JorgeVB/code_unlearning_dataset · Datasets at Hugging Face

text stringlengths 81 112k
Parameters ---------- keyarr : list-like Indexer to convert. Returns ------- indexer : numpy.ndarray or None Return an ndarray or None if cannot convert. keyarr : numpy.ndarray Return tuple-safe keys. def _convert_listlike_indexer(sel...
Consistent invalid indexer message. def _invalid_indexer(self, form, key): """ Consistent invalid indexer message. """ raise TypeError("cannot do {form} indexing on {klass} with these " "indexers [{key}] of {kind}".format( form=form, k...
Create index with target's values (move/add/delete values as necessary). Parameters ---------- target : an iterable Returns ------- new_index : pd.Index Resulting index. indexer : np.ndarray or None Indices of output values in ori...
Create a new index with target's values (move/add/delete values as necessary) use with non-unique Index and a possibly non-unique target. Parameters ---------- target : an iterable Returns ------- new_index : pd.Index Resulting index. indexer...
The join method only affects the level of the resulting MultiIndex. Otherwise it just exactly aligns the Index data to the labels of the level in the MultiIndex. If ```keep_order == True```, the order of the data indexed by the MultiIndex will not be changed; otherwise, it will tie ou...
Attempt to convert an array of data into an integer index. Parameters ---------- data : The data to convert. copy : Whether to copy the data or not. name : The name of the index returned. Returns ------- int_index : data converted to either an Int64Index...
Coerces data to ndarray. Converts other iterables to list first and then to array. Does not touch ndarrays. Raises ------ TypeError When the data passed in is a scalar. def _coerce_to_ndarray(cls, data): """ Coerces data to ndarray. Convert...
We need to coerce a scalar to a compat for our index type. Parameters ---------- item : scalar item to coerce def _coerce_scalar_to_index(self, item): """ We need to coerce a scalar to a compat for our index type. Parameters ---------- item : scalar ite...
Check value is valid for scalar op. def _assert_can_do_op(self, value): """ Check value is valid for scalar op. """ if not is_scalar(value): msg = "'value' must be a scalar, passed: {0}" raise TypeError(msg.format(type(value).__name__))
Faster check for ``name in self`` when we know `name` is a Python identifier (e.g. in NDFrame.__getattr__, which hits this to support . key lookup). For indexes that can't hold identifiers (everything but object & categorical) we just return False. https://github.com/pandas-dev/pandas/i...
Append a collection of Index options together. Parameters ---------- other : Index or list/tuple of indices Returns ------- appended : Index def append(self, other): """ Append a collection of Index options together. Parameters --------...
Return a new Index of the values set with the mask. See Also -------- numpy.ndarray.putmask def putmask(self, mask, value): """ Return a new Index of the values set with the mask. See Also -------- numpy.ndarray.putmask """ values = self...
Determine if two Index objects contain the same elements. def equals(self, other): """ Determine if two Index objects contain the same elements. """ if self.is_(other): return True if not isinstance(other, Index): return False if is_object_dtype...
Similar to equals, but check that other comparable attributes are also equal. def identical(self, other): """ Similar to equals, but check that other comparable attributes are also equal. """ return (self.equals(other) and all((getattr(self, c, None) == g...
Return the label from the index, or, if not present, the previous one. Assuming that the index is sorted, return the passed index label if it is in the index, or return the previous index label if the passed one is not in the index. Parameters ---------- label : object ...
Find the locations (indices) of the labels from the index for every entry in the `where` argument. As in the `asof` function, if the label (a particular entry in `where`) is not in the index, the latest index label upto the passed label is chosen and its index returned. If all ...
Return a sorted copy of the index. Return a sorted copy of the index, and optionally return the indices that sorted the index itself. Parameters ---------- return_indexer : bool, default False Should the indices that would sort the index be returned. ascendi...
Return the integer indices that would sort the index. Parameters ---------- args Passed to `numpy.ndarray.argsort`. *kwargs Passed to `numpy.ndarray.argsort`. Returns ------- numpy.ndarray Integer indices that would sort the...
Fast lookup of value from 1-dimensional ndarray. Only use this if you know what you're doing. def get_value(self, series, key): """ Fast lookup of value from 1-dimensional ndarray. Only use this if you know what you're doing. """ # if we have something that is Index-lik...
Fast lookup of value from 1-dimensional ndarray. Notes ----- Only use this if you know what you're doing. def set_value(self, arr, key, value): """ Fast lookup of value from 1-dimensional ndarray. Notes ----- Only use this if you know what you're doing....
Guaranteed return of an indexer even when non-unique. This dispatches to get_indexer or get_indexer_nonunique as appropriate. def get_indexer_for(self, target, **kwargs): """ Guaranteed return of an indexer even when non-unique. This dispatches to get_indexer or get_indexer_no...
Group the index labels by a given array of values. Parameters ---------- values : array Values used to determine the groups. Returns ------- groups : dict {group name -> group labels} def groupby(self, values): """ Group the inde...
Map values using input correspondence (a dict, Series, or function). Parameters ---------- mapper : function, dict, or Series Mapping correspondence. na_action : {None, 'ignore'} If 'ignore', propagate NA values, without passing them to the mapping co...
Return a boolean array where the index values are in `values`. Compute boolean array of whether each index value is found in the passed set of values. The length of the returned boolean array matches the length of the index. Parameters ---------- values : set or list-li...
For an ordered or unique index, compute the slice indexer for input labels and step. Parameters ---------- start : label, default None If None, defaults to the beginning end : label, default None If None, defaults to the end step : int, default No...
