[Python] Polars group_by + describe: return all columns as single dataframe

I'm slowly migrating to polars from pandas and I have found that in some cases the polars syntax is tricky.

I'm seeking help to do a group_by followed by a describe using less (or more readable) code.

See this example:

from io import BytesIO
import pandas as pd
import polars as pl

S = b'''group,value\n3,245\n3,28\n3,48\n1,113\n1,288\n1,165\n2,90\n2,21\n2,109'''

pl_df = pl.read_csv(BytesIO(S))
pd_df = pd.read_csv(BytesIO(S))

# Polars' way
pl_df.group_by('group').map_groups(
lambda df: (
df['value']
.describe()
.with_columns(
group=pl.lit(df['group'][0])
)
)
).pivot(index='group', on='statistic')


Something similar in pandas would be:

# pandas'
pd_df.groupby('group').value.describe()

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