polars.Series.cumulative_eval

Series.cumulative_eval(
    expr: Expr,
    *,
    min_samples: int = 1,
    parallel: bool = False,
) → Series

Run an expression over a sliding window that increases 1 slot every iteration.

Warning

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

Changed in version 1.21.0: The min_periods parameter was renamed min_samples.

Parameters:

expr

Expression to evaluate

min_samples

Number of valid values there should be in the window before the expression is evaluated. valid values = length - null_count

parallel

Run in parallel. Don’t do this in a group by or another operation that already has much parallelization.

Warning

This can be really slow as it can have O(n^2) complexity. Don’t use this for operations that visit all elements.

Examples

>>> s = pl.Series("values", [1, 2, 3, 4, 5])
>>> s.cumulative_eval(pl.element().first() - pl.element().last() ** 2)
shape: (5,)
Series: 'values' [i64]
[
    0
    -3
    -8
    -15
    -24
]