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.