generic.py

"""
Define the SeriesGroupBy and DataFrameGroupBy
classes that hold the groupby interfaces (and some implementations).

These are user facing as the result of the ``df.groupby(...)`` operations,
which here returns a DataFrameGroupBy object.
"""
from __future__ import annotations

from collections import (
    abc,
    namedtuple,
)
import copy
from functools import partial
from textwrap import dedent
from typing import (
    Any,
    Callable,
    Hashable,
    Iterable,
    Mapping,
    TypeVar,
    Union,
    cast,
)
import warnings

import numpy as np

from pandas._libs import (
    lib,
    reduction as libreduction,
)
from pandas._typing import (
    ArrayLike,
    FrameOrSeries,
    FrameOrSeriesUnion,
    Manager2D,
)
from pandas.util._decorators import (
    Appender,
    Substitution,
    doc,
)

from pandas.core.dtypes.cast import (
    find_common_type,
    maybe_downcast_numeric,
)
from pandas.core.dtypes.common import (
    ensure_int64,
    is_bool,
    is_categorical_dtype,
    is_dict_like,
    is_integer_dtype,
    is_interval_dtype,
    is_numeric_dtype,
    is_scalar,
)
from pandas.core.dtypes.missing import (
    isna,
    notna,
)

from pandas.core import (
    algorithms,
    nanops,
)
from pandas.core.aggregation import (
    maybe_mangle_lambdas,
    reconstruct_func,
    validate_func_kwargs,
)
from pandas.core.apply import GroupByApply
from pandas.core.arrays import Categorical
from pandas.core.base import (
    DataError,
    SpecificationError,
)
import pandas.core.common as com
from pandas.core.construction import create_series_with_explicit_dtype
from pandas.core.frame import DataFrame
from pandas.core.generic import NDFrame
from pandas.core.groupby import base
from pandas.core.groupby.groupby import (
    GroupBy,
    _agg_template,
    _apply_docs,
    _transform_template,
    get_groupby,
    group_selection_context,
)
from pandas.core.indexes.api import (
    Index,
    MultiIndex,
    all_indexes_same,
)
import pandas.core.indexes.base as ibase
from pandas.core.internals import ArrayManager
from pandas.core.series import Series
from pandas.core.util.numba_ import maybe_use_numba

from pandas.plotting import boxplot_frame_groupby

NamedAgg = namedtuple("NamedAgg", ["column", "aggfunc"])
# TODO(typing) the return value on this callable should be any *scalar*.
AggScalar = Union[str, Callable[..., Any]]
# TODO: validate types on ScalarResult and move to _typing
# Blocked from using by https://github.com/python/mypy/issues/1484
# See note at _mangle_lambda_list
ScalarResult = TypeVar("ScalarResult")


def generate_property(name: str, klass: type[FrameOrSeries]):
    """
    Create a property for a GroupBy subclass to dispatch to DataFrame/Series.

    Parameters
    ----------
    name : str
    klass : {DataFrame, Series}

    Returns
    -------
    property
    """

    def prop(self):
        return self._make_wrapper(name)

    parent_method = getattr(klass, name)
    prop.__doc__ = parent_method.__doc__ or ""
    prop.__name__ = name
    return property(prop)


def pin_allowlisted_properties(klass: type[FrameOrSeries], allowlist: frozenset[str]):
    """
    Create GroupBy member defs for DataFrame/Series names in a allowlist.

    Parameters
    ----------
    klass : DataFrame or Series class
        class where members are defined.
    allowlist : frozenset[str]
        Set of names of klass methods to be constructed

    Returns
    -------
    class decorator

    Notes
    -----
    Since we don't want to override methods explicitly defined in the
    base class, any such name is skipped.
    """

    def pinner(cls):
        for name in allowlist:
            if hasattr(cls, name):
                # don't override anything that was explicitly defined
                #  in the base class
                continue

            prop = generate_property(name, klass)
            setattr(cls, name, prop)

        return cls

    return pinner


@pin_allowlisted_properties(Series, base.series_apply_allowlist)
class SeriesGroupBy(GroupBy[Series]):
    _apply_allowlist = base.series_apply_allowlist

    def _iterate_slices(self) -> Iterable[Series]:
        yield self._selected_obj

    @property
    def _selection_name(self):
        """
        since we are a series, we by definition only have
        a single name, but may be the result of a selection or
        the name of our object
        """
        if self._selection is None:
            return self.obj.name
        else:
            return self._selection

    _agg_examples_doc = dedent(
        """
    Examples
    --------
    >>> s = pd.Series([1, 2, 3, 4])

    >>> s
    0    1
    1    2
    2    3
    3    4
    dtype: int64

    >>> s.groupby([1, 1, 2, 2]).min()
    1    1
    2    3
    dtype: int64

    >>> s.groupby([1, 1, 2, 2]).agg('min')
    1    1
    2    3
    dtype: int64

    >>> s.groupby([1, 1, 2, 2]).agg(['min', 'max'])
       min  max
    1    1    2
    2    3    4

    The output column names can be controlled by passing
    the desired column names and aggregations as keyword arguments.

    >>> s.groupby([1, 1, 2, 2]).agg(
    ...     minimum='min',
    ...     maximum='max',
    ... )
       minimum  maximum
    1        1        2
    2        3        4"""
    )

    @Appender(
        _apply_docs["template"].format(
            input="series", examples=_apply_docs["series_examples"]
        )
    )
    def apply(self, func, *args, **kwargs):
        return super().apply(func, *args, **kwargs)

    @doc(_agg_template, examples=_agg_examples_doc, klass="Series")
    def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs):

        if maybe_use_numba(engine):
            with group_selection_context(self):
                data = self._selected_obj
            result, index = self._aggregate_with_numba(
                data.to_frame(), func, *args, engine_kwargs=engine_kwargs, **kwargs
            )
            return self.obj._constructor(result.ravel(), index=index, name=data.name)

        relabeling = func is None
        columns = None
        if relabeling:
            columns, func = validate_func_kwargs(kwargs)
            kwargs = {}

        if isinstance(func, str):
            return getattr(self, func)(*args, **kwargs)

        elif isinstance(func, abc.Iterable):
            # Catch instances of lists / tuples
            # but not the class list / tuple itself.
            func = maybe_mangle_lambdas(func)
            ret = self._aggregate_multiple_funcs(func)
            if relabeling:
                ret.columns = columns
        else:
            cyfunc = com.get_cython_func(func)
            if cyfunc and not args and not kwargs:
                return getattr(self, cyfunc)()

            if self.grouper.nkeys > 1:
                return self._python_agg_general(func, *args, **kwargs)

            try:
                return self._python_agg_general(func, *args, **kwargs)
            except (ValueError, KeyError):
                # TODO: KeyError is raised in _python_agg_general,
                #  see test_groupby.test_basic
                result = self._aggregate_named(func, *args, **kwargs)

            index = Index(sorted(result), name=self.grouper.names[0])
            ret = create_series_with_explicit_dtype(
                result, index=index, dtype_if_empty=object
            )

        if not self.as_index:  # pragma: no cover
            print("Warning, ignoring as_index=True")

        if isinstance(ret, dict):
            from pandas import concat

            ret = concat(ret.values(), axis=1, keys=[key.label for key in ret.keys()])
        return ret

    agg = aggregate

    def _aggregate_multiple_funcs(self, arg):
        if isinstance(arg, dict):

