polars.Expr.kurtosis#

Expr.kurtosis(*, fisher: bool = True, bias: bool = True) → Self[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

>>> df = pl.DataFrame({"a": [1, 2, 3, 2, 1]})
>>> df.select(pl.col("a").kurtosis())
shape: (1, 1)
┌───────────┐
│ a         │
│ ---       │
│ f64       │
╞═══════════╡
│ -1.153061 │
└───────────┘