.nan, 101.0, 50.0]), + False, + False, + [ + (1.0 - (1.0 / (1.0 + 2.0))) ** 3, + np.nan, + (1.0 - (1.0 / (1.0 + 2.0))) * (1.0 / (1.0 + 2.0)), + (1.0 / (1.0 + 2.0)) + * ((1.0 - (1.0 / (1.0 + 2.0))) ** 2 + (1.0 / (1.0 + 2.0))), + ], + ), + ( + Series([1.0, np.nan, 101.0, 50.0]), + False, + True, + [ + (1.0 - (1.0 / (1.0 + 2.0))) ** 2, + np.nan, + (1.0 - (1.0 / (1.0 + 2.0))) * (1.0 / (1.0 + 2.0)), + (1.0 / (1.0 + 2.0)), + ], + ), + ], +) +def test_ewma_nan_handling_cases(s, adjust, ignore_na, w): + # GH 7603 + expected = (s.multiply(w).cumsum() / Series(w).cumsum()).ffill() + result = s.ewm(com=2.0, adjust=adjust, ignore_na=ignore_na).mean() + + tm.assert_series_equal(result, expected) + if ignore_na is False: + # check that ignore_na defaults to False + result = s.ewm(com=2.0, adjust=adjust).mean() + tm.assert_series_equal(result, expected) + + +def test_ewm_alpha(): + # GH 10789 + arr = np.random.default_rng(2).standard_normal(100) + locs = np.arange(20, 40) + arr[locs] = np.nan + + s = Series(arr) + a = s.ewm(alpha=0.61722699889169674).mean() + b = s.ewm(com=0.62014947789973052).mean() + c = s.ewm(span=2.240298955799461).mean() + d = s.ewm(halflife=0.721792864318).mean() + tm.assert_series_equal(a, b) + tm.assert_series_equal(a, c) + tm.assert_series_equal(a, d) + + +def test_ewm_domain_checks(): + # GH 12492 + arr = np.random.default_rng(2).standard_normal(100) + locs = np.arange(20, 40) + arr[locs] = np.nan + + s = Series(arr) + msg = "comass must satisfy: comass >= 0" + with pytest.raises(ValueError, match=msg): + s.ewm(com=-0.1) + s.ewm(com=0.0) + s.ewm(com=0.1) + + msg = "span must satisfy: span >= 1" + with pytest.raises(ValueError, match=msg): + s.ewm(span=-0.1) + with pytest.raises(ValueError, match=msg): + s.ewm(span=0.0) + with pytest.raises(ValueError, match=msg): + s.ewm(span=0.9) + s.ewm(span=1.0) + s.ewm(span=1.1) + + msg = "halflife must satisfy: halflife > 0" + with pytest.raises(ValueError, match=msg): + s.ewm(halflife=-0.1) + with pytest.raises(ValueError, match=msg): + s.ewm(halflife=0.0) + s.ewm(halflife=0.1) + + msg = "alpha must satisfy: 0 < alpha <= 1" + with pytest.raises(ValueError, match=msg): + s.ewm(alpha=-0.1) + with pytest.raises(ValueError, match=msg): + s.ewm(alpha=0.0) + s.ewm(alpha=0.1) + s.ewm(alpha=1.0) + with pytest.raises(ValueError, match=msg): + s.ewm(alpha=1.1) + + +@pytest.mark.parametrize("method", ["mean", "std", "var"]) +def test_ew_empty_series(method): + vals = Series([], dtype=np.float64) + + ewm = vals.ewm(3) + result = getattr(ewm, method)() + tm.assert_almost_equal(result, vals) + + +@pytest.mark.parametrize("min_periods", [0, 1]) +@pytest.mark.parametrize("name", ["mean", "var", "std"]) +def test_ew_min_periods(min_periods, name): + # excluding NaNs correctly + arr = np.random.default_rng(2).standard_normal(50) + arr[:10] = np.nan + arr[-10:] = np.nan + s = Series(arr) + + # check min_periods + # GH 7898 + result = getattr(s.ewm(com=50, min_periods=2), name)() + assert result[:11].isna().all() + assert not result[11:].isna().any() + + result = getattr(s.ewm(com=50, min_periods=min_periods), name)() + if name == "mean": + assert result[:10].isna().all() + assert not result[10:].isna().any() + else: + # ewm.std, ewm.var (with bias=False) require at least + # two values + assert result[:11].isna().all() + assert not result[11:].isna().any() + + # check series of length 0 + result = getattr(Series(dtype=object).ewm(com=50, min_periods=min_periods), name)() + tm.assert_series_equal(result, Series(dtype="float64")) + + # check series of length 1 + result = getattr(Series([1.0]).ewm(50, min_periods=min_periods), name)() + if name == "mean": + tm.assert_series_equal(result, Series([1.0])) + else: + # ewm.std, ewm.var with bias=False require at least + # two values + tm.assert_series_equal(result, Series([np.nan])) + + # pass in ints + result2 = getattr(Series(np.arange(50)).ewm(span=10), name)() + assert result2.dtype == np.float64 + + +@pytest.mark.parametrize("name", ["cov", "corr"]) +def test_ewm_corr_cov(name): + A = Series(np.random.default_rng(2).standard_normal(50), index=range(50)) + B = A[2:] + np.random.default_rng(2).standard_normal(48) + + A[:10] = np.nan + B.iloc[-10:] = np.nan + + result = getattr(A.ewm(com=20, min_periods=5), name)(B) + assert np.isnan(result.values[:14]).all() + assert not np.isnan(result.values[14:]).any() + + +@pytest.mark.parametrize("min_periods", [0, 1, 2]) +@pytest.mark.parametrize("name", ["cov", "corr"]) +def test_ewm_corr_cov_min_periods(name, min_periods): + # GH 7898 + A = Series(np.random.default_rng(2).standard_normal(50), index=range(50)) + B = A[2:] + np.random.default_rng(2).standard_normal(48) + + A[:10] = np.nan + B.iloc[-10:] = np.nan + + result = getattr(A.ewm(com=20, min_periods=min_periods), name)(B) + # binary functions (ewmcov, ewmcorr) with bias=False require at + # least two values + assert np.isnan(result.values[:11]).all() + assert not np.isnan(result.values[11:]).any() + + # check series of length 0 + empty = Series([], dtype=np.float64) + result = getattr(empty.ewm(com=50, min_periods=min_periods), name)(empty) + tm.assert_series_equal(result, empty) + + # check series of length 1 + result = getattr(Series([1.0]).ewm(com=50, min_periods=min_periods), name)( + Series([1.0]) + ) + tm.assert_series_equal(result, Series([np.nan])) + + +@pytest.mark.parametrize("name", ["cov", "corr"]) +def test_different_input_array_raise_exception(name): + A = Series(np.random.default_rng(2).standard_normal(50), index=range(50)) + A[:10] = np.nan + + msg = "other must be a DataFrame or Series" + # exception raised is Exception + with pytest.raises(ValueError, match=msg): + getattr(A.ewm(com=20, min_periods=5), name)( + np.random.default_rng(2).standard_normal(50) + ) + + +@pytest.mark.parametrize("name", ["var", "std", "mean"]) +def test_ewma_series(series, name): + series_result = getattr(series.ewm(com=10), name)() + assert isinstance(series_result, Series) + + +@pytest.mark.parametrize("name", ["var", "std", "mean"]) +def test_ewma_frame(frame, name): + frame_result = getattr(frame.ewm(com=10), name)() + assert isinstance(frame_result, DataFrame) + + +def test_ewma_span_com_args(series): + A = series.ewm(com=9.5).mean() + B = series.ewm(span=20).mean() + tm.assert_almost_equal(A, B) + msg = "comass, span, halflife, and alpha are mutually exclusive" + with pytest.raises(ValueError, match=msg): + series.ewm(com=9.5, span=20) + + msg = "Must pass one of comass, span, halflife, or alpha" + with pytest.raises(ValueError, match=msg): + series.ewm().mean() + + +def test_ewma_halflife_arg(series): + A = series.ewm(com=13.932726172912965).mean() + B = series.ewm(halflife=10.0).mean() + tm.assert_almost_equal(A, B) + msg = "comass, span, halflife, and alpha are mutually exclusive" + with pytest.raises(ValueError, match=msg): + series.ewm(span=20, halflife=50) + with pytest.raises(ValueError, match=msg): + series.ewm(com=9.5, halflife=50) + with pytest.raises(ValueError, match=msg): + series.ewm(com=9.5, span=20, halflife=50) + msg = "Must pass one of comass, span, halflife, or alpha" + with pytest.raises(ValueError, match=msg): + series.ewm() + + +def test_ewm_alpha_arg(series): + # GH 10789 + s = series + msg = "Must pass one of comass, span, halflife, or alpha" + with pytest.raises(ValueError, match=msg): + s.ewm() + + msg = "comass, span, halflife, and alpha are mutually exclusive" + with pytest.raises(ValueError, match=msg): + s.ewm(com=10.0, alpha=0.5) + with pytest.raises(ValueError, match=msg): + s.ewm(span=10.0, alpha=0.5) + with pytest.raises(ValueError, match=msg): + s.ewm(halflife=10.0, alpha=0.5) + + +@pytest.mark.parametrize("func", ["cov", "corr"]) +def test_ewm_pairwise_cov_corr(func, frame): + result = getattr(frame.ewm(span=10, min_periods=5), func)() + result = result.loc[(slice(None), 1), 5] + result.index = result.index.droplevel(1) + expected = getattr(frame[1].ewm(span=10, min_periods=5), func)(frame[5]) + tm.assert_series_equal(result, expected, check_names=False) + + +def test_numeric_only_frame(arithmetic_win_operators, numeric_only): + # GH#46560 + kernel = arithmetic_win_operators + df = DataFrame({"a": [1], "b": 2, "c": 3}) + df["c"] = df["c"].astype(object) + ewm = df.ewm(span=2, min_periods=1) + op = getattr(ewm, kernel, None) + if op is not None: + result = op(numeric_only=numeric_only) + + columns = ["a", "b"] if numeric_only else ["a", "b", "c"] + expected = df[columns].agg([kernel]).reset_index(drop=True).astype(float) + assert list(expected.columns) == columns + + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("kernel", ["corr", "cov"]) +@pytest.mark.parametrize("use_arg", [True, False]) +def test_numeric_only_corr_cov_frame(kernel, numeric_only, use_arg): + # GH#46560 + df = DataFrame({"a": [1, 2, 3], "b": 2, "c": 3}) + df["c"] = df["c"].astype(object) + arg = (df,) if use_arg else () + ewm = df.ewm(span=2, min_periods=1) + op = getattr(ewm, kernel) + result = op(*arg, numeric_only=numeric_only) + + # Compare result to op using float dtypes, dropping c when numeric_only is True + columns = ["a", "b"] if numeric_only else ["a", "b", "c"] + df2 = df[columns].astype(float) + arg2 = (df2,) if use_arg else () + ewm2 = df2.ewm(span=2, min_periods=1) + op2 = getattr(ewm2, kernel) + expected = op2(*arg2, numeric_only=numeric_only) + + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("dtype", [int, object]) +def test_numeric_only_series(arithmetic_win_operators, numeric_only, dtype): + # GH#46560 + kernel = arithmetic_win_operators + ser = Series([1], dtype=dtype) + ewm = ser.ewm(span=2, min_periods=1) + op = getattr(ewm, kernel, None) + if op is None: + # Nothing to test + pytest.skip("No op to test") + if numeric_only and dtype is object: + msg = f"ExponentialMovingWindow.{kernel} does not implement numeric_only" + with pytest.raises(NotImplementedError, match=msg): + op(numeric_only=numeric_only) + else: + result = op(numeric_only=numeric_only) + expected = ser.agg([kernel]).reset_index(drop=True).astype(float) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("kernel", ["corr", "cov"]) +@pytest.mark.parametrize("use_arg", [True, False]) +@pytest.mark.parametrize("dtype", [int, object]) +def test_numeric_only_corr_cov_series(kernel, use_arg, numeric_only, dtype): + # GH#46560 + ser = Series([1, 2, 3], dtype=dtype) + arg = (ser,) if use_arg else () + ewm = ser.ewm(span=2, min_periods=1) + op = getattr(ewm, kernel) + if numeric_only and dtype is object: + msg = f"ExponentialMovingWindow.{kernel} does not implement numeric_only" + with pytest.raises(NotImplementedError, match=msg): + op(*arg, numeric_only=numeric_only) + else: + result = op(*arg, numeric_only=numeric_only) + + ser2 = ser.astype(float) + arg2 = (ser2,) if use_arg else () + ewm2 = ser2.ewm(span=2, min_periods=1) + op2 = getattr(ewm2, kernel) + expected = op2(*arg2, numeric_only=numeric_only) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_expanding.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_expanding.py new file mode 100644 index 0000000000000000000000000000000000000000..aebb9e86c763f265b740e79e3e1e76e7ffe2dd94 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_expanding.py @@ -0,0 +1,723 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + DatetimeIndex, + Index, + MultiIndex, + Series, + isna, + notna, +) +import pandas._testing as tm + + +def test_doc_string(): + df = DataFrame({"B": [0, 1, 2, np.nan, 4]}) + df + df.expanding(2).sum() + + +def test_constructor(frame_or_series): + # GH 12669 + + c = frame_or_series(range(5)).expanding + + # valid + c(min_periods=1) + + +@pytest.mark.parametrize("w", [2.0, "foo", np.array([2])]) +def test_constructor_invalid(frame_or_series, w): + # not valid + + c = frame_or_series(range(5)).expanding + msg = "min_periods must be an integer" + with pytest.raises(ValueError, match=msg): + c(min_periods=w) + + +@pytest.mark.parametrize( + "expander", + [ + 1, + pytest.param( + "ls", + marks=pytest.mark.xfail( + reason="GH#16425 expanding with offset not supported" + ), + ), + ], +) +def test_empty_df_expanding(expander): + # GH 15819 Verifies that datetime and integer expanding windows can be + # applied to empty DataFrames + + expected = DataFrame() + result = DataFrame().expanding(expander).sum() + tm.assert_frame_equal(result, expected) + + # Verifies that datetime and integer expanding windows can be applied + # to empty DataFrames with datetime index + expected = DataFrame(index=DatetimeIndex([])) + result = DataFrame(index=DatetimeIndex([])).expanding(expander).sum() + tm.assert_frame_equal(result, expected) + + +def test_missing_minp_zero(): + # https://github.com/pandas-dev/pandas/pull/18921 + # minp=0 + x = Series([np.nan]) + result = x.expanding(min_periods=0).sum() + expected = Series([0.0]) + tm.assert_series_equal(result, expected) + + # minp=1 + result = x.expanding(min_periods=1).sum() + expected = Series([np.nan]) + tm.assert_series_equal(result, expected) + + +def test_expanding_axis(axis_frame): + # see gh-23372. + df = DataFrame(np.ones((10, 20))) + axis = df._get_axis_number(axis_frame) + + if axis == 0: + msg = "The 'axis' keyword in DataFrame.expanding is deprecated" + expected = DataFrame( + {i: [np.nan] * 2 + [float(j) for j in range(3, 11)] for i in range(20)} + ) + else: + # axis == 1 + msg = "Support for axis=1 in DataFrame.expanding is deprecated" + expected = DataFrame([[np.nan] * 2 + [float(i) for i in range(3, 21)]] * 10) + + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.expanding(3, axis=axis_frame).sum() + tm.assert_frame_equal(result, expected) + + +def test_expanding_count_with_min_periods(frame_or_series): + # GH 26996 + result = frame_or_series(range(5)).expanding(min_periods=3).count() + expected = frame_or_series([np.nan, np.nan, 3.0, 4.0, 5.0]) + tm.assert_equal(result, expected) + + +def test_expanding_count_default_min_periods_with_null_values(frame_or_series): + # GH 26996 + values = [1, 2, 3, np.nan, 4, 5, 6] + expected_counts = [1.0, 2.0, 3.0, 3.0, 4.0, 5.0, 6.0] + + result = frame_or_series(values).expanding().count() + expected = frame_or_series(expected_counts) + tm.assert_equal(result, expected) + + +def test_expanding_count_with_min_periods_exceeding_series_length(frame_or_series): + # GH 25857 + result = frame_or_series(range(5)).expanding(min_periods=6).count() + expected = frame_or_series([np.nan, np.nan, np.nan, np.nan, np.nan]) + tm.assert_equal(result, expected) + + +@pytest.mark.parametrize( + "df,expected,min_periods", + [ + ( + DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}), + [ + ({"A": [1], "B": [4]}, [0]), + ({"A": [1, 2], "B": [4, 5]}, [0, 1]), + ({"A": [1, 2, 3], "B": [4, 5, 6]}, [0, 1, 2]), + ], + 3, + ), + ( + DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}), + [ + ({"A": [1], "B": [4]}, [0]), + ({"A": [1, 2], "B": [4, 5]}, [0, 1]), + ({"A": [1, 2, 3], "B": [4, 5, 6]}, [0, 1, 2]), + ], + 2, + ), + ( + DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}), + [ + ({"A": [1], "B": [4]}, [0]), + ({"A": [1, 2], "B": [4, 5]}, [0, 1]), + ({"A": [1, 2, 3], "B": [4, 5, 6]}, [0, 1, 2]), + ], + 1, + ), + (DataFrame({"A": [1], "B": [4]}), [], 2), + (DataFrame(), [({}, [])], 1), + ( + DataFrame({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}), + [ + ({"A": [1.0], "B": [np.nan]}, [0]), + ({"A": [1, np.nan], "B": [np.nan, 5]}, [0, 1]), + ({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}, [0, 1, 2]), + ], + 3, + ), + ( + DataFrame({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}), + [ + ({"A": [1.0], "B": [np.nan]}, [0]), + ({"A": [1, np.nan], "B": [np.nan, 5]}, [0, 1]), + ({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}, [0, 1, 2]), + ], + 2, + ), + ( + DataFrame({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}), + [ + ({"A": [1.0], "B": [np.nan]}, [0]), + ({"A": [1, np.nan], "B": [np.nan, 5]}, [0, 1]), + ({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}, [0, 1, 2]), + ], + 1, + ), + ], +) +def test_iter_expanding_dataframe(df, expected, min_periods): + # GH 11704 + expected = [DataFrame(values, index=index) for (values, index) in expected] + + for expected, actual in zip(expected, df.expanding(min_periods)): + tm.assert_frame_equal(actual, expected) + + +@pytest.mark.parametrize( + "ser,expected,min_periods", + [ + (Series([1, 2, 3]), [([1], [0]), ([1, 2], [0, 1]), ([1, 2, 3], [0, 1, 2])], 3), + (Series([1, 2, 3]), [([1], [0]), ([1, 2], [0, 1]), ([1, 2, 3], [0, 1, 2])], 2), + (Series([1, 2, 3]), [([1], [0]), ([1, 2], [0, 1]), ([1, 2, 3], [0, 1, 2])], 1), + (Series([1, 2]), [([1], [0]), ([1, 2], [0, 1])], 2), + (Series([np.nan, 2]), [([np.nan], [0]), ([np.nan, 2], [0, 1])], 2), + (Series([], dtype="int64"), [], 2), + ], +) +def test_iter_expanding_series(ser, expected, min_periods): + # GH 11704 + expected = [Series(values, index=index) for (values, index) in expected] + + for expected, actual in zip(expected, ser.expanding(min_periods)): + tm.assert_series_equal(actual, expected) + + +def test_center_invalid(): + # GH 20647 + df = DataFrame() + with pytest.raises(TypeError, match=".* got an unexpected keyword"): + df.expanding(center=True) + + +def test_expanding_sem(frame_or_series): + # GH: 26476 + obj = frame_or_series([0, 1, 2]) + result = obj.expanding().sem() + if isinstance(result, DataFrame): + result = Series(result[0].values) + expected = Series([np.nan] + [0.707107] * 2) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("method", ["skew", "kurt"]) +def test_expanding_skew_kurt_numerical_stability(method): + # GH: 6929 + s = Series(np.random.default_rng(2).random(10)) + expected = getattr(s.expanding(3), method)() + s = s + 5000 + result = getattr(s.expanding(3), method)() + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("window", [1, 3, 10, 20]) +@pytest.mark.parametrize("method", ["min", "max", "average"]) +@pytest.mark.parametrize("pct", [True, False]) +@pytest.mark.parametrize("ascending", [True, False]) +@pytest.mark.parametrize("test_data", ["default", "duplicates", "nans"]) +def test_rank(window, method, pct, ascending, test_data): + length = 20 + if test_data == "default": + ser = Series(data=np.random.default_rng(2).random(length)) + elif test_data == "duplicates": + ser = Series(data=np.random.default_rng(2).choice(3, length)) + elif test_data == "nans": + ser = Series( + data=np.random.default_rng(2).choice( + [1.0, 0.25, 0.75, np.nan, np.inf, -np.inf], length + ) + ) + + expected = ser.expanding(window).apply( + lambda x: x.rank(method=method, pct=pct, ascending=ascending).iloc[-1] + ) + result = ser.expanding(window).rank(method=method, pct=pct, ascending=ascending) + + tm.assert_series_equal(result, expected) + + +def test_expanding_corr(series): + A = series.dropna() + B = (A + np.random.default_rng(2).standard_normal(len(A)))[:-5] + + result = A.expanding().corr(B) + + rolling_result = A.rolling(window=len(A), min_periods=1).corr(B) + + tm.assert_almost_equal(rolling_result, result) + + +def test_expanding_count(series): + result = series.expanding(min_periods=0).count() + tm.assert_almost_equal( + result, series.rolling(window=len(series), min_periods=0).count() + ) + + +def test_expanding_quantile(series): + result = series.expanding().quantile(0.5) + + rolling_result = series.rolling(window=len(series), min_periods=1).quantile(0.5) + + tm.assert_almost_equal(result, rolling_result) + + +def test_expanding_cov(series): + A = series + B = (A + np.random.default_rng(2).standard_normal(len(A)))[:-5] + + result = A.expanding().cov(B) + + rolling_result = A.rolling(window=len(A), min_periods=1).cov(B) + + tm.assert_almost_equal(rolling_result, result) + + +def test_expanding_cov_pairwise(frame): + result = frame.expanding().cov() + + rolling_result = frame.rolling(window=len(frame), min_periods=1).cov() + + tm.assert_frame_equal(result, rolling_result) + + +def test_expanding_corr_pairwise(frame): + result = frame.expanding().corr() + + rolling_result = frame.rolling(window=len(frame), min_periods=1).corr() + tm.assert_frame_equal(result, rolling_result) + + +@pytest.mark.parametrize( + "func,static_comp", + [ + ("sum", np.sum), + ("mean", lambda x: np.mean(x, axis=0)), + ("max", lambda x: np.max(x, axis=0)), + ("min", lambda x: np.min(x, axis=0)), + ], + ids=["sum", "mean", "max", "min"], +) +def test_expanding_func(func, static_comp, frame_or_series): + data = frame_or_series(np.array(list(range(10)) + [np.nan] * 10)) + + msg = "The 'axis' keyword in (Series|DataFrame).expanding is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + obj = data.expanding(min_periods=1, axis=0) + result = getattr(obj, func)() + assert isinstance(result, frame_or_series) + + msg = "The behavior of DataFrame.sum with axis=None is deprecated" + warn = None + if frame_or_series is DataFrame and static_comp is np.sum: + warn = FutureWarning + with tm.assert_produces_warning(warn, match=msg, check_stacklevel=False): + expected = static_comp(data[:11]) + if frame_or_series is Series: + tm.assert_almost_equal(result[10], expected) + else: + tm.assert_series_equal(result.iloc[10], expected, check_names=False) + + +@pytest.mark.parametrize( + "func,static_comp", + [("sum", np.sum), ("mean", np.mean), ("max", np.max), ("min", np.min)], + ids=["sum", "mean", "max", "min"], +) +def test_expanding_min_periods(func, static_comp): + ser = Series(np.random.default_rng(2).standard_normal(50)) + + msg = "The 'axis' keyword in Series.expanding is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = getattr(ser.expanding(min_periods=30, axis=0), func)() + assert result[:29].isna().all() + tm.assert_almost_equal(result.iloc[-1], static_comp(ser[:50])) + + # min_periods is working correctly + with tm.assert_produces_warning(FutureWarning, match=msg): + result = getattr(ser.expanding(min_periods=15, axis=0), func)() + assert isna(result.iloc[13]) + assert notna(result.iloc[14]) + + ser2 = Series(np.random.default_rng(2).standard_normal(20)) + with tm.assert_produces_warning(FutureWarning, match=msg): + result = getattr(ser2.expanding(min_periods=5, axis=0), func)() + assert isna(result[3]) + assert notna(result[4]) + + # min_periods=0 + with tm.assert_produces_warning(FutureWarning, match=msg): + result0 = getattr(ser.expanding(min_periods=0, axis=0), func)() + with tm.assert_produces_warning(FutureWarning, match=msg): + result1 = getattr(ser.expanding(min_periods=1, axis=0), func)() + tm.assert_almost_equal(result0, result1) + + with tm.assert_produces_warning(FutureWarning, match=msg): + result = getattr(ser.expanding(min_periods=1, axis=0), func)() + tm.assert_almost_equal(result.iloc[-1], static_comp(ser[:50])) + + +def test_expanding_apply(engine_and_raw, frame_or_series): + engine, raw = engine_and_raw + data = frame_or_series(np.array(list(range(10)) + [np.nan] * 10)) + result = data.expanding(min_periods=1).apply( + lambda x: x.mean(), raw=raw, engine=engine + ) + assert isinstance(result, frame_or_series) + + if frame_or_series is Series: + tm.assert_almost_equal(result[9], np.mean(data[:11], axis=0)) + else: + tm.assert_series_equal( + result.iloc[9], np.mean(data[:11], axis=0), check_names=False + ) + + +def test_expanding_min_periods_apply(engine_and_raw): + engine, raw = engine_and_raw + ser = Series(np.random.default_rng(2).standard_normal(50)) + + result = ser.expanding(min_periods=30).apply( + lambda x: x.mean(), raw=raw, engine=engine + ) + assert result[:29].isna().all() + tm.assert_almost_equal(result.iloc[-1], np.mean(ser[:50])) + + # min_periods is working correctly + result = ser.expanding(min_periods=15).apply( + lambda x: x.mean(), raw=raw, engine=engine + ) + assert isna(result.iloc[13]) + assert notna(result.iloc[14]) + + ser2 = Series(np.random.default_rng(2).standard_normal(20)) + result = ser2.expanding(min_periods=5).apply( + lambda x: x.mean(), raw=raw, engine=engine + ) + assert isna(result[3]) + assert notna(result[4]) + + # min_periods=0 + result0 = ser.expanding(min_periods=0).apply( + lambda x: x.mean(), raw=raw, engine=engine + ) + result1 = ser.expanding(min_periods=1).apply( + lambda x: x.mean(), raw=raw, engine=engine + ) + tm.assert_almost_equal(result0, result1) + + result = ser.expanding(min_periods=1).apply( + lambda x: x.mean(), raw=raw, engine=engine + ) + tm.assert_almost_equal(result.iloc[-1], np.mean(ser[:50])) + + +@pytest.mark.parametrize( + "f", + [ + lambda x: (x.expanding(min_periods=5).cov(x, pairwise=True)), + lambda x: (x.expanding(min_periods=5).corr(x, pairwise=True)), + ], +) +def test_moment_functions_zero_length_pairwise(f): + df1 = DataFrame() + df2 = DataFrame(columns=Index(["a"], name="foo"), index=Index([], name="bar")) + df2["a"] = df2["a"].astype("float64") + + df1_expected = DataFrame(index=MultiIndex.from_product([df1.index, df1.columns])) + df2_expected = DataFrame( + index=MultiIndex.from_product([df2.index, df2.columns], names=["bar", "foo"]), + columns=Index(["a"], name="foo"), + dtype="float64", + ) + + df1_result = f(df1) + tm.assert_frame_equal(df1_result, df1_expected) + + df2_result = f(df2) + tm.assert_frame_equal(df2_result, df2_expected) + + +@pytest.mark.parametrize( + "f", + [ + lambda x: x.expanding().count(), + lambda x: x.expanding(min_periods=5).cov(x, pairwise=False), + lambda x: x.expanding(min_periods=5).corr(x, pairwise=False), + lambda x: x.expanding(min_periods=5).max(), + lambda x: x.expanding(min_periods=5).min(), + lambda x: x.expanding(min_periods=5).sum(), + lambda x: x.expanding(min_periods=5).mean(), + lambda x: x.expanding(min_periods=5).std(), + lambda x: x.expanding(min_periods=5).var(), + lambda x: x.expanding(min_periods=5).skew(), + lambda x: x.expanding(min_periods=5).kurt(), + lambda x: x.expanding(min_periods=5).quantile(0.5), + lambda x: x.expanding(min_periods=5).median(), + lambda x: x.expanding(min_periods=5).apply(sum, raw=False), + lambda x: x.expanding(min_periods=5).apply(sum, raw=True), + ], +) +def test_moment_functions_zero_length(f): + # GH 8056 + s = Series(dtype=np.float64) + s_expected = s + df1 = DataFrame() + df1_expected = df1 + df2 = DataFrame(columns=["a"]) + df2["a"] = df2["a"].astype("float64") + df2_expected = df2 + + s_result = f(s) + tm.assert_series_equal(s_result, s_expected) + + df1_result = f(df1) + tm.assert_frame_equal(df1_result, df1_expected) + + df2_result = f(df2) + tm.assert_frame_equal(df2_result, df2_expected) + + +def test_expanding_apply_empty_series(engine_and_raw): + engine, raw = engine_and_raw + ser = Series([], dtype=np.float64) + tm.assert_series_equal( + ser, ser.expanding().apply(lambda x: x.mean(), raw=raw, engine=engine) + ) + + +def test_expanding_apply_min_periods_0(engine_and_raw): + # GH 8080 + engine, raw = engine_and_raw + s = Series([None, None, None]) + result = s.expanding(min_periods=0).apply(lambda x: len(x), raw=raw, engine=engine) + expected = Series([1.0, 2.0, 3.0]) + tm.assert_series_equal(result, expected) + + +def test_expanding_cov_diff_index(): + # GH 7512 + s1 = Series([1, 2, 3], index=[0, 1, 2]) + s2 = Series([1, 3], index=[0, 2]) + result = s1.expanding().cov(s2) + expected = Series([None, None, 2.0]) + tm.assert_series_equal(result, expected) + + s2a = Series([1, None, 3], index=[0, 1, 2]) + result = s1.expanding().cov(s2a) + tm.assert_series_equal(result, expected) + + s1 = Series([7, 8, 10], index=[0, 1, 3]) + s2 = Series([7, 9, 10], index=[0, 2, 3]) + result = s1.expanding().cov(s2) + expected = Series([None, None, None, 4.5]) + tm.assert_series_equal(result, expected) + + +def test_expanding_corr_diff_index(): + # GH 7512 + s1 = Series([1, 2, 3], index=[0, 1, 2]) + s2 = Series([1, 3], index=[0, 2]) + result = s1.expanding().corr(s2) + expected = Series([None, None, 1.0]) + tm.assert_series_equal(result, expected) + + s2a = Series([1, None, 3], index=[0, 1, 2]) + result = s1.expanding().corr(s2a) + tm.assert_series_equal(result, expected) + + s1 = Series([7, 8, 10], index=[0, 1, 3]) + s2 = Series([7, 9, 10], index=[0, 2, 3]) + result = s1.expanding().corr(s2) + expected = Series([None, None, None, 1.0]) + tm.assert_series_equal(result, expected) + + +def test_expanding_cov_pairwise_diff_length(): + # GH 7512 + df1 = DataFrame([[1, 5], [3, 2], [3, 9]], columns=Index(["A", "B"], name="foo")) + df1a = DataFrame( + [[1, 5], [3, 9]], index=[0, 2], columns=Index(["A", "B"], name="foo") + ) + df2 = DataFrame( + [[5, 6], [None, None], [2, 1]], columns=Index(["X", "Y"], name="foo") + ) + df2a = DataFrame( + [[5, 6], [2, 1]], index=[0, 2], columns=Index(["X", "Y"], name="foo") + ) + # TODO: xref gh-15826 + # .loc is not preserving the names + result1 = df1.expanding().cov(df2, pairwise=True).loc[2] + result2 = df1.expanding().cov(df2a, pairwise=True).loc[2] + result3 = df1a.expanding().cov(df2, pairwise=True).loc[2] + result4 = df1a.expanding().cov(df2a, pairwise=True).loc[2] + expected = DataFrame( + [[-3.0, -6.0], [-5.0, -10.0]], + columns=Index(["A", "B"], name="foo"), + index=Index(["X", "Y"], name="foo"), + ) + tm.assert_frame_equal(result1, expected) + tm.assert_frame_equal(result2, expected) + tm.assert_frame_equal(result3, expected) + tm.assert_frame_equal(result4, expected) + + +def test_expanding_corr_pairwise_diff_length(): + # GH 7512 + df1 = DataFrame( + [[1, 2], [3, 2], [3, 4]], columns=["A", "B"], index=Index(range(3), name="bar") + ) + df1a = DataFrame( + [[1, 2], [3, 4]], index=Index([0, 2], name="bar"), columns=["A", "B"] + ) + df2 = DataFrame( + [[5, 6], [None, None], [2, 1]], + columns=["X", "Y"], + index=Index(range(3), name="bar"), + ) + df2a = DataFrame( + [[5, 6], [2, 1]], index=Index([0, 2], name="bar"), columns=["X", "Y"] + ) + result1 = df1.expanding().corr(df2, pairwise=True).loc[2] + result2 = df1.expanding().corr(df2a, pairwise=True).loc[2] + result3 = df1a.expanding().corr(df2, pairwise=True).loc[2] + result4 = df1a.expanding().corr(df2a, pairwise=True).loc[2] + expected = DataFrame( + [[-1.0, -1.0], [-1.0, -1.0]], columns=["A", "B"], index=Index(["X", "Y"]) + ) + tm.assert_frame_equal(result1, expected) + tm.assert_frame_equal(result2, expected) + tm.assert_frame_equal(result3, expected) + tm.assert_frame_equal(result4, expected) + + +def test_expanding_apply_args_kwargs(engine_and_raw): + def mean_w_arg(x, const): + return np.mean(x) + const + + engine, raw = engine_and_raw + + df = DataFrame(np.random.default_rng(2).random((20, 3))) + + expected = df.expanding().apply(np.mean, engine=engine, raw=raw) + 20.0 + + result = df.expanding().apply(mean_w_arg, engine=engine, raw=raw, args=(20,)) + tm.assert_frame_equal(result, expected) + + result = df.expanding().apply(mean_w_arg, raw=raw, kwargs={"const": 20}) + tm.assert_frame_equal(result, expected) + + +def test_numeric_only_frame(arithmetic_win_operators, numeric_only): + # GH#46560 + kernel = arithmetic_win_operators + df = DataFrame({"a": [1], "b": 2, "c": 3}) + df["c"] = df["c"].astype(object) + expanding = df.expanding() + op = getattr(expanding, kernel, None) + if op is not None: + result = op(numeric_only=numeric_only) + + columns = ["a", "b"] if numeric_only else ["a", "b", "c"] + expected = df[columns].agg([kernel]).reset_index(drop=True).astype(float) + assert list(expected.columns) == columns + + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("kernel", ["corr", "cov"]) +@pytest.mark.parametrize("use_arg", [True, False]) +def test_numeric_only_corr_cov_frame(kernel, numeric_only, use_arg): + # GH#46560 + df = DataFrame({"a": [1, 2, 3], "b": 2, "c": 3}) + df["c"] = df["c"].astype(object) + arg = (df,) if use_arg else () + expanding = df.expanding() + op = getattr(expanding, kernel) + result = op(*arg, numeric_only=numeric_only) + + # Compare result to op using float dtypes, dropping c when numeric_only is True + columns = ["a", "b"] if numeric_only else ["a", "b", "c"] + df2 = df[columns].astype(float) + arg2 = (df2,) if use_arg else () + expanding2 = df2.expanding() + op2 = getattr(expanding2, kernel) + expected = op2(*arg2, numeric_only=numeric_only) + + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("dtype", [int, object]) +def test_numeric_only_series(arithmetic_win_operators, numeric_only, dtype): + # GH#46560 + kernel = arithmetic_win_operators + ser = Series([1], dtype=dtype) + expanding = ser.expanding() + op = getattr(expanding, kernel) + if numeric_only and dtype is object: + msg = f"Expanding.{kernel} does not implement numeric_only" + with pytest.raises(NotImplementedError, match=msg): + op(numeric_only=numeric_only) + else: + result = op(numeric_only=numeric_only) + expected = ser.agg([kernel]).reset_index(drop=True).astype(float) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("kernel", ["corr", "cov"]) +@pytest.mark.parametrize("use_arg", [True, False]) +@pytest.mark.parametrize("dtype", [int, object]) +def test_numeric_only_corr_cov_series(kernel, use_arg, numeric_only, dtype): + # GH#46560 + ser = Series([1, 2, 3], dtype=dtype) + arg = (ser,) if use_arg else () + expanding = ser.expanding() + op = getattr(expanding, kernel) + if numeric_only and dtype is object: + msg = f"Expanding.{kernel} does not implement numeric_only" + with pytest.raises(NotImplementedError, match=msg): + op(*arg, numeric_only=numeric_only) + else: + result = op(*arg, numeric_only=numeric_only) + + ser2 = ser.astype(float) + arg2 = (ser2,) if use_arg else () + expanding2 = ser2.expanding() + op2 = getattr(expanding2, kernel) + expected = op2(*arg2, numeric_only=numeric_only) + tm.assert_series_equal(result, expected) + + +def test_keyword_quantile_deprecated(): + # GH #52550 + ser = Series([1, 2, 3, 4]) + with tm.assert_produces_warning(FutureWarning): + ser.expanding().quantile(quantile=0.5) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_groupby.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_groupby.py new file mode 100644 index 0000000000000000000000000000000000000000..400bf10817ab871abafcfc39baf57c340d3cbb0a --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_groupby.py @@ -0,0 +1,1318 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + DatetimeIndex, + Index, + MultiIndex, + Series, + Timestamp, + date_range, + to_datetime, +) +import pandas._testing as tm +from pandas.api.indexers import BaseIndexer +from pandas.core.groupby.groupby import get_groupby + + +@pytest.fixture +def times_frame(): + """Frame for testing times argument in EWM groupby.""" + return DataFrame( + { + "A": ["a", "b", "c", "a", "b", "c", "a", "b", "c", "a"], + "B": [0, 0, 0, 1, 1, 1, 2, 2, 2, 3], + "C": to_datetime( + [ + "2020-01-01", + "2020-01-01", + "2020-01-01", + "2020-01-02", + "2020-01-10", + "2020-01-22", + "2020-01-03", + "2020-01-23", + "2020-01-23", + "2020-01-04", + ] + ), + } + ) + + +@pytest.fixture +def roll_frame(): + return DataFrame({"A": [1] * 20 + [2] * 12 + [3] * 8, "B": np.arange(40)}) + + +class TestRolling: + def test_groupby_unsupported_argument(self, roll_frame): + msg = r"groupby\(\) got an unexpected keyword argument 'foo'" + with pytest.raises(TypeError, match=msg): + roll_frame.groupby("A", foo=1) + + def test_getitem(self, roll_frame): + g = roll_frame.groupby("A") + g_mutated = get_groupby(roll_frame, by="A") + + expected = g_mutated.B.apply(lambda x: x.rolling(2).mean()) + + result = g.rolling(2).mean().B + tm.assert_series_equal(result, expected) + + result = g.rolling(2).B.mean() + tm.assert_series_equal(result, expected) + + result = g.B.rolling(2).mean() + tm.assert_series_equal(result, expected) + + result = roll_frame.B.groupby(roll_frame.A).rolling(2).mean() + tm.assert_series_equal(result, expected) + + def test_getitem_multiple(self, roll_frame): + # GH 13174 + g = roll_frame.groupby("A") + r = g.rolling(2, min_periods=0) + g_mutated = get_groupby(roll_frame, by="A") + expected = g_mutated.B.apply(lambda x: x.rolling(2, min_periods=0).count()) + + result = r.B.count() + tm.assert_series_equal(result, expected) + + result = r.B.count() + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "f", + [ + "sum", + "mean", + "min", + "max", + "count", + "kurt", + "skew", + ], + ) + def test_rolling(self, f, roll_frame): + g = roll_frame.groupby("A", group_keys=False) + r = g.rolling(window=4) + + result = getattr(r, f)() + msg = "DataFrameGroupBy.apply operated on the grouping columns" + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = g.apply(lambda x: getattr(x.rolling(4), f)()) + # groupby.apply doesn't drop the grouped-by column + expected = expected.drop("A", axis=1) + # GH 39732 + expected_index = MultiIndex.from_arrays([roll_frame["A"], range(40)]) + expected.index = expected_index + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("f", ["std", "var"]) + def test_rolling_ddof(self, f, roll_frame): + g = roll_frame.groupby("A", group_keys=False) + r = g.rolling(window=4) + + result = getattr(r, f)(ddof=1) + msg = "DataFrameGroupBy.apply operated on the grouping columns" + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = g.apply(lambda x: getattr(x.rolling(4), f)(ddof=1)) + # groupby.apply doesn't drop the grouped-by column + expected = expected.drop("A", axis=1) + # GH 39732 + expected_index = MultiIndex.from_arrays([roll_frame["A"], range(40)]) + expected.index = expected_index + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "interpolation", ["linear", "lower", "higher", "midpoint", "nearest"] + ) + def test_rolling_quantile(self, interpolation, roll_frame): + g = roll_frame.groupby("A", group_keys=False) + r = g.rolling(window=4) + + result = r.quantile(0.4, interpolation=interpolation) + msg = "DataFrameGroupBy.apply operated on the grouping columns" + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = g.apply( + lambda x: x.rolling(4).quantile(0.4, interpolation=interpolation) + ) + # groupby.apply doesn't drop the grouped-by column + expected = expected.drop("A", axis=1) + # GH 39732 + expected_index = MultiIndex.from_arrays([roll_frame["A"], range(40)]) + expected.index = expected_index + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("f, expected_val", [["corr", 1], ["cov", 0.5]]) + def test_rolling_corr_cov_other_same_size_as_groups(self, f, expected_val): + # GH 42915 + df = DataFrame( + {"value": range(10), "idx1": [1] * 5 + [2] * 5, "idx2": [1, 2, 3, 4, 5] * 2} + ).set_index(["idx1", "idx2"]) + other = DataFrame({"value": range(5), "idx2": [1, 2, 3, 4, 5]}).set_index( + "idx2" + ) + result = getattr(df.groupby(level=0).rolling(2), f)(other) + expected_data = ([np.nan] + [expected_val] * 4) * 2 + expected = DataFrame( + expected_data, + columns=["value"], + index=MultiIndex.from_arrays( + [ + [1] * 5 + [2] * 5, + [1] * 5 + [2] * 5, + list(range(1, 6)) * 2, + ], + names=["idx1", "idx1", "idx2"], + ), + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("f", ["corr", "cov"]) + def test_rolling_corr_cov_other_diff_size_as_groups(self, f, roll_frame): + g = roll_frame.groupby("A") + r = g.rolling(window=4) + + result = getattr(r, f)(roll_frame) + + def func(x): + return getattr(x.rolling(4), f)(roll_frame) + + msg = "DataFrameGroupBy.apply operated on the grouping columns" + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = g.apply(func) + # GH 39591: The grouped column should be all np.nan + # (groupby.apply inserts 0s for cov) + expected["A"] = np.nan + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("f", ["corr", "cov"]) + def test_rolling_corr_cov_pairwise(self, f, roll_frame): + g = roll_frame.groupby("A") + r = g.rolling(window=4) + + result = getattr(r.B, f)(pairwise=True) + + def func(x): + return getattr(x.B.rolling(4), f)(pairwise=True) + + msg = "DataFrameGroupBy.apply operated on the grouping columns" + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = g.apply(func) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "func, expected_values", + [("cov", [[1.0, 1.0], [1.0, 4.0]]), ("corr", [[1.0, 0.5], [0.5, 1.0]])], + ) + def test_rolling_corr_cov_unordered(self, func, expected_values): + # GH 43386 + df = DataFrame( + { + "a": ["g1", "g2", "g1", "g1"], + "b": [0, 0, 1, 2], + "c": [2, 0, 6, 4], + } + ) + rol = df.groupby("a").rolling(3) + result = getattr(rol, func)() + expected = DataFrame( + { + "b": 4 * [np.nan] + expected_values[0] + 2 * [np.nan], + "c": 4 * [np.nan] + expected_values[1] + 2 * [np.nan], + }, + index=MultiIndex.from_tuples( + [ + ("g1", 