If we have a float key and are not a floating index, then try to cast to an int if equivalent. def _maybe_cast_indexer(self, key): """ If we have a float key and are not a floating index, then try to cast to an int if equivalent. """ if is_float(key) and not self.is_flo...
If we are positional indexer, validate that we have appropriate typed bounds must be an integer. def _validate_indexer(self, form, key, kind): """ If we are positional indexer, validate that we have appropriate typed bounds must be an integer. """ assert kind in ['ix', '...
Calculate slice bound that corresponds to given label. Returns leftmost (one-past-the-rightmost if ``side=='right'``) position of given label. Parameters ---------- label : object side : {'left', 'right'} kind : {'ix', 'loc', 'getitem'} def get_slice_bound(self...
Compute slice locations for input labels. Parameters ---------- start : label, default None If None, defaults to the beginning end : label, default None If None, defaults to the end step : int, defaults None If None, defaults to 1 kind...
Make new Index with passed location(-s) deleted. Returns ------- new_index : Index def delete(self, loc): """ Make new Index with passed location(-s) deleted. Returns ------- new_index : Index """ return self._shallow_copy(np.delete(self...
Make new Index inserting new item at location. Follows Python list.append semantics for negative values. Parameters ---------- loc : int item : object Returns ------- new_index : Index def insert(self, loc, item): """ Make new Index ins...
Make new Index with passed list of labels deleted. Parameters ---------- labels : array-like errors : {'ignore', 'raise'}, default 'raise' If 'ignore', suppress error and existing labels are dropped. Returns ------- dropped : Index Raises ...
Add in comparison methods. def _add_comparison_methods(cls): """ Add in comparison methods. """ cls.__eq__ = _make_comparison_op(operator.eq, cls) cls.__ne__ = _make_comparison_op(operator.ne, cls) cls.__lt__ = _make_comparison_op(operator.lt, cls) cls.__gt__ = _...
Add in the numeric add/sub methods to disable. def _add_numeric_methods_add_sub_disabled(cls): """ Add in the numeric add/sub methods to disable. """ cls.__add__ = make_invalid_op('__add__') cls.__radd__ = make_invalid_op('__radd__') cls.__iadd__ = make_invalid_op('__iad...
Add in numeric methods to disable other than add/sub. def _add_numeric_methods_disabled(cls): """ Add in numeric methods to disable other than add/sub. """ cls.__pow__ = make_invalid_op('__pow__') cls.__rpow__ = make_invalid_op('__rpow__') cls.__mul__ = make_invalid_op('...
Validate if we can perform a numeric unary operation. def _validate_for_numeric_unaryop(self, op, opstr): """ Validate if we can perform a numeric unary operation. """ if not self._is_numeric_dtype: raise TypeError("cannot evaluate a numeric op " ...
Return valid other; evaluate or raise TypeError if we are not of the appropriate type. Notes ----- This is an internal method called by ops. def _validate_for_numeric_binop(self, other, op): """ Return valid other; evaluate or raise TypeError if we are not of th...
Add in numeric methods. def _add_numeric_methods_binary(cls): """ Add in numeric methods. """ cls.__add__ = _make_arithmetic_op(operator.add, cls) cls.__radd__ = _make_arithmetic_op(ops.radd, cls) cls.__sub__ = _make_arithmetic_op(operator.sub, cls) cls.__rsub__ ...
Add in numeric unary methods. def _add_numeric_methods_unary(cls): """ Add in numeric unary methods. """ def _make_evaluate_unary(op, opstr): def _evaluate_numeric_unary(self): self._validate_for_numeric_unaryop(op, opstr) attrs = self._get_...
Add in logical methods. def _add_logical_methods(cls): """ Add in logical methods. """ _doc = """ %(desc)s Parameters ---------- args These parameters will be passed to numpy.%(outname)s. *kwargs These parameters will be...
create and return a BaseGrouper, which is an internal mapping of how to create the grouper indexers. This may be composed of multiple Grouping objects, indicating multiple groupers Groupers are ultimately index mappings. They can originate as: index mappings, keys to columns, functions, or Groupers...
Parameters ---------- obj : the subject object validate : boolean, default True if True, validate the grouper Returns ------- a tuple of binner, grouper, obj (possibly sorted) def _get_grouper(self, obj, validate=True): """ Parameters ...
given an object and the specifications, setup the internal grouper for this particular specification Parameters ---------- obj : the subject object sort : bool, default False whether the resulting grouper should be sorted def _set_grouper(self, obj, sort=False): ...
Pickle (serialize) object to file. Parameters ---------- obj : any object Any python object. path : str File path where the pickled object will be stored. compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer' A string representing the compression to use ...
Load pickled pandas object (or any object) from file. .. warning:: Loading pickled data received from untrusted sources can be unsafe. See `here <https://docs.python.org/3/library/pickle.html>`__. Parameters ---------- path : str File path where the pickled object will be loaded...
Return a masking array of same size/shape as arr with entries equaling any member of values_to_mask set to True def mask_missing(arr, values_to_mask): """ Return a masking array of same size/shape as arr with entries equaling any member of values_to_mask set to True """ dtype, values_to_mask = ...
Logic for the 1-d interpolation. The result should be 1-d, inputs xvalues and yvalues will each be 1-d arrays of the same length. Bounds_error is currently hardcoded to False since non-scipy ones don't take it as an argument. def interpolate_1d(xvalues, yvalues, method='linear', limit=None, ...
Passed off to scipy.interpolate.interp1d. method is scipy's kind. Returns an array interpolated at new_x. Add any new methods to the list in _clean_interp_method. def _interpolate_scipy_wrapper(x, y, new_x, method, fill_value=None, bounds_error=False, order=None, **kwargs): ...
Convenience function for interpolate.BPoly.from_derivatives. Construct a piecewise polynomial in the Bernstein basis, compatible with the specified values and derivatives at breakpoints. Parameters ---------- xi : array_like sorted 1D array of x-coordinates yi : array_like or list of a...
