""" Provide classes to perform the groupby aggregate operations. These are not exposed to the user and provide implementations of the grouping operations, primarily in cython. These classes (BaseGrouper and BinGrouper) are contained *in* the SeriesGroupBy and DataFrameGroupBy objects. """ from __future__ import annotations import collections import functools from typing import ( TYPE_CHECKING, Callable, Generic, Hashable, Iterator, NoReturn, Sequence, final, ) import numpy as np from pandas._libs import ( NaT, lib, ) import pandas._libs.groupby as libgroupby import pandas._libs.reduction as libreduction from pandas._typing import ( ArrayLike, DtypeObj, NDFrameT, Shape, npt, ) from pandas.errors import AbstractMethodError from pandas.util._decorators import cache_readonly from pandas.core.dtypes.cast import ( maybe_cast_pointwise_result, maybe_downcast_to_dtype, ) from pandas.core.dtypes.common import ( ensure_float64, ensure_int64, ensure_platform_int, ensure_uint64, is_1d_only_ea_dtype, is_bool_dtype, is_complex_dtype, is_datetime64_any_dtype, is_float_dtype, is_integer_dtype, is_numeric_dtype, is_sparse, is_timedelta64_dtype, needs_i8_conversion, ) from pandas.core.dtypes.dtypes import CategoricalDtype from pandas.core.dtypes.missing import ( isna, maybe_fill, ) from pandas.core.arrays import ( Categorical, DatetimeArray, ExtensionArray, PeriodArray, TimedeltaArray, ) from pandas.core.arrays.boolean import BooleanDtype from pandas.core.arrays.floating import FloatingDtype from pandas.core.arrays.integer import IntegerDtype from pandas.core.arrays.masked import ( BaseMaskedArray, BaseMaskedDtype, ) from pandas.core.arrays.string_ import StringDtype from pandas.core.frame import DataFrame from pandas.core.groupby import grouper from pandas.core.indexes.api import ( CategoricalIndex, Index, MultiIndex, ensure_index, ) from pandas.core.series import Series from pandas.core.sorting import ( compress_group_index, decons_obs_group_ids, get_flattened_list, get_group_index, get_group_index_sorter, get_indexer_dict, ) if TYPE_CHECKING: from pandas.core.generic import NDFrame class WrappedCythonOp: """ Dispatch logic for functions defined in _libs.groupby Parameters ---------- kind: str Whether the operation is an aggregate or transform. how: str Operation name, e.g. "mean". has_dropped_na: bool True precisely when dropna=True and the grouper contains a null value. """ # Functions for which we do _not_ attempt to cast the cython result # back to the original dtype. cast_blocklist = frozenset(["rank", "count", "size", "idxmin", "idxmax"]) def __init__(self, kind: str, how: str, has_dropped_na: bool) -> None: self.kind = kind self.how = how self.has_dropped_na = has_dropped_na _CYTHON_FUNCTIONS = { "aggregate": { "sum": "group_sum", "prod": "group_prod", "min": "group_min", "max": "group_max", "mean": "group_mean", "median": "group_median_float64", "var": "group_var", "first": "group_nth", "last": "group_last", "ohlc": "group_ohlc", }, "transform": { "cumprod": "group_cumprod_float64", "cumsum": "group_cumsum", "cummin": "group_cummin", "cummax": "group_cummax", "rank": "group_rank", }, } # "group_any" and "group_all" are also support masks, but don't go # through WrappedCythonOp _MASKED_CYTHON_FUNCTIONS = { "cummin", "cummax", "min", "max", "last", "first", "rank", "sum", "ohlc", "cumsum", "prod", } _cython_arity = {"ohlc": 4} # OHLC # Note: we make this a classmethod and pass kind+how so that caching # works at the class level and not the instance level @classmethod @functools.lru_cache(maxsize=None) def _get_cython_function( cls, kind: str, how: str, dtype: np.dtype, is_numeric: bool ): dtype_str = dtype.name ftype = cls._CYTHON_FUNCTIONS[kind][how] # see if there is a fused-type version of function # only valid for numeric f = getattr(libgroupby, ftype) if is_numeric: return f elif dtype == np.dtype(object): if how in ["median", "cumprod"]: # no fused types -> no __signatures__ raise NotImplementedError( f"function is not implemented for this dtype: " f"[how->{how},dtype->{dtype_str}]" ) elif "object" not in f.