0, "b"), + ("g1", 0, "c"), + ("g1", 2, "b"), + ("g1", 2, "c"), + ("g1", 3, "b"), + ("g1", 3, "c"), + ("g2", 1, "b"), + ("g2", 1, "c"), + ], + names=["a", None, None], + ), + ) + tm.assert_frame_equal(result, expected) + + def test_rolling_apply(self, raw, roll_frame): + g = roll_frame.groupby("A", group_keys=False) + r = g.rolling(window=4) + + # reduction + result = r.apply(lambda x: x.sum(), raw=raw) + msg = "DataFrameGroupBy.apply operated on the grouping columns" + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = g.apply(lambda x: x.rolling(4).apply(lambda y: y.sum(), raw=raw)) + # groupby.apply doesn't drop the grouped-by column + expected = expected.drop("A", axis=1) + # GH 39732 + expected_index = MultiIndex.from_arrays([roll_frame["A"], range(40)]) + expected.index = expected_index + tm.assert_frame_equal(result, expected) + + def test_rolling_apply_mutability(self): + # GH 14013 + df = DataFrame({"A": ["foo"] * 3 + ["bar"] * 3, "B": [1] * 6}) + g = df.groupby("A") + + mi = MultiIndex.from_tuples( + [("bar", 3), ("bar", 4), ("bar", 5), ("foo", 0), ("foo", 1), ("foo", 2)] + ) + + mi.names = ["A", None] + # Grouped column should not be a part of the output + expected = DataFrame([np.nan, 2.0, 2.0] * 2, columns=["B"], index=mi) + + result = g.rolling(window=2).sum() + tm.assert_frame_equal(result, expected) + + # Call an arbitrary function on the groupby + g.sum() + + # Make sure nothing has been mutated + result = g.rolling(window=2).sum() + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("expected_value,raw_value", [[1.0, True], [0.0, False]]) + def test_groupby_rolling(self, expected_value, raw_value): + # GH 31754 + + def isnumpyarray(x): + return int(isinstance(x, np.ndarray)) + + df = DataFrame({"id": [1, 1, 1], "value": [1, 2, 3]}) + result = df.groupby("id").value.rolling(1).apply(isnumpyarray, raw=raw_value) + expected = Series( + [expected_value] * 3, + index=MultiIndex.from_tuples(((1, 0), (1, 1), (1, 2)), names=["id", None]), + name="value", + ) + tm.assert_series_equal(result, expected) + + def test_groupby_rolling_center_center(self): + # GH 35552 + series = Series(range(1, 6)) + result = series.groupby(series).rolling(center=True, window=3).mean() + expected = Series( + [np.nan] * 5, + index=MultiIndex.from_tuples(((1, 0), (2, 1), (3, 2), (4, 3), (5, 4))), + ) + tm.assert_series_equal(result, expected) + + series = Series(range(1, 5)) + result = series.groupby(series).rolling(center=True, window=3).mean() + expected = Series( + [np.nan] * 4, + index=MultiIndex.from_tuples(((1, 0), (2, 1), (3, 2), (4, 3))), + ) + tm.assert_series_equal(result, expected) + + df = DataFrame({"a": ["a"] * 5 + ["b"] * 6, "b": range(11)}) + result = df.groupby("a").rolling(center=True, window=3).mean() + expected = DataFrame( + [np.nan, 1, 2, 3, np.nan, np.nan, 6, 7, 8, 9, np.nan], + index=MultiIndex.from_tuples( + ( + ("a", 0), + ("a", 1), + ("a", 2), + ("a", 3), + ("a", 4), + ("b", 5), + ("b", 6), + ("b", 7), + ("b", 8), + ("b", 9), + ("b", 10), + ), + names=["a", None], + ), + columns=["b"], + ) + tm.assert_frame_equal(result, expected) + + df = DataFrame({"a": ["a"] * 5 + ["b"] * 5, "b": range(10)}) + result = df.groupby("a").rolling(center=True, window=3).mean() + expected = DataFrame( + [np.nan, 1, 2, 3, np.nan, np.nan, 6, 7, 8, np.nan], + index=MultiIndex.from_tuples( + ( + ("a", 0), + ("a", 1), + ("a", 2), + ("a", 3), + ("a", 4), + ("b", 5), + ("b", 6), + ("b", 7), + ("b", 8), + ("b", 9), + ), + names=["a", None], + ), + columns=["b"], + ) + tm.assert_frame_equal(result, expected) + + def test_groupby_rolling_center_on(self): + # GH 37141 + df = DataFrame( + data={ + "Date": date_range("2020-01-01", "2020-01-10"), + "gb": ["group_1"] * 6 + ["group_2"] * 4, + "value": range(10), + } + ) + result = ( + df.groupby("gb") + .rolling(6, on="Date", center=True, min_periods=1) + .value.mean() + ) + mi = MultiIndex.from_arrays([df["gb"], df["Date"]], names=["gb", "Date"]) + expected = Series( + [1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 7.0, 7.5, 7.5, 7.5], + name="value", + index=mi, + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("min_periods", [5, 4, 3]) + def test_groupby_rolling_center_min_periods(self, min_periods): + # GH 36040 + df = DataFrame({"group": ["A"] * 10 + ["B"] * 10, "data": range(20)}) + + window_size = 5 + result = ( + df.groupby("group") + .rolling(window_size, center=True, min_periods=min_periods) + .mean() + ) + result = result.reset_index()[["group", "data"]] + + grp_A_mean = [1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 7.5, 8.0] + grp_B_mean = [x + 10.0 for x in grp_A_mean] + + num_nans = max(0, min_periods - 3) # For window_size of 5 + nans = [np.nan] * num_nans + grp_A_expected = nans + grp_A_mean[num_nans : 10 - num_nans] + nans + grp_B_expected = nans + grp_B_mean[num_nans : 10 - num_nans] + nans + + expected = DataFrame( + {"group": ["A"] * 10 + ["B"] * 10, "data": grp_A_expected + grp_B_expected} + ) + + tm.assert_frame_equal(result, expected) + + def test_groupby_subselect_rolling(self): + # GH 35486 + df = DataFrame( + {"a": [1, 2, 3, 2], "b": [4.0, 2.0, 3.0, 1.0], "c": [10, 20, 30, 20]} + ) + result = df.groupby("a")[["b"]].rolling(2).max() + expected = DataFrame( + [np.nan, np.nan, 2.0, np.nan], + columns=["b"], + index=MultiIndex.from_tuples( + ((1, 0), (2, 1), (2, 3), (3, 2)), names=["a", None] + ), + ) + tm.assert_frame_equal(result, expected) + + result = df.groupby("a")["b"].rolling(2).max() + expected = Series( + [np.nan, np.nan, 2.0, np.nan], + index=MultiIndex.from_tuples( + ((1, 0), (2, 1), (2, 3), (3, 2)), names=["a", None] + ), + name="b", + ) + tm.assert_series_equal(result, expected) + + def test_groupby_rolling_custom_indexer(self): + # GH 35557 + class SimpleIndexer(BaseIndexer): + def get_window_bounds( + self, + num_values=0, + min_periods=None, + center=None, + closed=None, + step=None, + ): + min_periods = self.window_size if min_periods is None else 0 + end = np.arange(num_values, dtype=np.int64) + 1 + start = end.copy() - self.window_size + start[start < 0] = min_periods + return start, end + + df = DataFrame( + {"a": [1.0, 2.0, 3.0, 4.0, 5.0] * 3}, index=[0] * 5 + [1] * 5 + [2] * 5 + ) + result = ( + df.groupby(df.index) + .rolling(SimpleIndexer(window_size=3), min_periods=1) + .sum() + ) + expected = df.groupby(df.index).rolling(window=3, min_periods=1).sum() + tm.assert_frame_equal(result, expected) + + def test_groupby_rolling_subset_with_closed(self): + # GH 35549 + df = DataFrame( + { + "column1": range(8), + "column2": range(8), + "group": ["A"] * 4 + ["B"] * 4, + "date": [ + Timestamp(date) + for date in ["2019-01-01", "2019-01-01", "2019-01-02", "2019-01-02"] + ] + * 2, + } + ) + result = ( + df.groupby("group").rolling("1D", on="date", closed="left")["column1"].sum() + ) + expected = Series( + [np.nan, np.nan, 1.0, 1.0, np.nan, np.nan, 9.0, 9.0], + index=MultiIndex.from_frame( + df[["group", "date"]], + names=["group", "date"], + ), + name="column1", + ) + tm.assert_series_equal(result, expected) + + def test_groupby_subset_rolling_subset_with_closed(self): + # GH 35549 + df = DataFrame( + { + "column1": range(8), + "column2": range(8), + "group": ["A"] * 4 + ["B"] * 4, + "date": [ + Timestamp(date) + for date in ["2019-01-01", "2019-01-01", "2019-01-02", "2019-01-02"] + ] + * 2, + } + ) + + result = ( + df.groupby("group")[["column1", "date"]] + .rolling("1D", on="date", closed="left")["column1"] + .sum() + ) + expected = Series( + [np.nan, np.nan, 1.0, 1.0, np.nan, np.nan, 9.0, 9.0], + index=MultiIndex.from_frame( + df[["group", "date"]], + names=["group", "date"], + ), + name="column1", + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("func", ["max", "min"]) + def test_groupby_rolling_index_changed(self, func): + # GH: #36018 nlevels of MultiIndex changed + ds = Series( + [1, 2, 2], + index=MultiIndex.from_tuples( + [("a", "x"), ("a", "y"), ("c", "z")], names=["1", "2"] + ), + name="a", + ) + + result = getattr(ds.groupby(ds).rolling(2), func)() + expected = Series( + [np.nan, np.nan, 2.0], + index=MultiIndex.from_tuples( + [(1, "a", "x"), (2, "a", "y"), (2, "c", "z")], names=["a", "1", "2"] + ), + name="a", + ) + tm.assert_series_equal(result, expected) + + def test_groupby_rolling_empty_frame(self): + # GH 36197 + expected = DataFrame({"s1": []}) + result = expected.groupby("s1").rolling(window=1).sum() + # GH 32262 + expected = expected.drop(columns="s1") + # GH-38057 from_tuples gives empty object dtype, we now get float/int levels + # expected.index = MultiIndex.from_tuples([], names=["s1", None]) + expected.index = MultiIndex.from_product( + [Index([], dtype="float64"), Index([], dtype="int64")], names=["s1", None] + ) + tm.assert_frame_equal(result, expected) + + expected = DataFrame({"s1": [], "s2": []}) + result = expected.groupby(["s1", "s2"]).rolling(window=1).sum() + # GH 32262 + expected = expected.drop(columns=["s1", "s2"]) + expected.index = MultiIndex.from_product( + [ + Index([], dtype="float64"), + Index([], dtype="float64"), + Index([], dtype="int64"), + ], + names=["s1", "s2", None], + ) + tm.assert_frame_equal(result, expected) + + def test_groupby_rolling_string_index(self): + # GH: 36727 + df = DataFrame( + [ + ["A", "group_1", Timestamp(2019, 1, 1, 9)], + ["B", "group_1", Timestamp(2019, 1, 2, 9)], + ["Z", "group_2", Timestamp(2019, 1, 3, 9)], + ["H", "group_1", Timestamp(2019, 1, 6, 9)], + ["E", "group_2", Timestamp(2019, 1, 20, 9)], + ], + columns=["index", "group", "eventTime"], + ).set_index("index") + + groups = df.groupby("group") + df["count_to_date"] = groups.cumcount() + rolling_groups = groups.rolling("10d", on="eventTime") + result = rolling_groups.apply(lambda df: df.shape[0]) + expected = DataFrame( + [ + ["A", "group_1", Timestamp(2019, 1, 1, 9), 1.0], + ["B", "group_1", Timestamp(2019, 1, 2, 9), 2.0], + ["H", "group_1", Timestamp(2019, 1, 6, 9), 3.0], + ["Z", "group_2", Timestamp(2019, 1, 3, 9), 1.0], + ["E", "group_2", Timestamp(2019, 1, 20, 9), 1.0], + ], + columns=["index", "group", "eventTime", "count_to_date"], + ).set_index(["group", "index"]) + tm.assert_frame_equal(result, expected) + + def test_groupby_rolling_no_sort(self): + # GH 36889 + result = ( + DataFrame({"foo": [2, 1], "bar": [2, 1]}) + .groupby("foo", sort=False) + .rolling(1) + .min() + ) + expected = DataFrame( + np.array([[2.0, 2.0], [1.0, 1.0]]), + columns=["foo", "bar"], + index=MultiIndex.from_tuples([(2, 0), (1, 1)], names=["foo", None]), + ) + # GH 32262 + expected = expected.drop(columns="foo") + tm.assert_frame_equal(result, expected) + + def test_groupby_rolling_count_closed_on(self, unit): + # GH 35869 + df = DataFrame( + { + "column1": range(6), + "column2": range(6), + "group": 3 * ["A", "B"], + "date": date_range(end="20190101", periods=6, unit=unit), + } + ) + result = ( + df.groupby("group") + .rolling("3d", on="date", closed="left")["column1"] + .count() + ) + dti = DatetimeIndex( + [ + "2018-12-27", + "2018-12-29", + "2018-12-31", + "2018-12-28", + "2018-12-30", + "2019-01-01", + ], + dtype=f"M8[{unit}]", + ) + mi = MultiIndex.from_arrays( + [ + ["A", "A", "A", "B", "B", "B"], + dti, + ], + names=["group", "date"], + ) + expected = Series( + [np.nan, 1.0, 1.0, np.nan, 1.0, 1.0], + name="column1", + index=mi, + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + ("func", "kwargs"), + [("rolling", {"window": 2, "min_periods": 1}), ("expanding", {})], + ) + def test_groupby_rolling_sem(self, func, kwargs): + # GH: 26476 + df = DataFrame( + [["a", 1], ["a", 2], ["b", 1], ["b", 2], ["b", 3]], columns=["a", "b"] + ) + result = getattr(df.groupby("a"), func)(**kwargs).sem() + expected = DataFrame( + {"a": [np.nan] * 5, "b": [np.nan, 0.70711, np.nan, 0.70711, 0.70711]}, + index=MultiIndex.from_tuples( + [("a", 0), ("a", 1), ("b", 2), ("b", 3), ("b", 4)], names=["a", None] + ), + ) + # GH 32262 + expected = expected.drop(columns="a") + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + ("rollings", "key"), [({"on": "a"}, "a"), ({"on": None}, "index")] + ) + def test_groupby_rolling_nans_in_index(self, rollings, key): + # GH: 34617 + df = DataFrame( + { + "a": to_datetime(["2020-06-01 12:00", "2020-06-01 14:00", np.nan]), + "b": [1, 2, 3], + "c": [1, 1, 1], + } + ) + if key == "index": + df = df.set_index("a") + with pytest.raises(ValueError, match=f"{key} values must not have NaT"): + df.groupby("c").rolling("60min", **rollings) + + @pytest.mark.parametrize("group_keys", [True, False]) + def test_groupby_rolling_group_keys(self, group_keys): + # GH 37641 + # GH 38523: GH 37641 actually was not a bug. + # group_keys only applies to groupby.apply directly + arrays = [["val1", "val1", "val2"], ["val1", "val1", "val2"]] + index = MultiIndex.from_arrays(arrays, names=("idx1", "idx2")) + + s = Series([1, 2, 3], index=index) + result = s.groupby(["idx1", "idx2"], group_keys=group_keys).rolling(1).mean() + expected = Series( + [1.0, 2.0, 3.0], + index=MultiIndex.from_tuples( + [ + ("val1", "val1", "val1", "val1"), + ("val1", "val1", "val1", "val1"), + ("val2", "val2", "val2", "val2"), + ], + names=["idx1", "idx2", "idx1", "idx2"], + ), + ) + tm.assert_series_equal(result, expected) + + def test_groupby_rolling_index_level_and_column_label(self): + # The groupby keys should not appear as a resulting column + arrays = [["val1", "val1", "val2"], ["val1", "val1", "val2"]] + index = MultiIndex.from_arrays(arrays, names=("idx1", "idx2")) + + df = DataFrame({"A": [1, 1, 2], "B": range(3)}, index=index) + result = df.groupby(["idx1", "A"]).rolling(1).mean() + expected = DataFrame( + {"B": [0.0, 1.0, 2.0]}, + index=MultiIndex.from_tuples( + [ + ("val1", 1, "val1", "val1"), + ("val1", 1, "val1", "val1"), + ("val2", 2, "val2", "val2"), + ], + names=["idx1", "A", "idx1", "idx2"], + ), + ) + tm.assert_frame_equal(result, expected) + + def test_groupby_rolling_resulting_multiindex(self): + # a few different cases checking the created MultiIndex of the result + # https://github.com/pandas-dev/pandas/pull/38057 + + # grouping by 1 columns -> 2-level MI as result + df = DataFrame({"a": np.arange(8.0), "b": [1, 2] * 4}) + result = df.groupby("b").rolling(3).mean() + expected_index = MultiIndex.from_tuples( + [(1, 0), (1, 2), (1, 4), (1, 6), (2, 1), (2, 3), (2, 5), (2, 7)], + names=["b", None], + ) + tm.assert_index_equal(result.index, expected_index) + + def test_groupby_rolling_resulting_multiindex2(self): + # grouping by 2 columns -> 3-level MI as result + df = DataFrame({"a": np.arange(12.0), "b": [1, 2] * 6, "c": [1, 2, 3, 4] * 3}) + result = df.groupby(["b", "c"]).rolling(2).sum() + expected_index = MultiIndex.from_tuples( + [ + (1, 1, 0), + (1, 1, 4), + (1, 1, 8), + (1, 3, 2), + (1, 3, 6), + (1, 3, 10), + (2, 2, 1), + (2, 2, 5), + (2, 2, 9), + (2, 4, 3), + (2, 4, 7), + (2, 4, 11), + ], + names=["b", "c", None], + ) + tm.assert_index_equal(result.index, expected_index) + + def test_groupby_rolling_resulting_multiindex3(self): + # grouping with 1 level on dataframe with 2-level MI -> 3-level MI as result + df = DataFrame({"a": np.arange(8.0), "b": [1, 2] * 4, "c": [1, 2, 3, 4] * 2}) + df = df.set_index("c", append=True) + result = df.groupby("b").rolling(3).mean() + expected_index = MultiIndex.from_tuples( + [ + (1, 0, 1), + (1, 2, 3), + (1, 4, 1), + (1, 6, 3), + (2, 1, 2), + (2, 3, 4), + (2, 5, 2), + (2, 7, 4), + ], + names=["b", None, "c"], + ) + tm.assert_index_equal(result.index, expected_index, exact="equiv") + + def test_groupby_rolling_object_doesnt_affect_groupby_apply(self, roll_frame): + # GH 39732 + g = roll_frame.groupby("A", group_keys=False) + msg = "DataFrameGroupBy.apply operated on the grouping columns" + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = g.apply(lambda x: x.rolling(4).sum()).index + _ = g.rolling(window=4) + msg = "DataFrameGroupBy.apply operated on the grouping columns" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = g.apply(lambda x: x.rolling(4).sum()).index + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize( + ("window", "min_periods", "closed", "expected"), + [ + (2, 0, "left", [None, 0.0, 1.0, 1.0, None, 0.0, 1.0, 1.0]), + (2, 2, "left", [None, None, 1.0, 1.0, None, None, 1.0, 1.0]), + (4, 4, "left", [None, None, None, None, None, None, None, None]), + (4, 4, "right", [None, None, None, 5.0, None, None, None, 5.0]), + ], + ) + def test_groupby_rolling_var(self, window, min_periods, closed, expected): + df = DataFrame([1, 2, 3, 4, 5, 6, 7, 8]) + result = ( + df.groupby([1, 2, 1, 2, 1, 2, 1, 2]) + .rolling(window=window, min_periods=min_periods, closed=closed) + .var(0) + ) + expected_result = DataFrame( + np.array(expected, dtype="float64"), + index=MultiIndex( + levels=[np.array([1, 2]), [0, 1, 2, 3, 4, 5, 6, 7]], + codes=[[0, 0, 0, 0, 1, 1, 1, 1], [0, 2, 4, 6, 1, 3, 5, 7]], + ), + ) + tm.assert_frame_equal(result, expected_result) + + @pytest.mark.parametrize( + "columns", [MultiIndex.from_tuples([("A", ""), ("B", "C")]), ["A", "B"]] + ) + def test_by_column_not_in_values(self, columns): + # GH 32262 + df = DataFrame([[1, 0]] * 20 + [[2, 0]] * 12 + [[3, 0]] * 8, columns=columns) + g = df.groupby("A") + original_obj = g.obj.copy(deep=True) + r = g.rolling(4) + result = r.sum() + assert "A" not in result.columns + tm.assert_frame_equal(g.obj, original_obj) + + def test_groupby_level(self): + # GH 38523, 38787 + arrays = [ + ["Falcon", "Falcon", "Parrot", "Parrot"], + ["Captive", "Wild", "Captive", "Wild"], + ] + index = MultiIndex.from_arrays(arrays, names=("Animal", "Type")) + df = DataFrame({"Max Speed": [390.0, 350.0, 30.0, 20.0]}, index=index) + result = df.groupby(level=0)["Max Speed"].rolling(2).sum() + expected = Series( + [np.nan, 740.0, np.nan, 50.0], + index=MultiIndex.from_tuples( + [ + ("Falcon", "Falcon", "Captive"), + ("Falcon", "Falcon", "Wild"), + ("Parrot", "Parrot", "Captive"), + ("Parrot", "Parrot", "Wild"), + ], + names=["Animal", "Animal", "Type"], + ), + name="Max Speed", + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "by, expected_data", + [ + [["id"], {"num": [100.0, 150.0, 150.0, 200.0]}], + [ + ["id", "index"], + { + "date": [ + Timestamp("2018-01-01"), + Timestamp("2018-01-02"), + Timestamp("2018-01-01"), + Timestamp("2018-01-02"), + ], + "num": [100.0, 200.0, 150.0, 250.0], + }, + ], + ], + ) + def test_as_index_false(self, by, expected_data, unit): + # GH 39433 + data = [ + ["A", "2018-01-01", 100.0], + ["A", "2018-01-02", 200.0], + ["B", "2018-01-01", 150.0], + ["B", "2018-01-02", 250.0], + ] + df = DataFrame(data, columns=["id", "date", "num"]) + df["date"] = df["date"].astype(f"M8[{unit}]") + df = df.set_index(["date"]) + + gp_by = [getattr(df, attr) for attr in by] + result = ( + df.groupby(gp_by, as_index=False).rolling(window=2, min_periods=1).mean() + ) + + expected = {"id": ["A", "A", "B", "B"]} + expected.update(expected_data) + expected = DataFrame( + expected, + index=df.index, + ) + if "date" in expected_data: + expected["date"] = expected["date"].astype(f"M8[{unit}]") + tm.assert_frame_equal(result, expected) + + def test_nan_and_zero_endpoints(self, any_int_numpy_dtype): + # https://github.com/twosigma/pandas/issues/53 + typ = np.dtype(any_int_numpy_dtype).type + size = 1000 + idx = np.repeat(typ(0), size) + idx[-1] = 1 + + val = 5e25 + arr = np.repeat(val, size) + arr[0] = np.nan + arr[-1] = 0 + + df = DataFrame( + { + "index": idx, + "adl2": arr, + } + ).set_index("index") + result = df.groupby("index")["adl2"].rolling(window=10, min_periods=1).mean() + expected = Series( + arr, + name="adl2", + index=MultiIndex.from_arrays( + [ + Index([0] * 999 + [1], dtype=typ, name="index"), + Index([0] * 999 + [1], dtype=typ, name="index"), + ], + ), + ) + tm.assert_series_equal(result, expected) + + def test_groupby_rolling_non_monotonic(self): + # GH 43909 + + shuffled = [3, 0, 1, 2] + sec = 1_000 + df = DataFrame( + [{"t": Timestamp(2 * x * sec), "x": x + 1, "c": 42} for x in shuffled] + ) + with pytest.raises(ValueError, match=r".* must be monotonic"): + df.groupby("c").rolling(on="t", window="3s") + + def test_groupby_monotonic(self): + # GH 15130 + # we don't need to validate monotonicity when grouping + + # GH 43909 we should raise an error here to match + # behaviour of non-groupby rolling. + + data = [ + ["David", "1/1/2015", 100], + ["David", "1/5/2015", 500], + ["David", "5/30/2015", 50], + ["David", "7/25/2015", 50], + ["Ryan", "1/4/2014", 100], + ["Ryan", "1/19/2015", 500], + ["Ryan", "3/31/2016", 50], + ["Joe", "7/1/2015", 100], + ["Joe", "9/9/2015", 500], + ["Joe", "10/15/2015", 50], + ] + + df = DataFrame(data=data, columns=["name", "date", "amount"]) + df["date"] = to_datetime(df["date"]) + df = df.sort_values("date") + + msg = "DataFrameGroupBy.apply operated on the grouping columns" + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = ( + df.set_index("date") + .groupby("name") + .apply(lambda x: x.rolling("180D")["amount"].sum()) + ) + result = df.groupby("name").rolling("180D", on="date")["amount"].sum() + tm.assert_series_equal(result, expected) + + def test_datelike_on_monotonic_within_each_group(self): + # GH 13966 (similar to #15130, closed by #15175) + + # superseded by 43909 + # GH 46061: OK if the on is monotonic relative to each each group + + dates = date_range(start="2016-01-01 09:30:00", periods=20, freq="s") + df = DataFrame( + { + "A": [1] * 20 + [2] * 12 + [3] * 8, + "B": np.concatenate((dates, dates)), + "C": np.arange(40), + } + ) + + msg = "DataFrameGroupBy.apply operated on the grouping columns" + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = ( + df.set_index("B") + .groupby("A") + .apply(lambda x: x.rolling("4s")["C"].mean()) + ) + result = df.groupby("A").rolling("4s", on="B").C.mean() + tm.assert_series_equal(result, expected) + + def test_datelike_on_not_monotonic_within_each_group(self): + # GH 46061 + df = DataFrame( + { + "A": [1] * 3 + [2] * 3, + "B": [Timestamp(year, 1, 1) for year in [2020, 2021, 2019]] * 2, + "C": range(6), + } + ) + with pytest.raises(ValueError, match="Each group within B must be monotonic."): + df.groupby("A").rolling("365D", on="B") + + +class TestExpanding: + @pytest.fixture + def frame(self): + return DataFrame({"A": [1] * 20 + [2] * 12 + [3] * 8, "B": np.arange(40)}) + + @pytest.mark.parametrize( + "f", ["sum", "mean", "min", "max", "count", "kurt", "skew"] + ) + def test_expanding(self, f, frame): + g = frame.groupby("A", group_keys=False) + r = g.expanding() + + result = getattr(r, f)() + msg = "DataFrameGroupBy.apply operated on the grouping columns" + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = g.apply(lambda x: getattr(x.expanding(), f)()) + # groupby.apply doesn't drop the grouped-by column + expected = expected.drop("A", axis=1) + # GH 39732 + expected_index = MultiIndex.from_arrays([frame["A"], range(40)]) + expected.index = expected_index + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("f", ["std", "var"]) + def test_expanding_ddof(self, f, frame): + g = frame.groupby("A", group_keys=False) + r = g.expanding() + + result = getattr(r, f)(ddof=0) + msg = "DataFrameGroupBy.apply operated on the grouping columns" + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = g.apply(lambda x: getattr(x.expanding(), f)(ddof=0)) + # groupby.apply doesn't drop the grouped-by column + expected = expected.drop("A", axis=1) + # GH 39732 + expected_index = MultiIndex.from_arrays([frame["A"], range(40)]) + expected.index = expected_index + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "interpolation", ["linear", "lower", "higher", "midpoint", "nearest"] + ) + def test_expanding_quantile(self, interpolation, frame): + g = frame.groupby("A", group_keys=False) + r = g.expanding() + + result = r.quantile(0.4, interpolation=interpolation) + msg = "DataFrameGroupBy.apply operated on the grouping columns" + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = g.apply( + lambda x: x.expanding().quantile(0.4, interpolation=interpolation) + ) + # groupby.apply doesn't drop the grouped-by column + expected = expected.drop("A", axis=1) + # GH 39732 + expected_index = MultiIndex.from_arrays([frame["A"], range(40)]) + expected.index = expected_index + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("f", ["corr", "cov"]) + def test_expanding_corr_cov(self, f, frame): + g = frame.groupby("A") + r = g.expanding() + + result = getattr(r, f)(frame) + + def func_0(x): + return getattr(x.expanding(), f)(frame) + + msg = "DataFrameGroupBy.apply operated on the grouping columns" + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = g.apply(func_0) + # GH 39591: groupby.apply returns 1 instead of nan for windows + # with all nan values + null_idx = list(range(20, 61)) + list(range(72, 113)) + expected.iloc[null_idx, 1] = np.nan + # GH 39591: The grouped column should be all np.nan + # (groupby.apply inserts 0s for cov) + expected["A"] = np.nan + tm.assert_frame_equal(result, expected) + + result = getattr(r.B, f)(pairwise=True) + + def func_1(x): + return getattr(x.B.expanding(), f)(pairwise=True) + + msg = "DataFrameGroupBy.apply operated on the grouping columns" + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = g.apply(func_1) + tm.assert_series_equal(result, expected) + + def test_expanding_apply(self, raw, frame): + g = frame.groupby("A", group_keys=False) + r = g.expanding() + + # reduction + result = r.apply(lambda x: x.sum(), raw=raw) + msg = "DataFrameGroupBy.apply operated on the grouping columns" + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = g.apply( + lambda x: x.expanding().apply(lambda y: y.sum(), raw=raw) + ) + # groupby.apply doesn't drop the grouped-by column + expected = expected.drop("A", axis=1) + # GH 39732 + expected_index = MultiIndex.from_arrays([frame["A"], range(40)]) + expected.index = expected_index + tm.assert_frame_equal(result, expected) + + +class TestEWM: + @pytest.mark.parametrize( + "method, expected_data", + [ + ["mean", [0.0, 0.6666666666666666, 1.4285714285714286, 2.2666666666666666]], + ["std", [np.nan, 0.707107, 0.963624, 1.177164]], + ["var", [np.nan, 0.5, 0.9285714285714286, 1.3857142857142857]], + ], + ) + def test_methods(self, method, expected_data): + # GH 16037 + df = DataFrame({"A": ["a"] * 4, "B": range(4)}) + result = getattr(df.groupby("A").ewm(com=1.0), method)() + expected = DataFrame( + {"B": expected_data}, + index=MultiIndex.from_tuples( + [ + ("a", 0), + ("a", 1), + ("a", 2), + ("a", 3), + ], + names=["A", None], + ), + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "method, expected_data", + [["corr", [np.nan, 1.0, 1.0, 1]], ["cov", [np.nan, 0.5, 0.928571, 1.385714]]], + ) + def test_pairwise_methods(self, method, expected_data): + # GH 16037 + df = DataFrame({"A": ["a"] * 4, "B": range(4)}) + result = getattr(df.groupby("A").ewm(com=1.0), method)() + expected = DataFrame( + {"B": expected_data}, + index=MultiIndex.from_tuples( + [ + ("a", 0, "B"), + ("a", 1, "B"), + ("a", 2, "B"), + ("a", 3, "B"), + ], + names=["A", None, None], + ), + ) + tm.assert_frame_equal(result, expected) + + expected = df.groupby("A")[["B"]].apply( + lambda x: getattr(x.ewm(com=1.0), method)() + ) + tm.assert_frame_equal(result, expected) + + def test_times(self, times_frame): + # GH 40951 + halflife = "23 days" + # GH#42738 + times = times_frame.pop("C") + result = times_frame.groupby("A").ewm(halflife=halflife, times=times).mean() + expected = DataFrame( + { + "B": [ + 0.0, + 0.507534, + 1.020088, + 1.537661, + 0.0, + 0.567395, + 1.221209, + 0.0, + 0.653141, + 1.195003, + ] + }, + index=MultiIndex.from_tuples( + [ + ("a", 0), + ("a", 3), + ("a", 6), + ("a", 9), + ("b", 1), + ("b", 4), + ("b", 7), + ("c", 2), + ("c", 5), + ("c", 8), + ], + names=["A", None], + ), + ) + tm.assert_frame_equal(result, expected) + + def test_times_array(self, times_frame): + # GH 40951 + halflife = "23 days" + times = times_frame.pop("C") + gb = times_frame.groupby("A") + result = gb.ewm(halflife=halflife, times=times).mean() + expected = gb.ewm(halflife=halflife, times=times.values).mean() + tm.assert_frame_equal(result, expected) + + def test_dont_mutate_obj_after_slicing(self): + # GH 43355 + df = DataFrame( + { + "id": ["a", "a", "b", "b", "b"], + "timestamp": date_range("2021-9-1", periods=5, freq="h"), + "y": range(5), + } + ) + grp = df.groupby("id").rolling("1h", on="timestamp") + result = grp.count() + expected_df = DataFrame( + { + "timestamp": date_range("2021-9-1", periods=5, freq="h"), + "y": [1.0] * 5, + }, + index=MultiIndex.from_arrays( + [["a", "a", "b", "b", "b"], list(range(5))], names=["id", None] + ), + ) + tm.assert_frame_equal(result, expected_df) + + result = grp["y"].count() + expected_series = Series( + [1.0] * 5, + index=MultiIndex.from_arrays( + [ + ["a", "a", "b", "b", "b"], + date_range("2021-9-1", periods=5, freq="h"), + ], + names=["id", "timestamp"], + ), + name="y", + ) + tm.assert_series_equal(result, expected_series) + # This is the key test + result = grp.count() + tm.assert_frame_equal(result, expected_df) + + +def test_rolling_corr_with_single_integer_in_index(): + # GH 44078 + df = DataFrame({"a": [(1,), (1,), (1,)], "b": [4, 5, 6]}) + gb = df.groupby(["a"]) + result = gb.rolling(2).corr(other=df) + index = MultiIndex.from_tuples([((1,), 0), ((1,), 1), ((1,), 2)], names=["a", None]) + expected = DataFrame( + {"a": [np.nan, np.nan, np.nan], "b": [np.nan, 1.0, 1.0]}, index=index + ) + tm.assert_frame_equal(result, expected) + + +def test_rolling_corr_with_tuples_in_index(): + # GH 44078 + df = DataFrame( + { + "a": [ + ( + 1, + 2, + ), + ( + 1, + 2, + ), + ( + 1, + 2, + ), + ], + "b": [4, 5, 6], + } + ) + gb = df.groupby(["a"]) + result = gb.rolling(2).corr(other=df) + index = MultiIndex.from_tuples( + [((1, 2), 0), ((1, 2), 1), ((1, 2), 2)], names=["a", None] + ) + expected = DataFrame( + {"a": [np.nan, np.nan, np.nan], "b": [np.nan, 1.0, 1.0]}, index=index + ) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_numba.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_numba.py new file mode 100644 index 0000000000000000000000000000000000000000..9ee7ed0c2f3e66935a8b2de3f8af75da8f49c773 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_numba.py @@ -0,0 +1,465 @@ +import numpy as np +import pytest + +from pandas.compat import is_platform_arm +from pandas.errors import NumbaUtilError +import pandas.util._test_decorators as td + +from pandas import ( + DataFrame, + Series, + option_context, + to_datetime, +) +import pandas._testing as tm +from pandas.util.version import Version + +pytestmark = [pytest.mark.single_cpu] + +numba = pytest.importorskip("numba") +pytestmark.append( + pytest.mark.skipif( + Version(numba.__version__) == Version("0.61") and is_platform_arm(), + reason=f"Segfaults on ARM platforms with numba {numba.__version__}", + ) +) + + +@pytest.fixture(params=["single", "table"]) +def method(request): + """method keyword in rolling/expanding/ewm constructor""" + return request.param + + +@pytest.fixture( + params=[ + ["sum", {}], + ["mean", {}], + ["median", {}], + ["max", {}], + ["min", {}], + ["var", {}], + ["var", {"ddof": 0}], + ["std", {}], + ["std", {"ddof": 0}], + ] +) +def arithmetic_numba_supported_operators(request): + return request.param + + +@td.skip_if_no("numba") +@pytest.mark.filterwarnings("ignore") +# Filter warnings when parallel=True and the function can't be parallelized by Numba +class TestEngine: + @pytest.mark.parametrize("jit", [True, False]) + def test_numba_vs_cython_apply(self, jit, nogil, parallel, nopython, center, step): + def f(x, *args): + arg_sum = 0 + for arg in args: + arg_sum += arg + return np.mean(x) + arg_sum + + if jit: + import numba + + f = numba.jit(f) + + engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython} + args = (2,) + + s = Series(range(10)) + result = s.rolling(2, center=center, step=step).apply( + f, args=args, engine="numba", engine_kwargs=engine_kwargs, raw=True + ) + expected = s.rolling(2, center=center, step=step).apply( + f, engine="cython", args=args, raw=True + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "data", + [ + DataFrame(np.eye(5)), + DataFrame( + [ + [5, 7, 7, 7, np.nan, np.inf, 4, 3, 3, 3], + [5, 7, 7, 7, np.nan, np.inf, 7, 3, 3, 3], + [np.nan, np.nan, 5, 6, 7, 5, 5, 5, 5, 5], + ] + ).T, + Series(range(5), name="foo"), + Series([20, 10, 10, np.inf, 1, 1, 2, 3]), + Series([20, 10, 10, np.nan, 10, 1, 2, 3]), + ], + ) + def test_numba_vs_cython_rolling_methods( + self, + data, + nogil, + parallel, + nopython, + arithmetic_numba_supported_operators, + step, + ): + method, kwargs = arithmetic_numba_supported_operators + + engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython} + + roll = data.rolling(3, step=step) + result = getattr(roll, method)( + engine="numba", engine_kwargs=engine_kwargs, **kwargs + ) + expected = getattr(roll, method)(engine="cython", **kwargs) + tm.assert_equal(result, expected) + + @pytest.mark.parametrize( + "data", [DataFrame(np.eye(5)), Series(range(5), name="foo")] + ) + def test_numba_vs_cython_expanding_methods( + self, data, nogil, parallel, nopython, arithmetic_numba_supported_operators + ): + method, kwargs = arithmetic_numba_supported_operators + + engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython} + + data = DataFrame(np.eye(5)) + expand = data.expanding() + result = getattr(expand, method)( + engine="numba", engine_kwargs=engine_kwargs, **kwargs + ) + expected = getattr(expand, method)(engine="cython", **kwargs) + tm.assert_equal(result, expected) + + @pytest.mark.parametrize("jit", [True, False]) + def test_cache_apply(self, jit, nogil, parallel, nopython, step): + # Test that the functions are cached correctly if we switch functions + def func_1(x): + return np.mean(x) + 4 + + def func_2(x): + return np.std(x) * 5 + + if jit: + import numba + + func_1 = numba.jit(func_1) + func_2 = numba.jit(func_2) + + engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython} + + roll = Series(range(10)).rolling(2, step=step) + result = roll.apply( + func_1, engine="numba", engine_kwargs=engine_kwargs, raw=True + ) + expected = roll.apply(func_1, engine="cython", raw=True) + tm.assert_series_equal(result, expected) + + result = roll.apply( + func_2, engine="numba", engine_kwargs=engine_kwargs, raw=True + ) + expected = roll.apply(func_2, engine="cython", raw=True) + tm.assert_series_equal(result, expected) + # This run should use the cached func_1 + result = roll.apply( + func_1, engine="numba", engine_kwargs=engine_kwargs, raw=True + ) + expected = roll.apply(func_1, engine="cython", raw=True) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "window,window_kwargs", + [ + ["rolling", {"window": 3, "min_periods": 0}], + ["expanding", {}], + ], + ) + def test_dont_cache_args( + self, window, window_kwargs, nogil, parallel, nopython, method + ): + # GH 42287 + + def add(values, x): + return np.sum(values) + x + + engine_kwargs = {"nopython": nopython, "nogil": nogil, "parallel": parallel} + df = DataFrame({"value": [0, 0, 0]}) + result = getattr(df, window)(method=method, **window_kwargs).apply( + add, raw=True, engine="numba", engine_kwargs=engine_kwargs, args=(1,) + ) + expected = DataFrame({"value": [1.0, 1.0, 1.0]}) + tm.assert_frame_equal(result, expected) + + result = getattr(df, window)(method=method, **window_kwargs).apply( + add, raw=True, engine="numba", engine_kwargs=engine_kwargs, args=(2,) + ) + expected = DataFrame({"value": [2.0, 2.0, 2.0]}) + tm.assert_frame_equal(result, expected) + + def test_dont_cache_engine_kwargs(self): + # If the user passes a different set of engine_kwargs don't return the same + # jitted function + nogil = False + parallel = True + nopython = True + + def func(x): + return nogil + parallel + nopython + + engine_kwargs = {"nopython": nopython, "nogil": nogil, "parallel": parallel} + df = DataFrame({"value": [0, 0, 0]}) + result = df.rolling(1).apply( + func, raw=True, engine="numba", engine_kwargs=engine_kwargs + ) + expected = DataFrame({"value": [2.0, 2.0, 2.0]}) + tm.assert_frame_equal(result, expected) + + parallel = False + engine_kwargs = {"nopython": nopython, "nogil": nogil, "parallel": parallel} + result = df.rolling(1).apply( + func, raw=True, engine="numba", engine_kwargs=engine_kwargs + ) + expected = DataFrame({"value": [1.0, 1.0, 1.0]}) + tm.assert_frame_equal(result, expected) + + +@td.skip_if_no("numba") +class TestEWM: + @pytest.mark.parametrize( + "grouper", [lambda x: x, lambda x: x.groupby("A")], ids=["None", "groupby"] + ) + @pytest.mark.parametrize("method", ["mean", "sum"]) + def test_invalid_engine(self, grouper, method): + df = DataFrame({"A": ["a", "b", "a", "b"], "B": range(4)}) + with pytest.raises(ValueError, match="engine must be either"): + getattr(grouper(df).ewm(com=1.0), method)(engine="foo") + + @pytest.mark.parametrize( + "grouper", [lambda x: x, lambda x: x.groupby("A")], ids=["None", "groupby"] + ) + @pytest.mark.parametrize("method", ["mean", "sum"]) + def test_invalid_engine_kwargs(self, grouper, method): + df = DataFrame({"A": ["a", "b", "a", "b"], "B": range(4)}) + with pytest.raises(ValueError, match="cython engine does not"): + getattr(grouper(df).ewm(com=1.0), method)( + engine="cython", engine_kwargs={"nopython": True} + ) + + @pytest.mark.parametrize("grouper", ["None", "groupby"]) + @pytest.mark.parametrize("method", ["mean", "sum"]) + def test_cython_vs_numba( + self, grouper, method, nogil, parallel, nopython, ignore_na, adjust + ): + df = DataFrame({"B": range(4)}) + if grouper == "None": + grouper = lambda x: x + else: + df["A"] = ["a", "b", "a", "b"] + grouper = lambda x: x.groupby("A") + if method == "sum": + adjust = True + ewm = grouper(df).ewm(com=1.0, adjust=adjust, ignore_na=ignore_na) + + engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython} + result = getattr(ewm, method)(engine="numba", engine_kwargs=engine_kwargs) + expected = getattr(ewm, method)(engine="cython") + + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("grouper", ["None", "groupby"]) + def test_cython_vs_numba_times(self, grouper, nogil, parallel, nopython, ignore_na): + # GH 40951 + + df = DataFrame({"B": [0, 0, 1, 1, 2, 2]}) + if grouper == "None": + grouper = lambda x: x + else: + grouper = lambda x: x.groupby("A") + df["A"] = ["a", "b", "a", "b", "b", "a"] + + halflife = "23 days" + times = to_datetime( + [ + "2020-01-01", + "2020-01-01", + "2020-01-02", + "2020-01-10", + "2020-02-23", + "2020-01-03", + ] + ) + ewm = grouper(df).ewm( + halflife=halflife, adjust=True, ignore_na=ignore_na, times=times + ) + + engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython} + + result = ewm.mean(engine="numba", engine_kwargs=engine_kwargs) + expected = ewm.mean(engine="cython") + + tm.assert_frame_equal(result, expected) + + +@td.skip_if_no("numba") +def test_use_global_config(): + def f(x): + return np.mean(x) + 2 + + s = Series(range(10)) + with option_context("compute.use_numba", True): + result = s.rolling(2).apply(f, engine=None, raw=True) + expected = s.rolling(2).apply(f, engine="numba", raw=True) + tm.assert_series_equal(expected, result) + + +@td.skip_if_no("numba") +def test_invalid_kwargs_nopython(): + with pytest.raises(NumbaUtilError, match="numba does not support kwargs with"): + Series(range(1)).rolling(1).apply( + lambda x: x, kwargs={"a": 1}, engine="numba", raw=True + ) + + +@td.skip_if_no("numba") +@pytest.mark.slow +@pytest.mark.filterwarnings("ignore") +# Filter warnings when parallel=True and the function can't be parallelized by Numba +class TestTableMethod: + def test_table_series_valueerror(self): + def f(x): + return np.sum(x, axis=0) + 1 + + with pytest.raises( + ValueError, match="method='table' not applicable for Series objects." + ): + Series(range(1)).rolling(1, method="table").apply( + f, engine="numba", raw=True + ) + + def test_table_method_rolling_methods( + self, + axis, + nogil, + parallel, + nopython, + arithmetic_numba_supported_operators, + step, + ): + method, kwargs = arithmetic_numba_supported_operators + + engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython} + + df = DataFrame(np.eye(3)) + roll_table = df.rolling(2, method="table", axis=axis, min_periods=0, step=step) + if method in ("var", "std"): + with pytest.raises(NotImplementedError, match=f"{method} not supported"): + getattr(roll_table, method)( + engine_kwargs=engine_kwargs, engine="numba", **kwargs + ) + else: + roll_single = df.rolling( + 2, method="single", axis=axis, min_periods=0, step=step + ) + result = getattr(roll_table, method)( + engine_kwargs=engine_kwargs, engine="numba", **kwargs + ) + expected = getattr(roll_single, method)( + engine_kwargs=engine_kwargs, engine="numba", **kwargs + ) + tm.assert_frame_equal(result, expected) + + def test_table_method_rolling_apply(self, axis, nogil, parallel, nopython, step): + engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython} + + def f(x): + return np.sum(x, axis=0) + 1 + + df = DataFrame(np.eye(3)) + result = df.rolling( + 2, method="table", axis=axis, min_periods=0, step=step + ).apply(f, raw=True, engine_kwargs=engine_kwargs, engine="numba") + expected = df.rolling( + 2, method="single", axis=axis, min_periods=0, step=step + ).apply(f, raw=True, engine_kwargs=engine_kwargs, engine="numba") + tm.assert_frame_equal(result, expected) + + def test_table_method_rolling_weighted_mean(self, step): + def weighted_mean(x): + arr = np.ones((1, x.shape[1])) + arr[:, :2] = (x[:, :2] * x[:, 2]).sum(axis=0) / x[:, 2].sum() + return arr + + df = DataFrame([[1, 2, 0.6], [2, 3, 0.4], [3, 4, 0.2], [4, 5, 0.7]]) + result = df.rolling(2, method="table", min_periods=0, step=step).apply( + weighted_mean, raw=True, engine="numba" + ) + expected = DataFrame( + [ + [1.0, 2.0, 1.0], + [1.8, 2.0, 1.0], + [3.333333, 2.333333, 1.0], + [1.555556, 7, 1.0], + ] + )[::step] + tm.assert_frame_equal(result, expected) + + def test_table_method_expanding_apply(self, axis, nogil, parallel, nopython): + engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython} + + def f(x): + return np.sum(x, axis=0) + 1 + + df = DataFrame(np.eye(3)) + result = df.expanding(method="table", axis=axis).apply( + f, raw=True, engine_kwargs=engine_kwargs, engine="numba" + ) + expected = df.expanding(method="single", axis=axis).apply( + f, raw=True, engine_kwargs=engine_kwargs, engine="numba" + ) + tm.assert_frame_equal(result, expected) + + def test_table_method_expanding_methods( + self, axis, nogil, parallel, nopython, arithmetic_numba_supported_operators + ): + method, kwargs = arithmetic_numba_supported_operators + + engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython} + + df = DataFrame(np.eye(3)) + expand_table = df.expanding(method="table", axis=axis) + if method in ("var", "std"): + with pytest.raises(NotImplementedError, match=f"{method} not supported"): + getattr(expand_table, method)( + engine_kwargs=engine_kwargs, engine="numba", **kwargs + ) + else: + expand_single = df.expanding(method="single", axis=axis) + result = getattr(expand_table, method)( + engine_kwargs=engine_kwargs, engine="numba", **kwargs + ) + expected = getattr(expand_single, method)( + engine_kwargs=engine_kwargs, engine="numba", **kwargs + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("data", [np.eye(3), np.ones((2, 3)), np.ones((3, 2))]) + @pytest.mark.parametrize("method", ["mean", "sum"]) + def test_table_method_ewm(self, data, method, axis, nogil, parallel, nopython): + engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython} + + df = DataFrame(data) + + result = getattr(df.ewm(com=1, method="table", axis=axis), method)( + engine_kwargs=engine_kwargs, engine="numba" + ) + expected = getattr(df.ewm(com=1, method="single", axis=axis), method)( + engine_kwargs=engine_kwargs, engine="numba" + ) + tm.assert_frame_equal(result, expected) + + +@td.skip_if_no("numba") +def test_npfunc_no_warnings(): + df = DataFrame({"col1": [1, 2, 3, 4, 5]}) + with tm.assert_produces_warning(False): + df.col1.rolling(2).apply(np.prod, raw=True, engine="numba") diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_online.