polars cum sum to create a set and not actually sum

I'd like to use a function like cumsum, but that would create a set of all values contained in the column up to the point, and not to sum them

df = pl.DataFrame({"a": [1, 2, 3, 4]})
df["a"].cum_sum()
shape: (4,)
Series: 'a' [i64]
[
    1
    3
    6
    10
]

but I'd like to have something like

df["a"].cum_sum()
shape: (4,)
Series: 'a' [i64]
[
    {1}
    {1, 2}
    {1, 2, 3}
    {1, 2, 3, 4}
]

also note that I'm working on big (several Millions of rows) df, so I'd like to avoid indexing and map_elements (as I've read that it slows down a lot)

This can be achieved using pl.Expr.cumulative_eval together with pl.Expr.unique and pl.Expr.implode as follows.

df.with_columns(
    res=pl.col("a").cumulative_eval(pl.element().unique().implode())
)

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