polars.Expr.rolling_skew#

Expr.rolling_skew(window_size: int, *, bias: bool = True) → Self[source]#

Compute a rolling skew.

The window at a given row includes the row itself and the window_size - 1 elements before it.

Parameters:

window_size: Integer size of the rolling window.
bias: If False, the calculations are corrected for statistical bias.

Examples

>>> df = pl.DataFrame({"a": [1, 4, 2, 9]})
>>> df.select(pl.col("a").rolling_skew(3))
shape: (4, 1)
┌──────────┐
│ a        │
│ ---      │
│ f64      │
╞══════════╡
│ null     │
│ null     │
│ 0.381802 │
│ 0.47033  │
└──────────┘

Note how the values match the following:

>>> pl.Series([1, 4, 2]).skew(), pl.Series([4, 2, 9]).skew()
(0.38180177416060584, 0.47033046033698594)