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_online.py new file mode 100644 index 0000000000000000000000000000000000000000..43d55a7992b3ce52255a6813e8b9e93b82a45324 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_online.py @@ -0,0 +1,112 @@ +import numpy as np +import pytest + +from pandas.compat import is_platform_arm + +from pandas import ( + DataFrame, + Series, +) +import pandas._testing as tm +from pandas.util.version import Version + +pytestmark = [pytest.mark.single_cpu] + +numba = pytest.importorskip("numba") +pytestmark.append( + pytest.mark.skipif( + Version(numba.__version__) == Version("0.61") and is_platform_arm(), + reason=f"Segfaults on ARM platforms with numba {numba.__version__}", + ) +) + + +@pytest.mark.filterwarnings("ignore") +# Filter warnings when parallel=True and the function can't be parallelized by Numba +class TestEWM: + def test_invalid_update(self): + df = DataFrame({"a": range(5), "b": range(5)}) + online_ewm = df.head(2).ewm(0.5).online() + with pytest.raises( + ValueError, + match="Must call mean with update=None first before passing update", + ): + online_ewm.mean(update=df.head(1)) + + @pytest.mark.slow + @pytest.mark.parametrize( + "obj", [DataFrame({"a": range(5), "b": range(5)}), Series(range(5), name="foo")] + ) + def test_online_vs_non_online_mean( + self, obj, nogil, parallel, nopython, adjust, ignore_na + ): + expected = obj.ewm(0.5, adjust=adjust, ignore_na=ignore_na).mean() + engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython} + + online_ewm = ( + obj.head(2) + .ewm(0.5, adjust=adjust, ignore_na=ignore_na) + .online(engine_kwargs=engine_kwargs) + ) + # Test resetting once + for _ in range(2): + result = online_ewm.mean() + tm.assert_equal(result, expected.head(2)) + + result = online_ewm.mean(update=obj.tail(3)) + tm.assert_equal(result, expected.tail(3)) + + online_ewm.reset() + + @pytest.mark.xfail(raises=NotImplementedError) + @pytest.mark.parametrize( + "obj", [DataFrame({"a": range(5), "b": range(5)}), Series(range(5), name="foo")] + ) + def test_update_times_mean( + self, obj, nogil, parallel, nopython, adjust, ignore_na, halflife_with_times + ): + times = Series( + np.array( + ["2020-01-01", "2020-01-05", "2020-01-07", "2020-01-17", "2020-01-21"], + dtype="datetime64[ns]", + ) + ) + expected = obj.ewm( + 0.5, + adjust=adjust, + ignore_na=ignore_na, + times=times, + halflife=halflife_with_times, + ).mean() + + engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython} + online_ewm = ( + obj.head(2) + .ewm( + 0.5, + adjust=adjust, + ignore_na=ignore_na, + times=times.head(2), + halflife=halflife_with_times, + ) + .online(engine_kwargs=engine_kwargs) + ) + # Test resetting once + for _ in range(2): + result = online_ewm.mean() + tm.assert_equal(result, expected.head(2)) + + result = online_ewm.mean(update=obj.tail(3), update_times=times.tail(3)) + tm.assert_equal(result, expected.tail(3)) + + online_ewm.reset() + + @pytest.mark.parametrize("method", ["aggregate", "std", "corr", "cov", "var"]) + def test_ewm_notimplementederror_raises(self, method): + ser = Series(range(10)) + kwargs = {} + if method == "aggregate": + kwargs["func"] = lambda x: x + + with pytest.raises(NotImplementedError, match=".* is not implemented."): + getattr(ser.ewm(1).online(), method)(**kwargs) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_pairwise.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_pairwise.py new file mode 100644 index 0000000000000000000000000000000000000000..3ceb58756bac6cbc5f3b1862523d4185c1ad8b47 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_pairwise.py @@ -0,0 +1,445 @@ +import numpy as np +import pytest + +from pandas.compat import IS64 + +from pandas import ( + DataFrame, + Index, + MultiIndex, + Series, + date_range, +) +import pandas._testing as tm +from pandas.core.algorithms import safe_sort + + +@pytest.fixture( + params=[ + DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=[1, 0]), + DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=[1, 1]), + DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=["C", "C"]), + DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=[1.0, 0]), + DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=[0.0, 1]), + DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=["C", 1]), + DataFrame([[2.0, 4.0], [1.0, 2.0], [5.0, 2.0], [8.0, 1.0]], columns=[1, 0.0]), + DataFrame([[2, 4.0], [1, 2.0], [5, 2.0], [8, 1.0]], columns=[0, 1.0]), + DataFrame([[2, 4], [1, 2], [5, 2], [8, 1.0]], columns=[1.0, "X"]), + ] +) +def pairwise_frames(request): + """Pairwise frames test_pairwise""" + return request.param + + +@pytest.fixture +def pairwise_target_frame(): + """Pairwise target frame for test_pairwise""" + return DataFrame([[2, 4], [1, 2], [5, 2], [8, 1]], columns=[0, 1]) + + +@pytest.fixture +def pairwise_other_frame(): + """Pairwise other frame for test_pairwise""" + return DataFrame( + [[None, 1, 1], [None, 1, 2], [None, 3, 2], [None, 8, 1]], + columns=["Y", "Z", "X"], + ) + + +def test_rolling_cov(series): + A = series + B = A + np.random.default_rng(2).standard_normal(len(A)) + + result = A.rolling(window=50, min_periods=25).cov(B) + tm.assert_almost_equal(result.iloc[-1], np.cov(A[-50:], B[-50:])[0, 1]) + + +def test_rolling_corr(series): + A = series + B = A + np.random.default_rng(2).standard_normal(len(A)) + + result = A.rolling(window=50, min_periods=25).corr(B) + tm.assert_almost_equal(result.iloc[-1], np.corrcoef(A[-50:], B[-50:])[0, 1]) + + +def test_rolling_corr_bias_correction(): + # test for correct bias correction + a = Series( + np.arange(20, dtype=np.float64), index=date_range("2020-01-01", periods=20) + ) + b = a.copy() + a[:5] = np.nan + b[:10] = np.nan + + result = a.rolling(window=len(a), min_periods=1).corr(b) + tm.assert_almost_equal(result.iloc[-1], a.corr(b)) + + +@pytest.mark.parametrize("func", ["cov", "corr"]) +def test_rolling_pairwise_cov_corr(func, frame): + result = getattr(frame.rolling(window=10, min_periods=5), func)() + result = result.loc[(slice(None), 1), 5] + result.index = result.index.droplevel(1) + expected = getattr(frame[1].rolling(window=10, min_periods=5), func)(frame[5]) + tm.assert_series_equal(result, expected, check_names=False) + + +@pytest.mark.parametrize("method", ["corr", "cov"]) +def test_flex_binary_frame(method, frame): + series = frame[1] + + res = getattr(series.rolling(window=10), method)(frame) + res2 = getattr(frame.rolling(window=10), method)(series) + exp = frame.apply(lambda x: getattr(series.rolling(window=10), method)(x)) + + tm.assert_frame_equal(res, exp) + tm.assert_frame_equal(res2, exp) + + frame2 = frame.copy() + frame2 = DataFrame( + np.random.default_rng(2).standard_normal(frame2.shape), + index=frame2.index, + columns=frame2.columns, + ) + + res3 = getattr(frame.rolling(window=10), method)(frame2) + exp = DataFrame( + {k: getattr(frame[k].rolling(window=10), method)(frame2[k]) for k in frame} + ) + tm.assert_frame_equal(res3, exp) + + +@pytest.mark.parametrize("window", range(7)) +def test_rolling_corr_with_zero_variance(window): + # GH 18430 + s = Series(np.zeros(20)) + other = Series(np.arange(20)) + + assert s.rolling(window=window).corr(other=other).isna().all() + + +def test_corr_sanity(): + # GH 3155 + df = DataFrame( + np.array( + [ + [0.87024726, 0.18505595], + [0.64355431, 0.3091617], + [0.92372966, 0.50552513], + [0.00203756, 0.04520709], + [0.84780328, 0.33394331], + [0.78369152, 0.63919667], + ] + ) + ) + + res = df[0].rolling(5, center=True).corr(df[1]) + assert all(np.abs(np.nan_to_num(x)) <= 1 for x in res) + + df = DataFrame(np.random.default_rng(2).random((30, 2))) + res = df[0].rolling(5, center=True).corr(df[1]) + assert all(np.abs(np.nan_to_num(x)) <= 1 for x in res) + + +def test_rolling_cov_diff_length(): + # GH 7512 + s1 = Series([1, 2, 3], index=[0, 1, 2]) + s2 = Series([1, 3], index=[0, 2]) + result = s1.rolling(window=3, min_periods=2).cov(s2) + expected = Series([None, None, 2.0]) + tm.assert_series_equal(result, expected) + + s2a = Series([1, None, 3], index=[0, 1, 2]) + result = s1.rolling(window=3, min_periods=2).cov(s2a) + tm.assert_series_equal(result, expected) + + +def test_rolling_corr_diff_length(): + # GH 7512 + s1 = Series([1, 2, 3], index=[0, 1, 2]) + s2 = Series([1, 3], index=[0, 2]) + result = s1.rolling(window=3, min_periods=2).corr(s2) + expected = Series([None, None, 1.0]) + tm.assert_series_equal(result, expected) + + s2a = Series([1, None, 3], index=[0, 1, 2]) + result = s1.rolling(window=3, min_periods=2).corr(s2a) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "f", + [ + lambda x: (x.rolling(window=10, min_periods=5).cov(x, pairwise=True)), + lambda x: (x.rolling(window=10, min_periods=5).corr(x, pairwise=True)), + ], +) +def test_rolling_functions_window_non_shrinkage_binary(f): + # corr/cov return a MI DataFrame + df = DataFrame( + [[1, 5], [3, 2], [3, 9], [-1, 0]], + columns=Index(["A", "B"], name="foo"), + index=Index(range(4), name="bar"), + ) + df_expected = DataFrame( + columns=Index(["A", "B"], name="foo"), + index=MultiIndex.from_product([df.index, df.columns], names=["bar", "foo"]), + dtype="float64", + ) + df_result = f(df) + tm.assert_frame_equal(df_result, df_expected) + + +@pytest.mark.parametrize( + "f", + [ + lambda x: (x.rolling(window=10, min_periods=5).cov(x, pairwise=True)), + lambda x: (x.rolling(window=10, min_periods=5).corr(x, pairwise=True)), + ], +) +def test_moment_functions_zero_length_pairwise(f): + df1 = DataFrame() + df2 = DataFrame(columns=Index(["a"], name="foo"), index=Index([], name="bar")) + df2["a"] = df2["a"].astype("float64") + + df1_expected = DataFrame(index=MultiIndex.from_product([df1.index, df1.columns])) + df2_expected = DataFrame( + index=MultiIndex.from_product([df2.index, df2.columns], names=["bar", "foo"]), + columns=Index(["a"], name="foo"), + dtype="float64", + ) + + df1_result = f(df1) + tm.assert_frame_equal(df1_result, df1_expected) + + df2_result = f(df2) + tm.assert_frame_equal(df2_result, df2_expected) + + +class TestPairwise: + # GH 7738 + @pytest.mark.parametrize("f", [lambda x: x.cov(), lambda x: x.corr()]) + def test_no_flex(self, pairwise_frames, pairwise_target_frame, f): + # DataFrame methods (which do not call flex_binary_moment()) + + result = f(pairwise_frames) + tm.assert_index_equal(result.index, pairwise_frames.columns) + tm.assert_index_equal(result.columns, pairwise_frames.columns) + expected = f(pairwise_target_frame) + # since we have sorted the results + # we can only compare non-nans + result = result.dropna().values + expected = expected.dropna().values + + tm.assert_numpy_array_equal(result, expected, check_dtype=False) + + @pytest.mark.parametrize( + "f", + [ + lambda x: x.expanding().cov(pairwise=True), + lambda x: x.expanding().corr(pairwise=True), + lambda x: x.rolling(window=3).cov(pairwise=True), + lambda x: x.rolling(window=3).corr(pairwise=True), + lambda x: x.ewm(com=3).cov(pairwise=True), + lambda x: x.ewm(com=3).corr(pairwise=True), + ], + ) + def test_pairwise_with_self(self, pairwise_frames, pairwise_target_frame, f): + # DataFrame with itself, pairwise=True + # note that we may construct the 1st level of the MI + # in a non-monotonic way, so compare accordingly + result = f(pairwise_frames) + tm.assert_index_equal( + result.index.levels[0], pairwise_frames.index, check_names=False + ) + tm.assert_index_equal( + safe_sort(result.index.levels[1]), + safe_sort(pairwise_frames.columns.unique()), + ) + tm.assert_index_equal(result.columns, pairwise_frames.columns) + expected = f(pairwise_target_frame) + # since we have sorted the results + # we can only compare non-nans + result = result.dropna().values + expected = expected.dropna().values + + tm.assert_numpy_array_equal(result, expected, check_dtype=False) + + @pytest.mark.parametrize( + "f", + [ + lambda x: x.expanding().cov(pairwise=False), + lambda x: x.expanding().corr(pairwise=False), + lambda x: x.rolling(window=3).cov(pairwise=False), + lambda x: x.rolling(window=3).corr(pairwise=False), + lambda x: x.ewm(com=3).cov(pairwise=False), + lambda x: x.ewm(com=3).corr(pairwise=False), + ], + ) + def test_no_pairwise_with_self(self, pairwise_frames, pairwise_target_frame, f): + # DataFrame with itself, pairwise=False + result = f(pairwise_frames) + tm.assert_index_equal(result.index, pairwise_frames.index) + tm.assert_index_equal(result.columns, pairwise_frames.columns) + expected = f(pairwise_target_frame) + # since we have sorted the results + # we can only compare non-nans + result = result.dropna().values + expected = expected.dropna().values + + tm.assert_numpy_array_equal(result, expected, check_dtype=False) + + @pytest.mark.parametrize( + "f", + [ + lambda x, y: x.expanding().cov(y, pairwise=True), + lambda x, y: x.expanding().corr(y, pairwise=True), + lambda x, y: x.rolling(window=3).cov(y, pairwise=True), + # TODO: We're missing a flag somewhere in meson + pytest.param( + lambda x, y: x.rolling(window=3).corr(y, pairwise=True), + marks=pytest.mark.xfail( + not IS64, reason="Precision issues on 32 bit", strict=False + ), + ), + lambda x, y: x.ewm(com=3).cov(y, pairwise=True), + lambda x, y: x.ewm(com=3).corr(y, pairwise=True), + ], + ) + def test_pairwise_with_other( + self, pairwise_frames, pairwise_target_frame, pairwise_other_frame, f + ): + # DataFrame with another DataFrame, pairwise=True + result = f(pairwise_frames, pairwise_other_frame) + tm.assert_index_equal( + result.index.levels[0], pairwise_frames.index, check_names=False + ) + tm.assert_index_equal( + safe_sort(result.index.levels[1]), + safe_sort(pairwise_other_frame.columns.unique()), + ) + expected = f(pairwise_target_frame, pairwise_other_frame) + # since we have sorted the results + # we can only compare non-nans + result = result.dropna().values + expected = expected.dropna().values + + tm.assert_numpy_array_equal(result, expected, check_dtype=False) + + @pytest.mark.filterwarnings("ignore:RuntimeWarning") + @pytest.mark.parametrize( + "f", + [ + lambda x, y: x.expanding().cov(y, pairwise=False), + lambda x, y: x.expanding().corr(y, pairwise=False), + lambda x, y: x.rolling(window=3).cov(y, pairwise=False), + lambda x, y: x.rolling(window=3).corr(y, pairwise=False), + lambda x, y: x.ewm(com=3).cov(y, pairwise=False), + lambda x, y: x.ewm(com=3).corr(y, pairwise=False), + ], + ) + def test_no_pairwise_with_other(self, pairwise_frames, pairwise_other_frame, f): + # DataFrame with another DataFrame, pairwise=False + result = ( + f(pairwise_frames, pairwise_other_frame) + if pairwise_frames.columns.is_unique + else None + ) + if result is not None: + # we can have int and str columns + expected_index = pairwise_frames.index.union(pairwise_other_frame.index) + expected_columns = pairwise_frames.columns.union( + pairwise_other_frame.columns + ) + tm.assert_index_equal(result.index, expected_index) + tm.assert_index_equal(result.columns, expected_columns) + else: + with pytest.raises(ValueError, match="'arg1' columns are not unique"): + f(pairwise_frames, pairwise_other_frame) + with pytest.raises(ValueError, match="'arg2' columns are not unique"): + f(pairwise_other_frame, pairwise_frames) + + @pytest.mark.parametrize( + "f", + [ + lambda x, y: x.expanding().cov(y), + lambda x, y: x.expanding().corr(y), + lambda x, y: x.rolling(window=3).cov(y), + lambda x, y: x.rolling(window=3).corr(y), + lambda x, y: x.ewm(com=3).cov(y), + lambda x, y: x.ewm(com=3).corr(y), + ], + ) + def test_pairwise_with_series(self, pairwise_frames, pairwise_target_frame, f): + # DataFrame with a Series + result = f(pairwise_frames, Series([1, 1, 3, 8])) + tm.assert_index_equal(result.index, pairwise_frames.index) + tm.assert_index_equal(result.columns, pairwise_frames.columns) + expected = f(pairwise_target_frame, Series([1, 1, 3, 8])) + # since we have sorted the results + # we can only compare non-nans + result = result.dropna().values + expected = expected.dropna().values + tm.assert_numpy_array_equal(result, expected, check_dtype=False) + + result = f(Series([1, 1, 3, 8]), pairwise_frames) + tm.assert_index_equal(result.index, pairwise_frames.index) + tm.assert_index_equal(result.columns, pairwise_frames.columns) + expected = f(Series([1, 1, 3, 8]), pairwise_target_frame) + # since we have sorted the results + # we can only compare non-nans + result = result.dropna().values + expected = expected.dropna().values + tm.assert_numpy_array_equal(result, expected, check_dtype=False) + + def test_corr_freq_memory_error(self): + # GH 31789 + s = Series(range(5), index=date_range("2020", periods=5)) + result = s.rolling("12h").corr(s) + expected = Series([np.nan] * 5, index=date_range("2020", periods=5)) + tm.assert_series_equal(result, expected) + + def test_cov_mulittindex(self): + # GH 34440 + + columns = MultiIndex.from_product([list("ab"), list("xy"), list("AB")]) + index = range(3) + df = DataFrame(np.arange(24).reshape(3, 8), index=index, columns=columns) + + result = df.ewm(alpha=0.1).cov() + + index = MultiIndex.from_product([range(3), list("ab"), list("xy"), list("AB")]) + columns = MultiIndex.from_product([list("ab"), list("xy"), list("AB")]) + expected = DataFrame( + np.vstack( + ( + np.full((8, 8), np.nan), + np.full((8, 8), 32.000000), + np.full((8, 8), 63.881919), + ) + ), + index=index, + columns=columns, + ) + + tm.assert_frame_equal(result, expected) + + def test_multindex_columns_pairwise_func(self): + # GH 21157 + columns = MultiIndex.from_arrays([["M", "N"], ["P", "Q"]], names=["a", "b"]) + df = DataFrame(np.ones((5, 2)), columns=columns) + result = df.rolling(3).corr() + expected = DataFrame( + np.nan, + index=MultiIndex.from_arrays( + [ + np.repeat(np.arange(5, dtype=np.int64), 2), + ["M", "N"] * 5, + ["P", "Q"] * 5, + ], + names=[None, "a", "b"], + ), + columns=columns, + ) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_rolling.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_rolling.py new file mode 100644 index 0000000000000000000000000000000000000000..f353a7fa2f0fe5f0480c31b40a2907943ff5a5c6 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_rolling.py @@ -0,0 +1,1979 @@ +from datetime import ( + datetime, + timedelta, +) + +import numpy as np +import pytest + +from pandas.compat import ( + IS64, + is_platform_arm, + is_platform_power, +) + +from pandas import ( + DataFrame, + DatetimeIndex, + MultiIndex, + Series, + Timedelta, + Timestamp, + date_range, + period_range, + to_datetime, + to_timedelta, +) +import pandas._testing as tm +from pandas.api.indexers import BaseIndexer +from pandas.core.indexers.objects import VariableOffsetWindowIndexer + +from pandas.tseries.offsets import BusinessDay + + +def test_doc_string(): + df = DataFrame({"B": [0, 1, 2, np.nan, 4]}) + df + df.rolling(2).sum() + df.rolling(2, min_periods=1).sum() + + +def test_constructor(frame_or_series): + # GH 12669 + + c = frame_or_series(range(5)).rolling + + # valid + c(0) + c(window=2) + c(window=2, min_periods=1) + c(window=2, min_periods=1, center=True) + c(window=2, min_periods=1, center=False) + + # GH 13383 + + msg = "window must be an integer 0 or greater" + + with pytest.raises(ValueError, match=msg): + c(-1) + + +@pytest.mark.parametrize("w", [2.0, "foo", np.array([2])]) +def test_invalid_constructor(frame_or_series, w): + # not valid + + c = frame_or_series(range(5)).rolling + + msg = "|".join( + [ + "window must be an integer", + "passed window foo is not compatible with a datetimelike index", + ] + ) + with pytest.raises(ValueError, match=msg): + c(window=w) + + msg = "min_periods must be an integer" + with pytest.raises(ValueError, match=msg): + c(window=2, min_periods=w) + + msg = "center must be a boolean" + with pytest.raises(ValueError, match=msg): + c(window=2, min_periods=1, center=w) + + +@pytest.mark.parametrize( + "window", + [ + timedelta(days=3), + Timedelta(days=3), + "3D", + VariableOffsetWindowIndexer( + index=date_range("2015-12-25", periods=5), offset=BusinessDay(1) + ), + ], +) +def test_freq_window_not_implemented(window): + # GH 15354 + df = DataFrame( + np.arange(10), + index=date_range("2015-12-24", periods=10, freq="D"), + ) + with pytest.raises( + NotImplementedError, match="^step (not implemented|is not supported)" + ): + df.rolling(window, step=3).sum() + + +@pytest.mark.parametrize("agg", ["cov", "corr"]) +def test_step_not_implemented_for_cov_corr(agg): + # GH 15354 + roll = DataFrame(range(2)).rolling(1, step=2) + with pytest.raises(NotImplementedError, match="step not implemented"): + getattr(roll, agg)() + + +@pytest.mark.parametrize("window", [timedelta(days=3), Timedelta(days=3)]) +def test_constructor_with_timedelta_window(window): + # GH 15440 + n = 10 + df = DataFrame( + {"value": np.arange(n)}, + index=date_range("2015-12-24", periods=n, freq="D"), + ) + expected_data = np.append([0.0, 1.0], np.arange(3.0, 27.0, 3)) + + result = df.rolling(window=window).sum() + expected = DataFrame( + {"value": expected_data}, + index=date_range("2015-12-24", periods=n, freq="D"), + ) + tm.assert_frame_equal(result, expected) + expected = df.rolling("3D").sum() + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("window", [timedelta(days=3), Timedelta(days=3), "3D"]) +def test_constructor_timedelta_window_and_minperiods(window, raw): + # GH 15305 + n = 10 + df = DataFrame( + {"value": np.arange(n)}, + index=date_range("2017-08-08", periods=n, freq="D"), + ) + expected = DataFrame( + {"value": np.append([np.nan, 1.0], np.arange(3.0, 27.0, 3))}, + index=date_range("2017-08-08", periods=n, freq="D"), + ) + result_roll_sum = df.rolling(window=window, min_periods=2).sum() + result_roll_generic = df.rolling(window=window, min_periods=2).apply(sum, raw=raw) + tm.assert_frame_equal(result_roll_sum, expected) + tm.assert_frame_equal(result_roll_generic, expected) + + +def test_closed_fixed(closed, arithmetic_win_operators): + # GH 34315 + func_name = arithmetic_win_operators + df_fixed = DataFrame({"A": [0, 1, 2, 3, 4]}) + df_time = DataFrame({"A": [0, 1, 2, 3, 4]}, index=date_range("2020", periods=5)) + + result = getattr( + df_fixed.rolling(2, closed=closed, min_periods=1), + func_name, + )() + expected = getattr( + df_time.rolling("2D", closed=closed, min_periods=1), + func_name, + )().reset_index(drop=True) + + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "closed, window_selections", + [ + ( + "both", + [ + [True, True, False, False, False], + [True, True, True, False, False], + [False, True, True, True, False], + [False, False, True, True, True], + [False, False, False, True, True], + ], + ), + ( + "left", + [ + [True, False, False, False, False], + [True, True, False, False, False], + [False, True, True, False, False], + [False, False, True, True, False], + [False, False, False, True, True], + ], + ), + ( + "right", + [ + [True, True, False, False, False], + [False, True, True, False, False], + [False, False, True, True, False], + [False, False, False, True, True], + [False, False, False, False, True], + ], + ), + ( + "neither", + [ + [True, False, False, False, False], + [False, True, False, False, False], + [False, False, True, False, False], + [False, False, False, True, False], + [False, False, False, False, True], + ], + ), + ], +) +def test_datetimelike_centered_selections( + closed, window_selections, arithmetic_win_operators +): + # GH 34315 + func_name = arithmetic_win_operators + df_time = DataFrame( + {"A": [0.0, 1.0, 2.0, 3.0, 4.0]}, index=date_range("2020", periods=5) + ) + + expected = DataFrame( + {"A": [getattr(df_time["A"].iloc[s], func_name)() for s in window_selections]}, + index=date_range("2020", periods=5), + ) + + if func_name == "sem": + kwargs = {"ddof": 0} + else: + kwargs = {} + + result = getattr( + df_time.rolling("2D", closed=closed, min_periods=1, center=True), + func_name, + )(**kwargs) + + tm.assert_frame_equal(result, expected, check_dtype=False) + + +@pytest.mark.parametrize( + "window,closed,expected", + [ + ("3s", "right", [3.0, 3.0, 3.0]), + ("3s", "both", [3.0, 3.0, 3.0]), + ("3s", "left", [3.0, 3.0, 3.0]), + ("3s", "neither", [3.0, 3.0, 3.0]), + ("2s", "right", [3.0, 2.0, 2.0]), + ("2s", "both", [3.0, 3.0, 3.0]), + ("2s", "left", [1.0, 3.0, 3.0]), + ("2s", "neither", [1.0, 2.0, 2.0]), + ], +) +def test_datetimelike_centered_offset_covers_all( + window, closed, expected, frame_or_series +): + # GH 42753 + + index = [ + Timestamp("20130101 09:00:01"), + Timestamp("20130101 09:00:02"), + Timestamp("20130101 09:00:02"), + ] + df = frame_or_series([1, 1, 1], index=index) + + result = df.rolling(window, closed=closed, center=True).sum() + expected = frame_or_series(expected, index=index) + tm.assert_equal(result, expected) + + +@pytest.mark.parametrize( + "window,closed,expected", + [ + ("2D", "right", [4, 4, 4, 4, 4, 4, 2, 2]), + ("2D", "left", [2, 2, 4, 4, 4, 4, 4, 4]), + ("2D", "both", [4, 4, 6, 6, 6, 6, 4, 4]), + ("2D", "neither", [2, 2, 2, 2, 2, 2, 2, 2]), + ], +) +def test_datetimelike_nonunique_index_centering( + window, closed, expected, frame_or_series +): + index = DatetimeIndex( + [ + "2020-01-01", + "2020-01-01", + "2020-01-02", + "2020-01-02", + "2020-01-03", + "2020-01-03", + "2020-01-04", + "2020-01-04", + ] + ) + + df = frame_or_series([1] * 8, index=index, dtype=float) + expected = frame_or_series(expected, index=index, dtype=float) + + result = df.rolling(window, center=True, closed=closed).sum() + + tm.assert_equal(result, expected) + + +@pytest.mark.parametrize( + "closed,expected", + [ + ("left", [np.nan, np.nan, 1, 1, 1, 10, 14, 14, 18, 21]), + ("neither", [np.nan, np.nan, 1, 1, 1, 9, 5, 5, 13, 8]), + ("right", [0, 1, 3, 6, 10, 14, 11, 18, 21, 17]), + ("both", [0, 1, 3, 6, 10, 15, 20, 27, 26, 30]), + ], +) +def test_variable_window_nonunique(closed, expected, frame_or_series): + # GH 20712 + index = DatetimeIndex( + [ + "2011-01-01", + "2011-01-01", + "2011-01-02", + "2011-01-02", + "2011-01-02", + "2011-01-03", + "2011-01-04", + "2011-01-04", + "2011-01-05", + "2011-01-06", + ] + ) + + df = frame_or_series(range(10), index=index, dtype=float) + expected = frame_or_series(expected, index=index, dtype=float) + + result = df.rolling("2D", closed=closed).sum() + + tm.assert_equal(result, expected) + + +@pytest.mark.parametrize( + "closed,expected", + [ + ("left", [np.nan, np.nan, 1, 1, 1, 10, 15, 15, 18, 21]), + ("neither", [np.nan, np.nan, 1, 1, 1, 10, 15, 15, 13, 8]), + ("right", [0, 1, 3, 6, 10, 15, 21, 28, 21, 17]), + ("both", [0, 1, 3, 6, 10, 15, 21, 28, 26, 30]), + ], +) +def test_variable_offset_window_nonunique(closed, expected, frame_or_series): + # GH 20712 + index = DatetimeIndex( + [ + "2011-01-01", + "2011-01-01", + "2011-01-02", + "2011-01-02", + "2011-01-02", + "2011-01-03", + "2011-01-04", + "2011-01-04", + "2011-01-05", + "2011-01-06", + ] + ) + + df = frame_or_series(range(10), index=index, dtype=float) + expected = frame_or_series(expected, index=index, dtype=float) + + offset = BusinessDay(2) + indexer = VariableOffsetWindowIndexer(index=index, offset=offset) + result = df.rolling(indexer, closed=closed, min_periods=1).sum() + + tm.assert_equal(result, expected) + + +def test_even_number_window_alignment(): + # see discussion in GH 38780 + s = Series(range(3), index=date_range(start="2020-01-01", freq="D", periods=3)) + + # behavior of index- and datetime-based windows differs here! + # s.rolling(window=2, min_periods=1, center=True).mean() + + result = s.rolling(window="2D", min_periods=1, center=True).mean() + + expected = Series([0.5, 1.5, 2], index=s.index) + + tm.assert_series_equal(result, expected) + + +def test_closed_fixed_binary_col(center, step): + # GH 34315 + data = [0, 1, 1, 0, 0, 1, 0, 1] + df = DataFrame( + {"binary_col": data}, + index=date_range(start="2020-01-01", freq="min", periods=len(data)), + ) + + if center: + expected_data = [2 / 3, 0.5, 0.4, 0.5, 0.428571, 0.5, 0.571429, 0.5] + else: + expected_data = [np.nan, 0, 0.5, 2 / 3, 0.5, 0.4, 0.5, 0.428571] + + expected = DataFrame( + expected_data, + columns=["binary_col"], + index=date_range(start="2020-01-01", freq="min", periods=len(expected_data)), + )[::step] + + rolling = df.rolling( + window=len(df), closed="left", min_periods=1, center=center, step=step + ) + result = rolling.mean() + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("closed", ["neither", "left"]) +def test_closed_empty(closed, arithmetic_win_operators): + # GH 26005 + func_name = arithmetic_win_operators + ser = Series(data=np.arange(5), index=date_range("2000", periods=5, freq="2D")) + roll = ser.rolling("1D", closed=closed) + + result = getattr(roll, func_name)() + expected = Series([np.nan] * 5, index=ser.index) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("func", ["min", "max"]) +def test_closed_one_entry(func): + # GH24718 + ser = Series(data=[2], index=date_range("2000", periods=1)) + result = getattr(ser.rolling("10D", closed="left"), func)() + tm.assert_series_equal(result, Series([np.nan], index=ser.index)) + + +@pytest.mark.parametrize("func", ["min", "max"]) +def test_closed_one_entry_groupby(func): + # GH24718 + ser = DataFrame( + data={"A": [1, 1, 2], "B": [3, 2, 1]}, + index=date_range("2000", periods=3), + ) + result = getattr( + ser.groupby("A", sort=False)["B"].rolling("10D", closed="left"), func + )() + exp_idx = MultiIndex.from_arrays(arrays=[[1, 1, 2], ser.index], names=("A", None)) + expected = Series(data=[np.nan, 3, np.nan], index=exp_idx, name="B") + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("input_dtype", ["int", "float"]) +@pytest.mark.parametrize( + "func,closed,expected", + [ + ("min", "right", [0.0, 0, 0, 1, 2, 3, 4, 5, 6, 7]), + ("min", "both", [0.0, 0, 0, 0, 1, 2, 3, 4, 5, 6]), + ("min", "neither", [np.nan, 0, 0, 1, 2, 3, 4, 5, 6, 7]), + ("min", "left", [np.nan, 0, 0, 0, 1, 2, 3, 4, 5, 6]), + ("max", "right", [0.0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), + ("max", "both", [0.0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), + ("max", "neither", [np.nan, 0, 1, 2, 3, 4, 5, 6, 7, 8]), + ("max", "left", [np.nan, 0, 1, 2, 3, 4, 5, 6, 7, 8]), + ], +) +def test_closed_min_max_datetime(input_dtype, func, closed, expected): + # see gh-21704 + ser = Series( + data=np.arange(10).astype(input_dtype), + index=date_range("2000", periods=10), + ) + + result = getattr(ser.rolling("3D", closed=closed), func)() + expected = Series(expected, index=ser.index) + tm.assert_series_equal(result, expected) + + +def test_closed_uneven(): + # see gh-21704 + ser = Series(data=np.arange(10), index=date_range("2000", periods=10)) + + # uneven + ser = ser.drop(index=ser.index[[1, 5]]) + result = ser.rolling("3D", closed="left").min() + expected = Series([np.nan, 0, 0, 2, 3, 4, 6, 6], index=ser.index) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "func,closed,expected", + [ + ("min", "right", [np.nan, 0, 0, 1, 2, 3, 4, 5, np.nan, np.nan]), + ("min", "both", [np.nan, 0, 0, 0, 1, 2, 3, 4, 5, np.nan]), + ("min", "neither", [np.nan, np.nan, 0, 1, 2, 3, 4, 5, np.nan, np.nan]), + ("min", "left", [np.nan, np.nan, 0, 0, 1, 2, 3, 4, 5, np.nan]), + ("max", "right", [np.nan, 1, 2, 3, 4, 5, 6, 6, np.nan, np.nan]), + ("max", "both", [np.nan, 1, 2, 3, 4, 5, 6, 6, 6, np.nan]), + ("max", "neither", [np.nan, np.nan, 1, 2, 3, 4, 5, 6, np.nan, np.nan]), + ("max", "left", [np.nan, np.nan, 1, 2, 3, 4, 5, 6, 6, np.nan]), + ], +) +def test_closed_min_max_minp(func, closed, expected): + # see gh-21704 + ser = Series(data=np.arange(10), index=date_range("2000", periods=10)) + # Explicit cast to float to avoid implicit cast when setting nan + ser = ser.astype("float") + ser[ser.index[-3:]] = np.nan + result = getattr(ser.rolling("3D", min_periods=2, closed=closed), func)() + expected = Series(expected, index=ser.index) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "closed,expected", + [ + ("right", [0, 0.5, 1, 2, 3, 4, 5, 6, 7, 8]), + ("both", [0, 0.5, 1, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5]), + ("neither", [np.nan, 0, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5]), + ("left", [np.nan, 0, 0.5, 1, 2, 3, 4, 5, 6, 7]), + ], +) +def test_closed_median_quantile(closed, expected): + # GH 26005 + ser = Series(data=np.arange(10), index=date_range("2000", periods=10)) + roll = ser.rolling("3D", closed=closed) + expected = Series(expected, index=ser.index) + + result = roll.median() + tm.assert_series_equal(result, expected) + + result = roll.quantile(0.5) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("roller", ["1s", 1]) +def tests_empty_df_rolling(roller): + # GH 15819 Verifies that datetime and integer rolling windows can be + # applied to empty DataFrames + expected = DataFrame() + result = DataFrame().rolling(roller).sum() + tm.assert_frame_equal(result, expected) + + # Verifies that datetime and integer rolling windows can be applied to + # empty DataFrames with datetime index + expected = DataFrame(index=DatetimeIndex([])) + result = DataFrame(index=DatetimeIndex([])).rolling(roller).sum() + tm.assert_frame_equal(result, expected) + + +def test_empty_window_median_quantile(): + # GH 26005 + expected = Series([np.nan, np.nan, np.nan]) + roll = Series(np.arange(3)).rolling(0) + + result = roll.median() + tm.assert_series_equal(result, expected) + + result = roll.quantile(0.1) + tm.assert_series_equal(result, expected) + + +def test_missing_minp_zero(): + # https://github.com/pandas-dev/pandas/pull/18921 + # minp=0 + x = Series([np.nan]) + result = x.rolling(1, min_periods=0).sum() + expected = Series([0.0]) + tm.assert_series_equal(result, expected) + + # minp=1 + result = x.rolling(1, min_periods=1).sum() + expected = Series([np.nan]) + tm.assert_series_equal(result, expected) + + +def test_missing_minp_zero_variable(): + # https://github.com/pandas-dev/pandas/pull/18921 + x = Series( + [np.nan] * 4, + index=DatetimeIndex(["2017-01-01", "2017-01-04", "2017-01-06", "2017-01-07"]), + ) + result = x.rolling(Timedelta("2d"), min_periods=0).sum() + expected = Series(0.0, index=x.index) + tm.assert_series_equal(result, expected) + + +def test_multi_index_names(): + # GH 16789, 16825 + cols = MultiIndex.from_product([["A", "B"], ["C", "D", "E"]], names=["1", "2"]) + df = DataFrame(np.ones((10, 6)), columns=cols) + result = df.rolling(3).cov() + + tm.assert_index_equal(result.columns, df.columns) + assert result.index.names == [None, "1", "2"] + + +def test_rolling_axis_sum(axis_frame): + # see gh-23372. + df = DataFrame(np.ones((10, 20))) + axis = df._get_axis_number(axis_frame) + + if axis == 0: + msg = "The 'axis' keyword in DataFrame.rolling" + expected = DataFrame({i: [np.nan] * 2 + [3.0] * 8 for i in range(20)}) + else: + # axis == 1 + msg = "Support for axis=1 in DataFrame.rolling is deprecated" + expected = DataFrame([[np.nan] * 2 + [3.0] * 18] * 10) + + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.rolling(3, axis=axis_frame).sum() + tm.assert_frame_equal(result, expected) + + +def test_rolling_axis_count(axis_frame): + # see gh-26055 + df = DataFrame({"x": range(3), "y": range(3)}) + + axis = df._get_axis_number(axis_frame) + + if axis in [0, "index"]: + msg = "The 'axis' keyword in DataFrame.rolling" + expected = DataFrame({"x": [1.0, 2.0, 2.0], "y": [1.0, 2.0, 2.0]}) + else: + msg = "Support for axis=1 in DataFrame.rolling is deprecated" + expected = DataFrame({"x": [1.0, 1.0, 1.0], "y": [2.0, 2.0, 2.0]}) + + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.rolling(2, axis=axis_frame, min_periods=0).count() + tm.assert_frame_equal(result, expected) + + +def test_readonly_array(): + # GH-27766 + arr = np.array([1, 3, np.nan, 3, 5]) + arr.setflags(write=False) + result = Series(arr).rolling(2).mean() + expected = Series([np.nan, 2, np.nan, np.nan, 4]) + tm.assert_series_equal(result, expected) + + +def test_rolling_datetime(axis_frame, tz_naive_fixture): + # GH-28192 + tz = tz_naive_fixture + df = DataFrame( + {i: [1] * 2 for i in date_range("2019-8-01", "2019-08-03", freq="D", tz=tz)} + ) + + if axis_frame in [0, "index"]: + msg = "The 'axis' keyword in DataFrame.rolling" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.T.rolling("2D", axis=axis_frame).sum().T + else: + msg = "Support for axis=1 in DataFrame.rolling" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.rolling("2D", axis=axis_frame).sum() + expected = DataFrame( + { + **{ + i: [1.0] * 2 + for i in date_range("2019-8-01", periods=1, freq="D", tz=tz) + }, + **{ + i: [2.0] * 2 + for i in