""" Provide the groupby split-apply-combine paradigm. Define the GroupBy class providing the base-class of operations. The SeriesGroupBy and DataFrameGroupBy sub-class (defined in pandas.core.groupby.generic) expose these user-facing objects to provide specific functionality. """ from __future__ import annotations from collections.abc import ( Callable, Hashable, Iterable, Iterator, Mapping, Sequence, ) import datetime from functools import ( partial, wraps, ) from typing import ( TYPE_CHECKING, Concatenate, Literal, Self, TypeAlias, TypeVar, Union, cast, final, overload, ) import warnings import numpy as np from pandas._libs import ( Timestamp, lib, ) from pandas._libs.algos import rank_1d import pandas._libs.groupby as libgroupby from pandas._libs.missing import NA from pandas._typing import ( AnyArrayLike, ArrayLike, DtypeObj, IndexLabel, IntervalClosedType, NDFrameT, PositionalIndexer, RandomState, npt, ) from pandas.compat.numpy import function as nv from pandas.errors import ( AbstractMethodError, DataError, Pandas4Warning, ) from pandas.util._decorators import cache_readonly from pandas.util._exceptions import find_stack_level from pandas.core.dtypes.cast import ( coerce_indexer_dtype, ensure_dtype_can_hold_na, ) from pandas.core.dtypes.common import ( is_bool, is_bool_dtype, is_float_dtype, is_hashable, is_integer, is_integer_dtype, is_list_like, is_numeric_dtype, is_object_dtype, is_scalar, is_string_dtype, needs_i8_conversion, pandas_dtype, ) from pandas.core.dtypes.missing import ( isna, na_value_for_dtype, notna, ) from pandas.core import ( algorithms, sample, ) from pandas.core._numba import executor from pandas.core.arrays import ( ArrowExtensionArray, BaseMaskedArray, ExtensionArray, FloatingArray, IntegerArray, SparseArray, ) from pandas.core.arrays.string_ import StringDtype from pandas.core.arrays.string_arrow import ArrowStringArray from pandas.core.base import ( PandasObject, SelectionMixin, ) import pandas.core.common as com from pandas.core.frame import DataFrame from pandas.core.generic import NDFrame from pandas.core.groupby import ( base, numba_, ops, ) from pandas.core.groupby.grouper import get_grouper from pandas.core.groupby.indexing import ( GroupByIndexingMixin, GroupByNthSelector, ) from pandas.core.indexes.api import ( Index, MultiIndex, default_index, ) from pandas.core.internals.blocks import ensure_block_shape from pandas.core.series import Series from pandas.core.sorting import get_group_index_sorter from pandas.core.util.numba_ import ( get_jit_arguments, maybe_use_numba, prepare_function_arguments, ) if TYPE_CHECKING: from pandas._libs.tslibs import BaseOffset from pandas._libs.tslibs.timedeltas import Timedelta from pandas._typing import ( Any, P, T, ) from pandas.core.indexers.objects import BaseIndexer from pandas.core.resample import Resampler from pandas.core.window import ( ExpandingGroupby, ExponentialMovingWindowGroupby, RollingGroupby, ) _groupby_agg_method_engine_template = """ Compute {fname} of group values. Parameters ---------- numeric_only : bool, default {no} Include only float, int, boolean columns. .. versionchanged:: 2.0.0 numeric_only no longer accepts ``None``. min_count : int, default {mc} The required number of valid values to perform the operation. If fewer than ``min_count`` non-NA values are present the result will be NA. engine : str, default None {e} * ``'cython'`` : Runs rolling apply through C-extensions from cython. * ``'numba'`` : Runs rolling apply through JIT compiled code from numba. Only available when ``raw`` is set to ``True``. * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba`` engine_kwargs : dict, default None {ek} * For ``'cython'`` engine, there are no accepted ``engine_kwargs`` * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil`` and ``parallel`` dictionary keys. The values must either be ``True`` or ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` and will be applied to both the ``func`` and the ``apply`` groupby aggregation. Returns ------- Series or DataFrame Computed {fname} of values within each group. See Also -------- SeriesGroupBy.min : Return the min of the group values. DataFrameGroupBy.min : Return the min of the group values. SeriesGroupBy.max : Return the max of the group values. DataFrameGroupBy.max : Return the max of the group values. SeriesGroupBy.sum : Return the sum of the group values. DataFrameGroupBy.sum : Return the sum of the group values. Examples -------- {example} """ _groupby_agg_method_skipna_engine_template = """ Compute {fname} of group values. Parameters ---------- numeric_only : bool, default {no} Include only float, int, boolean columns. .. versionchanged:: 2.0.0 numeric_only no longer accepts ``None``. min_count : int, default {mc} The required number of valid values to perform the operation. If fewer than ``min_count`` non-NA values are present the result will be NA. skipna : bool, default {s} Exclude NA/null values. If the entire group is NA and ``skipna`` is ``True``, the result will be NA. .. versionchanged:: 3.0.0 engine : str, default None {e} * ``'cython'`` : Runs rolling apply through C-extensions from cython. * ``'numba'`` : Runs rolling apply through JIT compiled code from numba. Only available when ``raw`` is set to ``True``. * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba`` engine_kwargs : dict, default None {ek} * For ``'cython'`` engine, there are no accepted ``engine_kwargs`` * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil`` and ``parallel`` dictionary keys. The values must either be ``True`` or ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` and will be applied to both the ``func`` and the ``apply`` groupby aggregation. Returns ------- Series or DataFrame Computed {fname} of values within each group. See Also -------- SeriesGroupBy.min : Return the min of the group values. DataFrameGroupBy.min : Return the min of the group values. SeriesGroupBy.max : Return the max of the group values. DataFrameGroupBy.max : Return the max of the group values. SeriesGroupBy.sum : Return the sum of the group values. DataFrameGroupBy.sum : Return the sum of the group values. Examples -------- {example} """ _pipe_template = """ Apply a ``func`` with arguments to this %(klass)s object and return its result. Use `.pipe` when you want to improve readability by chaining together functions that expect Series, DataFrames, GroupBy or Resampler objects. Instead of writing >>> h = lambda x, arg2, arg3: x + 1 - arg2 * arg3 >>> g = lambda x, arg1: x * 5 / arg1 >>> f = lambda x: x ** 4 >>> df = pd.DataFrame([["a", 4], ["b", 5]], columns=["group", "value"]) >>> h(g(f(df.groupby('group')), arg1=1), arg2=2, arg3=3) # doctest: +SKIP You can write >>> (df.groupby('group') ... .pipe(f) ... .pipe(g, arg1=1) ... .pipe(h, arg2=2, arg3=3)) # doctest: +SKIP which is much more readable. Parameters ---------- func : callable or tuple of (callable, str) Function to apply to this %(klass)s object or, alternatively, a `(callable, data_keyword)` tuple where `data_keyword` is a string indicating the keyword of `callable` that expects the %(klass)s object. *args : iterable, optional Positional arguments passed into `func`. **kwargs : dict, optional A dictionary of keyword arguments passed into `func`. Returns ------- %(klass)s The original object with the function `func` applied. See Also -------- Series.pipe : Apply a function with arguments to a series. DataFrame.pipe: Apply a function with arguments to a dataframe. apply : Apply function to each group instead of to the full %(klass)s object. Notes ----- See more `here `_ Examples -------- %(examples)s """ _transform_template = """ Call function producing a same-indexed %(klass)s on each group. Returns a %(klass)s having the same indexes as the original object filled with the transformed values. Parameters ---------- func : function, str Function to apply to each group. See the Notes section below for requirements. Accepted inputs are: - String - Python function - Numba JIT function with ``engine='numba'`` specified. Only passing a single function is supported with this engine. If the ``'numba'`` engine is chosen, the function must be a user defined function with ``values`` and ``index`` as the first and second arguments respectively in the function signature. Each group's index will be passed to the user defined function and optionally available for use. If a string is chosen, then it needs to be the name of the groupby method you want to use. *args Positional arguments to pass to func. engine : str, default None * ``'cython'`` : Runs the function through C-extensions from cython. * ``'numba'`` : Runs the function through JIT compiled code from numba. * ``None`` : Defaults to ``'cython'`` or the global setting ``compute.use_numba`` engine_kwargs : dict, default None * For ``'cython'`` engine, there are no accepted ``engine_kwargs`` * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil`` and ``parallel`` dictionary keys. The values must either be ``True`` or ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is ``{'nopython': True, 'nogil': False, 'parallel': False}`` and will be applied to the function **kwargs Keyword arguments to be passed into func. Returns ------- %(klass)s %(klass)s with the same indexes as the original object filled with transformed values. See Also -------- %(klass)s.groupby.apply : Apply function ``func`` group-wise and combine the results together. %(klass)s.groupby.aggregate : Aggregate using one or more operations. %(klass)s.transform : Call ``func`` on self producing a %(klass)s with the same axis shape as self. Notes ----- Each group is endowed the attribute 'name' in case you need to know which group you are working on. The current implementation imposes three requirements on f: * f must return a value that either has the same shape as the input subframe or can be broadcast to the shape of the input subframe. For example, if `f` returns a scalar it will be broadcast to have the same shape as the input subframe. * if this is a DataFrame, f must support application column-by-column in the subframe. If f also supports application to the entire subframe, then a fast path is used starting from the second chunk. * f must not mutate groups. Mutation is not supported and may produce unexpected results. See :ref:`gotchas.udf-mutation` for more details. When using ``engine='numba'``, there will be no "fall back" behavior internally. The group data and group index will be passed as numpy arrays to the JITed user defined function, and no alternative execution attempts will be tried. The resulting dtype will reflect the return value of the passed ``func``, see the examples below. .. versionchanged:: 2.0.0 When using ``.transform`` on a grouped DataFrame and the transformation function returns a DataFrame, pandas now aligns the result's index with the input's index. You can call ``.to_numpy()`` on the result of the transformation function to avoid alignment. Examples -------- %(example)s""" @final class GroupByPlot(PandasObject): """ Class implementing the .plot attribute for groupby objects. """ def __init__(self, groupby: GroupBy) -> None: self._groupby = groupby def __call__(self, *args, **kwargs): def f(self): return self.plot(*args, **kwargs) f.__name__ = "plot" return self._groupby._python_apply_general(f, self._groupby._selected_obj) def __getattr__(self, name: str): def attr(*args, **kwargs): def f(self): return getattr(self.plot, name)(*args, **kwargs) return self._groupby._python_apply_general(f, self._groupby._selected_obj) return attr _KeysArgType: TypeAlias = ( Hashable | list[Hashable] | Callable[[Hashable], Hashable] | list[Callable[[Hashable], Hashable]] | Mapping[Hashable, Hashable] ) class BaseGroupBy(PandasObject, SelectionMixin[NDFrameT], GroupByIndexingMixin): _hidden_attrs = PandasObject._hidden_attrs | { "as_index", "dropna", "exclusions", "grouper", "group_keys", "keys", "level", "obj", "observed", "sort", } _grouper: ops.BaseGrouper keys: _KeysArgType | None = None level: IndexLabel | None = None group_keys: bool @final def __len__(self) -> int: return self._grouper.ngroups @final def __repr__(self) -> str: # TODO: Better repr for GroupBy object return object.__repr__(self) @final @property def groups(self) -> dict[Hashable, Index]: """ Dict {group name -> group labels}. This property provides a dictionary representation of the groupings formed during a groupby operation, where each key represents a unique group value from the specified column(s), and each value is a list of index labels that belong to that group. See Also -------- core.groupby.DataFrameGroupBy.get_group : Retrieve group from a ``DataFrameGroupBy`` object with provided name. core.groupby.SeriesGroupBy.get_group : Retrieve group from a ``SeriesGroupBy`` object with provided name. core.resample.Resampler.get_group : Retrieve group from a ``Resampler`` object with provided name. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b"] >>> ser = pd.Series([1, 2, 3], index=lst) >>> ser a 1 a 2 b 3 dtype: int64 >>> ser.groupby(level=0).groups {'a': ['a', 'a'], 'b': ['b']} For DataFrameGroupBy: >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]] >>> df = pd.DataFrame(data, columns=["a", "b", "c"]) >>> df a b c 0 1 2 3 1 1 5 6 2 7 8 9 >>> df.groupby(by="a").groups {1: [0, 1], 7: [2]} For Resampler: >>> ser = pd.Series( ... [1, 2, 3, 4], ... index=pd.DatetimeIndex( ... ["2023-01-01", "2023-01-15", "2023-02-01", "2023-02-15"] ... ), ... ) >>> ser 2023-01-01 1 2023-01-15 2 2023-02-01 3 2023-02-15 4 dtype: int64 >>> ser.resample("MS").groups {Timestamp('2023-01-01 00:00:00'): np.int64(2), Timestamp('2023-02-01 00:00:00'): np.int64(4)} """ if isinstance(self.keys, list) and len(self.keys) == 1: warnings.warn( "In a future version, the keys of `groups` will be a " f"tuple with a single element, e.g. ({self.keys[0]},) , " f"instead of a scalar, e.g. {self.keys[0]}, when grouping " "by a list with a single element. Use ``df.groupby(by='a').groups`` " "instead of ``df.groupby(by=['a']).groups`` to avoid this warning", Pandas4Warning, stacklevel=find_stack_level(), ) return self._grouper.groups @final @property def ngroups(self) -> int: return self._grouper.ngroups @final @property def indices(self) -> dict[Hashable, npt.NDArray[np.intp]]: """ Dict {group name -> group indices}. The dictionary keys represent the group labels (e.g., timestamps for a time-based resampling operation), and the values are arrays of integer positions indicating where the elements of each group are located in the original data. This property is particularly useful when working with resampled data, as it provides insight into how the original time-series data has been grouped. See Also -------- core.groupby.DataFrameGroupBy.indices : Provides a mapping of group rows to positions of the elements. core.groupby.SeriesGroupBy.indices : Provides a mapping of group rows to positions of the elements. core.resample.Resampler.indices : Provides a mapping of group rows to positions of the elements. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b"] >>> ser = pd.Series([1, 2, 3], index=lst) >>> ser a 1 a 2 b 3 dtype: int64 >>> ser.groupby(level=0).indices {'a': array([0, 1]), 'b': array([2])} For DataFrameGroupBy: >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]] >>> df = pd.DataFrame( ... data, columns=["a", "b", "c"], index=["owl", "toucan", "eagle"] ... ) >>> df a b c owl 1 2 3 toucan 1 5 6 eagle 7 8 9 >>> df.groupby(by=["a"]).indices {np.int64(1): array([0, 1]), np.int64(7): array([2])} For Resampler: >>> ser = pd.Series( ... [1, 2, 3, 4], ... index=pd.DatetimeIndex( ... ["2023-01-01", "2023-01-15", "2023-02-01", "2023-02-15"] ... ), ... ) >>> ser 2023-01-01 1 2023-01-15 2 2023-02-01 3 2023-02-15 4 dtype: int64 >>> ser.resample("MS").indices defaultdict(, {Timestamp('2023-01-01 00:00:00'): [0, 1], Timestamp('2023-02-01 00:00:00'): [2, 3]}) """ return self._grouper.indices @final def _get_index(self, name): """ Safe get multiple indices, translate keys for datelike to underlying repr. """ def get_converter(s): # possibly convert to the actual key types # in the indices, could be a Timestamp or an np.datetime64 if isinstance(s, datetime.datetime): return lambda key: Timestamp(key) elif isinstance(s, np.datetime64): return lambda key: Timestamp(key).asm8 else: return lambda key: key if isna(name): return self.indices.get(np.nan, []) if isinstance(name, tuple): name = tuple(np.nan if isna(comp) else comp for comp in name) if len(self.indices) > 0: index_sample = next(iter(self.indices)) else: index_sample = None # Dummy sample if isinstance(index_sample, tuple): if not isinstance(name, tuple): msg = "must supply a tuple to get_group with multiple grouping keys" raise ValueError(msg) if not len(name) == len(index_sample): try: # If the original grouper was a tuple return self.indices[name] except KeyError as err: # turns out it wasn't a tuple msg = ( "must supply a same-length tuple to get_group " "with multiple grouping keys" ) raise ValueError(msg) from err converters = (get_converter(s) for s in index_sample) name = tuple(f(n) for f, n in zip(converters, name, strict=True)) else: converter = get_converter(index_sample) name = converter(name) return self.indices.get(name, []) @final @cache_readonly def _selected_obj(self): # Note: _selected_obj is always just `self.obj` for SeriesGroupBy if isinstance(self.obj, Series): return self.obj if self._selection is not None: if is_hashable(self._selection): # i.e. a single key, so selecting it will return a Series. # In this case, _obj_with_exclusions would wrap the key # in a list and return a single-column DataFrame. return self.obj[self._selection] # Otherwise _selection is equivalent to _selection_list, so # _selected_obj matches _obj_with_exclusions, so we can reuse # that and avoid making a copy. return self._obj_with_exclusions return self.obj @final def _dir_additions(self) -> set[str]: return self.obj._dir_additions() @overload def pipe( self, func: Callable[Concatenate[Self, P], T], *args: P.args, **kwargs: P.kwargs, ) -> T: ... @overload def pipe( self, func: tuple[Callable[..., T], str], *args: Any, **kwargs: Any, ) -> T: ... def pipe( self, func: Callable[Concatenate[Self, P], T] | tuple[Callable[..., T], str], *args: Any, **kwargs: Any, ) -> T: """ Apply a ``func`` with arguments to this GroupBy object and return its result. Use `.pipe` when you want to improve readability by chaining together functions that expect Series, DataFrames, GroupBy or Resampler objects. Instead of writing >>> h = lambda x, arg2, arg3: x + 1 - arg2 * arg3 >>> g = lambda x, arg1: x * 5 / arg1 >>> f = lambda x: x**4 >>> df = pd.DataFrame([["a", 4], ["b", 5]], columns=["group", "value"]) >>> h(g(f(df.groupby("group")), arg1=1), arg2=2, arg3=3) # doctest: +SKIP You can write >>> ( ... df.groupby("group").pipe(f).pipe(g, arg1=1).pipe(h, arg2=2, arg3=3) ... ) # doctest: +SKIP which is much more readable. Parameters ---------- func : callable or tuple of (callable, str) Function to apply to this GroupBy object or, alternatively, a `(callable, data_keyword)` tuple where `data_keyword` is a string indicating the keyword of `callable` that expects the GroupBy object. *args : iterable, optional Positional arguments passed into `func`. **kwargs : dict, optional A dictionary of keyword arguments passed into `func`. Returns ------- GroupBy The return type of `func`. See Also -------- Series.pipe : Apply a function with arguments to a series. DataFrame.pipe : Apply a function with arguments to a dataframe. apply : Apply function to each group instead of to the full GroupBy object. Notes ----- See more `here `_ Examples -------- >>> df = pd.DataFrame({"A": "a b a b".split(), "B": [1, 2, 3, 4]}) >>> df A B 0 a 1 1 b 2 2 a 3 3 b 4 To get the difference between each groups maximum and minimum value in one pass, you can do >>> df.groupby("A").pipe(lambda x: x.max() - x.min()) B A a 2 b 2 """ return com.pipe(self, func, *args, **kwargs) @final def get_group(self, name) -> DataFrame | Series: """ Construct DataFrame from group with provided name. Parameters ---------- name : object The name of the group to get as a DataFrame. Returns ------- Series or DataFrame Get the respective Series or DataFrame corresponding to the group provided. See Also -------- DataFrameGroupBy.groups: Dictionary representation of the groupings formed during a groupby operation. DataFrameGroupBy.indices: Provides a mapping of group rows to positions of the elements. SeriesGroupBy.groups: Dictionary representation of the groupings formed during a groupby operation. SeriesGroupBy.indices: Provides a mapping of group rows to positions of the elements. