""" Provide user facing operators for doing the split part of the split-apply-combine paradigm. """ from __future__ import annotations from typing import ( TYPE_CHECKING, Any, Hashable, final, ) import warnings import numpy as np from pandas._typing import ( ArrayLike, NDFrameT, npt, ) from pandas.errors import InvalidIndexError from pandas.util._decorators import cache_readonly from pandas.util._exceptions import find_stack_level from pandas.core.dtypes.cast import sanitize_to_nanoseconds from pandas.core.dtypes.common import ( is_categorical_dtype, is_list_like, is_scalar, ) import pandas.core.algorithms as algorithms from pandas.core.arrays import ( Categorical, ExtensionArray, ) import pandas.core.common as com from pandas.core.frame import DataFrame from pandas.core.groupby import ops from pandas.core.groupby.categorical import ( recode_for_groupby, recode_from_groupby, ) from pandas.core.indexes.api import ( CategoricalIndex, Index, MultiIndex, ) from pandas.core.series import Series from pandas.io.formats.printing import pprint_thing if TYPE_CHECKING: from pandas.core.generic import NDFrame class Grouper: """ A Grouper allows the user to specify a groupby instruction for an object. This specification will select a column via the key parameter, or if the level and/or axis parameters are given, a level of the index of the target object. If `axis` and/or `level` are passed as keywords to both `Grouper` and `groupby`, the values passed to `Grouper` take precedence. Parameters ---------- key : str, defaults to None Groupby key, which selects the grouping column of the target. level : name/number, defaults to None The level for the target index. freq : str / frequency object, defaults to None This will groupby the specified frequency if the target selection (via key or level) is a datetime-like object. For full specification of available frequencies, please see `here `_. axis : str, int, defaults to 0 Number/name of the axis. sort : bool, default to False Whether to sort the resulting labels. closed : {'left' or 'right'} Closed end of interval. Only when `freq` parameter is passed. label : {'left' or 'right'} Interval boundary to use for labeling. Only when `freq` parameter is passed. convention : {'start', 'end', 'e', 's'} If grouper is PeriodIndex and `freq` parameter is passed. base : int, default 0 Only when `freq` parameter is passed. For frequencies that evenly subdivide 1 day, the "origin" of the aggregated intervals. For example, for '5min' frequency, base could range from 0 through 4. Defaults to 0. .. deprecated:: 1.1.0 The new arguments that you should use are 'offset' or 'origin'. loffset : str, DateOffset, timedelta object Only when `freq` parameter is passed. .. deprecated:: 1.1.0 loffset is only working for ``.resample(...)`` and not for Grouper (:issue:`28302`). However, loffset is also deprecated for ``.resample(...)`` See: :class:`DataFrame.resample` origin : Timestamp or str, default 'start_day' The timestamp on which to adjust the grouping. The timezone of origin must match the timezone of the index. If string, must be one of the following: - 'epoch': `origin` is 1970-01-01 - 'start': `origin` is the first value of the timeseries - 'start_day': `origin` is the first day at midnight of the timeseries .. versionadded:: 1.1.0 - 'end': `origin` is the last value of the timeseries - 'end_day': `origin` is the ceiling midnight of the last day .. versionadded:: 1.3.0 offset : Timedelta or str, default is None An offset timedelta added to the origin. .. versionadded:: 1.1.0 dropna : bool, default True If True, and if group keys contain NA values, NA values together with row/column will be dropped. If False, NA values will also be treated as the key in groups. .. versionadded:: 1.2.0 Returns ------- A