title stringclasses 1
value | text stringlengths 30 426k | id stringlengths 27 30 |
|---|---|---|
asv_bench/benchmarks/strings.py/Dummies/time_get_dummies
class Dummies:
def time_get_dummies(self, dtype):
self.s.str.get_dummies("|") | negative_train_query0_00098 | |
asv_bench/benchmarks/strings.py/Encode/setup
class Encode:
def setup(self):
self.ser = Series(Index([f"i-{i}" for i in range(10_000)], dtype=object)) | negative_train_query0_00099 | |
asv_bench/benchmarks/strings.py/Encode/time_encode_decode
class Encode:
def time_encode_decode(self):
self.ser.str.encode("utf-8").str.decode("utf-8") | negative_train_query0_00100 | |
asv_bench/benchmarks/strings.py/Slice/setup
class Slice:
def setup(self):
self.s = Series(["abcdefg", np.nan] * 500000) | negative_train_query0_00101 | |
asv_bench/benchmarks/strings.py/Slice/time_vector_slice
class Slice:
def time_vector_slice(self):
# GH 2602
self.s.str[:5] | negative_train_query0_00102 | |
asv_bench/benchmarks/strings.py/Iter/time_iter
class Iter:
def time_iter(self, dtype):
for i in self.s:
pass | negative_train_query0_00103 | |
asv_bench/benchmarks/strings.py/StringArrayConstruction/setup
class StringArrayConstruction:
def setup(self):
self.series_arr = np.array([str(i) * 10 for i in range(10**5)], dtype=object)
self.series_arr_nan = np.concatenate([self.series_arr, np.array([NA] * 1000)]) | negative_train_query0_00104 | |
asv_bench/benchmarks/strings.py/StringArrayConstruction/time_string_array_construction
class StringArrayConstruction:
def time_string_array_construction(self):
StringArray(self.series_arr) | negative_train_query0_00105 | |
asv_bench/benchmarks/strings.py/StringArrayConstruction/time_string_array_with_nan_construction
class StringArrayConstruction:
def time_string_array_with_nan_construction(self):
StringArray(self.series_arr_nan) | negative_train_query0_00106 | |
asv_bench/benchmarks/strings.py/StringArrayConstruction/peakmem_stringarray_construction
class StringArrayConstruction:
def peakmem_stringarray_construction(self):
StringArray(self.series_arr) | negative_train_query0_00107 | |
asv_bench/benchmarks/frame_ctor.py/FromDicts/setup
class FromDicts:
def setup(self):
N, K = 5000, 50
self.index = pd.Index([f"i-{i}" for i in range(N)], dtype=object)
self.columns = pd.Index([f"i-{i}" for i in range(K)], dtype=object)
frame = DataFrame(np.random.randn(N, K), index=self.i... | negative_train_query0_00108 | |
asv_bench/benchmarks/frame_ctor.py/FromDicts/time_list_of_dict
class FromDicts:
def time_list_of_dict(self):
DataFrame(self.dict_list) | negative_train_query0_00109 | |
asv_bench/benchmarks/frame_ctor.py/FromDicts/time_nested_dict
class FromDicts:
def time_nested_dict(self):
DataFrame(self.data) | negative_train_query0_00110 | |
asv_bench/benchmarks/frame_ctor.py/FromDicts/time_nested_dict_index
class FromDicts:
def time_nested_dict_index(self):
DataFrame(self.data, index=self.index) | negative_train_query0_00111 | |
asv_bench/benchmarks/frame_ctor.py/FromDicts/time_nested_dict_columns
class FromDicts:
def time_nested_dict_columns(self):
DataFrame(self.data, columns=self.columns) | negative_train_query0_00112 | |
asv_bench/benchmarks/frame_ctor.py/FromDicts/time_nested_dict_index_columns
class FromDicts:
def time_nested_dict_index_columns(self):
DataFrame(self.data, index=self.index, columns=self.columns) | negative_train_query0_00113 | |
