[Python] Polars group_by + value_counts

I need some help with polars:

I have a dataframe with a categorical values column

┌───────────────────┬──────────────┬────────┐
│ session_id ┆ elapsed_time ┆ fqid │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i32 ┆ cat │
╞═══════════════════╪══════════════╪════════╡
│ 20090312431273200 ┆ 0 ┆ intro │
│ 20090312431273200 ┆ 1323 ┆ gramps │
│ 20090312431273200 ┆ 831 ┆ gramps │
│ 20090312431273200 ┆ 1147 ┆ gramps │
│ … ┆ … ┆ … │
│ 20090312431273200 ┆ 5197 ┆ teddy │
│ 20090312431273200 ┆ 6180 ┆ teddy │
│ 20090312431273200 ┆ 7014 ┆ teddy │
│ 20090312431273200 ┆ 7946 ┆ teddy │
└───────────────────┴──────────────┴────────┘


And I want to transform the fqid-column to look like this:

┌───────────────────┬─────────────┬────────────┬────────────┐
│ session_id ┆ fqid_gramps ┆ fqid_intro ┆ fqid_teddy │
│ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ i32 ┆ i32 ┆ i32 │
╞═══════════════════╪═════════════╪════════════╪════════════╡
│ 20090312431273200 ┆ 1 ┆ 1 ┆ 4 │
└───────────────────┴─────────────┴────────────┴────────────┘


That is, I would like to:

• Group_by over session_id,
• Make a value_counts() over fqid,
• Rename columns so that it would be 'fqid_' + category,
• Turn them into columns (transpose),
• Add them to the result.

Technically, I could achieve this without groupby by using something like

column_values = train['fqid'].value_counts().with_columns(pl.concat_str(pl.lit('fqid' + '_').alias('fqid'), pl.col('fqid').cast(pl.String))).transpose()
column_values = column_values.rename(column_values.head(1).to_dicts().pop()).slice(1)


But when I am trying to make an aggregating function from this replacing train['fqid'] with pl.col('fqid') and making a group_by('session_id').aggregate(func('fqid')) it gives me nothing but errors like AttributeError: 'Expr' object has no attribute 'with_columns'.

Could you kindly suggest a proper way of making this operation?

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