pyspark.pandas.Series.kurtosis

Series.kurtosis(axis: Union[int, str, None] = None, skipna: bool = True, numeric_only: bool = None) → Union[int, float, bool, str, bytes, decimal.Decimal, datetime.date, datetime.datetime, None, Series]

Return unbiased kurtosis 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.

skipna: bool, default True

Exclude NA/null values when computing the result.

Changed in version 3.4.0: Supported including NA/null values.

numeric_only: bool, default None

Include only float, int, boolean columns. False is not supported. This parameter is mainly for pandas compatibility.

Returns

kurt: scalar for a Series, and a Series for a DataFrame.

Examples

>>> df = ps.DataFrame({'a': [1, 2, 3, np.nan, 6], 'b': [0.1, 0.2, 0.3, np.nan, 0.8]},
...                   columns=['a', 'b'])

On a DataFrame:

>>> df.kurtosis()
a    1.500000
b    2.703924
dtype: float64

On a Series:

>>> df['a'].kurtosis()
1.5

pyspark.pandas.Series.var pyspark.pandas.Series.unique