polars.fold#

polars.fold( acc: IntoExpr, function: Callable[[Series, Series], Series], exprs: Sequence[Expr | str] | Expr, ) → Expr[source]#

Accumulate over multiple columns horizontally/ row wise with a left fold.

Parameters:

acc: Accumulator Expression. This is the value that will be initialized when the fold starts. For a sum this could for instance be lit(0).
function: Function to apply over the accumulator and the value. Fn(acc, value) -> new_value
exprs: Expressions to aggregate over. May also be a wildcard expression.

Notes

If you simply want the first encountered expression as accumulator, consider using reduce.

Examples

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, 3],
...         "b": [3, 4, 5],
...         "c": [5, 6, 7],
...     }
... )
>>> df
shape: (3, 3)
┌─────┬─────┬─────┐
│ a   ┆ b   ┆ c   │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ 1   ┆ 3   ┆ 5   │
│ 2   ┆ 4   ┆ 6   │
│ 3   ┆ 5   ┆ 7   │
└─────┴─────┴─────┘

Horizontally sum over all columns and add 1.

>>> df.select(
...     pl.fold(
...         acc=pl.lit(1), function=lambda acc, x: acc + x, exprs=pl.col("*")
...     ).alias("sum"),
... )
shape: (3, 1)
┌─────┐
│ sum │
│ --- │
│ i64 │
╞═════╡
│ 10  │
│ 13  │
│ 16  │
└─────┘

You can also apply a condition/predicate on all columns:

>>> df = pl.DataFrame(
...     {
...         "a": [1, 2, 3],
...         "b": [0, 1, 2],
...     }
... )
>>> df
shape: (3, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1   ┆ 0   │
│ 2   ┆ 1   │
│ 3   ┆ 2   │
└─────┴─────┘

>>> df.filter(
...     pl.fold(
...         acc=pl.lit(True),
...         function=lambda acc, x: acc & x,
...         exprs=pl.col("*") > 1,
...     )
... )
shape: (1, 2)
┌─────┬─────┐
│ a   ┆ b   │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 3   ┆ 2   │
└─────┴─────┘