date_range("2019-8-02", "2019-8-03", freq="D", tz=tz) + }, + } + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("center", [True, False]) +def test_rolling_window_as_string(center): + # see gh-22590 + date_today = datetime.now() + days = date_range(date_today, date_today + timedelta(365), freq="D") + + data = np.ones(len(days)) + df = DataFrame({"DateCol": days, "metric": data}) + + df.set_index("DateCol", inplace=True) + result = df.rolling(window="21D", min_periods=2, closed="left", center=center)[ + "metric" + ].agg("max") + + index = days.rename("DateCol") + index = index._with_freq(None) + expected_data = np.ones(len(days), dtype=np.float64) + if not center: + expected_data[:2] = np.nan + expected = Series(expected_data, index=index, name="metric") + tm.assert_series_equal(result, expected) + + +def test_min_periods1(): + # GH#6795 + df = DataFrame([0, 1, 2, 1, 0], columns=["a"]) + result = df["a"].rolling(3, center=True, min_periods=1).max() + expected = Series([1.0, 2.0, 2.0, 2.0, 1.0], name="a") + tm.assert_series_equal(result, expected) + + +def test_rolling_count_with_min_periods(frame_or_series): + # GH 26996 + result = frame_or_series(range(5)).rolling(3, min_periods=3).count() + expected = frame_or_series([np.nan, np.nan, 3.0, 3.0, 3.0]) + tm.assert_equal(result, expected) + + +def test_rolling_count_default_min_periods_with_null_values(frame_or_series): + # GH 26996 + values = [1, 2, 3, np.nan, 4, 5, 6] + expected_counts = [1.0, 2.0, 3.0, 2.0, 2.0, 2.0, 3.0] + + # GH 31302 + result = frame_or_series(values).rolling(3, min_periods=0).count() + expected = frame_or_series(expected_counts) + tm.assert_equal(result, expected) + + +@pytest.mark.parametrize( + "df,expected,window,min_periods", + [ + ( + DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}), + [ + ({"A": [1], "B": [4]}, [0]), + ({"A": [1, 2], "B": [4, 5]}, [0, 1]), + ({"A": [1, 2, 3], "B": [4, 5, 6]}, [0, 1, 2]), + ], + 3, + None, + ), + ( + DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}), + [ + ({"A": [1], "B": [4]}, [0]), + ({"A": [1, 2], "B": [4, 5]}, [0, 1]), + ({"A": [2, 3], "B": [5, 6]}, [1, 2]), + ], + 2, + 1, + ), + ( + DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}), + [ + ({"A": [1], "B": [4]}, [0]), + ({"A": [1, 2], "B": [4, 5]}, [0, 1]), + ({"A": [2, 3], "B": [5, 6]}, [1, 2]), + ], + 2, + 2, + ), + ( + DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}), + [ + ({"A": [1], "B": [4]}, [0]), + ({"A": [2], "B": [5]}, [1]), + ({"A": [3], "B": [6]}, [2]), + ], + 1, + 1, + ), + ( + DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}), + [ + ({"A": [1], "B": [4]}, [0]), + ({"A": [2], "B": [5]}, [1]), + ({"A": [3], "B": [6]}, [2]), + ], + 1, + 0, + ), + (DataFrame({"A": [1], "B": [4]}), [], 2, None), + (DataFrame({"A": [1], "B": [4]}), [], 2, 1), + (DataFrame(), [({}, [])], 2, None), + ( + DataFrame({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}), + [ + ({"A": [1.0], "B": [np.nan]}, [0]), + ({"A": [1, np.nan], "B": [np.nan, 5]}, [0, 1]), + ({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}, [0, 1, 2]), + ], + 3, + 2, + ), + ], +) +def test_iter_rolling_dataframe(df, expected, window, min_periods): + # GH 11704 + expected = [DataFrame(values, index=index) for (values, index) in expected] + + for expected, actual in zip(expected, df.rolling(window, min_periods=min_periods)): + tm.assert_frame_equal(actual, expected) + + +@pytest.mark.parametrize( + "expected,window", + [ + ( + [ + ({"A": [1], "B": [4]}, [0]), + ({"A": [1, 2], "B": [4, 5]}, [0, 1]), + ({"A": [2, 3], "B": [5, 6]}, [1, 2]), + ], + "2D", + ), + ( + [ + ({"A": [1], "B": [4]}, [0]), + ({"A": [1, 2], "B": [4, 5]}, [0, 1]), + ({"A": [1, 2, 3], "B": [4, 5, 6]}, [0, 1, 2]), + ], + "3D", + ), + ( + [ + ({"A": [1], "B": [4]}, [0]), + ({"A": [2], "B": [5]}, [1]), + ({"A": [3], "B": [6]}, [2]), + ], + "1D", + ), + ], +) +def test_iter_rolling_on_dataframe(expected, window): + # GH 11704, 40373 + df = DataFrame( + { + "A": [1, 2, 3, 4, 5], + "B": [4, 5, 6, 7, 8], + "C": date_range(start="2016-01-01", periods=5, freq="D"), + } + ) + + expected = [ + DataFrame(values, index=df.loc[index, "C"]) for (values, index) in expected + ] + for expected, actual in zip(expected, df.rolling(window, on="C")): + tm.assert_frame_equal(actual, expected) + + +def test_iter_rolling_on_dataframe_unordered(): + # GH 43386 + df = DataFrame({"a": ["x", "y", "x"], "b": [0, 1, 2]}) + results = list(df.groupby("a").rolling(2)) + expecteds = [df.iloc[idx, [1]] for idx in [[0], [0, 2], [1]]] + for result, expected in zip(results, expecteds): + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "ser,expected,window, min_periods", + [ + ( + Series([1, 2, 3]), + [([1], [0]), ([1, 2], [0, 1]), ([1, 2, 3], [0, 1, 2])], + 3, + None, + ), + ( + Series([1, 2, 3]), + [([1], [0]), ([1, 2], [0, 1]), ([1, 2, 3], [0, 1, 2])], + 3, + 1, + ), + ( + Series([1, 2, 3]), + [([1], [0]), ([1, 2], [0, 1]), ([2, 3], [1, 2])], + 2, + 1, + ), + ( + Series([1, 2, 3]), + [([1], [0]), ([1, 2], [0, 1]), ([2, 3], [1, 2])], + 2, + 2, + ), + (Series([1, 2, 3]), [([1], [0]), ([2], [1]), ([3], [2])], 1, 0), + (Series([1, 2, 3]), [([1], [0]), ([2], [1]), ([3], [2])], 1, 1), + (Series([1, 2]), [([1], [0]), ([1, 2], [0, 1])], 2, 0), + (Series([], dtype="int64"), [], 2, 1), + ], +) +def test_iter_rolling_series(ser, expected, window, min_periods): + # GH 11704 + expected = [Series(values, index=index) for (values, index) in expected] + + for expected, actual in zip(expected, ser.rolling(window, min_periods=min_periods)): + tm.assert_series_equal(actual, expected) + + +@pytest.mark.parametrize( + "expected,expected_index,window", + [ + ( + [[0], [1], [2], [3], [4]], + [ + date_range("2020-01-01", periods=1, freq="D"), + date_range("2020-01-02", periods=1, freq="D"), + date_range("2020-01-03", periods=1, freq="D"), + date_range("2020-01-04", periods=1, freq="D"), + date_range("2020-01-05", periods=1, freq="D"), + ], + "1D", + ), + ( + [[0], [0, 1], [1, 2], [2, 3], [3, 4]], + [ + date_range("2020-01-01", periods=1, freq="D"), + date_range("2020-01-01", periods=2, freq="D"), + date_range("2020-01-02", periods=2, freq="D"), + date_range("2020-01-03", periods=2, freq="D"), + date_range("2020-01-04", periods=2, freq="D"), + ], + "2D", + ), + ( + [[0], [0, 1], [0, 1, 2], [1, 2, 3], [2, 3, 4]], + [ + date_range("2020-01-01", periods=1, freq="D"), + date_range("2020-01-01", periods=2, freq="D"), + date_range("2020-01-01", periods=3, freq="D"), + date_range("2020-01-02", periods=3, freq="D"), + date_range("2020-01-03", periods=3, freq="D"), + ], + "3D", + ), + ], +) +def test_iter_rolling_datetime(expected, expected_index, window): + # GH 11704 + ser = Series(range(5), index=date_range(start="2020-01-01", periods=5, freq="D")) + + expected = [ + Series(values, index=idx) for (values, idx) in zip(expected, expected_index) + ] + + for expected, actual in zip(expected, ser.rolling(window)): + tm.assert_series_equal(actual, expected) + + +@pytest.mark.parametrize( + "grouping,_index", + [ + ( + {"level": 0}, + MultiIndex.from_tuples( + [(0, 0), (0, 0), (1, 1), (1, 1), (1, 1)], names=[None, None] + ), + ), + ( + {"by": "X"}, + MultiIndex.from_tuples( + [(0, 0), (1, 0), (2, 1), (3, 1), (4, 1)], names=["X", None] + ), + ), + ], +) +def test_rolling_positional_argument(grouping, _index, raw): + # GH 34605 + + def scaled_sum(*args): + if len(args) < 2: + raise ValueError("The function needs two arguments") + array, scale = args + return array.sum() / scale + + df = DataFrame(data={"X": range(5)}, index=[0, 0, 1, 1, 1]) + + expected = DataFrame(data={"X": [0.0, 0.5, 1.0, 1.5, 2.0]}, index=_index) + # GH 40341 + if "by" in grouping: + expected = expected.drop(columns="X", errors="ignore") + result = df.groupby(**grouping).rolling(1).apply(scaled_sum, raw=raw, args=(2,)) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("add", [0.0, 2.0]) +def test_rolling_numerical_accuracy_kahan_mean(add, unit): + # GH: 36031 implementing kahan summation + dti = DatetimeIndex( + [ + Timestamp("19700101 09:00:00"), + Timestamp("19700101 09:00:03"), + Timestamp("19700101 09:00:06"), + ] + ).as_unit(unit) + df = DataFrame( + {"A": [3002399751580331.0 + add, -0.0, -0.0]}, + index=dti, + ) + result = ( + df.resample("1s").ffill().rolling("3s", closed="left", min_periods=3).mean() + ) + dates = date_range("19700101 09:00:00", periods=7, freq="s", unit=unit) + expected = DataFrame( + { + "A": [ + np.nan, + np.nan, + np.nan, + 3002399751580330.5, + 2001599834386887.25, + 1000799917193443.625, + 0.0, + ] + }, + index=dates, + ) + tm.assert_frame_equal(result, expected) + + +def test_rolling_numerical_accuracy_kahan_sum(): + # GH: 13254 + df = DataFrame([2.186, -1.647, 0.0, 0.0, 0.0, 0.0], columns=["x"]) + result = df["x"].rolling(3).sum() + expected = Series([np.nan, np.nan, 0.539, -1.647, 0.0, 0.0], name="x") + tm.assert_series_equal(result, expected) + + +def test_rolling_numerical_accuracy_jump(): + # GH: 32761 + index = date_range(start="2020-01-01", end="2020-01-02", freq="60s").append( + DatetimeIndex(["2020-01-03"]) + ) + data = np.random.default_rng(2).random(len(index)) + + df = DataFrame({"data": data}, index=index) + result = df.rolling("60s").mean() + tm.assert_frame_equal(result, df[["data"]]) + + +def test_rolling_numerical_accuracy_small_values(): + # GH: 10319 + s = Series( + data=[0.00012456, 0.0003, -0.0, -0.0], + index=date_range("1999-02-03", "1999-02-06"), + ) + result = s.rolling(1).mean() + tm.assert_series_equal(result, s) + + +def test_rolling_numerical_too_large_numbers(): + # GH: 11645 + dates = date_range("2015-01-01", periods=10, freq="D") + ds = Series(data=range(10), index=dates, dtype=np.float64) + ds.iloc[2] = -9e33 + result = ds.rolling(5).mean() + expected = Series( + [ + np.nan, + np.nan, + np.nan, + np.nan, + -1.8e33, + -1.8e33, + -1.8e33, + 5.0, + 6.0, + 7.0, + ], + index=dates, + ) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + ("func", "value"), + [("sum", 2.0), ("max", 1.0), ("min", 1.0), ("mean", 1.0), ("median", 1.0)], +) +def test_rolling_mixed_dtypes_axis_1(func, value): + # GH: 20649 + df = DataFrame(1, index=[1, 2], columns=["a", "b", "c"]) + df["c"] = 1.0 + msg = "Support for axis=1 in DataFrame.rolling is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + roll = df.rolling(window=2, min_periods=1, axis=1) + result = getattr(roll, func)() + expected = DataFrame( + {"a": [1.0, 1.0], "b": [value, value], "c": [value, value]}, + index=[1, 2], + ) + tm.assert_frame_equal(result, expected) + + +def test_rolling_axis_one_with_nan(): + # GH: 35596 + df = DataFrame( + [ + [0, 1, 2, 4, np.nan, np.nan, np.nan], + [0, 1, 2, np.nan, np.nan, np.nan, np.nan], + [0, 2, 2, np.nan, 2, np.nan, 1], + ] + ) + msg = "Support for axis=1 in DataFrame.rolling is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.rolling(window=7, min_periods=1, axis="columns").sum() + expected = DataFrame( + [ + [0.0, 1.0, 3.0, 7.0, 7.0, 7.0, 7.0], + [0.0, 1.0, 3.0, 3.0, 3.0, 3.0, 3.0], + [0.0, 2.0, 4.0, 4.0, 6.0, 6.0, 7.0], + ] + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "value", + ["test", to_datetime("2019-12-31"), to_timedelta("1 days 06:05:01.00003")], +) +def test_rolling_axis_1_non_numeric_dtypes(value): + # GH: 20649 + df = DataFrame({"a": [1, 2]}) + df["b"] = value + msg = "Support for axis=1 in DataFrame.rolling is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.rolling(window=2, min_periods=1, axis=1).sum() + expected = DataFrame({"a": [1.0, 2.0]}) + tm.assert_frame_equal(result, expected) + + +def test_rolling_on_df_transposed(): + # GH: 32724 + df = DataFrame({"A": [1, None], "B": [4, 5], "C": [7, 8]}) + expected = DataFrame({"A": [1.0, np.nan], "B": [5.0, 5.0], "C": [11.0, 13.0]}) + msg = "Support for axis=1 in DataFrame.rolling is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.rolling(min_periods=1, window=2, axis=1).sum() + tm.assert_frame_equal(result, expected) + + result = df.T.rolling(min_periods=1, window=2).sum().T + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + ("index", "window"), + [ + ( + period_range(start="2020-01-01 08:00", end="2020-01-01 08:08", freq="min"), + "2min", + ), + ( + period_range( + start="2020-01-01 08:00", end="2020-01-01 12:00", freq="30min" + ), + "1h", + ), + ], +) +@pytest.mark.parametrize( + ("func", "values"), + [ + ("min", [np.nan, 0, 0, 1, 2, 3, 4, 5, 6]), + ("max", [np.nan, 0, 1, 2, 3, 4, 5, 6, 7]), + ("sum", [np.nan, 0, 1, 3, 5, 7, 9, 11, 13]), + ], +) +def test_rolling_period_index(index, window, func, values): + # GH: 34225 + ds = Series([0, 1, 2, 3, 4, 5, 6, 7, 8], index=index) + result = getattr(ds.rolling(window, closed="left"), func)() + expected = Series(values, index=index) + tm.assert_series_equal(result, expected) + + +def test_rolling_sem(frame_or_series): + # GH: 26476 + obj = frame_or_series([0, 1, 2]) + result = obj.rolling(2, min_periods=1).sem() + if isinstance(result, DataFrame): + result = Series(result[0].values) + expected = Series([np.nan] + [0.7071067811865476] * 2) + tm.assert_series_equal(result, expected) + + +@pytest.mark.xfail( + is_platform_arm() or is_platform_power(), + reason="GH 38921", +) +@pytest.mark.parametrize( + ("func", "third_value", "values"), + [ + ("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]), + ("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]), + ("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]), + ("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]), + ], +) +def test_rolling_var_numerical_issues(func, third_value, values): + # GH: 37051 + ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1]) + result = getattr(ds.rolling(2), func)() + expected = Series([np.nan] + values) + tm.assert_series_equal(result, expected) + # GH 42064 + # new `roll_var` will output 0.0 correctly + tm.assert_series_equal(result == 0, expected == 0) + + +def test_timeoffset_as_window_parameter_for_corr(unit): + # GH: 28266 + dti = DatetimeIndex( + [ + Timestamp("20130101 09:00:00"), + Timestamp("20130102 09:00:02"), + Timestamp("20130103 09:00:03"), + Timestamp("20130105 09:00:05"), + Timestamp("20130106 09:00:06"), + ] + ).as_unit(unit) + mi = MultiIndex.from_product([dti, ["B", "A"]]) + + exp = DataFrame( + { + "B": [ + np.nan, + np.nan, + 0.9999999999999998, + -1.0, + 1.0, + -0.3273268353539892, + 0.9999999999999998, + 1.0, + 0.9999999999999998, + 1.0, + ], + "A": [ + np.nan, + np.nan, + -1.0, + 1.0000000000000002, + -0.3273268353539892, + 0.9999999999999966, + 1.0, + 1.0000000000000002, + 1.0, + 1.0000000000000002, + ], + }, + index=mi, + ) + + df = DataFrame( + {"B": [0, 1, 2, 4, 3], "A": [7, 4, 6, 9, 3]}, + index=dti, + ) + + res = df.rolling(window="3d").corr() + + tm.assert_frame_equal(exp, res) + + +@pytest.mark.parametrize("method", ["var", "sum", "mean", "skew", "kurt", "min", "max"]) +def test_rolling_decreasing_indices(method): + """ + Make sure that decreasing indices give the same results as increasing indices. + + GH 36933 + """ + df = DataFrame({"values": np.arange(-15, 10) ** 2}) + df_reverse = DataFrame({"values": df["values"][::-1]}, index=df.index[::-1]) + + increasing = getattr(df.rolling(window=5), method)() + decreasing = getattr(df_reverse.rolling(window=5), method)() + + assert np.abs(decreasing.values[::-1][:-4] - increasing.values[4:]).max() < 1e-12 + + +@pytest.mark.parametrize( + "window,closed,expected", + [ + ("2s", "right", [1.0, 3.0, 5.0, 3.0]), + ("2s", "left", [0.0, 1.0, 3.0, 5.0]), + ("2s", "both", [1.0, 3.0, 6.0, 5.0]), + ("2s", "neither", [0.0, 1.0, 2.0, 3.0]), + ("3s", "right", [1.0, 3.0, 6.0, 5.0]), + ("3s", "left", [1.0, 3.0, 6.0, 5.0]), + ("3s", "both", [1.0, 3.0, 6.0, 5.0]), + ("3s", "neither", [1.0, 3.0, 6.0, 5.0]), + ], +) +def test_rolling_decreasing_indices_centered(window, closed, expected, frame_or_series): + """ + Ensure that a symmetrical inverted index return same result as non-inverted. + """ + # GH 43927 + + index = date_range("2020", periods=4, freq="1s") + df_inc = frame_or_series(range(4), index=index) + df_dec = frame_or_series(range(4), index=index[::-1]) + + expected_inc = frame_or_series(expected, index=index) + expected_dec = frame_or_series(expected, index=index[::-1]) + + result_inc = df_inc.rolling(window, closed=closed, center=True).sum() + result_dec = df_dec.rolling(window, closed=closed, center=True).sum() + + tm.assert_equal(result_inc, expected_inc) + tm.assert_equal(result_dec, expected_dec) + + +@pytest.mark.parametrize( + "window,expected", + [ + ("1ns", [1.0, 1.0, 1.0, 1.0]), + ("3ns", [2.0, 3.0, 3.0, 2.0]), + ], +) +def test_rolling_center_nanosecond_resolution( + window, closed, expected, frame_or_series +): + index = date_range("2020", periods=4, freq="1ns") + df = frame_or_series([1, 1, 1, 1], index=index, dtype=float) + expected = frame_or_series(expected, index=index, dtype=float) + result = df.rolling(window, closed=closed, center=True).sum() + tm.assert_equal(result, expected) + + +@pytest.mark.parametrize( + "method,expected", + [ + ( + "var", + [ + float("nan"), + 43.0, + float("nan"), + 136.333333, + 43.5, + 94.966667, + 182.0, + 318.0, + ], + ), + ( + "mean", + [float("nan"), 7.5, float("nan"), 21.5, 6.0, 9.166667, 13.0, 17.5], + ), + ( + "sum", + [float("nan"), 30.0, float("nan"), 86.0, 30.0, 55.0, 91.0, 140.0], + ), + ( + "skew", + [ + float("nan"), + 0.709296, + float("nan"), + 0.407073, + 0.984656, + 0.919184, + 0.874674, + 0.842418, + ], + ), + ( + "kurt", + [ + float("nan"), + -0.5916711736073559, + float("nan"), + -1.0028993131317954, + -0.06103844629409494, + -0.254143227116194, + -0.37362637362637585, + -0.45439658241367054, + ], + ), + ], +) +def test_rolling_non_monotonic(method, expected): + """ + Make sure the (rare) branch of non-monotonic indices is covered by a test. + + output from 1.1.3 is assumed to be the expected output. Output of sum/mean has + manually been verified. + + GH 36933. + """ + # Based on an example found in computation.rst + use_expanding = [True, False, True, False, True, True, True, True] + df = DataFrame({"values": np.arange(len(use_expanding)) ** 2}) + + class CustomIndexer(BaseIndexer): + def get_window_bounds(self, num_values, min_periods, center, closed, step): + start = np.empty(num_values, dtype=np.int64) + end = np.empty(num_values, dtype=np.int64) + for i in range(num_values): + if self.use_expanding[i]: + start[i] = 0 + end[i] = i + 1 + else: + start[i] = i + end[i] = i + self.window_size + return start, end + + indexer = CustomIndexer(window_size=4, use_expanding=use_expanding) + + result = getattr(df.rolling(indexer), method)() + expected = DataFrame({"values": expected}) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + ("index", "window"), + [ + ([0, 1, 2, 3, 4], 2), + (date_range("2001-01-01", freq="D", periods=5), "2D"), + ], +) +def test_rolling_corr_timedelta_index(index, window): + # GH: 31286 + x = Series([1, 2, 3, 4, 5], index=index) + y = x.copy() + x.iloc[0:2] = 0.0 + result = x.rolling(window).corr(y) + expected = Series([np.nan, np.nan, 1, 1, 1], index=index) + tm.assert_almost_equal(result, expected) + + +def test_groupby_rolling_nan_included(): + # GH 35542 + data = {"group": ["g1", np.nan, "g1", "g2", np.nan], "B": [0, 1, 2, 3, 4]} + df = DataFrame(data) + result = df.groupby("group", dropna=False).rolling(1, min_periods=1).mean() + expected = DataFrame( + {"B": [0.0, 2.0, 3.0, 1.0, 4.0]}, + # GH-38057 from_tuples puts the NaNs in the codes, result expects them + # to be in the levels, at the moment + # index=MultiIndex.from_tuples( + # [("g1", 0), ("g1", 2), ("g2", 3), (np.nan, 1), (np.nan, 4)], + # names=["group", None], + # ), + index=MultiIndex( + [["g1", "g2", np.nan], [0, 1, 2, 3, 4]], + [[0, 0, 1, 2, 2], [0, 2, 3, 1, 4]], + names=["group", None], + ), + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("method", ["skew", "kurt"]) +def test_rolling_skew_kurt_numerical_stability(method): + # GH#6929 + ser = Series(np.random.default_rng(2).random(10)) + ser_copy = ser.copy() + expected = getattr(ser.rolling(3), method)() + tm.assert_series_equal(ser, ser_copy) + ser = ser + 50000 + result = getattr(ser.rolling(3), method)() + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + ("method", "values"), + [ + ("skew", [2.0, 0.854563, 0.0, 1.999984]), + ("kurt", [4.0, -1.289256, -1.2, 3.999946]), + ], +) +def test_rolling_skew_kurt_large_value_range(method, values): + # GH: 37557 + s = Series([3000000, 1, 1, 2, 3, 4, 999]) + result = getattr(s.rolling(4), method)() + expected = Series([np.nan] * 3 + values) + tm.assert_series_equal(result, expected) + + +def test_invalid_method(): + with pytest.raises(ValueError, match="method must be 'table' or 'single"): + Series(range(1)).rolling(1, method="foo") + + +@pytest.mark.parametrize("window", [1, "1d"]) +def test_rolling_descending_date_order_with_offset(window, frame_or_series): + # GH#40002 + idx = date_range(start="2020-01-01", end="2020-01-03", freq="1d") + obj = frame_or_series(range(1, 4), index=idx) + result = obj.rolling("1d", closed="left").sum() + expected = frame_or_series([np.nan, 1, 2], index=idx) + tm.assert_equal(result, expected) + + result = obj.iloc[::-1].rolling("1d", closed="left").sum() + idx = date_range(start="2020-01-03", end="2020-01-01", freq="-1d") + expected = frame_or_series([np.nan, 3, 2], index=idx) + tm.assert_equal(result, expected) + + +def test_rolling_var_floating_artifact_precision(): + # GH 37051 + s = Series([7, 5, 5, 5]) + result = s.rolling(3).var() + expected = Series([np.nan, np.nan, 4 / 3, 0]) + tm.assert_series_equal(result, expected, atol=1.0e-15, rtol=1.0e-15) + # GH 42064 + # new `roll_var` will output 0.0 correctly + tm.assert_series_equal(result == 0, expected == 0) + + +def test_rolling_std_small_values(): + # GH 37051 + s = Series( + [ + 0.00000054, + 0.00000053, + 0.00000054, + ] + ) + result = s.rolling(2).std() + expected = Series([np.nan, 7.071068e-9, 7.071068e-9]) + tm.assert_series_equal(result, expected, atol=1.0e-15, rtol=1.0e-15) + + +@pytest.mark.parametrize( + "start, exp_values", + [ + (1, [0.03, 0.0155, 0.0155, 0.011, 0.01025]), + (2, [0.001, 0.001, 0.0015, 0.00366666]), + ], +) +def test_rolling_mean_all_nan_window_floating_artifacts(start, exp_values): + # GH#41053 + df = DataFrame( + [ + 0.03, + 0.03, + 0.001, + np.nan, + 0.002, + 0.008, + np.nan, + np.nan, + np.nan, + np.nan, + np.nan, + np.nan, + 0.005, + 0.2, + ] + ) + + values = exp_values + [ + 0.00366666, + 0.005, + 0.005, + 0.008, + np.nan, + np.nan, + 0.005, + 0.102500, + ] + expected = DataFrame( + values, + index=list(range(start, len(values) + start)), + ) + result = df.iloc[start:].rolling(5, min_periods=0).mean() + tm.assert_frame_equal(result, expected) + + +def test_rolling_sum_all_nan_window_floating_artifacts(): + # GH#41053 + df = DataFrame([0.002, 0.008, 0.005, np.nan, np.nan, np.nan]) + result = df.rolling(3, min_periods=0).sum() + expected = DataFrame([0.002, 0.010, 0.015, 0.013, 0.005, 0.0]) + tm.assert_frame_equal(result, expected) + + +def test_rolling_zero_window(): + # GH 22719 + s = Series(range(1)) + result = s.rolling(0).min() + expected = Series([np.nan]) + tm.assert_series_equal(result, expected) + + +def test_rolling_float_dtype(float_numpy_dtype): + # GH#42452 + df = DataFrame({"A": range(5), "B": range(10, 15)}, dtype=float_numpy_dtype) + expected = DataFrame( + {"A": [np.nan] * 5, "B": range(10, 20, 2)}, + dtype=float_numpy_dtype, + ) + msg = "Support for axis=1 in DataFrame.rolling is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.rolling(2, axis=1).sum() + tm.assert_frame_equal(result, expected, check_dtype=False) + + +def test_rolling_numeric_dtypes(): + # GH#41779 + df = DataFrame(np.arange(40).reshape(4, 10), columns=list("abcdefghij")).astype( + { + "a": "float16", + "b": "float32", + "c": "float64", + "d": "int8", + "e": "int16", + "f": "int32", + "g": "uint8", + "h": "uint16", + "i": "uint32", + "j": "uint64", + } + ) + msg = "Support for axis=1 in DataFrame.rolling is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.rolling(window=2, min_periods=1, axis=1).min() + expected = DataFrame( + { + "a": range(0, 40, 10), + "b": range(0, 40, 10), + "c": range(1, 40, 10), + "d": range(2, 40, 10), + "e": range(3, 40, 10), + "f": range(4, 40, 10), + "g": range(5, 40, 10), + "h": range(6, 40, 10), + "i": range(7, 40, 10), + "j": range(8, 40, 10), + }, + dtype="float64", + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("window", [1, 3, 10, 20]) +@pytest.mark.parametrize("method", ["min", "max", "average"]) +@pytest.mark.parametrize("pct", [True, False]) +@pytest.mark.parametrize("ascending", [True, False]) +@pytest.mark.parametrize("test_data", ["default", "duplicates", "nans"]) +def test_rank(window, method, pct, ascending, test_data): + length = 20 + if test_data == "default": + ser = Series(data=np.random.default_rng(2).random(length)) + elif test_data == "duplicates": + ser = Series(data=np.random.default_rng(2).choice(3, length)) + elif test_data == "nans": + ser = Series( + data=np.random.default_rng(2).choice( + [1.0, 0.25, 0.75, np.nan, np.inf, -np.inf], length + ) + ) + + expected = ser.rolling(window).apply( + lambda x: x.rank(method=method, pct=pct, ascending=ascending).iloc[-1] + ) + result = ser.rolling(window).rank(method=method, pct=pct, ascending=ascending) + + tm.assert_series_equal(result, expected) + + +def test_rolling_quantile_np_percentile(): + # #9413: Tests that rolling window's quantile default behavior + # is analogous to Numpy's percentile + row = 10 + col = 5 + idx = date_range("20100101", periods=row, freq="B") + df = DataFrame( + np.random.default_rng(2).random(row * col).reshape((row, -1)), index=idx + ) + + df_quantile = df.quantile([0.25, 0.5, 0.75], axis=0) + np_percentile = np.percentile(df, [25, 50, 75], axis=0) + + tm.assert_almost_equal(df_quantile.values, np.array(np_percentile)) + + +@pytest.mark.parametrize("quantile", [0.0, 0.1, 0.45, 0.5, 1]) +@pytest.mark.parametrize( + "interpolation", ["linear", "lower", "higher", "nearest", "midpoint"] +) +@pytest.mark.parametrize( + "data", + [ + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0], + [8.0, 1.0, 3.0, 4.0, 5.0, 2.0, 6.0, 7.0], + [0.0, np.nan, 0.2, np.nan, 0.4], + [np.nan, np.nan, np.nan, np.nan], + [np.nan, 0.1, np.nan, 0.3, 0.4, 0.5], + [0.5], + [np.nan, 0.7, 0.6], + ], +) +def test_rolling_quantile_interpolation_options(quantile, interpolation, data): + # Tests that rolling window's quantile behavior is analogous to + # Series' quantile for each interpolation option + s = Series(data) + + q1 = s.quantile(quantile, interpolation) + q2 = s.expanding(min_periods=1).quantile(quantile, interpolation).iloc[-1] + + if np.isnan(q1): + assert np.isnan(q2) + else: + if not IS64: + # Less precision on 32-bit + assert np.allclose([q1], [q2], rtol=1e-07, atol=0) + else: + assert q1 == q2 + + +def test_invalid_quantile_value(): + data = np.arange(5) + s = Series(data) + + msg = "Interpolation 'invalid' is not supported" + with pytest.raises(ValueError, match=msg): + s.rolling(len(data), min_periods=1).quantile(0.5, interpolation="invalid") + + +def test_rolling_quantile_param(): + ser = Series([0.0, 0.1, 0.5, 0.9, 1.0]) + msg = "quantile value -0.1 not in \\[0, 1\\]" + with pytest.raises(ValueError, match=msg): + ser.rolling(3).quantile(-0.1) + + msg = "quantile value 10.0 not in \\[0, 1\\]" + with pytest.raises(ValueError, match=msg): + ser.rolling(3).quantile(10.0) + + msg = "must be real number, not str" + with pytest.raises(TypeError, match=msg): + ser.rolling(3).quantile("foo") + + +def test_rolling_std_1obs(): + vals = Series([1.0, 2.0, 3.0, 4.0, 5.0]) + + result = vals.rolling(1, min_periods=1).std() + expected = Series([np.nan] * 5) + tm.assert_series_equal(result, expected) + + result = vals.rolling(1, min_periods=1).std(ddof=0) + expected = Series([0.0] * 5) + tm.assert_series_equal(result, expected) + + result = Series([np.nan, np.nan, 3, 4, 5]).rolling(3, min_periods=2).std() + assert np.isnan(result[2]) + + +def test_rolling_std_neg_sqrt(): + # unit test from Bottleneck + + # Test move_nanstd for neg sqrt. + + a = Series( + [ + 0.0011448196318903589, + 0.00028718669878572767, + 0.00028718669878572767, + 0.00028718669878572767, + 0.00028718669878572767, + ] + ) + b = a.rolling(window=3).std() + assert np.isfinite(b[2:]).all() + + b = a.ewm(span=3).std() + assert np.isfinite(b[2:]).all() + + +def test_step_not_integer_raises(): + with pytest.raises(ValueError, match="step must be an integer"): + DataFrame(range(2)).rolling(1, step="foo") + + +def test_step_not_positive_raises(): + with pytest.raises(ValueError, match="step must be >= 0"): + DataFrame(range(2)).rolling(1, step=-1) + + +@pytest.mark.parametrize( + ["values", "window", "min_periods", "expected"], + [ + [ + [20, 10, 10, np.inf, 1, 1, 2, 3], + 3, + 1, + [np.nan, 50, 100 / 3, 0, 40.5, 0, 1 / 3, 1], + ], + [ + [20, 10, 10, np.nan, 10, 1, 2, 3], + 3, + 1, + [np.nan, 50, 100 / 3, 0, 0, 40.5, 73 / 3, 1], + ], + [ + [np.nan, 5, 6, 7, 5, 5, 5], + 3, + 3, + [np.nan] * 3 + [1, 1, 4 / 3, 0], + ], + [ + [5, 7, 7, 7, np.nan, np.inf, 4, 3, 3, 3], + 3, + 3, + [np.nan] * 2 + [4 / 3, 0] + [np.nan] * 4 + [1 / 3, 0], + ], + [ + [5, 7, 7, 7, np.nan, np.inf, 7, 3, 3, 3], + 3, + 3, + [np.nan] * 2 + [4 / 3, 0] + [np.nan] * 4 + [16 / 3, 0], + ], + [ + [5, 7] * 4, + 3, + 3, + [np.nan] * 2 + [4 / 3] * 6, + ], + [ + [5, 7, 5, np.nan, 7, 5, 7], + 3, + 2, + [np.nan, 2, 4 / 3] + [2] * 3 + [4 / 3], + ], + ], +) +def test_rolling_var_same_value_count_logic(values, window, min_periods, expected): + # GH 42064. + + expected = Series(expected) + sr = Series(values) + + # With new algo implemented, result will be set to .0 in rolling var + # if sufficient amount of consecutively same values are found. + result_var = sr.rolling(window, min_periods=min_periods).var() + + # use `assert_series_equal` twice to check for equality, + # because `check_exact=True` will fail in 32-bit tests due to + # precision loss. + + # 1. result should be close to correct value + # non-zero values can still differ slightly from "truth" + # as the result of online algorithm + tm.assert_series_equal(result_var, expected) + # 2. zeros should be exactly the same since the new algo takes effect here + tm.assert_series_equal(expected == 0, result_var == 0) + + # std should also pass as it's just a sqrt of var + result_std = sr.rolling(window, min_periods=min_periods).std() + tm.assert_series_equal(result_std, np.sqrt(expected)) + tm.assert_series_equal(expected == 0, result_std == 0) + + +def test_rolling_mean_sum_floating_artifacts(): + # GH 42064. + + sr = Series([1 / 3, 4, 0, 0, 0, 0, 0]) + r = sr.rolling(3) + result = r.mean() + assert (result[-3:] == 0).all() + result = r.sum() + assert (result[-3:] == 0).all() + + +def test_rolling_skew_kurt_floating_artifacts(): + # GH 42064 46431 + + sr = Series([1 / 3, 4, 0, 0, 0, 0, 0]) + r = sr.rolling(4) + result = r.skew() + assert (result[-2:] == 0).all() + result = r.kurt() + assert (result[-2:] == -3).all() + + +def test_numeric_only_frame(arithmetic_win_operators, numeric_only): + # GH#46560 + kernel = arithmetic_win_operators + df = DataFrame({"a": [1], "b": 2, "c": 3}) + df["c"] = df["c"].astype(object) + rolling = df.rolling(2, min_periods=1) + op = getattr(rolling, kernel) + result = op(numeric_only=numeric_only) + + columns = ["a", "b"] if numeric_only else ["a", "b", "c"] + expected = df[columns].agg([kernel]).reset_index(drop=True).astype(float) + assert list(expected.columns) == columns + + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("kernel", ["corr", "cov"]) +@pytest.mark.parametrize("use_arg", [True, False]) +def test_numeric_only_corr_cov_frame(kernel, numeric_only, use_arg): + # GH#46560 + df = DataFrame({"a": [1, 2, 3], "b": 2, "c": 3}) + df["c"] = df["c"].astype(object) + arg = (df,) if use_arg else () + rolling = df.rolling(2, min_periods=1) + op = getattr(rolling, kernel) + result = op(*arg, numeric_only=numeric_only) + + # Compare result to op using float dtypes, dropping c when numeric_only is True + columns = ["a", "b"] if numeric_only else ["a", "b", "c"] + df2 = df[columns].astype(float) + arg2 = (df2,) if use_arg else () + rolling2 = df2.rolling(2, min_periods=1) + op2 = getattr(rolling2, kernel) + expected = op2(*arg2, numeric_only=numeric_only) + + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("dtype", [int, object]) +def test_numeric_only_series(arithmetic_win_operators, numeric_only, dtype): + # GH#46560 + kernel = arithmetic_win_operators + ser = Series([1], dtype=dtype) + rolling = ser.rolling(2, min_periods=1) + op = getattr(rolling, kernel) + if numeric_only and dtype is object: + msg = f"Rolling.{kernel} does not implement numeric_only" + with pytest.raises(NotImplementedError, match=msg): + op(numeric_only=numeric_only) + else: + result = op(numeric_only=numeric_only) + expected = ser.agg([kernel]).reset_index(drop=True).astype(float) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("kernel", ["corr", "cov"]) +@pytest.mark.parametrize("use_arg", [True, False]) +@pytest.mark.parametrize("dtype", [int, object]) +def test_numeric_only_corr_cov_series(kernel, use_arg, numeric_only, dtype): + # GH#46560 + ser = Series([1, 2, 3], dtype=dtype) + arg = (ser,) if use_arg else () + rolling = ser.rolling(2, min_periods=1) + op = getattr(rolling, kernel) + if numeric_only and dtype is object: + msg = f"Rolling.{kernel} does not implement numeric_only" + with pytest.raises(NotImplementedError, match=msg): + op(*arg, numeric_only=numeric_only) + else: + result = op(*arg, numeric_only=numeric_only) + + ser2 = ser.astype(float) + arg2 = (ser2,) if use_arg else () + rolling2 = ser2.rolling(2, min_periods=1) + op2 = getattr(rolling2, kernel) + expected = op2(*arg2, numeric_only=numeric_only) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"]) +@pytest.mark.parametrize("tz", [None, "UTC", "Europe/Prague"]) +def test_rolling_timedelta_window_non_nanoseconds(unit, tz): + # Test Sum, GH#55106 + df_time = DataFrame( + {"A": range(5)}, index=date_range("2013-01-01", freq="1s", periods=5, tz=tz) + ) + sum_in_nanosecs = df_time.rolling("1s").sum() + # microseconds / milliseconds should not break the correct rolling + df_time.index = df_time.index.as_unit(unit) + sum_in_microsecs = df_time.rolling("1s").sum() + sum_in_microsecs.index = sum_in_microsecs.index.as_unit("ns") + tm.assert_frame_equal(sum_in_nanosecs, sum_in_microsecs) + + # Test max, GH#55026 + ref_dates = date_range("2023-01-01", "2023-01-10", unit="ns", tz=tz) + ref_series = Series(0, index=ref_dates) + ref_series.iloc[0] = 1 + ref_max_series = ref_series.rolling(Timedelta(days=4)).max() + + dates = date_range("2023-01-01", "2023-01-10", unit=unit, tz=tz) + series = Series(0, index=dates) + series.iloc[0] = 1 + max_series = series.rolling(Timedelta(days=4)).max() + + ref_df = DataFrame(ref_max_series) + df = DataFrame(max_series) + df.index = df.index.as_unit("ns") + + tm.assert_frame_equal(ref_df, df) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_rolling_functions.