""" Provide user facing operators for doing the split part of the split-apply-combine paradigm. """ from typing import Dict, Hashable, List, Optional, Set, Tuple import warnings import numpy as np from pandas._typing import FrameOrSeries, Label, final from pandas.errors import InvalidIndexError from pandas.util._decorators import cache_readonly from pandas.core.dtypes.common import ( is_categorical_dtype, is_datetime64_dtype, is_list_like, is_scalar, is_timedelta64_dtype, ) 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 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 : {'epoch', 'start', 'start_day'}, 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 a timestamp is not used, these values are also supported: - '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 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 Parrot 10 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 """ _attributes: Tuple[str, ...] = ("key", "level", "freq", "axis", "sort") def __new__(cls, *args, **kwargs): if kwargs.get("freq") is not None: from pandas.core.resample import TimeGrouper # 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 # hacky way to set the stacklevel: if cls is TimeGrouper it means # that the call comes from a pandas internal call of resample, # otherwise it comes from pd.Grouper stacklevel = 4 if cls is TimeGrouper else 2 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=stacklevel, ) 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=stacklevel, ) cls = TimeGrouper return super().__new__(cls) def __init__( self, key=None, level=None, freq=None, axis=0, sort=False, dropna=True ): self.key = key self.level = level self.freq = freq self.axis = axis self.sort = sort self.grouper = None self.obj = None self.indexer = None self.binner = None self._grouper = None self._indexer = None self.dropna = dropna @final @property def ax(self): return self.grouper def _get_grouper(self, obj, validate: bool = True): """ Parameters ---------- obj : the subject object validate : boolean, default True if True, validate the grouper Returns ------- a tuple of binner, grouper, obj (possibly sorted) """ self._set_grouper(obj) # pandas\core\groupby\grouper.py:310: error: Value of type variable # "FrameOrSeries" of "get_grouper" cannot be "Optional[Any]" # [type-var] self.grouper, _, self.obj = get_grouper( # type: ignore[type-var] self.obj, [self.key], axis=self.axis, level=self.level, sort=self.sort, validate=validate, dropna=self.dropna, ) return self.binner, self.grouper, self.obj @final def _set_grouper(self, obj: FrameOrSeries, sort: bool = False): """ 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: self._grouper = self.grouper 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.grouper, "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: # use stable sort to support first, last, nth indexer = self.indexer = ax.argsort(kind="mergesort") ax = ax.take(indexer) obj = obj.take(indexer, axis=self.axis) self.obj = obj self.grouper = ax return self.grouper @final @property def groups(self): # pandas\core\groupby\grouper.py:382: error: Item "None" of # "Optional[Any]" has no attribute "groups" [union-attr] return self.grouper.groups # type: ignore[union-attr] @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} """ def __init__( self, index: Index, grouper=None, obj: Optional[FrameOrSeries] = None, name=None, level=None, sort: bool = True, observed: bool = False, in_axis: bool = False, dropna: bool = True, ): self.name = name self.level = level self.grouper = _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 # right place for this? if isinstance(grouper, (Series, Index)) and name is None: self.name = grouper.name if isinstance(grouper, MultiIndex): self.grouper = grouper._values # we have a single grouper which may be a myriad of things, # some of which are dependent on the passing in level if level is not None: if not isinstance(level, int): if level not in index.names: raise AssertionError(f"Level {level} not in index") level = index.names.index(level) if self.name is None: self.name = index.names[level] ( self.grouper, self._codes, self._group_index, ) = index._get_grouper_for_level(self.grouper, level) # 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.grouper, 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) _, grouper, _ = self.grouper._get_grouper(self.obj, validate=False) if self.name is None: self.name = grouper.result_index.name self.obj = self.grouper.obj self.grouper = grouper._get_grouper() else: if self.grouper is None and self.name is not None and self.obj is not None: self.grouper = self.obj[self.name] elif isinstance(self.grouper, (list, tuple)): self.grouper = com.asarray_tuplesafe(self.grouper) # a passed Categorical elif