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# pandas.Series.skew

Series.skew(*, axis=0, skipna=True, numeric_only=False, **kwargs)

  Return unbiased skew over requested axis.

  Normalized by N-1.

  Parameters:
    axis{index (0)}

      Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

      For DataFrames, specifying `axis=None` will apply the aggregation across both axes.

      Added in version 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:
    scalar or scalar

      Value containing the calculation referenced in the description.

  See also

  `Series.skew`

    Return unbiased skew over requested axis.

  `Series.var`

    Return unbiased variance over requested axis.

  `Series.std`

    Return unbiased standard deviation over requested axis.

  Examples

      >>> s = pd.Series([1, 2, 3])
      >>> s.skew()
      0.0

  With a DataFrame

      >>> df = pd.DataFrame({'a': [1, 2, 3], 'b': [2, 3, 4], 'c': [1, 3, 5]},
      ...                   index=['tiger', 'zebra', 'cow'])
      >>> df
              a   b   c
      tiger   1   2   1
      zebra   2   3   3
      cow     3   4   5
      >>> df.skew()
      a   0.0
      b   0.0
      c   0.0
      dtype: float64

  Using axis=1

      >>> df.skew(axis=1)
      tiger   1.732051
      zebra  -1.732051
      cow     0.000000
      dtype: float64

  In this case, numeric_only should be set to True to avoid getting an error.

      >>> df = pd.DataFrame({'a': [1, 2, 3], 'b': ['T', 'Z', 'X']},
      ...                   index=['tiger', 'zebra', 'cow'])
      >>> df.skew(numeric_only=True)
      a   0.0
      dtype: float64