asv_bench/benchmarks/frame_ctor.py/FromDicts/time_nested_dict_int64
class FromDicts:
def time_nested_dict_int64(self):
# nested dict, integer indexes, regression described in #621
DataFrame(self.data2) | negative_train_query0_00114 | |
asv_bench/benchmarks/frame_ctor.py/FromDicts/time_dict_of_categoricals
class FromDicts:
def time_dict_of_categoricals(self):
# dict of arrays that we won't consolidate
DataFrame(self.dict_of_categoricals) | negative_train_query0_00115 | |
asv_bench/benchmarks/frame_ctor.py/FromSeries/setup
class FromSeries:
def setup(self):
mi = MultiIndex.from_product([range(100), range(100)])
self.s = Series(np.random.randn(10000), index=mi) | negative_train_query0_00116 | |
asv_bench/benchmarks/frame_ctor.py/FromSeries/time_mi_series
class FromSeries:
def time_mi_series(self):
DataFrame(self.s) | negative_train_query0_00117 | |
asv_bench/benchmarks/frame_ctor.py/FromDictwithTimestamp/setup
class FromDictwithTimestamp:
def setup(self, offset):
N = 10**3
idx = date_range(Timestamp("1/1/1900"), freq=offset, periods=N)
df = DataFrame(np.random.randn(N, 10), index=idx)
self.d = df.to_dict() | negative_train_query0_00118 | |
asv_bench/benchmarks/frame_ctor.py/FromDictwithTimestamp/time_dict_with_timestamp_offsets
class FromDictwithTimestamp:
def time_dict_with_timestamp_offsets(self, offset):
DataFrame(self.d) | negative_train_query0_00119 | |
asv_bench/benchmarks/frame_ctor.py/FromRecords/setup
class FromRecords:
def setup(self, nrows):
N = 100000
self.gen = ((x, (x * 20), (x * 100)) for x in range(N)) | negative_train_query0_00120 | |
asv_bench/benchmarks/frame_ctor.py/FromRecords/time_frame_from_records_generator
class FromRecords:
def time_frame_from_records_generator(self, nrows):
# issue-6700
self.df = DataFrame.from_records(self.gen, nrows=nrows) | negative_train_query0_00121 | |
asv_bench/benchmarks/frame_ctor.py/FromNDArray/setup
class FromNDArray:
def setup(self):
N = 100000
self.data = np.random.randn(N) | negative_train_query0_00122 | |
asv_bench/benchmarks/frame_ctor.py/FromNDArray/time_frame_from_ndarray
class FromNDArray:
def time_frame_from_ndarray(self):
self.df = DataFrame(self.data) | negative_train_query0_00123 | |
asv_bench/benchmarks/frame_ctor.py/FromLists/setup
class FromLists:
def setup(self):
N = 1000
M = 100
self.data = [list(range(M)) for i in range(N)] | negative_train_query0_00124 | |
asv_bench/benchmarks/frame_ctor.py/FromLists/time_frame_from_lists
class FromLists:
def time_frame_from_lists(self):
self.df = DataFrame(self.data) | negative_train_query0_00125 | |
asv_bench/benchmarks/frame_ctor.py/FromRange/setup
class FromRange:
def setup(self):
N = 1_000_000
self.data = range(N) | negative_train_query0_00126 | |
asv_bench/benchmarks/frame_ctor.py/FromRange/time_frame_from_range
class FromRange:
def time_frame_from_range(self):
self.df = DataFrame(self.data) | negative_train_query0_00127 | |
asv_bench/benchmarks/frame_ctor.py/FromScalar/setup
class FromScalar:
def setup(self):
self.nrows = 100_000 | negative_train_query0_00128 | |
asv_bench/benchmarks/frame_ctor.py/FromScalar/time_frame_from_scalar_ea_float64
class FromScalar:
def time_frame_from_scalar_ea_float64(self):
DataFrame(
1.0,
index=range(self.nrows),
columns=list("abc"),
dtype=Float64Dtype(),
) | negative_train_query0_00129 | |
