""" Provide user facing operators for doing the split part of the split-apply-combine paradigm. """ from __future__ import annotations from typing import ( TYPE_CHECKING, Hashable, Iterator, final, ) import warnings import numpy as np from pandas._config import using_copy_on_write from pandas._typing import ( ArrayLike, Axis, 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.common import ( is_categorical_dtype, is_list_like, is_scalar, ) from pandas.core import 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 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. 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 """ 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 cls = TimeGrouper return super().__new__(cls) def __init__( self, key=None, level=None, freq=None, axis: Axis = 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_deprecated = None self._indexer_deprecated = None self._obj_deprecated = None self._gpr_index = None self.binner = None self._grouper = None self._indexer = None def _get_grouper( self, obj: NDFrameT, validate: bool = True ) -> tuple[ops.BaseGrouper, NDFrameT]: """ Parameters ---------- obj : Series or DataFrame validate : bool, default True if True, validate the grouper Returns ------- a tuple of grouper, obj (possibly sorted) """ obj, _, _ = self._set_grouper(obj) grouper, _, obj = get_grouper( obj, [self.key], axis=self.axis, level=self.level, sort=self.sort, validate=validate, dropna=self.dropna, ) # Without setting this, subsequent lookups to .groups raise # error: Incompatible types in assignment (expression has type "BaseGrouper", # variable has type "None") self._grouper_deprecated = grouper # type: ignore[assignment] return grouper, obj @final def _set_grouper( self, obj: NDFrame, sort: bool = False, *, gpr_index: Index | None = 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 gpr_index : Index or None, default None Returns ------- NDFrame Index np.ndarray[np.intp] | None """ assert obj is not None indexer = 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 = gpr_index self._indexer = self._indexer_deprecated # the key must be a valid info item if self.key is not None: key = self.key # The 'on' is already defined if getattr(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_deprecated = 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_deprecated = obj # type: ignore[assignment] self._gpr_index = ax return obj, ax, indexer @final @property def ax(self) -> Index: warnings.warn( f"{type(self).__name__}.ax is deprecated and will be removed in a " "future version. Use Resampler.ax instead", FutureWarning, stacklevel=find_stack_level(), ) index = self._gpr_index if index is None: raise ValueError("_set_grouper must be called before ax is accessed") return index @final @property def indexer(self): warnings.warn( f"{type(self).__name__}.indexer is deprecated and will be removed " "in a future version. Use Resampler.indexer instead.", FutureWarning, stacklevel=find_stack_level(), ) return self._indexer_deprecated @final @property def obj(self): warnings.warn( f"{type(self).__name__}.obj is deprecated and will be removed " "in a future version. Use GroupBy.indexer instead.", FutureWarning, stacklevel=find_stack_level(), ) return self._obj_deprecated @final @property def grouper(self): warnings.warn( f"{type(self).__name__}.grouper is deprecated and will be removed " "in a future version. Use GroupBy.grouper instead.", FutureWarning, stacklevel=find_stack_level(), ) return self._grouper_deprecated @final @property def groups(self): warnings.warn( f"{type(self).__name__}.groups is deprecated and will be removed " "in a future version. Use GroupBy.groups instead.", FutureWarning, stacklevel=find_stack_level(), ) # error: "None" has no attribute "groups" return self._grouper_deprecated.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 dropna : bool, default True Whether to drop NA groups. uniques : Array-like, optional When specified, will be used for unique values. Enables including empty groups in the result for a BinGrouper. Must not contain duplicates. Attributes ------- indices : dict Mapping of {group -> index_list} codes : ndarray Group codes group_index : Index or None unique groups groups : dict Mapping of {group -> label_list} """ _codes: npt.NDArray[np.signedinteger] | None = None _group_index: Index | None = None _all_grouper: Categorical | None _orig_cats: Index | 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, uniques: ArrayLike | None = None, ) -> None: self.level = level self._orig_grouper = grouper grouping_vector = _convert_grouper(index, grouper) self._all_grouper = None self._orig_cats = None self._index = index self._sort = sort self.obj = obj self._observed = observed self.in_axis = in_axis self._dropna = dropna self._uniques = uniques # 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: # In extant tests, the new self.grouping_vector matches # `index.get_level_values(ilevel)` whenever # mapper is None and isinstance(index, MultiIndex) if isinstance(index, MultiIndex): index_level = index.get_level_values(ilevel) else: index_level = index if grouping_vector is None: grouping_vector = index_level else: mapper = grouping_vector grouping_vector = index_level.map(mapper) # 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(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 = grouping_vector._get_grouper(self.obj, validate=False) self.obj = newobj if isinstance(newgrouper, ops.BinGrouper): # TODO: can we unwrap this and get a tighter typing # for