            # show the deprecation, but only if we
            # have not shown a higher level one
            # GH 15931
            if isinstance(self._selected_obj, Series):
                raise SpecificationError("nested renamer is not supported")

            columns = list(arg.keys())
            arg = arg.items()
        elif any(isinstance(x, (tuple, list)) for x in arg):
            arg = [(x, x) if not isinstance(x, (tuple, list)) else x for x in arg]

            # indicated column order
            columns = next(zip(*arg))
        else:
            # list of functions / function names
            columns = []
            for f in arg:
                columns.append(com.get_callable_name(f) or f)

            arg = zip(columns, arg)

        results: dict[base.OutputKey, FrameOrSeriesUnion] = {}
        for idx, (name, func) in enumerate(arg):
            obj = self

            # reset the cache so that we
            # only include the named selection
            if name in self._selected_obj:
                obj = copy.copy(obj)
                obj._reset_cache()
                obj._selection = name
            results[base.OutputKey(label=name, position=idx)] = obj.aggregate(func)

        if any(isinstance(x, DataFrame) for x in results.values()):
            # let higher level handle
            return results

        indexed_output = {key.position: val for key, val in results.items()}
        output = self.obj._constructor_expanddim(indexed_output, index=None)
        output.columns = Index(key.label for key in results)

        output = self._reindex_output(output)
        return output

    def _cython_agg_general(
        self, how: str, alt=None, numeric_only: bool = True, min_count: int = -1
    ):
        output: dict[base.OutputKey, ArrayLike] = {}
        # Ideally we would be able to enumerate self._iterate_slices and use
        # the index from enumeration as the key of output, but ohlc in particular
        # returns a (n x 4) array. Output requires 1D ndarrays as values, so we
        # need to slice that up into 1D arrays
        idx = 0
        for obj in self._iterate_slices():
            name = obj.name
            is_numeric = is_numeric_dtype(obj.dtype)
            if numeric_only and not is_numeric:
                continue

            result = self.grouper._cython_operation(
                "aggregate", obj._values, how, axis=0, min_count=min_count
            )
            assert result.ndim == 1
            key = base.OutputKey(label=name, position=idx)
            output[key] = result
            idx += 1

        if not output:
            raise DataError("No numeric types to aggregate")

        return self._wrap_aggregated_output(output)

    def _wrap_aggregated_output(
        self,
        output: Mapping[base.OutputKey, Series | ArrayLike],
    ) -> Series:
        """
        Wraps the output of a SeriesGroupBy aggregation into the expected result.

        Parameters
        ----------
        output : Mapping[base.OutputKey, Union[Series, ArrayLike]]
            Data to wrap.

        Returns
        -------
        Series

        Notes
        -----
        In the vast majority of cases output will only contain one element.
        The exception is operations that expand dimensions, like ohlc.
        """
        assert len(output) == 1

        name = self.obj.name
        index = self.grouper.result_index
        values = list(output.values())[0]

        result = self.obj._constructor(values, index=index, name=name)
        return self._reindex_output(result)

    def _wrap_transformed_output(
        self, output: Mapping[base.OutputKey, Series | ArrayLike]
    ) -> Series:
        """
        Wraps the output of a SeriesGroupBy aggregation into the expected result.

        Parameters
        ----------
        output : dict[base.OutputKey, Union[Series, np.ndarray, ExtensionArray]]
            Dict with a sole key of 0 and a value of the result values.

        Returns
        -------
        Series

        Notes
        -----
        output should always contain one element. It is specified as a dict
        for consistency with DataFrame methods and _wrap_aggregated_output.
        """
        assert len(output) == 1

        name = self.obj.name
        values = list(output.values())[0]
        result = self.obj._constructor(values, index=self.obj.index, name=name)

        # No transformations increase the ndim of the result
        assert isinstance(result, Series)
        return result

    def _wrap_applied_output(
        self,
        data: Series,
        keys: Index,
        values: list[Any] | None,
        not_indexed_same: bool = False,
    ) -> FrameOrSeriesUnion:
        """
        Wrap the output of SeriesGroupBy.apply into the expected result.

        Parameters
        ----------
        data : Series
            Input data for groupby operation.
        keys : Index
            Keys of groups that Series was grouped by.
        values : Optional[List[Any]]
            Applied output for each group.
        not_indexed_same : bool, default False
            Whether the applied outputs are not indexed the same as the group axes.

        Returns
        -------
        DataFrame or Series
        """
        if len(keys) == 0:
            # GH #6265
            return self.obj._constructor(
                [],
                name=self._selection_name,
                index=self.grouper.result_index,
                dtype=data.dtype,
            )
        assert values is not None

        def _get_index() -> Index:
            if self.grouper.nkeys > 1:
                index = MultiIndex.from_tuples(keys, names=self.grouper.names)
            else:
                index = Index(keys, name=self.grouper.names[0])
            return index

        if isinstance(values[0], dict):
            # GH #823 #24880
            index = _get_index()
            result: FrameOrSeriesUnion = self._reindex_output(
                self.obj._constructor_expanddim(values, index=index)
            )
            # if self.observed is False,
            # keep all-NaN rows created while re-indexing
            result = result.stack(dropna=self.observed)
            result.name = self._selection_name
            return result
        elif isinstance(values[0], (Series, DataFrame)):
            return self._concat_objects(keys, values, not_indexed_same=not_indexed_same)
        else:
            # GH #6265 #24880
            result = self.obj._constructor(
                data=values, index=_get_index(), name=self._selection_name
            )
            return self._reindex_output(result)

    def _aggregate_named(self, func, *args, **kwargs):
        result = {}
        initialized = False

        for name, group in self:
            # Each step of this loop corresponds to
            #  libreduction._BaseGrouper._apply_to_group
            group.name = name  # NB: libreduction does not pin name

            output = func(group, *args, **kwargs)
            output = libreduction.extract_result(output)
            if not initialized:
                # We only do this validation on the first iteration
                libreduction.check_result_array(output, 0)
                initialized = True
            result[name] = output

        return result

    @Substitution(klass="Series")
    @Appender(_transform_template)
    def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):

        if maybe_use_numba(engine):
            with group_selection_context(self):
                data = self._selected_obj
            result = self._transform_with_numba(
                data.to_frame(), func, *args, engine_kwargs=engine_kwargs, **kwargs
            )
            return self.obj._constructor(
                result.ravel(), index=data.index, name=data.name
            )

        func = com.get_cython_func(func) or func

        if not isinstance(func, str):
            return self._transform_general(func, *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"
            return getattr(self, func)(*args, **kwargs)
        # If func is a reduction, we need to broadcast the
        # result to the whole group. Compute func result
        # and deal with possible broadcasting below.
        # Temporarily set observed for dealing with categoricals.
        with com.temp_setattr(self, "observed", True):
            result = getattr(self, func)(*args, **kwargs)
        return self._transform_fast(result)

    def _transform_general(self, func, *args, **kwargs):
        """
        Transform with a non-str `func`.
        """
        klass = type(self._selected_obj)

        results = []
        for name, group in self:
            object.__setattr__(group, "name", name)
            res = func(group, *args, **kwargs)

            if isinstance(res, (DataFrame, Series)):
                res = res._values

            results.append(klass(res, index=group.index))