Convenience function for akima interpolation. xi and yi are arrays of values used to approximate some function f, with ``yi = f(xi)``. See `Akima1DInterpolator` for details. Parameters ---------- xi : array_like A sorted list of x-coordinates, of length N. yi : array_like ...
Perform an actual interpolation of values, values will be make 2-d if needed fills inplace, returns the result. def interpolate_2d(values, method='pad', axis=0, limit=None, fill_value=None, dtype=None): """ Perform an actual interpolation of values, values will be make 2-d if needed ...
Cast values to a dtype that algos.pad and algos.backfill can handle. def _cast_values_for_fillna(values, dtype): """ Cast values to a dtype that algos.pad and algos.backfill can handle. """ # TODO: for int-dtypes we make a copy, but for everything else this # alters the values in-place. Is this i...
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 the fill, return the result. Mask the nan's from x. def fill_zeros(result, x, y, name, fill): """ If this is a reversed op, then flip x,y If we have an integer value (or ar...
Set results of 0 / 0 or 0 // 0 to np.nan, regardless of the dtypes of the numerator or the denominator. Parameters ---------- x : ndarray y : ndarray result : ndarray copy : bool (default False) Whether to always create a new array or try to fill in the existing array if pos...
Fill nulls caused by division by zero, casting to a diffferent dtype if necessary. Parameters ---------- op : function (operator.add, operator.div, ...) left : object (Index for non-reversed ops) right : object (Index fof reversed ops) result : ndarray Returns ------- result : ...
Get indexers of values that won't be filled because they exceed the limits. Parameters ---------- invalid : boolean ndarray fw_limit : int or None forward limit to index bw_limit : int or None backward limit to index Returns ------- set of indexers Notes --...
[True, True, False, True, False], 2 -> [ [True, True], [True, False], [False, True], [True, False], ] def _rolling_window(a, window): """ [True, True, False, True, False], 2 -> [ [True, True], [True, False], [False, True], [True, F...
Return console size as tuple = (width, height). Returns (None,None) in non-interactive session. def get_console_size(): """Return console size as tuple = (width, height). Returns (None,None) in non-interactive session. """ from pandas import get_option display_width = get_option('display.wid...
check if we're running in an interactive shell returns True if running under python/ipython interactive shell def in_interactive_session(): """ check if we're running in an interactive shell returns True if running under python/ipython interactive shell """ from pandas import get_option def ...
Code the categories to ensure we can groupby for categoricals. If observed=True, we return a new Categorical with the observed categories only. If sort=False, return a copy of self, coded with categories as returned by .unique(), followed by any categories not appearing in the data. If sort=True, ...
Reverse the codes_to_groupby to account for sort / observed. Parameters ---------- c : Categorical sort : boolean The value of the sort parameter groupby was called with. ci : CategoricalIndex The codes / categories to recode Returns ------- CategoricalIndex def recode...
return our implementation def get_engine(engine): """ return our implementation """ if engine == 'auto': engine = get_option('io.parquet.engine') if engine == 'auto': # try engines in this order try: return PyArrowImpl() except ImportError: pass ...
Write a DataFrame to the parquet format. Parameters ---------- path : str File path or Root Directory path. Will be used as Root Directory path while writing a partitioned dataset. .. versionchanged:: 0.24.0 engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto' P...
Load a parquet object from the file path, returning a DataFrame. .. versionadded 0.21.0 Parameters ---------- path : string File path engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto' Parquet library to use. If 'auto', then the option ``io.parquet.engine`` is used...
Generate bin edge offsets and bin labels for one array using another array which has bin edge values. Both arrays must be sorted. Parameters ---------- values : array of values binner : a comparable array of values representing bins into which to bin the first array. Note, 'values' end-poin...
Groupby iterator Returns ------- Generator yielding sequence of (name, subsetted object) for each group def get_iterator(self, data, axis=0): """ Groupby iterator Returns ------- Generator yielding sequence of (name, subsetted object) fo...
dict {group name -> group indices} def indices(self): """ dict {group name -> group indices} """ if len(self.groupings) == 1: return self.groupings[0].indices else: label_list = [ping.labels for ping in self.groupings] keys = [com.values_from_object(ping.grou...
Compute group sizes def size(self): """ Compute group sizes """ ids, _, ngroup = self.group_info ids = ensure_platform_int(ids) if ngroup: out = np.bincount(ids[ids != -1], minlength=ngroup) else: out = [] return Series(out, ...
dict {group name -> group labels} def groups(self): """ dict {group name -> group labels} """ if len(self.groupings) == 1: return self.groupings[0].groups else: to_groupby = lzip(*(ping.grouper for ping in self.groupings)) to_groupby = Index(to_groupby) ...
dict {group name -> group labels} def groups(self): """ dict {group name -> group labels} """ # this is mainly for compat # GH 3881 result = {key: value for key, value in zip(self.binlabels, self.bins) if key is not NaT} return result
Groupby iterator Returns ------- Generator yielding sequence of (name, subsetted object) for each group def get_iterator(self, data, axis=0): """ Groupby iterator Returns ------- Generator yielding sequence of (name, subsetted object) fo...
Normalize semi-structured JSON data into a flat table. Parameters ---------- data : dict or list of dicts Unserialized JSON objects record_path : string or list of strings, default None Path in each object to list of records. If not passed, data will be assumed to be an array of...
Reshape long-format data to wide. Generalized inverse of DataFrame.pivot Parameters ---------- data : DataFrame groups : dict {new_name : list_of_columns} dropna : boolean, default True Examples -------- >>> data = pd.DataFrame({'hr1': [514, 573], 'hr2': [545, 526], ... ...
r""" Wide panel to long format. Less flexible but more user-friendly than melt. With stubnames ['A', 'B'], this function expects to find one or more group of columns with format A-suffix1, A-suffix2,..., B-suffix1, B-suffix2,... You specify what you want to call this suffix in the resulting long fo...