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b"] >>> ser = pd.Series([1, 2, 3], index=lst) >>> ser a 1 a 2 b 3 dtype: int64 >>> ser.groupby(level=0).get_group("a") a 1 a 2 dtype: int64 For DataFrameGroupBy: >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]] >>> df = pd.DataFrame( ... data, columns=["a", "b", "c"], index=["owl", "toucan", "eagle"] ... ) >>> df a b c owl 1 2 3 toucan 1 5 6 eagle 7 8 9 >>> df.groupby(by=["a"]).get_group((1,)) a b c owl 1 2 3 toucan 1 5 6 For Resampler: >>> ser = pd.Series( ... [1, 2, 3, 4], ... index=pd.DatetimeIndex( ... ["2023-01-01", "2023-01-15", "2023-02-01", "2023-02-15"] ... ), ... ) >>> ser 2023-01-01 1 2023-01-15 2 2023-02-01 3 2023-02-15 4 dtype: int64 >>> ser.resample("MS").get_group("2023-01-01") 2023-01-01 1 2023-01-15 2 dtype: int64 """ keys = self.keys level = self.level # mypy doesn't recognize level/keys as being sized when passed to len if (is_list_like(level) and len(level) == 1) or ( # type: ignore[arg-type] is_list_like(keys) and len(keys) == 1 # type: ignore[arg-type] ): # GH#25971 if isinstance(name, tuple) and len(name) == 1: name = name[0] else: raise KeyError(name) inds = self._get_index(name) if not len(inds): raise KeyError(name) return self._selected_obj.iloc[inds] @final def __iter__(self) -> Iterator[tuple[Hashable, NDFrameT]]: """ Groupby iterator. This method provides an iterator over the groups created by the ``resample`` or ``groupby`` operation on the object. The method yields tuples where the first element is the label (group key) corresponding to each group or resampled bin, and the second element is the subset of the data that falls within that group or bin. Returns ------- Iterator Generator yielding a sequence of (name, subsetted object) for each group. See Also -------- Series.groupby : Group data by a specific key or column. DataFrame.groupby : Group DataFrame using mapper or by columns. DataFrame.resample : Resample a DataFrame. Series.resample : Resample a Series. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b"] >>> ser = pd.Series([1, 2, 3], index=lst) >>> ser a 1 a 2 b 3 dtype: int64 >>> for x, y in ser.groupby(level=0): ... print(f"{x}\\n{y}\\n") a a 1 a 2 dtype: int64 b b 3 dtype: int64 For DataFrameGroupBy: >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]] >>> df = pd.DataFrame(data, columns=["a", "b", "c"]) >>> df a b c 0 1 2 3 1 1 5 6 2 7 8 9 >>> for x, y in df.groupby(by=["a"]): ... print(f"{x}\\n{y}\\n") (1,) a b c 0 1 2 3 1 1 5 6 (7,) a b c 2 7 8 9 For Resampler: >>> ser = pd.Series( ... [1, 2, 3, 4], ... index=pd.DatetimeIndex( ... ["2023-01-01", "2023-01-15", "2023-02-01", "2023-02-15"] ... ), ... ) >>> ser 2023-01-01 1 2023-01-15 2 2023-02-01 3 2023-02-15 4 dtype: int64 >>> for x, y in ser.resample("MS"): ... print(f"{x}\\n{y}\\n") 2023-01-01 00:00:00 2023-01-01 1 2023-01-15 2 dtype: int64 2023-02-01 00:00:00 2023-02-01 3 2023-02-15 4 dtype: int64 """ keys = self.keys level = self.level result = self._grouper.get_iterator(self._selected_obj) # mypy: Argument 1 to "len" has incompatible type "Hashable"; expected "Sized" if (is_list_like(level) and len(level) == 1) or ( # type: ignore[arg-type] isinstance(keys, list) and len(keys) == 1 ): # GH#42795 - when keys is a list, return tuples even when length is 1 result = (((key,), group) for key, group in result) return result # To track operations that expand dimensions, like ohlc OutputFrameOrSeries = TypeVar("OutputFrameOrSeries", bound=NDFrame) class GroupBy(BaseGroupBy[NDFrameT]): """ Class for grouping and aggregating relational data. See aggregate, transform, and apply functions on this object. It's easiest to use obj.groupby(...) to use GroupBy, but you can also do: :: grouped = groupby(obj, ...) Parameters ---------- obj : pandas object level : int, default None Level of MultiIndex groupings : list of Grouping objects Most users should ignore this exclusions : array-like, optional List of columns to exclude name : str Most users should ignore this Returns ------- **Attributes** groups : dict {group name -> group labels} len(grouped) : int Number of groups Notes ----- After grouping, see aggregate, apply, and transform functions. Here are some other brief notes about usage. When grouping by multiple groups, the result index will be a MultiIndex (hierarchical) by default. Iteration produces (key, group) tuples, i.e. chunking the data by group. So you can write code like: :: grouped = obj.groupby(keys) for key, group in grouped: # do something with the data Function calls on GroupBy, if not specially implemented, "dispatch" to the grouped data. So if you group a DataFrame and wish to invoke the std() method on each group, you can simply do: :: df.groupby(mapper).std() rather than :: df.groupby(mapper).aggregate(np.std) You can pass arguments to these "wrapped" functions, too. See the online documentation for full exposition on these topics and much more """ _grouper: ops.BaseGrouper as_index: bool @final def __init__( self, obj: NDFrameT, keys: _KeysArgType | None = None, level: IndexLabel | None = None, grouper: ops.BaseGrouper | None = None, exclusions: frozenset[Hashable] | None = None, selection: IndexLabel | None = None, as_index: bool = True, sort: bool = True, group_keys: bool = True, observed: bool = False, dropna: bool = True, ) -> None: self._selection = selection assert isinstance(obj, NDFrame), type(obj) self.level = level self.as_index = as_index self.keys = keys self.sort = sort self.group_keys = group_keys self.dropna = dropna if grouper is None: grouper, exclusions, obj = get_grouper( obj, keys, level=level, sort=sort, observed=observed, dropna=self.dropna, ) self.observed = observed self.obj = obj self._grouper = grouper self.exclusions = frozenset(exclusions) if exclusions else frozenset() def __getattr__(self, attr: str): if attr in self._internal_names_set: return object.__getattribute__(self, attr) if attr in self.obj: return self[attr] raise AttributeError( f"'{type(self).__name__}' object has no attribute '{attr}'" ) @final def _op_via_apply(self, name: str, *args, **kwargs): """Compute the result of an operation by using GroupBy's apply.""" f = getattr(type(self._obj_with_exclusions), name) def curried(x): return f(x, *args, **kwargs) # preserve the name so we can detect it when calling plot methods, # to avoid duplicates curried.__name__ = name # special case otherwise extra plots are created when catching the # exception below if name in base.plotting_methods: return self._python_apply_general(curried, self._selected_obj) is_transform = name in base.transformation_kernels result = self._python_apply_general( curried, self._obj_with_exclusions, is_transform=is_transform, not_indexed_same=not is_transform, ) if self._grouper.has_dropped_na and is_transform: # result will have dropped rows due to nans, fill with null # and ensure index is ordered same as the input result = self._set_result_index_ordered(result) return result # ----------------------------------------------------------------- # Dispatch/Wrapping @final def _concat_objects( self, values, not_indexed_same: bool = False, is_transform: bool = False, ): from pandas.core.reshape.concat import concat if self.group_keys and not is_transform: if self.as_index: # possible MI return case group_keys = self._grouper.result_index group_levels = self._grouper.levels group_names = self._grouper.names result = concat( values, axis=0, keys=group_keys, levels=group_levels, names=group_names, sort=False, ) else: result = concat(values, axis=0) elif not not_indexed_same: result = concat(values, axis=0) ax = self._selected_obj.index if self.dropna: labels = self._grouper.ids mask = labels != -1 ax = ax[mask] # this is a very unfortunate situation # we can't use reindex to restore the original order # when the ax has duplicates # so we resort to this # GH 14776, 30667 # TODO: can we reuse e.g. _reindex_non_unique? if ax.has_duplicates and not result.axes[0].equals(ax): # e.g. test_category_order_transformer target = algorithms.unique1d(ax._values) indexer, _ = result.index.get_indexer_non_unique(target) result = result.take(indexer, axis=0) else: result = result.reindex(ax, axis=0) else: result = concat(values, axis=0) if self.obj.ndim == 1: name = self.obj.name elif is_hashable(self._selection): name = self._selection else: name = None if isinstance(result, Series) and name is not None: result.name = name return result.__finalize__(self.obj, method="groupby") @final def _set_result_index_ordered( self, result: OutputFrameOrSeries ) -> OutputFrameOrSeries: # set the result index on the passed values object and # return the new object, xref 8046 index = self.obj.index if self._grouper.is_monotonic and not self._grouper.has_dropped_na: # shortcut if we have an already ordered grouper result = result.set_axis(index, axis=0) return result # row order is scrambled => sort the rows by position in original index original_positions = Index(self._grouper.result_ilocs, copy=False) result = result.set_axis(original_positions, axis=0) result = result.sort_index(axis=0) if self._grouper.has_dropped_na: # Add back in any missing rows due to dropna - index here is integral # with values referring to the row of the input so can use RangeIndex result = result.reindex(default_index(len(index)), axis=0) result = result.set_axis(index, axis=0) return result @final def _insert_inaxis_grouper( self, result: Series | DataFrame, qs: npt.NDArray[np.float64] | None = None ) -> DataFrame: if isinstance(result, Series): result = result.to_frame() n_groupings = len(self._grouper.groupings) if qs is not None: result.insert( 0, f"level_{n_groupings}", np.tile(qs, len(result) // len(qs)) ) # zip in reverse so we can always insert at loc 0 for level, (name, lev) in enumerate( zip( reversed(self._grouper.names), self._grouper.get_group_levels(), strict=True, ) ): if name is None: # Behave the same as .reset_index() when a level is unnamed name = ( "index" if n_groupings == 1 and qs is None else f"level_{n_groupings - level - 1}" ) # GH #28549 # When using .apply(-), name will be in columns already if name not in result.columns: # if in_axis: if qs is None: result.insert(0, name, lev) else: result.insert(0, name, Index(np.repeat(lev, len(qs)), copy=False)) return result @final def _wrap_aggregated_output( self, result: Series | DataFrame, qs: npt.NDArray[np.float64] | None = None, ): """ Wraps the output of GroupBy aggregations into the expected result. Parameters ---------- result : Series, DataFrame Returns ------- Series or DataFrame """ # ATM we do not get here for SeriesGroupBy; when we do, we will # need to require that result.name already match self.obj.name if not self.as_index: # `not self.as_index` is only relevant for DataFrameGroupBy, # enforced in __init__ result = self._insert_inaxis_grouper(result, qs=qs) result = result._consolidate() result.index = default_index(len(result)) else: index = self._grouper.result_index if qs is not None: # We get here with len(qs) != 1 and not self.as_index # in test_pass_args_kwargs index = _insert_quantile_level(index, qs) result.index = index return result def _wrap_applied_output( self, data, values: list, not_indexed_same: bool = False, is_transform: bool = False, ): raise AbstractMethodError(self) # ----------------------------------------------------------------- # numba @final def _numba_prep(self, data: DataFrame): ngroups = self._grouper.ngroups sorted_index = self._grouper.result_ilocs sorted_ids = self._grouper._sorted_ids sorted_data = data.take(sorted_index, axis=0).to_numpy() # GH 46867 index_data = data.index if isinstance(index_data, MultiIndex): if len(self._grouper.groupings) > 1: raise NotImplementedError( "Grouping with more than 1 grouping labels and " "a MultiIndex is not supported with engine='numba'" ) group_key = self._grouper.groupings[0].name index_data = index_data.get_level_values(group_key) sorted_index_data = index_data.take(sorted_index).to_numpy() starts, ends = lib.generate_slices(sorted_ids, ngroups) return ( starts, ends, sorted_index_data, sorted_data, ) def _numba_agg_general( self, func: Callable, dtype_mapping: dict[np.dtype, Any], engine_kwargs: dict[str, bool] | None, **aggregator_kwargs, ): """ Perform groupby with a standard numerical aggregation function (e.g. mean) with Numba. """ if not self.as_index: raise NotImplementedError( "as_index=False is not supported. Use .reset_index() instead." ) data = self._obj_with_exclusions df = data if data.ndim == 2 else data.to_frame() aggregator = executor.generate_shared_aggregator( func, dtype_mapping, True, # is_grouped_kernel **get_jit_arguments(engine_kwargs), ) # Pass group ids to kernel directly if it can handle it # (This is faster since it doesn't require a sort) ids = self._grouper.ids ngroups = self._grouper.ngroups res_mgr = df._mgr.apply( aggregator, labels=ids, ngroups=ngroups, **aggregator_kwargs ) res_mgr.axes[1] = self._grouper.result_index result = df._constructor_from_mgr(res_mgr, axes=res_mgr.axes) if data.ndim == 1: result = result.squeeze("columns") result.name = data.name else: result.columns = data.columns return result @final def _transform_with_numba(self, func, *args, engine_kwargs=None, **kwargs): """ Perform groupby transform routine with the numba engine. This routine mimics the data splitting routine of the DataSplitter class to generate the indices of each group in the sorted data and then passes the data and indices into a Numba jitted function. """ data = self._obj_with_exclusions index_sorting = self._grouper.result_ilocs df = data if data.ndim == 2 else data.to_frame() starts, ends, sorted_index, sorted_data = self._numba_prep(df) numba_.validate_udf(func) args, kwargs = prepare_function_arguments( func, args, kwargs, num_required_args=2 ) numba_transform_func = numba_.generate_numba_transform_func( func, **get_jit_arguments(engine_kwargs) ) result = numba_transform_func( sorted_data, sorted_index, starts, ends, len(df.columns), *args, ) # result values needs to be resorted to their original positions since we # evaluated the data sorted by group result = result.take(np.argsort(index_sorting), axis=0) index = data.index if data.ndim == 1: result_kwargs = {"name": data.name} result = result.ravel() else: result_kwargs = {"columns": data.columns} return data._constructor(result, index=index, **result_kwargs) @final def _aggregate_with_numba(self, func, *args, engine_kwargs=None, **kwargs): """ Perform groupby aggregation routine with the numba engine. This routine mimics the data splitting routine of the DataSplitter class to generate the indices of each group in the sorted data and then passes the data and indices into a Numba jitted function. """ data = self._obj_with_exclusions df = data if data.ndim == 2 else data.to_frame() starts, ends, sorted_index, sorted_data = self._numba_prep(df) numba_.validate_udf(func) args, kwargs = prepare_function_arguments( func, args, kwargs, num_required_args=2 ) numba_agg_func = numba_.generate_numba_agg_func( func, **get_jit_arguments(engine_kwargs) ) result = numba_agg_func( sorted_data, sorted_index, starts, ends, len(df.columns), *args, ) index = self._grouper.result_index if data.ndim == 1: result_kwargs = {"name": data.name} result = result.ravel() else: result_kwargs = {"columns": data.columns} res = data._constructor(result, index=index, **result_kwargs) if not self.as_index: res = self._insert_inaxis_grouper(res) res.index = default_index(len(res)) return res # ----------------------------------------------------------------- # apply/agg/transform def apply(self, func, *args, include_groups: bool = False, **kwargs) -> NDFrameT: """ Apply function ``func`` group-wise and combine the results together. The function passed to ``apply`` must take a dataframe as its first argument and return a DataFrame, Series or scalar. ``apply`` will then take care of combining the results back together into a single dataframe or series. ``apply`` is therefore a highly flexible grouping method. While ``apply`` is a very flexible method, its downside is that using it can be quite a bit slower than using more specific methods like ``agg`` or ``transform``. Pandas offers a wide range of method that will be much faster than using ``apply`` for their specific purposes, so try to use them before reaching for ``apply``. Parameters ---------- func : callable A callable that takes a dataframe as its first argument, and returns a dataframe, a series or a scalar. In addition the callable may take positional and keyword arguments. *args : tuple Optional positional arguments to pass to ``func``. include_groups : bool, default False When True, will attempt to apply ``func`` to the groupings in the case that they are columns of the DataFrame. If this raises a TypeError, the result will be computed with the groupings excluded. When False, the groupings will be excluded when applying ``func``. .. versionadded:: 2.2.0 .. versionchanged:: 3.0.0 The default changed from True to False, and True is no longer allowed. **kwargs : dict Optional keyword arguments to pass to ``func``. Returns ------- Series or DataFrame A pandas object with the result of applying ``func`` to each group. See Also -------- pipe : Apply function to the full GroupBy object instead of to each group. aggregate : Apply aggregate function to the GroupBy object. transform : Apply function column-by-column to the GroupBy object. Series.apply : Apply a function to a Series. DataFrame.apply : Apply a function to each row or column of a DataFrame. Notes ----- The resulting dtype will reflect the return value of the passed ``func``, see the examples below. Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See :ref:`gotchas.udf-mutation` for more details. Examples -------- >>> df = pd.DataFrame({"A": "a a b".split(), "B": [1, 2, 3], "C": [4, 6, 5]}) >>> g1 = df.groupby("A", group_keys=False) >>> g2 = df.groupby("A", group_keys=True) Notice that ``g1`` and ``g2`` have two groups, ``a`` and ``b``, and only differ in their ``group_keys`` argument. Calling `apply` in various ways, we can get different grouping results: Example 1: below the function passed to `apply` takes a DataFrame as its argument and returns a DataFrame. `apply` combines the result for each group together into a new DataFrame: >>> g1[["B", "C"]].apply(lambda x: x / x.sum()) B C 0 0.333333 0.4 1 0.666667 0.6 2 1.000000 1.0 In the above, the groups are not part of the index. We can have them included by using ``g2`` where ``group_keys=True``: >>> g2[["B", "C"]].apply(lambda x: x / x.sum()) B C A a 0 0.333333 0.4 1 0.666667 0.6 b 2 1.000000 1.0 Example 2: The function passed to `apply` takes a DataFrame as its argument and returns a Series. `apply` combines the result for each group together into a new DataFrame. The resulting dtype will reflect the return value of the passed ``func``. >>> g1[["B", "C"]].apply(lambda x: x.astype(float).max() - x.min()) B C A a 1.0 2.0 b 0.0 0.0 >>> g2[["B", "C"]].apply(lambda x: x.astype(float).max() - x.min()) B C A a 1.0 2.0 b 0.0 0.0 The ``group_keys`` argument has no effect here because the result is not like-indexed (i.e. :ref:`a transform `) when compared to the input. Example 3: The function passed to `apply` takes a DataFrame as its argument and returns a scalar. `apply` combines the result for each group together into a Series, including setting the index as appropriate: >>> g1.apply(lambda x: x.C.max() - x.B.min()) A a 5 b 2 dtype: int64 Example 4: The function passed to ``apply`` returns ``None`` for one of the group. This group is filtered from the result: >>> g1.apply(lambda x: None if x.iloc[0, 0] == 3 else x) B C 0 1 4 1 2 6 """ if include_groups: raise ValueError("include_groups=True is no longer allowed.") if isinstance(func, str): if hasattr(self, func): res = getattr(self, func) if callable(res): return res(*args, **kwargs) elif args or kwargs: raise ValueError(f"Cannot pass arguments to property {func}") return res else: raise TypeError(f"apply func should be callable, not '{func}'") elif args or kwargs: if callable(func): @wraps(func) def f(g): return func(g, *args, **kwargs) else: raise ValueError( "func must be a callable if args or kwargs are supplied" ) else: f = func return self._python_apply_general(f, self._obj_with_exclusions) @final def _python_apply_general( self, f: Callable, data: DataFrame | Series, not_indexed_same: bool | None = None, is_transform: bool = False, is_agg: bool = False, ) -> NDFrameT: """ Apply function f in python space Parameters ---------- f : callable Function to apply data : Series or DataFrame Data to apply f to not_indexed_same: bool, optional When specified, overrides the value of not_indexed_same. Apply behaves differently when the result index is equal to the input index, but this can be coincidental leading to value-dependent behavior. is_transform : bool, default False Indicator for whether the function is actually a transform and should not have group keys prepended. is_agg : bool, default False Indicator for whether the function is an aggregation. When the result is empty, we don't want to warn for this case. See _GroupBy._python_agg_general. Returns ------- Series or DataFrame data after applying f """ values, mutated = self._grouper.apply_groupwise(f, data) if not_indexed_same is None: not_indexed_same = mutated return self._wrap_applied_output( data, values, not_indexed_same, is_transform, ) @final def _agg_general( self, numeric_only: bool = False, min_count: int = -1, *, alias: str, npfunc: Callable | None = None, **kwargs, ): result = self._cython_agg_general( how=alias, alt=npfunc, numeric_only=numeric_only, min_count=min_count, **kwargs, ) return result.__finalize__(self.obj, method="groupby") def _agg_py_fallback( self, how: str, values: ArrayLike, ndim: int, alt: Callable ) -> ArrayLike: """ Fallback to pure-python aggregation if _cython_operation raises NotImplementedError. """ # We get here with a) EADtypes and b) object dtype assert alt is not None if values.ndim == 1: # For DataFrameGroupBy we only get here with ExtensionArray ser = Series(values, copy=False) else: # We only get here with values.dtype == object df = DataFrame(values.T, dtype=values.dtype) # bc we split object blocks in grouped_reduce, we have only 1 col # otherwise we'd have to worry about block-splitting GH#39329 assert df.shape[1] == 1 # Avoid call to self.values that can occur in DataFrame # reductions; see GH#28949 ser = df.iloc[:, 0] # We do not get here with UDFs, so we know that our dtype # should always be preserved by the implemented aggregations # TODO: Is this exactly right; see WrappedCythonOp get_result_dtype? try: res_values = self._grouper.agg_series(ser, alt, preserve_dtype=True) except Exception as err: msg = f"agg function failed [how->{how},dtype->{ser.dtype}]" # preserve the kind of exception that raised raise type(err)(msg) from err dtype = ser.dtype if dtype == object: res_values = res_values.astype(object, copy=False) elif is_string_dtype(dtype): # mypy doesn't infer dtype is an ExtensionDtype string_array_cls = dtype.construct_array_type() # type: ignore[union-attr] res_values = string_array_cls._from_sequence(res_values, dtype=dtype) # If we are DataFrameGroupBy and went through a SeriesGroupByPath # then we need to reshape # GH#32223 includes case with IntegerArray values, ndarray res_values # test_groupby_duplicate_columns with object dtype values return ensure_block_shape(res_values, ndim=ndim) @final def _cython_agg_general( self, how: str, alt: Callable | None = None, numeric_only: bool = False, min_count: int = -1, **kwargs, ): # Note: we never get here with how="ohlc" for DataFrameGroupBy; # that goes through SeriesGroupBy if not is_bool(numeric_only): raise ValueError("numeric_only accepts only Boolean values") data = self._get_data_to_aggregate(numeric_only=numeric_only, name=how) def array_func(values: ArrayLike) -> ArrayLike: try: result = self._grouper._cython_operation( "aggregate", values, how, axis=data.ndim - 1, min_count=min_count, **kwargs, ) except NotImplementedError: # generally if we have numeric_only=False # and non-applicable functions # try to python agg # TODO: shouldn't min_count matter? # TODO: avoid special casing SparseArray here if how in ["any", "all"] and isinstance(values, SparseArray): pass elif alt is None or how in ["any", "all", "std", "sem"]: raise # TODO: re-raise as TypeError? should not be reached else: return result assert alt is not None result = self._agg_py_fallback(how, values, ndim=data.ndim, alt=alt) return result new_mgr = data.grouped_reduce(array_func) res = self._wrap_agged_manager(new_mgr) if how in ["idxmin", "idxmax"]: # mypy expects how to be Literal["idxmin", "idxmax"]. res = self._wrap_idxmax_idxmin(res, how=how, skipna=kwargs["skipna"]) # type: ignore[arg-type] out = self._wrap_aggregated_output(res) return out def _cython_transform(self, how: str, numeric_only: bool = False, **kwargs): raise AbstractMethodError(self) @final def _transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs): # optimized transforms if not isinstance(func, str): return self._transform_general(func, engine, engine_kwargs, *args, **kwargs) elif func not in base.transform_kernel_allowlist: msg = f"'{func}' is not a valid function name for transform(name)" raise ValueError(msg) elif func in base.cythonized_kernels or func in base.transformation_kernels: # cythonized transform or canned "agg+broadcast" if engine is not None: kwargs["engine"] = engine kwargs["engine_kwargs"] = engine_kwargs return getattr(self, func)(*args, **kwargs) else: # i.e. func in base.reduction_kernels if self.observed: return self._reduction_kernel_transform( func, *args, engine=engine, engine_kwargs=engine_kwargs, **kwargs ) with ( com.temp_setattr(self, "observed", True), com.temp_setattr(self, "_grouper", self._grouper.observed_grouper), ): return self._reduction_kernel_transform( func, *args, engine=engine, engine_kwargs=engine_kwargs, **kwargs ) @final def _reduction_kernel_transform( self, func, *args, engine=None, engine_kwargs=None, **kwargs ): # GH#30918 Use _transform_fast only when we know func is an aggregation # If func is a reduction, we need to broadcast the # result to the whole group. Compute func result # and deal with possible broadcasting below. with com.temp_setattr(self, "as_index", True): # GH#49834 - result needs groups in the index for # _wrap_transform_fast_result if func in ["idxmin", "idxmax"]: func = cast(Literal["idxmin", "idxmax"], func) result = self._idxmax_idxmin(func, True, *args, **kwargs) else: if engine is not None: kwargs["engine"] = engine kwargs["engine_kwargs"] = engine_kwargs result = getattr(self, func)(*args, **kwargs) return self._wrap_transform_fast_result(result) @final def _wrap_transform_fast_result(self, result: NDFrameT) -> NDFrameT: """ Fast transform path for aggregations. """ obj = self._obj_with_exclusions # for each col, reshape to size of original frame by take operation ids = self._grouper.ids result = result.reindex(self._grouper.result_index, axis=0) if self.obj.ndim == 1: # i.e. SeriesGroupBy out = algorithms.take_nd(result._values, ids) output = obj._constructor(out, index=obj.index, name=obj.name) else: # `.size()` gives Series output on DataFrame input, need axis 0 # GH#46209 # Don't convert indices: negative indices need to give rise # to null values in the result new_ax = result.index.take(ids) output = result._reindex_with_indexers({0: (new_ax, ids)}, allow_dups=True) output = output.set_axis(obj.index, axis=0) return output # ----------------------------------------------------------------- # Utilities @final def _apply_filter(self, indices, dropna): if len(indices) == 0: indices = np.array([], dtype="int64") else: indices = np.sort(np.concatenate(indices)) if dropna: filtered = self._selected_obj.take(indices, axis=0) else: mask = np.empty(len(self._selected_obj.index), dtype=bool) mask.fill(False) mask[indices.astype(int)] = True # mask fails to broadcast when passed to where; broadcast manually. mask = np.tile(mask, [*self._selected_obj.shape[1:], 1]).T filtered = self._selected_obj.where(mask) # Fill with NaNs. return filtered @final def _cumcount_array(self, ascending: bool = True) -> np.ndarray: """ Parameters ---------- ascending : bool, default True If False, number in reverse, from length of group - 1 to 0. Notes ----- this is currently implementing sort=False (though the default is sort=True) for groupby in general """ ids = self._grouper.ids ngroups = self._grouper.ngroups sorter = get_group_index_sorter(ids, ngroups) ids, count = ids[sorter], len(ids) if count == 0: return np.empty(0, dtype=np.int64) run = np.r_[True, ids[:-1] != ids[1:]] rep = np.diff(np.r_[np.nonzero(run)[0], count]) out = (~run).cumsum() if ascending: out -= np.repeat(out[run], rep) else: out = np.repeat(out[np.r_[run[1:], True]], rep) - out if self._grouper.has_dropped_na: out = np.where(ids == -1, np.nan, out.astype(np.float64, copy=False)) else: out = out.astype(np.int64, copy=False) rev = np.empty(count, dtype=np.intp) rev[sorter] = np.arange(count, dtype=np.intp) return out[rev] # ----------------------------------------------------------------- @final @property def _obj_1d_constructor(self) -> Callable: # GH28330 preserve subclassed Series/DataFrames if isinstance(self.obj, DataFrame): return self.obj._constructor_sliced assert isinstance(self.obj, Series) return self.obj._constructor @final def any(self, skipna: bool = True) -> NDFrameT: """ Return True if any value in the group is truthful, else False. Parameters ---------- skipna : bool, default True Flag to ignore nan values during truth testing. Returns ------- Series or DataFrame DataFrame or Series of boolean values, where a value is True if any element is True within its respective group, False otherwise. See Also -------- Series.any : Apply function any to a Series. DataFrame.any : Apply function any to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b"] >>> ser = pd.Series([1, 2, 0], index=lst) >>> ser a 1 a 2 b 0 dtype: int64 >>> ser.groupby(level=0).any() a True b False dtype: bool For DataFrameGroupBy: >>> data = [[1, 0, 3], [1, 0, 6], [7, 1, 9]] >>> df = pd.DataFrame( ... data, columns=["a", "b", "c"], index=["ostrich", "penguin", "parrot"] ... ) >>> df a b c ostrich 1 0 3 penguin 1 0 6 parrot 7 1 9 >>> df.groupby(by=["a"]).any() b c a 1 False True 7 True True """ return self._cython_agg_general( "any", alt=lambda x: Series(x, copy=False).any(skipna=skipna), skipna=skipna, ) @final def all(self, skipna: bool = True) -> NDFrameT: """ Return True if all values in the group are truthful, else False. Parameters ---------- skipna : bool, default True Flag to ignore nan values during truth testing. Returns ------- Series or DataFrame DataFrame or Series of boolean values, where a value is True if all elements are True within its respective group, False otherwise. See Also -------- Series.all : Apply function all to a Series. DataFrame.all : Apply function all to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b"] >>> ser = pd.Series([1, 2, 0], index=lst) >>> ser a 1 a 2 b 0 dtype: int64 >>> ser.groupby(level=0).all() a True b False dtype: bool For DataFrameGroupBy: >>> data = [[1, 0, 3], [1, 5, 6], [7, 8, 9]] >>> df = pd.DataFrame( ... data, columns=["a", "b", "c"], index=["ostrich", "penguin", "parrot"] ... ) >>> df a b c ostrich 1 0 3 penguin 1 5 6 parrot 7 8 9 >>> df.groupby(by=["a"]).all() b c a 1 False True 7 True True """ return self._cython_agg_general( "all", alt=lambda x: Series(x, copy=False).all(skipna=skipna), skipna=skipna, ) @final def count(self) -> NDFrameT: """ Compute count of group, excluding missing values. Returns ------- Series or DataFrame Count of values within each group. See Also -------- Series.count : Apply function count to a Series. DataFrame.count : Apply function count to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b"] >>> ser = pd.Series([1, 2, np.nan], index=lst) >>> ser a 1.0 a 2.0 b NaN dtype: float64 >>> ser.groupby(level=0).count() a 2 b 0 dtype: int64 For DataFrameGroupBy: >>> data = [[1, np.nan, 3], [1, np.nan, 6], [7, 8, 9]] >>> df = pd.DataFrame( ... data, columns=["a", "b", "c"], index=["cow", "horse", "bull"] ... ) >>> df a b c cow 1 NaN 3 horse 1 NaN 6 bull 7 8.0 9 >>> df.groupby("a").count() b c a 1 0 2 7 1 1 For Resampler: >>> ser = pd.Series( ... [1, 2, 3, 4], ... index=pd.DatetimeIndex( ... ["2023-01-01", "2023-01-15", "2023-02-01", "2023-02-15"] ... ), ... ) >>> ser 2023-01-01 1 2023-01-15 2 2023-02-01 3 2023-02-15 4 dtype: int64 >>> ser.resample("MS").count() 2023-01-01 2 2023-02-01 2 Freq: MS, dtype: int64 """ data = self._get_data_to_aggregate() ids = self._grouper.ids ngroups = self._grouper.ngroups mask = ids != -1 is_series = data.ndim == 1 def hfunc(bvalues: ArrayLike) -> ArrayLike: # TODO(EA2D): reshape would not be necessary with 2D EAs if bvalues.ndim == 1: # EA masked = mask & ~isna(bvalues).reshape(1, -1) else: masked = mask & ~isna(bvalues) counted = lib.count_level_2d(masked, labels=ids, max_bin=ngroups) if isinstance(bvalues, BaseMaskedArray): return IntegerArray( counted[0], mask=np.zeros(counted.shape[1], dtype=np.bool_) ) elif isinstance(bvalues, ArrowExtensionArray) and not isinstance( bvalues.dtype, StringDtype ): dtype = pandas_dtype("int64[pyarrow]") return type(bvalues)._from_sequence(counted[0], dtype=dtype) if is_series: assert counted.ndim == 2 assert counted.shape[0] == 1 return counted[0] return counted new_mgr = data.grouped_reduce(hfunc) new_obj = self._wrap_agged_manager(new_mgr) result = self._wrap_aggregated_output(new_obj) return result @final def mean( self, numeric_only: bool = False, skipna: bool = True, engine: Literal["cython", "numba"] | None = None, engine_kwargs: dict[str, bool] | None = None, ): """ Compute mean of groups, excluding missing values. Parameters ---------- numeric_only : bool, default False Include only float, int, boolean columns. .. versionchanged:: 2.0.0 numeric_only no longer accepts ``None`` and defaults to ``False``. skipna : bool, default True Exclude NA/null values. If an entire group is NA, the result will be NA. engine : str, default None * ``'cython'`` : Runs the operation through C-extensions from cython. * ``'numba'`` : Runs the operation through JIT compiled code from numba. * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba`` engine_kwargs : dict, default None * For ``'cython'`` engine, there are no accepted ``engine_kwargs`` * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil`` and ``parallel`` dictionary keys. The values must either be ``True`` or ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` Returns ------- pandas.Series or pandas.DataFrame Mean of values within each group. Same object type as the caller. See Also -------- Series.mean : Apply function mean to a Series. DataFrame.mean : Apply function mean to each row or column of a DataFrame. Examples -------- >>> df = pd.DataFrame( ... {"A": [1, 1, 2, 1, 2], "B": [np.nan, 2, 3, 4, 5], "C": [1, 2, 1, 1, 2]}, ... columns=["A", "B", "C"], ... ) Groupby one column and return the mean of the remaining columns in each group. >>> df.groupby("A").mean() B C A 1 3.0 1.333333 2 4.0 1.500000 Groupby two columns and return the mean of the remaining column. >>> df.groupby(["A", "B"]).mean() C A B 1 2.0 2.0 4.0 1.0 2 3.0 1.0 5.0 2.0 Groupby one column and return the mean of only particular column in the group. >>> df.groupby("A")["B"].mean() A 1 3.0 2 4.0 Name: B, dtype: float64 """ if maybe_use_numba(engine): from pandas.core._numba.kernels import grouped_mean return self._numba_agg_general( grouped_mean, executor.float_dtype_mapping, engine_kwargs, min_periods=0, skipna=skipna, ) else: result = self._cython_agg_general( "mean", alt=lambda x: Series(x, copy=False).mean( numeric_only=numeric_only, skipna=skipna ), numeric_only=numeric_only, skipna=skipna, ) return result.