        # check for empty "results" to avoid concat ValueError
        if results:
            from pandas.core.reshape.concat import concat

            concatenated = concat(results)
            result = self._set_result_index_ordered(concatenated)
        else:
            result = self.obj._constructor(dtype=np.float64)
        # we will only try to coerce the result type if
        # we have a numeric dtype, as these are *always* user-defined funcs
        # the cython take a different path (and casting)
        if is_numeric_dtype(result.dtype):
            common_dtype = find_common_type([self._selected_obj.dtype, result.dtype])
            if common_dtype is result.dtype:
                result = maybe_downcast_numeric(result, self._selected_obj.dtype)

        result.name = self._selected_obj.name
        return result

    def _transform_fast(self, result) -> Series:
        """
        fast version of transform, only applicable to
        builtin/cythonizable functions
        """
        ids, _, ngroup = self.grouper.group_info
        result = result.reindex(self.grouper.result_index, copy=False)
        out = algorithms.take_nd(result._values, ids)
        return self.obj._constructor(out, index=self.obj.index, name=self.obj.name)

    def filter(self, func, dropna=True, *args, **kwargs):
        """
        Return a copy of a Series excluding elements from groups that
        do not satisfy the boolean criterion specified by func.

        Parameters
        ----------
        func : function
            To apply to each group. Should return True or False.
        dropna : Drop groups that do not pass the filter. True by default;
            if False, groups that evaluate False are filled with NaNs.

        Notes
        -----
        Functions that mutate the passed object can produce unexpected
        behavior or errors and are not supported. See :ref:`udf-mutation`
        for more details.

        Examples
        --------
        >>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
        ...                           'foo', 'bar'],
        ...                    'B' : [1, 2, 3, 4, 5, 6],
        ...                    'C' : [2.0, 5., 8., 1., 2., 9.]})
        >>> grouped = df.groupby('A')
        >>> df.groupby('A').B.filter(lambda x: x.mean() > 3.)
        1    2
        3    4
        5    6
        Name: B, dtype: int64

        Returns
        -------
        filtered : Series
        """
        if isinstance(func, str):
            wrapper = lambda x: getattr(x, func)(*args, **kwargs)
        else:
            wrapper = lambda x: func(x, *args, **kwargs)

        # Interpret np.nan as False.
        def true_and_notna(x) -> bool:
            b = wrapper(x)
            return b and notna(b)

        try:
            indices = [
                self._get_index(name) for name, group in self if true_and_notna(group)
            ]
        except (ValueError, TypeError) as err:
            raise TypeError("the filter must return a boolean result") from err

        filtered = self._apply_filter(indices, dropna)
        return filtered

    def nunique(self, dropna: bool = True) -> Series:
        """
        Return number of unique elements in the group.

        Returns
        -------
        Series
            Number of unique values within each group.
        """
        ids, _, _ = self.grouper.group_info

        val = self.obj._values

        codes, _ = algorithms.factorize(val, sort=False)
        sorter = np.lexsort((codes, ids))
        codes = codes[sorter]
        ids = ids[sorter]

        # group boundaries are where group ids change
        # unique observations are where sorted values change
        idx = np.r_[0, 1 + np.nonzero(ids[1:] != ids[:-1])[0]]
        inc = np.r_[1, codes[1:] != codes[:-1]]

        # 1st item of each group is a new unique observation
        mask = codes == -1
        if dropna:
            inc[idx] = 1
            inc[mask] = 0
        else:
            inc[mask & np.r_[False, mask[:-1]]] = 0
            inc[idx] = 1

        out = np.add.reduceat(inc, idx).astype("int64", copy=False)
        if len(ids):
            # NaN/NaT group exists if the head of ids is -1,
            # so remove it from res and exclude its index from idx
            if ids[0] == -1:
                res = out[1:]
                idx = idx[np.flatnonzero(idx)]
            else:
                res = out
        else:
            res = out[1:]
        ri = self.grouper.result_index

        # we might have duplications among the bins
        if len(res) != len(ri):
            res, out = np.zeros(len(ri), dtype=out.dtype), res
            res[ids[idx]] = out

        result = self.obj._constructor(res, index=ri, name=self._selection_name)
        return self._reindex_output(result, fill_value=0)

    @doc(Series.describe)
    def describe(self, **kwargs):
        result = self.apply(lambda x: x.describe(**kwargs))
        if self.axis == 1:
            return result.T
        return result.unstack()

    def value_counts(
        self,
        normalize=False,
        sort=True,
        ascending=False,
        bins=None,
        dropna: bool = True,
    ):

        from pandas.core.reshape.merge import get_join_indexers
        from pandas.core.reshape.tile import cut

        ids, _, _ = self.grouper.group_info
        val = self.obj._values

        def apply_series_value_counts():
            return self.apply(
                Series.value_counts,
                normalize=normalize,
                sort=sort,
                ascending=ascending,
                bins=bins,
            )

        if bins is not None:
            if not np.iterable(bins):
                # scalar bins cannot be done at top level
                # in a backward compatible way
                return apply_series_value_counts()
        elif is_categorical_dtype(val):
            # GH38672
            return apply_series_value_counts()

        # groupby removes null keys from groupings
        mask = ids != -1
        ids, val = ids[mask], val[mask]

        if bins is None:
            lab, lev = algorithms.factorize(val, sort=True)
            llab = lambda lab, inc: lab[inc]
        else:

            # lab is a Categorical with categories an IntervalIndex
            lab = cut(Series(val), bins, include_lowest=True)
            # error: "ndarray" has no attribute "cat"
            lev = lab.cat.categories  # type: ignore[attr-defined]
            # error: No overload variant of "take" of "_ArrayOrScalarCommon" matches
            # argument types "Any", "bool", "Union[Any, float]"
            lab = lev.take(  # type: ignore[call-overload]
                # error: "ndarray" has no attribute "cat"
                lab.cat.codes,  # type: ignore[attr-defined]
                allow_fill=True,
                # error: Item "ndarray" of "Union[ndarray, Index]" has no attribute
                # "_na_value"
                fill_value=lev._na_value,  # type: ignore[union-attr]
            )
            llab = lambda lab, inc: lab[inc]._multiindex.codes[-1]

        if is_interval_dtype(lab.dtype):
            # TODO: should we do this inside II?