specification for a groupby instruction Examples -------- Syntactic sugar for ``df.groupby('A')`` >>> df = pd.DataFrame( ... { ... "Animal": ["Falcon", "Parrot", "Falcon", "Falcon", "Parrot"], ... "Speed": [100, 5, 200, 300, 15], ... } ... ) >>> df Animal Speed 0 Falcon 100 1 Parrot 5 2 Falcon 200 3 Falcon 300 4 Parrot 15 >>> df.groupby(pd.Grouper(key="Animal")).mean() Speed Animal Falcon 200.0 Parrot 10.0 Specify a resample operation on the column 'Publish date' >>> df = pd.DataFrame( ... { ... "Publish date": [ ... pd.Timestamp("2000-01-02"), ... pd.Timestamp("2000-01-02"), ... pd.Timestamp("2000-01-09"), ... pd.Timestamp("2000-01-16") ... ], ... "ID": [0, 1, 2, 3], ... "Price": [10, 20, 30, 40] ... } ... ) >>> df Publish date ID Price 0 2000-01-02 0 10 1 2000-01-02 1 20 2 2000-01-09 2 30 3 2000-01-16 3 40 >>> df.groupby(pd.Grouper(key="Publish date", freq="1W")).mean() ID Price Publish date 2000-01-02 0.5 15.0 2000-01-09 2.0 30.0 2000-01-16 3.0 40.0 If you want to adjust the start of the bins based on a fixed timestamp: >>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00' >>> rng = pd.date_range(start, end, freq='7min') >>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng) >>> ts 2000-10-01 23:30:00 0 2000-10-01 23:37:00 3 2000-10-01 23:44:00 6 2000-10-01 23:51:00 9 2000-10-01 23:58:00 12 2000-10-02 00:05:00 15 2000-10-02 00:12:00 18 2000-10-02 00:19:00 21 2000-10-02 00:26:00 24 Freq: 7T, dtype: int64 >>> ts.groupby(pd.Grouper(freq='17min')).sum() 2000-10-01 23:14:00 0 2000-10-01 23:31:00 9 2000-10-01 23:48:00 21 2000-10-02 00:05:00 54 2000-10-02 00:22:00 24 Freq: 17T, dtype: int64 >>> ts.groupby(pd.Grouper(freq='17min', origin='epoch')).sum() 2000-10-01 23:18:00 0 2000-10-01 23:35:00 18 2000-10-01 23:52:00 27 2000-10-02 00:09:00 39 2000-10-02 00:26:00 24 Freq: 17T, dtype: int64 >>> ts.groupby(pd.Grouper(freq='17min', origin='2000-01-01')).sum() 2000-10-01 23:24:00 3 2000-10-01 23:41:00 15 2000-10-01 23:58:00 45 2000-10-02 00:15:00 45 Freq: 17T, dtype: int64 If you want to adjust the start of the bins with an `offset` Timedelta, the two following lines are equivalent: >>> ts.groupby(pd.Grouper(freq='17min', origin='start')).sum() 2000-10-01 23:30:00 9 2000-10-01 23:47:00 21 2000-10-02 00:04:00 54 2000-10-02 00:21:00 24 Freq: 17T, dtype: int64 >>> ts.groupby(pd.Grouper(freq='17min', offset='23h30min')).sum() 2000-10-01 23:30:00 9 2000-10-01 23:47:00 21 2000-10-02 00:04:00 54 2000-10-02 00:21:00 24 Freq: 17T, dtype: int64 To replace the use of the deprecated `base` argument, you can now use `offset`, in this example it is equivalent to have `base=2`: >>> ts.groupby(pd.Grouper(freq='17min', offset='2min')).sum() 2000-10-01 23:16:00 0 2000-10-01 23:33:00 9 2000-10-01 23:50:00 36 2000-10-02 00:07:00 39 2000-10-02 00:24:00 24 Freq: 17T, dtype: int64 """ axis: int sort: bool dropna: bool _gpr_index: Index | None _grouper: Index | None _attributes: tuple[str, ...] = ("key", "level", "freq", "axis", "sort", "dropna") def __new__(cls, *args, **kwargs): if kwargs.get("freq") is not None: from pandas.core.resample import TimeGrouper _check_deprecated_resample_kwargs(kwargs, origin=cls) cls = TimeGrouper return super().__new__(cls) def __init__( self, key=None, level=None, freq=None, axis: int = 0, sort: bool = False, dropna: bool = True, ) -> None: self.key = key self.level = level self.freq = freq self.axis = axis self.sort = sort self.dropna = dropna self.grouper = None self._gpr_index = None self.obj = None self.indexer = None self.binner = None self._grouper = None self._indexer = None @final @property def ax(self) -> Index: index = self._gpr_index if index is None: raise ValueError("_set_grouper must be called before ax is accessed") return index def _get_grouper( self, obj: NDFrameT, validate: bool = True ) -> tuple[Any, ops.BaseGrouper, NDFrameT]: """ Parameters ---------- obj : Series or DataFrame validate : bool, default True if True, validate the grouper Returns ------- a tuple of binner, grouper, obj (possibly sorted) """ self._set_grouper(obj) # error: Value of type variable "NDFrameT" of "get_grouper" cannot be # "Optional[Any]" # error: Incompatible types in assignment (expression has type "BaseGrouper", # variable has type "None") self.grouper, _, self.obj = get_grouper( # type: ignore[type-var,assignment] self.obj, [self.key], axis=self.axis, level=self.level, sort=self.sort, validate=validate, dropna=self.dropna, ) # error: Incompatible return value type (got "Tuple[None, None, None]", # expected "Tuple[Any, BaseGrouper, NDFrameT]") return self.binner, self.grouper, self.obj # type: ignore[return-value] @final def _set_grouper(self, obj: NDFrame, sort: bool = False) -> None: """ given an object and the specifications, setup the internal grouper for this particular specification Parameters ---------- obj : Series or DataFrame sort : bool, default False whether the resulting grouper should be sorted """ assert obj is not None if self.key is not None and self.level is not None: raise ValueError("The Grouper cannot specify both a key and a level!") # Keep self.grouper value before overriding if self._grouper is None: # TODO: What are we assuming about subsequent calls? self._grouper = self._gpr_index self._indexer = self.indexer # the key must be a valid info item if self.key is not None: key = self.key # The 'on' is already defined if getattr(self._gpr_index, "name", None) == key and isinstance( obj, Series ): # Sometimes self._grouper will have been resorted while # obj has not. In this case there is a mismatch when we # call self._grouper.take(obj.index) so we need to undo the sorting # before we call _grouper.take. assert self._grouper is not None if self._indexer is not None: reverse_indexer = self._indexer.argsort() unsorted_ax = self._grouper.take(reverse_indexer) ax = unsorted_ax.take(obj.index) else: ax = self._grouper.take(obj.index) else: if key not in obj._info_axis: raise KeyError(f"The grouper name {key} is not found") ax = Index(obj[key], name=key) else: ax = obj._get_axis(self.axis) if self.level is not None: level = self.level # if a level is given it must be a mi level or # equivalent to the axis name if isinstance(ax, MultiIndex): level = ax._get_level_number(level) ax = Index(ax._get_level_values(level), name=ax.names[level]) else: if level not in (0, ax.name): raise ValueError(f"The level {level} is not valid") # possibly sort if (self.sort or sort) and not ax.is_monotonic_increasing: # use stable sort to support first, last, nth # TODO: why does putting na_position="first" fix datetimelike cases? indexer = self.indexer = ax.array.argsort( kind="mergesort", na_position="first" ) ax = ax.take(indexer) obj = obj.take(indexer, axis=self.axis) # error: Incompatible types in assignment (expression has type # "NDFrameT", variable has type "None") self.obj = obj # type: ignore[assignment] self._gpr_index = ax @final @property def groups(self): # error: "None" has no attribute "groups" return self.grouper.groups # type: ignore[attr-defined] @final def __repr__(self) -> str: attrs_list = ( f"{attr_name}={repr(getattr(self, attr_name))}" for attr_name in self._attributes if getattr(self, attr_name) is not None ) attrs = ", ".join(attrs_list) cls_name = type(self).