__signatures__: # raise NotImplementedError here rather than TypeError later raise NotImplementedError( f"function is not implemented for this dtype: " f"[how->{how},dtype->{dtype_str}]" ) return f else: raise NotImplementedError( "This should not be reached. Please report a bug at " "github.com/pandas-dev/pandas/", dtype, ) def _get_cython_vals(self, values: np.ndarray) -> np.ndarray: """ Cast numeric dtypes to float64 for functions that only support that. Parameters ---------- values : np.ndarray Returns ------- values : np.ndarray """ how = self.how if how in ["median", "cumprod"]: # these two only have float64 implementations # We should only get here with is_numeric, as non-numeric cases # should raise in _get_cython_function values = ensure_float64(values) elif values.dtype.kind in ["i", "u"]: if how in ["var", "mean"] or ( self.kind == "transform" and self.has_dropped_na ): # result may still include NaN, so we have to cast values = ensure_float64(values) elif how in ["sum", "ohlc", "prod", "cumsum"]: # Avoid overflow during group op if values.dtype.kind == "i": values = ensure_int64(values) else: values = ensure_uint64(values) return values # TODO: general case implementation overridable by EAs. def _disallow_invalid_ops(self, dtype: DtypeObj, is_numeric: bool = False): """ Check if we can do this operation with our cython functions. Raises ------ NotImplementedError This is either not a valid function for this dtype, or valid but not implemented in cython. """ how = self.how if is_numeric: # never an invalid op for those dtypes, so return early as fastpath return if isinstance(dtype, CategoricalDtype): # NotImplementedError for methods that can fall back to a # non-cython implementation. if how in ["sum", "prod", "cumsum", "cumprod"]: raise TypeError(f"{dtype} type does not support {how} operations") elif how not in ["rank"]: # only "rank" is implemented in cython raise NotImplementedError(f"{dtype} dtype not supported") elif not dtype.ordered: # TODO: TypeError? raise NotImplementedError(f"{dtype} dtype not supported") elif is_sparse(dtype): # categoricals are only 1d, so we # are not setup for dim transforming raise NotImplementedError(f"{dtype} dtype not supported") elif is_datetime64_any_dtype(dtype): # TODO: same for period_dtype? no for these methods with Period # we raise NotImplemented if this is an invalid operation # entirely, e.g. adding datetimes if how in ["sum", "prod", "cumsum", "cumprod"]: raise TypeError(f"datetime64 type does not support {how} operations") elif is_timedelta64_dtype(dtype): if how in ["prod", "cumprod"]: raise TypeError(f"timedelta64 type does not support {how} operations") def _get_output_shape(self, ngroups: int, values: np.ndarray) -> Shape: how = self.how kind = self.kind arity = self._cython_arity.get(how, 1) out_shape: Shape if how == "ohlc": out_shape = (ngroups, 4) elif arity > 1: raise NotImplementedError( "arity of more than 1 is not supported for the 'how' argument" ) elif kind == "transform": out_shape = values.shape else: out_shape = (ngroups,) + values.shape[1:] return out_shape def _get_out_dtype(self, dtype: np.dtype) -> np.dtype: how = self.how if how == "rank": out_dtype = "float64" else: if is_numeric_dtype(dtype): out_dtype = f"{dtype.kind}{dtype.itemsize}" else: out_dtype = "object" return np.dtype(out_dtype) def _get_result_dtype(self, dtype: np.dtype) -> np.dtype: """ Get the desired dtype of a result based on the input dtype and how it was computed. Parameters ---------- dtype : np.dtype Returns ------- np.dtype The desired dtype of the result. """ how = self.how if how in ["sum", "cumsum", "sum", "prod"]: if dtype == np.dtype(bool): return np.dtype(np.int64) elif how in ["mean", "median", "var"]: if is_float_dtype(dtype) or is_complex_dtype(dtype): return dtype elif is_numeric_dtype(dtype): return np.dtype(np.float64) return dtype def uses_mask(self) -> bool: return self.how in self._MASKED_CYTHON_FUNCTIONS @final def _ea_wrap_cython_operation( self, values: ExtensionArray, min_count: int, ngroups: int, comp_ids: np.ndarray, **kwargs, ) -> ArrayLike: """ If we have an ExtensionArray, unwrap, call _cython_operation, and re-wrap if appropriate. """ if isinstance(values, BaseMaskedArray) and self.uses_mask(): return self._masked_ea_wrap_cython_operation( values, min_count=min_count, ngroups=ngroups, comp_ids=comp_ids, **kwargs, ) elif isinstance(values, Categorical) and self.uses_mask(): assert self.how == "rank" # the only one implemented ATM assert values.ordered # checked earlier mask = values.isna() npvalues = values._ndarray res_values = self._cython_op_ndim_compat( npvalues, min_count=min_count, ngroups=ngroups, comp_ids=comp_ids, mask=mask, **kwargs, ) # If we ever have more than just "rank" here, we'll need to do # `if self.how in self.cast_blocklist` like we do for other dtypes. return res_values npvalues = self._ea_to_cython_values(values) res_values = self._cython_op_ndim_compat( npvalues, min_count=min_count, ngroups=ngroups, comp_ids=comp_ids, mask=None, **kwargs, ) if self.how in self.cast_blocklist: # i.e. how in ["rank"], since other cast_blocklist methods dont go # through cython_operation return res_values return self._reconstruct_ea_result(values, res_values) # TODO: general case implementation overridable by EAs. def _ea_to_cython_values(self, values: ExtensionArray) -> np.ndarray: # GH#43682 if isinstance(values, (DatetimeArray, PeriodArray, TimedeltaArray)): # All of the functions implemented here are ordinal, so we can # operate on the tz-naive equivalents npvalues = values._ndarray.view("M8[ns]") elif isinstance(values.dtype, (BooleanDtype, IntegerDtype)): # IntegerArray or BooleanArray npvalues = values.to_numpy("float64", na_value=np.nan) elif isinstance(values.dtype, FloatingDtype): # FloatingArray npvalues = values.to_numpy(values.dtype.numpy_dtype, na_value=np.nan) elif isinstance(values.dtype, StringDtype): # StringArray npvalues = values.to_numpy(object, na_value=np.nan) else: raise NotImplementedError( f"function is not implemented for this dtype: {values.dtype}" ) return npvalues # TODO: general case implementation overridable by EAs. def _reconstruct_ea_result( self, values: ExtensionArray, res_values: np.ndarray ) -> ExtensionArray: """ Construct an ExtensionArray result from an ndarray result. """ dtype: BaseMaskedDtype | StringDtype if isinstance(values.dtype, StringDtype): dtype = values.dtype string_array_cls = dtype.construct_array_type() return string_array_cls._from_sequence(res_values, dtype=dtype) elif isinstance(values.dtype, BaseMaskedDtype): new_dtype = self._get_result_dtype(values.dtype.numpy_dtype) dtype = BaseMaskedDtype.from_numpy_dtype(new_dtype) masked_array_cls = dtype.construct_array_type() return masked_array_cls._from_sequence(res_values, dtype=dtype) elif isinstance(values, (DatetimeArray, TimedeltaArray, PeriodArray)): # In to_cython_values we took a view as M8[ns] assert res_values.dtype == "M8[ns]" res_values = res_values.view(values._ndarray.dtype) return values._from_backing_data(res_values) raise NotImplementedError @final def _masked_ea_wrap_cython_operation( self, values: BaseMaskedArray, min_count: int, ngroups: int, comp_ids: np.ndarray, **kwargs, ) -> BaseMaskedArray: """ Equivalent of `_ea_wrap_cython_operation`, but optimized for masked EA's and cython algorithms which accept a mask. """ orig_values = values # libgroupby functions are responsible for NOT altering mask mask = values._mask if self.kind != "aggregate": result_mask = mask.copy() else: result_mask = np.zeros(ngroups, dtype=bool) arr = values._data res_values = self._cython_op_ndim_compat( arr, min_count=min_count, ngroups=ngroups, comp_ids=comp_ids, mask=mask, result_mask=result_mask, **kwargs, ) if self.how == "ohlc": result_mask = np.tile(result_mask, (4, 1)).T # res_values should already have the correct dtype, we just need to # wrap in a MaskedArray return orig_values._maybe_mask_result(res_values, result_mask) @final