            # error: "ndarray" has no attribute "left"
            # error: "ndarray" has no attribute "right"
            sorter = np.lexsort(
                (lab.left, lab.right, ids)  # type: ignore[attr-defined]
            )
        else:
            sorter = np.lexsort((lab, ids))

        ids, lab = ids[sorter], lab[sorter]

        # group boundaries are where group ids change
        idchanges = 1 + np.nonzero(ids[1:] != ids[:-1])[0]
        idx = np.r_[0, idchanges]
        if not len(ids):
            idx = idchanges

        # new values are where sorted labels change
        lchanges = llab(lab, slice(1, None)) != llab(lab, slice(None, -1))
        inc = np.r_[True, lchanges]
        if not len(lchanges):
            inc = lchanges
        inc[idx] = True  # group boundaries are also new values
        out = np.diff(np.nonzero(np.r_[inc, True])[0])  # value counts

        # num. of times each group should be repeated
        rep = partial(np.repeat, repeats=np.add.reduceat(inc, idx))

        # multi-index components
        codes = self.grouper.reconstructed_codes
        codes = [rep(level_codes) for level_codes in codes] + [llab(lab, inc)]
        # error: List item 0 has incompatible type "Union[ndarray, Any]";
        # expected "Index"
        levels = [ping.group_index for ping in self.grouper.groupings] + [
            lev  # type: ignore[list-item]
        ]
        names = self.grouper.names + [self._selection_name]

        if dropna:
            mask = codes[-1] != -1
            if mask.all():
                dropna = False
            else:
                out, codes = out[mask], [level_codes[mask] for level_codes in codes]

        if normalize:
            out = out.astype("float")
            d = np.diff(np.r_[idx, len(ids)])
            if dropna:
                m = ids[lab == -1]
                np.add.at(d, m, -1)
                acc = rep(d)[mask]
            else:
                acc = rep(d)
            out /= acc

        if sort and bins is None:
            cat = ids[inc][mask] if dropna else ids[inc]
            sorter = np.lexsort((out if ascending else -out, cat))
            out, codes[-1] = out[sorter], codes[-1][sorter]

        if bins is None:
            mi = MultiIndex(
                levels=levels, codes=codes, names=names, verify_integrity=False
            )

            if is_integer_dtype(out):
                out = ensure_int64(out)
            return self.obj._constructor(out, index=mi, name=self._selection_name)

        # for compat. with libgroupby.value_counts need to ensure every
        # bin is present at every index level, null filled with zeros
        diff = np.zeros(len(out), dtype="bool")
        for level_codes in codes[:-1]:
            diff |= np.r_[True, level_codes[1:] != level_codes[:-1]]

        ncat, nbin = diff.sum(), len(levels[-1])

        left = [np.repeat(np.arange(ncat), nbin), np.tile(np.arange(nbin), ncat)]

        right = [diff.cumsum() - 1, codes[-1]]

        _, idx = get_join_indexers(left, right, sort=False, how="left")
        out = np.where(idx != -1, out[idx], 0)

        if sort:
            sorter = np.lexsort((out if ascending else -out, left[0]))
            out, left[-1] = out[sorter], left[-1][sorter]

        # build the multi-index w/ full levels
        def build_codes(lev_codes: np.ndarray) -> np.ndarray:
            return np.repeat(lev_codes[diff], nbin)

        codes = [build_codes(lev_codes) for lev_codes in codes[:-1]]
        codes.append(left[-1])

        mi = MultiIndex(levels=levels, codes=codes, names=names, verify_integrity=False)

        if is_integer_dtype(out):
            out = ensure_int64(out)
        return self.obj._constructor(out, index=mi, name=self._selection_name)

    def count(self) -> Series:
        """
        Compute count of group, excluding missing values.

        Returns
        -------
        Series
            Count of values within each group.
        """
        ids, _, ngroups = self.grouper.group_info
        val = self.obj._values

        mask = (ids != -1) & ~isna(val)
        minlength = ngroups or 0
        out = np.bincount(ids[mask], minlength=minlength)

        result = self.obj._constructor(
            out,
            index=self.grouper.result_index,
            name=self._selection_name,
            dtype="int64",
        )
        return self._reindex_output(result, fill_value=0)

    def pct_change(self, periods=1, fill_method="pad", limit=None, freq=None):
        """Calculate pct_change of each value to previous entry in group"""
        # TODO: Remove this conditional when #23918 is fixed
        if freq:
            return self.apply(
                lambda x: x.pct_change(
                    periods=periods, fill_method=fill_method, limit=limit, freq=freq
                )
            )
        if fill_method is None:  # GH30463
            fill_method = "pad"
            limit = 0
        filled = getattr(self, fill_method)(limit=limit)
        fill_grp = filled.groupby(self.grouper.codes)
        shifted = fill_grp.shift(periods=periods, freq=freq)

        return (filled / shifted) - 1


@pin_allowlisted_properties(DataFrame, base.dataframe_apply_allowlist)
class DataFrameGroupBy(GroupBy[DataFrame]):

    _apply_allowlist = base.dataframe_apply_allowlist

    _agg_examples_doc = dedent(
        """
    Examples
    --------
    >>> df = pd.DataFrame(
    ...     {
    ...         "A": [1, 1, 2, 2],
    ...         "B": [1, 2, 3, 4],
    ...         "C": [0.362838, 0.227877, 1.267767, -0.562860],
    ...     }
    ... )

    >>> df
       A  B         C
    0  1  1  0.362838
    1  1  2  0.227877
    2  2  3  1.267767
    3  2  4 -0.562860

    The aggregation is for each column.

    >>> df.groupby('A').agg('min')
       B         C
    A
    1  1  0.227877
    2  3 -0.562860

    Multiple aggregations

    >>> df.groupby('A').agg(['min', 'max'])
        B             C
      min max       min       max
    A
    1   1   2  0.227877  0.362838
    2   3   4 -0.562860  1.267767

    Select a column for aggregation

    >>> df.groupby('A').B.agg(['min', 'max'])
       min  max
    A
    1    1    2
    2    3    4

    Different aggregations per column

    >>> df.groupby('A').agg({'B': ['min', 'max'], 'C': 'sum'})
        B             C
      min max       sum
    A
    1   1   2  0.590715
    2   3   4  0.704907

    To control the output names with different aggregations per column,
    pandas supports "named aggregation"

    >>> df.groupby("A").agg(
    ...     b_min=pd.NamedAgg(column="B", aggfunc="min"),
    ...     c_sum=pd.NamedAgg(column="C", aggfunc="sum"))
       b_min     c_sum
    A
    1      1  0.590715
    2      3  0.704907

    - The keywords are the *output* column names
    - The values are tuples whose first element is the column to select
      and the second element is the aggregation to apply to that column.
      Pandas provides the ``pandas.NamedAgg`` namedtuple with the fields
      ``['column', 'aggfunc']`` to make it clearer what the arguments are.
      As usual, the aggregation can be a callable or a string alias.