__name__ return f"{cls_name}({attrs})" @final class Grouping: """ Holds the grouping information for a single key Parameters ---------- index : Index grouper : obj : DataFrame or Series name : Label level : observed : bool, default False If we are a Categorical, use the observed values in_axis : if the Grouping is a column in self.obj and hence among Groupby.exclusions list Returns ------- **Attributes**: * indices : dict of {group -> index_list} * codes : ndarray, group codes * group_index : unique groups * groups : dict of {group -> label_list} """ _codes: npt.NDArray[np.signedinteger] | None = None _group_index: Index | None = None _passed_categorical: bool _all_grouper: Categorical | None _index: Index def __init__( self, index: Index, grouper=None, obj: NDFrame | None = None, level=None, sort: bool = True, observed: bool = False, in_axis: bool = False, dropna: bool = True, ) -> None: self.level = level self._orig_grouper = grouper self.grouping_vector = _convert_grouper(index, grouper) self._all_grouper = None self._index = index self._sort = sort self.obj = obj self._observed = observed self.in_axis = in_axis self._dropna = dropna self._passed_categorical = False # we have a single grouper which may be a myriad of things, # some of which are dependent on the passing in level ilevel = self._ilevel if ilevel is not None: mapper = self.grouping_vector # In extant tests, the new self.grouping_vector matches # `index.get_level_values(ilevel)` whenever # mapper is None and isinstance(index, MultiIndex) ( self.grouping_vector, # Index self._codes, self._group_index, ) = index._get_grouper_for_level(mapper, level=ilevel, dropna=dropna) # a passed Grouper like, directly get the grouper in the same way # as single grouper groupby, use the group_info to get codes elif isinstance(self.grouping_vector, Grouper): # get the new grouper; we already have disambiguated # what key/level refer to exactly, don't need to # check again as we have by this point converted these # to an actual value (rather than a pd.Grouper) assert self.obj is not None # for mypy _, newgrouper, newobj = self.grouping_vector._get_grouper( self.obj, validate=False ) self.obj = newobj ng = newgrouper._get_grouper() if isinstance(newgrouper, ops.BinGrouper): # in this case we have `ng is newgrouper` self.grouping_vector = ng else: # ops.BaseGrouper # use Index instead of ndarray so we can recover the name self.grouping_vector = Index(ng, name=newgrouper.result_index.name) elif is_categorical_dtype(self.grouping_vector): # a passed Categorical self._passed_categorical = True self.grouping_vector, self._all_grouper = recode_for_groupby( self.grouping_vector, sort, observed ) elif not isinstance( self.grouping_vector, (Series, Index, ExtensionArray, np.ndarray) ): # no level passed if getattr(self.grouping_vector, "ndim", 1) != 1: t = self.name or str(type(self.grouping_vector)) raise ValueError(f"Grouper for '{t}' not 1-dimensional") self.grouping_vector = index.map(self.grouping_vector) if not ( hasattr(self.grouping_vector, "__len__") and len(self.grouping_vector) == len(index) ): grper = pprint_thing(self.grouping_vector) errmsg = ( "Grouper result violates len(labels) == " f"len(data)\nresult: {grper}" ) self.grouping_vector = None # Try for sanity raise AssertionError(errmsg) if isinstance(self.grouping_vector, np.ndarray): # if we have a date/time-like grouper, make sure that we have # Timestamps like self.grouping_vector = sanitize_to_nanoseconds(self.grouping_vector) def __repr__(self) -> str: return f"Grouping({self.name})" def __iter__(self): return iter(self.indices) @cache_readonly def name(self) -> Hashable: ilevel = self._ilevel if ilevel is not None: return self._index.names[ilevel] if isinstance(self._orig_grouper, (Index, Series)): return self._orig_grouper.name elif isinstance(self.grouping_vector, ops.BaseGrouper): return self.grouping_vector.result_index.name elif isinstance(self.grouping_vector, Index): return self.grouping_vector.name # otherwise we have ndarray or ExtensionArray -> no name return None @cache_readonly def _ilevel(self) -> int | None: """ If