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# pandas.api.typing.DataFrameGroupBy.skew

DataFrameGroupBy.skew(skipna=True, numeric_only=False, **kwargs)

  Return unbiased skew within groups.

  Normalized by N-1.

  Parameters:
    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.

      Deprecated since version 3.1.0: Passing `**kwargs` to DataFrameGroupBy.skew is deprecated and will be removed in a future version of pandas.

  Returns:
    DataFrame

      Unbiased skew within groups.

  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