[Python] Determine if value exists in previous rows

I'm trying to do this:

import polars as pl

df = pl.DataFrame({
'j': [1, 2, 3, 4],
'k': [3, 1, 2, 2],
})

df = df.with_row_index().with_columns([
pl.struct(['index', 'j']).map_elements(
lambda x: df.slice(0, x['index'])['k']
.to_list().count(x['j']) > 0,
pl.Boolean
).alias('l')
])

print(df)

shape: (4, 4)
┌───────┬─────┬─────┬───────┐
│ index ┆ j ┆ k ┆ l │
│ --- ┆ --- ┆ --- ┆ --- │
│ u32 ┆ i64 ┆ i64 ┆ bool │
╞═══════╪═════╪═════╪═══════╡
│ 0 ┆ 1 ┆ 3 ┆ false │
│ 1 ┆ 2 ┆ 1 ┆ false │
│ 2 ┆ 3 ┆ 2 ┆ true │
│ 3 ┆ 4 ┆ 2 ┆ false │
└───────┴─────┴─────┴───────┘


where each row in l is true if the value in j has been seen in previous rows in k.

Is there a way to do this without escaping into python via map_elements?

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