Safe get multiple indices, translate keys for datelike to underlying repr. def _get_indices(self, names): """ Safe get multiple indices, translate keys for datelike to underlying repr. """ def get_converter(s): # possibly convert to the actual key types ...
Create group based selection. Used when selection is not passed directly but instead via a grouper. NOTE: this should be paired with a call to _reset_group_selection def _set_group_selection(self): """ Create group based selection. Used when selection is not passed directly b...
Construct NDFrame from group with provided name. Parameters ---------- name : object the name of the group to get as a DataFrame obj : NDFrame, default None the NDFrame to take the DataFrame out of. If it is None, the object groupby was called on wil...
Parameters ---------- ascending : bool, default True If False, number in reverse, from length of group - 1 to 0. Notes ----- this is currently implementing sort=False (though the default is sort=True) for groupby in general def _cumcount_array(self, ascendin...
Try to cast the result to our obj original type, we may have roundtripped through object in the mean-time. If numeric_only is True, then only try to cast numerics and not datetimelikes. def _try_cast(self, result, obj, numeric_only=False): """ Try to cast the result to our obj ...
Parameters: ----------- func_nm: str The name of the aggregation function being performed Returns: -------- bool Whether transform should attempt to cast the result of aggregation def _transform_should_cast(self, func_nm): """ Parameters:...
Shared func to call any / all Cython GroupBy implementations. def _bool_agg(self, val_test, skipna): """ Shared func to call any / all Cython GroupBy implementations. """ def objs_to_bool(vals: np.ndarray) -> Tuple[np.ndarray, Type]: if is_object_dtype(vals): ...
Compute mean of groups, excluding missing values. Returns ------- pandas.Series or pandas.DataFrame %(see_also)s Examples -------- >>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2], ... 'B': [np.nan, 2, 3, 4, 5], ... ...
Compute median of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex def median(self, **kwargs): """ Compute median of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex """ try: ...
Compute standard deviation of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : integer, default 1 degrees of freedom def std(self, ddof=1, args, *kwargs): """ Compute standard de...
Compute variance of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : integer, default 1 degrees of freedom def var(self, ddof=1, args, *kwargs): """ Compute variance of groups, e...
Compute standard error of the mean of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : integer, default 1 degrees of freedom def sem(self, ddof=1): """ Compute standard error of th...
Compute group sizes. def size(self): """ Compute group sizes. """ result = self.grouper.size() if isinstance(self.obj, Series): result.name = getattr(self.obj, 'name', None) return result
Add numeric operations to the GroupBy generically. def _add_numeric_operations(cls): """ Add numeric operations to the GroupBy generically. """ def groupby_function(name, alias, npfunc, numeric_only=True, _convert=False, min_cou...
Provide resampling when using a TimeGrouper. Given a grouper, the function resamples it according to a string "string" -> "frequency". See the :ref:`frequency aliases <timeseries.offset_aliases>` documentation for more details. Parameters ---------- rule : str ...
Return a rolling grouper, providing rolling functionality per group. def rolling(self, args, kwargs): """ Return a rolling grouper, providing rolling functionality per group. """ from pandas.core.window import RollingGroupby return RollingGroupby(self, args, **kwargs)
Return an expanding grouper, providing expanding functionality per group. def expanding(self, args, *kwargs): """ Return an expanding grouper, providing expanding functionality per group. """ from pandas.core.window import ExpandingGroupby return ExpandingGroup...
Shared function for `pad` and `backfill` to call Cython method. Parameters ---------- direction : {'ffill', 'bfill'} Direction passed to underlying Cython function. `bfill` will cause values to be filled backwards. `ffill` and any other values will default to...
Take the nth row from each group if n is an int, or a subset of rows if n is a list of ints. If dropna, will take the nth non-null row, dropna is either Truthy (if a Series) or 'all', 'any' (if a DataFrame); this is equivalent to calling dropna(how=dropna) before the groupby. ...
Return group values at the given quantile, a la numpy.percentile. Parameters ---------- q : float or array-like, default 0.5 (50% quantile) Value(s) between 0 and 1 providing the quantile(s) to compute. interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} ...
Number each group from 0 to the number of groups - 1. This is the enumerative complement of cumcount. Note that the numbers given to the groups match the order in which the groups would be seen when iterating over the groupby object, not the order they are first observed. .. v...
Number each item in each group from 0 to the length of that group - 1. Essentially this is equivalent to >>> self.apply(lambda x: pd.Series(np.arange(len(x)), x.index)) Parameters ---------- ascending : bool, default True If False, number in reverse, from length of...
Provide the rank of values within each group. Parameters ---------- method : {'average', 'min', 'max', 'first', 'dense'}, default 'average' * average: average rank of group * min: lowest rank in group * max: highest rank in group * first: ranks as...
Cumulative product for each group. def cumprod(self, axis=0, args, *kwargs): """ Cumulative product for each group. """ nv.validate_groupby_func('cumprod', args, kwargs, ['numeric_only', 'skipna']) if axis != 0: return self.apply(la...
Cumulative min for each group. def cummin(self, axis=0, **kwargs): """ Cumulative min for each group. """ if axis != 0: return self.apply(lambda x: np.minimum.accumulate(x, axis)) return self._cython_transform('cummin', numeric_only=False)
Cumulative max for each group. def cummax(self, axis=0, **kwargs): """ Cumulative max for each group. """ if axis != 0: return self.apply(lambda x: np.maximum.accumulate(x, axis)) return self._cython_transform('cummax', numeric_only=False)