def _cython_op_ndim_compat( self, values: np.ndarray, *, min_count: int, ngroups: int, comp_ids: np.ndarray, mask: npt.NDArray[np.bool_] | None = None, result_mask: npt.NDArray[np.bool_] | None = None, **kwargs, ) -> np.ndarray: if values.ndim == 1: # expand to 2d, dispatch, then squeeze if appropriate values2d = values[None, :] if mask is not None: mask = mask[None, :] if result_mask is not None: result_mask = result_mask[None, :] res = self._call_cython_op( values2d, min_count=min_count, ngroups=ngroups, comp_ids=comp_ids, mask=mask, result_mask=result_mask, **kwargs, ) if res.shape[0] == 1: return res[0] # otherwise we have OHLC return res.T return self._call_cython_op( values, min_count=min_count, ngroups=ngroups, comp_ids=comp_ids, mask=mask, result_mask=result_mask, **kwargs, ) @final def _call_cython_op( self, values: np.ndarray, # np.ndarray[ndim=2] *, min_count: int, ngroups: int, comp_ids: np.ndarray, mask: npt.NDArray[np.bool_] | None, result_mask: npt.NDArray[np.bool_] | None, **kwargs, ) -> np.ndarray: # np.ndarray[ndim=2] orig_values = values dtype = values.dtype is_numeric = is_numeric_dtype(dtype) is_datetimelike = needs_i8_conversion(dtype) if is_datetimelike: values = values.view("int64") is_numeric = True elif is_bool_dtype(dtype): values = values.view("uint8") if values.dtype == "float16": values = values.astype(np.float32) values = values.T if mask is not None: mask = mask.T if result_mask is not None: result_mask = result_mask.T out_shape = self._get_output_shape(ngroups, values) func = self._get_cython_function(self.kind, self.how, values.dtype, is_numeric) values = self._get_cython_vals(values) out_dtype = self._get_out_dtype(values.dtype) result = maybe_fill(np.empty(out_shape, dtype=out_dtype)) if self.kind == "aggregate": counts = np.zeros(ngroups, dtype=np.int64) if self.how in ["min", "max", "mean", "last", "first"]: func( out=result, counts=counts, values=values, labels=comp_ids, min_count=min_count, mask=mask, result_mask=result_mask, is_datetimelike=is_datetimelike, ) elif self.how in ["sum"]: # We support datetimelike func( out=result, counts=counts, values=values, labels=comp_ids, mask=mask, result_mask=result_mask, min_count=min_count, is_datetimelike=is_datetimelike, ) elif self.how in ["ohlc", "prod"]: func( result, counts, values, comp_ids, min_count=min_count, mask=mask, result_mask=result_mask, ) else: func(result, counts, values, comp_ids, min_count, **kwargs) else: # TODO: min_count if self.uses_mask(): if self.how != "rank": # TODO: should rank take result_mask? kwargs["result_mask"] = result_mask func( out=result, values=values, labels=comp_ids, ngroups=ngroups, is_datetimelike=is_datetimelike, mask=mask, **kwargs, ) else: func( out=result, values=values, labels=comp_ids, ngroups=ngroups, is_datetimelike=is_datetimelike, **kwargs, ) if self.kind == "aggregate": # i.e. counts is defined. Locations where count ArrayLike: """ Call our cython function, with appropriate pre- and post- processing. """ if values.ndim > 2: raise NotImplementedError("number of dimensions is currently limited to 2") elif values.ndim == 2: assert axis == 1, axis elif not is_1d_only_ea_dtype(values.dtype): # Note: it is *not* the case that axis is always 0 for 1-dim values, # as we can have 1D ExtensionArrays that we need to treat as 2D assert axis == 0 dtype = values.dtype is_numeric = is_numeric_dtype(dtype) # can we do this operation with our cython functions # if not raise NotImplementedError self._disallow_invalid_ops(dtype, is_numeric) if not isinstance(values, np.ndarray): # i.e. ExtensionArray return self._ea_wrap_cython_operation( values, min_count=min_count, ngroups=ngroups, comp_ids=comp_ids, **kwargs, ) return self._cython_op_ndim_compat( values, min_count=min_count, ngroups=ngroups, comp_ids=comp_ids, mask=None, **kwargs, ) class BaseGrouper: """ This is an internal Grouper class, which actually holds the generated groups Parameters ---------- axis : Index groupings : Sequence[Grouping] all the grouping