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

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.


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