Parameters ---------- keyarr : list-like Indexer to convert. Returns ------- indexer : numpy.ndarray or None Return an ndarray or None if cannot convert. keyarr : numpy.ndarray Return tuple-safe keys. def _convert_listlike_indexer(sel...

Consistent invalid indexer message. def _invalid_indexer(self, form, key): """ Consistent invalid indexer message. """ raise TypeError("cannot do {form} indexing on {klass} with these " "indexers [{key}] of {kind}".format( form=form, k...

Create index with target's values (move/add/delete values as necessary). Parameters ---------- target : an iterable Returns ------- new_index : pd.Index Resulting index. indexer : np.ndarray or None Indices of output values in ori...

Create a new index with target's values (move/add/delete values as necessary) use with non-unique Index and a possibly non-unique target. Parameters ---------- target : an iterable Returns ------- new_index : pd.Index Resulting index. indexer...

The join method *only* affects the level of the resulting MultiIndex. Otherwise it just exactly aligns the Index data to the labels of the level in the MultiIndex. If ```keep_order == True```, the order of the data indexed by the MultiIndex will not be changed; otherwise, it will tie ou...

Attempt to convert an array of data into an integer index. Parameters ---------- data : The data to convert. copy : Whether to copy the data or not. name : The name of the index returned. Returns ------- int_index : data converted to either an Int64Index...

Coerces data to ndarray. Converts other iterables to list first and then to array. Does not touch ndarrays. Raises ------ TypeError When the data passed in is a scalar. def _coerce_to_ndarray(cls, data): """ Coerces data to ndarray. Convert...

We need to coerce a scalar to a compat for our index type. Parameters ---------- item : scalar item to coerce def _coerce_scalar_to_index(self, item): """ We need to coerce a scalar to a compat for our index type. Parameters ---------- item : scalar ite...

Check value is valid for scalar op. def _assert_can_do_op(self, value): """ Check value is valid for scalar op. """ if not is_scalar(value): msg = "'value' must be a scalar, passed: {0}" raise TypeError(msg.format(type(value).__name__))

Faster check for ``name in self`` when we know `name` is a Python identifier (e.g. in NDFrame.__getattr__, which hits this to support . key lookup). For indexes that can't hold identifiers (everything but object & categorical) we just return False. https://github.com/pandas-dev/pandas/i...

Append a collection of Index options together. Parameters ---------- other : Index or list/tuple of indices Returns ------- appended : Index def append(self, other): """ Append a collection of Index options together. Parameters --------...

Return a new Index of the values set with the mask. See Also -------- numpy.ndarray.putmask def putmask(self, mask, value): """ Return a new Index of the values set with the mask. See Also -------- numpy.ndarray.putmask """ values = self...

Determine if two Index objects contain the same elements. def equals(self, other): """ Determine if two Index objects contain the same elements. """ if self.is_(other): return True if not isinstance(other, Index): return False if is_object_dtype...

Similar to equals, but check that other comparable attributes are also equal. def identical(self, other): """ Similar to equals, but check that other comparable attributes are also equal. """ return (self.equals(other) and all((getattr(self, c, None) == g...

Return the label from the index, or, if not present, the previous one. Assuming that the index is sorted, return the passed index label if it is in the index, or return the previous index label if the passed one is not in the index. Parameters ---------- label : object ...

Find the locations (indices) of the labels from the index for every entry in the `where` argument. As in the `asof` function, if the label (a particular entry in `where`) is not in the index, the latest index label upto the passed label is chosen and its index returned. If all ...

Return a sorted copy of the index. Return a sorted copy of the index, and optionally return the indices that sorted the index itself. Parameters ---------- return_indexer : bool, default False Should the indices that would sort the index be returned. ascendi...

Return the integer indices that would sort the index. Parameters ---------- *args Passed to `numpy.ndarray.argsort`. **kwargs Passed to `numpy.ndarray.argsort`. Returns ------- numpy.ndarray Integer indices that would sort the...

Fast lookup of value from 1-dimensional ndarray. Only use this if you know what you're doing. def get_value(self, series, key): """ Fast lookup of value from 1-dimensional ndarray. Only use this if you know what you're doing. """ # if we have something that is Index-lik...

Fast lookup of value from 1-dimensional ndarray. Notes ----- Only use this if you know what you're doing. def set_value(self, arr, key, value): """ Fast lookup of value from 1-dimensional ndarray. Notes ----- Only use this if you know what you're doing....

Guaranteed return of an indexer even when non-unique. This dispatches to get_indexer or get_indexer_nonunique as appropriate. def get_indexer_for(self, target, **kwargs): """ Guaranteed return of an indexer even when non-unique. This dispatches to get_indexer or get_indexer_no...

Group the index labels by a given array of values. Parameters ---------- values : array Values used to determine the groups. Returns ------- groups : dict {group name -> group labels} def groupby(self, values): """ Group the inde...

Map values using input correspondence (a dict, Series, or function). Parameters ---------- mapper : function, dict, or Series Mapping correspondence. na_action : {None, 'ignore'} If 'ignore', propagate NA values, without passing them to the mapping co...

Return a boolean array where the index values are in `values`. Compute boolean array of whether each index value is found in the passed set of values. The length of the returned boolean array matches the length of the index. Parameters ---------- values : set or list-li...

For an ordered or unique index, compute the slice indexer for input labels and step. Parameters ---------- start : label, default None If None, defaults to the beginning end : label, default None If None, defaults to the end step : int, default No...

If we have a float key and are not a floating index, then try to cast to an int if equivalent. def _maybe_cast_indexer(self, key): """ If we have a float key and are not a floating index, then try to cast to an int if equivalent. """ if is_float(key) and not self.is_flo...