    See :ref:`groupby.aggregate.named` for more."""
    )

    @doc(_agg_template, examples=_agg_examples_doc, klass="DataFrame")
    def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs):

        if maybe_use_numba(engine):
            with group_selection_context(self):
                data = self._selected_obj
            result, index = self._aggregate_with_numba(
                data, func, *args, engine_kwargs=engine_kwargs, **kwargs
            )
            return self.obj._constructor(result, index=index, columns=data.columns)

        relabeling, func, columns, order = reconstruct_func(func, **kwargs)
        func = maybe_mangle_lambdas(func)

        op = GroupByApply(self, func, args, kwargs)
        result = op.agg()
        if not is_dict_like(func) and result is not None:
            return result

        if result is None:

            # grouper specific aggregations
            if self.grouper.nkeys > 1:
                return self._python_agg_general(func, *args, **kwargs)
            elif args or kwargs:
                result = self._aggregate_frame(func, *args, **kwargs)

            elif self.axis == 1:
                # _aggregate_multiple_funcs does not allow self.axis == 1
                result = self._aggregate_frame(func)

            else:

                # try to treat as if we are passing a list
                try:
                    result = GroupByApply(self, [func], args=(), kwargs={}).agg()

                    # select everything except for the last level, which is the one
                    # containing the name of the function(s), see GH 32040
                    result.columns = result.columns.rename(
                        [self._selected_obj.columns.name] * result.columns.nlevels
                    ).droplevel(-1)

                except ValueError as err:
                    if "no results" not in str(err):
                        # raised directly by _aggregate_multiple_funcs
                        raise
                    result = self._aggregate_frame(func)
                except AttributeError:
                    # catch exception from line 969
                    # (Series does not have attribute "columns"), see GH 35246
                    result = self._aggregate_frame(func)

        if relabeling:

            # used reordered index of columns
            result = result.iloc[:, order]
            result.columns = columns

        if not self.as_index:
            self._insert_inaxis_grouper_inplace(result)
            result.index = np.arange(len(result))

        return result._convert(datetime=True)

    agg = aggregate

    def _iterate_slices(self) -> Iterable[Series]:
        obj = self._selected_obj
        if self.axis == 1:
            obj = obj.T

        if isinstance(obj, Series) and obj.name not in self.exclusions:
            # Occurs when doing DataFrameGroupBy(...)["X"]
            yield obj
        else:
            for label, values in obj.items():
                if label in self.exclusions:
                    continue

                yield values

    def _cython_agg_general(
        self, how: str, alt=None, numeric_only: bool = True, min_count: int = -1
    ) -> DataFrame:
        # Note: we never get here with how="ohlc"; that goes through SeriesGroupBy

        data: Manager2D = self._get_data_to_aggregate()

        if numeric_only:
            data = data.get_numeric_data(copy=False)

        using_array_manager = isinstance(data, ArrayManager)

        def cast_agg_result(
            result: ArrayLike, values: ArrayLike, how: str
        ) -> ArrayLike:
            # see if we can cast the values to the desired dtype
            # this may not be the original dtype

            if isinstance(values, Categorical) and isinstance(result, np.ndarray):
                # If the Categorical op didn't raise, it is dtype-preserving
                # We get here with how="first", "last", "min", "max"
                result = type(values)._from_sequence(result.ravel(), dtype=values.dtype)
                # Note this will have result.dtype == dtype from above

            elif (
                not using_array_manager
                and isinstance(result, np.ndarray)
                and result.ndim == 1
            ):
                # We went through a SeriesGroupByPath and need to reshape
                # GH#32223 includes case with IntegerArray values
                # We only get here with values.dtype == object
                result = result.reshape(1, -1)
                # test_groupby_duplicate_columns gets here with
                #  result.dtype == int64, values.dtype=object, how="min"

            return result

        def py_fallback(values: ArrayLike) -> ArrayLike:
            # if self.grouper.aggregate fails, we fall back to a pure-python
            #  solution

            # We get here with a) EADtypes and b) object dtype
            obj: FrameOrSeriesUnion

            # call our grouper again with only this block
            if values.ndim == 1:
                # We only get here with ExtensionArray

                obj = Series(values)
            else:
                # We only get here with values.dtype == object
                # TODO special case not needed with ArrayManager
                obj = DataFrame(values.T)
                if obj.shape[1] == 1:
                    # Avoid call to self.values that can occur in DataFrame
                    #  reductions; see GH#28949
                    obj = obj.iloc[:, 0]

            # Create SeriesGroupBy with observed=True so that it does
            # not try to add missing categories if grouping over multiple
            # Categoricals. This will done by later self._reindex_output()
            # Doing it here creates an error. See GH#34951
            sgb = get_groupby(obj, self.grouper, observed=True)
            result = sgb.aggregate(lambda x: alt(x, axis=self.axis))

            # In the case of object dtype block, it may have been split
            #  in the operation.  We un-split here.
            result = result._consolidate()
            # unwrap DataFrame/Series to get array
            mgr = result._mgr
            arrays = mgr.arrays
            if len(arrays) != 1:
                # We've split an object block! Everything we've assumed
                # about a single block input returning a single block output
                # is a lie. See eg GH-39329
                return mgr.as_array()
            else:
                # We are a single block from a BlockManager
                # or one array from SingleArrayManager
                return arrays[0]

        def array_func(values: ArrayLike) -> ArrayLike:

            try:
                result = self.grouper._cython_operation(
                    "aggregate", values, how, axis=1, min_count=min_count
                )
            except NotImplementedError:
                # generally if we have numeric_only=False
                # and non-applicable functions
                # try to python agg
                result = py_fallback(values)

                return cast_agg_result(result, values, how)
            return result

        # TypeError -> we may have an exception in trying to aggregate
        #  continue and exclude the block
        new_mgr = data.grouped_reduce(array_func, ignore_failures=True)

        if not len(new_mgr):
            raise DataError("No numeric types to aggregate")

        return self._wrap_agged_manager(new_mgr)

    def _aggregate_frame(self, func, *args, **kwargs) -> DataFrame:
        if self.grouper.nkeys != 1:
            raise AssertionError("Number of keys must be 1")

        axis = self.axis
        obj = self._obj_with_exclusions

        result: dict[Hashable, NDFrame | np.ndarray] = {}
        if axis != obj._info_axis_number:
            for name, data in self:
                fres = func(data, *args, **kwargs)
                result[name] = fres
        else:
            for name in self.indices:
                data = self.get_group(name, obj=obj)
                fres = func(data, *args, **kwargs)
                result[name] = fres

        return self._wrap_frame_output(result, obj)

    def _aggregate_item_by_item(self, func, *args, **kwargs) -> DataFrame:
        # only for axis==0

        obj = self._obj_with_exclusions
        result: dict[int | str, NDFrame] = {}
        cannot_agg = []
        for item in obj:
            data = obj[item]
            colg = SeriesGroupBy(data, selection=item, grouper=self.grouper)

            try:
                result[item] = colg.aggregate(func, *args, **kwargs)

            except ValueError as err:
                if "Must produce aggregated value" in str(err):
                    # raised in _aggregate_named, handle at higher level
                    #  see test_apply_with_mutated_index
                    raise
                # otherwise we get here from an AttributeError in _make_wrapper
                cannot_agg.append(item)
                continue

        result_columns = obj.columns
        if cannot_agg:
            result_columns = result_columns.drop(cannot_agg)

        return self.obj._constructor(result, columns=result_columns)

    def _wrap_applied_output(self, data, keys, values, not_indexed_same=False):
        if len(keys) == 0:
            result = self.obj._constructor(
                index=self.grouper.result_index, columns=data.columns
            )
            result = result.astype(data.dtypes.to_dict(), copy=False)
            return result