necessary, converted index level name to index level position. """ level = self.level if level is None: return None if not isinstance(level, int): index = self._index if level not in index.names: raise AssertionError(f"Level {level} not in index") return index.names.index(level) return level @property def ngroups(self) -> int: return len(self.group_index) @cache_readonly def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]: # we have a list of groupers if isinstance(self.grouping_vector, ops.BaseGrouper): return self.grouping_vector.indices values = Categorical(self.grouping_vector) return values._reverse_indexer() @property def codes(self) -> npt.NDArray[np.signedinteger]: if self._codes is not None: # _codes is set in __init__ for MultiIndex cases return self._codes return self._codes_and_uniques[0] @cache_readonly def group_arraylike(self) -> ArrayLike: """ Analogous to result_index, but holding an ArrayLike to ensure we can retain ExtensionDtypes. """ if self._group_index is not None: # _group_index is set in __init__ for MultiIndex cases return self._group_index._values elif self._all_grouper is not None: # retain dtype for categories, including unobserved ones return self.result_index._values return self._codes_and_uniques[1] @cache_readonly def result_index(self) -> Index: # result_index retains dtype for categories, including unobserved ones, # which group_index does not if self._all_grouper is not None: group_idx = self.group_index assert isinstance(group_idx, CategoricalIndex) return recode_from_groupby(self._all_grouper, self._sort, group_idx) return self.group_index @cache_readonly def group_index(self) -> Index: if self._group_index is not None: # _group_index is set in __init__ for MultiIndex cases return self._group_index uniques = self._codes_and_uniques[1] return Index._with_infer(uniques, name=self.name) @cache_readonly def _codes_and_uniques(self) -> tuple[npt.NDArray[np.signedinteger], ArrayLike]: if self._passed_categorical: # we make a CategoricalIndex out of the cat grouper # preserving the categories / ordered attributes; # doesn't (yet - GH#46909) handle dropna=False cat = self.grouping_vector categories = cat.categories if self._observed: ucodes = algorithms.unique1d(cat.codes) ucodes = ucodes[ucodes != -1] if self._sort or cat.ordered: ucodes = np.sort(ucodes) else: ucodes = np.arange(len(categories)) uniques = Categorical.from_codes( codes=ucodes, categories=categories, ordered=cat.ordered ) return cat.codes, uniques elif isinstance(self.grouping_vector, ops.BaseGrouper): # we have a list of groupers codes = self.grouping_vector.codes_info # error: Incompatible types in assignment (expression has type "Union # [ExtensionArray, ndarray[Any, Any]]", variable has type "Categorical") uniques = ( self.grouping_vector.result_index._values # type: ignore[assignment] ) else: # GH35667, replace dropna=False with use_na_sentinel=False # error: Incompatible types in assignment (expression has type "Union[ # ndarray[Any, Any], Index]", variable has type "Categorical") codes, uniques = algorithms.factorize( # type: ignore[assignment] self.grouping_vector, sort=self._sort, use_na_sentinel=self._dropna ) return codes, uniques @cache_readonly def groups(self) -> dict[Hashable, np.ndarray]: return self._index.groupby(Categorical.from_codes(self.codes, self.group_index)) def get_grouper( obj: NDFrameT, key=None, axis: int = 0, level=None, sort: bool = True, observed: bool = False, mutated: bool = False, validate: bool = True, dropna: bool = True, ) -> tuple[ops.BaseGrouper, frozenset[Hashable], NDFrameT]: """ 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 Groupers enable local references to axis,level,sort, while the passed in axis, level, and sort are 'global'. This routine tries to figure out