instances to handle in this grouper for example for grouper list to groupby, need to pass the list sort : bool, default True whether this grouper will give sorted result or not group_keys : bool, default True mutated : bool, default False indexer : np.ndarray[np.intp], optional the indexer created by Grouper some groupers (TimeGrouper) will sort its axis and its group_info is also sorted, so need the indexer to reorder """ axis: Index def __init__( self, axis: Index, groupings: Sequence[grouper.Grouping], sort: bool = True, group_keys: bool = True, mutated: bool = False, indexer: npt.NDArray[np.intp] | None = None, dropna: bool = True, ) -> None: assert isinstance(axis, Index), axis self.axis = axis self._groupings: list[grouper.Grouping] = list(groupings) self._sort = sort self.group_keys = group_keys self.mutated = mutated self.indexer = indexer self.dropna = dropna @property def groupings(self) -> list[grouper.Grouping]: return self._groupings @property def shape(self) -> Shape: return tuple(ping.ngroups for ping in self.groupings) def __iter__(self) -> Iterator[Hashable]: return iter(self.indices) @property def nkeys(self) -> int: return len(self.groupings) def get_iterator( self, data: NDFrameT, axis: int = 0 ) -> Iterator[tuple[Hashable, NDFrameT]]: """ Groupby iterator Returns ------- Generator yielding sequence of (name, subsetted object) for each group """ splitter = self._get_splitter(data, axis=axis) keys = self.group_keys_seq yield from zip(keys, splitter) @final def _get_splitter(self, data: NDFrame, axis: int = 0) -> DataSplitter: """ Returns ------- Generator yielding subsetted objects """ ids, _, ngroups = self.group_info return get_splitter(data, ids, ngroups, axis=axis) def _get_grouper(self): """ We are a grouper as part of another's groupings. We have a specific method of grouping, so cannot convert to a Index for our grouper. """ return self.groupings[0].grouping_vector @final @cache_readonly def group_keys_seq(self): if len(self.groupings) == 1: return self.levels[0] else: ids, _, ngroups = self.group_info # provide "flattened" iterator for multi-group setting return get_flattened_list(ids, ngroups, self.levels, self.codes) @final def apply( self, f: Callable, data: DataFrame | Series, axis: int = 0 ) -> tuple[list, bool]: mutated = self.mutated splitter = self._get_splitter(data, axis=axis) group_keys = self.group_keys_seq result_values = [] # This calls DataSplitter.__iter__ zipped = zip(group_keys, splitter) for key, group in zipped: object.__setattr__(group, "name", key) # group might be modified group_axes = group.axes res = f(group) if not mutated and not _is_indexed_like(res, group_axes, axis): mutated = True result_values.append(res) # getattr pattern for __name__ is needed for functools.partial objects if len(group_keys) == 0 and getattr(f, "__name__", None) in [ "mad", "skew", "sum", "prod", ]: # If group_keys is empty, then no function calls have been made, # so we will not have raised even if this is an invalid dtype. # So do one dummy call here to raise appropriate TypeError. f(data.iloc[:0]) return result_values, mutated @cache_readonly def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]: """dict {group name -> group indices}""" if len(self.groupings) == 1 and isinstance(self.result_index, CategoricalIndex): # This shows unused categories in indices GH#38642 return self.groupings[0].indices codes_list = [ping.codes for ping in self.groupings] keys = [ping.group_index for ping in self.groupings] return get_indexer_dict(codes_list, keys) @final def result_ilocs(self) -> npt.NDArray[np.intp]: """ Get the original integer locations of result_index in the input. """ # Original indices are where group_index would go via sorting. # But when dropna is true, we need to remove null values while accounting for # any gaps that then occur because of them. group_index = get_group_index( self.codes, self.shape, sort=self._sort, xnull=True ) group_index, _ = compress_group_index(group_index, sort=self._sort) if