How to create new columns based on a grouping method for one column in Polars?

I have some data structed as showed at the first picture. Where I like to restructure the dataframe. Short piece of the initial data:

id	time	value
2050	02-01	20
2051	02-01	25
2050	02-02	21
2051	02-02	22
2051	02-03	23

The way I would like the restructured dataframe is with a timestamp column and then a column for each externallogid. I have done it with use of pandas, but since the file is quite huge, and must be used multiple times, I will like to do it in Polars due to the speed.

Excpected output:

time	2050	2051
02-01	20	25
02-02	21	22
02-03	nan	23

I have tried an use the groupby function, and join/hstack/concat. But seems to have problems when also trying to use Lazyframes.

Thanks

To produce the data:

import polars as pl

lf = pl.DataFrame({'id': [2050, 2051, 2050, 2051, 2051],
                    'time': ['2023-05-01',
                             '2023-05-01',
                             '2023-05-02',
                             '2023-05-02',
                             '2023-05-03'],
                   'value': [20, 25, 21, 22, 23]})
lf = lf.with_columns(pl.col("time").str.to_datetime("%Y-%m-%d"))

Solution

You should .pivot();

In [29]: lf.pivot('id', index='time')
Out[29]:
shape: (3, 3)
┌─────────────────────┬──────┬──────┐
│ time                ┆ 2050 ┆ 2051 │
│ ---                 ┆ ---  ┆ ---  │
│ datetime[μs]        ┆ i64  ┆ i64  │
╞═════════════════════╪══════╪══════╡
│ 2023-05-01 00:00:00 ┆ 20   ┆ 25   │
│ 2023-05-02 00:00:00 ┆ 21   ┆ 22   │
│ 2023-05-03 00:00:00 ┆ null ┆ 23   │
└─────────────────────┴──────┴──────┘