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_rolling_functions.py new file mode 100644 index 0000000000000000000000000000000000000000..5906ff52db098dfca00d8fdc0a5fa1460b7a35f1 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_rolling_functions.py @@ -0,0 +1,532 @@ +from datetime import datetime + +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +from pandas import ( + DataFrame, + DatetimeIndex, + Series, + concat, + isna, + notna, +) +import pandas._testing as tm + +from pandas.tseries import offsets + + +@pytest.mark.parametrize( + "compare_func, roll_func, kwargs", + [ + [np.mean, "mean", {}], + [np.nansum, "sum", {}], + [ + lambda x: np.isfinite(x).astype(float).sum(), + "count", + {}, + ], + [np.median, "median", {}], + [np.min, "min", {}], + [np.max, "max", {}], + [lambda x: np.std(x, ddof=1), "std", {}], + [lambda x: np.std(x, ddof=0), "std", {"ddof": 0}], + [lambda x: np.var(x, ddof=1), "var", {}], + [lambda x: np.var(x, ddof=0), "var", {"ddof": 0}], + ], +) +def test_series(series, compare_func, roll_func, kwargs, step): + result = getattr(series.rolling(50, step=step), roll_func)(**kwargs) + assert isinstance(result, Series) + end = range(0, len(series), step or 1)[-1] + 1 + tm.assert_almost_equal(result.iloc[-1], compare_func(series[end - 50 : end])) + + +@pytest.mark.parametrize( + "compare_func, roll_func, kwargs", + [ + [np.mean, "mean", {}], + [np.nansum, "sum", {}], + [ + lambda x: np.isfinite(x).astype(float).sum(), + "count", + {}, + ], + [np.median, "median", {}], + [np.min, "min", {}], + [np.max, "max", {}], + [lambda x: np.std(x, ddof=1), "std", {}], + [lambda x: np.std(x, ddof=0), "std", {"ddof": 0}], + [lambda x: np.var(x, ddof=1), "var", {}], + [lambda x: np.var(x, ddof=0), "var", {"ddof": 0}], + ], +) +def test_frame(raw, frame, compare_func, roll_func, kwargs, step): + result = getattr(frame.rolling(50, step=step), roll_func)(**kwargs) + assert isinstance(result, DataFrame) + end = range(0, len(frame), step or 1)[-1] + 1 + tm.assert_series_equal( + result.iloc[-1, :], + frame.iloc[end - 50 : end, :].apply(compare_func, axis=0, raw=raw), + check_names=False, + ) + + +@pytest.mark.parametrize( + "compare_func, roll_func, kwargs, minp", + [ + [np.mean, "mean", {}, 10], + [np.nansum, "sum", {}, 10], + [lambda x: np.isfinite(x).astype(float).sum(), "count", {}, 0], + [np.median, "median", {}, 10], + [np.min, "min", {}, 10], + [np.max, "max", {}, 10], + [lambda x: np.std(x, ddof=1), "std", {}, 10], + [lambda x: np.std(x, ddof=0), "std", {"ddof": 0}, 10], + [lambda x: np.var(x, ddof=1), "var", {}, 10], + [lambda x: np.var(x, ddof=0), "var", {"ddof": 0}, 10], + ], +) +def test_time_rule_series(series, compare_func, roll_func, kwargs, minp): + win = 25 + ser = series[::2].resample("B").mean() + series_result = getattr(ser.rolling(window=win, min_periods=minp), roll_func)( + **kwargs + ) + last_date = series_result.index[-1] + prev_date = last_date - 24 * offsets.BDay() + + trunc_series = series[::2].truncate(prev_date, last_date) + tm.assert_almost_equal(series_result.iloc[-1], compare_func(trunc_series)) + + +@pytest.mark.parametrize( + "compare_func, roll_func, kwargs, minp", + [ + [np.mean, "mean", {}, 10], + [np.nansum, "sum", {}, 10], + [lambda x: np.isfinite(x).astype(float).sum(), "count", {}, 0], + [np.median, "median", {}, 10], + [np.min, "min", {}, 10], + [np.max, "max", {}, 10], + [lambda x: np.std(x, ddof=1), "std", {}, 10], + [lambda x: np.std(x, ddof=0), "std", {"ddof": 0}, 10], + [lambda x: np.var(x, ddof=1), "var", {}, 10], + [lambda x: np.var(x, ddof=0), "var", {"ddof": 0}, 10], + ], +) +def test_time_rule_frame(raw, frame, compare_func, roll_func, kwargs, minp): + win = 25 + frm = frame[::2].resample("B").mean() + frame_result = getattr(frm.rolling(window=win, min_periods=minp), roll_func)( + **kwargs + ) + last_date = frame_result.index[-1] + prev_date = last_date - 24 * offsets.BDay() + + trunc_frame = frame[::2].truncate(prev_date, last_date) + tm.assert_series_equal( + frame_result.xs(last_date), + trunc_frame.apply(compare_func, raw=raw), + check_names=False, + ) + + +@pytest.mark.parametrize( + "compare_func, roll_func, kwargs", + [ + [np.mean, "mean", {}], + [np.nansum, "sum", {}], + [np.median, "median", {}], + [np.min, "min", {}], + [np.max, "max", {}], + [lambda x: np.std(x, ddof=1), "std", {}], + [lambda x: np.std(x, ddof=0), "std", {"ddof": 0}], + [lambda x: np.var(x, ddof=1), "var", {}], + [lambda x: np.var(x, ddof=0), "var", {"ddof": 0}], + ], +) +def test_nans(compare_func, roll_func, kwargs): + obj = Series(np.random.default_rng(2).standard_normal(50)) + obj[:10] = np.nan + obj[-10:] = np.nan + + result = getattr(obj.rolling(50, min_periods=30), roll_func)(**kwargs) + tm.assert_almost_equal(result.iloc[-1], compare_func(obj[10:-10])) + + # min_periods is working correctly + result = getattr(obj.rolling(20, min_periods=15), roll_func)(**kwargs) + assert isna(result.iloc[23]) + assert not isna(result.iloc[24]) + + assert not isna(result.iloc[-6]) + assert isna(result.iloc[-5]) + + obj2 = Series(np.random.default_rng(2).standard_normal(20)) + result = getattr(obj2.rolling(10, min_periods=5), roll_func)(**kwargs) + assert isna(result.iloc[3]) + assert notna(result.iloc[4]) + + if roll_func != "sum": + result0 = getattr(obj.rolling(20, min_periods=0), roll_func)(**kwargs) + result1 = getattr(obj.rolling(20, min_periods=1), roll_func)(**kwargs) + tm.assert_almost_equal(result0, result1) + + +def test_nans_count(): + obj = Series(np.random.default_rng(2).standard_normal(50)) + obj[:10] = np.nan + obj[-10:] = np.nan + result = obj.rolling(50, min_periods=30).count() + tm.assert_almost_equal( + result.iloc[-1], np.isfinite(obj[10:-10]).astype(float).sum() + ) + + +@pytest.mark.parametrize( + "roll_func, kwargs", + [ + ["mean", {}], + ["sum", {}], + ["median", {}], + ["min", {}], + ["max", {}], + ["std", {}], + ["std", {"ddof": 0}], + ["var", {}], + ["var", {"ddof": 0}], + ], +) +@pytest.mark.parametrize("minp", [0, 99, 100]) +def test_min_periods(series, minp, roll_func, kwargs, step): + result = getattr( + series.rolling(len(series) + 1, min_periods=minp, step=step), roll_func + )(**kwargs) + expected = getattr( + series.rolling(len(series), min_periods=minp, step=step), roll_func + )(**kwargs) + nan_mask = isna(result) + tm.assert_series_equal(nan_mask, isna(expected)) + + nan_mask = ~nan_mask + tm.assert_almost_equal(result[nan_mask], expected[nan_mask]) + + +def test_min_periods_count(series, step): + result = series.rolling(len(series) + 1, min_periods=0, step=step).count() + expected = series.rolling(len(series), min_periods=0, step=step).count() + nan_mask = isna(result) + tm.assert_series_equal(nan_mask, isna(expected)) + + nan_mask = ~nan_mask + tm.assert_almost_equal(result[nan_mask], expected[nan_mask]) + + +@pytest.mark.parametrize( + "roll_func, kwargs, minp", + [ + ["mean", {}, 15], + ["sum", {}, 15], + ["count", {}, 0], + ["median", {}, 15], + ["min", {}, 15], + ["max", {}, 15], + ["std", {}, 15], + ["std", {"ddof": 0}, 15], + ["var", {}, 15], + ["var", {"ddof": 0}, 15], + ], +) +def test_center(roll_func, kwargs, minp): + obj = Series(np.random.default_rng(2).standard_normal(50)) + obj[:10] = np.nan + obj[-10:] = np.nan + + result = getattr(obj.rolling(20, min_periods=minp, center=True), roll_func)( + **kwargs + ) + expected = ( + getattr( + concat([obj, Series([np.nan] * 9)]).rolling(20, min_periods=minp), roll_func + )(**kwargs) + .iloc[9:] + .reset_index(drop=True) + ) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "roll_func, kwargs, minp, fill_value", + [ + ["mean", {}, 10, None], + ["sum", {}, 10, None], + ["count", {}, 0, 0], + ["median", {}, 10, None], + ["min", {}, 10, None], + ["max", {}, 10, None], + ["std", {}, 10, None], + ["std", {"ddof": 0}, 10, None], + ["var", {}, 10, None], + ["var", {"ddof": 0}, 10, None], + ], +) +def test_center_reindex_series(series, roll_func, kwargs, minp, fill_value): + # shifter index + s = [f"x{x:d}" for x in range(12)] + + series_xp = ( + getattr( + series.reindex(list(series.index) + s).rolling(window=25, min_periods=minp), + roll_func, + )(**kwargs) + .shift(-12) + .reindex(series.index) + ) + series_rs = getattr( + series.rolling(window=25, min_periods=minp, center=True), roll_func + )(**kwargs) + if fill_value is not None: + series_xp = series_xp.fillna(fill_value) + tm.assert_series_equal(series_xp, series_rs) + + +@pytest.mark.parametrize( + "roll_func, kwargs, minp, fill_value", + [ + ["mean", {}, 10, None], + ["sum", {}, 10, None], + ["count", {}, 0, 0], + ["median", {}, 10, None], + ["min", {}, 10, None], + ["max", {}, 10, None], + ["std", {}, 10, None], + ["std", {"ddof": 0}, 10, None], + ["var", {}, 10, None], + ["var", {"ddof": 0}, 10, None], + ], +) +def test_center_reindex_frame(frame, roll_func, kwargs, minp, fill_value): + # shifter index + s = [f"x{x:d}" for x in range(12)] + + frame_xp = ( + getattr( + frame.reindex(list(frame.index) + s).rolling(window=25, min_periods=minp), + roll_func, + )(**kwargs) + .shift(-12) + .reindex(frame.index) + ) + frame_rs = getattr( + frame.rolling(window=25, min_periods=minp, center=True), roll_func + )(**kwargs) + if fill_value is not None: + frame_xp = frame_xp.fillna(fill_value) + tm.assert_frame_equal(frame_xp, frame_rs) + + +@pytest.mark.parametrize( + "f", + [ + lambda x: x.rolling(window=10, min_periods=5).cov(x, pairwise=False), + lambda x: x.rolling(window=10, min_periods=5).corr(x, pairwise=False), + lambda x: x.rolling(window=10, min_periods=5).max(), + lambda x: x.rolling(window=10, min_periods=5).min(), + lambda x: x.rolling(window=10, min_periods=5).sum(), + lambda x: x.rolling(window=10, min_periods=5).mean(), + lambda x: x.rolling(window=10, min_periods=5).std(), + lambda x: x.rolling(window=10, min_periods=5).var(), + lambda x: x.rolling(window=10, min_periods=5).skew(), + lambda x: x.rolling(window=10, min_periods=5).kurt(), + lambda x: x.rolling(window=10, min_periods=5).quantile(q=0.5), + lambda x: x.rolling(window=10, min_periods=5).median(), + lambda x: x.rolling(window=10, min_periods=5).apply(sum, raw=False), + lambda x: x.rolling(window=10, min_periods=5).apply(sum, raw=True), + pytest.param( + lambda x: x.rolling(win_type="boxcar", window=10, min_periods=5).mean(), + marks=td.skip_if_no("scipy"), + ), + ], +) +def test_rolling_functions_window_non_shrinkage(f): + # GH 7764 + s = Series(range(4)) + s_expected = Series(np.nan, index=s.index) + df = DataFrame([[1, 5], [3, 2], [3, 9], [-1, 0]], columns=["A", "B"]) + df_expected = DataFrame(np.nan, index=df.index, columns=df.columns) + + s_result = f(s) + tm.assert_series_equal(s_result, s_expected) + + df_result = f(df) + tm.assert_frame_equal(df_result, df_expected) + + +def test_rolling_max_gh6297(step): + """Replicate result expected in GH #6297""" + indices = [datetime(1975, 1, i) for i in range(1, 6)] + # So that we can have 2 datapoints on one of the days + indices.append(datetime(1975, 1, 3, 6, 0)) + series = Series(range(1, 7), index=indices) + # Use floats instead of ints as values + series = series.map(lambda x: float(x)) + # Sort chronologically + series = series.sort_index() + + expected = Series( + [1.0, 2.0, 6.0, 4.0, 5.0], + index=DatetimeIndex([datetime(1975, 1, i, 0) for i in range(1, 6)], freq="D"), + )[::step] + x = series.resample("D").max().rolling(window=1, step=step).max() + tm.assert_series_equal(expected, x) + + +def test_rolling_max_resample(step): + indices = [datetime(1975, 1, i) for i in range(1, 6)] + # So that we can have 3 datapoints on last day (4, 10, and 20) + indices.append(datetime(1975, 1, 5, 1)) + indices.append(datetime(1975, 1, 5, 2)) + series = Series(list(range(5)) + [10, 20], index=indices) + # Use floats instead of ints as values + series = series.map(lambda x: float(x)) + # Sort chronologically + series = series.sort_index() + + # Default how should be max + expected = Series( + [0.0, 1.0, 2.0, 3.0, 20.0], + index=DatetimeIndex([datetime(1975, 1, i, 0) for i in range(1, 6)], freq="D"), + )[::step] + x = series.resample("D").max().rolling(window=1, step=step).max() + tm.assert_series_equal(expected, x) + + # Now specify median (10.0) + expected = Series( + [0.0, 1.0, 2.0, 3.0, 10.0], + index=DatetimeIndex([datetime(1975, 1, i, 0) for i in range(1, 6)], freq="D"), + )[::step] + x = series.resample("D").median().rolling(window=1, step=step).max() + tm.assert_series_equal(expected, x) + + # Now specify mean (4+10+20)/3 + v = (4.0 + 10.0 + 20.0) / 3.0 + expected = Series( + [0.0, 1.0, 2.0, 3.0, v], + index=DatetimeIndex([datetime(1975, 1, i, 0) for i in range(1, 6)], freq="D"), + )[::step] + x = series.resample("D").mean().rolling(window=1, step=step).max() + tm.assert_series_equal(expected, x) + + +def test_rolling_min_resample(step): + indices = [datetime(1975, 1, i) for i in range(1, 6)] + # So that we can have 3 datapoints on last day (4, 10, and 20) + indices.append(datetime(1975, 1, 5, 1)) + indices.append(datetime(1975, 1, 5, 2)) + series = Series(list(range(5)) + [10, 20], index=indices) + # Use floats instead of ints as values + series = series.map(lambda x: float(x)) + # Sort chronologically + series = series.sort_index() + + # Default how should be min + expected = Series( + [0.0, 1.0, 2.0, 3.0, 4.0], + index=DatetimeIndex([datetime(1975, 1, i, 0) for i in range(1, 6)], freq="D"), + )[::step] + r = series.resample("D").min().rolling(window=1, step=step) + tm.assert_series_equal(expected, r.min()) + + +def test_rolling_median_resample(): + indices = [datetime(1975, 1, i) for i in range(1, 6)] + # So that we can have 3 datapoints on last day (4, 10, and 20) + indices.append(datetime(1975, 1, 5, 1)) + indices.append(datetime(1975, 1, 5, 2)) + series = Series(list(range(5)) + [10, 20], index=indices) + # Use floats instead of ints as values + series = series.map(lambda x: float(x)) + # Sort chronologically + series = series.sort_index() + + # Default how should be median + expected = Series( + [0.0, 1.0, 2.0, 3.0, 10], + index=DatetimeIndex([datetime(1975, 1, i, 0) for i in range(1, 6)], freq="D"), + ) + x = series.resample("D").median().rolling(window=1).median() + tm.assert_series_equal(expected, x) + + +def test_rolling_median_memory_error(): + # GH11722 + n = 20000 + Series(np.random.default_rng(2).standard_normal(n)).rolling( + window=2, center=False + ).median() + Series(np.random.default_rng(2).standard_normal(n)).rolling( + window=2, center=False + ).median() + + +@pytest.mark.parametrize( + "data_type", + [np.dtype(f"f{width}") for width in [4, 8]] + + [np.dtype(f"{sign}{width}") for width in [1, 2, 4, 8] for sign in "ui"], +) +def test_rolling_min_max_numeric_types(data_type): + # GH12373 + + # Just testing that these don't throw exceptions and that + # the return type is float64. Other tests will cover quantitative + # correctness + result = DataFrame(np.arange(20, dtype=data_type)).rolling(window=5).max() + assert result.dtypes[0] == np.dtype("f8") + result = DataFrame(np.arange(20, dtype=data_type)).rolling(window=5).min() + assert result.dtypes[0] == np.dtype("f8") + + +@pytest.mark.parametrize( + "f", + [ + lambda x: x.rolling(window=10, min_periods=0).count(), + lambda x: x.rolling(window=10, min_periods=5).cov(x, pairwise=False), + lambda x: x.rolling(window=10, min_periods=5).corr(x, pairwise=False), + lambda x: x.rolling(window=10, min_periods=5).max(), + lambda x: x.rolling(window=10, min_periods=5).min(), + lambda x: x.rolling(window=10, min_periods=5).sum(), + lambda x: x.rolling(window=10, min_periods=5).mean(), + lambda x: x.rolling(window=10, min_periods=5).std(), + lambda x: x.rolling(window=10, min_periods=5).var(), + lambda x: x.rolling(window=10, min_periods=5).skew(), + lambda x: x.rolling(window=10, min_periods=5).kurt(), + lambda x: x.rolling(window=10, min_periods=5).quantile(0.5), + lambda x: x.rolling(window=10, min_periods=5).median(), + lambda x: x.rolling(window=10, min_periods=5).apply(sum, raw=False), + lambda x: x.rolling(window=10, min_periods=5).apply(sum, raw=True), + pytest.param( + lambda x: x.rolling(win_type="boxcar", window=10, min_periods=5).mean(), + marks=td.skip_if_no("scipy"), + ), + ], +) +def test_moment_functions_zero_length(f): + # GH 8056 + s = Series(dtype=np.float64) + s_expected = s + df1 = DataFrame() + df1_expected = df1 + df2 = DataFrame(columns=["a"]) + df2["a"] = df2["a"].astype("float64") + df2_expected = df2 + + s_result = f(s) + tm.assert_series_equal(s_result, s_expected) + + df1_result = f(df1) + tm.assert_frame_equal(df1_result, df1_expected) + + df2_result = f(df2) + tm.assert_frame_equal(df2_result, df2_expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_rolling_quantile.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_rolling_quantile.py new file mode 100644 index 0000000000000000000000000000000000000000..d5a7010923563c99b335fd9a309a1d8762d21651 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_rolling_quantile.py @@ -0,0 +1,182 @@ +from functools import partial + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Series, + concat, + isna, + notna, +) +import pandas._testing as tm + +from pandas.tseries import offsets + + +def scoreatpercentile(a, per): + values = np.sort(a, axis=0) + + idx = int(per / 1.0 * (values.shape[0] - 1)) + + if idx == values.shape[0] - 1: + retval = values[-1] + + else: + qlow = idx / (values.shape[0] - 1) + qhig = (idx + 1) / (values.shape[0] - 1) + vlow = values[idx] + vhig = values[idx + 1] + retval = vlow + (vhig - vlow) * (per - qlow) / (qhig - qlow) + + return retval + + +@pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0]) +def test_series(series, q, step): + compare_func = partial(scoreatpercentile, per=q) + result = series.rolling(50, step=step).quantile(q) + assert isinstance(result, Series) + end = range(0, len(series), step or 1)[-1] + 1 + tm.assert_almost_equal(result.iloc[-1], compare_func(series[end - 50 : end])) + + +@pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0]) +def test_frame(raw, frame, q, step): + compare_func = partial(scoreatpercentile, per=q) + result = frame.rolling(50, step=step).quantile(q) + assert isinstance(result, DataFrame) + end = range(0, len(frame), step or 1)[-1] + 1 + tm.assert_series_equal( + result.iloc[-1, :], + frame.iloc[end - 50 : end, :].apply(compare_func, axis=0, raw=raw), + check_names=False, + ) + + +@pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0]) +def test_time_rule_series(series, q): + compare_func = partial(scoreatpercentile, per=q) + win = 25 + ser = series[::2].resample("B").mean() + series_result = ser.rolling(window=win, min_periods=10).quantile(q) + last_date = series_result.index[-1] + prev_date = last_date - 24 * offsets.BDay() + + trunc_series = series[::2].truncate(prev_date, last_date) + tm.assert_almost_equal(series_result.iloc[-1], compare_func(trunc_series)) + + +@pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0]) +def test_time_rule_frame(raw, frame, q): + compare_func = partial(scoreatpercentile, per=q) + win = 25 + frm = frame[::2].resample("B").mean() + frame_result = frm.rolling(window=win, min_periods=10).quantile(q) + last_date = frame_result.index[-1] + prev_date = last_date - 24 * offsets.BDay() + + trunc_frame = frame[::2].truncate(prev_date, last_date) + tm.assert_series_equal( + frame_result.xs(last_date), + trunc_frame.apply(compare_func, raw=raw), + check_names=False, + ) + + +@pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0]) +def test_nans(q): + compare_func = partial(scoreatpercentile, per=q) + obj = Series(np.random.default_rng(2).standard_normal(50)) + obj[:10] = np.nan + obj[-10:] = np.nan + + result = obj.rolling(50, min_periods=30).quantile(q) + tm.assert_almost_equal(result.iloc[-1], compare_func(obj[10:-10])) + + # min_periods is working correctly + result = obj.rolling(20, min_periods=15).quantile(q) + assert isna(result.iloc[23]) + assert not isna(result.iloc[24]) + + assert not isna(result.iloc[-6]) + assert isna(result.iloc[-5]) + + obj2 = Series(np.random.default_rng(2).standard_normal(20)) + result = obj2.rolling(10, min_periods=5).quantile(q) + assert isna(result.iloc[3]) + assert notna(result.iloc[4]) + + result0 = obj.rolling(20, min_periods=0).quantile(q) + result1 = obj.rolling(20, min_periods=1).quantile(q) + tm.assert_almost_equal(result0, result1) + + +@pytest.mark.parametrize("minp", [0, 99, 100]) +@pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0]) +def test_min_periods(series, minp, q, step): + result = series.rolling(len(series) + 1, min_periods=minp, step=step).quantile(q) + expected = series.rolling(len(series), min_periods=minp, step=step).quantile(q) + nan_mask = isna(result) + tm.assert_series_equal(nan_mask, isna(expected)) + + nan_mask = ~nan_mask + tm.assert_almost_equal(result[nan_mask], expected[nan_mask]) + + +@pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0]) +def test_center(q): + obj = Series(np.random.default_rng(2).standard_normal(50)) + obj[:10] = np.nan + obj[-10:] = np.nan + + result = obj.rolling(20, center=True).quantile(q) + expected = ( + concat([obj, Series([np.nan] * 9)]) + .rolling(20) + .quantile(q) + .iloc[9:] + .reset_index(drop=True) + ) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0]) +def test_center_reindex_series(series, q): + # shifter index + s = [f"x{x:d}" for x in range(12)] + + series_xp = ( + series.reindex(list(series.index) + s) + .rolling(window=25) + .quantile(q) + .shift(-12) + .reindex(series.index) + ) + + series_rs = series.rolling(window=25, center=True).quantile(q) + tm.assert_series_equal(series_xp, series_rs) + + +@pytest.mark.parametrize("q", [0.0, 0.1, 0.5, 0.9, 1.0]) +def test_center_reindex_frame(frame, q): + # shifter index + s = [f"x{x:d}" for x in range(12)] + + frame_xp = ( + frame.reindex(list(frame.index) + s) + .rolling(window=25) + .quantile(q) + .shift(-12) + .reindex(frame.index) + ) + frame_rs = frame.rolling(window=25, center=True).quantile(q) + tm.assert_frame_equal(frame_xp, frame_rs) + + +def test_keyword_quantile_deprecated(): + # GH #52550 + s = Series([1, 2, 3, 4]) + with tm.assert_produces_warning(FutureWarning): + s.rolling(2).quantile(quantile=0.4) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_rolling_skew_kurt.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_rolling_skew_kurt.py new file mode 100644 index 0000000000000000000000000000000000000000..79c14f243e7cc93b395ea84e05ec6bc79942b79b --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_rolling_skew_kurt.py @@ -0,0 +1,227 @@ +from functools import partial + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Series, + concat, + isna, + notna, +) +import pandas._testing as tm + +from pandas.tseries import offsets + + +@pytest.mark.parametrize("sp_func, roll_func", [["kurtosis", "kurt"], ["skew", "skew"]]) +def test_series(series, sp_func, roll_func): + sp_stats = pytest.importorskip("scipy.stats") + + compare_func = partial(getattr(sp_stats, sp_func), bias=False) + result = getattr(series.rolling(50), roll_func)() + assert isinstance(result, Series) + tm.assert_almost_equal(result.iloc[-1], compare_func(series[-50:])) + + +@pytest.mark.parametrize("sp_func, roll_func", [["kurtosis", "kurt"], ["skew", "skew"]]) +def test_frame(raw, frame, sp_func, roll_func): + sp_stats = pytest.importorskip("scipy.stats") + + compare_func = partial(getattr(sp_stats, sp_func), bias=False) + result = getattr(frame.rolling(50), roll_func)() + assert isinstance(result, DataFrame) + tm.assert_series_equal( + result.iloc[-1, :], + frame.iloc[-50:, :].apply(compare_func, axis=0, raw=raw), + check_names=False, + ) + + +@pytest.mark.parametrize("sp_func, roll_func", [["kurtosis", "kurt"], ["skew", "skew"]]) +def test_time_rule_series(series, sp_func, roll_func): + sp_stats = pytest.importorskip("scipy.stats") + + compare_func = partial(getattr(sp_stats, sp_func), bias=False) + win = 25 + ser = series[::2].resample("B").mean() + series_result = getattr(ser.rolling(window=win, min_periods=10), roll_func)() + last_date = series_result.index[-1] + prev_date = last_date - 24 * offsets.BDay() + + trunc_series = series[::2].truncate(prev_date, last_date) + tm.assert_almost_equal(series_result.iloc[-1], compare_func(trunc_series)) + + +@pytest.mark.parametrize("sp_func, roll_func", [["kurtosis", "kurt"], ["skew", "skew"]]) +def test_time_rule_frame(raw, frame, sp_func, roll_func): + sp_stats = pytest.importorskip("scipy.stats") + + compare_func = partial(getattr(sp_stats, sp_func), bias=False) + win = 25 + frm = frame[::2].resample("B").mean() + frame_result = getattr(frm.rolling(window=win, min_periods=10), roll_func)() + last_date = frame_result.index[-1] + prev_date = last_date - 24 * offsets.BDay() + + trunc_frame = frame[::2].truncate(prev_date, last_date) + tm.assert_series_equal( + frame_result.xs(last_date), + trunc_frame.apply(compare_func, raw=raw), + check_names=False, + ) + + +@pytest.mark.parametrize("sp_func, roll_func", [["kurtosis", "kurt"], ["skew", "skew"]]) +def test_nans(sp_func, roll_func): + sp_stats = pytest.importorskip("scipy.stats") + + compare_func = partial(getattr(sp_stats, sp_func), bias=False) + obj = Series(np.random.default_rng(2).standard_normal(50)) + obj[:10] = np.nan + obj[-10:] = np.nan + + result = getattr(obj.rolling(50, min_periods=30), roll_func)() + tm.assert_almost_equal(result.iloc[-1], compare_func(obj[10:-10])) + + # min_periods is working correctly + result = getattr(obj.rolling(20, min_periods=15), roll_func)() + assert isna(result.iloc[23]) + assert not isna(result.iloc[24]) + + assert not isna(result.iloc[-6]) + assert isna(result.iloc[-5]) + + obj2 = Series(np.random.default_rng(2).standard_normal(20)) + result = getattr(obj2.rolling(10, min_periods=5), roll_func)() + assert isna(result.iloc[3]) + assert notna(result.iloc[4]) + + result0 = getattr(obj.rolling(20, min_periods=0), roll_func)() + result1 = getattr(obj.rolling(20, min_periods=1), roll_func)() + tm.assert_almost_equal(result0, result1) + + +@pytest.mark.parametrize("minp", [0, 99, 100]) +@pytest.mark.parametrize("roll_func", ["kurt", "skew"]) +def test_min_periods(series, minp, roll_func, step): + result = getattr( + series.rolling(len(series) + 1, min_periods=minp, step=step), roll_func + )() + expected = getattr( + series.rolling(len(series), min_periods=minp, step=step), roll_func + )() + nan_mask = isna(result) + tm.assert_series_equal(nan_mask, isna(expected)) + + nan_mask = ~nan_mask + tm.assert_almost_equal(result[nan_mask], expected[nan_mask]) + + +@pytest.mark.parametrize("roll_func", ["kurt", "skew"]) +def test_center(roll_func): + obj = Series(np.random.default_rng(2).standard_normal(50)) + obj[:10] = np.nan + obj[-10:] = np.nan + + result = getattr(obj.rolling(20, center=True), roll_func)() + expected = ( + getattr(concat([obj, Series([np.nan] * 9)]).rolling(20), roll_func)() + .iloc[9:] + .reset_index(drop=True) + ) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("roll_func", ["kurt", "skew"]) +def test_center_reindex_series(series, roll_func): + # shifter index + s = [f"x{x:d}" for x in range(12)] + + series_xp = ( + getattr( + series.reindex(list(series.index) + s).rolling(window=25), + roll_func, + )() + .shift(-12) + .reindex(series.index) + ) + series_rs = getattr(series.rolling(window=25, center=True), roll_func)() + tm.assert_series_equal(series_xp, series_rs) + + +@pytest.mark.slow +@pytest.mark.parametrize("roll_func", ["kurt", "skew"]) +def test_center_reindex_frame(frame, roll_func): + # shifter index + s = [f"x{x:d}" for x in range(12)] + + frame_xp = ( + getattr( + frame.reindex(list(frame.index) + s).rolling(window=25), + roll_func, + )() + .shift(-12) + .reindex(frame.index) + ) + frame_rs = getattr(frame.rolling(window=25, center=True), roll_func)() + tm.assert_frame_equal(frame_xp, frame_rs) + + +def test_rolling_skew_edge_cases(step): + expected = Series([np.nan] * 4 + [0.0])[::step] + # yields all NaN (0 variance) + d = Series([1] * 5) + x = d.rolling(window=5, step=step).skew() + # index 4 should be 0 as it contains 5 same obs + tm.assert_series_equal(expected, x) + + expected = Series([np.nan] * 5)[::step] + # yields all NaN (window too small) + d = Series(np.random.default_rng(2).standard_normal(5)) + x = d.rolling(window=2, step=step).skew() + tm.assert_series_equal(expected, x) + + # yields [NaN, NaN, NaN, 0.177994, 1.548824] + d = Series([-1.50837035, -0.1297039, 0.19501095, 1.73508164, 0.41941401]) + expected = Series([np.nan, np.nan, np.nan, 0.177994, 1.548824])[::step] + x = d.rolling(window=4, step=step).skew() + tm.assert_series_equal(expected, x) + + +def test_rolling_kurt_edge_cases(step): + expected = Series([np.nan] * 4 + [-3.0])[::step] + + # yields all NaN (0 variance) + d = Series([1] * 5) + x = d.rolling(window=5, step=step).kurt() + tm.assert_series_equal(expected, x) + + # yields all NaN (window too small) + expected = Series([np.nan] * 5)[::step] + d = Series(np.random.default_rng(2).standard_normal(5)) + x = d.rolling(window=3, step=step).kurt() + tm.assert_series_equal(expected, x) + + # yields [NaN, NaN, NaN, 1.224307, 2.671499] + d = Series([-1.50837035, -0.1297039, 0.19501095, 1.73508164, 0.41941401]) + expected = Series([np.nan, np.nan, np.nan, 1.224307, 2.671499])[::step] + x = d.rolling(window=4, step=step).kurt() + tm.assert_series_equal(expected, x) + + +def test_rolling_skew_eq_value_fperr(step): + # #18804 all rolling skew for all equal values should return Nan + # #46717 update: all equal values should return 0 instead of NaN + a = Series([1.1] * 15).rolling(window=10, step=step).skew() + assert (a[a.index >= 9] == 0).all() + assert a[a.index < 9].isna().all() + + +def test_rolling_kurt_eq_value_fperr(step): + # #18804 all rolling kurt for all equal values should return Nan + # #46717 update: all equal values should return -3 instead of NaN + a = Series([1.1] * 15).rolling(window=10, step=step).kurt() + assert (a[a.index >= 9] == -3).all() + assert a[a.index < 9].isna().all() diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_timeseries_window.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_timeseries_window.py new file mode 100644 index 0000000000000000000000000000000000000000..bd0fadeb3e47565b6bad3e2e96dd7962b0b31d07 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_timeseries_window.py @@ -0,0 +1,715 @@ +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +from pandas import ( + DataFrame, + DatetimeIndex, + Index, + MultiIndex, + NaT, + Series, + Timestamp, + date_range, +) +import pandas._testing as tm + +from pandas.tseries import offsets + + +@pytest.fixture +def regular(): + return DataFrame( + {"A": date_range("20130101", periods=5, freq="s"), "B": range(5)} + ).set_index("A") + + +@pytest.fixture +def ragged(): + df = DataFrame({"B": range(5)}) + df.index = [ + Timestamp("20130101 09:00:00"), + Timestamp("20130101 09:00:02"), + Timestamp("20130101 09:00:03"), + Timestamp("20130101 09:00:05"), + Timestamp("20130101 09:00:06"), + ] + return df + + +class TestRollingTS: + # rolling time-series friendly + # xref GH13327 + + def test_doc_string(self): + df = DataFrame( + {"B": [0, 1, 2, np.nan, 4]}, + index=[ + Timestamp("20130101 09:00:00"), + Timestamp("20130101 09:00:02"), + Timestamp("20130101 09:00:03"), + Timestamp("20130101 09:00:05"), + Timestamp("20130101 09:00:06"), + ], + ) + df + df.rolling("2s").sum() + + def test_invalid_window_non_int(self, regular): + # not a valid freq + msg = "passed window foobar is not compatible with a datetimelike index" + with pytest.raises(ValueError, match=msg): + regular.rolling(window="foobar") + # not a datetimelike index + msg = "window must be an integer" + with pytest.raises(ValueError, match=msg): + regular.reset_index().rolling(window="foobar") + + @pytest.mark.parametrize("freq", ["2MS", offsets.MonthBegin(2)]) + def test_invalid_window_nonfixed(self, freq, regular): + # non-fixed freqs + msg = "\\<2 \\* MonthBegins\\> is a non-fixed frequency" + with pytest.raises(ValueError, match=msg): + regular.rolling(window=freq) + + @pytest.mark.parametrize("freq", ["1D", offsets.Day(2), "2ms"]) + def test_valid_window(self, freq, regular): + regular.rolling(window=freq) + + @pytest.mark.parametrize("minp", [1.0, "foo", np.array([1, 2, 3])]) + def test_invalid_minp(self, minp, regular): + # non-integer min_periods + msg = ( + r"local variable 'minp' referenced before assignment|" + "min_periods must be an integer" + ) + with pytest.raises(ValueError, match=msg): + regular.rolling(window="1D", min_periods=minp) + + def test_on(self, regular): + df = regular + + # not a valid column + msg = ( + r"invalid on specified as foobar, must be a column " + "\\(of DataFrame\\), an Index or None" + ) + with pytest.raises(ValueError, match=msg): + df.rolling(window="2s", on="foobar") + + # column is valid + df = df.copy() + df["C"] = date_range("20130101", periods=len(df)) + df.rolling(window="2d", on="C").sum() + + # invalid columns + msg = "window must be an integer" + with pytest.raises(ValueError, match=msg): + df.rolling(window="2d", on="B") + + # ok even though on non-selected + df.rolling(window="2d", on="C").B.sum() + + def test_monotonic_on(self): + # on/index must be monotonic + df = DataFrame( + {"A": date_range("20130101", periods=5, freq="s"), "B": range(5)} + ) + + assert df.A.is_monotonic_increasing + df.rolling("2s", on="A").sum() + + df = df.set_index("A") + assert df.index.is_monotonic_increasing + df.rolling("2s").sum() + + def test_non_monotonic_on(self): + # GH 19248 + df = DataFrame( + {"A": date_range("20130101", periods=5, freq="s"), "B": range(5)} + ) + df = df.set_index("A") + non_monotonic_index = df.index.to_list() + non_monotonic_index[0] = non_monotonic_index[3] + df.index = non_monotonic_index + + assert not df.index.is_monotonic_increasing + + msg = "index values must be monotonic" + with pytest.raises(ValueError, match=msg): + df.rolling("2s").sum() + + df = df.reset_index() + + msg = ( + r"invalid on specified as A, must be a column " + "\\(of DataFrame\\), an Index or None" + ) + with pytest.raises(ValueError, match=msg): + df.rolling("2s", on="A").sum() + + def test_frame_on(self): + df = DataFrame( + {"B": range(5), "C": date_range("20130101 09:00:00", periods=5, freq="3s")} + ) + + df["A"] = [ + Timestamp("20130101 09:00:00"), + Timestamp("20130101 09:00:02"), + Timestamp("20130101 09:00:03"), + Timestamp("20130101 09:00:05"), + Timestamp("20130101 09:00:06"), + ] + + # we are doing simulating using 'on' + expected = df.set_index("A").rolling("2s").B.sum().reset_index(drop=True) + + result = df.rolling("2s", on="A").B.sum() + tm.assert_series_equal(result, expected) + + # test as a frame + # we should be ignoring the 'on' as an aggregation column + # note that the expected is setting, computing, and resetting + # so the columns need to be switched compared + # to the actual result where they are ordered as in the + # original + expected = ( + df.set_index("A").rolling("2s")[["B"]].sum().reset_index()[["B", "A"]] + ) + + result = df.rolling("2s", on="A")[["B"]].sum() + tm.assert_frame_equal(result, expected) + + def test_frame_on2(self, unit): + # using multiple aggregation columns + dti = DatetimeIndex( + [ + Timestamp("20130101 09:00:00"), + Timestamp("20130101 09:00:02"), + Timestamp("20130101 09:00:03"), + Timestamp("20130101 09:00:05"), + Timestamp("20130101 09:00:06"), + ] + ).as_unit(unit) + df = DataFrame( + { + "A": [0, 1, 2, 3, 4], + "B": [0, 1, 2, np.nan, 4], + "C": dti, + }, + columns=["A", "C", "B"], + ) + + expected1 = DataFrame( + {"A": [0.0, 1, 3, 3, 7], "B": [0, 1, 3, np.nan, 4], "C": df["C"]}, + columns=["A", "C", "B"], + ) + + result = df.rolling("2s", on="C").sum() + expected = expected1 + tm.assert_frame_equal(result, expected) + + expected = Series([0, 1, 3, np.nan, 4], name="B") + result = df.rolling("2s", on="C").B.sum() + tm.assert_series_equal(result, expected) + + expected = expected1[["A", "B", "C"]] + result = df.rolling("2s", on="C")[["A", "B", "C"]].sum() + tm.assert_frame_equal(result, expected) + + def test_basic_regular(self, regular): + df = regular.copy() + + df.index = date_range("20130101", periods=5, freq="D") + expected = df.rolling(window=1, min_periods=1).sum() + result = df.rolling(window="1D").sum() + tm.assert_frame_equal(result, expected) + + df.index = date_range("20130101", periods=5, freq="2D") + expected = df.rolling(window=1, min_periods=1).sum() + result = df.rolling(window="2D", min_periods=1).sum() + tm.assert_frame_equal(result, expected) + + expected = df.rolling(window=1, min_periods=1).sum() + result = df.rolling(window="2D", min_periods=1).sum() + tm.assert_frame_equal(result, expected) + + expected = df.rolling(window=1).sum() + result = df.rolling(window="2D").sum() + tm.assert_frame_equal(result, expected) + + def test_min_periods(self, regular): + # compare for min_periods + df = regular + + # these slightly different + expected = df.rolling(2, min_periods=1).sum() + result = df.rolling("2s").sum() + tm.assert_frame_equal(result, expected) + + expected = df.rolling(2, min_periods=1).sum() + result = df.rolling("2s", min_periods=1).sum() + tm.assert_frame_equal(result, expected) + + def test_closed(self, regular, unit): + # xref GH13965 + + dti = DatetimeIndex( + [ + Timestamp("20130101 09:00:01"), + Timestamp("20130101 09:00:02"), + Timestamp("20130101 09:00:03"), + Timestamp("20130101 09:00:04"), + Timestamp("20130101 09:00:06"), + ] + ).as_unit(unit) + + df = DataFrame( + {"A": [1] * 5}, + index=dti, + ) + + # closed must be 'right', 'left', 'both', 'neither' + msg = "closed must be 'right', 'left', 'both' or 'neither'" + with pytest.raises(ValueError, match=msg): + regular.rolling(window="2s", closed="blabla") + + expected = df.copy() + expected["A"] = [1.0, 2, 2, 2, 1] + result = df.rolling("2s", closed="right").sum() + tm.assert_frame_equal(result, expected) + + # default should be 'right' + result = df.rolling("2s").sum() + tm.assert_frame_equal(result, expected) + + expected = df.copy() + expected["A"] = [1.0, 2, 3, 3, 2] + result = df.rolling("2s", closed="both").sum() + tm.assert_frame_equal(result, expected) + + expected = df.copy() + expected["A"] = [np.nan, 1.0, 2, 2, 1] + result = df.rolling("2s", closed="left").sum() + tm.assert_frame_equal(result, expected) + + expected = df.copy() + expected["A"] = [np.nan, 1.0, 1, 1, np.nan] + result = df.rolling("2s", closed="neither").sum() + tm.assert_frame_equal(result, expected) + + def test_ragged_sum(self, ragged): + df = ragged + result = df.rolling(window="1s", min_periods=1).sum() + expected = df.copy() + expected["B"] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="2s", min_periods=1).sum() + expected = df.copy() + expected["B"] = [0.0, 1, 3, 3, 7] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="2s", min_periods=2).sum() + expected = df.copy() + expected["B"] = [np.nan, np.nan, 3, np.nan, 7] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="3s", min_periods=1).sum() + expected = df.copy() + expected["B"] = [0.0, 1, 3, 5, 7] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="3s").sum() + expected = df.copy() + expected["B"] = [0.0, 1, 3, 5, 7] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="4s", min_periods=1).sum() + expected = df.copy() + expected["B"] = [0.0, 1, 3, 6, 9] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="4s", min_periods=3).sum() + expected = df.copy() + expected["B"] = [np.nan, np.nan, 3, 6, 9] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="5s", min_periods=1).sum() + expected = df.copy() + expected["B"] = [0.0, 1, 3, 6, 10] + tm.assert_frame_equal(result, expected) + + def test_ragged_mean(self, ragged): + df = ragged + result = df.rolling(window="1s", min_periods=1).mean() + expected = df.copy() + expected["B"] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="2s", min_periods=1).mean() + expected = df.copy() + expected["B"] = [0.0, 1, 1.5, 3.0, 3.5] + tm.assert_frame_equal(result, expected) + + def test_ragged_median(self, ragged): + df = ragged + result = df.rolling(window="1s", min_periods=1).median() + expected = df.copy() + expected["B"] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="2s", min_periods=1).median() + expected = df.copy() + expected["B"] = [0.0, 1, 1.5, 3.0, 3.5] + tm.assert_frame_equal(result, expected) + + def test_ragged_quantile(self, ragged): + df = ragged + result = df.rolling(window="1s", min_periods=1).quantile(0.5) + expected = df.copy() + expected["B"] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="2s", min_periods=1).quantile(0.5) + expected = df.copy() + expected["B"] = [0.0, 1, 1.5, 3.0, 3.5] + tm.assert_frame_equal(result, expected) + + def test_ragged_std(self, ragged): + df = ragged + result = df.rolling(window="1s", min_periods=1).std(ddof=0) + expected = df.copy() + expected["B"] = [0.0] * 5 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="1s", min_periods=1).std(ddof=1) + expected = df.copy() + expected["B"] = [np.nan] * 5 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="3s", min_periods=1).std(ddof=0) + expected = df.copy() + expected["B"] = [0.0] + [0.5] * 4 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="5s", min_periods=1).std(ddof=1) + expected = df.copy() + expected["B"] = [np.nan, 0.707107, 1.0, 1.0, 1.290994] + tm.assert_frame_equal(result, expected) + + def test_ragged_var(self, ragged): + df = ragged + result = df.rolling(window="1s", min_periods=1).var(ddof=0) + expected = df.copy() + expected["B"] = [0.0] * 5 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="1s", min_periods=1).var(ddof=1) + expected = df.copy() + expected["B"] = [np.nan] * 5 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="3s", min_periods=1).var(ddof=0) + expected = df.copy() + expected["B"] = [0.0] + [0.25] * 4 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="5s", min_periods=1).var(ddof=1) + expected = df.copy() + expected["B"] = [np.nan, 0.5, 1.0, 1.0, 1 + 2 / 3.0] + tm.assert_frame_equal(result, expected) + + def test_ragged_skew(self, ragged): + df = ragged + result = df.rolling(window="3s", min_periods=1).skew() + expected = df.copy() + expected["B"] = [np.nan] * 5 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="5s", min_periods=1).skew() + expected = df.copy() + expected["B"] = [np.nan] * 2 + [0.0, 0.0, 0.0] + tm.assert_frame_equal(result, expected) + + def test_ragged_kurt(self, ragged): + df = ragged + result = df.rolling(window="3s", min_periods=1).kurt() + expected = df.copy() + expected["B"] = [np.nan] * 5 + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="5s", min_periods=1).kurt() + expected = df.copy() + expected["B"] = [np.nan] * 4 + [-1.2] + tm.assert_frame_equal(result, expected) + + def test_ragged_count(self, ragged): + df = ragged + result = df.rolling(window="1s", min_periods=1).count() + expected = df.copy() + expected["B"] = [1.0, 1, 1, 1, 1] + tm.assert_frame_equal(result, expected) + + df = ragged + result = df.rolling(window="1s").count() + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="2s", min_periods=1).count() + expected = df.copy() + expected["B"] = [1.0, 1, 2, 1, 2] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="2s", min_periods=2).count() + expected = df.copy() + expected["B"] = [np.nan, np.nan, 2, np.nan, 2] + tm.assert_frame_equal(result, expected) + + def test_regular_min(self): + df = DataFrame( + {"A": date_range("20130101", periods=5, freq="s"), "B": [0.0, 1, 2, 3, 4]} + ).set_index("A") + result = df.rolling("1s").min() + expected = df.copy() + expected["B"] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + df = DataFrame( + {"A": date_range("20130101", periods=5, freq="s"), "B": [5, 4, 3, 4, 5]} + ).set_index("A") + + tm.assert_frame_equal(result, expected) + result = df.rolling("2s").min() + expected = df.copy() + expected["B"] = [5.0, 4, 3, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling("5s").min() + expected = df.copy() + expected["B"] = [5.0, 4, 3, 3, 3] + tm.assert_frame_equal(result, expected) + + def test_ragged_min(self, ragged): + df = ragged + + result = df.rolling(window="1s", min_periods=1).min() + expected = df.copy() + expected["B"] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="2s", min_periods=1).min() + expected = df.copy() + expected["B"] = [0.0, 1, 1, 3, 3] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="5s", min_periods=1).min() + expected = df.copy() + expected["B"] = [0.0, 0, 0, 1, 1] + tm.assert_frame_equal(result, expected) + + def test_perf_min(self): + N = 10000 + + dfp = DataFrame( + {"B": np.random.default_rng(2).standard_normal(N)}, + index=date_range("20130101", periods=N, freq="s"), + ) + expected = dfp.rolling(2, min_periods=1).min() + result = dfp.rolling("2s").min() + assert ((result - expected) < 0.01).all().all() + + expected = dfp.rolling(200, min_periods=1).min() + result = dfp.rolling("200s").min() + assert ((result - expected) < 0.01).all().all() + + def test_ragged_max(self, ragged): + df = ragged + + result = df.rolling(window="1s", min_periods=1).max() + expected = df.copy() + expected["B"] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="2s", min_periods=1).max() + expected = df.copy() + expected["B"] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + result = df.rolling(window="5s", min_periods=1).max() + expected = df.copy() + expected["B"] = [0.0, 1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "freq, op, result_data", + [ + ("ms", "min", [0.0] * 10), + ("ms", "mean", [0.0] * 9 + [2.0 / 9]), + ("ms", "max", [0.0] * 9 + [2.0]), + ("s", "min", [0.0] * 10), + ("s", "mean", [0.0] * 9 + [2.0 / 9]), + ("s", "max", [0.0] * 9 + [2.0]), + ("min", "min", [0.0] * 10), + ("min", "mean", [0.0] * 9 + [2.0 / 9]), + ("min", "max", [0.0] * 9 + [2.0]), + ("h", "min", [0.0] * 10), + ("h", "mean", [0.0] * 9 + [2.0 / 9]), + ("h", "max", [0.0] * 9 + [2.0]), + ("D", "min", [0.0] * 10), + ("D", "mean", [0.0] * 9 + [2.0 / 9]), + ("D", "max", [0.0] * 9 + [2.0]), + ], + ) + def test_freqs_ops(self, freq, op, result_data): + # GH 21096 + index = date_range(start="2018-1-1 01:00:00", freq=f"1{freq}", periods=10) + # Explicit cast to float to avoid implicit cast when setting nan + s = Series(data=0, index=index, dtype="float") + s.iloc[1] = np.nan + s.iloc[-1] = 2 + result = getattr(s.rolling(window=f"10{freq}"), op)() + expected = Series(data=result_data, index=index) + + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "f", + [ + "sum", + "mean", + "count", + "median", + "std", + "var", + "kurt", + "skew", + "min", + "max", + ], + ) + def test_all(self, f, regular): + # simple comparison of integer vs time-based windowing + df = regular * 2 + er = df.rolling(window=1) + r = df.rolling(window="1s") + + result = getattr(r, f)() + expected = getattr(er, f)() + tm.assert_frame_equal(result, expected) + + result = r.quantile(0.5) + expected = er.quantile(0.5) + tm.assert_frame_equal(result, expected) + + def test_all2(self, arithmetic_win_operators): + f = arithmetic_win_operators + # more sophisticated comparison of integer vs. + # time-based windowing + df = DataFrame( + {"B": np.arange(50)}, index=date_range("20130101", periods=50, freq="h") + ) + # in-range data + dft = df.between_time("09:00", "16:00") + + r = dft.rolling(window="5h") + + result = getattr(r, f)() + + # we need to roll the days separately + # to compare with a time-based roll + # finally groupby-apply will return a multi-index + # so we need to drop the day + def agg_by_day(x): + x = x.between_time("09:00", "16:00") + return getattr(x.rolling(5, min_periods=1), f)() + + expected = ( + df.groupby(df.index.day).apply(agg_by_day).reset_index(level=0, drop=True) + ) + + tm.assert_frame_equal(result, expected) + + def test_rolling_cov_offset(self): + # GH16058 + + idx = date_range("2017-01-01", periods=24, freq="1h") + ss = Series(np.arange(len(idx)), index=idx) + + result = ss.rolling("2h").cov() + expected = Series([np.nan] + [0.5] * (len(idx) - 1), index=idx) + tm.assert_series_equal(result, expected) + + expected2 = ss.rolling(2, min_periods=1).cov() + tm.assert_series_equal(result, expected2) + + result = ss.rolling("3h").cov() + expected = Series([np.nan, 0.5] + [1.0] * (len(idx) - 2), index=idx) + tm.assert_series_equal(result, expected) + + expected2 = ss.rolling(3, min_periods=1).cov() + tm.assert_series_equal(result, expected2) + + def test_rolling_on_decreasing_index(self, unit): + # GH-19248, GH-32385 + index = DatetimeIndex( + [ + Timestamp("20190101 09:00:30"), + Timestamp("20190101 09:00:27"), + Timestamp("20190101 09:00:20"), + Timestamp("20190101 09:00:18"), + Timestamp("20190101 09:00:10"), + ] + ).as_unit(unit) + + df = DataFrame({"column": [3, 4, 4, 5, 6]}, index=index) + result = df.rolling("5s").min() + expected = DataFrame({"column": [3.0, 3.0, 4.0, 4.0, 6.0]}, index=index) + tm.assert_frame_equal(result, expected) + + def test_rolling_on_empty(self): + # GH-32385 + df = DataFrame({"column": []}, index=[]) + result = df.rolling("5s").min() + expected = DataFrame({"column": []}, index=[]) + tm.assert_frame_equal(result, expected) + + def test_rolling_on_multi_index_level(self): + # GH-15584 + df = DataFrame( + {"column": range(6)}, + index=MultiIndex.from_product( + [date_range("20190101", periods=3), range(2)], names=["date", "seq"] + ), + ) + result = df.rolling("10d", on=df.index.get_level_values("date")).sum() + expected = DataFrame( + {"column": [0.0, 1.0, 3.0, 6.0, 10.0, 15.0]}, index=df.index + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("msg, axis", [["column", 1], ["index", 0]]) +def test_nat_axis_error(msg, axis): + idx = [Timestamp("2020"), NaT] + kwargs = {"columns" if axis == 1 else "index": idx} + df = DataFrame(np.eye(2), **kwargs) + warn_msg = "The 'axis' keyword in DataFrame.rolling is deprecated" + if axis == 1: + warn_msg = "Support for axis=1 in DataFrame.rolling is deprecated" + with pytest.raises(ValueError, match=f"{msg} values must not have NaT"): + with tm.assert_produces_warning(FutureWarning, match=warn_msg): + df.rolling("D", axis=axis).mean() + + +@td.skip_if_no("pyarrow") +def test_arrow_datetime_axis(): + # GH 55849 + expected = Series( + np.arange(5, dtype=np.float64), + index=Index( + date_range("2020-01-01", periods=5), dtype="timestamp[ns][pyarrow]" + ), + ) + result = expected.rolling("1D").sum() + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_win_type.