self.has_dropped_na: mask = np.where(group_index >= 0) # Count how many gaps are caused by previous null values for each position null_gaps = np.cumsum(group_index == -1)[mask] group_index = group_index[mask] result = get_group_index_sorter(group_index, self.ngroups) if self.has_dropped_na: # Shift by the number of prior null gaps result += np.take(null_gaps, result) return result @final @property def codes(self) -> list[npt.NDArray[np.signedinteger]]: return [ping.codes for ping in self.groupings] @property def levels(self) -> list[Index]: return [ping.group_index for ping in self.groupings] @property def names(self) -> list[Hashable]: return [ping.name for ping in self.groupings] @final def size(self) -> Series: """ Compute group sizes. """ ids, _, ngroups = self.group_info out: np.ndarray | list if ngroups: out = np.bincount(ids[ids != -1], minlength=ngroups) else: out = [] return Series(out, index=self.result_index, dtype="int64") @cache_readonly def groups(self) -> dict[Hashable, np.ndarray]: """dict {group name -> group labels}""" if len(self.groupings) == 1: return self.groupings[0].groups else: to_groupby = zip(*(ping.grouping_vector for ping in self.groupings)) index = Index(to_groupby) return self.axis.groupby(index) @final @cache_readonly def is_monotonic(self) -> bool: # return if my group orderings are monotonic return Index(self.group_info[0]).is_monotonic_increasing @final @cache_readonly def has_dropped_na(self) -> bool: """ Whether grouper has null value(s) that are dropped. """ return bool((self.group_info[0] < 0).any()) @cache_readonly def group_info(self) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp], int]: comp_ids, obs_group_ids = self._get_compressed_codes() ngroups = len(obs_group_ids) comp_ids = ensure_platform_int(comp_ids) return comp_ids, obs_group_ids, ngroups @final @cache_readonly def codes_info(self) -> npt.NDArray[np.intp]: # return the codes of items in original grouped axis ids, _, _ = self.group_info if self.indexer is not None: sorter = np.lexsort((ids, self.indexer)) ids = ids[sorter] ids = ensure_platform_int(ids) # TODO: if numpy annotates np.lexsort, this ensure_platform_int # may become unnecessary return ids @final def _get_compressed_codes( self, ) -> tuple[npt.NDArray[np.signedinteger], npt.NDArray[np.intp]]: # The first returned ndarray may have any signed integer dtype if len(self.groupings) > 1: group_index = get_group_index(self.codes, self.shape, sort=True, xnull=True) return compress_group_index(group_index, sort=self._sort) # FIXME: compress_group_index's second return value is int64, not intp ping = self.groupings[0] return ping.codes, np.arange(len(ping.group_index), dtype=np.intp) @final @cache_readonly def ngroups(self) -> int: return len(self.result_index) @property def reconstructed_codes(self) -> list[npt.NDArray[np.intp]]: codes = self.codes ids, obs_ids, _ = self.group_info return decons_obs_group_ids(ids, obs_ids, self.shape, codes, xnull=True) @cache_readonly def result_index(self) -> Index: if len(self.groupings) == 1: return self.groupings[0].result_index.rename(self.names[0]) codes = self.reconstructed_codes levels = [ping.result_index for ping in self.groupings] return MultiIndex( levels=levels, codes=codes, verify_integrity=False, names=self.names ) @final def get_group_levels(self) -> list[ArrayLike]: # Note: only called from _insert_inaxis_grouper_inplace, which # is only called for BaseGrouper, never for BinGrouper if len(self.groupings) == 1: return [self.groupings[0].group_arraylike] name_list = [] for ping, codes in zip(self.groupings, self.reconstructed_codes): codes = ensure_platform_int(codes) levels = ping.group_arraylike.take(codes) name_list.append(levels) return name_list # ------------------------------------------------------------ # Aggregation functions @final def _cython_operation( self, kind: str, values, how: str, axis: int, min_count: int = -1, **kwargs, ) -> ArrayLike: """ Returns the values of a cython operation. """ assert kind in ["transform", "aggregate"] cy_op = WrappedCythonOp(kind=kind, how=how, has_dropped_na=self.has_dropped_na) ids, _, _ = self.group_info ngroups = self.ngroups return cy_op.cython_operation( values=values, axis=axis, min_count=min_count, comp_ids=ids, ngroups=ngroups, **kwargs, ) @final def agg_series( self, obj: Series, func: Callable, preserve_dtype: bool = False ) -> ArrayLike: """ Parameters ---------- obj : Series func : function taking a Series and returning a scalar-like preserve_dtype : bool Whether the aggregation is known to be dtype-preserving. Returns ------- np.ndarray or ExtensionArray """ # test_groupby_empty_with_category gets here with self.ngroups == 0 # and len(obj) > 0 if len(obj) == 0: # SeriesGrouper would raise if we were to call _aggregate_series_fast result = self._aggregate_series_pure_python(obj, func) elif not isinstance(obj._values, np.ndarray): result = self._aggregate_series_pure_python(obj, func) # we can preserve a little bit more aggressively with EA dtype # because maybe_cast_pointwise_result will do a try/except # with _from_sequence. NB we are assuming here that _from_sequence # is sufficiently strict that it casts appropriately. preserve_dtype = True else: result = self._aggregate_series_pure_python(obj, func) npvalues = lib.maybe_convert_objects(result, try_float=False) if preserve_dtype: out = maybe_cast_pointwise_result(npvalues, obj.dtype, numeric_only=True) else: out = npvalues return out @final def _aggregate_series_pure_python( self, obj: Series, func: Callable ) -> npt.NDArray[np.object_]: ids, _, ngroups = self.group_info counts = np.zeros(ngroups, dtype=int) result = np.empty(ngroups, dtype="O") initialized = False # equiv: splitter = self._get_splitter(obj, axis=0) splitter = get_splitter(obj, ids, ngroups, axis=0) for i, group in enumerate(splitter): res = func(group) res = libreduction.extract_result(res) if not initialized: # We only do this validation on the first iteration libreduction.check_result_array(res, group.dtype) initialized = True counts[i] = group.shape[0] result[i] = res return result class BinGrouper(BaseGrouper): """ This is an internal Grouper class Parameters ---------- bins : the split index of binlabels to group the item of axis binlabels : the label list mutated : bool, default False indexer : np.ndarray[np.intp] Examples -------- bins: [2, 4, 6, 8, 10] binlabels: DatetimeIndex(['2005-01-01', '2005-01-03', '2005-01-05', '2005-01-07', '2005-01-09'], dtype='datetime64[ns]', freq='2D') the group_info, which contains the label of each item in grouped axis, the index of label in label list, group number, is (array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4]), array([0, 1, 2, 3, 4]), 5) means that, the grouped axis has 10 items, can be grouped into 5 labels, the first and second items belong to the first label, the third and forth items belong to the second label, and so on """ bins: npt.NDArray[np.int64] binlabels: Index mutated: bool def __init__( self, bins, binlabels, mutated: bool = False, indexer=None, ) -> None: self.bins = ensure_int64(bins) self.binlabels = ensure_index(binlabels) self.mutated = mutated self.indexer = indexer # These lengths must match, otherwise we could call agg_series # with empty self.bins, which would raise in libreduction. assert len(self.binlabels) == len(self.bins) @cache_readonly 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 @property def nkeys(self) -> int: # still matches len(self.groupings), but we can hard-code return 1 def _get_grouper(self): """ We are a grouper as part of another's groupings. We have a specific method of grouping, so cannot convert to a Index for our grouper. """ return self def get_iterator(self, data: NDFrame, axis: int = 0): """ Groupby iterator Returns ------- Generator yielding sequence of (name, subsetted object) for each group """ if axis == 0: slicer = lambda start, edge: data.iloc[start:edge] else: slicer = lambda start, edge: data.iloc[:, start:edge] length = len(data.axes[axis]) start = 0 for