__finalize__(self.obj, method="groupby") @final def median(self, numeric_only: bool = False, skipna: bool = True) -> NDFrameT: """ Compute median of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex Parameters ---------- numeric_only : bool, default False Include only float, int, boolean columns. .. versionchanged:: 2.0.0 numeric_only no longer accepts ``None`` and defaults to False. skipna : bool, default True Exclude NA/null values. If an entire group is NA, the result will be NA. .. versionadded:: 3.0.0 Returns ------- Series or DataFrame Median of values within each group. See Also -------- Series.median : Apply function median to a Series. DataFrame.median : Apply function median to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "a", "b", "b", "b"] >>> ser = pd.Series([7, 2, 8, 4, 3, 3], index=lst) >>> ser a 7 a 2 a 8 b 4 b 3 b 3 dtype: int64 >>> ser.groupby(level=0).median() a 7.0 b 3.0 dtype: float64 For DataFrameGroupBy: >>> data = {"a": [1, 3, 5, 7, 7, 8, 3], "b": [1, 4, 8, 4, 4, 2, 1]} >>> df = pd.DataFrame( ... data, index=["dog", "dog", "dog", "mouse", "mouse", "mouse", "mouse"] ... ) >>> df a b dog 1 1 dog 3 4 dog 5 8 mouse 7 4 mouse 7 4 mouse 8 2 mouse 3 1 >>> df.groupby(level=0).median() a b dog 3.0 4.0 mouse 7.0 3.0 For Resampler: >>> ser = pd.Series( ... [1, 2, 3, 3, 4, 5], ... index=pd.DatetimeIndex( ... [ ... "2023-01-01", ... "2023-01-10", ... "2023-01-15", ... "2023-02-01", ... "2023-02-10", ... "2023-02-15", ... ] ... ), ... ) >>> ser.resample("MS").median() 2023-01-01 2.0 2023-02-01 4.0 Freq: MS, dtype: float64 """ result = self._cython_agg_general( "median", alt=lambda x: Series(x, copy=False).median( numeric_only=numeric_only, skipna=skipna ), numeric_only=numeric_only, skipna=skipna, ) return result.__finalize__(self.obj, method="groupby") @final def std( self, ddof: int = 1, engine: Literal["cython", "numba"] | None = None, engine_kwargs: dict[str, bool] | None = None, numeric_only: bool = False, skipna: bool = True, ): """ Compute standard deviation of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : int, default 1 Delta Degrees of Freedom. The divisor used in calculations is ``N - ddof``, where ``N`` represents the number of elements. engine : str, default None * ``'cython'`` : Runs the operation through C-extensions from cython. * ``'numba'`` : Runs the operation through JIT compiled code from numba. * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba`` engine_kwargs : dict, default None * For ``'cython'`` engine, there are no accepted ``engine_kwargs`` * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil`` and ``parallel`` dictionary keys. The values must either be ``True`` or ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` numeric_only : bool, default False Include only `float`, `int` or `boolean` data. .. versionchanged:: 2.0.0 numeric_only now defaults to ``False``. skipna : bool, default True Exclude NA/null values. If an entire group is NA, the result will be NA. .. versionadded:: 3.0.0 Returns ------- Series or DataFrame Standard deviation of values within each group. See Also -------- Series.std : Apply function std to a Series. DataFrame.std : Apply function std to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "a", "b", "b", "b"] >>> ser = pd.Series([7, 2, 8, 4, 3, 3], index=lst) >>> ser a 7 a 2 a 8 b 4 b 3 b 3 dtype: int64 >>> ser.groupby(level=0).std() a 3.21455 b 0.57735 dtype: float64 For DataFrameGroupBy: >>> data = {"a": [1, 3, 5, 7, 7, 8, 3], "b": [1, 4, 8, 4, 4, 2, 1]} >>> df = pd.DataFrame( ... data, index=["dog", "dog", "dog", "mouse", "mouse", "mouse", "mouse"] ... ) >>> df a b dog 1 1 dog 3 4 dog 5 8 mouse 7 4 mouse 7 4 mouse 8 2 mouse 3 1 >>> df.groupby(level=0).std() a b dog 2.000000 3.511885 mouse 2.217356 1.500000 """ if maybe_use_numba(engine): from pandas.core._numba.kernels import grouped_var return np.sqrt( self._numba_agg_general( grouped_var, executor.float_dtype_mapping, engine_kwargs, min_periods=0, ddof=ddof, skipna=skipna, ) ) else: return self._cython_agg_general( "std", alt=lambda x: Series(x, copy=False).std(ddof=ddof, skipna=skipna), numeric_only=numeric_only, ddof=ddof, skipna=skipna, ) @final def var( self, ddof: int = 1, engine: Literal["cython", "numba"] | None = None, engine_kwargs: dict[str, bool] | None = None, numeric_only: bool = False, skipna: bool = True, ): """ Compute variance of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : int, default 1 Degrees of freedom. engine : str, default None * ``'cython'`` : Runs the operation through C-extensions from cython. * ``'numba'`` : Runs the operation through JIT compiled code from numba. * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba`` engine_kwargs : dict, default None * For ``'cython'`` engine, there are no accepted ``engine_kwargs`` * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil`` and ``parallel`` dictionary keys. The values must either be ``True`` or ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is ``{{'nopython': True, 'nogil': False, 'parallel': False}}`` numeric_only : bool, default False Include only `float`, `int` or `boolean` data. .. versionchanged:: 2.0.0 numeric_only now defaults to ``False``. skipna : bool, default True Exclude NA/null values. If an entire group is NA, the result will be NA. .. versionadded:: 3.0.0 Returns ------- Series or DataFrame Variance of values within each group. See Also -------- Series.var : Apply function var to a Series. DataFrame.var : Apply function var to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "a", "b", "b", "b"] >>> ser = pd.Series([7, 2, 8, 4, 3, 3], index=lst) >>> ser a 7 a 2 a 8 b 4 b 3 b 3 dtype: int64 >>> ser.groupby(level=0).var() a 10.333333 b 0.333333 dtype: float64 For DataFrameGroupBy: >>> data = {"a": [1, 3, 5, 7, 7, 8, 3], "b": [1, 4, 8, 4, 4, 2, 1]} >>> df = pd.DataFrame( ... data, index=["dog", "dog", "dog", "mouse", "mouse", "mouse", "mouse"] ... ) >>> df a b dog 1 1 dog 3 4 dog 5 8 mouse 7 4 mouse 7 4 mouse 8 2 mouse 3 1 >>> df.groupby(level=0).var() a b dog 4.000000 12.333333 mouse 4.916667 2.250000 """ if maybe_use_numba(engine): from pandas.core._numba.kernels import grouped_var return self._numba_agg_general( grouped_var, executor.float_dtype_mapping, engine_kwargs, min_periods=0, ddof=ddof, skipna=skipna, ) else: return self._cython_agg_general( "var", alt=lambda x: Series(x, copy=False).var(ddof=ddof, skipna=skipna), numeric_only=numeric_only, ddof=ddof, skipna=skipna, ) @final def _value_counts( self, subset: Sequence[Hashable] | None = None, normalize: bool = False, sort: bool = True, ascending: bool = False, dropna: bool = True, ) -> DataFrame | Series: """ Shared implementation of value_counts for SeriesGroupBy and DataFrameGroupBy. SeriesGroupBy additionally supports a bins argument. See the docstring of DataFrameGroupBy.value_counts for a description of arguments. """ name = "proportion" if normalize else "count" df = self.obj obj = self._obj_with_exclusions in_axis_names = { grouping.name for grouping in self._grouper.groupings if grouping.in_axis } if isinstance(obj, Series): _name = obj.name keys: Iterable[Series] = [] if _name in in_axis_names else [obj] else: unique_cols = set(obj.columns) if subset is not None: subsetted = set(subset) clashing = subsetted & set(in_axis_names) if clashing: raise ValueError( f"Keys {clashing} in subset cannot be in " "the groupby column keys." ) doesnt_exist = subsetted - unique_cols if doesnt_exist: raise ValueError( f"Keys {doesnt_exist} in subset do not exist in the DataFrame." ) else: subsetted = unique_cols keys = ( # Can't use .values because the column label needs to be preserved obj.iloc[:, idx] for idx, _name in enumerate(obj.columns) if _name not in in_axis_names and _name in subsetted ) groupings = list(self._grouper.groupings) for key in keys: grouper, _, _ = get_grouper( df, key=key, sort=False, observed=False, dropna=dropna, ) groupings += list(grouper.groupings) # Take the size of the overall columns gb = df.groupby( groupings, sort=False, observed=self.observed, dropna=self.dropna, ) result_series = cast(Series, gb.size()) result_series.name = name if sort: # Sort by the values result_series = result_series.sort_values( ascending=ascending, kind="stable" ) if self.sort: # Sort by the groupings names = result_series.index.names # GH#55951 - Temporarily replace names in case they are integers result_series.index.names = range(len(names)) index_level = range(len(self._grouper.groupings)) result_series = result_series.sort_index( level=index_level, sort_remaining=False ) result_series.index.names = names if normalize: # Normalize the results by dividing by the original group sizes. # We are guaranteed to have the first N levels be the # user-requested grouping. levels = list( range(len(self._grouper.groupings), result_series.index.nlevels) ) indexed_group_size = result_series.groupby( result_series.index.droplevel(levels), sort=self.sort, dropna=self.dropna, # GH#43999 - deprecation of observed=False observed=False, ).transform("sum") result_series /= indexed_group_size # Handle groups of non-observed categories result_series = result_series.fillna(0.0) result: Series | DataFrame if self.as_index: result = result_series else: # Convert to frame index = result_series.index columns = com.fill_missing_names(index.names) if name in columns: raise ValueError(f"Column label '{name}' is duplicate of result column") result_series.name = name result_series.index = index.set_names(range(len(columns))) result_frame = result_series.reset_index() orig_dtype = self._grouper.groupings[0].obj.columns.dtype # type: ignore[union-attr] cols = Index(columns, dtype=orig_dtype).insert(len(columns), name) result_frame.columns = cols result = result_frame return result.__finalize__(self.obj, method="value_counts") @final def sem( self, ddof: int = 1, numeric_only: bool = False, skipna: bool = True ) -> NDFrameT: """ Compute standard error of the mean of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters ---------- ddof : int, default 1 Degrees of freedom. numeric_only : bool, default False Include only `float`, `int` or `boolean` data. .. versionchanged:: 2.0.0 numeric_only now defaults to ``False``. skipna : bool, default True Exclude NA/null values. If an entire group is NA, the result will be NA. .. versionadded:: 3.0.0 Returns ------- Series or DataFrame Standard error of the mean of values within each group. See Also -------- DataFrame.sem : Return unbiased standard error of the mean over requested axis. Series.sem : Return unbiased standard error of the mean over requested axis. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b", "b"] >>> ser = pd.Series([5, 10, 8, 14], index=lst) >>> ser a 5 a 10 b 8 b 14 dtype: int64 >>> ser.groupby(level=0).sem() a 2.5 b 3.0 dtype: float64 For DataFrameGroupBy: >>> data = [[1, 12, 11], [1, 15, 2], [2, 5, 8], [2, 6, 12]] >>> df = pd.DataFrame( ... data, ... columns=["a", "b", "c"], ... index=["tuna", "salmon", "catfish", "goldfish"], ... ) >>> df a b c tuna 1 12 11 salmon 1 15 2 catfish 2 5 8 goldfish 2 6 12 >>> df.groupby("a").sem() b c a 1 1.5 4.5 2 0.5 2.0 For Resampler: >>> ser = pd.Series( ... [1, 3, 2, 4, 3, 8], ... index=pd.DatetimeIndex( ... [ ... "2023-01-01", ... "2023-01-10", ... "2023-01-15", ... "2023-02-01", ... "2023-02-10", ... "2023-02-15", ... ] ... ), ... ) >>> ser.resample("MS").sem() 2023-01-01 0.577350 2023-02-01 1.527525 Freq: MS, dtype: float64 """ if numeric_only and self.obj.ndim == 1 and not is_numeric_dtype(self.obj.dtype): raise TypeError( f"{type(self).__name__}.sem called with " f"numeric_only={numeric_only} and dtype {self.obj.dtype}" ) return self._cython_agg_general( "sem", alt=lambda x: Series(x, copy=False).sem(ddof=ddof, skipna=skipna), numeric_only=numeric_only, ddof=ddof, skipna=skipna, ) @final def size(self) -> DataFrame | Series: """ Compute group sizes. Returns ------- DataFrame or Series Number of rows in each group as a Series if as_index is True or a DataFrame if as_index is False. See Also -------- Series.size : Apply function size to a Series. DataFrame.size : Apply function size to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b"] >>> ser = pd.Series([1, 2, 3], index=lst) >>> ser a 1 a 2 b 3 dtype: int64 >>> ser.groupby(level=0).size() a 2 b 1 dtype: int64 >>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]] >>> df = pd.DataFrame( ... data, columns=["a", "b", "c"], index=["owl", "toucan", "eagle"] ... ) >>> df a b c owl 1 2 3 toucan 1 5 6 eagle 7 8 9 >>> df.groupby("a").size() a 1 2 7 1 dtype: int64 For Resampler: >>> ser = pd.Series( ... [1, 2, 3], ... index=pd.DatetimeIndex(["2023-01-01", "2023-01-15", "2023-02-01"]), ... ) >>> ser 2023-01-01 1 2023-01-15 2 2023-02-01 3 dtype: int64 >>> ser.resample("MS").size() 2023-01-01 2 2023-02-01 1 Freq: MS, dtype: int64 """ result = self._grouper.size() dtype_backend: None | Literal["pyarrow", "numpy_nullable"] = None if isinstance(self.obj, Series): if isinstance(self.obj.array, ArrowExtensionArray): if isinstance(self.obj.array, ArrowStringArray): if self.obj.array.dtype.na_value is np.nan: dtype_backend = None else: dtype_backend = "numpy_nullable" else: dtype_backend = "pyarrow" elif isinstance(self.obj.array, BaseMaskedArray): dtype_backend = "numpy_nullable" # TODO: For DataFrames what if columns are mixed arrow/numpy/masked? # GH28330 preserve subclassed Series/DataFrames through calls if isinstance(self.obj, Series): result = self._obj_1d_constructor(result, name=self.obj.name) else: result = self._obj_1d_constructor(result) if dtype_backend is not None: result = result.convert_dtypes( infer_objects=False, convert_string=False, convert_boolean=False, convert_floating=False, dtype_backend=dtype_backend, ) if not self.as_index: result = result.rename("size").reset_index() return result @final def sum( self, numeric_only: bool = False, min_count: int = 0, skipna: bool = True, engine: Literal["cython", "numba"] | None = None, engine_kwargs: dict[str, bool] | None = None, ): """ Compute sum of group values. Parameters ---------- numeric_only : bool, default False Include only float, int, boolean columns. .. versionchanged:: 2.0.0 numeric_only no longer accepts ``None``. min_count : int, default 0 The required number of valid values to perform the operation. If fewer than ``min_count`` non-NA values are present the result will be NA. skipna : bool, default True Exclude NA/null values. If the entire group is NA and ``skipna`` is ``True``, the result will be NA. .. versionchanged:: 3.0.0 engine : str, default None None * ``'cython'`` : Runs rolling apply through C-extensions from cython. * ``'numba'`` : Runs rolling apply through JIT compiled code from numba. Only available when ``raw`` is set to ``True``. * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba`` engine_kwargs : dict, default None None * For ``'cython'`` engine, there are no accepted ``engine_kwargs`` * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil`` and ``parallel`` dictionary keys. The values must either be ``True`` or ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is ``{'nopython': True, 'nogil': False, 'parallel': False}`` and will be applied to both the ``func`` and the ``apply`` groupby aggregation. Returns ------- Series or DataFrame Computed sum of values within each group. See Also -------- SeriesGroupBy.min : Return the min of the group values. DataFrameGroupBy.min : Return the min of the group values. SeriesGroupBy.max : Return the max of the group values. DataFrameGroupBy.max : Return the max of the group values. SeriesGroupBy.sum : Return the sum of the group values. DataFrameGroupBy.sum : Return the sum of the group values. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b", "b"] >>> ser = pd.Series([1, 2, 3, 4], index=lst) >>> ser a 1 a 2 b 3 b 4 dtype: int64 >>> ser.groupby(level=0).sum() a 3 b 7 dtype: int64 For DataFrameGroupBy: >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]] >>> df = pd.DataFrame( ... data, ... columns=["a", "b", "c"], ... index=["tiger", "leopard", "cheetah", "lion"], ... ) >>> df a b c tiger 1 8 2 leopard 1 2 5 cheetah 2 5 8 lion 2 6 9 >>> df.groupby("a").sum() b c a 1 10 7 2 11 17 """ if maybe_use_numba(engine): from pandas.core._numba.kernels import grouped_sum return self._numba_agg_general( grouped_sum, executor.default_dtype_mapping, engine_kwargs, min_periods=min_count, skipna=skipna, ) else: # If we are grouping on categoricals we want unobserved categories to # return zero, rather than the default of NaN which the reindexing in # _agg_general() returns. GH #31422 with com.temp_setattr(self, "observed", True): result = self._agg_general( numeric_only=numeric_only, min_count=min_count, alias="sum", npfunc=np.sum, skipna=skipna, ) return result @final def prod( self, numeric_only: bool = False, min_count: int = 0, skipna: bool = True ) -> NDFrameT: """ Compute prod of group values. Parameters ---------- numeric_only : bool, default False Include only float, int, boolean columns. .. versionchanged:: 2.0.0 numeric_only no longer accepts ``None``. min_count : int, default 0 The required number of valid values to perform the operation. If fewer than ``min_count`` non-NA values are present the result will be NA. skipna : bool, default True Exclude NA/null values. If an entire group is NA, the result will be NA. .. versionadded:: 3.0.0 Returns ------- Series or DataFrame Computed prod of values within each group. See Also -------- Series.prod : Return the product of the values over the requested axis. DataFrame.prod : Return the product of the values over the requested axis. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b", "b"] >>> ser = pd.Series([1, 2, 3, 4], index=lst) >>> ser a 1 a 2 b 3 b 4 dtype: int64 >>> ser.groupby(level=0).prod() a 2 b 12 dtype: int64 For DataFrameGroupBy: >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]] >>> df = pd.DataFrame( ... data, ... columns=["a", "b", "c"], ... index=["tiger", "leopard", "cheetah", "lion"], ... ) >>> df a b c tiger 1 8 2 leopard 1 2 5 cheetah 2 5 8 lion 2 6 9 >>> df.groupby("a").prod() b c a 1 16 10 2 30 72 """ return self._agg_general( numeric_only=numeric_only, min_count=min_count, skipna=skipna, alias="prod", npfunc=np.prod, ) @final def min( self, numeric_only: bool = False, min_count: int = -1, skipna: bool = True, engine: Literal["cython", "numba"] | None = None, engine_kwargs: dict[str, bool] | None = None, ): """ Compute min of group values. Parameters ---------- numeric_only : bool, default False Include only float, int, boolean columns. .. versionchanged:: 2.0.0 numeric_only no longer accepts ``None``. min_count : int, default -1 The required number of valid values to perform the operation. If fewer than ``min_count`` non-NA values are present the result will be NA. skipna : bool, default True Exclude NA/null values. If the entire group is NA and ``skipna`` is ``True``, the result will be NA. .. versionchanged:: 3.0.0 engine : str, default None None * ``'cython'`` : Runs rolling apply through C-extensions from cython. * ``'numba'`` : Runs rolling apply through JIT compiled code from numba. Only available when ``raw`` is set to ``True``. * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba`` engine_kwargs : dict, default None None * For ``'cython'`` engine, there are no accepted ``engine_kwargs`` * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil`` and ``parallel`` dictionary keys. The values must either be ``True`` or ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is ``{'nopython': True, 'nogil': False, 'parallel': False}`` and will be applied to both the ``func`` and the ``apply`` groupby aggregation. Returns ------- Series or DataFrame Computed min of values within each group. See Also -------- SeriesGroupBy.min : Return the min of the group values. DataFrameGroupBy.min : Return the min of the group values. SeriesGroupBy.max : Return the max of the group values. DataFrameGroupBy.max : Return the max of the group values. SeriesGroupBy.sum : Return the sum of the group values. DataFrameGroupBy.sum : Return the sum of the group values. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b", "b"] >>> ser = pd.Series([1, 2, 3, 4], index=lst) >>> ser a 1 a 2 b 3 b 4 dtype: int64 >>> ser.groupby(level=0).min() a 1 b 3 dtype: int64 For DataFrameGroupBy: >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]] >>> df = pd.DataFrame( ... data, ... columns=["a", "b", "c"], ... index=["tiger", "leopard", "cheetah", "lion"], ... ) >>> df a b c tiger 1 8 2 leopard 1 2 5 cheetah 2 5 8 lion 2 6 9 >>> df.groupby("a").min() b c a 1 2 2 2 5 8 """ if maybe_use_numba(engine): from pandas.core._numba.kernels import grouped_min_max return self._numba_agg_general( grouped_min_max, executor.identity_dtype_mapping, engine_kwargs, min_periods=min_count, is_max=False, skipna=skipna, ) else: return self._agg_general( numeric_only=numeric_only, min_count=min_count, skipna=skipna, alias="min", npfunc=np.min, ) @final def max( self, numeric_only: bool = False, min_count: int = -1, skipna: bool = True, engine: Literal["cython", "numba"] | None = None, engine_kwargs: dict[str, bool] | None = None, ): """ Compute max of group values. Parameters ---------- numeric_only : bool, default False Include only float, int, boolean columns. .. versionchanged:: 2.0.0 numeric_only no longer accepts ``None``. min_count : int, default -1 The required number of valid values to perform the operation. If fewer than ``min_count`` non-NA values are present the result will be NA. skipna : bool, default True Exclude NA/null values. If the entire group is NA and ``skipna`` is ``True``, the result will be NA. .. versionchanged:: 3.0.0 engine : str, default None None * ``'cython'`` : Runs rolling apply through C-extensions from cython. * ``'numba'`` : Runs rolling apply through JIT compiled code from numba. Only available when ``raw`` is set to ``True``. * ``None`` : Defaults to ``'cython'`` or globally setting ``compute.use_numba`` engine_kwargs : dict, default None None * For ``'cython'`` engine, there are no accepted ``engine_kwargs`` * For ``'numba'`` engine, the engine can accept ``nopython``, ``nogil`` and ``parallel`` dictionary keys. The values must either be ``True`` or ``False``. The default ``engine_kwargs`` for the ``'numba'`` engine is ``{'nopython': True, 'nogil': False, 'parallel': False}`` and will be applied to both the ``func`` and the ``apply`` groupby aggregation. Returns ------- Series or DataFrame Computed max of values within each group. See Also -------- SeriesGroupBy.min : Return the min of the group values. DataFrameGroupBy.min : Return the min of the group values. SeriesGroupBy.max : Return the max of the group values. DataFrameGroupBy.max : Return the max of the group values. SeriesGroupBy.sum : Return the sum of the group values. DataFrameGroupBy.sum : Return the sum of the group values. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b", "b"] >>> ser = pd.Series([1, 2, 3, 4], index=lst) >>> ser a 1 a 2 b 3 b 4 dtype: int64 >>> ser.groupby(level=0).max() a 2 b 4 dtype: int64 For DataFrameGroupBy: >>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]] >>> df = pd.DataFrame( ... data, ... columns=["a", "b", "c"], ... index=["tiger", "leopard", "cheetah", "lion"], ... ) >>> df a b c tiger 1 8 2 leopard 1 2 5 cheetah 2 5 8 lion 2 6 9 >>> df.groupby("a").max() b c a 1 8 5 2 6 9 """ if maybe_use_numba(engine): from pandas.core._numba.kernels import grouped_min_max return self._numba_agg_general( grouped_min_max, executor.identity_dtype_mapping, engine_kwargs, min_periods=min_count, is_max=True, skipna=skipna, ) else: return self._agg_general( numeric_only=numeric_only, min_count=min_count, skipna=skipna, alias="max", npfunc=np.max, ) @final def first( self, numeric_only: bool = False, min_count: int = -1, skipna: bool = True ) -> NDFrameT: """ Compute the first entry of each column within each group. Defaults to skipping NA elements. Parameters ---------- numeric_only : bool, default False Include only float, int, boolean columns. min_count : int, default -1 The required number of valid values to perform the operation. If fewer than ``min_count`` valid values are present the result will be NA. skipna : bool, default True Exclude NA/null values. If an entire group is NA, the result will be NA. .. versionadded:: 2.2.1 Returns ------- Series or DataFrame First values within each group. See Also -------- DataFrame.groupby : Apply a function groupby to each row or column of a DataFrame. core.groupby.DataFrameGroupBy.last : Compute the last non-null entry of each column. core.groupby.DataFrameGroupBy.nth : Take the nth row from each group. Examples -------- >>> df = pd.DataFrame( ... dict( ... A=[1, 1, 3], ... B=[None, 5, 6], ... C=[1, 2, 3], ... D=["3/11/2000", "3/12/2000", "3/13/2000"], ... ) ... ) >>> df["D"] = pd.to_datetime(df["D"]) >>> df.groupby("A").first() B C D A 1 5.0 1 2000-03-11 3 6.0 3 2000-03-13 >>> df.groupby("A").first(min_count=2) B C D A 1 NaN 1.0 2000-03-11 3 NaN NaN NaT >>> df.groupby("A").first(numeric_only=True) B C A 1 5.0 1 3 6.0 3 """ def first_compat(obj: NDFrameT): def first(x: Series): """Helper function for first item that isn't NA.""" arr = x.array[notna(x.array)] if not len(arr): return x.array.dtype.na_value return arr[0] if isinstance(obj, DataFrame): return obj.apply(first) elif isinstance(obj, Series): return first(obj) else: # pragma: no cover raise TypeError(type(obj)) return self._agg_general( numeric_only=numeric_only, min_count=min_count, alias="first", npfunc=first_compat, skipna=skipna, ) @final def last( self, numeric_only: bool = False, min_count: int = -1, skipna: bool = True ) -> NDFrameT: """ Compute the last entry of each column within each group. Defaults to skipping NA elements. Parameters ---------- numeric_only : bool, default False Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. min_count : int, default -1 The required number of valid values to perform the operation. If fewer than ``min_count`` valid values are present the result will be NA. skipna : bool, default True Exclude NA/null values. If an entire group is NA, the result will be NA. .. versionadded:: 2.2.1 Returns ------- Series or DataFrame Last of values within each group. See Also -------- DataFrame.groupby : Apply a function groupby to each row or column of a DataFrame. core.groupby.DataFrameGroupBy.first : Compute the first non-null entry of each column. core.groupby.DataFrameGroupBy.nth : Take the nth row from each group. Examples -------- >>> df = pd.DataFrame(dict(A=[1, 1, 3], B=[5, None, 6], C=[1, 2, 3])) >>> df.groupby("A").last() B C A 1 5.0 2 3 6.0 3 """ def last_compat(obj: NDFrameT): def last(x: Series): """Helper function for last item that isn't NA.""" arr = x.array[notna(x.array)] if not len(arr): return x.array.dtype.na_value return arr[-1] if isinstance(obj, DataFrame): return obj.apply(last) elif isinstance(obj, Series): return last(obj) else: # pragma: no cover raise TypeError(type(obj)) return self._agg_general( numeric_only=numeric_only, min_count=min_count, alias="last", npfunc=last_compat, skipna=skipna, ) @final def ohlc(self) -> DataFrame: """ Compute open, high, low and close values of a group, excluding missing values. For multiple groupings, the result index will be a MultiIndex Returns ------- DataFrame Open, high, low and close values within each group. See Also -------- DataFrame.agg : Aggregate using one or more operations over the specified axis. DataFrame.resample : Resample time-series data. DataFrame.groupby : Group DataFrame using a mapper or by a Series of columns. Examples -------- For SeriesGroupBy: >>> lst = [ ... "SPX", ... "CAC", ... "SPX", ... "CAC", ... "SPX", ... "CAC", ... "SPX", ... "CAC", ... ] >>> ser = pd.Series([3.4, 9.0, 7.2, 5.2, 8.8, 9.4, 0.1, 0.5], index=lst) >>> ser SPX 3.4 CAC 9.0 SPX 7.2 CAC 5.2 SPX 8.8 CAC 9.4 SPX 0.1 CAC 0.5 dtype: float64 >>> ser.groupby(level=0).ohlc() open high low close CAC 9.0 9.4 0.5 0.5 SPX 3.4 8.8 0.1 0.1 For DataFrameGroupBy: >>> data = { ... 2022: [1.2, 2.3, 8.9, 4.5, 4.4, 3, 2, 1], ... 2023: [3.4, 9.0, 7.2, 5.2, 8.8, 9.4, 8.2, 1.0], ... } >>> df = pd.DataFrame( ... data, index=["SPX", "CAC", "SPX", "CAC", "SPX", "CAC", "SPX", "CAC"] ... ) >>> df 2022 2023 SPX 1.2 3.4 CAC 2.3 9.0 SPX 8.9 7.2 CAC 4.5 5.2 SPX 4.4 8.8 CAC 3.0 9.4 SPX 2.0 8.2 CAC 1.0 1.0 >>> df.groupby(level=0).ohlc() 2022 2023 open high low close open high low close CAC 2.3 4.5 1.0 1.0 9.0 9.4 1.0 1.0 SPX 1.2 8.9 1.2 2.0 3.4 8.8 3.4 8.2 For Resampler: >>> ser = pd.Series( ... [1, 3, 2, 4, 3, 5], ... index=pd.DatetimeIndex( ... [ ... "2023-01-01", ... "2023-01-10", ... "2023-01-15", ... "2023-02-01", ... "2023-02-10", ... "2023-02-15", ... ] ... ), ... ) >>> ser.resample("MS").ohlc() open high low close 2023-01-01 1 3 1 2 2023-02-01 4 5 3 5 """ if self.obj.ndim == 1: obj = self._selected_obj is_numeric = is_numeric_dtype(obj.dtype) if not is_numeric: raise DataError("No numeric types to aggregate") res_values = self._grouper._cython_operation( "aggregate", obj._values, "ohlc", axis=0, min_count=-1 ) agg_names = ["open", "high", "low", "close"] result = self.obj._constructor_expanddim( res_values, index=self._grouper.result_index, columns=agg_names ) return result result = self._apply_to_column_groupbys(lambda sgb: sgb.ohlc()) return result def describe( self, percentiles=None, include=None, exclude=None, ) -> NDFrameT: """ Generate descriptive statistics. Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset's distribution, excluding ``NaN`` values. Analyzes both numeric and object series, as well as ``DataFrame`` column sets of mixed data types. The output will vary depending on what is provided. Refer to the notes below for more detail. Parameters ---------- percentiles : list-like of numbers, optional The percentiles to include in the output. All should fall between 0 and 1. The default, ``None``, will automatically return the 25th, 50th, and 75th percentiles. include : 'all', list-like of dtypes or None (default), optional A white list of data types to include in the result. Ignored for ``Series``. Here are the options: - 'all' : All columns of the input will be included in the output. - A list-like of dtypes : Limits the results to the provided data types. To limit the result to numeric types submit ``numpy.number``. To limit it instead to object columns submit the ``numpy.object`` data type. Strings can also be used in the style of ``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To select pandas categorical columns, use ``'category'`` - None (default) : The result will include all numeric columns. exclude : list-like of dtypes or None (default), optional, A black list of data types to omit from the result. Ignored for ``Series``. Here are the options: - A list-like of dtypes : Excludes the provided data types from the result. To exclude numeric types submit ``numpy.number``. To exclude object columns submit the data type ``numpy.object``. Strings can also be used in the style of ``select_dtypes`` (e.g. ``df.describe(exclude=['O'])``). To exclude pandas categorical columns, use ``'category'`` - None (default) : The result will exclude nothing. Returns ------- Series or DataFrame Summary statistics of the Series or Dataframe provided. See Also -------- DataFrame.count: Count number of non-NA/null observations. DataFrame.max: Maximum of the values in the object. DataFrame.min: Minimum of the values in the object. DataFrame.mean: Mean of the values. DataFrame.std: Standard deviation of the observations. DataFrame.select_dtypes: Subset of a DataFrame including/excluding columns based on their dtype. Notes ----- For numeric data, the result's index will include ``count``, ``mean``, ``std``, ``min``, ``max`` as well as lower, ``50`` and upper percentiles. By default the lower percentile is ``25`` and the upper percentile is ``75``. The ``50`` percentile is the same as the median. For object data (e.g. strings), the result's index will include ``count``, ``unique``, ``top``, and ``freq``. The ``top`` is the most common value. The ``freq`` is the most common value's frequency. If multiple object values have the highest count, then the ``count`` and ``top`` results will be arbitrarily chosen from among those with the highest count. For mixed data types provided via a ``DataFrame``, the default is to return only an analysis of numeric columns. If the DataFrame consists only of object and categorical data without any numeric columns, the default is to return an analysis of both the object and categorical columns. If ``include='all'`` is provided as an option, the result will include a union of attributes of each type. The `include` and `exclude` parameters can be used to limit which columns in a ``DataFrame`` are analyzed for the output. The parameters are ignored when analyzing a ``Series``. Examples -------- Describing a numeric ``Series``. >>> s = pd.Series([1, 2, 3]) >>> s.describe() count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 dtype: float64 Describing a categorical ``Series``. >>> s = pd.Series(["a", "a", "b", "c"]) >>> s.describe() count 4 unique 3 top a freq 2 dtype: object Describing a timestamp ``Series``. >>> s = pd.Series( ... [ ... np.datetime64("2000-01-01"), ... np.datetime64("2010-01-01"), ... np.datetime64("2010-01-01"), ... ] ... ) >>> s.describe() count 3 mean 2006-09-01 08:00:00 min 2000-01-01 00:00:00 25% 2004-12-31 12:00:00 50% 2010-01-01 00:00:00 75% 2010-01-01 00:00:00 max 2010-01-01 00:00:00 dtype: object Describing a ``DataFrame``. By default only numeric fields are returned. >>> df = pd.DataFrame( ... { ... "categorical": pd.Categorical(["d", "e", "f"]), ... "numeric": [1, 2, 3], ... "object": ["a", "b", "c"], ... } ... ) >>> df.describe() numeric count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 Describing all columns of a ``DataFrame`` regardless of data type. >>> df.describe(include="all") # doctest: +SKIP categorical numeric object count 3 3.0 3 unique 3 NaN 3 top f NaN a freq 1 NaN 1 mean NaN 2.0 NaN std NaN 1.0 NaN min NaN 1.0 NaN 25% NaN 1.5 NaN 50% NaN 2.0 NaN 75% NaN 2.5 NaN max NaN 3.0 NaN Describing a column from a ``DataFrame`` by accessing it as an attribute. >>> df.numeric.describe() count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 Name: numeric, dtype: float64 Including only