Joining two dataframes that share "index columns" (id columns), but not data columns, so that the resulting dataframe has a full spine of ids?

I find myself doing this:

import polars as pl
import sys

red_data = pl.DataFrame(
    [
        pl.Series("id", [0, 1, 2], dtype=pl.UInt8()),
        pl.Series("red_data", [1, 0, 1], dtype=pl.UInt8()),
    ]
)
blue_data = pl.DataFrame(
    [
        pl.Series("id", [0, 2, 3], dtype=pl.UInt8()),
        pl.Series("blue_data", [0, 1, 1], dtype=pl.UInt8()),
    ]
)

# in both red and blue
red_and_blue = red_data.join(blue_data, on=["id"])
# in red, but not blue
red_not_blue = red_data.join(blue_data, on=["id"], how="anti").with_columns(
    blue_data=pl.lit(None, dtype=pl.UInt8())
)
# in blue, but not red
blue_not_red = blue_data.join(red_data, on=["id"], how="anti").with_columns(
    red_data=pl.lit(None, dtype=pl.UInt8())
)

columns = ["id", "red_data", "blue_data"]
sys.displayhook(
    pl.concat(
        [
            red_and_blue.select(columns),
            red_not_blue.select(columns),
            blue_not_red.select(columns),
        ]
    )
)

shape: (4, 3)
┌─────┬──────────┬───────────┐
│ id  ┆ red_data ┆ blue_data │
│ --- ┆ ---      ┆ ---       │
│ u8  ┆ u8       ┆ u8        │
╞═════╪══════════╪═══════════╡
│ 0   ┆ 1        ┆ 0         │
│ 2   ┆ 1        ┆ 1         │
│ 1   ┆ 0        ┆ null      │
│ 3   ┆ null     ┆ 1         │
└─────┴──────────┴───────────┘

It seems like you are looking for a simple pl.DataFrame.join with how="full" and coalesce=True.

red_data.join(blue_data, on="id", how="full", coalesce=True)

shape: (4, 3)
┌─────┬──────────┬───────────┐
│ id  ┆ red_data ┆ blue_data │
│ --- ┆ ---      ┆ ---       │
│ u8  ┆ u8       ┆ u8        │
╞═════╪══════════╪═══════════╡
│ 0   ┆ 1        ┆ 0         │
│ 1   ┆ 0        ┆ null      │
│ 2   ┆ 1        ┆ 1         │
│ 3   ┆ null     ┆ 1         │
└─────┴──────────┴───────────┘