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_win_type.py new file mode 100644 index 0000000000000000000000000000000000000000..5052019ddb7264c4f81e99ccdd79d88d86865ec4 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tests/window/test_win_type.py @@ -0,0 +1,688 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Series, + Timedelta, + concat, + date_range, +) +import pandas._testing as tm +from pandas.api.indexers import BaseIndexer + + +@pytest.fixture( + params=[ + "triang", + "blackman", + "hamming", + "bartlett", + "bohman", + "blackmanharris", + "nuttall", + "barthann", + ] +) +def win_types(request): + return request.param + + +@pytest.fixture(params=["kaiser", "gaussian", "general_gaussian", "exponential"]) +def win_types_special(request): + return request.param + + +def test_constructor(frame_or_series): + # GH 12669 + pytest.importorskip("scipy") + c = frame_or_series(range(5)).rolling + + # valid + c(win_type="boxcar", window=2, min_periods=1) + c(win_type="boxcar", window=2, min_periods=1, center=True) + c(win_type="boxcar", window=2, min_periods=1, center=False) + + +@pytest.mark.parametrize("w", [2.0, "foo", np.array([2])]) +def test_invalid_constructor(frame_or_series, w): + # not valid + pytest.importorskip("scipy") + c = frame_or_series(range(5)).rolling + with pytest.raises(ValueError, match="min_periods must be an integer"): + c(win_type="boxcar", window=2, min_periods=w) + with pytest.raises(ValueError, match="center must be a boolean"): + c(win_type="boxcar", window=2, min_periods=1, center=w) + + +@pytest.mark.parametrize("wt", ["foobar", 1]) +def test_invalid_constructor_wintype(frame_or_series, wt): + pytest.importorskip("scipy") + c = frame_or_series(range(5)).rolling + with pytest.raises(ValueError, match="Invalid win_type"): + c(win_type=wt, window=2) + + +def test_constructor_with_win_type(frame_or_series, win_types): + # GH 12669 + pytest.importorskip("scipy") + c = frame_or_series(range(5)).rolling + c(win_type=win_types, window=2) + + +@pytest.mark.parametrize("arg", ["median", "kurt", "skew"]) +def test_agg_function_support(arg): + pytest.importorskip("scipy") + df = DataFrame({"A": np.arange(5)}) + roll = df.rolling(2, win_type="triang") + + msg = f"'{arg}' is not a valid function for 'Window' object" + with pytest.raises(AttributeError, match=msg): + roll.agg(arg) + + with pytest.raises(AttributeError, match=msg): + roll.agg([arg]) + + with pytest.raises(AttributeError, match=msg): + roll.agg({"A": arg}) + + +def test_invalid_scipy_arg(): + # This error is raised by scipy + pytest.importorskip("scipy") + msg = r"boxcar\(\) got an unexpected" + with pytest.raises(TypeError, match=msg): + Series(range(3)).rolling(1, win_type="boxcar").mean(foo="bar") + + +def test_constructor_with_win_type_invalid(frame_or_series): + # GH 13383 + pytest.importorskip("scipy") + c = frame_or_series(range(5)).rolling + + msg = "window must be an integer 0 or greater" + + with pytest.raises(ValueError, match=msg): + c(-1, win_type="boxcar") + + +def test_window_with_args(step): + # make sure that we are aggregating window functions correctly with arg + pytest.importorskip("scipy") + r = Series(np.random.default_rng(2).standard_normal(100)).rolling( + window=10, min_periods=1, win_type="gaussian", step=step + ) + expected = concat([r.mean(std=10), r.mean(std=0.01)], axis=1) + expected.columns = ["", ""] + result = r.aggregate([lambda x: x.mean(std=10), lambda x: x.mean(std=0.01)]) + tm.assert_frame_equal(result, expected) + + def a(x): + return x.mean(std=10) + + def b(x): + return x.mean(std=0.01) + + expected = concat([r.mean(std=10), r.mean(std=0.01)], axis=1) + expected.columns = ["a", "b"] + result = r.aggregate([a, b]) + tm.assert_frame_equal(result, expected) + + +def test_win_type_with_method_invalid(): + pytest.importorskip("scipy") + with pytest.raises( + NotImplementedError, match="'single' is the only supported method type." + ): + Series(range(1)).rolling(1, win_type="triang", method="table") + + +@pytest.mark.parametrize("arg", [2000000000, "2s", Timedelta("2s")]) +def test_consistent_win_type_freq(arg): + # GH 15969 + pytest.importorskip("scipy") + s = Series(range(1)) + with pytest.raises(ValueError, match="Invalid win_type freq"): + s.rolling(arg, win_type="freq") + + +def test_win_type_freq_return_none(): + # GH 48838 + freq_roll = Series(range(2), index=date_range("2020", periods=2)).rolling("2s") + assert freq_roll.win_type is None + + +def test_win_type_not_implemented(): + pytest.importorskip("scipy") + + class CustomIndexer(BaseIndexer): + def get_window_bounds(self, num_values, min_periods, center, closed, step): + return np.array([0, 1]), np.array([1, 2]) + + df = DataFrame({"values": range(2)}) + indexer = CustomIndexer() + with pytest.raises(NotImplementedError, match="BaseIndexer subclasses not"): + df.rolling(indexer, win_type="boxcar") + + +def test_cmov_mean(step): + # GH 8238 + pytest.importorskip("scipy") + vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81, 13.49, 16.68, 9.48, 10.63, 14.48]) + result = Series(vals).rolling(5, center=True, step=step).mean() + expected_values = [ + np.nan, + np.nan, + 9.962, + 11.27, + 11.564, + 12.516, + 12.818, + 12.952, + np.nan, + np.nan, + ] + expected = Series(expected_values)[::step] + tm.assert_series_equal(expected, result) + + +def test_cmov_window(step): + # GH 8238 + pytest.importorskip("scipy") + vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81, 13.49, 16.68, 9.48, 10.63, 14.48]) + result = Series(vals).rolling(5, win_type="boxcar", center=True, step=step).mean() + expected_values = [ + np.nan, + np.nan, + 9.962, + 11.27, + 11.564, + 12.516, + 12.818, + 12.952, + np.nan, + np.nan, + ] + expected = Series(expected_values)[::step] + tm.assert_series_equal(expected, result) + + +def test_cmov_window_corner(step): + # GH 8238 + # all nan + pytest.importorskip("scipy") + vals = Series([np.nan] * 10) + result = vals.rolling(5, center=True, win_type="boxcar", step=step).mean() + assert np.isnan(result).all() + + # empty + vals = Series([], dtype=object) + result = vals.rolling(5, center=True, win_type="boxcar", step=step).mean() + assert len(result) == 0 + + # shorter than window + vals = Series(np.random.default_rng(2).standard_normal(5)) + result = vals.rolling(10, win_type="boxcar", step=step).mean() + assert np.isnan(result).all() + assert len(result) == len(range(0, 5, step or 1)) + + +@pytest.mark.parametrize( + "f,xp", + [ + ( + "mean", + [ + [np.nan, np.nan], + [np.nan, np.nan], + [9.252, 9.392], + [8.644, 9.906], + [8.87, 10.208], + [6.81, 8.588], + [7.792, 8.644], + [9.05, 7.824], + [np.nan, np.nan], + [np.nan, np.nan], + ], + ), + ( + "std", + [ + [np.nan, np.nan], + [np.nan, np.nan], + [3.789706, 4.068313], + [3.429232, 3.237411], + [3.589269, 3.220810], + [3.405195, 2.380655], + [3.281839, 2.369869], + [3.676846, 1.801799], + [np.nan, np.nan], + [np.nan, np.nan], + ], + ), + ( + "var", + [ + [np.nan, np.nan], + [np.nan, np.nan], + [14.36187, 16.55117], + [11.75963, 10.48083], + [12.88285, 10.37362], + [11.59535, 5.66752], + [10.77047, 5.61628], + [13.51920, 3.24648], + [np.nan, np.nan], + [np.nan, np.nan], + ], + ), + ( + "sum", + [ + [np.nan, np.nan], + [np.nan, np.nan], + [46.26, 46.96], + [43.22, 49.53], + [44.35, 51.04], + [34.05, 42.94], + [38.96, 43.22], + [45.25, 39.12], + [np.nan, np.nan], + [np.nan, np.nan], + ], + ), + ], +) +def test_cmov_window_frame(f, xp, step): + # Gh 8238 + pytest.importorskip("scipy") + df = DataFrame( + np.array( + [ + [12.18, 3.64], + [10.18, 9.16], + [13.24, 14.61], + [4.51, 8.11], + [6.15, 11.44], + [9.14, 6.21], + [11.31, 10.67], + [2.94, 6.51], + [9.42, 8.39], + [12.44, 7.34], + ] + ) + ) + xp = DataFrame(np.array(xp))[::step] + + roll = df.rolling(5, win_type="boxcar", center=True, step=step) + rs = getattr(roll, f)() + + tm.assert_frame_equal(xp, rs) + + +@pytest.mark.parametrize("min_periods", [0, 1, 2, 3, 4, 5]) +def test_cmov_window_na_min_periods(step, min_periods): + pytest.importorskip("scipy") + vals = Series(np.random.default_rng(2).standard_normal(10)) + vals[4] = np.nan + vals[8] = np.nan + + xp = vals.rolling(5, min_periods=min_periods, center=True, step=step).mean() + rs = vals.rolling( + 5, win_type="boxcar", min_periods=min_periods, center=True, step=step + ).mean() + tm.assert_series_equal(xp, rs) + + +def test_cmov_window_regular(win_types, step): + # GH 8238 + pytest.importorskip("scipy") + vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81, 13.49, 16.68, 9.48, 10.63, 14.48]) + xps = { + "hamming": [ + np.nan, + np.nan, + 8.71384, + 9.56348, + 12.38009, + 14.03687, + 13.8567, + 11.81473, + np.nan, + np.nan, + ], + "triang": [ + np.nan, + np.nan, + 9.28667, + 10.34667, + 12.00556, + 13.33889, + 13.38, + 12.33667, + np.nan, + np.nan, + ], + "barthann": [ + np.nan, + np.nan, + 8.4425, + 9.1925, + 12.5575, + 14.3675, + 14.0825, + 11.5675, + np.nan, + np.nan, + ], + "bohman": [ + np.nan, + np.nan, + 7.61599, + 9.1764, + 12.83559, + 14.17267, + 14.65923, + 11.10401, + np.nan, + np.nan, + ], + "blackmanharris": [ + np.nan, + np.nan, + 6.97691, + 9.16438, + 13.05052, + 14.02156, + 15.10512, + 10.74574, + np.nan, + np.nan, + ], + "nuttall": [ + np.nan, + np.nan, + 7.04618, + 9.16786, + 13.02671, + 14.03559, + 15.05657, + 10.78514, + np.nan, + np.nan, + ], + "blackman": [ + np.nan, + np.nan, + 7.73345, + 9.17869, + 12.79607, + 14.20036, + 14.57726, + 11.16988, + np.nan, + np.nan, + ], + "bartlett": [ + np.nan, + np.nan, + 8.4425, + 9.1925, + 12.5575, + 14.3675, + 14.0825, + 11.5675, + np.nan, + np.nan, + ], + } + + xp = Series(xps[win_types])[::step] + rs = Series(vals).rolling(5, win_type=win_types, center=True, step=step).mean() + tm.assert_series_equal(xp, rs) + + +def test_cmov_window_regular_linear_range(win_types, step): + # GH 8238 + pytest.importorskip("scipy") + vals = np.array(range(10), dtype=float) + xp = vals.copy() + xp[:2] = np.nan + xp[-2:] = np.nan + xp = Series(xp)[::step] + + rs = Series(vals).rolling(5, win_type=win_types, center=True, step=step).mean() + tm.assert_series_equal(xp, rs) + + +def test_cmov_window_regular_missing_data(win_types, step): + # GH 8238 + pytest.importorskip("scipy") + vals = np.array( + [6.95, 15.21, 4.72, 9.12, 13.81, 13.49, 16.68, np.nan, 10.63, 14.48] + ) + xps = { + "bartlett": [ + np.nan, + np.nan, + 9.70333, + 10.5225, + 8.4425, + 9.1925, + 12.5575, + 14.3675, + 15.61667, + 13.655, + ], + "blackman": [ + np.nan, + np.nan, + 9.04582, + 11.41536, + 7.73345, + 9.17869, + 12.79607, + 14.20036, + 15.8706, + 13.655, + ], + "barthann": [ + np.nan, + np.nan, + 9.70333, + 10.5225, + 8.4425, + 9.1925, + 12.5575, + 14.3675, + 15.61667, + 13.655, + ], + "bohman": [ + np.nan, + np.nan, + 8.9444, + 11.56327, + 7.61599, + 9.1764, + 12.83559, + 14.17267, + 15.90976, + 13.655, + ], + "hamming": [ + np.nan, + np.nan, + 9.59321, + 10.29694, + 8.71384, + 9.56348, + 12.38009, + 14.20565, + 15.24694, + 13.69758, + ], + "nuttall": [ + np.nan, + np.nan, + 8.47693, + 12.2821, + 7.04618, + 9.16786, + 13.02671, + 14.03673, + 16.08759, + 13.65553, + ], + "triang": [ + np.nan, + np.nan, + 9.33167, + 9.76125, + 9.28667, + 10.34667, + 12.00556, + 13.82125, + 14.49429, + 13.765, + ], + "blackmanharris": [ + np.nan, + np.nan, + 8.42526, + 12.36824, + 6.97691, + 9.16438, + 13.05052, + 14.02175, + 16.1098, + 13.65509, + ], + } + + xp = Series(xps[win_types])[::step] + rs = Series(vals).rolling(5, win_type=win_types, min_periods=3, step=step).mean() + tm.assert_series_equal(xp, rs) + + +def test_cmov_window_special(win_types_special, step): + # GH 8238 + pytest.importorskip("scipy") + kwds = { + "kaiser": {"beta": 1.0}, + "gaussian": {"std": 1.0}, + "general_gaussian": {"p": 2.0, "sig": 2.0}, + "exponential": {"tau": 10}, + } + + vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81, 13.49, 16.68, 9.48, 10.63, 14.48]) + + xps = { + "gaussian": [ + np.nan, + np.nan, + 8.97297, + 9.76077, + 12.24763, + 13.89053, + 13.65671, + 12.01002, + np.nan, + np.nan, + ], + "general_gaussian": [ + np.nan, + np.nan, + 9.85011, + 10.71589, + 11.73161, + 13.08516, + 12.95111, + 12.74577, + np.nan, + np.nan, + ], + "kaiser": [ + np.nan, + np.nan, + 9.86851, + 11.02969, + 11.65161, + 12.75129, + 12.90702, + 12.83757, + np.nan, + np.nan, + ], + "exponential": [ + np.nan, + np.nan, + 9.83364, + 11.10472, + 11.64551, + 12.66138, + 12.92379, + 12.83770, + np.nan, + np.nan, + ], + } + + xp = Series(xps[win_types_special])[::step] + rs = ( + Series(vals) + .rolling(5, win_type=win_types_special, center=True, step=step) + .mean(**kwds[win_types_special]) + ) + tm.assert_series_equal(xp, rs) + + +def test_cmov_window_special_linear_range(win_types_special, step): + # GH 8238 + pytest.importorskip("scipy") + kwds = { + "kaiser": {"beta": 1.0}, + "gaussian": {"std": 1.0}, + "general_gaussian": {"p": 2.0, "sig": 2.0}, + "slepian": {"width": 0.5}, + "exponential": {"tau": 10}, + } + + vals = np.array(range(10), dtype=float) + xp = vals.copy() + xp[:2] = np.nan + xp[-2:] = np.nan + xp = Series(xp)[::step] + + rs = ( + Series(vals) + .rolling(5, win_type=win_types_special, center=True, step=step) + .mean(**kwds[win_types_special]) + ) + tm.assert_series_equal(xp, rs) + + +def test_weighted_var_big_window_no_segfault(win_types, center): + # GitHub Issue #46772 + pytest.importorskip("scipy") + x = Series(0) + result = x.rolling(window=16, center=center, win_type=win_types).var() + expected = Series(np.nan) + + tm.assert_series_equal(result, expected) + + +def test_rolling_center_axis_1(): + pytest.importorskip("scipy") + df = DataFrame( + {"a": [1, 1, 0, 0, 0, 1], "b": [1, 0, 0, 1, 0, 0], "c": [1, 0, 0, 1, 0, 1]} + ) + + msg = "Support for axis=1 in DataFrame.rolling is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.rolling(window=3, axis=1, win_type="boxcar", center=True).sum() + + expected = DataFrame( + {"a": [np.nan] * 6, "b": [3.0, 1.0, 0.0, 2.0, 0.0, 2.0], "c": [np.nan] * 6} + ) + + tm.assert_frame_equal(result, expected, check_dtype=True) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tseries/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tseries/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e361726dc6f80d41cb4975641b44624427b489d6 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tseries/__init__.py @@ -0,0 +1,12 @@ +# ruff: noqa: TCH004 +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + # import modules that have public classes/functions: + from pandas.tseries import ( + frequencies, + offsets, + ) + + # and mark only those modules as public + __all__ = ["frequencies", "offsets"] diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tseries/__pycache__/__init__.cpython-310.pyc b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tseries/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..83c3b5a6b3f7e59a7cf85024554faa9086e73acd Binary files /dev/null and b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tseries/__pycache__/__init__.cpython-310.pyc differ diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tseries/__pycache__/api.cpython-310.pyc b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tseries/__pycache__/api.cpython-310.pyc new file mode 100644 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b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tseries/__pycache__/offsets.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e82191074931698ddc35b72413fa8a5a85159b69 Binary files /dev/null and b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tseries/__pycache__/offsets.cpython-310.pyc differ diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tseries/api.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tseries/api.py new file mode 100644 index 0000000000000000000000000000000000000000..ec2d7d230483947d78f737af42684effa3e60514 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tseries/api.py @@ -0,0 +1,10 @@ +""" +Timeseries API +""" + +from pandas._libs.tslibs.parsing import guess_datetime_format + +from pandas.tseries import offsets +from pandas.tseries.frequencies import infer_freq + +__all__ = ["infer_freq", "offsets", "guess_datetime_format"] diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tseries/frequencies.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tseries/frequencies.py new file mode 100644 index 0000000000000000000000000000000000000000..4a1a668426b36ef10f56352653121b6420070882 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tseries/frequencies.py @@ -0,0 +1,602 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np + +from pandas._libs import lib +from pandas._libs.algos import unique_deltas +from pandas._libs.tslibs import ( + Timestamp, + get_unit_from_dtype, + periods_per_day, + tz_convert_from_utc, +) +from pandas._libs.tslibs.ccalendar import ( + DAYS, + MONTH_ALIASES, + MONTH_NUMBERS, + MONTHS, + int_to_weekday, +) +from pandas._libs.tslibs.dtypes import ( + OFFSET_TO_PERIOD_FREQSTR, + freq_to_period_freqstr, +) +from pandas._libs.tslibs.fields import ( + build_field_sarray, + month_position_check, +) +from pandas._libs.tslibs.offsets import ( + DateOffset, + Day, + to_offset, +) +from pandas._libs.tslibs.parsing import get_rule_month +from pandas.util._decorators import cache_readonly + +from pandas.core.dtypes.common import is_numeric_dtype +from pandas.core.dtypes.dtypes import ( + DatetimeTZDtype, + PeriodDtype, +) +from pandas.core.dtypes.generic import ( + ABCIndex, + ABCSeries, +) + +from pandas.core.algorithms import unique + +if TYPE_CHECKING: + from pandas._typing import npt + + from pandas import ( + DatetimeIndex, + Series, + TimedeltaIndex, + ) + from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin +# -------------------------------------------------------------------- +# Offset related functions + +_need_suffix = ["QS", "BQE", "BQS", "YS", "BYE", "BYS"] + +for _prefix in _need_suffix: + for _m in MONTHS: + key = f"{_prefix}-{_m}" + OFFSET_TO_PERIOD_FREQSTR[key] = OFFSET_TO_PERIOD_FREQSTR[_prefix] + +for _prefix in ["Y", "Q"]: + for _m in MONTHS: + _alias = f"{_prefix}-{_m}" + OFFSET_TO_PERIOD_FREQSTR[_alias] = _alias + +for _d in DAYS: + OFFSET_TO_PERIOD_FREQSTR[f"W-{_d}"] = f"W-{_d}" + + +def get_period_alias(offset_str: str) -> str | None: + """ + Alias to closest period strings BQ->Q etc. + """ + return OFFSET_TO_PERIOD_FREQSTR.get(offset_str, None) + + +# --------------------------------------------------------------------- +# Period codes + + +def infer_freq( + index: DatetimeIndex | TimedeltaIndex | Series | DatetimeLikeArrayMixin, +) -> str | None: + """ + Infer the most likely frequency given the input index. + + Parameters + ---------- + index : DatetimeIndex, TimedeltaIndex, Series or array-like + If passed a Series will use the values of the series (NOT THE INDEX). + + Returns + ------- + str or None + None if no discernible frequency. + + Raises + ------ + TypeError + If the index is not datetime-like. + ValueError + If there are fewer than three values. + + Examples + -------- + >>> idx = pd.date_range(start='2020/12/01', end='2020/12/30', periods=30) + >>> pd.infer_freq(idx) + 'D' + """ + from pandas.core.api import DatetimeIndex + + if isinstance(index, ABCSeries): + values = index._values + if not ( + lib.is_np_dtype(values.dtype, "mM") + or isinstance(values.dtype, DatetimeTZDtype) + or values.dtype == object + ): + raise TypeError( + "cannot infer freq from a non-convertible dtype " + f"on a Series of {index.dtype}" + ) + index = values + + inferer: _FrequencyInferer + + if not hasattr(index, "dtype"): + pass + elif isinstance(index.dtype, PeriodDtype): + raise TypeError( + "PeriodIndex given. Check the `freq` attribute " + "instead of using infer_freq." + ) + elif lib.is_np_dtype(index.dtype, "m"): + # Allow TimedeltaIndex and TimedeltaArray + inferer = _TimedeltaFrequencyInferer(index) + return inferer.get_freq() + + elif is_numeric_dtype(index.dtype): + raise TypeError( + f"cannot infer freq from a non-convertible index of dtype {index.dtype}" + ) + + if not isinstance(index, DatetimeIndex): + index = DatetimeIndex(index) + + inferer = _FrequencyInferer(index) + return inferer.get_freq() + + +class _FrequencyInferer: + """ + Not sure if I can avoid the state machine here + """ + + def __init__(self, index) -> None: + self.index = index + self.i8values = index.asi8 + + # For get_unit_from_dtype we need the dtype to the underlying ndarray, + # which for tz-aware is not the same as index.dtype + if isinstance(index, ABCIndex): + # error: Item "ndarray[Any, Any]" of "Union[ExtensionArray, + # ndarray[Any, Any]]" has no attribute "_ndarray" + self._creso = get_unit_from_dtype( + index._data._ndarray.dtype # type: ignore[union-attr] + ) + else: + # otherwise we have DTA/TDA + self._creso = get_unit_from_dtype(index._ndarray.dtype) + + # This moves the values, which are implicitly in UTC, to the + # the timezone so they are in local time + if hasattr(index, "tz"): + if index.tz is not None: + self.i8values = tz_convert_from_utc( + self.i8values, index.tz, reso=self._creso + ) + + if len(index) < 3: + raise ValueError("Need at least 3 dates to infer frequency") + + self.is_monotonic = ( + self.index._is_monotonic_increasing or self.index._is_monotonic_decreasing + ) + + @cache_readonly + def deltas(self) -> npt.NDArray[np.int64]: + return unique_deltas(self.i8values) + + @cache_readonly + def deltas_asi8(self) -> npt.NDArray[np.int64]: + # NB: we cannot use self.i8values here because we may have converted + # the tz in __init__ + return unique_deltas(self.index.asi8) + + @cache_readonly + def is_unique(self) -> bool: + return len(self.deltas) == 1 + + @cache_readonly + def is_unique_asi8(self) -> bool: + return len(self.deltas_asi8) == 1 + + def get_freq(self) -> str | None: + """ + Find the appropriate frequency string to describe the inferred + frequency of self.i8values + + Returns + ------- + str or None + """ + if not self.is_monotonic or not self.index._is_unique: + return None + + delta = self.deltas[0] + ppd = periods_per_day(self._creso) + if delta and _is_multiple(delta, ppd): + return self._infer_daily_rule() + + # Business hourly, maybe. 17: one day / 65: one weekend + if self.hour_deltas in ([1, 17], [1, 65], [1, 17, 65]): + return "bh" + + # Possibly intraday frequency. Here we use the + # original .asi8 values as the modified values + # will not work around DST transitions. See #8772 + if not self.is_unique_asi8: + return None + + delta = self.deltas_asi8[0] + pph = ppd // 24 + ppm = pph // 60 + pps = ppm // 60 + if _is_multiple(delta, pph): + # Hours + return _maybe_add_count("h", delta / pph) + elif _is_multiple(delta, ppm): + # Minutes + return _maybe_add_count("min", delta / ppm) + elif _is_multiple(delta, pps): + # Seconds + return _maybe_add_count("s", delta / pps) + elif _is_multiple(delta, (pps // 1000)): + # Milliseconds + return _maybe_add_count("ms", delta / (pps // 1000)) + elif _is_multiple(delta, (pps // 1_000_000)): + # Microseconds + return _maybe_add_count("us", delta / (pps // 1_000_000)) + else: + # Nanoseconds + return _maybe_add_count("ns", delta) + + @cache_readonly + def day_deltas(self) -> list[int]: + ppd = periods_per_day(self._creso) + return [x / ppd for x in self.deltas] + + @cache_readonly + def hour_deltas(self) -> list[int]: + pph = periods_per_day(self._creso) // 24 + return [x / pph for x in self.deltas] + + @cache_readonly + def fields(self) -> np.ndarray: # structured array of fields + return build_field_sarray(self.i8values, reso=self._creso) + + @cache_readonly + def rep_stamp(self) -> Timestamp: + return Timestamp(self.i8values[0], unit=self.index.unit) + + def month_position_check(self) -> str | None: + return month_position_check(self.fields, self.index.dayofweek) + + @cache_readonly + def mdiffs(self) -> npt.NDArray[np.int64]: + nmonths = self.fields["Y"] * 12 + self.fields["M"] + return unique_deltas(nmonths.astype("i8")) + + @cache_readonly + def ydiffs(self) -> npt.NDArray[np.int64]: + return unique_deltas(self.fields["Y"].astype("i8")) + + def _infer_daily_rule(self) -> str | None: + annual_rule = self._get_annual_rule() + if annual_rule: + nyears = self.ydiffs[0] + month = MONTH_ALIASES[self.rep_stamp.month] + alias = f"{annual_rule}-{month}" + return _maybe_add_count(alias, nyears) + + quarterly_rule = self._get_quarterly_rule() + if quarterly_rule: + nquarters = self.mdiffs[0] / 3 + mod_dict = {0: 12, 2: 11, 1: 10} + month = MONTH_ALIASES[mod_dict[self.rep_stamp.month % 3]] + alias = f"{quarterly_rule}-{month}" + return _maybe_add_count(alias, nquarters) + + monthly_rule = self._get_monthly_rule() + if monthly_rule: + return _maybe_add_count(monthly_rule, self.mdiffs[0]) + + if self.is_unique: + return self._get_daily_rule() + + if self._is_business_daily(): + return "B" + + wom_rule = self._get_wom_rule() + if wom_rule: + return wom_rule + + return None + + def _get_daily_rule(self) -> str | None: + ppd = periods_per_day(self._creso) + days = self.deltas[0] / ppd + if days % 7 == 0: + # Weekly + wd = int_to_weekday[self.rep_stamp.weekday()] + alias = f"W-{wd}" + return _maybe_add_count(alias, days / 7) + else: + return _maybe_add_count("D", days) + + def _get_annual_rule(self) -> str | None: + if len(self.ydiffs) > 1: + return None + + if len(unique(self.fields["M"])) > 1: + return None + + pos_check = self.month_position_check() + + if pos_check is None: + return None + else: + return {"cs": "YS", "bs": "BYS", "ce": "YE", "be": "BYE"}.get(pos_check) + + def _get_quarterly_rule(self) -> str | None: + if len(self.mdiffs) > 1: + return None + + if not self.mdiffs[0] % 3 == 0: + return None + + pos_check = self.month_position_check() + + if pos_check is None: + return None + else: + return {"cs": "QS", "bs": "BQS", "ce": "QE", "be": "BQE"}.get(pos_check) + + def _get_monthly_rule(self) -> str | None: + if len(self.mdiffs) > 1: + return None + pos_check = self.month_position_check() + + if pos_check is None: + return None + else: + return {"cs": "MS", "bs": "BMS", "ce": "ME", "be": "BME"}.get(pos_check) + + def _is_business_daily(self) -> bool: + # quick check: cannot be business daily + if self.day_deltas != [1, 3]: + return False + + # probably business daily, but need to confirm + first_weekday = self.index[0].weekday() + shifts = np.diff(self.i8values) + ppd = periods_per_day(self._creso) + shifts = np.floor_divide(shifts, ppd) + weekdays = np.mod(first_weekday + np.cumsum(shifts), 7) + + return bool( + np.all( + ((weekdays == 0) & (shifts == 3)) + | ((weekdays > 0) & (weekdays <= 4) & (shifts == 1)) + ) + ) + + def _get_wom_rule(self) -> str | None: + weekdays = unique(self.index.weekday) + if len(weekdays) > 1: + return None + + week_of_months = unique((self.index.day - 1) // 7) + # Only attempt to infer up to WOM-4. See #9425 + week_of_months = week_of_months[week_of_months < 4] + if len(week_of_months) == 0 or len(week_of_months) > 1: + return None + + # get which week + week = week_of_months[0] + 1 + wd = int_to_weekday[weekdays[0]] + + return f"WOM-{week}{wd}" + + +class _TimedeltaFrequencyInferer(_FrequencyInferer): + def _infer_daily_rule(self): + if self.is_unique: + return self._get_daily_rule() + + +def _is_multiple(us, mult: int) -> bool: + return us % mult == 0 + + +def _maybe_add_count(base: str, count: float) -> str: + if count != 1: + assert count == int(count) + count = int(count) + return f"{count}{base}" + else: + return base + + +# ---------------------------------------------------------------------- +# Frequency comparison + + +def is_subperiod(source, target) -> bool: + """ + Returns True if downsampling is possible between source and target + frequencies + + Parameters + ---------- + source : str or DateOffset + Frequency converting from + target : str or DateOffset + Frequency converting to + + Returns + ------- + bool + """ + if target is None or source is None: + return False + source = _maybe_coerce_freq(source) + target = _maybe_coerce_freq(target) + + if _is_annual(target): + if _is_quarterly(source): + return _quarter_months_conform( + get_rule_month(source), get_rule_month(target) + ) + return source in {"D", "C", "B", "M", "h", "min", "s", "ms", "us", "ns"} + elif _is_quarterly(target): + return source in {"D", "C", "B", "M", "h", "min", "s", "ms", "us", "ns"} + elif _is_monthly(target): + return source in {"D", "C", "B", "h", "min", "s", "ms", "us", "ns"} + elif _is_weekly(target): + return source in {target, "D", "C", "B", "h", "min", "s", "ms", "us", "ns"} + elif target == "B": + return source in {"B", "h", "min", "s", "ms", "us", "ns"} + elif target == "C": + return source in {"C", "h", "min", "s", "ms", "us", "ns"} + elif target == "D": + return source in {"D", "h", "min", "s", "ms", "us", "ns"} + elif target == "h": + return source in {"h", "min", "s", "ms", "us", "ns"} + elif target == "min": + return source in {"min", "s", "ms", "us", "ns"} + elif target == "s": + return source in {"s", "ms", "us", "ns"} + elif target == "ms": + return source in {"ms", "us", "ns"} + elif target == "us": + return source in {"us", "ns"} + elif target == "ns": + return source in {"ns"} + else: + return False + + +def is_superperiod(source, target) -> bool: + """ + Returns True if upsampling is possible between source and target + frequencies + + Parameters + ---------- + source : str or DateOffset + Frequency converting from + target : str or DateOffset + Frequency converting to + + Returns + ------- + bool + """ + if target is None or source is None: + return False + source = _maybe_coerce_freq(source) + target = _maybe_coerce_freq(target) + + if _is_annual(source): + if _is_annual(target): + return get_rule_month(source) == get_rule_month(target) + + if _is_quarterly(target): + smonth = get_rule_month(source) + tmonth = get_rule_month(target) + return _quarter_months_conform(smonth, tmonth) + return target in {"D", "C", "B", "M", "h", "min", "s", "ms", "us", "ns"} + elif _is_quarterly(source): + return target in {"D", "C", "B", "M", "h", "min", "s", "ms", "us", "ns"} + elif _is_monthly(source): + return target in {"D", "C", "B", "h", "min", "s", "ms", "us", "ns"} + elif _is_weekly(source): + return target in {source, "D", "C", "B", "h", "min", "s", "ms", "us", "ns"} + elif source == "B": + return target in {"D", "C", "B", "h", "min", "s", "ms", "us", "ns"} + elif source == "C": + return target in {"D", "C", "B", "h", "min", "s", "ms", "us", "ns"} + elif source == "D": + return target in {"D", "C", "B", "h", "min", "s", "ms", "us", "ns"} + elif source == "h": + return target in {"h", "min", "s", "ms", "us", "ns"} + elif source == "min": + return target in {"min", "s", "ms", "us", "ns"} + elif source == "s": + return target in {"s", "ms", "us", "ns"} + elif source == "ms": + return target in {"ms", "us", "ns"} + elif source == "us": + return target in {"us", "ns"} + elif source == "ns": + return target in {"ns"} + else: + return False + + +def _maybe_coerce_freq(code) -> str: + """we might need to coerce a code to a rule_code + and uppercase it + + Parameters + ---------- + source : str or DateOffset + Frequency converting from + + Returns + ------- + str + """ + assert code is not None + if isinstance(code, DateOffset): + code = freq_to_period_freqstr(1, code.name) + if code in {"h", "min", "s", "ms", "us", "ns"}: + return code + else: + return code.upper() + + +def _quarter_months_conform(source: str, target: str) -> bool: + snum = MONTH_NUMBERS[source] + tnum = MONTH_NUMBERS[target] + return snum % 3 == tnum % 3 + + +def _is_annual(rule: str) -> bool: + rule = rule.upper() + return rule == "Y" or rule.startswith("Y-") + + +def _is_quarterly(rule: str) -> bool: + rule = rule.upper() + return rule == "Q" or rule.startswith(("Q-", "BQ")) + + +def _is_monthly(rule: str) -> bool: + rule = rule.upper() + return rule in ("M", "BM") + + +def _is_weekly(rule: str) -> bool: + rule = rule.upper() + return rule == "W" or rule.startswith("W-") + + +__all__ = [ + "Day", + "get_period_alias", + "infer_freq", + "is_subperiod", + "is_superperiod", + "to_offset", +] diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tseries/holiday.