numeric columns in a ``DataFrame`` description. >>> df.describe(include=[np.number]) numeric count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 Including only string columns in a ``DataFrame`` description. >>> df.describe(include=[object]) # doctest: +SKIP object count 3 unique 3 top a freq 1 Including only categorical columns from a ``DataFrame`` description. >>> df.describe(include=["category"]) categorical count 3 unique 3 top d freq 1 Excluding numeric columns from a ``DataFrame`` description. >>> df.describe(exclude=[np.number]) # doctest: +SKIP categorical object count 3 3 unique 3 3 top f a freq 1 1 Excluding object columns from a ``DataFrame`` description. >>> df.describe(exclude=[object]) # doctest: +SKIP categorical numeric count 3 3.0 unique 3 NaN top f NaN freq 1 NaN mean NaN 2.0 std NaN 1.0 min NaN 1.0 25% NaN 1.5 50% NaN 2.0 75% NaN 2.5 max NaN 3.0 """ obj = self._obj_with_exclusions if len(obj) == 0: described = obj.describe( percentiles=percentiles, include=include, exclude=exclude ) if obj.ndim == 1: result = described else: result = described.unstack() return result.to_frame().T.iloc[:0] with com.temp_setattr(self, "as_index", True): result = self._python_apply_general( lambda x: x.describe( percentiles=percentiles, include=include, exclude=exclude ), obj, not_indexed_same=True, ) # GH#49256 - properly handle the grouping column(s) result = result.unstack() if not self.as_index: result = self._insert_inaxis_grouper(result) result.index = default_index(len(result)) return result @final def resample( self, rule, *args, include_groups: bool = False, **kwargs ) -> Resampler: """ Provide resampling when using a TimeGrouper. Given a grouper, the function resamples it according to a string "string" -> "frequency". See the :ref:`frequency aliases ` documentation for more details. Parameters ---------- rule : str or DateOffset The offset string or object representing target grouper conversion. *args Possible arguments are `how`, `fill_method`, `limit`, `kind` and `on`, and other arguments of `TimeGrouper`. include_groups : bool, default True When True, will attempt to include the groupings in the operation in the case that they are columns of the DataFrame. If this raises a TypeError, the result will be computed with the groupings excluded. When False, the groupings will be excluded when applying ``func``. .. versionadded:: 2.2.0 .. versionchanged:: 3.0 The default was changed to False, and True is no longer allowed. **kwargs Possible arguments are `how`, `fill_method`, `limit`, `kind` and `on`, and other arguments of `TimeGrouper`. Returns ------- DatetimeIndexResampler, PeriodIndexResampler or TimdeltaResampler Resampler object for the type of the index. See Also -------- Grouper : Specify a frequency to resample with when grouping by a key. DatetimeIndex.resample : Frequency conversion and resampling of time series. Examples -------- >>> idx = pd.date_range("1/1/2000", periods=4, freq="min") >>> df = pd.DataFrame(data=4 * [range(2)], index=idx, columns=["a", "b"]) >>> df.iloc[2, 0] = 5 >>> df a b 2000-01-01 00:00:00 0 1 2000-01-01 00:01:00 0 1 2000-01-01 00:02:00 5 1 2000-01-01 00:03:00 0 1 Downsample the DataFrame into 3 minute bins and sum the values of the timestamps falling into a bin. >>> df.groupby("a").resample("3min").sum() b a 0 2000-01-01 00:00:00 2 2000-01-01 00:03:00 1 5 2000-01-01 00:00:00 1 Upsample the series into 30 second bins. >>> df.groupby("a").resample("30s").sum() b a 0 2000-01-01 00:00:00 1 2000-01-01 00:00:30 0 2000-01-01 00:01:00 1 2000-01-01 00:01:30 0 2000-01-01 00:02:00 0 2000-01-01 00:02:30 0 2000-01-01 00:03:00 1 5 2000-01-01 00:02:00 1 Resample by month. Values are assigned to the month of the period. >>> df.groupby("a").resample("ME").sum() b a 0 2000-01-31 3 5 2000-01-31 1 Downsample the series into 3 minute bins as above, but close the right side of the bin interval. >>> (df.groupby("a").resample("3min", closed="right").sum()) b a 0 1999-12-31 23:57:00 1 2000-01-01 00:00:00 2 5 2000-01-01 00:00:00 1 Downsample the series into 3 minute bins and close the right side of the bin interval, but label each bin using the right edge instead of the left. >>> (df.groupby("a").resample("3min", closed="right", label="right").sum()) b a 0 2000-01-01 00:00:00 1 2000-01-01 00:03:00 2 5 2000-01-01 00:03:00 1 """ from pandas.core.resample import get_resampler_for_grouping if include_groups: raise ValueError("include_groups=True is no longer allowed.") return get_resampler_for_grouping(self, rule, *args, **kwargs) @final def rolling( self, window: int | datetime.timedelta | str | BaseOffset | BaseIndexer, min_periods: int | None = None, center: bool = False, win_type: str | None = None, on: str | None = None, closed: IntervalClosedType | None = None, method: str = "single", ) -> RollingGroupby: """ Return a rolling grouper, providing rolling functionality per group. Parameters ---------- window : int, timedelta, str, offset, or BaseIndexer subclass Interval of the moving window. If an integer, the delta between the start and end of each window. The number of points in the window depends on the ``closed`` argument. If a timedelta, str, or offset, the time period of each window. Each window will be a variable sized based on the observations included in the time-period. This is only valid for datetimelike indexes. To learn more about the offsets & frequency strings, please see :ref:`this link`. If a BaseIndexer subclass, the window boundaries based on the defined ``get_window_bounds`` method. Additional rolling keyword arguments, namely ``min_periods``, ``center``, ``closed`` and ``step`` will be passed to ``get_window_bounds``. min_periods : int, default None Minimum number of observations in window required to have a value; otherwise, result is ``np.nan``. For a window that is specified by an offset, ``min_periods`` will default to 1. For a window that is specified by an integer, ``min_periods`` will default to the size of the window. center : bool, default False If False, set the window labels as the right edge of the window index. If True, set the window labels as the center of the window index. win_type : str, default None If ``None``, all points are evenly weighted. If a string, it must be a valid `scipy.signal window function `__. Certain Scipy window types require additional parameters to be passed in the aggregation function. The additional parameters must match the keywords specified in the Scipy window type method signature. on : str, optional For a DataFrame, a column label or Index level on which to calculate the rolling window, rather than the DataFrame's index. Provided integer column is ignored and excluded from result since an integer index is not used to calculate the rolling window. closed : str, default None Determines the inclusivity of points in the window If ``'right'``, uses the window (first, last] meaning the last point is included in the calculations. If ``'left'``, uses the window [first, last) meaning the first point is included in the calculations. If ``'both'``, uses the window [first, last] meaning all points in the window are included in the calculations. If ``'neither'``, uses the window (first, last) meaning the first and last points in the window are excluded from calculations. () and [] are referencing open and closed set notation respetively. Default ``None`` (``'right'``). method : str {'single', 'table'}, default 'single' Execute the rolling operation per single column or row (``'single'``) or over the entire object (``'table'``). This argument is only implemented when specifying ``engine='numba'`` in the method call. Returns ------- pandas.api.typing.RollingGroupby Return a new grouper with our rolling appended. See Also -------- Series.rolling : Calling object with Series data. DataFrame.rolling : Calling object with DataFrames. Series.groupby : Apply a function groupby to a Series. DataFrame.groupby : Apply a function groupby. Examples -------- >>> df = pd.DataFrame( ... { ... "A": [1, 1, 2, 2], ... "B": [1, 2, 3, 4], ... "C": [0.362, 0.227, 1.267, -0.562], ... } ... ) >>> df A B C 0 1 1 0.362 1 1 2 0.227 2 2 3 1.267 3 2 4 -0.562 >>> df.groupby("A").rolling(2).sum() B C A 1 0 NaN NaN 1 3.0 0.589 2 2 NaN NaN 3 7.0 0.705 >>> df.groupby("A").rolling(2, min_periods=1).sum() B C A 1 0 1.0 0.362 1 3.0 0.589 2 2 3.0 1.267 3 7.0 0.705 >>> df.groupby("A").rolling(2, on="B").sum() B C A 1 0 1 NaN 1 2 0.589 2 2 3 NaN 3 4 0.705 """ from pandas.core.window import RollingGroupby return RollingGroupby( self._selected_obj, window=window, min_periods=min_periods, center=center, win_type=win_type, on=on, closed=closed, method=method, _grouper=self._grouper, _as_index=self.as_index, ) @final def expanding( self, min_periods: int = 1, method: str = "single", ) -> ExpandingGroupby: """ Return an expanding grouper, providing expanding functionality per group. Parameters ---------- min_periods : int, default 1 Minimum number of observations in window required to have a value; otherwise, result is ``np.nan``. method : str {'single', 'table'}, default 'single' Execute the expanding operation per single column or row (``'single'``) or over the entire object (``'table'``). This argument is only implemented when specifying ``engine='numba'`` in the method call. Returns ------- pandas.api.typing.ExpandingGroupby An object that supports expanding transformations over each group. See Also -------- Series.expanding : Expanding transformations for Series. DataFrame.expanding : Expanding transformations for DataFrames. Series.groupby : Apply a function groupby to a Series. DataFrame.groupby : Apply a function groupby. Examples -------- >>> df = pd.DataFrame( ... { ... "Class": ["A", "A", "A", "B", "B", "B"], ... "Value": [10, 20, 30, 40, 50, 60], ... } ... ) >>> df Class Value 0 A 10 1 A 20 2 A 30 3 B 40 4 B 50 5 B 60 >>> df.groupby("Class").expanding().mean() Value Class A 0 10.0 1 15.0 2 20.0 B 3 40.0 4 45.0 5 50.0 """ from pandas.core.window import ExpandingGroupby return ExpandingGroupby( self._selected_obj, min_periods=min_periods, method=method, _grouper=self._grouper, ) @final def ewm( self, com: float | None = None, span: float | None = None, halflife: float | str | Timedelta | None = None, alpha: float | None = None, min_periods: int | None = 0, adjust: bool = True, ignore_na: bool = False, times: np.ndarray | Series | None = None, method: str = "single", ) -> ExponentialMovingWindowGroupby: """ Return an ewm grouper, providing ewm functionality per group. Parameters ---------- com : float, optional Specify decay in terms of center of mass. Alternative to ``span``, ``halflife``, and ``alpha``. span : float, optional Specify decay in terms of span. halflife : float, str, or Timedelta, optional Specify decay in terms of half-life. alpha : float, optional Specify smoothing factor directly. min_periods : int, default 0 Minimum number of observations in the window required to have a value; otherwise, result is ``np.nan``. adjust : bool, default True Divide by decaying adjustment factor to account for imbalance in relative weights. ignore_na : bool, default False Ignore missing values when calculating weights. times : str or array-like of datetime64, optional Times corresponding to the observations. method : {'single', 'table'}, default 'single' Execute the operation per group independently (``'single'``) or over the entire object before regrouping (``'table'``). Only applicable to ``mean()``, and only when using ``engine='numba'``. Returns ------- pandas.api.typing.ExponentialMovingWindowGroupby An object that supports exponentially weighted moving transformations over each group. See Also -------- Series.ewm : EWM transformations for Series. DataFrame.ewm : EWM transformations for DataFrames. Series.groupby : Apply a function groupby to a Series. DataFrame.groupby : Apply a function groupby. Examples -------- >>> df = pd.DataFrame( ... { ... "Class": ["A", "A", "A", "B", "B", "B"], ... "Value": [10, 20, 30, 40, 50, 60], ... } ... ) >>> df Class Value 0 A 10 1 A 20 2 A 30 3 B 40 4 B 50 5 B 60 >>> df.groupby("Class").ewm(com=0.5).mean() Value Class A 0 10.000000 1 17.500000 2 26.153846 B 3 40.000000 4 47.500000 5 56.153846 """ from pandas.core.window import ExponentialMovingWindowGroupby return ExponentialMovingWindowGroupby( self._selected_obj, com=com, span=span, halflife=halflife, alpha=alpha, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na, times=times, method=method, _grouper=self._grouper, ) @final def _fill(self, direction: Literal["ffill", "bfill"], limit: int | None = None): """ Shared function for `pad` and `backfill` to call Cython method. Parameters ---------- direction : {'ffill', 'bfill'} Direction passed to underlying Cython function. `bfill` will cause values to be filled backwards. `ffill` and any other values will default to a forward fill limit : int, default None Maximum number of consecutive values to fill. If `None`, this method will convert to -1 prior to passing to Cython Returns ------- `Series` or `DataFrame` with filled values See Also -------- pad : Returns Series with minimum number of char in object. backfill : Backward fill the missing values in the dataset. """ # Need int value for Cython if limit is None: limit = -1 ids = self._grouper.ids ngroups = self._grouper.ngroups col_func = partial( libgroupby.group_fillna_indexer, labels=ids, limit=limit, compute_ffill=(direction == "ffill"), ngroups=ngroups, ) def blk_func(values: ArrayLike) -> ArrayLike: mask = isna(values) if values.ndim == 1: indexer = np.empty(values.shape, dtype=np.intp) col_func(out=indexer, mask=mask) # type: ignore[arg-type] return algorithms.take_nd(values, indexer) else: # We broadcast algorithms.take_nd analogous to # np.take_along_axis if isinstance(values, np.ndarray): dtype = values.dtype if self._grouper.has_dropped_na: # dropped null groups give rise to nan in the result dtype = ensure_dtype_can_hold_na(values.dtype) out = np.empty(values.shape, dtype=dtype) else: # Note: we only get here with backfill/pad, # so if we have a dtype that cannot hold NAs, # then there will be no -1s in indexer, so we can use # the original dtype (no need to ensure_dtype_can_hold_na) out = type(values)._empty(values.shape, dtype=values.dtype) for i, value_element in enumerate(values): # call group_fillna_indexer column-wise indexer = np.empty(values.shape[1], dtype=np.intp) col_func(out=indexer, mask=mask[i]) out[i, :] = algorithms.take_nd(value_element, indexer) return out mgr = self._get_data_to_aggregate() res_mgr = mgr.apply(blk_func) new_obj = self._wrap_agged_manager(res_mgr) new_obj.index = self.obj.index return new_obj @final def ffill(self, limit: int | None = None): """ Forward fill the values. Parameters ---------- limit : int, optional Limit of how many values to fill. Returns ------- Series or DataFrame Object with missing values filled. See Also -------- Series.ffill: Returns Series with minimum number of char in object. DataFrame.ffill: Object with missing values filled or None if inplace=True. Series.fillna: Fill NaN values of a Series. DataFrame.fillna: Fill NaN values of a DataFrame. Examples -------- For SeriesGroupBy: >>> key = [0, 0, 1, 1] >>> ser = pd.Series([np.nan, 2, 3, np.nan], index=key) >>> ser 0 NaN 0 2.0 1 3.0 1 NaN dtype: float64 >>> ser.groupby(level=0).ffill() 0 NaN 0 2.0 1 3.0 1 3.0 dtype: float64 For DataFrameGroupBy: >>> df = pd.DataFrame( ... { ... "key": [0, 0, 1, 1, 1], ... "A": [np.nan, 2, np.nan, 3, np.nan], ... "B": [2, 3, np.nan, np.nan, np.nan], ... "C": [np.nan, np.nan, 2, np.nan, np.nan], ... } ... ) >>> df key A B C 0 0 NaN 2.0 NaN 1 0 2.0 3.0 NaN 2 1 NaN NaN 2.0 3 1 3.0 NaN NaN 4 1 NaN NaN NaN Propagate non-null values forward or backward within each group along columns. >>> df.groupby("key").ffill() A B C 0 NaN 2.0 NaN 1 2.0 3.0 NaN 2 NaN NaN 2.0 3 3.0 NaN 2.0 4 3.0 NaN 2.0 Propagate non-null values forward or backward within each group along rows. >>> df.T.groupby(np.array([0, 0, 1, 1])).ffill().T key A B C 0 0.0 0.0 2.0 2.0 1 0.0 2.0 3.0 3.0 2 1.0 1.0 NaN 2.0 3 1.0 3.0 NaN NaN 4 1.0 1.0 NaN NaN Only replace the first NaN element within a group along columns. >>> df.groupby("key").ffill(limit=1) A B C 0 NaN 2.0 NaN 1 2.0 3.0 NaN 2 NaN NaN 2.0 3 3.0 NaN 2.0 4 3.0 NaN NaN """ return self._fill("ffill", limit=limit) @final def bfill(self, limit: int | None = None): """ Backward fill the values. Parameters ---------- limit : int, optional Limit of how many values to fill. Returns ------- Series or DataFrame Object with missing values filled. See Also -------- Series.bfill : Backward fill the missing values in the dataset. DataFrame.bfill: Backward fill the missing values in the dataset. Series.fillna: Fill NaN values of a Series. DataFrame.fillna: Fill NaN values of a DataFrame. Examples -------- With Series: >>> index = ["Falcon", "Falcon", "Parrot", "Parrot", "Parrot"] >>> s = pd.Series([None, 1, None, None, 3], index=index) >>> s Falcon NaN Falcon 1.0 Parrot NaN Parrot NaN Parrot 3.0 dtype: float64 >>> s.groupby(level=0).bfill() Falcon 1.0 Falcon 1.0 Parrot 3.0 Parrot 3.0 Parrot 3.0 dtype: float64 >>> s.groupby(level=0).bfill(limit=1) Falcon 1.0 Falcon 1.0 Parrot NaN Parrot 3.0 Parrot 3.0 dtype: float64 With DataFrame: >>> df = pd.DataFrame( ... {"A": [1, None, None, None, 4], "B": [None, None, 5, None, 7]}, ... index=index, ... ) >>> df A B Falcon 1.0 NaN Falcon NaN NaN Parrot NaN 5.0 Parrot NaN NaN Parrot 4.0 7.0 >>> df.groupby(level=0).bfill() A B Falcon 1.0 NaN Falcon NaN NaN Parrot 4.0 5.0 Parrot 4.0 7.0 Parrot 4.0 7.0 >>> df.groupby(level=0).bfill(limit=1) A B Falcon 1.0 NaN Falcon NaN NaN Parrot NaN 5.0 Parrot 4.0 7.0 Parrot 4.0 7.0 """ return self._fill("bfill", limit=limit) @final @property def nth(self) -> GroupByNthSelector: """ Take the nth row from each group if n is an int, otherwise a subset of rows. Can be either a call or an index. dropna is not available with index notation. Index notation accepts a comma separated list of integers and slices. If dropna, will take the nth non-null row, dropna is either 'all' or 'any'; this is equivalent to calling dropna(how=dropna) before the groupby. Returns ------- Series or DataFrame