polars.Series.cumulative_eval#

Series.cumulative_eval( expr: Expr, min_periods: int = 1, *, parallel: bool = False, ) → Series[source]#

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

Parameters:

expr: Expression to evaluate
min_periods: 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 groupby or another operation that already has much parallelization.

Warning

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

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' [f64]
[
    0.0
    -3.0
    -8.0
    -15.0
    -24.0
]