xorbits.pandas.DataFrame.kurtosis#

DataFrame.kurtosis(axis=None, skipna=True, level=None, numeric_only=None, combine_size=None, bias=False, fisher=True, method=None)#

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.

    New in version 2.0.0(pandas).

  • skipna (bool, default True) – Exclude NA/null values when computing the result.

  • numeric_only (bool, default False) – Include only float, int, boolean columns. Not implemented for Series.

  • **kwargs – Additional keyword arguments to be passed to the function.

Returns

  • Series or scalar – .. rubric:: 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).round(6)  
    -0.988693
    

    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
    
  • This docstring was copied from pandas.core.frame.DataFrame.