[Python] How to perform pandas reindex in polars

In pandas, I can reindex() the dataframe using multi-index to make the date range consistent for each group.

See example below using pandas:

import pandas as pd
import polars as pl

df = pl.from_repr("""
┌────────────┬───────┬───────┐
│ date ┆ group ┆ value │
│ --- ┆ --- ┆ --- │
│ date ┆ str ┆ i64 │
╞════════════╪═══════╪═══════╡
│ 2022-01-01 ┆ A ┆ 10 │
│ 2022-02-01 ┆ A ┆ 20 │
│ 2022-03-01 ┆ A ┆ 30 │
│ 2022-04-01 ┆ B ┆ 40 │
│ 2022-05-01 ┆ B ┆ 50 │
│ 2022-06-01 ┆ B ┆ 60 │
└────────────┴───────┴───────┘
""")

df_pd = df.to_pandas().set_index(["group", "date"])

new_index = pd.MultiIndex.from_product([df_pd.index.levels[0].tolist(), df_pd.index.levels[1].tolist()], names=["group", "date"])

result = df_pd.reindex(new_index)

with pl.Config(tbl_rows=13):
pl.from_pandas(result.reset_index())


which gives a result with both groups having the same date range:

shape: (12, 3)
┌───────┬─────────────────────┬───────┐
│ group ┆ date ┆ value │
│ --- ┆ --- ┆ --- │
│ str ┆ datetime[ns] ┆ f64 │
╞═══════╪═════════════════════╪═══════╡
│ A ┆ 2022-01-01 00:00:00 ┆ 10.0 │
│ A ┆ 2022-02-01 00:00:00 ┆ 20.0 │
│ A ┆ 2022-03-01 00:00:00 ┆ 30.0 │
│ A ┆ 2022-04-01 00:00:00 ┆ null │
│ A ┆ 2022-05-01 00:00:00 ┆ null │
│ A ┆ 2022-06-01 00:00:00 ┆ null │
│ B ┆ 2022-01-01 00:00:00 ┆ null │
│ B ┆ 2022-02-01 00:00:00 ┆ null │
│ B ┆ 2022-03-01 00:00:00 ┆ null │
│ B ┆ 2022-04-01 00:00:00 ┆ 40.0 │
│ B ┆ 2022-05-01 00:00:00 ┆ 50.0 │
│ B ┆ 2022-06-01 00:00:00 ┆ 60.0 │
└───────┴─────────────────────┴───────┘


Is there any way to produce the same result in polars directly?

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