[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

marimo/_plugins/ui/_impl/dataframes/transforms/print_code.py

@@ -282,29 +282,29 @@ def generate_where_clause_polars(where: Condition) -> str:
                 agg_alias = f"{column_id}_{aggregation}"
                 if aggregation == "count":
                     aggs.append(
-                        f'pl.col({col_ref}).count().alias({_as_literal(agg_alias)})'
+                        f"pl.col({col_ref}).count().alias({_as_literal(agg_alias)})"
                     )
                 elif aggregation == "sum":
                     aggs.append(
-                        f'pl.col({col_ref}).sum().alias({_as_literal(agg_alias)})'
+                        f"pl.col({col_ref}).sum().alias({_as_literal(agg_alias)})"
                     )
                 elif aggregation == "mean":
                     aggs.append(
-                        f'pl.col({col_ref}).mean().alias({_as_literal(agg_alias)})'
+                        f"pl.col({col_ref}).mean().alias({_as_literal(agg_alias)})"
                     )
                 elif aggregation == "median":
                     aggs.append(
-                        f'pl.col({col_ref}).median().alias({_as_literal(agg_alias)})'
+                        f"pl.col({col_ref}).median().alias({_as_literal(agg_alias)})"
                     )
                 elif aggregation == "min":
                     aggs.append(
-                        f'pl.col({col_ref}).min().alias({_as_literal(agg_alias)})'
+                        f"pl.col({col_ref}).min().alias({_as_literal(agg_alias)})"
                     )
                 elif aggregation == "max":
                     aggs.append(
-                        f'pl.col({col_ref}).max().alias({_as_literal(agg_alias)})'
+                        f"pl.col({col_ref}).max().alias({_as_literal(agg_alias)})"
                     )
-        group_cols = [f'pl.col({_as_literal(col)})' for col in column_ids]
+        group_cols = [f"pl.col({_as_literal(col)})" for col in column_ids]
         return f"{df_name}.group_by([{', '.join(group_cols)}], maintain_order=True).agg([{', '.join(aggs)}])"  # noqa: E501
 
     elif transform.type == TransformType.SELECT_COLUMNS:

tests/_plugins/ui/_impl/dataframes/test_dataframe.py

@@ -280,24 +280,26 @@ def test_polars_groupby_alias() -> None:
         import marimo as mo
 
         # Create a test dataframe with age and group columns
-        df = pl.DataFrame({
-            "group": ["a", "a", "b", "b"],
-            "age": [10, 20, 30, 40],
-        })
+        df = pl.DataFrame(
+            {
+                "group": ["a", "a", "b", "b"],
+                "age": [10, 20, 30, 40],
+            }
+        )
         # Test the transformation directly using TransformsContainer
         from marimo._plugins.ui._impl.dataframes.transforms.apply import (
             TransformsContainer,
             get_handler_for_dataframe,
         )
         from marimo._plugins.ui._impl.dataframes.transforms.types import (
             GroupByTransform,
-            TransformType,
             Transformations,
+            TransformType,
         )
 
         handler = get_handler_for_dataframe(df)
         transform_container = TransformsContainer(df, handler)
-        
+
         # Create and apply the transformation
         transform = GroupByTransform(
             type=TransformType.GROUP_BY,
@@ -313,7 +315,10 @@ def test_polars_groupby_alias() -> None:
         assert "group" in transformed_df.columns
         assert "age_max" in transformed_df.columns
         assert transformed_df.shape == (2, 2)
-        assert transformed_df["age_max"].to_list() == [20, 40]  # max age for each group
+        assert transformed_df["age_max"].to_list() == [
+            20,
+            40,
+        ]  # max age for each group
 
         # The resulting frame should have correct column names and values
         # Convert to dict and verify values
@@ -330,6 +335,7 @@ def test_polars_groupby_alias() -> None:
         from marimo._plugins.ui._impl.dataframes.transforms.print_code import (
             python_print_polars,
         )
+
         code = python_print_polars(
             "df",
             ["group", "age"],