        # GH12824
        first_not_none = next(com.not_none(*values), None)

        if first_not_none is None:
            # GH9684 - All values are None, return an empty frame.
            return self.obj._constructor()
        elif isinstance(first_not_none, DataFrame):
            return self._concat_objects(keys, values, not_indexed_same=not_indexed_same)

        key_index = self.grouper.result_index if self.as_index else None

        if isinstance(first_not_none, (np.ndarray, Index)):
            # GH#1738: values is list of arrays of unequal lengths
            #  fall through to the outer else clause
            # TODO: sure this is right?  we used to do this
            #  after raising AttributeError above
            return self.obj._constructor_sliced(
                values, index=key_index, name=self._selection_name
            )
        elif not isinstance(first_not_none, Series):
            # values are not series or array-like but scalars
            # self._selection_name not passed through to Series as the
            # result should not take the name of original selection
            # of columns
            if self.as_index:
                return self.obj._constructor_sliced(values, index=key_index)
            else:
                result = DataFrame(values, index=key_index, columns=[self._selection])
                self._insert_inaxis_grouper_inplace(result)
                return result
        else:
            # values are Series
            return self._wrap_applied_output_series(
                keys, values, not_indexed_same, first_not_none, key_index
            )

    def _wrap_applied_output_series(
        self,
        keys,
        values: list[Series],
        not_indexed_same: bool,
        first_not_none,
        key_index,
    ) -> FrameOrSeriesUnion:
        # this is to silence a DeprecationWarning
        # TODO: Remove when default dtype of empty Series is object
        kwargs = first_not_none._construct_axes_dict()
        backup = create_series_with_explicit_dtype(dtype_if_empty=object, **kwargs)
        values = [x if (x is not None) else backup for x in values]

        all_indexed_same = all_indexes_same(x.index for x in values)

        # GH3596
        # provide a reduction (Frame -> Series) if groups are
        # unique
        if self.squeeze:
            applied_index = self._selected_obj._get_axis(self.axis)
            singular_series = len(values) == 1 and applied_index.nlevels == 1

            # assign the name to this series
            if singular_series:
                values[0].name = keys[0]

                # GH2893
                # we have series in the values array, we want to
                # produce a series:
                # if any of the sub-series are not indexed the same
                # OR we don't have a multi-index and we have only a
                # single values
                return self._concat_objects(
                    keys, values, not_indexed_same=not_indexed_same
                )

            # still a series
            # path added as of GH 5545
            elif all_indexed_same:
                from pandas.core.reshape.concat import concat

                return concat(values)

        if not all_indexed_same:
            # GH 8467
            return self._concat_objects(keys, values, not_indexed_same=True)

        # Combine values
        # vstack+constructor is faster than concat and handles MI-columns
        stacked_values = np.vstack([np.asarray(v) for v in values])

        if self.axis == 0:
            index = key_index
            columns = first_not_none.index.copy()
            if columns.name is None:
                # GH6124 - propagate name of Series when it's consistent
                names = {v.name for v in values}
                if len(names) == 1:
                    columns.name = list(names)[0]
        else:
            index = first_not_none.index
            columns = key_index
            stacked_values = stacked_values.T

        result = self.obj._constructor(stacked_values, index=index, columns=columns)

        # if we have date/time like in the original, then coerce dates
        # as we are stacking can easily have object dtypes here
        result = result._convert(datetime=True)

        if not self.as_index:
            self._insert_inaxis_grouper_inplace(result)

        return self._reindex_output(result)

    def _transform_general(self, func, *args, **kwargs):
        from pandas.core.reshape.concat import concat

        applied = []
        obj = self._obj_with_exclusions
        gen = self.grouper.get_iterator(obj, axis=self.axis)
        fast_path, slow_path = self._define_paths(func, *args, **kwargs)

        for name, group in gen:
            object.__setattr__(group, "name", name)

            # Try slow path and fast path.
            try:
                path, res = self._choose_path(fast_path, slow_path, group)
            except TypeError:
                return self._transform_item_by_item(obj, fast_path)
            except ValueError as err:
                msg = "transform must return a scalar value for each group"
                raise ValueError(msg) from err

            if isinstance(res, Series):

                # we need to broadcast across the
                # other dimension; this will preserve dtypes
                # GH14457
                if not np.prod(group.shape):
                    continue
                elif res.index.is_(obj.index):
                    r = concat([res] * len(group.columns), axis=1)
                    r.columns = group.columns
                    r.index = group.index
                else:
                    r = self.obj._constructor(
                        np.concatenate([res.values] * len(group.index)).reshape(
                            group.shape
                        ),
                        columns=group.columns,
                        index=group.index,
                    )

                applied.append(r)
            else:
                applied.append(res)

        concat_index = obj.columns if self.axis == 0 else obj.index
        other_axis = 1 if self.axis == 0 else 0  # switches between 0 & 1
        concatenated = concat(applied, axis=self.axis, verify_integrity=False)
        concatenated = concatenated.reindex(concat_index, axis=other_axis, copy=False)
        return self._set_result_index_ordered(concatenated)

    @Substitution(klass="DataFrame")
    @Appender(_transform_template)
    def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):

        if maybe_use_numba(engine):
            with group_selection_context(self):
                data = self._selected_obj
            result = self._transform_with_numba(
                data, func, *args, engine_kwargs=engine_kwargs, **kwargs
            )
            return self.obj._constructor(result, index=data.index, columns=data.columns)

        # optimized transforms
        func = com.get_cython_func(func) or func

        if not isinstance(func, str):
            return self._transform_general(func, *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 transformation or canned "reduction+broadcast"
            return getattr(self, func)(*args, **kwargs)
        # GH 30918
        # Use _transform_fast only when we know func is an aggregation
        if func in base.reduction_kernels:
            # If func is a reduction, we need to broadcast the
            # result to the whole group. Compute func result
            # and deal with possible broadcasting below.
            # Temporarily set observed for dealing with categoricals.
            with com.temp_setattr(self, "observed", True):
                result = getattr(self, func)(*args, **kwargs)

            if isinstance(result, DataFrame) and result.columns.equals(
                self._obj_with_exclusions.columns
            ):
                return self._transform_fast(result)

        return self._transform_general(func, *args, **kwargs)

    def _transform_fast(self, result: DataFrame) -> DataFrame:
        """
        Fast transform path for aggregations
        """
        obj = self._obj_with_exclusions

