pandas.DataFrame.kurt#

DataFrame.kurt(axis=0,skipna=True,numeric_only=False,**kwargs)[source]#

Return unbiased kurtosis over requested axis.

Kurtosis obtained using Fisher’s definition ofkurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Parameters:

axis{index (0), columns (1)}

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

For DataFrames, specifyingaxis=None will apply the aggregationacross both axes.

Added in version 2.0.0.

skipnabool, default True

Exclude NA/null values when computing the result.

numeric_onlybool, 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

Examples

>>>s=pd.Series([1,2,2,3],index=['cat','dog','dog','mouse'])>>>scat    1dog    2dog    2mouse  3dtype: 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   4mouse  3   4>>>df.kurt()a   1.5b   4.0dtype: 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   -6.0dog   -6.0dtype: float64