asv_bench/benchmarks/frame_ctor.py/FromScalar/time_frame_from_scalar_ea_float64_na
class FromScalar:
def time_frame_from_scalar_ea_float64_na(self):
DataFrame(
NA,
index=range(self.nrows),
columns=list("abc"),
dtype=Float64Dtype(),
) | negative_train_query0_00130 | |
asv_bench/benchmarks/frame_ctor.py/FromArrays/setup
class FromArrays:
def setup(self):
N_rows = 1000
N_cols = 1000
self.float_arrays = [np.random.randn(N_rows) for _ in range(N_cols)]
self.sparse_arrays = [
pd.arrays.SparseArray(np.random.randint(0, 2, N_rows), dtype="float64... | negative_train_query0_00131 | |
asv_bench/benchmarks/frame_ctor.py/FromArrays/time_frame_from_arrays_float
class FromArrays:
def time_frame_from_arrays_float(self):
self.df = DataFrame._from_arrays(
self.float_arrays,
index=self.index,
columns=self.columns,
verify_integrity=False,
) | negative_train_query0_00132 | |
asv_bench/benchmarks/frame_ctor.py/FromArrays/time_frame_from_arrays_int
class FromArrays:
def time_frame_from_arrays_int(self):
self.df = DataFrame._from_arrays(
self.int_arrays,
index=self.index,
columns=self.columns,
verify_integrity=False,
) | negative_train_query0_00133 | |
asv_bench/benchmarks/frame_ctor.py/FromArrays/time_frame_from_arrays_sparse
class FromArrays:
def time_frame_from_arrays_sparse(self):
self.df = DataFrame._from_arrays(
self.sparse_arrays,
index=self.index,
columns=self.columns,
verify_integrity=False,
) | negative_train_query0_00134 | |
asv_bench/benchmarks/join_merge.py/Concat/setup
class Concat:
def setup(self, axis):
N = 1000
s = Series(N, index=Index([f"i-{i}" for i in range(N)], dtype=object))
self.series = [s[i:-i] for i in range(1, 10)] * 50
self.small_frames = [DataFrame(np.random.randn(5, 4))] * 1000
df... | negative_train_query0_00135 | |
asv_bench/benchmarks/join_merge.py/Concat/time_concat_series
class Concat:
def time_concat_series(self, axis):
concat(self.series, axis=axis, sort=False) | negative_train_query0_00136 | |
asv_bench/benchmarks/join_merge.py/Concat/time_concat_small_frames
class Concat:
def time_concat_small_frames(self, axis):
concat(self.small_frames, axis=axis) | negative_train_query0_00137 | |
asv_bench/benchmarks/join_merge.py/Concat/time_concat_empty_right
class Concat:
def time_concat_empty_right(self, axis):
concat(self.empty_right, axis=axis) | negative_train_query0_00138 | |
asv_bench/benchmarks/join_merge.py/Concat/time_concat_empty_left
class Concat:
def time_concat_empty_left(self, axis):
concat(self.empty_left, axis=axis) | negative_train_query0_00139 | |
asv_bench/benchmarks/join_merge.py/Concat/time_concat_mixed_ndims
class Concat:
def time_concat_mixed_ndims(self, axis):
concat(self.mixed_ndims, axis=axis) | negative_train_query0_00140 | |
asv_bench/benchmarks/join_merge.py/ConcatDataFrames/setup
class ConcatDataFrames:
def setup(self, axis, ignore_index):
frame_c = DataFrame(np.zeros((10000, 200), dtype=np.float32, order="C"))
self.frame_c = [frame_c] * 20
frame_f = DataFrame(np.zeros((10000, 200), dtype=np.float32, order="F"))
... | negative_train_query0_00141 | |
asv_bench/benchmarks/join_merge.py/ConcatDataFrames/time_c_ordered
class ConcatDataFrames:
def time_c_ordered(self, axis, ignore_index):
concat(self.frame_c, axis=axis, ignore_index=ignore_index) | negative_train_query0_00142 | |
asv_bench/benchmarks/join_merge.py/ConcatDataFrames/time_f_ordered
class ConcatDataFrames:
def time_f_ordered(self, axis, ignore_index):
