polars.Expr.cumulative_eval#

Expr.cumulative_eval( expr: Expr, min_periods: int = 1, *, parallel: bool = False, ) → Self[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

>>> df = pl.DataFrame({"values": [1, 2, 3, 4, 5]})
>>> df.select(
...     [
...         pl.col("values").cumulative_eval(
...             pl.element().first() - pl.element().last() ** 2
...         )
...     ]
... )
shape: (5, 1)
┌────────┐
│ values │
│ ---    │
│ f64    │
╞════════╡
│ 0.0    │
│ -3.0   │
│ -8.0   │
│ -15.0  │
│ -24.0  │
└────────┘