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tseries/holiday.py new file mode 100644 index 0000000000000000000000000000000000000000..3c429a960b45181c831055cb3e877dbc4cad3b30 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tseries/holiday.py @@ -0,0 +1,634 @@ +from __future__ import annotations + +from datetime import ( + datetime, + timedelta, +) +import warnings + +from dateutil.relativedelta import ( + FR, + MO, + SA, + SU, + TH, + TU, + WE, +) +import numpy as np + +from pandas.errors import PerformanceWarning + +from pandas import ( + DateOffset, + DatetimeIndex, + Series, + Timestamp, + concat, + date_range, +) + +from pandas.tseries.offsets import ( + Day, + Easter, +) + + +def next_monday(dt: datetime) -> datetime: + """ + If holiday falls on Saturday, use following Monday instead; + if holiday falls on Sunday, use Monday instead + """ + if dt.weekday() == 5: + return dt + timedelta(2) + elif dt.weekday() == 6: + return dt + timedelta(1) + return dt + + +def next_monday_or_tuesday(dt: datetime) -> datetime: + """ + For second holiday of two adjacent ones! + If holiday falls on Saturday, use following Monday instead; + if holiday falls on Sunday or Monday, use following Tuesday instead + (because Monday is already taken by adjacent holiday on the day before) + """ + dow = dt.weekday() + if dow in (5, 6): + return dt + timedelta(2) + if dow == 0: + return dt + timedelta(1) + return dt + + +def previous_friday(dt: datetime) -> datetime: + """ + If holiday falls on Saturday or Sunday, use previous Friday instead. + """ + if dt.weekday() == 5: + return dt - timedelta(1) + elif dt.weekday() == 6: + return dt - timedelta(2) + return dt + + +def sunday_to_monday(dt: datetime) -> datetime: + """ + If holiday falls on Sunday, use day thereafter (Monday) instead. + """ + if dt.weekday() == 6: + return dt + timedelta(1) + return dt + + +def weekend_to_monday(dt: datetime) -> datetime: + """ + If holiday falls on Sunday or Saturday, + use day thereafter (Monday) instead. + Needed for holidays such as Christmas observation in Europe + """ + if dt.weekday() == 6: + return dt + timedelta(1) + elif dt.weekday() == 5: + return dt + timedelta(2) + return dt + + +def nearest_workday(dt: datetime) -> datetime: + """ + If holiday falls on Saturday, use day before (Friday) instead; + if holiday falls on Sunday, use day thereafter (Monday) instead. + """ + if dt.weekday() == 5: + return dt - timedelta(1) + elif dt.weekday() == 6: + return dt + timedelta(1) + return dt + + +def next_workday(dt: datetime) -> datetime: + """ + returns next weekday used for observances + """ + dt += timedelta(days=1) + while dt.weekday() > 4: + # Mon-Fri are 0-4 + dt += timedelta(days=1) + return dt + + +def previous_workday(dt: datetime) -> datetime: + """ + returns previous weekday used for observances + """ + dt -= timedelta(days=1) + while dt.weekday() > 4: + # Mon-Fri are 0-4 + dt -= timedelta(days=1) + return dt + + +def before_nearest_workday(dt: datetime) -> datetime: + """ + returns previous workday after nearest workday + """ + return previous_workday(nearest_workday(dt)) + + +def after_nearest_workday(dt: datetime) -> datetime: + """ + returns next workday after nearest workday + needed for Boxing day or multiple holidays in a series + """ + return next_workday(nearest_workday(dt)) + + +class Holiday: + """ + Class that defines a holiday with start/end dates and rules + for observance. + """ + + start_date: Timestamp | None + end_date: Timestamp | None + days_of_week: tuple[int, ...] | None + + def __init__( + self, + name: str, + year=None, + month=None, + day=None, + offset=None, + observance=None, + start_date=None, + end_date=None, + days_of_week=None, + ) -> None: + """ + Parameters + ---------- + name : str + Name of the holiday , defaults to class name + offset : array of pandas.tseries.offsets or + class from pandas.tseries.offsets + computes offset from date + observance: function + computes when holiday is given a pandas Timestamp + days_of_week: + provide a tuple of days e.g (0,1,2,3,) for Monday Through Thursday + Monday=0,..,Sunday=6 + + Examples + -------- + >>> from dateutil.relativedelta import MO + + >>> USMemorialDay = pd.tseries.holiday.Holiday( + ... "Memorial Day", month=5, day=31, offset=pd.DateOffset(weekday=MO(-1)) + ... ) + >>> USMemorialDay + Holiday: Memorial Day (month=5, day=31, offset=) + + >>> USLaborDay = pd.tseries.holiday.Holiday( + ... "Labor Day", month=9, day=1, offset=pd.DateOffset(weekday=MO(1)) + ... ) + >>> USLaborDay + Holiday: Labor Day (month=9, day=1, offset=) + + >>> July3rd = pd.tseries.holiday.Holiday("July 3rd", month=7, day=3) + >>> July3rd + Holiday: July 3rd (month=7, day=3, ) + + >>> NewYears = pd.tseries.holiday.Holiday( + ... "New Years Day", month=1, day=1, + ... observance=pd.tseries.holiday.nearest_workday + ... ) + >>> NewYears # doctest: +SKIP + Holiday: New Years Day ( + month=1, day=1, observance= + ) + + >>> July3rd = pd.tseries.holiday.Holiday( + ... "July 3rd", month=7, day=3, + ... days_of_week=(0, 1, 2, 3) + ... ) + >>> July3rd + Holiday: July 3rd (month=7, day=3, ) + """ + if offset is not None and observance is not None: + raise NotImplementedError("Cannot use both offset and observance.") + + self.name = name + self.year = year + self.month = month + self.day = day + self.offset = offset + self.start_date = ( + Timestamp(start_date) if start_date is not None else start_date + ) + self.end_date = Timestamp(end_date) if end_date is not None else end_date + self.observance = observance + assert days_of_week is None or type(days_of_week) == tuple + self.days_of_week = days_of_week + + def __repr__(self) -> str: + info = "" + if self.year is not None: + info += f"year={self.year}, " + info += f"month={self.month}, day={self.day}, " + + if self.offset is not None: + info += f"offset={self.offset}" + + if self.observance is not None: + info += f"observance={self.observance}" + + repr = f"Holiday: {self.name} ({info})" + return repr + + def dates( + self, start_date, end_date, return_name: bool = False + ) -> Series | DatetimeIndex: + """ + Calculate holidays observed between start date and end date + + Parameters + ---------- + start_date : starting date, datetime-like, optional + end_date : ending date, datetime-like, optional + return_name : bool, optional, default=False + If True, return a series that has dates and holiday names. + False will only return dates. + + Returns + ------- + Series or DatetimeIndex + Series if return_name is True + """ + start_date = Timestamp(start_date) + end_date = Timestamp(end_date) + + filter_start_date = start_date + filter_end_date = end_date + + if self.year is not None: + dt = Timestamp(datetime(self.year, self.month, self.day)) + dti = DatetimeIndex([dt]) + if return_name: + return Series(self.name, index=dti) + else: + return dti + + dates = self._reference_dates(start_date, end_date) + holiday_dates = self._apply_rule(dates) + if self.days_of_week is not None: + holiday_dates = holiday_dates[ + np.isin( + # error: "DatetimeIndex" has no attribute "dayofweek" + holiday_dates.dayofweek, # type: ignore[attr-defined] + self.days_of_week, + ).ravel() + ] + + if self.start_date is not None: + filter_start_date = max( + self.start_date.tz_localize(filter_start_date.tz), filter_start_date + ) + if self.end_date is not None: + filter_end_date = min( + self.end_date.tz_localize(filter_end_date.tz), filter_end_date + ) + holiday_dates = holiday_dates[ + (holiday_dates >= filter_start_date) & (holiday_dates <= filter_end_date) + ] + if return_name: + return Series(self.name, index=holiday_dates) + return holiday_dates + + def _reference_dates( + self, start_date: Timestamp, end_date: Timestamp + ) -> DatetimeIndex: + """ + Get reference dates for the holiday. + + Return reference dates for the holiday also returning the year + prior to the start_date and year following the end_date. This ensures + that any offsets to be applied will yield the holidays within + the passed in dates. + """ + if self.start_date is not None: + start_date = self.start_date.tz_localize(start_date.tz) + + if self.end_date is not None: + end_date = self.end_date.tz_localize(start_date.tz) + + year_offset = DateOffset(years=1) + reference_start_date = Timestamp( + datetime(start_date.year - 1, self.month, self.day) + ) + + reference_end_date = Timestamp( + datetime(end_date.year + 1, self.month, self.day) + ) + # Don't process unnecessary holidays + dates = date_range( + start=reference_start_date, + end=reference_end_date, + freq=year_offset, + tz=start_date.tz, + ) + + return dates + + def _apply_rule(self, dates: DatetimeIndex) -> DatetimeIndex: + """ + Apply the given offset/observance to a DatetimeIndex of dates. + + Parameters + ---------- + dates : DatetimeIndex + Dates to apply the given offset/observance rule + + Returns + ------- + Dates with rules applied + """ + if dates.empty: + return dates.copy() + + if self.observance is not None: + return dates.map(lambda d: self.observance(d)) + + if self.offset is not None: + if not isinstance(self.offset, list): + offsets = [self.offset] + else: + offsets = self.offset + for offset in offsets: + # if we are adding a non-vectorized value + # ignore the PerformanceWarnings: + with warnings.catch_warnings(): + warnings.simplefilter("ignore", PerformanceWarning) + dates += offset + return dates + + +holiday_calendars = {} + + +def register(cls) -> None: + try: + name = cls.name + except AttributeError: + name = cls.__name__ + holiday_calendars[name] = cls + + +def get_calendar(name: str): + """ + Return an instance of a calendar based on its name. + + Parameters + ---------- + name : str + Calendar name to return an instance of + """ + return holiday_calendars[name]() + + +class HolidayCalendarMetaClass(type): + def __new__(cls, clsname: str, bases, attrs): + calendar_class = super().__new__(cls, clsname, bases, attrs) + register(calendar_class) + return calendar_class + + +class AbstractHolidayCalendar(metaclass=HolidayCalendarMetaClass): + """ + Abstract interface to create holidays following certain rules. + """ + + rules: list[Holiday] = [] + start_date = Timestamp(datetime(1970, 1, 1)) + end_date = Timestamp(datetime(2200, 12, 31)) + _cache = None + + def __init__(self, name: str = "", rules=None) -> None: + """ + Initializes holiday object with a given set a rules. Normally + classes just have the rules defined within them. + + Parameters + ---------- + name : str + Name of the holiday calendar, defaults to class name + rules : array of Holiday objects + A set of rules used to create the holidays. + """ + super().__init__() + if not name: + name = type(self).__name__ + self.name = name + + if rules is not None: + self.rules = rules + + def rule_from_name(self, name: str): + for rule in self.rules: + if rule.name == name: + return rule + + return None + + def holidays(self, start=None, end=None, return_name: bool = False): + """ + Returns a curve with holidays between start_date and end_date + + Parameters + ---------- + start : starting date, datetime-like, optional + end : ending date, datetime-like, optional + return_name : bool, optional + If True, return a series that has dates and holiday names. + False will only return a DatetimeIndex of dates. + + Returns + ------- + DatetimeIndex of holidays + """ + if self.rules is None: + raise Exception( + f"Holiday Calendar {self.name} does not have any rules specified" + ) + + if start is None: + start = AbstractHolidayCalendar.start_date + + if end is None: + end = AbstractHolidayCalendar.end_date + + start = Timestamp(start) + end = Timestamp(end) + + # If we don't have a cache or the dates are outside the prior cache, we + # get them again + if self._cache is None or start < self._cache[0] or end > self._cache[1]: + pre_holidays = [ + rule.dates(start, end, return_name=True) for rule in self.rules + ] + if pre_holidays: + # error: Argument 1 to "concat" has incompatible type + # "List[Union[Series, DatetimeIndex]]"; expected + # "Union[Iterable[DataFrame], Mapping[, DataFrame]]" + holidays = concat(pre_holidays) # type: ignore[arg-type] + else: + # error: Incompatible types in assignment (expression has type + # "Series", variable has type "DataFrame") + holidays = Series( + index=DatetimeIndex([]), dtype=object + ) # type: ignore[assignment] + + self._cache = (start, end, holidays.sort_index()) + + holidays = self._cache[2] + holidays = holidays[start:end] + + if return_name: + return holidays + else: + return holidays.index + + @staticmethod + def merge_class(base, other): + """ + Merge holiday calendars together. The base calendar + will take precedence to other. The merge will be done + based on each holiday's name. + + Parameters + ---------- + base : AbstractHolidayCalendar + instance/subclass or array of Holiday objects + other : AbstractHolidayCalendar + instance/subclass or array of Holiday objects + """ + try: + other = other.rules + except AttributeError: + pass + + if not isinstance(other, list): + other = [other] + other_holidays = {holiday.name: holiday for holiday in other} + + try: + base = base.rules + except AttributeError: + pass + + if not isinstance(base, list): + base = [base] + base_holidays = {holiday.name: holiday for holiday in base} + + other_holidays.update(base_holidays) + return list(other_holidays.values()) + + def merge(self, other, inplace: bool = False): + """ + Merge holiday calendars together. The caller's class + rules take precedence. The merge will be done + based on each holiday's name. + + Parameters + ---------- + other : holiday calendar + inplace : bool (default=False) + If True set rule_table to holidays, else return array of Holidays + """ + holidays = self.merge_class(self, other) + if inplace: + self.rules = holidays + else: + return holidays + + +USMemorialDay = Holiday( + "Memorial Day", month=5, day=31, offset=DateOffset(weekday=MO(-1)) +) +USLaborDay = Holiday("Labor Day", month=9, day=1, offset=DateOffset(weekday=MO(1))) +USColumbusDay = Holiday( + "Columbus Day", month=10, day=1, offset=DateOffset(weekday=MO(2)) +) +USThanksgivingDay = Holiday( + "Thanksgiving Day", month=11, day=1, offset=DateOffset(weekday=TH(4)) +) +USMartinLutherKingJr = Holiday( + "Birthday of Martin Luther King, Jr.", + start_date=datetime(1986, 1, 1), + month=1, + day=1, + offset=DateOffset(weekday=MO(3)), +) +USPresidentsDay = Holiday( + "Washington's Birthday", month=2, day=1, offset=DateOffset(weekday=MO(3)) +) +GoodFriday = Holiday("Good Friday", month=1, day=1, offset=[Easter(), Day(-2)]) + +EasterMonday = Holiday("Easter Monday", month=1, day=1, offset=[Easter(), Day(1)]) + + +class USFederalHolidayCalendar(AbstractHolidayCalendar): + """ + US Federal Government Holiday Calendar based on rules specified by: + https://www.opm.gov/policy-data-oversight/pay-leave/federal-holidays/ + """ + + rules = [ + Holiday("New Year's Day", month=1, day=1, observance=nearest_workday), + USMartinLutherKingJr, + USPresidentsDay, + USMemorialDay, + Holiday( + "Juneteenth National Independence Day", + month=6, + day=19, + start_date="2021-06-18", + observance=nearest_workday, + ), + Holiday("Independence Day", month=7, day=4, observance=nearest_workday), + USLaborDay, + USColumbusDay, + Holiday("Veterans Day", month=11, day=11, observance=nearest_workday), + USThanksgivingDay, + Holiday("Christmas Day", month=12, day=25, observance=nearest_workday), + ] + + +def HolidayCalendarFactory(name: str, base, other, base_class=AbstractHolidayCalendar): + rules = AbstractHolidayCalendar.merge_class(base, other) + calendar_class = type(name, (base_class,), {"rules": rules, "name": name}) + return calendar_class + + +__all__ = [ + "after_nearest_workday", + "before_nearest_workday", + "FR", + "get_calendar", + "HolidayCalendarFactory", + "MO", + "nearest_workday", + "next_monday", + "next_monday_or_tuesday", + "next_workday", + "previous_friday", + "previous_workday", + "register", + "SA", + "SU", + "sunday_to_monday", + "TH", + "TU", + "WE", + "weekend_to_monday", +] diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tseries/offsets.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tseries/offsets.py new file mode 100644 index 0000000000000000000000000000000000000000..169c9cc18a7fde6289a112ba5932cbb634eb3714 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/tseries/offsets.py @@ -0,0 +1,91 @@ +from __future__ import annotations + +from pandas._libs.tslibs.offsets import ( + FY5253, + BaseOffset, + BDay, + BMonthBegin, + BMonthEnd, + BQuarterBegin, + BQuarterEnd, + BusinessDay, + BusinessHour, + BusinessMonthBegin, + BusinessMonthEnd, + BYearBegin, + BYearEnd, + CBMonthBegin, + CBMonthEnd, + CDay, + CustomBusinessDay, + CustomBusinessHour, + CustomBusinessMonthBegin, + CustomBusinessMonthEnd, + DateOffset, + Day, + Easter, + FY5253Quarter, + Hour, + LastWeekOfMonth, + Micro, + Milli, + Minute, + MonthBegin, + MonthEnd, + Nano, + QuarterBegin, + QuarterEnd, + Second, + SemiMonthBegin, + SemiMonthEnd, + Tick, + Week, + WeekOfMonth, + YearBegin, + YearEnd, +) + +__all__ = [ + "Day", + "BaseOffset", + "BusinessDay", + "BusinessMonthBegin", + "BusinessMonthEnd", + "BDay", + "CustomBusinessDay", + "CustomBusinessMonthBegin", + "CustomBusinessMonthEnd", + "CDay", + "CBMonthEnd", + "CBMonthBegin", + "MonthBegin", + "BMonthBegin", + "MonthEnd", + "BMonthEnd", + "SemiMonthEnd", + "SemiMonthBegin", + "BusinessHour", + "CustomBusinessHour", + "YearBegin", + "BYearBegin", + "YearEnd", + "BYearEnd", + "QuarterBegin", + "BQuarterBegin", + "QuarterEnd", + "BQuarterEnd", + "LastWeekOfMonth", + "FY5253Quarter", + "FY5253", + "Week", + "WeekOfMonth", + "Easter", + "Tick", + "Hour", + "Minute", + "Second", + "Milli", + "Micro", + "Nano", + "DateOffset", +] diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..82b3aa56c653cd1241872c67e9d9016df04a6c5a --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/__init__.py @@ -0,0 +1,29 @@ +def __getattr__(key: str): + # These imports need to be lazy to avoid circular import errors + if key == "hash_array": + from pandas.core.util.hashing import hash_array + + return hash_array + if key == "hash_pandas_object": + from pandas.core.util.hashing import hash_pandas_object + + return hash_pandas_object + if key == "Appender": + from pandas.util._decorators import Appender + + return Appender + if key == "Substitution": + from pandas.util._decorators import Substitution + + return Substitution + + if key == "cache_readonly": + from pandas.util._decorators import cache_readonly + + return cache_readonly + + raise AttributeError(f"module 'pandas.util' has no attribute '{key}'") + + +def capitalize_first_letter(s): + return s[:1].upper() + s[1:] diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/__pycache__/__init__.cpython-310.pyc b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..eba94cfbe22c126cd171638613a4d1f623a65f07 Binary files /dev/null and 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0000000000000000000000000000000000000000..4e8189e72c427d75da0367532edfe04d0a7581e8 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/_decorators.py @@ -0,0 +1,508 @@ +from __future__ import annotations + +from functools import wraps +import inspect +from textwrap import dedent +from typing import ( + TYPE_CHECKING, + Any, + Callable, + cast, +) +import warnings + +from pandas._libs.properties import cache_readonly +from pandas._typing import ( + F, + T, +) +from pandas.util._exceptions import find_stack_level + +if TYPE_CHECKING: + from collections.abc import Mapping + + +def deprecate( + name: str, + alternative: Callable[..., Any], + version: str, + alt_name: str | None = None, + klass: type[Warning] | None = None, + stacklevel: int = 2, + msg: str | None = None, +) -> Callable[[F], F]: + """ + Return a new function that emits a deprecation warning on use. + + To use this method for a deprecated function, another function + `alternative` with the same signature must exist. The deprecated + function will emit a deprecation warning, and in the docstring + it will contain the deprecation directive with the provided version + so it can be detected for future removal. + + Parameters + ---------- + name : str + Name of function to deprecate. + alternative : func + Function to use instead. + version : str + Version of pandas in which the method has been deprecated. + alt_name : str, optional + Name to use in preference of alternative.__name__. + klass : Warning, default FutureWarning + stacklevel : int, default 2 + msg : str + The message to display in the warning. + Default is '{name} is deprecated. Use {alt_name} instead.' + """ + alt_name = alt_name or alternative.__name__ + klass = klass or FutureWarning + warning_msg = msg or f"{name} is deprecated, use {alt_name} instead." + + @wraps(alternative) + def wrapper(*args, **kwargs) -> Callable[..., Any]: + warnings.warn(warning_msg, klass, stacklevel=stacklevel) + return alternative(*args, **kwargs) + + # adding deprecated directive to the docstring + msg = msg or f"Use `{alt_name}` instead." + doc_error_msg = ( + "deprecate needs a correctly formatted docstring in " + "the target function (should have a one liner short " + "summary, and opening quotes should be in their own " + f"line). Found:\n{alternative.__doc__}" + ) + + # when python is running in optimized mode (i.e. `-OO`), docstrings are + # removed, so we check that a docstring with correct formatting is used + # but we allow empty docstrings + if alternative.__doc__: + if alternative.__doc__.count("\n") < 3: + raise AssertionError(doc_error_msg) + empty1, summary, empty2, doc_string = alternative.__doc__.split("\n", 3) + if empty1 or empty2 and not summary: + raise AssertionError(doc_error_msg) + wrapper.__doc__ = dedent( + f""" + {summary.strip()} + + .. deprecated:: {version} + {msg} + + {dedent(doc_string)}""" + ) + # error: Incompatible return value type (got "Callable[[VarArg(Any), KwArg(Any)], + # Callable[...,Any]]", expected "Callable[[F], F]") + return wrapper # type: ignore[return-value] + + +def deprecate_kwarg( + old_arg_name: str, + new_arg_name: str | None, + mapping: Mapping[Any, Any] | Callable[[Any], Any] | None = None, + stacklevel: int = 2, +) -> Callable[[F], F]: + """ + Decorator to deprecate a keyword argument of a function. + + Parameters + ---------- + old_arg_name : str + Name of argument in function to deprecate + new_arg_name : str or None + Name of preferred argument in function. Use None to raise warning that + ``old_arg_name`` keyword is deprecated. + mapping : dict or callable + If mapping is present, use it to translate old arguments to + new arguments. A callable must do its own value checking; + values not found in a dict will be forwarded unchanged. + + Examples + -------- + The following deprecates 'cols', using 'columns' instead + + >>> @deprecate_kwarg(old_arg_name='cols', new_arg_name='columns') + ... def f(columns=''): + ... print(columns) + ... + >>> f(columns='should work ok') + should work ok + + >>> f(cols='should raise warning') # doctest: +SKIP + FutureWarning: cols is deprecated, use columns instead + warnings.warn(msg, FutureWarning) + should raise warning + + >>> f(cols='should error', columns="can\'t pass do both") # doctest: +SKIP + TypeError: Can only specify 'cols' or 'columns', not both + + >>> @deprecate_kwarg('old', 'new', {'yes': True, 'no': False}) + ... def f(new=False): + ... print('yes!' if new else 'no!') + ... + >>> f(old='yes') # doctest: +SKIP + FutureWarning: old='yes' is deprecated, use new=True instead + warnings.warn(msg, FutureWarning) + yes! + + To raise a warning that a keyword will be removed entirely in the future + + >>> @deprecate_kwarg(old_arg_name='cols', new_arg_name=None) + ... def f(cols='', another_param=''): + ... print(cols) + ... + >>> f(cols='should raise warning') # doctest: +SKIP + FutureWarning: the 'cols' keyword is deprecated and will be removed in a + future version please takes steps to stop use of 'cols' + should raise warning + >>> f(another_param='should not raise warning') # doctest: +SKIP + should not raise warning + + >>> f(cols='should raise warning', another_param='') # doctest: +SKIP + FutureWarning: the 'cols' keyword is deprecated and will be removed in a + future version please takes steps to stop use of 'cols' + should raise warning + """ + if mapping is not None and not hasattr(mapping, "get") and not callable(mapping): + raise TypeError( + "mapping from old to new argument values must be dict or callable!" + ) + + def _deprecate_kwarg(func: F) -> F: + @wraps(func) + def wrapper(*args, **kwargs) -> Callable[..., Any]: + old_arg_value = kwargs.pop(old_arg_name, None) + + if old_arg_value is not None: + if new_arg_name is None: + msg = ( + f"the {repr(old_arg_name)} keyword is deprecated and " + "will be removed in a future version. Please take " + f"steps to stop the use of {repr(old_arg_name)}" + ) + warnings.warn(msg, FutureWarning, stacklevel=stacklevel) + kwargs[old_arg_name] = old_arg_value + return func(*args, **kwargs) + + elif mapping is not None: + if callable(mapping): + new_arg_value = mapping(old_arg_value) + else: + new_arg_value = mapping.get(old_arg_value, old_arg_value) + msg = ( + f"the {old_arg_name}={repr(old_arg_value)} keyword is " + "deprecated, use " + f"{new_arg_name}={repr(new_arg_value)} instead." + ) + else: + new_arg_value = old_arg_value + msg = ( + f"the {repr(old_arg_name)} keyword is deprecated, " + f"use {repr(new_arg_name)} instead." + ) + + warnings.warn(msg, FutureWarning, stacklevel=stacklevel) + if kwargs.get(new_arg_name) is not None: + msg = ( + f"Can only specify {repr(old_arg_name)} " + f"or {repr(new_arg_name)}, not both." + ) + raise TypeError(msg) + kwargs[new_arg_name] = new_arg_value + return func(*args, **kwargs) + + return cast(F, wrapper) + + return _deprecate_kwarg + + +def _format_argument_list(allow_args: list[str]) -> str: + """ + Convert the allow_args argument (either string or integer) of + `deprecate_nonkeyword_arguments` function to a string describing + it to be inserted into warning message. + + Parameters + ---------- + allowed_args : list, tuple or int + The `allowed_args` argument for `deprecate_nonkeyword_arguments`, + but None value is not allowed. + + Returns + ------- + str + The substring describing the argument list in best way to be + inserted to the warning message. + + Examples + -------- + `format_argument_list([])` -> '' + `format_argument_list(['a'])` -> "except for the arguments 'a'" + `format_argument_list(['a', 'b'])` -> "except for the arguments 'a' and 'b'" + `format_argument_list(['a', 'b', 'c'])` -> + "except for the arguments 'a', 'b' and 'c'" + """ + if "self" in allow_args: + allow_args.remove("self") + if not allow_args: + return "" + elif len(allow_args) == 1: + return f" except for the argument '{allow_args[0]}'" + else: + last = allow_args[-1] + args = ", ".join(["'" + x + "'" for x in allow_args[:-1]]) + return f" except for the arguments {args} and '{last}'" + + +def future_version_msg(version: str | None) -> str: + """Specify which version of pandas the deprecation will take place in.""" + if version is None: + return "In a future version of pandas" + else: + return f"Starting with pandas version {version}" + + +def deprecate_nonkeyword_arguments( + version: str | None, + allowed_args: list[str] | None = None, + name: str | None = None, +) -> Callable[[F], F]: + """ + Decorator to deprecate a use of non-keyword arguments of a function. + + Parameters + ---------- + version : str, optional + The version in which positional arguments will become + keyword-only. If None, then the warning message won't + specify any particular version. + + allowed_args : list, optional + In case of list, it must be the list of names of some + first arguments of the decorated functions that are + OK to be given as positional arguments. In case of None value, + defaults to list of all arguments not having the + default value. + + name : str, optional + The specific name of the function to show in the warning + message. If None, then the Qualified name of the function + is used. + """ + + def decorate(func): + old_sig = inspect.signature(func) + + if allowed_args is not None: + allow_args = allowed_args + else: + allow_args = [ + p.name + for p in old_sig.parameters.values() + if p.kind in (p.POSITIONAL_ONLY, p.POSITIONAL_OR_KEYWORD) + and p.default is p.empty + ] + + new_params = [ + p.replace(kind=p.KEYWORD_ONLY) + if ( + p.kind in (p.POSITIONAL_ONLY, p.POSITIONAL_OR_KEYWORD) + and p.name not in allow_args + ) + else p + for p in old_sig.parameters.values() + ] + new_params.sort(key=lambda p: p.kind) + new_sig = old_sig.replace(parameters=new_params) + + num_allow_args = len(allow_args) + msg = ( + f"{future_version_msg(version)} all arguments of " + f"{name or func.__qualname__}{{arguments}} will be keyword-only." + ) + + @wraps(func) + def wrapper(*args, **kwargs): + if len(args) > num_allow_args: + warnings.warn( + msg.format(arguments=_format_argument_list(allow_args)), + FutureWarning, + stacklevel=find_stack_level(), + ) + return func(*args, **kwargs) + + # error: "Callable[[VarArg(Any), KwArg(Any)], Any]" has no + # attribute "__signature__" + wrapper.__signature__ = new_sig # type: ignore[attr-defined] + return wrapper + + return decorate + + +def doc(*docstrings: None | str | Callable, **params) -> Callable[[F], F]: + """ + A decorator to take docstring templates, concatenate them and perform string + substitution on them. + + This decorator will add a variable "_docstring_components" to the wrapped + callable to keep track the original docstring template for potential usage. + If it should be consider as a template, it will be saved as a string. + Otherwise, it will be saved as callable, and later user __doc__ and dedent + to get docstring. + + Parameters + ---------- + *docstrings : None, str, or callable + The string / docstring / docstring template to be appended in order + after default docstring under callable. + **params + The string which would be used to format docstring template. + """ + + def decorator(decorated: F) -> F: + # collecting docstring and docstring templates + docstring_components: list[str | Callable] = [] + if decorated.__doc__: + docstring_components.append(dedent(decorated.__doc__)) + + for docstring in docstrings: + if docstring is None: + continue + if hasattr(docstring, "_docstring_components"): + docstring_components.extend( + docstring._docstring_components # pyright: ignore[reportGeneralTypeIssues] + ) + elif isinstance(docstring, str) or docstring.__doc__: + docstring_components.append(docstring) + + params_applied = [ + component.format(**params) + if isinstance(component, str) and len(params) > 0 + else component + for component in docstring_components + ] + + decorated.__doc__ = "".join( + [ + component + if isinstance(component, str) + else dedent(component.__doc__ or "") + for component in params_applied + ] + ) + + # error: "F" has no attribute "_docstring_components" + decorated._docstring_components = ( # type: ignore[attr-defined] + docstring_components + ) + return decorated + + return decorator + + +# Substitution and Appender are derived from matplotlib.docstring (1.1.0) +# module https://matplotlib.org/users/license.html + + +class Substitution: + """ + A decorator to take a function's docstring and perform string + substitution on it. + + This decorator should be robust even if func.__doc__ is None + (for example, if -OO was passed to the interpreter) + + Usage: construct a docstring.Substitution with a sequence or + dictionary suitable for performing substitution; then + decorate a suitable function with the constructed object. e.g. + + sub_author_name = Substitution(author='Jason') + + @sub_author_name + def some_function(x): + "%(author)s wrote this function" + + # note that some_function.__doc__ is now "Jason wrote this function" + + One can also use positional arguments. + + sub_first_last_names = Substitution('Edgar Allen', 'Poe') + + @sub_first_last_names + def some_function(x): + "%s %s wrote the Raven" + """ + + def __init__(self, *args, **kwargs) -> None: + if args and kwargs: + raise AssertionError("Only positional or keyword args are allowed") + + self.params = args or kwargs + + def __call__(self, func: F) -> F: + func.__doc__ = func.__doc__ and func.__doc__ % self.params + return func + + def update(self, *args, **kwargs) -> None: + """ + Update self.params with supplied args. + """ + if isinstance(self.params, dict): + self.params.update(*args, **kwargs) + + +class Appender: + """ + A function decorator that will append an addendum to the docstring + of the target function. + + This decorator should be robust even if func.__doc__ is None + (for example, if -OO was passed to the interpreter). + + Usage: construct a docstring.Appender with a string to be joined to + the original docstring. An optional 'join' parameter may be supplied + which will be used to join the docstring and addendum. e.g. + + add_copyright = Appender("Copyright (c) 2009", join='\n') + + @add_copyright + def my_dog(has='fleas'): + "This docstring will have a copyright below" + pass + """ + + addendum: str | None + + def __init__(self, addendum: str | None, join: str = "", indents: int = 0) -> None: + if indents > 0: + self.addendum = indent(addendum, indents=indents) + else: + self.addendum = addendum + self.join = join + + def __call__(self, func: T) -> T: + func.__doc__ = func.__doc__ if func.__doc__ else "" + self.addendum = self.addendum if self.addendum else "" + docitems = [func.__doc__, self.addendum] + func.__doc__ = dedent(self.join.join(docitems)) + return func + + +def indent(text: str | None, indents: int = 1) -> str: + if not text or not isinstance(text, str): + return "" + jointext = "".join(["\n"] + [" "] * indents) + return jointext.join(text.split("\n")) + + +__all__ = [ + "Appender", + "cache_readonly", + "deprecate", + "deprecate_kwarg", + "deprecate_nonkeyword_arguments", + "doc", + "future_version_msg", + "Substitution", +] diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/_doctools.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/_doctools.py new file mode 100644 index 0000000000000000000000000000000000000000..12619abf4baaf336dfd3d5ae78a9bc2133f310c0 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/_doctools.py @@ -0,0 +1,202 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np + +import pandas as pd + +if TYPE_CHECKING: + from collections.abc import Iterable + + +class TablePlotter: + """ + Layout some DataFrames in vertical/horizontal layout for explanation. + Used in merging.rst + """ + + def __init__( + self, + cell_width: float = 0.37, + cell_height: float = 0.25, + font_size: float = 7.5, + ) -> None: + self.cell_width = cell_width + self.cell_height = cell_height + self.font_size = font_size + + def _shape(self, df: pd.DataFrame) -> tuple[int, int]: + """ + Calculate table shape considering index levels. + """ + row, col = df.shape + return row + df.columns.nlevels, col + df.index.nlevels + + def _get_cells(self, left, right, vertical) -> tuple[int, int]: + """ + Calculate appropriate figure size based on left and right data. + """ + if vertical: + # calculate required number of cells + vcells = max(sum(self._shape(df)[0] for df in left), self._shape(right)[0]) + hcells = max(self._shape(df)[1] for df in left) + self._shape(right)[1] + else: + vcells = max([self._shape(df)[0] for df in left] + [self._shape(right)[0]]) + hcells = sum([self._shape(df)[1] for df in left] + [self._shape(right)[1]]) + return hcells, vcells + + def plot(self, left, right, labels: Iterable[str] = (), vertical: bool = True): + """ + Plot left / right DataFrames in specified layout. + + Parameters + ---------- + left : list of DataFrames before operation is applied + right : DataFrame of operation result + labels : list of str to be drawn as titles of left DataFrames + vertical : bool, default True + If True, use vertical layout. If False, use horizontal layout. + """ + from matplotlib import gridspec + import matplotlib.pyplot as plt + + if not isinstance(left, list): + left = [left] + left = [self._conv(df) for df in left] + right = self._conv(right) + + hcells, vcells = self._get_cells(left, right, vertical) + + if vertical: + figsize = self.cell_width * hcells, self.cell_height * vcells + else: + # include margin for titles + figsize = self.cell_width * hcells, self.cell_height * vcells + fig = plt.figure(figsize=figsize) + + if vertical: + gs = gridspec.GridSpec(len(left), hcells) + # left + max_left_cols = max(self._shape(df)[1] for df in left) + max_left_rows = max(self._shape(df)[0] for df in left) + for i, (_left, _label) in enumerate(zip(left, labels)): + ax = fig.add_subplot(gs[i, 0:max_left_cols]) + self._make_table(ax, _left, title=_label, height=1.0 / max_left_rows) + # right + ax = plt.subplot(gs[:, max_left_cols:]) + self._make_table(ax, right, title="Result", height=1.05 / vcells) + fig.subplots_adjust(top=0.9, bottom=0.05, left=0.05, right=0.95) + else: + max_rows = max(self._shape(df)[0] for df in left + [right]) + height = 1.0 / np.max(max_rows) + gs = gridspec.GridSpec(1, hcells) + # left + i = 0 + for df, _label in zip(left, labels): + sp = self._shape(df) + ax = fig.add_subplot(gs[0, i : i + sp[1]]) + self._make_table(ax, df, title=_label, height=height) + i += sp[1] + # right + ax = plt.subplot(gs[0, i:]) + self._make_table(ax, right, title="Result", height=height) + fig.subplots_adjust(top=0.85, bottom=0.05, left=0.05, right=0.95) + + return fig + + def _conv(self, data): + """ + Convert each input to appropriate for table outplot. + """ + if isinstance(data, pd.Series): + if data.name is None: + data = data.to_frame(name="") + else: + data = data.to_frame() + data = data.fillna("NaN") + return data + + def _insert_index(self, data): + # insert is destructive + data = data.copy() + idx_nlevels = data.index.nlevels + if idx_nlevels == 1: + data.insert(0, "Index", data.index) + else: + for i in range(idx_nlevels): + data.insert(i, f"Index{i}", data.index._get_level_values(i)) + + col_nlevels = data.columns.nlevels + if col_nlevels > 1: + col = data.columns._get_level_values(0) + values = [ + data.columns._get_level_values(i)._values for i in range(1, col_nlevels) + ] + col_df = pd.DataFrame(values) + data.columns = col_df.columns + data = pd.concat([col_df, data]) + data.columns = col + return data + + def _make_table(self, ax, df, title: str, height: float | None = None) -> None: + if df is None: + ax.set_visible(False) + return + + from pandas import plotting + + idx_nlevels = df.index.nlevels + col_nlevels = df.columns.nlevels + # must be convert here to get index levels for colorization + df = self._insert_index(df) + tb = plotting.table(ax, df, loc=9) + tb.set_fontsize(self.font_size) + + if height is None: + height = 1.0 / (len(df) + 1) + + props = tb.properties() + for (r, c), cell in props["celld"].items(): + if c == -1: + cell.set_visible(False) + elif r < col_nlevels and c < idx_nlevels: + cell.set_visible(False) + elif r < col_nlevels or c < idx_nlevels: + cell.set_facecolor("#AAAAAA") + cell.set_height(height) + + ax.set_title(title, size=self.font_size) + ax.axis("off") + + +def main() -> None: + import matplotlib.pyplot as plt + + p = TablePlotter() + + df1 = pd.DataFrame({"A": [10, 11, 12], "B": [20, 21, 22], "C": [30, 31, 32]}) + df2 = pd.DataFrame({"A": [10, 12], "C": [30, 32]}) + + p.plot([df1, df2], pd.concat([df1, df2]), labels=["df1", "df2"], vertical=True) + plt.show() + + df3 = pd.DataFrame({"X": [10, 12], "Z": [30, 32]}) + + p.plot( + [df1, df3], pd.concat([df1, df3], axis=1), labels=["df1", "df2"], vertical=False + ) + plt.show() + + idx = pd.MultiIndex.from_tuples( + [(1, "A"), (1, "B"), (1, "C"), (2, "A"), (2, "B"), (2, "C")] + ) + column = pd.MultiIndex.from_tuples([(1, "A"), (1, "B")]) + df3 = pd.DataFrame({"v1": [1, 2, 3, 4, 5, 6], "v2": [5, 6, 7, 8, 9, 10]}, index=idx) + df3.columns = column + p.plot(df3, df3, labels=["df3"]) + plt.show() + + +if __name__ == "__main__": + main() diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/_exceptions.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/_exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..5f50838d373154868ff7414775763a1c66853c65 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/_exceptions.py @@ -0,0 +1,103 @@ +from __future__ import annotations + +import contextlib +import inspect +import os +import re +from typing import TYPE_CHECKING +import warnings + +if TYPE_CHECKING: + from collections.abc import Generator + from types import FrameType + + +@contextlib.contextmanager +def rewrite_exception(old_name: str, new_name: str) -> Generator[None, None, None]: + """ + Rewrite the message of an exception. + """ + try: + yield + except Exception as err: + if not err.args: + raise + msg = str(err.args[0]) + msg = msg.replace(old_name, new_name) + args: tuple[str, ...] = (msg,) + if len(err.args) > 1: + args = args + err.args[1:] + err.args = args + raise + + +def find_stack_level() -> int: + """ + Find the first place in the stack that is not inside pandas + (tests notwithstanding). + """ + + import pandas as pd + + pkg_dir = os.path.dirname(pd.__file__) + test_dir = os.path.join(pkg_dir, "tests") + + # https://stackoverflow.com/questions/17407119/python-inspect-stack-is-slow + frame: FrameType | None = inspect.currentframe() + try: + n = 0 + while frame: + filename = inspect.getfile(frame) + if filename.startswith(pkg_dir) and not filename.startswith(test_dir): + frame = frame.f_back + n += 1 + else: + break + finally: + # See note in + # https://docs.python.org/3/library/inspect.html#inspect.Traceback + del frame + return n + + +@contextlib.contextmanager +def rewrite_warning( + target_message: str, + target_category: type[Warning], + new_message: str, + new_category: type[Warning] | None = None, +) -> Generator[None, None, None]: + """ + Rewrite the message of a warning. + + Parameters + ---------- + target_message : str + Warning message to match. + target_category : Warning + Warning type to match. + new_message : str + New warning message to emit. + new_category : Warning or None, default None + New warning type to emit. When None, will be the same as target_category. + """ + if new_category is None: + new_category = target_category + with warnings.catch_warnings(record=True) as record: + yield + if len(record) > 0: + match = re.compile(target_message) + for warning in record: + if warning.category is target_category and re.search( + match, str(warning.message) + ): + category = new_category + message: Warning | str = new_message + else: + category, message = warning.category, warning.message + warnings.warn_explicit( + message=message, + category=category, + filename=warning.filename, + lineno=warning.lineno, + ) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/_print_versions.