concat(self.frame_f, axis=axis, ignore_index=ignore_index) | negative_train_query0_00143 | |
asv_bench/benchmarks/join_merge.py/ConcatIndexDtype/setup
class ConcatIndexDtype:
def setup(self, dtype, structure, axis, sort):
N = 10_000
if dtype == "datetime64[ns]":
vals = date_range("1970-01-01", periods=N)
elif dtype in ("int64", "Int64", "int64[pyarrow]"):
vals = ... | negative_train_query0_00144 | |
asv_bench/benchmarks/join_merge.py/ConcatIndexDtype/time_concat_series
class ConcatIndexDtype:
def time_concat_series(self, dtype, structure, axis, sort):
concat(self.series, axis=axis, sort=sort) | negative_train_query0_00145 | |
asv_bench/benchmarks/join_merge.py/Join/setup
class Join:
def setup(self, sort):
level1 = Index([f"i-{i}" for i in range(10)], dtype=object).values
level2 = Index([f"i-{i}" for i in range(1000)], dtype=object).values
codes1 = np.arange(10).repeat(1000)
codes2 = np.tile(np.arange(1000), 1... | negative_train_query0_00146 | |
asv_bench/benchmarks/join_merge.py/Join/time_join_dataframe_index_multi
class Join:
def time_join_dataframe_index_multi(self, sort):
self.df.join(self.df_multi, on=["key1", "key2"], sort=sort) | negative_train_query0_00147 | |
asv_bench/benchmarks/join_merge.py/Join/time_join_dataframe_index_single_key_bigger
class Join:
def time_join_dataframe_index_single_key_bigger(self, sort):
self.df.join(self.df_key2, on="key2", sort=sort) | negative_train_query0_00148 | |
asv_bench/benchmarks/join_merge.py/Join/time_join_dataframe_index_single_key_small
class Join:
def time_join_dataframe_index_single_key_small(self, sort):
self.df.join(self.df_key1, on="key1", sort=sort) | negative_train_query0_00149 | |
asv_bench/benchmarks/join_merge.py/Join/time_join_dataframe_index_shuffle_key_bigger_sort
class Join:
def time_join_dataframe_index_shuffle_key_bigger_sort(self, sort):
self.df_shuf.join(self.df_key2, on="key2", sort=sort) | negative_train_query0_00150 | |
asv_bench/benchmarks/join_merge.py/Join/time_join_dataframes_cross
class Join:
def time_join_dataframes_cross(self, sort):
self.df.loc[:2000].join(self.df_key1, how="cross", sort=sort) | negative_train_query0_00151 | |
asv_bench/benchmarks/join_merge.py/JoinIndex/setup
class JoinIndex:
def setup(self):
N = 5000
self.left = DataFrame(
np.random.randint(1, N / 50, (N, 2)), columns=["jim", "joe"]
)
self.right = DataFrame(
np.random.randint(1, N / 50, (N, 2)), columns=["jolie", "jol... | negative_train_query0_00152 | |
asv_bench/benchmarks/join_merge.py/JoinIndex/time_left_outer_join_index
class JoinIndex:
def time_left_outer_join_index(self):
self.left.join(self.right, on="jim") | negative_train_query0_00153 | |
asv_bench/benchmarks/join_merge.py/JoinMultiindexSubset/setup
class JoinMultiindexSubset:
def setup(self):
N = 100_000
mi1 = MultiIndex.from_arrays([np.arange(N)] * 4, names=["a", "b", "c", "d"])
mi2 = MultiIndex.from_arrays([np.arange(N)] * 2, names=["a", "b"])
self.left = DataFrame({"c... | negative_train_query0_00154 | |
asv_bench/benchmarks/join_merge.py/JoinMultiindexSubset/time_join_multiindex_subset
class JoinMultiindexSubset:
def time_join_multiindex_subset(self):
self.left.join(self.right) | negative_train_query0_00155 | |
asv_bench/benchmarks/join_merge.py/JoinEmpty/setup
class JoinEmpty:
def setup(self):
N = 100_000
self.df = DataFrame({"A": np.arange(N)})
