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# pandas.Series.kurtosis

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

  Return unbiased kurtosis over requested axis.

  Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). 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 have no effect but might be accepted for compatibility with NumPy. See NumPy compatibility kwargs for more.

  Returns:
    scalar

      Unbiased kurtosis.

  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, 2, 3], index=["cat", "dog", "dog", "mouse"])
      >>> s
      cat    1
      dog    2
      dog    2
      mouse  3
      dtype: int64
      >>> round(s.kurt(), 6)
      1.5