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# pandas.DataFrame.kurtosis

DataFrame.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), columns (1)}

      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:
    Series or scalar

      Unbiased kurtosis over requested axis.

  See also

  `Dataframe.kurtosis`

    Returns unbiased kurtosis 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
      >>> s.kurt()
      1.5

  With a DataFrame

      >>> df = pd.DataFrame(
      ...     {"a": [1, 2, 2, 3], "b": [3, 4, 4, 4]},
      ...     index=["cat", "dog", "dog", "mouse"],
      ... )
      >>> df
             a   b
        cat  1   3
        dog  2   4
        dog  2   4
      mouse  3   4
      >>> df.kurt()
      a   1.5
      b   4.0
      dtype: float64

  With axis=None

      >>> df.kurt(axis=None)
      -0.9886927196984727

  Using axis=1

      >>> df = pd.DataFrame(
      ...     {"a": [1, 2], "b": [3, 4], "c": [3, 4], "d": [1, 2]},
      ...     index=["cat", "dog"],
      ... )
      >>> df.kurt(axis=1)
      cat   -6.0
      dog   -6.0
      dtype: float64