self.df_empty = DataFrame(columns=["B", "C"], dtype="int64") | negative_train_query0_00156 | |
asv_bench/benchmarks/join_merge.py/JoinEmpty/time_inner_join_left_empty
class JoinEmpty:
def time_inner_join_left_empty(self):
self.df_empty.join(self.df, how="inner") | negative_train_query0_00157 | |
asv_bench/benchmarks/join_merge.py/JoinEmpty/time_inner_join_right_empty
class JoinEmpty:
def time_inner_join_right_empty(self):
self.df.join(self.df_empty, how="inner") | negative_train_query0_00158 | |
asv_bench/benchmarks/join_merge.py/JoinNonUnique/setup
class JoinNonUnique:
def setup(self):
date_index = date_range("01-Jan-2013", "23-Jan-2013", freq="min")
daily_dates = date_index.to_period("D").to_timestamp("s", "s")
self.fracofday = date_index.values - daily_dates.values
self.fraco... | negative_train_query0_00159 | |
asv_bench/benchmarks/join_merge.py/JoinNonUnique/time_join_non_unique_equal
class JoinNonUnique:
def time_join_non_unique_equal(self):
self.fracofday * self.temp | negative_train_query0_00160 | |
asv_bench/benchmarks/join_merge.py/Merge/setup
class Merge:
def setup(self, sort):
N = 10000
indices = Index([f"i-{i}" for i in range(N)], dtype=object).values
indices2 = Index([f"i-{i}" for i in range(N)], dtype=object).values
key = np.tile(indices[:8000], 10)
key2 = np.tile(ind... | negative_train_query0_00161 | |
asv_bench/benchmarks/join_merge.py/Merge/time_merge_2intkey
class Merge:
def time_merge_2intkey(self, sort):
merge(self.left, self.right, sort=sort) | negative_train_query0_00162 | |
asv_bench/benchmarks/join_merge.py/Merge/time_merge_dataframe_integer_2key
class Merge:
def time_merge_dataframe_integer_2key(self, sort):
merge(self.df, self.df3, sort=sort) | negative_train_query0_00163 | |
asv_bench/benchmarks/join_merge.py/Merge/time_merge_dataframe_integer_key
class Merge:
def time_merge_dataframe_integer_key(self, sort):
merge(self.df, self.df2, on="key1", sort=sort) | negative_train_query0_00164 | |
asv_bench/benchmarks/join_merge.py/Merge/time_merge_dataframe_empty_right
class Merge:
def time_merge_dataframe_empty_right(self, sort):
merge(self.left, self.right.iloc[:0], sort=sort) | negative_train_query0_00165 | |
asv_bench/benchmarks/join_merge.py/Merge/time_merge_dataframe_empty_left
class Merge:
def time_merge_dataframe_empty_left(self, sort):
merge(self.left.iloc[:0], self.right, sort=sort) | negative_train_query0_00166 | |
asv_bench/benchmarks/join_merge.py/Merge/time_merge_dataframes_cross
class Merge:
def time_merge_dataframes_cross(self, sort):
merge(self.left.loc[:2000], self.right.loc[:2000], how="cross", sort=sort) | negative_train_query0_00167 | |
asv_bench/benchmarks/join_merge.py/MergeEA/setup
class MergeEA:
def setup(self, dtype, monotonic):
N = 10_000
indices = np.arange(1, N)
key = np.tile(indices[:8000], 10)
self.left = DataFrame(
{"key": Series(key, dtype=dtype), "value": np.random.randn(80000)}
)
... | negative_train_query0_00168 | |
asv_bench/benchmarks/join_merge.py/MergeEA/time_merge
class MergeEA:
def time_merge(self, dtype, monotonic):
merge(self.left, self.right) | negative_train_query0_00169 | |
asv_bench/benchmarks/join_merge.py/I8Merge/setup
class I8Merge:
def setup(self, how):
low, high, n = -1000, 1000, 10**6
self.left = DataFrame(
np.random.randint(low, high, (n, 7)), columns=list("ABCDEFG")
)
self.left["left"] = self.left.sum(axis=1)
