[Python] Polars-Python: Compare list columns

In Python Polars, I have a dataframe like the below:

df = pl.DataFrame(
{"sets": [[1, 2, 3], [1, 2], [9, 10]], "optional_members": [[1, 2, 3], [1, 2], [9, 0]]}
)

shape: (2, 2)
┌───────────┬──────────────────┐
│ sets ┆ optional_members │
│ --- ┆ --- │
│ list[i64] ┆ list[i64] │
╞═══════════╪══════════════════╡
│ [1, 4, 3] ┆ [1, 2, 3] │
│ [1, 0] ┆ [1, 2] │
└───────────┴──────────────────┘


I would like to build an expression that gets me the elements of the first column that are in the second, keeping the shape of the former, i.e:


shape: (2, 3)
┌───────────┬──────────────────┬─────────────────────┐
│ sets ┆ optional_members ┆ result │
│ --- ┆ --- ┆ --- │
│ list[i64] ┆ list[i64] ┆ list[bool] │
╞═══════════╪══════════════════╪═════════════════════╡
│ [1, 4, 3] ┆ [1, 2, 3] ┆ [true, false, true] │
│ [1, 0] ┆ [1, 2] ┆ [true, false] │
└───────────┴──────────────────┴─────────────────────┘



I have tried using eval over the first list, something like:


func = lambda x, y: y.list.contains(x)

df.with_columns(contains=
pl.col("optional_members")
.list.
eval(func(pl.element(), pl.col("optional_members"))))



But the pl.col() expression cannot be in an eval.

How could we aaddress this while keeping the solution in a single expression?

Thanks to @roman comment, a point need to be made: the check should be done regardless of the position.

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