pandas.Series.kurtosis — pandas 2.2.3 documentation (original) (raw)

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

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)}

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.

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:

scalar or scalar

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