self.right = self.l... | negative_train_query0_00170 | |
asv_bench/benchmarks/join_merge.py/I8Merge/time_i8merge
class I8Merge:
def time_i8merge(self, how):
merge(self.left, self.right, how=how) | negative_train_query0_00171 | |
asv_bench/benchmarks/join_merge.py/UniqueMerge/setup
class UniqueMerge:
def setup(self, unique_elements):
N = 1_000_000
self.left = DataFrame({"a": np.random.randint(1, unique_elements, (N,))})
self.right = DataFrame({"a": np.random.randint(1, unique_elements, (N,))})
uniques = self.righ... | negative_train_query0_00172 | |
asv_bench/benchmarks/join_merge.py/UniqueMerge/time_unique_merge
class UniqueMerge:
def time_unique_merge(self, unique_elements):
merge(self.left, self.right, how="inner") | negative_train_query0_00173 | |
asv_bench/benchmarks/join_merge.py/MergeDatetime/setup
class MergeDatetime:
def setup(self, units, tz, monotonic):
unit_left, unit_right = units
N = 10_000
keys = Series(date_range("2012-01-01", freq="min", periods=N, tz=tz))
self.left = DataFrame(
{
"key": ke... | negative_train_query0_00174 | |
asv_bench/benchmarks/join_merge.py/MergeDatetime/time_merge
class MergeDatetime:
def time_merge(self, units, tz, monotonic):
merge(self.left, self.right) | negative_train_query0_00175 | |
asv_bench/benchmarks/join_merge.py/MergeCategoricals/setup
class MergeCategoricals:
def setup(self):
self.left_object = DataFrame(
{
"X": np.random.choice(range(10), size=(10000,)),
"Y": np.random.choice(["one", "two", "three"], size=(10000,)),
}
)... | negative_train_query0_00176 | |
asv_bench/benchmarks/join_merge.py/MergeCategoricals/time_merge_object
class MergeCategoricals:
def time_merge_object(self):
merge(self.left_object, self.right_object, on="X") | negative_train_query0_00177 | |
asv_bench/benchmarks/join_merge.py/MergeCategoricals/time_merge_cat
class MergeCategoricals:
def time_merge_cat(self):
merge(self.left_cat, self.right_cat, on="X") | negative_train_query0_00178 | |
asv_bench/benchmarks/join_merge.py/MergeCategoricals/time_merge_on_cat_col
class MergeCategoricals:
def time_merge_on_cat_col(self):
merge(self.left_cat_col, self.right_cat_col, on="X") | negative_train_query0_00179 | |
asv_bench/benchmarks/join_merge.py/MergeCategoricals/time_merge_on_cat_idx
class MergeCategoricals:
def time_merge_on_cat_idx(self):
merge(self.left_cat_idx, self.right_cat_idx, on="X") | negative_train_query0_00180 | |
asv_bench/benchmarks/join_merge.py/MergeOrdered/setup
class MergeOrdered:
def setup(self):
groups = Index([f"i-{i}" for i in range(10)], dtype=object).values
self.left = DataFrame(
{
"group": groups.repeat(5000),
"key": np.tile(np.arange(0, 10000, 2), 10),
... | negative_train_query0_00181 | |
asv_bench/benchmarks/join_merge.py/MergeOrdered/time_merge_ordered
class MergeOrdered:
def time_merge_ordered(self):
merge_ordered(self.left, self.right, on="key", left_by="group") | negative_train_query0_00182 | |
asv_bench/benchmarks/join_merge.py/MergeAsof/setup
class MergeAsof:
def setup(self, direction, tolerance):
one_count = 200000
two_count = 1000000
df1 = DataFrame(
{
"time": np.random.randint(0, one_count / 20, one_count),
"key": np.random.choice(list(... | negative_train_query0_00183 | |
asv_bench/benchmarks/join_merge.py/MergeAsof/time_on_int
class MergeAsof:
def time_on_int(self, direction, tolerance):
merge_asof(
self.df1a, self.df2a, on="time", direction=direction, tolerance=tolerance
