[Python] How to count work days between date columns with Polars

I have the following DataFrame.

df = pl.from_repr("""
┌────────────┬───────────────┐
│ date ┆ maturity_date │
│ --- ┆ --- │
│ date ┆ date │
╞════════════╪═══════════════╡
│ 2000-01-04 ┆ 2000-01-17 │
│ 2000-01-04 ┆ 2000-02-15 │
│ 2000-01-04 ┆ 2000-03-15 │
│ 2000-01-04 ┆ 2000-04-17 │
│ 2000-01-04 ┆ 2000-05-15 │
└────────────┴───────────────┘
""")


I'm trying to get the number of the workdays between date and maturity_date (not counting saturday and sunday)

I'd also like to calculate diff days that use a given calendar like a trade date calendar of stock market which is different from a normal calendar.

I use this date_ranges to count workdays, but it seems only a little faster than map_elements

df.with_columns(
pl.date_ranges("date", "maturity_date")
.list.eval(pl.element().dt.weekday() <= 5)
.list.count_matches(True)
.alias("workdays_diff")
# pl.concat_list("date", "maturity_date").map_elements(lambda x: get_work_days(x[0], x[1]))
# .alias("workdays_diff")
)


shape: (5, 3)
┌────────────┬───────────────┬───────────────┐ # ┌────────────────┐
│ date ┆ maturity_date ┆ workdays_diff │ # │ tradedate_diff │
│ --- ┆ --- ┆ --- │ # │ --- │
│ date ┆ date ┆ u32 │ # │ i64 │
╞════════════╪═══════════════╪═══════════════╡ # ╞════════════════╡
│ 2000-01-04 ┆ 2000-01-17 ┆ 10 │ # │ 10 │
│ 2000-01-04 ┆ 2000-02-15 ┆ 31 │ # │ 21 │
│ 2000-01-04 ┆ 2000-03-15 ┆ 52 │ # │ 42 │
│ 2000-01-04 ┆ 2000-04-17 ┆ 75 │ # │ 65 │
│ 2000-01-04 ┆ 2000-05-15 ┆ 95 │ # │ 80 │
└────────────┴───────────────┴───────────────┘ # └────────────────┘


Is there a faster way?

Is there also a way to calculate tradedate_diff?

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