[Python] Polars Create Column with String Formatting

I have a polars dataframe:

df = pl.DataFrame({'schema_name': ['test_schema', 'test_schema_2'],
'table_name': ['test_table', 'test_table_2'],
'column_name': ['test_column, test_column_2','test_column']})

schema_name table_name column_name
test_schema test_table test_column, test_column_2
test_schema_2 test_table_2 test_column

I have a string:

date_field_value_max_query = '''
select '{0}' as schema_name,
'{1}' as table_name,
greatest({2})
from {0}.{1}
group by 1, 2
'''


I would like to use polars to add a column by using string formatting. The target dataframe is this:

schema_name table_name column_name query
test_schema test_table test_column, test_column_2 select test_schema, test_table, greatest(test_column, test_column_2) from test_schema.test_table group by 1, 2
test_schema_2 test_table_2 test_column select test_schema_2, test_table_2, greatest(test_column) from test_schema_2.test_table_2 group by 1, 2

In pandas, I would do something like this:

df.apply(lambda row: date_field_value_max_query.format(row['schema_name'], row['table_name'], row['column_name']), axis=1)


For polars, I tried this:

df.map_rows(lambda row: date_field_value_max_query.format(row[0], row[1], row[2]))


...but this returns only the one column, and I lose the original three columns. I know this approach is also not recommended for polars, when possible.

How can I perform string formatting across multiple dataframe columns with the output column attached to the original dataframe?

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