) | negative_train_query0_00184 | |
asv_bench/benchmarks/join_merge.py/MergeAsof/time_on_int32
class MergeAsof:
def time_on_int32(self, direction, tolerance):
merge_asof(
self.df1d, self.df2d, on="time32", direction=direction, tolerance=tolerance
) | negative_train_query0_00185 | |
asv_bench/benchmarks/join_merge.py/MergeAsof/time_on_uint64
class MergeAsof:
def time_on_uint64(self, direction, tolerance):
merge_asof(
self.df1f, self.df2f, on="timeu64", direction=direction, tolerance=tolerance
) | negative_train_query0_00186 | |
asv_bench/benchmarks/join_merge.py/MergeAsof/time_by_object
class MergeAsof:
def time_by_object(self, direction, tolerance):
merge_asof(
self.df1b,
self.df2b,
on="time",
by="key",
direction=direction,
tolerance=tolerance,
) | negative_train_query0_00187 | |
asv_bench/benchmarks/join_merge.py/MergeAsof/time_by_int
class MergeAsof:
def time_by_int(self, direction, tolerance):
merge_asof(
self.df1c,
self.df2c,
on="time",
by="key2",
direction=direction,
tolerance=tolerance,
) | negative_train_query0_00188 | |
asv_bench/benchmarks/join_merge.py/MergeAsof/time_multiby
class MergeAsof:
def time_multiby(self, direction, tolerance):
merge_asof(
self.df1e,
self.df2e,
on="time",
by=["key", "key2"],
direction=direction,
tolerance=tolerance,
) | negative_train_query0_00189 | |
asv_bench/benchmarks/join_merge.py/MergeMultiIndex/setup
class MergeMultiIndex:
def setup(self, dtypes, how):
n = 100_000
offset = 50_000
mi1 = MultiIndex.from_arrays(
[
array(np.arange(n), dtype=dtypes[0]),
array(np.arange(n), dtype=dtypes[1]),
... | negative_train_query0_00190 | |
asv_bench/benchmarks/join_merge.py/MergeMultiIndex/time_merge_sorted_multiindex
class MergeMultiIndex:
def time_merge_sorted_multiindex(self, dtypes, how):
# copy to avoid MultiIndex._values caching
df1 = self.df1.copy()
df2 = self.df2.copy()
merge(df1, df2, how=how, left_index=True, rig... | negative_train_query0_00191 | |
asv_bench/benchmarks/join_merge.py/Align/setup
class Align:
def setup(self):
size = 5 * 10**5
rng = np.arange(0, 10**13, 10**7)
stamps = np.datetime64("now").view("i8") + rng
idx1 = np.sort(np.random.choice(stamps, size, replace=False))
idx2 = np.sort(np.random.choice(stamps, siz... | negative_train_query0_00192 | |
asv_bench/benchmarks/join_merge.py/Align/time_series_align_int64_index
class Align:
def time_series_align_int64_index(self):
self.ts1 + self.ts2 | negative_train_query0_00193 | |
asv_bench/benchmarks/join_merge.py/Align/time_series_align_left_monotonic
class Align:
def time_series_align_left_monotonic(self):
self.ts1.align(self.ts2, join="left") | negative_train_query0_00194 | |
asv_bench/benchmarks/boolean.py/TimeLogicalOps/setup
class TimeLogicalOps:
def setup(self):
N = 10_000
left, right, lmask, rmask = np.random.randint(0, 2, size=(4, N)).astype("bool")
self.left = pd.arrays.BooleanArray(left, lmask)
self.right = pd.arrays.BooleanArray(right, rmask) | negative_train_query0_00195 | |
asv_bench/benchmarks/boolean.py/TimeLogicalOps/time_or_scalar
class TimeLogicalOps:
def time_or_scalar(self):
self.left | True
self.left | False | negative_train_query0_00196 | |
asv_bench/benchmarks/boolean.py/TimeLogicalOps/time_or_array
class TimeLogicalOps:
def time_or_array(self):
self.left | self.right | negative_train_query0_00197 |
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