[Python] Python Polars group by on both time and categorical values

There is a polars dataframe which consists of 3 fields listed below.

df = pl.from_repr("""
┌─────────┬─────────────────────┬─────────────┐
│ user_id ┆ date ┆ part_of_day │
│ --- ┆ --- ┆ --- │
│ i64 ┆ datetime[ns] ┆ cat │
╞═════════╪═════════════════════╪═════════════╡
│ 173367 ┆ 2021-08-03 00:00:00 ┆ day │
│ 132702 ┆ 2021-10-28 00:00:00 ┆ evening │
│ 100853 ┆ 2021-07-29 00:00:00 ┆ night │
│ 305810 ┆ 2021-08-24 00:00:00 ┆ day │
│ 305239 ┆ 2021-08-13 00:00:00 ┆ day │
└─────────┴─────────────────────┴─────────────┘
""")


My task is to calculate the number of unique users for each week and time of day. Polars provides three different types of grouping options::

1. group_by: This option allows for simple grouping by columns or expressions.
2. group_by_dynamic: This option only accepts a datetime column as the grouper.
3. rolling: This option also only accepts a datetime column as the grouper.

It seems like group_by_dynamic would be the best fit for this specific task. However, it does not allow for other columns to be used as the grouper. Therefore, my question is how can I accomplish this task using Polars?

Additionally, I have included some code that I would have used to solve this problem using Pandas.

(
df
.cast({"part_of_day": pl.String})
.to_pandas()
.groupby([pd.Grouper(key="date", freq="1W"), "part_of_day"])
.user_id
.nunique()
)


Desired result:

date part_of_day
2021-08-01 night 1
2021-08-08 day 1
2021-08-15 day 1
2021-08-29 day 1
2021-10-31 evening 1
Name: user_id, dtype: int64

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