        # for each col, reshape to size of original frame by take operation
        ids, _, ngroup = self.grouper.group_info
        result = result.reindex(self.grouper.result_index, copy=False)
        output = [
            algorithms.take_nd(result.iloc[:, i].values, ids)
            for i, _ in enumerate(result.columns)
        ]

        return self.obj._constructor._from_arrays(
            output, columns=result.columns, index=obj.index
        )

    def _define_paths(self, func, *args, **kwargs):
        if isinstance(func, str):
            fast_path = lambda group: getattr(group, func)(*args, **kwargs)
            slow_path = lambda group: group.apply(
                lambda x: getattr(x, func)(*args, **kwargs), axis=self.axis
            )
        else:
            fast_path = lambda group: func(group, *args, **kwargs)
            slow_path = lambda group: group.apply(
                lambda x: func(x, *args, **kwargs), axis=self.axis
            )
        return fast_path, slow_path

    def _choose_path(self, fast_path: Callable, slow_path: Callable, group: DataFrame):
        path = slow_path
        res = slow_path(group)

        # if we make it here, test if we can use the fast path
        try:
            res_fast = fast_path(group)
        except AssertionError:
            raise  # pragma: no cover
        except Exception:
            # GH#29631 For user-defined function, we can't predict what may be
            #  raised; see test_transform.test_transform_fastpath_raises
            return path, res

        # verify fast path does not change columns (and names), otherwise
        # its results cannot be joined with those of the slow path
        if not isinstance(res_fast, DataFrame):
            return path, res

        if not res_fast.columns.equals(group.columns):
            return path, res

        if res_fast.equals(res):
            path = fast_path

        return path, res

    def _transform_item_by_item(self, obj: DataFrame, wrapper) -> DataFrame:
        # iterate through columns
        output = {}
        inds = []
        for i, col in enumerate(obj):
            try:
                output[col] = self[col].transform(wrapper)
            except TypeError:
                # e.g. trying to call nanmean with string values
                pass
            else:
                inds.append(i)

        if not output:
            raise TypeError("Transform function invalid for data types")

        columns = obj.columns
        if len(output) < len(obj.columns):
            columns = columns.take(inds)

        return self.obj._constructor(output, index=obj.index, columns=columns)

    def filter(self, func, dropna=True, *args, **kwargs):
        """
        Return a copy of a DataFrame excluding filtered elements.

        Elements from groups are filtered if they do not satisfy the
        boolean criterion specified by func.

        Parameters
        ----------
        func : function
            Function to apply to each subframe. Should return True or False.
        dropna : Drop groups that do not pass the filter. True by default;
            If False, groups that evaluate False are filled with NaNs.

        Returns
        -------
        filtered : DataFrame

        Notes
        -----
        Each subframe is endowed the attribute 'name' in case you need to know
        which group you are working on.

        Functions that mutate the passed object can produce unexpected
        behavior or errors and are not supported. See :ref:`udf-mutation`
        for more details.

        Examples
        --------
        >>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
        ...                           'foo', 'bar'],
        ...                    'B' : [1, 2, 3, 4, 5, 6],
        ...                    'C' : [2.0, 5., 8., 1., 2., 9.]})
        >>> grouped = df.groupby('A')
        >>> grouped.filter(lambda x: x['B'].mean() > 3.)
             A  B    C
        1  bar  2  5.0
        3  bar  4  1.0
        5  bar  6  9.0
        """
        indices = []

        obj = self._selected_obj
        gen = self.grouper.get_iterator(obj, axis=self.axis)

        for name, group in gen:
            object.__setattr__(group, "name", name)

            res = func(group, *args, **kwargs)

            try:
                res = res.squeeze()
            except AttributeError:  # allow e.g., scalars and frames to pass
                pass

            # interpret the result of the filter
            if is_bool(res) or (is_scalar(res) and isna(res)):
                if res and notna(res):
                    indices.append(self._get_index(name))
            else:
                # non scalars aren't allowed
                raise TypeError(
                    f"filter function returned a {type(res).__name__}, "
                    "but expected a scalar bool"
                )

        return self._apply_filter(indices, dropna)

    def __getitem__(self, key):
        if self.axis == 1:
            # GH 37725
            raise ValueError("Cannot subset columns when using axis=1")
        # per GH 23566
        if isinstance(key, tuple) and len(key) > 1:
            # if len == 1, then it becomes a SeriesGroupBy and this is actually
            # valid syntax, so don't raise warning
            warnings.warn(
                "Indexing with multiple keys (implicitly converted to a tuple "
                "of keys) will be deprecated, use a list instead.",
                FutureWarning,
                stacklevel=2,
            )
        return super().__getitem__(key)

    def _gotitem(self, key, ndim: int, subset=None):
        """
        sub-classes to define
        return a sliced object

        Parameters
        ----------
        key : string / list of selections
        ndim : {1, 2}
            requested ndim of result
        subset : object, default None
            subset to act on
        """
        if ndim == 2:
            if subset is None:
                subset = self.obj
            return DataFrameGroupBy(
                subset,
                self.grouper,
                axis=self.axis,
                level=self.level,
                grouper=self.grouper,
                exclusions=self.exclusions,
                selection=key,
                as_index=self.as_index,
                sort=self.sort,
                group_keys=self.group_keys,
                squeeze=self.squeeze,
                observed=self.observed,
                mutated=self.mutated,
                dropna=self.dropna,
            )
        elif ndim == 1:
            if subset is None:
                subset = self.obj[key]
            return SeriesGroupBy(
                subset,
                level=self.level,
                grouper=self.grouper,
                selection=key,
                sort=self.sort,
                group_keys=self.group_keys,
                squeeze=self.squeeze,
                observed=self.observed,
                dropna=self.dropna,
            )

        raise AssertionError("invalid ndim for _gotitem")

    def _wrap_frame_output(self, result, obj: DataFrame) -> DataFrame:
        result_index = self.grouper.levels[0]

        if self.axis == 0:
            return self.obj._constructor(
                result, index=obj.columns, columns=result_index
            ).T
        else:
            return self.obj._constructor(result, index=obj.index, columns=result_index)

    def _get_data_to_aggregate(self) -> Manager2D:
        obj = self._obj_with_exclusions
        if self.axis == 1:
            return obj.T._mgr
        else:
            return obj._mgr

    def _insert_inaxis_grouper_inplace(self, result: DataFrame) -> None:
        # zip in reverse so we can always insert at loc 0
        columns = result.columns
        for name, lev, in_axis in zip(
            reversed(self.grouper.names),
            reversed(self.grouper.get_group_levels()),
            reversed([grp.in_axis for grp in self.grouper.groupings]),
        ):
            # GH #28549
            # When using .apply(-), name will be in columns already
            if in_axis and name not in columns:
                result.insert(0, name, lev)

    def _wrap_aggregated_output(
        self,
        output: Mapping[base.OutputKey, Series | np.ndarray],
    ) -> DataFrame:
        """
        Wraps the output of DataFrameGroupBy aggregations into the expected result.