If we are positional indexer, validate that we have appropriate typed bounds must be an integer. def _validate_indexer(self, form, key, kind): """ If we are positional indexer, validate that we have appropriate typed bounds must be an integer. """ assert kind in ['ix', '...

Calculate slice bound that corresponds to given label. Returns leftmost (one-past-the-rightmost if ``side=='right'``) position of given label. Parameters ---------- label : object side : {'left', 'right'} kind : {'ix', 'loc', 'getitem'} def get_slice_bound(self...

Compute slice locations for input labels. Parameters ---------- start : label, default None If None, defaults to the beginning end : label, default None If None, defaults to the end step : int, defaults None If None, defaults to 1 kind...

Make new Index with passed location(-s) deleted. Returns ------- new_index : Index def delete(self, loc): """ Make new Index with passed location(-s) deleted. Returns ------- new_index : Index """ return self._shallow_copy(np.delete(self...

Make new Index inserting new item at location. Follows Python list.append semantics for negative values. Parameters ---------- loc : int item : object Returns ------- new_index : Index def insert(self, loc, item): """ Make new Index ins...

Make new Index with passed list of labels deleted. Parameters ---------- labels : array-like errors : {'ignore', 'raise'}, default 'raise' If 'ignore', suppress error and existing labels are dropped. Returns ------- dropped : Index Raises ...

Add in comparison methods. def _add_comparison_methods(cls): """ Add in comparison methods. """ cls.__eq__ = _make_comparison_op(operator.eq, cls) cls.__ne__ = _make_comparison_op(operator.ne, cls) cls.__lt__ = _make_comparison_op(operator.lt, cls) cls.__gt__ = _...

Add in the numeric add/sub methods to disable. def _add_numeric_methods_add_sub_disabled(cls): """ Add in the numeric add/sub methods to disable. """ cls.__add__ = make_invalid_op('__add__') cls.__radd__ = make_invalid_op('__radd__') cls.__iadd__ = make_invalid_op('__iad...

Add in numeric methods to disable other than add/sub. def _add_numeric_methods_disabled(cls): """ Add in numeric methods to disable other than add/sub. """ cls.__pow__ = make_invalid_op('__pow__') cls.__rpow__ = make_invalid_op('__rpow__') cls.__mul__ = make_invalid_op('...

Validate if we can perform a numeric unary operation. def _validate_for_numeric_unaryop(self, op, opstr): """ Validate if we can perform a numeric unary operation. """ if not self._is_numeric_dtype: raise TypeError("cannot evaluate a numeric op " ...

Return valid other; evaluate or raise TypeError if we are not of the appropriate type. Notes ----- This is an internal method called by ops. def _validate_for_numeric_binop(self, other, op): """ Return valid other; evaluate or raise TypeError if we are not of th...

Add in numeric methods. def _add_numeric_methods_binary(cls): """ Add in numeric methods. """ cls.__add__ = _make_arithmetic_op(operator.add, cls) cls.__radd__ = _make_arithmetic_op(ops.radd, cls) cls.__sub__ = _make_arithmetic_op(operator.sub, cls) cls.__rsub__ ...

Add in numeric unary methods. def _add_numeric_methods_unary(cls): """ Add in numeric unary methods. """ def _make_evaluate_unary(op, opstr): def _evaluate_numeric_unary(self): self._validate_for_numeric_unaryop(op, opstr) attrs = self._get_...

Add in logical methods. def _add_logical_methods(cls): """ Add in logical methods. """ _doc = """ %(desc)s Parameters ---------- *args These parameters will be passed to numpy.%(outname)s. **kwargs These parameters will be...

create and return a BaseGrouper, which is an internal mapping of how to create the grouper indexers. This may be composed of multiple Grouping objects, indicating multiple groupers Groupers are ultimately index mappings. They can originate as: index mappings, keys to columns, functions, or Groupers...

Parameters ---------- obj : the subject object validate : boolean, default True if True, validate the grouper Returns ------- a tuple of binner, grouper, obj (possibly sorted) def _get_grouper(self, obj, validate=True): """ Parameters ...

given an object and the specifications, setup the internal grouper for this particular specification Parameters ---------- obj : the subject object sort : bool, default False whether the resulting grouper should be sorted def _set_grouper(self, obj, sort=False): ...

Pickle (serialize) object to file. Parameters ---------- obj : any object Any python object. path : str File path where the pickled object will be stored. compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer' A string representing the compression to use ...

Load pickled pandas object (or any object) from file. .. warning:: Loading pickled data received from untrusted sources can be unsafe. See `here <https://docs.python.org/3/library/pickle.html>`__. Parameters ---------- path : str File path where the pickled object will be loaded...

Return a masking array of same size/shape as arr with entries equaling any member of values_to_mask set to True def mask_missing(arr, values_to_mask): """ Return a masking array of same size/shape as arr with entries equaling any member of values_to_mask set to True """ dtype, values_to_mask = ...

Logic for the 1-d interpolation. The result should be 1-d, inputs xvalues and yvalues will each be 1-d arrays of the same length. Bounds_error is currently hardcoded to False since non-scipy ones don't take it as an argument. def interpolate_1d(xvalues, yvalues, method='linear', limit=None, ...

Passed off to scipy.interpolate.interp1d. method is scipy's kind. Returns an array interpolated at new_x. Add any new methods to the list in _clean_interp_method. def _interpolate_scipy_wrapper(x, y, new_x, method, fill_value=None, bounds_error=False, order=None, **kwargs): ...

Convenience function for interpolate.BPoly.from_derivatives. Construct a piecewise polynomial in the Bernstein basis, compatible with the specified values and derivatives at breakpoints. Parameters ---------- xi : array_like sorted 1D array of x-coordinates yi : array_like or list of a...