self.grouping_vector? grouping_vector = newgrouper else: # ops.BaseGrouper # TODO: 2023-02-03 no test cases with len(newgrouper.groupings) > 1. # If that were to occur, would we be throwing out information? # error: Cannot determine type of "grouping_vector" [has-type] ng = newgrouper.groupings[0].grouping_vector # type: ignore[has-type] # use Index instead of ndarray so we can recover the name grouping_vector = Index(ng, name=newgrouper.result_index.name) elif not isinstance( grouping_vector, (Series, Index, ExtensionArray, np.ndarray) ): # no level passed if getattr(grouping_vector, "ndim", 1) != 1: t = str(type(grouping_vector)) raise ValueError(f"Grouper for '{t}' not 1-dimensional") grouping_vector = index.map(grouping_vector) if not ( hasattr(grouping_vector, "__len__") and len(grouping_vector) == len(index) ): grper = pprint_thing(grouping_vector) errmsg = ( "Grouper result violates len(labels) == " f"len(data)\nresult: {grper}" ) raise AssertionError(errmsg) if isinstance(grouping_vector, np.ndarray): if grouping_vector.dtype.kind in ["m", "M"]: # if we have a date/time-like grouper, make sure that we have # Timestamps like # TODO 2022-10-08 we only have one test that gets here and # values are already in nanoseconds in that case. grouping_vector = Series(grouping_vector).to_numpy() elif is_categorical_dtype(grouping_vector): # a passed Categorical self._orig_cats = grouping_vector.categories grouping_vector, self._all_grouper = recode_for_groupby( grouping_vector, sort, observed ) self.grouping_vector = grouping_vector def __repr__(self) -> str: return f"Grouping({self.name})" def __iter__(self) -> Iterator: return iter(self.indices) @cache_readonly def _passed_categorical(self) -> bool: return is_categorical_dtype(self.grouping_vector) @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]: 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._all_grouper is not None: # retain dtype for categories, including unobserved ones return self.result_index._values elif self._passed_categorical: return self.group_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) cats = self._orig_cats # set_categories is dynamically added return group_idx.set_categories(cats) # type: ignore[attr-defined] return self.group_index @cache_readonly def group_index(self) -> Index: codes, uniques = self._codes_and_uniques if not self._dropna and self._passed_categorical: assert isinstance(uniques, Categorical) if self._sort and (codes == len(uniques)).any(): # Add NA value on the end when sorting uniques = Categorical.from_codes( np.append(uniques.codes, [-1]), uniques.categories ) elif len(codes) > 0: # Need to determine proper placement of NA value when not sorting cat = self.grouping_vector na_idx = (cat.codes < 0).argmax() if cat.codes[na_idx] < 0: # count number of unique codes that comes before the nan value na_unique_idx = algorithms.nunique_ints(cat.codes[:na_idx]) uniques = Categorical.from_codes( np.insert(uniques.codes, na_unique_idx, -1), uniques.categories ) return Index._with_infer(uniques, name=self.name) @cache_readonly def _codes_and_uniques(self) -> tuple[npt.NDArray[np.signedinteger], ArrayLike]: uniques: 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: ucodes = np.sort(ucodes) else: ucodes = np.arange(len(categories)) uniques = Categorical.from_codes( codes=ucodes, categories=categories, ordered=cat.ordered ) codes = cat.codes if not self._dropna: na_mask = codes < 0 if np.any(na_mask): if self._sort: # Replace NA codes with `largest code + 1` na_code = len(categories) codes = np.where(na_mask, na_code, codes) else: # Insert NA code into the codes based on first appearance # A negative code must exist, no need to check codes[na_idx] < 0 na_idx = na_mask.argmax() # count number of unique codes that comes before the nan value na_code = algorithms.nunique_ints(codes[:na_idx]) codes = np.where(codes >= na_code, codes + 1, codes) codes = np.where(na_mask, na_code, codes) if not self._observed: uniques = uniques.reorder_categories(self._orig_cats) return codes, uniques elif isinstance(self.grouping_vector, ops.BaseGrouper): # we have a list of groupers codes = self.grouping_vector.codes_info uniques = self.grouping_vector.result_index._values elif self._uniques is not None: # GH#50486 Code grouping_vector using _uniques; allows # including uniques that are not present in grouping_vector. cat = Categorical(self.grouping_vector, categories=self._uniques) codes = cat.codes uniques = self._uniques 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: Axis = 0, level=None, sort: bool = True, observed: 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): 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. an Index return False return True # if the grouper is obj[name] def is_in_obj(gpr) -> bool: if not hasattr(gpr, "name"): return False if using_copy_on_write(): # For the CoW case, we check the references to determine if the # series is part of the object try: obj_gpr_column = obj[gpr.name] except (KeyError, IndexError, InvalidIndexError): return False if isinstance(gpr, Series) and isinstance(obj_gpr_column, Series): return gpr._mgr.references_same_values( # type: ignore[union-attr] obj_gpr_column._mgr, 0 # type: ignore[arg-type] ) 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 obj.ndim != 1 and 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 = True 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!") if 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, 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