edge, label in zip(self.bins, self.binlabels): if label is not NaT: yield label, slicer(start, edge) start = edge if start < length: yield self.binlabels[-1], slicer(start, None) @cache_readonly def indices(self): indices = collections.defaultdict(list) i = 0 for label, bin in zip(self.binlabels, self.bins): if i < bin: if label is not NaT: indices[label] = list(range(i, bin)) i = bin return indices @cache_readonly def group_info(self) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp], int]: ngroups = self.ngroups obs_group_ids = np.arange(ngroups, dtype=np.intp) rep = np.diff(np.r_[0, self.bins]) rep = ensure_platform_int(rep) if ngroups == len(self.bins): comp_ids = np.repeat(np.arange(ngroups), rep) else: comp_ids = np.repeat(np.r_[-1, np.arange(ngroups)], rep) return ( ensure_platform_int(comp_ids), obs_group_ids, ngroups, ) @cache_readonly def reconstructed_codes(self) -> list[np.ndarray]: # get unique result indices, and prepend 0 as groupby starts from the first return [np.r_[0, np.flatnonzero(self.bins[1:] != self.bins[:-1]) + 1]] @cache_readonly def result_index(self) -> Index: if len(self.binlabels) != 0 and isna(self.binlabels[0]): return self.binlabels[1:] return self.binlabels @property def levels(self) -> list[Index]: return [self.binlabels] @property def names(self) -> list[Hashable]: return [self.binlabels.name] @property def groupings(self) -> list[grouper.Grouping]: lev = self.binlabels ping = grouper.Grouping(lev, lev, in_axis=False, level=None) return [ping] def _aggregate_series_fast(self, obj: Series, func: Callable) -> NoReturn: # -> np.ndarray[object] raise NotImplementedError( "This should not be reached; use _aggregate_series_pure_python" ) def _is_indexed_like(obj, axes, axis: int) -> bool: if isinstance(obj, Series): if len(axes) > 1: return False return obj.axes[axis].equals(axes[axis]) elif isinstance(obj, DataFrame): return obj.axes[axis].equals(axes[axis]) return False # ---------------------------------------------------------------------- # Splitting / application class DataSplitter(Generic[NDFrameT]): def __init__( self, data: NDFrameT, labels: npt.NDArray[np.intp], ngroups: int, axis: int = 0, ) -> None: self.data = data self.labels = ensure_platform_int(labels) # _should_ already be np.intp self.ngroups = ngroups self.axis = axis assert isinstance(axis, int), axis @cache_readonly def slabels(self) -> npt.NDArray[np.intp]: # Sorted labels return self.labels.take(self._sort_idx) @cache_readonly def _sort_idx(self) -> npt.NDArray[np.intp]: # Counting sort indexer return get_group_index_sorter(self.labels, self.ngroups) def __iter__(self): sdata = self.sorted_data if self.ngroups == 0: # we are inside a generator, rather than raise StopIteration # we merely return signal the end return starts, ends = lib.generate_slices(self.slabels, self.ngroups) for start, end in zip(starts, ends): yield self._chop(sdata, slice(start, end)) @cache_readonly def sorted_data(self) -> NDFrameT: return self.data.take(self._sort_idx, axis=self.axis) def _chop(self, sdata, slice_obj: slice) -> NDFrame: raise AbstractMethodError(self) class SeriesSplitter(DataSplitter): def _chop(self, sdata: Series, slice_obj: slice) -> Series: # fastpath equivalent to `sdata.iloc[slice_obj]` mgr = sdata._mgr.get_slice(slice_obj) ser = sdata._constructor(mgr, name=sdata.name, fastpath=True) return ser.__finalize__(sdata, method="groupby") class FrameSplitter(DataSplitter): def _chop(self, sdata: DataFrame, slice_obj: slice) -> DataFrame: # Fastpath equivalent to: # if self.axis == 0: # return sdata.iloc[slice_obj] # else: # return sdata.iloc[:, slice_obj] mgr = sdata._mgr.get_slice(slice_obj, axis=1 - self.axis) df = sdata._constructor(mgr) return df.__finalize__(sdata, method="groupby") def get_splitter( data: NDFrame, labels: np.ndarray, ngroups: int, axis: int = 0 ) -> DataSplitter: if isinstance(data, Series): klass: type[DataSplitter] = SeriesSplitter else: # i.e. DataFrame klass = FrameSplitter return klass(data, labels, ngroups, axis)