Skip to main content
`Ctrl`+`K`

# pandas.core.groupby.DataFrameGroupBy.skew

DataFrameGroupBy.skew(axis=<no_default>, skipna=True, numeric_only=False, **kwargs)

  Return unbiased 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.

      Added in version 2.0.0.

      Deprecated since version 2.1.0: For axis=1, operate on the underlying object instead. Otherwise the axis keyword is not necessary.

    skipnabool, default True

      Exclude NA/null values when computing the result.

    numeric_onlybool, 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