polars.Expr.rolling_skew#

Expr.rolling_skew( window_size: int, *, bias: bool = True, min_samples: int | None = None, center: bool = False, ) → Expr[source]#

Compute a rolling skew.

Warning

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

The window at a given row will include 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.: bias: bool = True,

min_samples

The number of values in the window that should be non-null before computing a result. If set to None (default), it will be set equal to window_size.

center

Set the labels at the center of the window.

See also

Expr.skew

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)