iased skew within groups.

        Normalized by N-1.

        Parameters
        ----------
        axis : {0 or 'index', 1 or 'columns', None}, default 0
            Axis for the function to be applied on.

            Specifying ``axis=None`` will apply the aggregation across both axes.

            .. versionadded:: 2.0.0

        skipna : bool, default True
            Exclude NA/null values when computing the result.

        numeric_only : bool, default False
            Include only float, int, boolean columns.

        **kwargs
            Additional keyword arguments to be passed to the function.

        Returns
        -------
        DataFrame

        See Also
        --------
        DataFrame.skew : Return unbiased skew over requested axis.

        Examples
        --------
        >>> arrays = [['falcon', 'parrot', 'cockatoo', 'kiwi',
        ...            'lion', 'monkey', 'rabbit'],
        ...           ['bird', 'bird', 'bird', 'bird',
        ...            'mammal', 'mammal', 'mammal']]
        >>> index = pd.MultiIndex.from_arrays(arrays, names=('name', 'class'))
        >>> df = pd.DataFrame({'max_speed': [389.0, 24.0, 70.0, np.nan,
        ...                                  80.5, 21.5, 15.0]},
        ...                   index=index)
        >>> df
                        max_speed
        name     class
        falcon   bird        389.0
        parrot   bird         24.0
        cockatoo bird         70.0
        kiwi     bird          NaN
        lion     mammal       80.5
        monkey   mammal       21.5
        rabbit   mammal       15.0
        >>> gb = df.groupby(["class"])
        >>> gb.skew()
                max_speed
        class
        bird     1.628296
        mammal   1.669046
        >>> gb.skew(skipna=False)
                max_speed
        class
        bird          NaN
        mammal   1.669046
        r–