Convenience function for akima interpolation. xi and yi are arrays of values used to approximate some function f, with ``yi = f(xi)``. See `Akima1DInterpolator` for details. Parameters ---------- xi : array_like A sorted list of x-coordinates, of length N. yi : array_like ...

Perform an actual interpolation of values, values will be make 2-d if needed fills inplace, returns the result. def interpolate_2d(values, method='pad', axis=0, limit=None, fill_value=None, dtype=None): """ Perform an actual interpolation of values, values will be make 2-d if needed ...

Cast values to a dtype that algos.pad and algos.backfill can handle. def _cast_values_for_fillna(values, dtype): """ Cast values to a dtype that algos.pad and algos.backfill can handle. """ # TODO: for int-dtypes we make a copy, but for everything else this # alters the values in-place. Is this i...

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 the fill, return the result. Mask the nan's from x. def fill_zeros(result, x, y, name, fill): """ If this is a reversed op, then flip x,y If we have an integer value (or ar...

Set results of 0 / 0 or 0 // 0 to np.nan, regardless of the dtypes of the numerator or the denominator. Parameters ---------- x : ndarray y : ndarray result : ndarray copy : bool (default False) Whether to always create a new array or try to fill in the existing array if pos...

Fill nulls caused by division by zero, casting to a diffferent dtype if necessary. Parameters ---------- op : function (operator.add, operator.div, ...) left : object (Index for non-reversed ops) right : object (Index fof reversed ops) result : ndarray Returns ------- result : ...

Get indexers of values that won't be filled because they exceed the limits. Parameters ---------- invalid : boolean ndarray fw_limit : int or None forward limit to index bw_limit : int or None backward limit to index Returns ------- set of indexers Notes --...

[True, True, False, True, False], 2 -> [ [True, True], [True, False], [False, True], [True, False], ] def _rolling_window(a, window): """ [True, True, False, True, False], 2 -> [ [True, True], [True, False], [False, True], [True, F...

Return console size as tuple = (width, height). Returns (None,None) in non-interactive session. def get_console_size(): """Return console size as tuple = (width, height). Returns (None,None) in non-interactive session. """ from pandas import get_option display_width = get_option('display.wid...

check if we're running in an interactive shell returns True if running under python/ipython interactive shell def in_interactive_session(): """ check if we're running in an interactive shell returns True if running under python/ipython interactive shell """ from pandas import get_option def ...

Code the categories to ensure we can groupby for categoricals. If observed=True, we return a new Categorical with the observed categories only. If sort=False, return a copy of self, coded with categories as returned by .unique(), followed by any categories not appearing in the data. If sort=True, ...

Reverse the codes_to_groupby to account for sort / observed. Parameters ---------- c : Categorical sort : boolean The value of the sort parameter groupby was called with. ci : CategoricalIndex The codes / categories to recode Returns ------- CategoricalIndex def recode...

return our implementation def get_engine(engine): """ return our implementation """ if engine == 'auto': engine = get_option('io.parquet.engine') if engine == 'auto': # try engines in this order try: return PyArrowImpl() except ImportError: pass ...

Write a DataFrame to the parquet format. Parameters ---------- path : str File path or Root Directory path. Will be used as Root Directory path while writing a partitioned dataset. .. versionchanged:: 0.24.0 engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto' P...

Load a parquet object from the file path, returning a DataFrame. .. versionadded 0.21.0 Parameters ---------- path : string File path engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto' Parquet library to use. If 'auto', then the option ``io.parquet.engine`` is used...

Generate bin edge offsets and bin labels for one array using another array which has bin edge values. Both arrays must be sorted. Parameters ---------- values : array of values binner : a comparable array of values representing bins into which to bin the first array. Note, 'values' end-poin...

Groupby iterator Returns ------- Generator yielding sequence of (name, subsetted object) for each group def get_iterator(self, data, axis=0): """ Groupby iterator Returns ------- Generator yielding sequence of (name, subsetted object) fo...

dict {group name -> group indices} def indices(self): """ dict {group name -> group indices} """ if len(self.groupings) == 1: return self.groupings[0].indices else: label_list = [ping.labels for ping in self.groupings] keys = [com.values_from_object(ping.grou...

Compute group sizes def size(self): """ Compute group sizes """ ids, _, ngroup = self.group_info ids = ensure_platform_int(ids) if ngroup: out = np.bincount(ids[ids != -1], minlength=ngroup) else: out = [] return Series(out, ...

dict {group name -> group labels} def groups(self): """ dict {group name -> group labels} """ if len(self.groupings) == 1: return self.groupings[0].groups else: to_groupby = lzip(*(ping.grouper for ping in self.groupings)) to_groupby = Index(to_groupby) ...

dict {group name -> group labels} def groups(self): """ dict {group name -> group labels} """ # this is mainly for compat # GH 3881 result = {key: value for key, value in zip(self.binlabels, self.bins) if key is not NaT} return result

Groupby iterator Returns ------- Generator yielding sequence of (name, subsetted object) for each group def get_iterator(self, data, axis=0): """ Groupby iterator Returns ------- Generator yielding sequence of (name, subsetted object) fo...

Normalize semi-structured JSON data into a flat table. Parameters ---------- data : dict or list of dicts Unserialized JSON objects record_path : string or list of strings, default None Path in each object to list of records. If not passed, data will be assumed to be an array of...

Reshape long-format data to wide. Generalized inverse of DataFrame.pivot Parameters ---------- data : DataFrame groups : dict {new_name : list_of_columns} dropna : boolean, default True Examples -------- >>> data = pd.DataFrame({'hr1': [514, 573], 'hr2': [545, 526], ... ...

r""" Wide panel to long format. Less flexible but more user-friendly than melt. With stubnames ['A', 'B'], this function expects to find one or more group of columns with format A-suffix1, A-suffix2,..., B-suffix1, B-suffix2,... You specify what you want to call this suffix in the resulting long fo...

