[Python] How to compute a column in polars dataframe using np.linspace

Consider the following pl.DataFrame:

df = pl.DataFrame(
data={
"np_linspace_start": [0, 0, 0],
"np_linspace_stop": [8, 6, 7],
"np_linspace_num": [5, 4, 4]
}
)

shape: (3, 3)
┌───────────────────┬──────────────────┬─────────────────┐
│ np_linspace_start ┆ np_linspace_stop ┆ np_linspace_num │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞═══════════════════╪══════════════════╪═════════════════╡
│ 0 ┆ 8 ┆ 5 │
│ 0 ┆ 6 ┆ 4 │
│ 0 ┆ 7 ┆ 4 │
└───────────────────┴──────────────────┴─────────────────┘


How can I create a new column ls, that is the result of the np.linspace function? This column will hold an np.array.

I was looking for something along those lines:

df.with_columns(
ls=np.linspace(
start=pl.col("np_linspace_start"),
stop=pl.col("np_linspace_stop"),
num=pl.col("np_linspace_num")
)
)


Is there a polars equivalent to np.linspace?

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