polars.Expr.rolling_var#

Expr.rolling_var(window_size: int | timedelta | str, weights: list[float] | None = None, min_periods: int | None = None, *, center: bool = False, by: str | None = None, closed: ClosedInterval = 'left') → Self[source]#

Compute a rolling variance.

A window of length window_size will traverse the array. The values that fill this window will (optionally) be multiplied with the weights given by the weight vector. The resulting values will be aggregated to their sum.

Parameters:
window_size

The length of the window. Can be a fixed integer size, or a dynamic temporal size indicated by a timedelta or the following string language:

  • 1ns (1 nanosecond)

  • 1us (1 microsecond)

  • 1ms (1 millisecond)

  • 1s (1 second)

  • 1m (1 minute)

  • 1h (1 hour)

  • 1d (1 day)

  • 1w (1 week)

  • 1mo (1 calendar month)

  • 1y (1 calendar year)

  • 1i (1 index count)

Suffix with “_saturating” to indicate that dates too large for their month should saturate at the largest date (e.g. 2022-02-29 -> 2022-02-28) instead of erroring.

If a timedelta or the dynamic string language is used, the by and closed arguments must also be set.

weights

An optional slice with the same length as the window that will be multiplied elementwise with the values in the window.

min_periods

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

center

Set the labels at the center of the window

by

If the window_size is temporal for instance “5h” or “3s”, you must set the column that will be used to determine the windows. This column must be of dtype {Date, Datetime}

closed{‘left’, ‘right’, ‘both’, ‘none’}

Define which sides of the temporal interval are closed (inclusive).

Warning

This functionality is experimental and may change without it being considered a breaking change.

Notes

If you want to compute multiple aggregation statistics over the same dynamic window, consider using groupby_rolling this method can cache the window size computation.

Examples

>>> df = pl.DataFrame({"A": [1.0, 2.0, 3.0, 4.0, 6.0, 8.0]})
>>> df.select(
...     [
...         pl.col("A").rolling_var(window_size=3),
...     ]
... )
shape: (6, 1)
┌──────────┐
│ A        │
│ ---      │
│ f64      │
╞══════════╡
│ null     │
│ null     │
│ 1.0      │
│ 1.0      │
│ 2.333333 │
│ 4.0      │
└──────────┘