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/_print_versions.py new file mode 100644 index 0000000000000000000000000000000000000000..2d8df330f1aaedcc3b0504d366007014a6750356 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/_print_versions.py @@ -0,0 +1,157 @@ +from __future__ import annotations + +import json +import locale +import os +import platform +import struct +import sys +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from pandas._typing import JSONSerializable + +from pandas.compat._optional import ( + VERSIONS, + get_version, + import_optional_dependency, +) + + +def _get_commit_hash() -> str | None: + """ + Use vendored versioneer code to get git hash, which handles + git worktree correctly. + """ + try: + from pandas._version_meson import ( # pyright: ignore [reportMissingImports] + __git_version__, + ) + + return __git_version__ + except ImportError: + from pandas._version import get_versions + + versions = get_versions() + return versions["full-revisionid"] + + +def _get_sys_info() -> dict[str, JSONSerializable]: + """ + Returns system information as a JSON serializable dictionary. + """ + uname_result = platform.uname() + language_code, encoding = locale.getlocale() + return { + "commit": _get_commit_hash(), + "python": platform.python_version(), + "python-bits": struct.calcsize("P") * 8, + "OS": uname_result.system, + "OS-release": uname_result.release, + "Version": uname_result.version, + "machine": uname_result.machine, + "processor": uname_result.processor, + "byteorder": sys.byteorder, + "LC_ALL": os.environ.get("LC_ALL"), + "LANG": os.environ.get("LANG"), + "LOCALE": {"language-code": language_code, "encoding": encoding}, + } + + +def _get_dependency_info() -> dict[str, JSONSerializable]: + """ + Returns dependency information as a JSON serializable dictionary. + """ + deps = [ + "pandas", + # required + "numpy", + "pytz", + "dateutil", + # install / build, + "pip", + "Cython", + # docs + "sphinx", + # Other, not imported. + "IPython", + ] + # Optional dependencies + deps.extend(list(VERSIONS)) + + result: dict[str, JSONSerializable] = {} + for modname in deps: + try: + mod = import_optional_dependency(modname, errors="ignore") + except Exception: + # Dependency conflicts may cause a non ImportError + result[modname] = "N/A" + else: + result[modname] = get_version(mod) if mod else None + return result + + +def show_versions(as_json: str | bool = False) -> None: + """ + Provide useful information, important for bug reports. + + It comprises info about hosting operation system, pandas version, + and versions of other installed relative packages. + + Parameters + ---------- + as_json : str or bool, default False + * If False, outputs info in a human readable form to the console. + * If str, it will be considered as a path to a file. + Info will be written to that file in JSON format. + * If True, outputs info in JSON format to the console. + + Examples + -------- + >>> pd.show_versions() # doctest: +SKIP + Your output may look something like this: + INSTALLED VERSIONS + ------------------ + commit : 37ea63d540fd27274cad6585082c91b1283f963d + python : 3.10.6.final.0 + python-bits : 64 + OS : Linux + OS-release : 5.10.102.1-microsoft-standard-WSL2 + Version : #1 SMP Wed Mar 2 00:30:59 UTC 2022 + machine : x86_64 + processor : x86_64 + byteorder : little + LC_ALL : None + LANG : en_GB.UTF-8 + LOCALE : en_GB.UTF-8 + pandas : 2.0.1 + numpy : 1.24.3 + ... + """ + sys_info = _get_sys_info() + deps = _get_dependency_info() + + if as_json: + j = {"system": sys_info, "dependencies": deps} + + if as_json is True: + sys.stdout.writelines(json.dumps(j, indent=2)) + else: + assert isinstance(as_json, str) # needed for mypy + with open(as_json, "w", encoding="utf-8") as f: + json.dump(j, f, indent=2) + + else: + assert isinstance(sys_info["LOCALE"], dict) # needed for mypy + language_code = sys_info["LOCALE"]["language-code"] + encoding = sys_info["LOCALE"]["encoding"] + sys_info["LOCALE"] = f"{language_code}.{encoding}" + + maxlen = max(len(x) for x in deps) + print("\nINSTALLED VERSIONS") + print("------------------") + for k, v in sys_info.items(): + print(f"{k:<{maxlen}}: {v}") + print("") + for k, v in deps.items(): + print(f"{k:<{maxlen}}: {v}") diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/_test_decorators.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/_test_decorators.py new file mode 100644 index 0000000000000000000000000000000000000000..2c1912bce856dd2694447d820ea2c5124be9c1a0 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/_test_decorators.py @@ -0,0 +1,173 @@ +""" +This module provides decorator functions which can be applied to test objects +in order to skip those objects when certain conditions occur. A sample use case +is to detect if the platform is missing ``matplotlib``. If so, any test objects +which require ``matplotlib`` and decorated with ``@td.skip_if_no("matplotlib")`` +will be skipped by ``pytest`` during the execution of the test suite. + +To illustrate, after importing this module: + +import pandas.util._test_decorators as td + +The decorators can be applied to classes: + +@td.skip_if_no("package") +class Foo: + ... + +Or individual functions: + +@td.skip_if_no("package") +def test_foo(): + ... + +For more information, refer to the ``pytest`` documentation on ``skipif``. +""" +from __future__ import annotations + +import locale +from typing import ( + TYPE_CHECKING, + Callable, +) + +import pytest + +from pandas._config import get_option + +if TYPE_CHECKING: + from pandas._typing import F + +from pandas._config.config import _get_option + +from pandas.compat import ( + IS64, + is_platform_windows, +) +from pandas.compat._optional import import_optional_dependency + + +def skip_if_installed(package: str) -> pytest.MarkDecorator: + """ + Skip a test if a package is installed. + + Parameters + ---------- + package : str + The name of the package. + + Returns + ------- + pytest.MarkDecorator + a pytest.mark.skipif to use as either a test decorator or a + parametrization mark. + """ + return pytest.mark.skipif( + bool(import_optional_dependency(package, errors="ignore")), + reason=f"Skipping because {package} is installed.", + ) + + +def skip_if_no(package: str, min_version: str | None = None) -> pytest.MarkDecorator: + """ + Generic function to help skip tests when required packages are not + present on the testing system. + + This function returns a pytest mark with a skip condition that will be + evaluated during test collection. An attempt will be made to import the + specified ``package`` and optionally ensure it meets the ``min_version`` + + The mark can be used as either a decorator for a test class or to be + applied to parameters in pytest.mark.parametrize calls or parametrized + fixtures. Use pytest.importorskip if an imported moduled is later needed + or for test functions. + + If the import and version check are unsuccessful, then the test function + (or test case when used in conjunction with parametrization) will be + skipped. + + Parameters + ---------- + package: str + The name of the required package. + min_version: str or None, default None + Optional minimum version of the package. + + Returns + ------- + pytest.MarkDecorator + a pytest.mark.skipif to use as either a test decorator or a + parametrization mark. + """ + msg = f"Could not import '{package}'" + if min_version: + msg += f" satisfying a min_version of {min_version}" + return pytest.mark.skipif( + not bool( + import_optional_dependency( + package, errors="ignore", min_version=min_version + ) + ), + reason=msg, + ) + + +skip_if_32bit = pytest.mark.skipif(not IS64, reason="skipping for 32 bit") +skip_if_windows = pytest.mark.skipif(is_platform_windows(), reason="Running on Windows") +skip_if_not_us_locale = pytest.mark.skipif( + locale.getlocale()[0] != "en_US", + reason=f"Set local {locale.getlocale()[0]} is not en_US", +) + + +def parametrize_fixture_doc(*args) -> Callable[[F], F]: + """ + Intended for use as a decorator for parametrized fixture, + this function will wrap the decorated function with a pytest + ``parametrize_fixture_doc`` mark. That mark will format + initial fixture docstring by replacing placeholders {0}, {1} etc + with parameters passed as arguments. + + Parameters + ---------- + args: iterable + Positional arguments for docstring. + + Returns + ------- + function + The decorated function wrapped within a pytest + ``parametrize_fixture_doc`` mark + """ + + def documented_fixture(fixture): + fixture.__doc__ = fixture.__doc__.format(*args) + return fixture + + return documented_fixture + + +def mark_array_manager_not_yet_implemented(request) -> None: + mark = pytest.mark.xfail(reason="Not yet implemented for ArrayManager") + request.applymarker(mark) + + +skip_array_manager_not_yet_implemented = pytest.mark.xfail( + _get_option("mode.data_manager", silent=True) == "array", + reason="Not yet implemented for ArrayManager", +) + +skip_array_manager_invalid_test = pytest.mark.skipif( + _get_option("mode.data_manager", silent=True) == "array", + reason="Test that relies on BlockManager internals or specific behaviour", +) + +skip_copy_on_write_not_yet_implemented = pytest.mark.xfail( + get_option("mode.copy_on_write") is True, + reason="Not yet implemented/adapted for Copy-on-Write mode", +) + +skip_copy_on_write_invalid_test = pytest.mark.skipif( + get_option("mode.copy_on_write") is True, + reason="Test not valid for Copy-on-Write mode", +) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/_tester.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/_tester.py new file mode 100644 index 0000000000000000000000000000000000000000..7cfddef7ddff87275ebf31eb7ec10e65d26f8668 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/_tester.py @@ -0,0 +1,53 @@ +""" +Entrypoint for testing from the top-level namespace. +""" +from __future__ import annotations + +import os +import sys + +from pandas.compat._optional import import_optional_dependency + +PKG = os.path.dirname(os.path.dirname(__file__)) + + +def test(extra_args: list[str] | None = None, run_doctests: bool = False) -> None: + """ + Run the pandas test suite using pytest. + + By default, runs with the marks -m "not slow and not network and not db" + + Parameters + ---------- + extra_args : list[str], default None + Extra marks to run the tests. + run_doctests : bool, default False + Whether to only run the Python and Cython doctests. If you would like to run + both doctests/regular tests, just append "--doctest-modules"/"--doctest-cython" + to extra_args. + + Examples + -------- + >>> pd.test() # doctest: +SKIP + running: pytest... + """ + pytest = import_optional_dependency("pytest") + import_optional_dependency("hypothesis") + cmd = ["-m not slow and not network and not db"] + if extra_args: + if not isinstance(extra_args, list): + extra_args = [extra_args] + cmd = extra_args + if run_doctests: + cmd = [ + "--doctest-modules", + "--doctest-cython", + f"--ignore={os.path.join(PKG, 'tests')}", + ] + cmd += [PKG] + joined = " ".join(cmd) + print(f"running: pytest {joined}") + sys.exit(pytest.main(cmd)) + + +__all__ = ["test"] diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/_validators.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/_validators.py new file mode 100644 index 0000000000000000000000000000000000000000..cb0b4d549f49ea972d50c97986c60be64c021c3c --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/_validators.py @@ -0,0 +1,456 @@ +""" +Module that contains many useful utilities +for validating data or function arguments +""" +from __future__ import annotations + +from collections.abc import ( + Iterable, + Sequence, +) +from typing import ( + TypeVar, + overload, +) + +import numpy as np + +from pandas._libs import lib + +from pandas.core.dtypes.common import ( + is_bool, + is_integer, +) + +BoolishT = TypeVar("BoolishT", bool, int) +BoolishNoneT = TypeVar("BoolishNoneT", bool, int, None) + + +def _check_arg_length(fname, args, max_fname_arg_count, compat_args) -> None: + """ + Checks whether 'args' has length of at most 'compat_args'. Raises + a TypeError if that is not the case, similar to in Python when a + function is called with too many arguments. + """ + if max_fname_arg_count < 0: + raise ValueError("'max_fname_arg_count' must be non-negative") + + if len(args) > len(compat_args): + max_arg_count = len(compat_args) + max_fname_arg_count + actual_arg_count = len(args) + max_fname_arg_count + argument = "argument" if max_arg_count == 1 else "arguments" + + raise TypeError( + f"{fname}() takes at most {max_arg_count} {argument} " + f"({actual_arg_count} given)" + ) + + +def _check_for_default_values(fname, arg_val_dict, compat_args) -> None: + """ + Check that the keys in `arg_val_dict` are mapped to their + default values as specified in `compat_args`. + + Note that this function is to be called only when it has been + checked that arg_val_dict.keys() is a subset of compat_args + """ + for key in arg_val_dict: + # try checking equality directly with '=' operator, + # as comparison may have been overridden for the left + # hand object + try: + v1 = arg_val_dict[key] + v2 = compat_args[key] + + # check for None-ness otherwise we could end up + # comparing a numpy array vs None + if (v1 is not None and v2 is None) or (v1 is None and v2 is not None): + match = False + else: + match = v1 == v2 + + if not is_bool(match): + raise ValueError("'match' is not a boolean") + + # could not compare them directly, so try comparison + # using the 'is' operator + except ValueError: + match = arg_val_dict[key] is compat_args[key] + + if not match: + raise ValueError( + f"the '{key}' parameter is not supported in " + f"the pandas implementation of {fname}()" + ) + + +def validate_args(fname, args, max_fname_arg_count, compat_args) -> None: + """ + Checks whether the length of the `*args` argument passed into a function + has at most `len(compat_args)` arguments and whether or not all of these + elements in `args` are set to their default values. + + Parameters + ---------- + fname : str + The name of the function being passed the `*args` parameter + args : tuple + The `*args` parameter passed into a function + max_fname_arg_count : int + The maximum number of arguments that the function `fname` + can accept, excluding those in `args`. Used for displaying + appropriate error messages. Must be non-negative. + compat_args : dict + A dictionary of keys and their associated default values. + In order to accommodate buggy behaviour in some versions of `numpy`, + where a signature displayed keyword arguments but then passed those + arguments **positionally** internally when calling downstream + implementations, a dict ensures that the original + order of the keyword arguments is enforced. + + Raises + ------ + TypeError + If `args` contains more values than there are `compat_args` + ValueError + If `args` contains values that do not correspond to those + of the default values specified in `compat_args` + """ + _check_arg_length(fname, args, max_fname_arg_count, compat_args) + + # We do this so that we can provide a more informative + # error message about the parameters that we are not + # supporting in the pandas implementation of 'fname' + kwargs = dict(zip(compat_args, args)) + _check_for_default_values(fname, kwargs, compat_args) + + +def _check_for_invalid_keys(fname, kwargs, compat_args) -> None: + """ + Checks whether 'kwargs' contains any keys that are not + in 'compat_args' and raises a TypeError if there is one. + """ + # set(dict) --> set of the dictionary's keys + diff = set(kwargs) - set(compat_args) + + if diff: + bad_arg = next(iter(diff)) + raise TypeError(f"{fname}() got an unexpected keyword argument '{bad_arg}'") + + +def validate_kwargs(fname, kwargs, compat_args) -> None: + """ + Checks whether parameters passed to the **kwargs argument in a + function `fname` are valid parameters as specified in `*compat_args` + and whether or not they are set to their default values. + + Parameters + ---------- + fname : str + The name of the function being passed the `**kwargs` parameter + kwargs : dict + The `**kwargs` parameter passed into `fname` + compat_args: dict + A dictionary of keys that `kwargs` is allowed to have and their + associated default values + + Raises + ------ + TypeError if `kwargs` contains keys not in `compat_args` + ValueError if `kwargs` contains keys in `compat_args` that do not + map to the default values specified in `compat_args` + """ + kwds = kwargs.copy() + _check_for_invalid_keys(fname, kwargs, compat_args) + _check_for_default_values(fname, kwds, compat_args) + + +def validate_args_and_kwargs( + fname, args, kwargs, max_fname_arg_count, compat_args +) -> None: + """ + Checks whether parameters passed to the *args and **kwargs argument in a + function `fname` are valid parameters as specified in `*compat_args` + and whether or not they are set to their default values. + + Parameters + ---------- + fname: str + The name of the function being passed the `**kwargs` parameter + args: tuple + The `*args` parameter passed into a function + kwargs: dict + The `**kwargs` parameter passed into `fname` + max_fname_arg_count: int + The minimum number of arguments that the function `fname` + requires, excluding those in `args`. Used for displaying + appropriate error messages. Must be non-negative. + compat_args: dict + A dictionary of keys that `kwargs` is allowed to + have and their associated default values. + + Raises + ------ + TypeError if `args` contains more values than there are + `compat_args` OR `kwargs` contains keys not in `compat_args` + ValueError if `args` contains values not at the default value (`None`) + `kwargs` contains keys in `compat_args` that do not map to the default + value as specified in `compat_args` + + See Also + -------- + validate_args : Purely args validation. + validate_kwargs : Purely kwargs validation. + + """ + # Check that the total number of arguments passed in (i.e. + # args and kwargs) does not exceed the length of compat_args + _check_arg_length( + fname, args + tuple(kwargs.values()), max_fname_arg_count, compat_args + ) + + # Check there is no overlap with the positional and keyword + # arguments, similar to what is done in actual Python functions + args_dict = dict(zip(compat_args, args)) + + for key in args_dict: + if key in kwargs: + raise TypeError( + f"{fname}() got multiple values for keyword argument '{key}'" + ) + + kwargs.update(args_dict) + validate_kwargs(fname, kwargs, compat_args) + + +def validate_bool_kwarg( + value: BoolishNoneT, + arg_name: str, + none_allowed: bool = True, + int_allowed: bool = False, +) -> BoolishNoneT: + """ + Ensure that argument passed in arg_name can be interpreted as boolean. + + Parameters + ---------- + value : bool + Value to be validated. + arg_name : str + Name of the argument. To be reflected in the error message. + none_allowed : bool, default True + Whether to consider None to be a valid boolean. + int_allowed : bool, default False + Whether to consider integer value to be a valid boolean. + + Returns + ------- + value + The same value as input. + + Raises + ------ + ValueError + If the value is not a valid boolean. + """ + good_value = is_bool(value) + if none_allowed: + good_value = good_value or (value is None) + + if int_allowed: + good_value = good_value or isinstance(value, int) + + if not good_value: + raise ValueError( + f'For argument "{arg_name}" expected type bool, received ' + f"type {type(value).__name__}." + ) + return value # pyright: ignore[reportGeneralTypeIssues] + + +def validate_fillna_kwargs(value, method, validate_scalar_dict_value: bool = True): + """ + Validate the keyword arguments to 'fillna'. + + This checks that exactly one of 'value' and 'method' is specified. + If 'method' is specified, this validates that it's a valid method. + + Parameters + ---------- + value, method : object + The 'value' and 'method' keyword arguments for 'fillna'. + validate_scalar_dict_value : bool, default True + Whether to validate that 'value' is a scalar or dict. Specifically, + validate that it is not a list or tuple. + + Returns + ------- + value, method : object + """ + from pandas.core.missing import clean_fill_method + + if value is None and method is None: + raise ValueError("Must specify a fill 'value' or 'method'.") + if value is None and method is not None: + method = clean_fill_method(method) + + elif value is not None and method is None: + if validate_scalar_dict_value and isinstance(value, (list, tuple)): + raise TypeError( + '"value" parameter must be a scalar or dict, but ' + f'you passed a "{type(value).__name__}"' + ) + + elif value is not None and method is not None: + raise ValueError("Cannot specify both 'value' and 'method'.") + + return value, method + + +def validate_percentile(q: float | Iterable[float]) -> np.ndarray: + """ + Validate percentiles (used by describe and quantile). + + This function checks if the given float or iterable of floats is a valid percentile + otherwise raises a ValueError. + + Parameters + ---------- + q: float or iterable of floats + A single percentile or an iterable of percentiles. + + Returns + ------- + ndarray + An ndarray of the percentiles if valid. + + Raises + ------ + ValueError if percentiles are not in given interval([0, 1]). + """ + q_arr = np.asarray(q) + # Don't change this to an f-string. The string formatting + # is too expensive for cases where we don't need it. + msg = "percentiles should all be in the interval [0, 1]" + if q_arr.ndim == 0: + if not 0 <= q_arr <= 1: + raise ValueError(msg) + else: + if not all(0 <= qs <= 1 for qs in q_arr): + raise ValueError(msg) + return q_arr + + +@overload +def validate_ascending(ascending: BoolishT) -> BoolishT: + ... + + +@overload +def validate_ascending(ascending: Sequence[BoolishT]) -> list[BoolishT]: + ... + + +def validate_ascending( + ascending: bool | int | Sequence[BoolishT], +) -> bool | int | list[BoolishT]: + """Validate ``ascending`` kwargs for ``sort_index`` method.""" + kwargs = {"none_allowed": False, "int_allowed": True} + if not isinstance(ascending, Sequence): + return validate_bool_kwarg(ascending, "ascending", **kwargs) + + return [validate_bool_kwarg(item, "ascending", **kwargs) for item in ascending] + + +def validate_endpoints(closed: str | None) -> tuple[bool, bool]: + """ + Check that the `closed` argument is among [None, "left", "right"] + + Parameters + ---------- + closed : {None, "left", "right"} + + Returns + ------- + left_closed : bool + right_closed : bool + + Raises + ------ + ValueError : if argument is not among valid values + """ + left_closed = False + right_closed = False + + if closed is None: + left_closed = True + right_closed = True + elif closed == "left": + left_closed = True + elif closed == "right": + right_closed = True + else: + raise ValueError("Closed has to be either 'left', 'right' or None") + + return left_closed, right_closed + + +def validate_inclusive(inclusive: str | None) -> tuple[bool, bool]: + """ + Check that the `inclusive` argument is among {"both", "neither", "left", "right"}. + + Parameters + ---------- + inclusive : {"both", "neither", "left", "right"} + + Returns + ------- + left_right_inclusive : tuple[bool, bool] + + Raises + ------ + ValueError : if argument is not among valid values + """ + left_right_inclusive: tuple[bool, bool] | None = None + + if isinstance(inclusive, str): + left_right_inclusive = { + "both": (True, True), + "left": (True, False), + "right": (False, True), + "neither": (False, False), + }.get(inclusive) + + if left_right_inclusive is None: + raise ValueError( + "Inclusive has to be either 'both', 'neither', 'left' or 'right'" + ) + + return left_right_inclusive + + +def validate_insert_loc(loc: int, length: int) -> int: + """ + Check that we have an integer between -length and length, inclusive. + + Standardize negative loc to within [0, length]. + + The exceptions we raise on failure match np.insert. + """ + if not is_integer(loc): + raise TypeError(f"loc must be an integer between -{length} and {length}") + + if loc < 0: + loc += length + if not 0 <= loc <= length: + raise IndexError(f"loc must be an integer between -{length} and {length}") + return loc # pyright: ignore[reportGeneralTypeIssues] + + +def check_dtype_backend(dtype_backend) -> None: + if dtype_backend is not lib.no_default: + if dtype_backend not in ["numpy_nullable", "pyarrow"]: + raise ValueError( + f"dtype_backend {dtype_backend} is invalid, only 'numpy_nullable' and " + f"'pyarrow' are allowed.", + ) diff --git a/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/version/__init__.py b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/version/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3a5efbbb09c1e5085bf0564d819be345edd8c827 --- /dev/null +++ b/Scripts_Climate_n_LAI_to_Yield/.venv/lib/python3.10/site-packages/pandas/util/version/__init__.py @@ -0,0 +1,579 @@ +# Vendored from https://github.com/pypa/packaging/blob/main/packaging/_structures.py +# and https://github.com/pypa/packaging/blob/main/packaging/_structures.py +# changeset ae891fd74d6dd4c6063bb04f2faeadaac6fc6313 +# 04/30/2021 + +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. Licence at LICENSES/PACKAGING_LICENSE +from __future__ import annotations + +import collections +from collections.abc import Iterator +import itertools +import re +from typing import ( + Callable, + SupportsInt, + Tuple, + Union, +) +import warnings + +__all__ = ["parse", "Version", "LegacyVersion", "InvalidVersion", "VERSION_PATTERN"] + + +class InfinityType: + def __repr__(self) -> str: + return "Infinity" + + def __hash__(self) -> int: + return hash(repr(self)) + + def __lt__(self, other: object) -> bool: + return False + + def __le__(self, other: object) -> bool: + return False + + def __eq__(self, other: object) -> bool: + return isinstance(other, type(self)) + + def __ne__(self, other: object) -> bool: + return not isinstance(other, type(self)) + + def __gt__(self, other: object) -> bool: + return True + + def __ge__(self, other: object) -> bool: + return True + + def __neg__(self: object) -> NegativeInfinityType: + return NegativeInfinity + + +Infinity = InfinityType() + + +class NegativeInfinityType: + def __repr__(self) -> str: + return "-Infinity" + + def __hash__(self) -> int: + return hash(repr(self)) + + def __lt__(self, other: object) -> bool: + return True + + def __le__(self, other: object) -> bool: + return True + + def __eq__(self, other: object) -> bool: + return isinstance(other, type(self)) + + def __ne__(self, other: object) -> bool: + return not isinstance(other, type(self)) + + def __gt__(self, other: object) -> bool: + return False + + def __ge__(self, other: object) -> bool: + return False + + def __neg__(self: object) -> InfinityType: + return Infinity + + +NegativeInfinity = NegativeInfinityType() + + +InfiniteTypes = Union[InfinityType, NegativeInfinityType] +PrePostDevType = Union[InfiniteTypes, tuple[str, int]] +SubLocalType = Union[InfiniteTypes, int, str] +LocalType = Union[ + NegativeInfinityType, + tuple[ + Union[ + SubLocalType, + tuple[SubLocalType, str], + tuple[NegativeInfinityType, SubLocalType], + ], + ..., + ], +] +CmpKey = tuple[ + int, tuple[int, ...], PrePostDevType, PrePostDevType, PrePostDevType, LocalType +] +LegacyCmpKey = tuple[int, tuple[str, ...]] +VersionComparisonMethod = Callable[ + [Union[CmpKey, LegacyCmpKey], Union[CmpKey, LegacyCmpKey]], bool +] + +_Version = collections.namedtuple( + "_Version", ["epoch", "release", "dev", "pre", "post", "local"] +) + + +def parse(version: str) -> LegacyVersion | Version: + """ + Parse the given version string and return either a :class:`Version` object + or a :class:`LegacyVersion` object depending on if the given version is + a valid PEP 440 version or a legacy version. + """ + try: + return Version(version) + except InvalidVersion: + return LegacyVersion(version) + + +class InvalidVersion(ValueError): + """ + An invalid version was found, users should refer to PEP 440. + + Examples + -------- + >>> pd.util.version.Version('1.') + Traceback (most recent call last): + InvalidVersion: Invalid version: '1.' + """ + + +class _BaseVersion: + _key: CmpKey | LegacyCmpKey + + def __hash__(self) -> int: + return hash(self._key) + + # Please keep the duplicated `isinstance` check + # in the six comparisons hereunder + # unless you find a way to avoid adding overhead function calls. + def __lt__(self, other: _BaseVersion) -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key < other._key + + def __le__(self, other: _BaseVersion) -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key <= other._key + + def __eq__(self, other: object) -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key == other._key + + def __ge__(self, other: _BaseVersion) -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key >= other._key + + def __gt__(self, other: _BaseVersion) -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key > other._key + + def __ne__(self, other: object) -> bool: + if not isinstance(other, _BaseVersion): + return NotImplemented + + return self._key != other._key + + +class LegacyVersion(_BaseVersion): + def __init__(self, version: str) -> None: + self._version = str(version) + self._key = _legacy_cmpkey(self._version) + + warnings.warn( + "Creating a LegacyVersion has been deprecated and will be " + "removed in the next major release.", + DeprecationWarning, + ) + + def __str__(self) -> str: + return self._version + + def __repr__(self) -> str: + return f"" + + @property + def public(self) -> str: + return self._version + + @property + def base_version(self) -> str: + return self._version + + @property + def epoch(self) -> int: + return -1 + + @property + def release(self) -> None: + return None + + @property + def pre(self) -> None: + return None + + @property + def post(self) -> None: + return None + + @property + def dev(self) -> None: + return None + + @property + def local(self) -> None: + return None + + @property + def is_prerelease(self) -> bool: + return False + + @property + def is_postrelease(self) -> bool: + return False + + @property + def is_devrelease(self) -> bool: + return False + + +_legacy_version_component_re = re.compile(r"(\d+ | [a-z]+ | \.| -)", re.VERBOSE) + +_legacy_version_replacement_map = { + "pre": "c", + "preview": "c", + "-": "final-", + "rc": "c", + "dev": "@", +} + + +def _parse_version_parts(s: str) -> Iterator[str]: + for part in _legacy_version_component_re.split(s): + mapped_part = _legacy_version_replacement_map.get(part, part) + + if not mapped_part or mapped_part == ".": + continue + + if mapped_part[:1] in "0123456789": + # pad for numeric comparison + yield mapped_part.zfill(8) + else: + yield "*" + mapped_part + + # ensure that alpha/beta/candidate are before final + yield "*final" + + +def _legacy_cmpkey(version: str) -> LegacyCmpKey: + # We hardcode an epoch of -1 here. A PEP 440 version can only have a epoch + # greater than or equal to 0. This will effectively put the LegacyVersion, + # which uses the defacto standard originally implemented by setuptools, + # as before all PEP 440 versions. + epoch = -1 + + # This scheme is taken from pkg_resources.parse_version setuptools prior to + # it's adoption of the packaging library. + parts: list[str] = [] + for part in _parse_version_parts(version.lower()): + if part.startswith("*"): + # remove "-" before a prerelease tag + if part < "*final": + while parts and parts[-1] == "*final-": + parts.pop() + + # remove trailing zeros from each series of numeric parts + while parts and parts[-1] == "00000000": + parts.pop() + + parts.append(part) + + return epoch, tuple(parts) + + +# Deliberately not anchored to the start and end of the string, to make it +# easier for 3rd party code to reuse +VERSION_PATTERN = r""" + v? + (?: + (?:(?P[0-9]+)!)? # epoch + (?P[0-9]+(?:\.[0-9]+)*) # release segment + (?P
                                          # pre-release
+            [-_\.]?
+            (?P(a|b|c|rc|alpha|beta|pre|preview))
+            [-_\.]?
+            (?P[0-9]+)?
+        )?
+        (?P                                         # post release
+            (?:-(?P[0-9]+))
+            |
+            (?:
+                [-_\.]?
+                (?Ppost|rev|r)
+                [-_\.]?
+                (?P[0-9]+)?
+            )
+        )?
+        (?P                                          # dev release
+            [-_\.]?
+            (?Pdev)
+            [-_\.]?
+            (?P[0-9]+)?
+        )?
+    )
+    (?:\+(?P[a-z0-9]+(?:[-_\.][a-z0-9]+)*))?       # local version
+"""
+
+
+class Version(_BaseVersion):
+    _regex = re.compile(r"^\s*" + VERSION_PATTERN + r"\s*$", re.VERBOSE | re.IGNORECASE)
+
+    def __init__(self, version: str) -> None:
+        # Validate the version and parse it into pieces
+        match = self._regex.search(version)
+        if not match:
+            raise InvalidVersion(f"Invalid version: '{version}'")
+
+        # Store the parsed out pieces of the version
+        self._version = _Version(
+            epoch=int(match.group("epoch")) if match.group("epoch") else 0,
+            release=tuple(int(i) for i in match.group("release").split(".")),
+            pre=_parse_letter_version(match.group("pre_l"), match.group("pre_n")),
+            post=_parse_letter_version(
+                match.group("post_l"), match.group("post_n1") or match.group("post_n2")
+            ),
+            dev=_parse_letter_version(match.group("dev_l"), match.group("dev_n")),
+            local=_parse_local_version(match.group("local")),
+        )
+
+        # Generate a key which will be used for sorting
+        self._key = _cmpkey(
+            self._version.epoch,
+            self._version.release,
+            self._version.pre,
+            self._version.post,
+            self._version.dev,
+            self._version.local,
+        )
+
+    def __repr__(self) -> str:
+        return f""
+
+    def __str__(self) -> str:
+        parts = []
+
+        # Epoch
+        if self.epoch != 0:
+            parts.append(f"{self.epoch}!")
+
+        # Release segment
+        parts.append(".".join([str(x) for x in self.release]))
+
+        # Pre-release
+        if self.pre is not None:
+            parts.append("".join([str(x) for x in self.pre]))
+
+        # Post-release
+        if self.post is not None:
+            parts.append(f".post{self.post}")
+
+        # Development release
+        if self.dev is not None:
+            parts.append(f".dev{self.dev}")
+
+        # Local version segment
+        if self.local is not None:
+            parts.append(f"+{self.local}")
+
+        return "".join(parts)
+
+    @property
+    def epoch(self) -> int:
+        _epoch: int = self._version.epoch
+        return _epoch
+
+    @property
+    def release(self) -> tuple[int, ...]:
+        _release: tuple[int, ...] = self._version.release
+        return _release
+
+    @property
+    def pre(self) -> tuple[str, int] | None:
+        _pre: tuple[str, int] | None = self._version.pre
+        return _pre
+
+    @property
+    def post(self) -> int | None:
+        return self._version.post[1] if self._version.post else None
+
+    @property
+    def dev(self) -> int | None:
+        return self._version.dev[1] if self._version.dev else None
+
+    @property
+    def local(self) -> str | None:
+        if self._version.local:
+            return ".".join([str(x) for x in self._version.local])
+        else:
+            return None
+
+    @property
+    def public(self) -> str:
+        return str(self).split("+", 1)[0]
+
+    @property
+    def base_version(self) -> str:
+        parts = []
+
+        # Epoch
+        if self.epoch != 0:
+            parts.append(f"{self.epoch}!")
+
+        # Release segment
+        parts.append(".".join([str(x) for x in self.release]))
+
+        return "".join(parts)
+
+    @property
+    def is_prerelease(self) -> bool:
+        return self.dev is not None or self.pre is not None
+
+    @property
+    def is_postrelease(self) -> bool:
+        return self.post is not None
+
+    @property
+    def is_devrelease(self) -> bool:
+        return self.dev is not None
+
+    @property
+    def major(self) -> int:
+        return self.release[0] if len(self.release) >= 1 else 0
+
+    @property
+    def minor(self) -> int:
+        return self.release[1] if len(self.release) >= 2 else 0
+
+    @property
+    def micro(self) -> int:
+        return self.release[2] if len(self.release) >= 3 else 0
+
+
+def _parse_letter_version(
+    letter: str, number: str | bytes | SupportsInt
+) -> tuple[str, int] | None:
+    if letter:
+        # We consider there to be an implicit 0 in a pre-release if there is
+        # not a numeral associated with it.
+        if number is None:
+            number = 0
+
+        # We normalize any letters to their lower case form
+        letter = letter.lower()
+
+        # We consider some words to be alternate spellings of other words and
+        # in those cases we want to normalize the spellings to our preferred
+        # spelling.
+        if letter == "alpha":
+            letter = "a"
+        elif letter == "beta":
+            letter = "b"
+        elif letter in ["c", "pre", "preview"]:
+            letter = "rc"
+        elif letter in ["rev", "r"]:
+            letter = "post"
+
+        return letter, int(number)
+    if not letter and number:
+        # We assume if we are given a number, but we are not given a letter
+        # then this is using the implicit post release syntax (e.g. 1.0-1)
+        letter = "post"
+
+        return letter, int(number)
+
+    return None
+
+
+_local_version_separators = re.compile(r"[\._-]")
+
+
+def _parse_local_version(local: str) -> LocalType | None:
+    """
+    Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve").
+    """
+    if local is not None:
+        return tuple(
+            part.lower() if not part.isdigit() else int(part)
+            for part in _local_version_separators.split(local)
+        )
+    return None
+
+
+def _cmpkey(
+    epoch: int,
+    release: tuple[int, ...],
+    pre: tuple[str, int] | None,
+    post: tuple[str, int] | None,
+    dev: tuple[str, int] | None,
+    local: tuple[SubLocalType] | None,
+) -> CmpKey:
+    # When we compare a release version, we want to compare it with all of the
+    # trailing zeros removed. So we'll use a reverse the list, drop all the now
+    # leading zeros until we come to something non zero, then take the rest
+    # re-reverse it back into the correct order and make it a tuple and use
+    # that for our sorting key.
+    _release = tuple(
+        reversed(list(itertools.dropwhile(lambda x: x == 0, reversed(release))))
+    )
+
+    # We need to "trick" the sorting algorithm to put 1.0.dev0 before 1.0a0.
+    # We'll do this by abusing the pre segment, but we _only_ want to do this
+    # if there is not a pre or a post segment. If we have one of those then
+    # the normal sorting rules will handle this case correctly.
+    if pre is None and post is None and dev is not None:
+        _pre: PrePostDevType = NegativeInfinity
+    # Versions without a pre-release (except as noted above) should sort after
+    # those with one.
+    elif pre is None:
+        _pre = Infinity
+    else:
+        _pre = pre
+
+    # Versions without a post segment should sort before those with one.
+    if post is None:
+        _post: PrePostDevType = NegativeInfinity
+
+    else:
+        _post = post
+
+    # Versions without a development segment should sort after those with one.
+    if dev is None:
+        _dev: PrePostDevType = Infinity
+
+    else:
+        _dev = dev
+
+    if local is None:
+        # Versions without a local segment should sort before those with one.
+        _local: LocalType = NegativeInfinity
+    else:
+        # Versions with a local segment need that segment parsed to implement
+        # the sorting rules in PEP440.
+        # - Alpha numeric segments sort before numeric segments
+        # - Alpha numeric segments sort lexicographically
+        # - Numeric segments sort numerically
+        # - Shorter versions sort before longer versions when the prefixes
+        #   match exactly
+        _local = tuple(
+            (i, "") if isinstance(i, int) else (NegativeInfinity, i) for i in local
+        )
+
+    return epoch, _release, _pre, _post, _dev, _local
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+size 202076
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