N-th value within each group. See Also -------- Series.nth : Apply function nth to a Series. DataFrame.nth : Apply function nth to each row or column of a DataFrame. Examples -------- >>> df = pd.DataFrame( ... {"A": [1, 1, 2, 1, 2], "B": [np.nan, 2, 3, 4, 5]}, columns=["A", "B"] ... ) >>> g = df.groupby("A") >>> g.nth(0) A B 0 1 NaN 2 2 3.0 >>> g.nth(1) A B 1 1 2.0 4 2 5.0 >>> g.nth(-1) A B 3 1 4.0 4 2 5.0 >>> g.nth([0, 1]) A B 0 1 NaN 1 1 2.0 2 2 3.0 4 2 5.0 >>> g.nth(slice(None, -1)) A B 0 1 NaN 1 1 2.0 2 2 3.0 Index notation may also be used >>> g.nth[0, 1] A B 0 1 NaN 1 1 2.0 2 2 3.0 4 2 5.0 >>> g.nth[:-1] A B 0 1 NaN 1 1 2.0 2 2 3.0 Specifying `dropna` allows ignoring ``NaN`` values >>> g.nth(0, dropna="any") A B 1 1 2.0 2 2 3.0 When the specified ``n`` is larger than any of the groups, an empty DataFrame is returned >>> g.nth(3, dropna="any") Empty DataFrame Columns: [A, B] Index: [] """ return GroupByNthSelector(self) def _nth( self, n: PositionalIndexer | tuple, dropna: Literal["any", "all"] | None = None, ) -> NDFrameT: if not dropna: mask = self._make_mask_from_positional_indexer(n) ids = self._grouper.ids # Drop NA values in grouping mask = mask & (ids != -1) out = self._mask_selected_obj(mask) return out # dropna is truthy if not is_integer(n): raise ValueError("dropna option only supported for an integer argument") if dropna not in ["any", "all"]: # Note: when agg-ing picker doesn't raise this, just returns NaN raise ValueError( "For a DataFrame or Series groupby.nth, dropna must be " "either None, 'any' or 'all', " f"(was passed {dropna})." ) # old behaviour, but with all and any support for DataFrames. # modified in GH 7559 to have better perf n = cast(int, n) dropped = self._selected_obj.dropna(how=dropna, axis=0) # get a new grouper for our dropped obj grouper: np.ndarray | Index | ops.BaseGrouper if len(dropped) == len(self._selected_obj): # Nothing was dropped, can use the same grouper grouper = self._grouper else: # we don't have the grouper info available # (e.g. we have selected out # a column that is not in the current object) axis = self._grouper.axis grouper = self._grouper.codes_info[axis.isin(dropped.index)] if self._grouper.has_dropped_na: # Null groups need to still be encoded as -1 when passed to groupby nulls = grouper == -1 # error: No overload variant of "where" matches argument types # "Any", "NAType", "Any" values = np.where(nulls, NA, grouper) # type: ignore[call-overload] grouper = Index(values, dtype="Int64", copy=False) grb = dropped.groupby(grouper, as_index=self.as_index, sort=self.sort) return grb.nth(n) @final def quantile( self, q: float | AnyArrayLike = 0.5, interpolation: Literal[ "linear", "lower", "higher", "nearest", "midpoint" ] = "linear", numeric_only: bool = False, ): """ Return group values at the given quantile, a la numpy.percentile. Parameters ---------- q : float or array-like, default 0.5 (50% quantile) Value(s) between 0 and 1 providing the quantile(s) to compute. interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} Method to use when the desired quantile falls between two points. numeric_only : bool, default False Include only `float`, `int` or `boolean` data. .. versionchanged:: 2.0.0 numeric_only now defaults to ``False``. Returns ------- Series or DataFrame Return type determined by caller of GroupBy object. See Also -------- Series.quantile : Similar method for Series. DataFrame.quantile : Similar method for DataFrame. numpy.percentile : NumPy method to compute qth percentile. Examples -------- >>> df = pd.DataFrame( ... [["a", 1], ["a", 2], ["a", 3], ["b", 1], ["b", 3], ["b", 5]], ... columns=["key", "val"], ... ) >>> df.groupby("key").quantile() val key a 2.0 b 3.0 """ mgr = self._get_data_to_aggregate(numeric_only=numeric_only, name="quantile") obj = self._wrap_agged_manager(mgr) splitter = self._grouper._get_splitter(obj) sdata = splitter._sorted_data starts, ends = lib.generate_slices(splitter._slabels, splitter.ngroups) def pre_processor(vals: ArrayLike) -> tuple[np.ndarray, DtypeObj | None]: if isinstance(vals.dtype, StringDtype) or is_object_dtype(vals.dtype): raise TypeError( f"dtype '{vals.dtype}' does not support operation 'quantile'" ) inference: DtypeObj | None = None if isinstance(vals, BaseMaskedArray) and is_numeric_dtype(vals.dtype): out = vals.to_numpy(dtype=float, na_value=np.nan) inference = vals.dtype elif is_integer_dtype(vals.dtype): if isinstance(vals, ExtensionArray): out = vals.to_numpy(dtype=float, na_value=np.nan) else: out = vals inference = np.dtype(np.int64) elif is_bool_dtype(vals.dtype) and isinstance(vals, ExtensionArray): out = vals.to_numpy(dtype=float, na_value=np.nan) elif is_bool_dtype(vals.dtype): # GH#51424 remove to match Series/DataFrame behavior raise TypeError("Cannot use quantile with bool dtype") elif needs_i8_conversion(vals.dtype): inference = vals.dtype # In this case we need to delay the casting until after the # np.lexsort below. # error: Incompatible return value type (got # "Tuple[Union[ExtensionArray, ndarray[Any, Any]], Union[Any, # ExtensionDtype]]", expected "Tuple[ndarray[Any, Any], # Optional[Union[dtype[Any], ExtensionDtype]]]") return vals, inference # type: ignore[return-value] elif isinstance(vals, ExtensionArray) and is_float_dtype(vals.dtype): inference = np.dtype(np.float64) out = vals.to_numpy(dtype=float, na_value=np.nan) else: out = np.asarray(vals) return out, inference def post_processor( vals: np.ndarray, inference: DtypeObj | None, result_mask: np.ndarray | None, orig_vals: ArrayLike, ) -> ArrayLike: if inference: # Check for edge case if isinstance(orig_vals, BaseMaskedArray): assert result_mask is not None # for mypy if interpolation in {"linear", "midpoint"} and not is_float_dtype( orig_vals ): return FloatingArray(vals, result_mask) else: # Item "ExtensionDtype" of "Union[ExtensionDtype, str, # dtype[Any], Type[object]]" has no attribute "numpy_dtype" # [union-attr] with warnings.catch_warnings(): # vals.astype with nan can warn with numpy >1.24 warnings.filterwarnings("ignore", category=RuntimeWarning) return type(orig_vals)( vals.astype( inference.numpy_dtype # type: ignore[union-attr] ), result_mask, ) elif not ( is_integer_dtype(inference) and interpolation in {"linear", "midpoint"} ): if needs_i8_conversion(inference): # error: Item "ExtensionArray" of "Union[ExtensionArray, # ndarray[Any, Any]]" has no attribute "_ndarray" vals = vals.astype("i8").view( orig_vals._ndarray.dtype # type: ignore[union-attr] ) # error: Item "ExtensionArray" of "Union[ExtensionArray, # ndarray[Any, Any]]" has no attribute "_from_backing_data" return orig_vals._from_backing_data( # type: ignore[union-attr] vals ) assert isinstance(inference, np.dtype) # for mypy return vals.astype(inference) return vals if is_scalar(q): qs = np.array([q], dtype=np.float64) pass_qs: None | np.ndarray = None else: qs = np.asarray(q, dtype=np.float64) pass_qs = qs ids = self._grouper.ids ngroups = self._grouper.ngroups if self.dropna: # splitter drops NA groups, we need to do the same ids = ids[ids >= 0] nqs = len(qs) func = partial( libgroupby.group_quantile, labels=ids, qs=qs, interpolation=interpolation, starts=starts, ends=ends, ) def blk_func(values: ArrayLike) -> ArrayLike: orig_vals = values if isinstance(values, BaseMaskedArray): mask = values._mask result_mask = np.zeros((ngroups, nqs), dtype=np.bool_) else: mask = isna(values) result_mask = None is_datetimelike = needs_i8_conversion(values.dtype) vals, inference = pre_processor(values) ncols = 1 if vals.ndim == 2: ncols = vals.shape[0] out = np.empty((ncols, ngroups, nqs), dtype=np.float64) if is_datetimelike: vals = vals.view("i8") if vals.ndim == 1: # EA is always 1d func( out[0], values=vals, mask=mask, # type: ignore[arg-type] result_mask=result_mask, is_datetimelike=is_datetimelike, ) else: for i in range(ncols): func( out[i], values=vals[i], mask=mask[i], result_mask=None, is_datetimelike=is_datetimelike, ) if vals.ndim == 1: out = out.ravel("K") # type: ignore[assignment] if result_mask is not None: result_mask = result_mask.ravel("K") # type: ignore[assignment] else: out = out.reshape(ncols, ngroups * nqs) # type: ignore[assignment] return post_processor(out, inference, result_mask, orig_vals) res_mgr = sdata._mgr.grouped_reduce(blk_func) res = self._wrap_agged_manager(res_mgr) return self._wrap_aggregated_output(res, qs=pass_qs) @final def ngroup(self, ascending: bool = True): """ Number each group from 0 to the number of groups - 1. This is the enumerative complement of cumcount. Note that the numbers given to the groups match the order in which the groups would be seen when iterating over the groupby object, not the order they are first observed. Groups with missing keys (where `pd.isna()` is True) will be labeled with `NaN` and will be skipped from the count. Parameters ---------- ascending : bool, default True If False, number in reverse, from number of group - 1 to 0. Returns ------- Series Unique numbers for each group. See Also -------- .cumcount : Number the rows in each group. Examples -------- >>> df = pd.DataFrame({"color": ["red", None, "red", "blue", "blue", "red"]}) >>> df color 0 red 1 NaN 2 red 3 blue 4 blue 5 red >>> df.groupby("color").ngroup() 0 1.0 1 NaN 2 1.0 3 0.0 4 0.0 5 1.0 dtype: float64 >>> df.groupby("color", dropna=False).ngroup() 0 1 1 2 2 1 3 0 4 0 5 1 dtype: int64 >>> df.groupby("color", dropna=False).ngroup(ascending=False) 0 1 1 0 2 1 3 2 4 2 5 1 dtype: int64 """ obj = self._obj_with_exclusions index = obj.index comp_ids = self._grouper.ids dtype: type if self._grouper.has_dropped_na: comp_ids = np.where(comp_ids == -1, np.nan, comp_ids) dtype = np.float64 else: dtype = np.int64 if any(ping._passed_categorical for ping in self._grouper.groupings): # comp_ids reflect non-observed groups, we need only observed comp_ids = rank_1d(comp_ids, ties_method="dense") - 1 result = self._obj_1d_constructor(comp_ids, index, dtype=dtype) if not ascending: result = self.ngroups - 1 - result return result @final def cumcount(self, ascending: bool = True): """ Number each item in each group from 0 to the length of that group - 1. Essentially this is equivalent to .. code-block:: python self.apply(lambda x: pd.Series(np.arange(len(x)), x.index)) Parameters ---------- ascending : bool, default True If False, number in reverse, from length of group - 1 to 0. Returns ------- Series Sequence number of each element within each group. See Also -------- .ngroup : Number the groups themselves. Examples -------- >>> df = pd.DataFrame([["a"], ["a"], ["a"], ["b"], ["b"], ["a"]], columns=["A"]) >>> df A 0 a 1 a 2 a 3 b 4 b 5 a >>> df.groupby("A").cumcount() 0 0 1 1 2 2 3 0 4 1 5 3 dtype: int64 >>> df.groupby("A").cumcount(ascending=False) 0 3 1 2 2 1 3 1 4 0 5 0 dtype: int64 """ index = self._obj_with_exclusions.index cumcounts = self._cumcount_array(ascending=ascending) return self._obj_1d_constructor(cumcounts, index) @final def rank( self, method: str = "average", ascending: bool = True, na_option: str = "keep", pct: bool = False, ) -> NDFrameT: """ Provide the rank of values within each group. Parameters ---------- method : {'average', 'min', 'max', 'first', 'dense'}, default 'average' * average: average rank of group. * min: lowest rank in group. * max: highest rank in group. * first: ranks assigned in order they appear in the array. * dense: like 'min', but rank always increases by 1 between groups. ascending : bool, default True False for ranks by high (1) to low (N). na_option : {'keep', 'top', 'bottom'}, default 'keep' * keep: leave NA values where they are. * top: smallest rank if ascending. * bottom: smallest rank if descending. pct : bool, default False Compute percentage rank of data within each group. Returns ------- DataFrame The ranking of values within each group. See Also -------- Series.rank : Apply function rank to a Series. DataFrame.rank : Apply function rank to each row or column of a DataFrame. Examples -------- >>> df = pd.DataFrame( ... { ... "group": ["a", "a", "a", "a", "a", "b", "b", "b", "b", "b"], ... "value": [2, 4, 2, 3, 5, 1, 2, 4, 1, 5], ... } ... ) >>> df group value 0 a 2 1 a 4 2 a 2 3 a 3 4 a 5 5 b 1 6 b 2 7 b 4 8 b 1 9 b 5 >>> for method in ["average", "min", "max", "dense", "first"]: ... df[f"{method}_rank"] = df.groupby("group")["value"].rank(method) >>> df group value average_rank min_rank max_rank dense_rank first_rank 0 a 2 1.5 1.0 2.0 1.0 1.0 1 a 4 4.0 4.0 4.0 3.0 4.0 2 a 2 1.5 1.0 2.0 1.0 2.0 3 a 3 3.0 3.0 3.0 2.0 3.0 4 a 5 5.0 5.0 5.0 4.0 5.0 5 b 1 1.5 1.0 2.0 1.0 1.0 6 b 2 3.0 3.0 3.0 2.0 3.0 7 b 4 4.0 4.0 4.0 3.0 4.0 8 b 1 1.5 1.0 2.0 1.0 2.0 9 b 5 5.0 5.0 5.0 4.0 5.0 """ if na_option not in {"keep", "top", "bottom"}: msg = "na_option must be one of 'keep', 'top', or 'bottom'" raise ValueError(msg) kwargs = { "ties_method": method, "ascending": ascending, "na_option": na_option, "pct": pct, } return self._cython_transform( "rank", numeric_only=False, **kwargs, ) @final def cumprod(self, numeric_only: bool = False, *args, **kwargs) -> NDFrameT: """ Cumulative product for each group. Parameters ---------- numeric_only : bool, default False Include only float, int, boolean columns. *args : tuple Positional arguments to be passed to `func`. **kwargs : dict Additional/specific keyword arguments to be passed to the function, such as `numeric_only` and `skipna`. Returns ------- Series or DataFrame Cumulative product for each group. Same object type as the caller. See Also -------- Series.cumprod : Apply function cumprod to a Series. DataFrame.cumprod : Apply function cumprod to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b"] >>> ser = pd.Series([6, 2, 0], index=lst) >>> ser a 6 a 2 b 0 dtype: int64 >>> ser.groupby(level=0).cumprod() a 6 a 12 b 0 dtype: int64 For DataFrameGroupBy: >>> data = [[1, 8, 2], [1, 2, 5], [2, 6, 9]] >>> df = pd.DataFrame( ... data, columns=["a", "b", "c"], index=["cow", "horse", "bull"] ... ) >>> df a b c cow 1 8 2 horse 1 2 5 bull 2 6 9 >>> df.groupby("a").groups {1: ['cow', 'horse'], 2: ['bull']} >>> df.groupby("a").cumprod() b c cow 8 2 horse 16 10 bull 6 9 """ nv.validate_groupby_func("cumprod", args, kwargs, ["skipna"]) return self._cython_transform("cumprod", numeric_only, **kwargs) @final def cumsum(self, numeric_only: bool = False, *args, **kwargs) -> NDFrameT: """ Cumulative sum for each group. Parameters ---------- numeric_only : bool, default False Include only float, int, boolean columns. *args : tuple Positional arguments to be passed to `func`. **kwargs : dict Additional/specific keyword arguments to be passed to the function, such as `numeric_only` and `skipna`. Returns ------- Series or DataFrame Cumulative sum for each group. Same object type as the caller. See Also -------- Series.cumsum : Apply function cumsum to a Series. DataFrame.cumsum : Apply function cumsum to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b"] >>> ser = pd.Series([6, 2, 0], index=lst) >>> ser a 6 a 2 b 0 dtype: int64 >>> ser.groupby(level=0).cumsum() a 6 a 8 b 0 dtype: int64 For DataFrameGroupBy: >>> data = [[1, 8, 2], [1, 2, 5], [2, 6, 9]] >>> df = pd.DataFrame( ... data, columns=["a", "b", "c"], index=["fox", "gorilla", "lion"] ... ) >>> df a b c fox 1 8 2 gorilla 1 2 5 lion 2 6 9 >>> df.groupby("a").groups {1: ['fox', 'gorilla'], 2: ['lion']} >>> df.groupby("a").cumsum() b c fox 8 2 gorilla 10 7 lion 6 9 """ nv.validate_groupby_func("cumsum", args, kwargs, ["skipna"]) return self._cython_transform("cumsum", numeric_only, **kwargs) @final def cummin( self, numeric_only: bool = False, **kwargs, ) -> NDFrameT: """ Cumulative min for each group. Parameters ---------- numeric_only : bool, default False Include only `float`, `int` or `boolean` data. **kwargs : dict, optional Additional keyword arguments to be passed to the function, such as `skipna`, to control whether NA/null values are ignored. Returns ------- Series or DataFrame Cumulative min for each group. Same object type as the caller. See Also -------- Series.cummin : Apply function cummin to a Series. DataFrame.cummin : Apply function cummin to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "a", "b", "b", "b"] >>> ser = pd.Series([1, 6, 2, 3, 0, 4], index=lst) >>> ser a 1 a 6 a 2 b 3 b 0 b 4 dtype: int64 >>> ser.groupby(level=0).cummin() a 1 a 1 a 1 b 3 b 0 b 0 dtype: int64 For DataFrameGroupBy: >>> data = [[1, 0, 2], [1, 1, 5], [6, 6, 9]] >>> df = pd.DataFrame( ... data, columns=["a", "b", "c"], index=["snake", "rabbit", "turtle"] ... ) >>> df a b c snake 1 0 2 rabbit 1 1 5 turtle 6 6 9 >>> df.groupby("a").groups {1: ['snake', 'rabbit'], 6: ['turtle']} >>> df.groupby("a").cummin() b c snake 0 2 rabbit 0 2 turtle 6 9 """ skipna = kwargs.get("skipna", True) return self._cython_transform( "cummin", numeric_only=numeric_only, skipna=skipna ) @final def cummax( self, numeric_only: bool = False, **kwargs, ) -> NDFrameT: """ Cumulative max for each group. Returns the cumulative maximum of values within each group. The result has the same size as the input, with each element representing the maximum of all preceding elements (including itself) within its group. Parameters ---------- numeric_only : bool, default False Include only `float`, `int` or `boolean` data. **kwargs : dict, optional Additional keyword arguments to be passed to the function, such as `skipna`, to control whether NA/null values are ignored. Returns ------- Series or DataFrame Cumulative max for each group. Same object type as the caller. See Also -------- Series.cummax : Apply function cummax to a Series. DataFrame.cummax : Apply function cummax to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "a", "b", "b", "b"] >>> ser = pd.Series([1, 6, 2, 3, 1, 4], index=lst) >>> ser a 1 a 6 a 2 b 3 b 1 b 4 dtype: int64 >>> ser.groupby(level=0).cummax() a 1 a 6 a 6 b 3 b 3 b 4 dtype: int64 For DataFrameGroupBy: >>> data = [[1, 8, 2], [1, 1, 0], [2, 6, 9]] >>> df = pd.DataFrame( ... data, columns=["a", "b", "c"], index=["cow", "horse", "bull"] ... ) >>> df a b c cow 1 8 2 horse 1 1 0 bull 2 6 9 >>> df.groupby("a").groups {1: ['cow', 'horse'], 2: ['bull']} >>> df.groupby("a").cummax() b c cow 8 2 horse 8 2 bull 6 9 """ skipna = kwargs.get("skipna", True) return self._cython_transform( "cummax", numeric_only=numeric_only, skipna=skipna ) @final def shift( self, periods: int | Sequence[int] = 1, freq=None, fill_value=lib.no_default, suffix: str | None = None, ): """ Shift each group by periods observations. If freq is passed, the index will be increased using the periods and the freq. Parameters ---------- periods : int | Sequence[int], default 1 Number of periods to shift. If a list of values, shift each group by each period. freq : str, optional Frequency string. fill_value : optional The scalar value to use for newly introduced missing values. .. versionchanged:: 2.1.0 Will raise a ``ValueError`` if ``freq`` is provided too. suffix : str, optional A string to add to each shifted column if there are multiple periods. Ignored otherwise. Returns ------- Series or DataFrame Object shifted within each group. See Also -------- Index.shift : Shift values of Index. