pyspark.pandas.DataFrame.kurtosis¶
-
DataFrame.kurtosis(axis: Union[int, str, None] = None, 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.
- numeric_onlybool, default None
Include only float, int, boolean columns. False is not supported. This parameter is mainly for pandas compatibility.
- Returns
- kurtscalar for a Series, and a Series for a DataFrame.
Examples
>>> df = ps.DataFrame({'a': [1, 2, 3, np.nan], 'b': [0.1, 0.2, 0.3, np.nan]}, ... columns=['a', 'b'])
On a DataFrame:
>>> df.kurtosis() a -1.5 b -1.5 dtype: float64
On a Series:
>>> df['a'].kurtosis() -1.5