is_categorical_dtype(self.grouper): self.grouper, self.all_grouper = recode_for_groupby( self.grouper, self.sort, observed ) categories = self.grouper.categories # we make a CategoricalIndex out of the cat grouper # preserving the categories / ordered attributes self._codes = self.grouper.codes if observed: codes = algorithms.unique1d(self.grouper.codes) codes = codes[codes != -1] if sort or self.grouper.ordered: codes = np.sort(codes) else: codes = np.arange(len(categories)) self._group_index = CategoricalIndex( Categorical.from_codes( codes=codes, categories=categories, ordered=self.grouper.ordered ), name=self.name, ) # we are done if isinstance(self.grouper, Grouping): self.grouper = self.grouper.grouper # no level passed elif not isinstance( self.grouper, (Series, Index, ExtensionArray, np.ndarray) ): if getattr(self.grouper, "ndim", 1) != 1: t = self.name or str(type(self.grouper)) raise ValueError(f"Grouper for '{t}' not 1-dimensional") self.grouper = self.index.map(self.grouper) if not ( hasattr(self.grouper, "__len__") and len(self.grouper) == len(self.index) ): grper = pprint_thing(self.grouper) errmsg = ( "Grouper result violates len(labels) == " f"len(data)\nresult: {grper}" ) self.grouper = None # Try for sanity raise AssertionError(errmsg) # if we have a date/time-like grouper, make sure that we have # Timestamps like if getattr(self.grouper, "dtype", None) is not None: if is_datetime64_dtype(self.grouper): self.grouper = self.grouper.astype("datetime64[ns]") elif is_timedelta64_dtype(self.grouper): self.grouper = self.grouper.astype("timedelta64[ns]") def __repr__(self) -> str: return f"Grouping({self.name})" def __iter__(self): return iter(self.indices) _codes: Optional[np.ndarray] = None _group_index: Optional[Index] = None @property def ngroups(self) -> int: return len(self.group_index) @cache_readonly def indices(self): # we have a list of groupers if isinstance(self.grouper, ops.BaseGrouper): return self.grouper.indices values = Categorical(self.grouper) return values._reverse_indexer() @property def codes(self) -> np.ndarray: if self._codes is None: self._make_codes() return self._codes @cache_readonly def result_index(self) -> Index: if self.all_grouper is not None: group_idx = self.group_index assert isinstance(group_idx, CategoricalIndex) # set in __init__ return recode_from_groupby(self.all_grouper, self.sort, group_idx) return self.group_index @property def group_index(self) -> Index: if self._group_index is None: self._make_codes() assert self._group_index is not None return self._group_index def _make_codes(self) -> None: if self._codes is not None and self._group_index is not None: return # we have a list of groupers if isinstance(self.grouper, ops.BaseGrouper): codes = self.grouper.codes_info uniques = self.grouper.result_index else: # GH35667, replace dropna=False with na_sentinel=None if not self.dropna: na_sentinel = None else: na_sentinel = -1 codes, uniques = algorithms.factorize( self.grouper, sort=self.sort, na_sentinel=na_sentinel ) uniques = Index(uniques, name=self.name) self._codes = codes self._group_index = 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: FrameOrSeries, 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", Set[Label], FrameOrSeries]: """ 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, set(), obj else: return grouper, {key.key}, obj # already have a BaseGrouper, just return it elif isinstance(key, ops.BaseGrouper): return key, set(), 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) 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[Label] = set() # if the actual grouper should be obj[key] def is_in_axis(key) -> bool: if not _is_label_like(key): # 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): # IndexError reached in e.g. test_skip_group_keys when we pass # lambda here return False for i, (gpr, level) in enumerate(zip(keys, levels)): if is_in_obj(gpr): # df.groupby(df['name']) in_axis, name = True, gpr.name exclusions.add(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] exclusions.add(name) elif obj._is_level_reference(gpr, axis=axis): in_axis, name, level, gpr = False, None, 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, name = False, None else: in_axis, name = False, None if is_categorical_dtype(gpr) and len(gpr) != obj.shape[axis]: raise ValueError( f"Length of grouper ({len(gpr)}) and axis ({obj.shape[axis]}) " "must be same length" ) # create the Grouping # allow us to passing the actual Grouping as the gpr ping = ( Grouping( group_axis, gpr, obj=obj, name=name, 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, 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, (list, Series, Index, np.ndarray)): if len(grouper) != len(axis): raise ValueError("Grouper and axis must be same length") return grouper else: return grouper