polars.Series.kurtosis#

Series.kurtosis(*, fisher: bool = True, bias: bool = True) → float | None[source]#

Compute the kurtosis (Fisher or Pearson) of a dataset.

Kurtosis is the fourth central moment divided by the square of the variance. If Fisher’s definition is used, then 3.0 is subtracted from the result to give 0.0 for a normal distribution. If bias is False then the kurtosis is calculated using k statistics to eliminate bias coming from biased moment estimators

See scipy.stats for more information

Parameters:

fisherbool, optional: If True, Fisher’s definition is used (normal ==> 0.0). If False, Pearson’s definition is used (normal ==> 3.0).
biasbool, optional: If False, the calculations are corrected for statistical bias.

Examples

>>> s = pl.Series("grades", [66, 79, 54, 97, 96, 70, 69, 85, 93, 75])
>>> s.kurtosis()
-1.0522623626787952
>>> s.kurtosis(fisher=False)
1.9477376373212048
>>> s.kurtosis(fisher=False, bias=False)
2.1040361802642726