        Parameters
        ----------
        output : Mapping[base.OutputKey, Union[Series, np.ndarray]]
           Data to wrap.

        Returns
        -------
        DataFrame
        """
        indexed_output = {key.position: val for key, val in output.items()}
        columns = Index([key.label for key in output])
        columns._set_names(self._obj_with_exclusions._get_axis(1 - self.axis).names)

        result = self.obj._constructor(indexed_output)
        result.columns = columns

        if not self.as_index:
            self._insert_inaxis_grouper_inplace(result)
            result = result._consolidate()
        else:
            result.index = self.grouper.result_index

        if self.axis == 1:
            result = result.T

        return self._reindex_output(result)

    def _wrap_transformed_output(
        self, output: Mapping[base.OutputKey, Series | ArrayLike]
    ) -> DataFrame:
        """
        Wraps the output of DataFrameGroupBy transformations into the expected result.

        Parameters
        ----------
        output : Mapping[base.OutputKey, Union[Series, np.ndarray, ExtensionArray]]
            Data to wrap.

        Returns
        -------
        DataFrame
        """
        indexed_output = {key.position: val for key, val in output.items()}
        result = self.obj._constructor(indexed_output)

        if self.axis == 1:
            result = result.T
            result.columns = self.obj.columns
        else:
            columns = Index(key.label for key in output)
            columns.name = self.obj.columns.name
            result.columns = columns

        result.index = self.obj.index

        return result

    def _wrap_agged_manager(self, mgr: Manager2D) -> DataFrame:
        if not self.as_index:
            index = np.arange(mgr.shape[1])
            mgr.set_axis(1, ibase.Index(index), verify_integrity=False)
            result = self.obj._constructor(mgr)

            self._insert_inaxis_grouper_inplace(result)
            result = result._consolidate()
        else:
            index = self.grouper.result_index
            mgr.set_axis(1, index, verify_integrity=False)
            result = self.obj._constructor(mgr)

        if self.axis == 1:
            result = result.T

        return self._reindex_output(result)._convert(datetime=True)

    def _iterate_column_groupbys(self):
        for i, colname in enumerate(self._selected_obj.columns):
            yield colname, SeriesGroupBy(
                self._selected_obj.iloc[:, i],
                selection=colname,
                grouper=self.grouper,
                exclusions=self.exclusions,
            )

    def _apply_to_column_groupbys(self, func) -> DataFrame:
        from pandas.core.reshape.concat import concat

        return concat(
            (func(col_groupby) for _, col_groupby in self._iterate_column_groupbys()),
            keys=self._selected_obj.columns,
            axis=1,
        )

    def count(self) -> DataFrame:
        """
        Compute count of group, excluding missing values.

        Returns
        -------
        DataFrame
            Count of values within each group.
        """
        data = self._get_data_to_aggregate()
        ids, _, ngroups = self.grouper.group_info
        mask = ids != -1

        using_array_manager = isinstance(data, ArrayManager)

        def hfunc(bvalues: ArrayLike) -> ArrayLike:
            # TODO(2DEA): 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, axis=1)
            if using_array_manager:
                # count_level_2d return (1, N) array for single column
                # -> extract 1D array
                counted = counted[0, :]
            return counted

        new_mgr = data.grouped_reduce(hfunc)

        # If we are grouping on categoricals we want unobserved categories to
        # return zero, rather than the default of NaN which the reindexing in
        # _wrap_agged_manager() returns. GH 35028
        with com.temp_setattr(self, "observed", True):
            result = self._wrap_agged_manager(new_mgr)

        return self._reindex_output(result, fill_value=0)

    def nunique(self, dropna: bool = True) -> DataFrame:
        """
        Return DataFrame with counts of unique elements in each position.

        Parameters
        ----------
        dropna : bool, default True
            Don't include NaN in the counts.

        Returns
        -------
        nunique: DataFrame

        Examples
        --------
        >>> df = pd.DataFrame({'id': ['spam', 'egg', 'egg', 'spam',
        ...                           'ham', 'ham'],
        ...                    'value1': [1, 5, 5, 2, 5, 5],
        ...                    'value2': list('abbaxy')})
        >>> df
             id  value1 value2
        0  spam       1      a
        1   egg       5      b
        2   egg       5      b
        3  spam       2      a
        4   ham       5      x
        5   ham       5      y

        >>> df.groupby('id').nunique()
              value1  value2
        id
        egg        1       1
        ham        1       2
        spam       2       1

        Check for rows with the same id but conflicting values:

        >>> df.groupby('id').filter(lambda g: (g.nunique() > 1).any())
             id  value1 value2
        0  spam       1      a
        3  spam       2      a
        4   ham       5      x
        5   ham       5      y
        """
        from pandas.core.reshape.concat import concat

        # TODO: this is duplicative of how GroupBy naturally works
        # Try to consolidate with normal wrapping functions

        obj = self._obj_with_exclusions
        axis_number = obj._get_axis_number(self.axis)
        other_axis = int(not axis_number)
        if axis_number == 0:
            iter_func = obj.items
        else:
            iter_func = obj.iterrows

        results = concat(
            [
                SeriesGroupBy(content, selection=label, grouper=self.grouper).nunique(
                    dropna
                )
                for label, content in iter_func()
            ],
            axis=1,
        )
        results = cast(DataFrame, results)

        if axis_number == 1:
            results = results.T

        results._get_axis(other_axis).names = obj._get_axis(other_axis).names

        if not self.as_index:
            results.index = ibase.default_index(len(results))
            self._insert_inaxis_grouper_inplace(results)
        return results

    @Appender(DataFrame.idxmax.__doc__)
    def idxmax(self, axis=0, skipna: bool = True):
        axis = DataFrame._get_axis_number(axis)
        numeric_only = None if axis == 0 else False

        def func(df):
            # NB: here we use numeric_only=None, in DataFrame it is False GH#38217
            res = df._reduce(
                nanops.nanargmax,
                "argmax",
                axis=axis,
                skipna=skipna,
                numeric_only=numeric_only,
            )
            indices = res._values
            index = df._get_axis(axis)
            result = [index[i] if i >= 0 else np.nan for i in indices]
            return df._constructor_sliced(result, index=res.index)

        return self._python_apply_general(func, self._obj_with_exclusions)

    @Appender(DataFrame.idxmin.__doc__)
    def idxmin(self, axis=0, skipna: bool = True):
        axis = DataFrame._get_axis_number(axis)
        numeric_only = None if axis == 0 else False

        def func(df):
            # NB: here we use numeric_only=None, in DataFrame it is False GH#38217
            res = df._reduce(
                nanops.nanargmin,
                "argmin",
                axis=axis,
                skipna=skipna,
                numeric_only=numeric_only,
            )
            indices = res._values
            index = df._get_axis(axis)
            result = [index[i] if i >= 0 else np.nan for i in indices]
            return df._constructor_sliced(result, index=res.index)

        return self._python_apply_general(func, self._obj_with_exclusions)

    boxplot = boxplot_frame_groupby