what the passing in references are and then creates a Grouping for each one, combined into a BaseGrouper. If observed & we have a categorical grouper, only show the observed values. If validate, then check for key/level overlaps. """ group_axis = obj._get_axis(axis) # validate that the passed single level is compatible with the passed # axis of the object if level is not None: # TODO: These if-block and else-block are almost same. # MultiIndex instance check is removable, but it seems that there are # some processes only for non-MultiIndex in else-block, # eg. `obj.index.name != level`. We have to consider carefully whether # these are applicable for MultiIndex. Even if these are applicable, # we need to check if it makes no side effect to subsequent processes # on the outside of this condition. # (GH 17621) if isinstance(group_axis, MultiIndex): if is_list_like(level) and len(level) == 1: level = level[0] if key is None and is_scalar(level): # Get the level values from group_axis key = group_axis.get_level_values(level) level = None else: # allow level to be a length-one list-like object # (e.g., level=[0]) # GH 13901 if is_list_like(level): nlevels = len(level) if nlevels == 1: level = level[0] elif nlevels == 0: raise ValueError("No group keys passed!") else: raise ValueError("multiple levels only valid with MultiIndex") if isinstance(level, str): if obj._get_axis(axis).name != level: raise ValueError( f"level name {level} is not the name " f"of the {obj._get_axis_name(axis)}" ) elif level > 0 or level < -1: raise ValueError("level > 0 or level < -1 only valid with MultiIndex") # NOTE: `group_axis` and `group_axis.get_level_values(level)` # are same in this section. level = None key = group_axis # a passed-in Grouper, directly convert if isinstance(key, Grouper): binner, grouper, obj = key._get_grouper(obj, validate=False) if key.key is None: return grouper, frozenset(), obj else: return grouper, frozenset({key.key}), obj # already have a BaseGrouper, just return it elif isinstance(key, ops.BaseGrouper): return key, frozenset(), obj if not isinstance(key, list): keys = [key] match_axis_length = False else: keys = key match_axis_length = len(keys) == len(group_axis) # what are we after, exactly? any_callable = any(callable(g) or isinstance(g, dict) for g in keys) any_groupers = any(isinstance(g, (Grouper, Grouping)) for g in keys) any_arraylike = any( isinstance(g, (list, tuple, Series, Index, np.ndarray)) for g in keys ) # is this an index replacement? if ( not any_callable and not any_arraylike and not any_groupers and match_axis_length and level is None ): if isinstance(obj, DataFrame): all_in_columns_index = all( g in obj.columns or g in obj.index.names for g in keys ) else: assert isinstance(obj, Series) all_in_columns_index = all(g in obj.index.names for g in keys) if not all_in_columns_index: keys = [com.asarray_tuplesafe(keys)] if isinstance(level, (tuple, list)): if key is None: keys = [None] * len(level) levels = level else: levels = [level] * len(keys) groupings: list[Grouping] = [] exclusions: set[Hashable] = set() # if the actual grouper should be obj[key] def is_in_axis(key) -> bool: if not _is_label_like(key): if obj.ndim == 1: return False # items -> .columns for DataFrame, .index for Series items = obj.axes[-1] try: items.get_loc(key) except (KeyError, TypeError, InvalidIndexError): # TypeError shows up here if we pass e.g. Int64Index return False return True # if the grouper is obj[name] def is_in_obj(gpr) -> bool: if not hasattr(gpr, "name"): return False try: return gpr is obj[gpr.name] except (KeyError, IndexError, InvalidIndexError): # IndexError reached in e.g. test_skip_group_keys when we pass # lambda here # InvalidIndexError raised on key-types inappropriate for