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b", "b"] >>> ser = pd.Series([1, 2, 3, 4], index=lst) >>> ser a 1 a 2 b 3 b 4 dtype: int64 >>> ser.groupby(level=0).shift(1) a NaN a 1.0 b NaN b 3.0 dtype: float64 For DataFrameGroupBy: >>> data = [[1, 2, 3], [1, 5, 6], [2, 5, 8], [2, 6, 9]] >>> df = pd.DataFrame( ... data, ... columns=["a", "b", "c"], ... index=["tuna", "salmon", "catfish", "goldfish"], ... ) >>> df a b c tuna 1 2 3 salmon 1 5 6 catfish 2 5 8 goldfish 2 6 9 >>> df.groupby("a").shift(1) b c tuna NaN NaN salmon 2.0 3.0 catfish NaN NaN goldfish 5.0 8.0 """ if is_list_like(periods): periods = cast(Sequence, periods) if len(periods) == 0: raise ValueError("If `periods` is an iterable, it cannot be empty.") from pandas.core.reshape.concat import concat add_suffix = True else: if not is_integer(periods): raise TypeError( f"Periods must be integer, but {periods} is {type(periods)}." ) if suffix: raise ValueError("Cannot specify `suffix` if `periods` is an int.") periods = [cast(int, periods)] add_suffix = False shifted_dataframes = [] for period in periods: if not is_integer(period): raise TypeError( f"Periods must be integer, but {period} is {type(period)}." ) period = cast(int, period) if freq is not None: f = lambda x: x.shift( period, freq, 0, # axis fill_value, ) shifted = self._python_apply_general( f, self._selected_obj, is_transform=True ) else: if fill_value is lib.no_default: fill_value = None ids = self._grouper.ids ngroups = self._grouper.ngroups res_indexer = np.zeros(len(ids), dtype=np.int64) libgroupby.group_shift_indexer(res_indexer, ids, ngroups, period) obj = self._obj_with_exclusions shifted = obj._reindex_with_indexers( {0: (obj.index, res_indexer)}, fill_value=fill_value, allow_dups=True, ) if add_suffix: if isinstance(shifted, Series): shifted = cast(NDFrameT, shifted.to_frame()) shifted = shifted.add_suffix( f"{suffix}_{period}" if suffix else f"_{period}" ) shifted_dataframes.append(cast(Union[Series, DataFrame], shifted)) return ( shifted_dataframes[0] if len(shifted_dataframes) == 1 else concat(shifted_dataframes, axis=1, sort=False) ) @final def diff( self, periods: int = 1, ) -> NDFrameT: """ First discrete difference of element. Calculates the difference of each element compared with another element in the group (default is element in previous row). Parameters ---------- periods : int, default 1 Periods to shift for calculating difference, accepts negative values. Returns ------- Series or DataFrame First differences. See Also -------- Series.diff : Apply function diff to a Series. DataFrame.diff : Apply function diff to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "a", "b", "b", "b"] >>> ser = pd.Series([7, 2, 8, 4, 3, 3], index=lst) >>> ser a 7 a 2 a 8 b 4 b 3 b 3 dtype: int64 >>> ser.groupby(level=0).diff() a NaN a -5.0 a 6.0 b NaN b -1.0 b 0.0 dtype: float64 For DataFrameGroupBy: >>> data = {"a": [1, 3, 5, 7, 7, 8, 3], "b": [1, 4, 8, 4, 4, 2, 1]} >>> df = pd.DataFrame( ... data, index=["dog", "dog", "dog", "mouse", "mouse", "mouse", "mouse"] ... ) >>> df a b dog 1 1 dog 3 4 dog 5 8 mouse 7 4 mouse 7 4 mouse 8 2 mouse 3 1 >>> df.groupby(level=0).diff() a b dog NaN NaN dog 2.0 3.0 dog 2.0 4.0 mouse NaN NaN mouse 0.0 0.0 mouse 1.0 -2.0 mouse -5.0 -1.0 """ obj = self._obj_with_exclusions shifted = self.shift(periods=periods) # GH45562 - to retain existing behavior and match behavior of Series.diff(), # int8 and int16 are coerced to float32 rather than float64. dtypes_to_f32 = ["int8", "int16"] if obj.ndim == 1: if obj.dtype in dtypes_to_f32: shifted = shifted.astype("float32") else: to_coerce = [c for c, dtype in obj.dtypes.items() if dtype in dtypes_to_f32] if to_coerce: shifted = shifted.astype(dict.fromkeys(to_coerce, "float32")) return obj - shifted @final def pct_change( self, periods: int = 1, fill_method: None = None, freq=None, ): """ Calculate pct_change of each value to previous entry in group. Parameters ---------- periods : int, default 1 Periods to shift for calculating percentage change. Comparing with a period of 1 means adjacent elements are compared, whereas a period of 2 compares every other element. fill_method : None Must be None. This argument will be removed in a future version of pandas. freq : str, pandas offset object, or None, default None The frequency increment for time series data (e.g., 'M' for month-end). If None, the frequency is inferred from the index. Relevant for time series data only. Returns ------- Series or DataFrame Percentage changes within each group. See Also -------- Series.pct_change : Apply function pct_change to a Series. DataFrame.pct_change : Apply function pct_change to each row or column of a DataFrame. Examples -------- For SeriesGroupBy: >>> lst = ["a", "a", "b", "b"] >>> ser = pd.Series([1, 2, 3, 4], index=lst) >>> ser a 1 a 2 b 3 b 4 dtype: int64 >>> ser.groupby(level=0).pct_change() a NaN a 1.000000 b NaN b 0.333333 dtype: float64 For DataFrameGroupBy: >>> data = [[1, 2, 3], [1, 5, 6], [2, 5, 8], [2, 6, 9]] >>> df = pd.DataFrame( ... data, ... columns=["a", "b", "c"], ... index=["tuna", "salmon", "catfish", "goldfish"], ... ) >>> df a b c tuna 1 2 3 salmon 1 5 6 catfish 2 5 8 goldfish 2 6 9 >>> df.groupby("a").pct_change() b c tuna NaN NaN salmon 1.5 1.000 catfish NaN NaN goldfish 0.2 0.125 """ # GH#53491 if fill_method is not None: raise ValueError(f"fill_method must be None; got {fill_method=}.") # TODO(GH#23918): Remove this conditional for SeriesGroupBy when # GH#23918 is fixed if freq is not None: f = lambda x: x.pct_change( periods=periods, freq=freq, axis=0, ) return self._python_apply_general(f, self._selected_obj, is_transform=True) if fill_method is None: # GH30463 op = "ffill" else: op = fill_method filled = getattr(self, op)(limit=0) fill_grp = filled.groupby(self._grouper.codes, group_keys=self.group_keys) shifted = fill_grp.shift(periods=periods, freq=freq) return (filled / shifted) - 1 @final def head(self, n: int = 5) -> NDFrameT: """ Return first n rows of each group. Similar to ``.apply(lambda x: x.head(n))``, but it returns a subset of rows from the original DataFrame with original index and order preserved (``as_index`` flag is ignored). Parameters ---------- n : int If positive: number of entries to include from start of each group. If negative: number of entries to exclude from end of each group. Returns ------- Series or DataFrame Subset of original Series or DataFrame as determined by n. See Also -------- Series.head : Apply function head to a Series. DataFrame.head : Apply function head to each row or column of a DataFrame. Examples -------- >>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=["A", "B"]) >>> df.groupby("A").head(1) A B 0 1 2 2 5 6 >>> df.groupby("A").head(-1) A B 0 1 2 """ mask = self._make_mask_from_positional_indexer(slice(None, n)) return self._mask_selected_obj(mask) @final def tail(self, n: int = 5) -> NDFrameT: """ Return last n rows of each group. Similar to ``.apply(lambda x: x.tail(n))``, but it returns a subset of rows from the original DataFrame with original index and order preserved (``as_index`` flag is ignored). Parameters ---------- n : int If positive: number of entries to include from end of each group. If negative: number of entries to exclude from start of each group. Returns ------- Series or DataFrame Subset of original Series or DataFrame as determined by n. See Also -------- Series.tail : Apply function tail to a Series. DataFrame.tail : Apply function tail to each row or column of a DataFrame. Examples -------- >>> df = pd.DataFrame( ... [["a", 1], ["a", 2], ["b", 1], ["b", 2]], columns=["A", "B"] ... ) >>> df.groupby("A").tail(1) A B 1 a 2 3 b 2 >>> df.groupby("A").tail(-1) A B 1 a 2 3 b 2 """ if n: mask = self._make_mask_from_positional_indexer(slice(-n, None)) else: mask = self._make_mask_from_positional_indexer([]) return self._mask_selected_obj(mask) @final def _mask_selected_obj(self, mask: npt.NDArray[np.bool_]) -> NDFrameT: """ Return _selected_obj with mask applied. Parameters ---------- mask : np.ndarray[bool] Boolean mask to apply. Returns ------- Series or DataFrame Filtered _selected_obj. """ ids = self._grouper.ids mask = mask & (ids != -1) return self._selected_obj[mask] @final def sample( self, n: int | None = None, frac: float | None = None, replace: bool = False, weights: Sequence | Series | None = None, random_state: RandomState | None = None, ): """ Return a random sample of items from each group. You can use `random_state` for reproducibility. Parameters ---------- n : int, optional Number of items to return for each group. Cannot be used with `frac` and must be no larger than the smallest group unless `replace` is True. Default is one if `frac` is None. frac : float, optional Fraction of items to return. Cannot be used with `n`. replace : bool, default False Allow or disallow sampling of the same row more than once. weights : list-like, optional Default None results in equal probability weighting. If passed a list-like then values must have the same length as the underlying DataFrame or Series object and will be used as sampling probabilities after normalization within each group. Values must be non-negative with at least one positive element within each group. random_state : int, array-like, BitGenerator, np.random.RandomState, np.random.Generator, optional If int, array-like, or BitGenerator, seed for random number generator. If np.random.RandomState or np.random.Generator, use as given. Default ``None`` results in sampling with the current state of np.random. Returns ------- Series or DataFrame A new object of same type as caller containing items randomly sampled within each group from the caller object. See Also -------- DataFrame.sample: Generate random samples from a DataFrame object. Series.sample: Generate random samples from a Series object. numpy.random.choice: Generate a random sample from a given 1-D numpy array. Examples -------- >>> df = pd.DataFrame( ... {"a": ["red"] * 2 + ["blue"] * 2 + ["black"] * 2, "b": range(6)} ... ) >>> df a b 0 red 0 1 red 1 2 blue 2 3 blue 3 4 black 4 5 black 5 Select one row at random for each distinct value in column a. The `random_state` argument can be used to guarantee reproducibility: >>> df.groupby("a").sample(n=1, random_state=1) a b 4 black 4 2 blue 2 1 red 1 Set `frac` to sample fixed proportions rather than counts: >>> df.groupby("a")["b"].sample(frac=0.5, random_state=2) 5 5 2 2 0 0 Name: b, dtype: int64 Control sample probabilities within groups by setting weights: >>> df.groupby("a").sample( ... n=1, ... weights=[1, 1, 1, 0, 0, 1], ... random_state=1, ... ) a b 5 black 5 2 blue 2 0 red 0 """ # noqa: E501 if self._selected_obj.empty: # GH48459 prevent ValueError when object is empty return self._selected_obj size = sample.process_sampling_size(n, frac, replace) if weights is not None: weights_arr = sample.preprocess_weights(self._selected_obj, weights, axis=0) random_state = com.random_state(random_state) group_iterator = self._grouper.get_iterator(self._selected_obj) sampled_indices = [] for labels, obj in group_iterator: grp_indices = self.indices[labels] group_size = len(grp_indices) if size is not None: sample_size = size else: assert frac is not None sample_size = round(frac * group_size) grp_sample = sample.sample( group_size, size=sample_size, replace=replace, weights=None if weights is None else weights_arr[grp_indices], random_state=random_state, ) sampled_indices.append(grp_indices[grp_sample]) sampled_indices = np.concatenate(sampled_indices) return self._selected_obj.take(sampled_indices, axis=0) def _idxmax_idxmin( self, how: Literal["idxmax", "idxmin"], ignore_unobserved: bool = False, skipna: bool = True, numeric_only: bool = False, ) -> NDFrameT: """Compute idxmax/idxmin. Parameters ---------- how : {'idxmin', 'idxmax'} Whether to compute idxmin or idxmax. numeric_only : bool, default False Include only float, int, boolean columns. skipna : bool, default True Exclude NA/null values. If an entire group is NA, the result will be NA. ignore_unobserved : bool, default False When True and an unobserved group is encountered, do not raise. This used for transform where unobserved groups do not play an impact on the result. Returns ------- Series or DataFrame idxmax or idxmin for the groupby operation. """ if not self.observed and any( ping._passed_categorical for ping in self._grouper.groupings ): expected_len = len(self._grouper.result_index) # TODO: Better way to find # of observed groups? group_sizes = self._grouper.size() result_len = group_sizes[group_sizes > 0].shape[0] assert result_len <= expected_len has_unobserved = result_len < expected_len raise_err: bool | np.bool_ = not ignore_unobserved and has_unobserved # Only raise an error if there are columns to compute; otherwise we return # an empty DataFrame with an index (possibly including unobserved) but no # columns data = self._obj_with_exclusions if raise_err and isinstance(data, DataFrame): if numeric_only: data = data._get_numeric_data() raise_err = len(data.columns) > 0 if raise_err: raise ValueError( f"Can't get {how} of an empty group due to unobserved categories. " "Specify observed=True in groupby instead." ) elif not skipna and self._obj_with_exclusions.isna().any(axis=None): raise ValueError(f"{how} with skipna=False encountered an NA value.") result = self._agg_general( numeric_only=numeric_only, min_count=1, alias=how, skipna=skipna, ) return result def _wrap_idxmax_idxmin( self, res: NDFrameT, how: Literal["idxmax", "idxmin"], skipna: bool ) -> NDFrameT: index = self.obj.index if res.size == 0: result = res.astype(index.dtype) elif skipna and res.lt(0).any(axis=None): raise ValueError( f"{how} with skipna=True encountered all NA values in a group." ) else: if isinstance(index, MultiIndex): index = index.to_flat_index() values = res._values assert isinstance(values, np.ndarray) na_value = na_value_for_dtype(index.dtype, compat=False) if isinstance(res, Series): # mypy: expression has type "Series", variable has type "NDFrameT" result = res._constructor( # type: ignore[assignment] index.array.take(values, allow_fill=True, fill_value=na_value), index=res.index, name=res.name, ) else: data = {} for k, column_values in enumerate(values.T): data[k] = index.array.take( column_values, allow_fill=True, fill_value=na_value ) result = self.obj._constructor(data, index=res.index) result.columns = res.columns return result def get_groupby( obj: NDFrame, by: _KeysArgType | None = None, grouper: ops.BaseGrouper | None = None, group_keys: bool = True, ) -> GroupBy: """ Class for grouping and aggregating relational data. See aggregate, transform, and apply functions on this object. It's easiest to use obj.groupby(...) to use GroupBy, but you can also do: :: grouped = groupby(obj, ...) Parameters ---------- obj : pandas object level : int, default None Level of MultiIndex groupings : list of Grouping objects Most users should ignore this exclusions : array-like, optional List of columns to exclude name : str Most users should ignore this Returns ------- **Attributes** groups : dict {group name -> group labels} len(grouped) : int Number of groups Notes ----- After grouping, see aggregate, apply, and transform functions. Here are some other brief notes about usage. When grouping by multiple groups, the result index will be a MultiIndex (hierarchical) by default. Iteration produces (key, group) tuples, i.e. chunking the data by group. So you can write code like: :: grouped = obj.groupby(keys) for key, group in grouped: # do something with the data Function calls on GroupBy, if not specially implemented, "dispatch" to the grouped data. So if you group a DataFrame and wish to invoke the std() method on each group, you can simply do: :: df.groupby(mapper).std() rather than :: df.groupby(mapper).aggregate(np.std) You can pass arguments to these "wrapped" functions, too. See the online documentation for full exposition on these topics and much more """ if isinstance(obj, Series): from pandas.core.groupby.generic import SeriesGroupBy return SeriesGroupBy( obj=obj, keys=by, grouper=grouper, group_keys=group_keys, ) elif isinstance(obj, DataFrame): from pandas.core.groupby.generic import DataFrameGroupBy return DataFrameGroupBy( obj=obj, keys=by, grouper=grouper, group_keys=group_keys, ) else: # pragma: no cover raise TypeError(f"invalid type: {obj}") def _insert_quantile_level(idx: Index, qs: npt.NDArray[np.float64]) -> MultiIndex: """ Insert the sequence 'qs' of quantiles as the inner-most level of a MultiIndex. The quantile level in the MultiIndex is a repeated copy of 'qs'. Parameters ---------- idx : Index qs : np.ndarray[float64] Returns ------- MultiIndex """ nqs = len(qs) lev_codes, lev = Index(qs, copy=False).factorize() lev_codes = coerce_indexer_dtype(lev_codes, lev) if idx._is_multi: idx = cast(MultiIndex, idx) levels = [*idx.levels, lev] codes = [np.repeat(x, nqs) for x in idx.codes] + [np.tile(lev_codes, len(idx))] mi = MultiIndex(levels=levels, codes=codes, names=[*idx.names, None]) else: nidx = len(idx) idx_codes = coerce_indexer_dtype(np.arange(nidx), idx) levels = [idx, lev] codes = [np.repeat(idx_codes, nqs), np.tile(lev_codes, nidx)] mi = MultiIndex(levels=levels, codes=codes, names=[idx.name, None]) return mi