Safe get multiple indices, translate keys for datelike to underlying repr. def _get_indices(self, names): """ Safe get multiple indices, translate keys for datelike to underlying repr. """ def get_converter(s): # possibly convert to the actual key types ...

Create group based selection. Used when selection is not passed directly but instead via a grouper. NOTE: this should be paired with a call to _reset_group_selection def _set_group_selection(self): """ Create group based selection. Used when selection is not passed directly b...

Construct NDFrame from group with provided name. Parameters ---------- name : object the name of the group to get as a DataFrame obj : NDFrame, default None the NDFrame to take the DataFrame out of. If it is None, the object groupby was called on wil...

Parameters ---------- ascending : bool, default True If False, number in reverse, from length of group - 1 to 0. Notes ----- this is currently implementing sort=False (though the default is sort=True) for groupby in general def _cumcount_array(self, ascendin...

Try to cast the result to our obj original type, we may have roundtripped through object in the mean-time. If numeric_only is True, then only try to cast numerics and not datetimelikes. def _try_cast(self, result, obj, numeric_only=False): """ Try to cast the result to our obj ...

Parameters: ----------- func_nm: str The name of the aggregation function being performed Returns: -------- bool Whether transform should attempt to cast the result of aggregation def _transform_should_cast(self, func_nm): """ Parameters:...

Shared func to call any / all Cython GroupBy implementations. def _bool_agg(self, val_test, skipna): """ Shared func to call any / all Cython GroupBy implementations. """ def objs_to_bool(vals: np.ndarray) -> Tuple[np.ndarray, Type]: if is_object_dtype(vals): ...

Compute mean of groups, excluding missing values. Returns ------- pandas.Series or pandas.DataFrame %(see_also)s Examples -------- >>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2], ... 'B': [np.nan, 2, 3, 4, 5], ... ...

Compute median of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex def median(self, **kwargs): """ Compute median of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex """ try: ...

Compute standard deviation of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : integer, default 1 degrees of freedom def std(self, ddof=1, *args, **kwargs): """ Compute standard de...

Compute variance of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : integer, default 1 degrees of freedom def var(self, ddof=1, *args, **kwargs): """ Compute variance of groups, e...

Compute standard error of the mean of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : integer, default 1 degrees of freedom def sem(self, ddof=1): """ Compute standard error of th...

Compute group sizes. def size(self): """ Compute group sizes. """ result = self.grouper.size() if isinstance(self.obj, Series): result.name = getattr(self.obj, 'name', None) return result

Add numeric operations to the GroupBy generically. def _add_numeric_operations(cls): """ Add numeric operations to the GroupBy generically. """ def groupby_function(name, alias, npfunc, numeric_only=True, _convert=False, min_cou...

Provide resampling when using a TimeGrouper. Given a grouper, the function resamples it according to a string "string" -> "frequency". See the :ref:`frequency aliases <timeseries.offset_aliases>` documentation for more details. Parameters ---------- rule : str ...

Return a rolling grouper, providing rolling functionality per group. def rolling(self, *args, **kwargs): """ Return a rolling grouper, providing rolling functionality per group. """ from pandas.core.window import RollingGroupby return RollingGroupby(self, *args, **kwargs)

Return an expanding grouper, providing expanding functionality per group. def expanding(self, *args, **kwargs): """ Return an expanding grouper, providing expanding functionality per group. """ from pandas.core.window import ExpandingGroupby return ExpandingGroup...

Shared function for `pad` and `backfill` to call Cython method. Parameters ---------- direction : {'ffill', 'bfill'} Direction passed to underlying Cython function. `bfill` will cause values to be filled backwards. `ffill` and any other values will default to...

Take the nth row from each group if n is an int, or a subset of rows if n is a list of ints. If dropna, will take the nth non-null row, dropna is either Truthy (if a Series) or 'all', 'any' (if a DataFrame); this is equivalent to calling dropna(how=dropna) before the groupby. ...

Return group values at the given quantile, a la numpy.percentile. Parameters ---------- q : float or array-like, default 0.5 (50% quantile) Value(s) between 0 and 1 providing the quantile(s) to compute. interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} ...

Number each group from 0 to the number of groups - 1. This is the enumerative complement of cumcount. Note that the numbers given to the groups match the order in which the groups would be seen when iterating over the groupby object, not the order they are first observed. .. v...

Number each item in each group from 0 to the length of that group - 1. Essentially this is equivalent to >>> self.apply(lambda x: pd.Series(np.arange(len(x)), x.index)) Parameters ---------- ascending : bool, default True If False, number in reverse, from length of...

Provide the rank of values within each group. Parameters ---------- method : {'average', 'min', 'max', 'first', 'dense'}, default 'average' * average: average rank of group * min: lowest rank in group * max: highest rank in group * first: ranks as...

Cumulative product for each group. def cumprod(self, axis=0, *args, **kwargs): """ Cumulative product for each group. """ nv.validate_groupby_func('cumprod', args, kwargs, ['numeric_only', 'skipna']) if axis != 0: return self.apply(la...

Cumulative min for each group. def cummin(self, axis=0, **kwargs): """ Cumulative min for each group. """ if axis != 0: return self.apply(lambda x: np.minimum.accumulate(x, axis)) return self._cython_transform('cummin', numeric_only=False)

Cumulative max for each group. def cummax(self, axis=0, **kwargs): """ Cumulative max for each group. """ if axis != 0: return self.apply(lambda x: np.maximum.accumulate(x, axis)) return self._cython_transform('cummax', numeric_only=False)