index, # e.g. DatetimeIndex.get_loc(tuple()) return False for gpr, level in zip(keys, levels): if is_in_obj(gpr): # df.groupby(df['name']) in_axis = True exclusions.add(gpr.name) elif is_in_axis(gpr): # df.groupby('name') if gpr in obj: if validate: obj._check_label_or_level_ambiguity(gpr, axis=axis) in_axis, name, gpr = True, gpr, obj[gpr] if gpr.ndim != 1: # non-unique columns; raise here to get the name in the # exception message raise ValueError(f"Grouper for '{name}' not 1-dimensional") exclusions.add(name) elif obj._is_level_reference(gpr, axis=axis): in_axis, level, gpr = False, gpr, None else: raise KeyError(gpr) elif isinstance(gpr, Grouper) and gpr.key is not None: # Add key to exclusions exclusions.add(gpr.key) in_axis = False else: in_axis = False # create the Grouping # allow us to passing the actual Grouping as the gpr ping = ( Grouping( group_axis, gpr, obj=obj, level=level, sort=sort, observed=observed, in_axis=in_axis, dropna=dropna, ) if not isinstance(gpr, Grouping) else gpr ) groupings.append(ping) if len(groupings) == 0 and len(obj): raise ValueError("No group keys passed!") elif len(groupings) == 0: groupings.append(Grouping(Index([], dtype="int"), np.array([], dtype=np.intp))) # create the internals grouper grouper = ops.BaseGrouper( group_axis, groupings, sort=sort, mutated=mutated, dropna=dropna ) return grouper, frozenset(exclusions), obj def _is_label_like(val) -> bool: return isinstance(val, (str, tuple)) or (val is not None and is_scalar(val)) def _convert_grouper(axis: Index, grouper): if isinstance(grouper, dict): return grouper.get elif isinstance(grouper, Series): if grouper.index.equals(axis): return grouper._values else: return grouper.reindex(axis)._values elif isinstance(grouper, MultiIndex): return grouper._values elif isinstance(grouper, (list, tuple, Index, Categorical, np.ndarray)): if len(grouper) != len(axis): raise ValueError("Grouper and axis must be same length") if isinstance(grouper, (list, tuple)): grouper = com.asarray_tuplesafe(grouper) return grouper else: return grouper def _check_deprecated_resample_kwargs(kwargs, origin): """ Check for use of deprecated parameters in ``resample`` and related functions. Raises the appropriate warnings if these parameters are detected. Only sets an approximate ``stacklevel`` for the warnings (see #37603, #36629). Parameters ---------- kwargs : dict Dictionary of keyword arguments to check for deprecated parameters. origin : object From where this function is being called; either Grouper or TimeGrouper. Used to determine an approximate stacklevel. """ # Deprecation warning of `base` and `loffset` since v1.1.0: # we are raising the warning here to be able to set the `stacklevel` # properly since we need to raise the `base` and `loffset` deprecation # warning from three different cases: # core/generic.py::NDFrame.resample # core/groupby/groupby.py::GroupBy.resample # core/groupby/grouper.py::Grouper # raising these warnings from TimeGrouper directly would fail the test: # tests/resample/test_deprecated.py::test_deprecating_on_loffset_and_base if kwargs.get("base", None) is not None: warnings.warn( "'base' in .resample() and in Grouper() is deprecated.\n" "The new arguments that you should use are 'offset' or 'origin'.\n" '\n>>> df.resample(freq="3s", base=2)\n' "\nbecomes:\n" '\n>>> df.resample(freq="3s", offset="2s")\n', FutureWarning, stacklevel=find_stack_level(), ) if kwargs.get("loffset", None) is not None: warnings.warn( "'loffset' in .resample() and in Grouper() is deprecated.\n" '\n>>> df.resample(freq="3s", loffset="8H")\n' "\nbecomes:\n" "\n>>> from pandas.tseries.frequencies import to_offset" '\n>>> df = df.resample(freq="3s").mean()' '\n>>> df.index = df.index.to_timestamp() + to_offset("8H")\n', FutureWarning, stacklevel=find_stack_level(), )