feat: Allow cast to Categorical inside list.eval

crates/polars-core/src/datatypes/dtype.rs

@@ -28,6 +28,10 @@ pub trait MetaDataExt: IntoMetadata {
     }
 
     fn pl_categorical_metadata(&self) -> Option<&str> {
+        // We ignore DTYPE_CATEGORICAL_LEGACY here, as we already map all
+        // string-typed arrow dictionaries to the global Categories, and the
+        // legacy metadata format only specifies the now-removed physical
+        // ordering parameter.
         Some(
             self.into_metadata_ref()
                 .get(DTYPE_CATEGORICAL_NEW)?
@@ -533,8 +537,6 @@ impl DataType {
         use DataType::*;
         match self {
             Binary | String => true,
-            #[cfg(feature = "dtype-categorical")]
-            Categorical(_, _) | Enum(_, _) => true, // TODO @ cat-rework: is this right?
             List(inner) => inner.contains_views(),
             #[cfg(feature = "dtype-array")]
             Array(inner, _) => inner.contains_views(),

crates/polars-plan/src/plans/conversion/dsl_to_ir/expr_to_ir.rs

@@ -350,25 +350,12 @@ pub(super) fn to_aexpr_impl(
             match variant {
                 EvalVariant::List => {
                     for (_, e) in ArenaExprIter::iter(&&*arena, evaluation) {
-                        match e {
-                            #[cfg(feature = "dtype-categorical")]
-                            AExpr::Cast {
-                                dtype: DataType::Categorical(_, _) | DataType::Enum(_, _),
-                                ..
-                            } => {
-                                // TODO @ cat-rework: why not?
-                                polars_bail!(
-                                    ComputeError: "casting to categorical not allowed in `list.eval`"
-                                )
-                            },
-                            AExpr::Column(name) => {
-                                polars_ensure!(
-                                    name.is_empty(),
-                                    ComputeError:
-                                    "named columns are not allowed in `list.eval`; consider using `element` or `col(\"\")`"
-                                );
-                            },
-                            _ => {},
+                        if let AExpr::Column(name) = e {
+                            polars_ensure!(
+                                name.is_empty(),
+                                ComputeError:
+                                "named columns are not allowed in `list.eval`; consider using `element` or `col(\"\")`"
+                            );
                         }
                     }
                 },

py-polars/tests/unit/datatypes/test_categorical.py

@@ -8,9 +8,6 @@
 
 import polars as pl
 from polars import StringCache
-from polars.exceptions import (
-    ComputeError,
-)
 from polars.testing import assert_frame_equal, assert_series_equal
 from tests.unit.conftest import with_string_cache_if_auto_streaming
 
@@ -425,21 +422,6 @@ def test_categorical_in_struct_nulls() -> None:
     assert s[2] == {"job": "waiter", "count": 1}
 
 
-@pytest.mark.usefixtures("test_global_and_local")
-def test_cast_inner_categorical() -> None:
-    dtype = pl.List(pl.Categorical)
-    out = pl.Series("foo", [["a"], ["a", "b"]]).cast(dtype)
-    assert out.dtype == dtype
-    assert out.to_list() == [["a"], ["a", "b"]]
-
-    with pytest.raises(
-        ComputeError, match=r"casting to categorical not allowed in `list.eval`"
-    ):
-        pl.Series("foo", [["a", "b"], ["a", "b"]]).list.eval(
-            pl.element().cast(pl.Categorical)
-        )
-
-
 @pytest.mark.slow
 def test_stringcache() -> None:
     N = 1_500

py-polars/tests/unit/operations/namespaces/list/test_eval.py

@@ -49,6 +49,16 @@ def test_list_eval_categorical() -> None:
     )
 
 
+def test_list_eval_cast_categorical() -> None:
+    df = pl.DataFrame({"test": [["a", None], ["c"], [], ["a", "b", "c"]]})
+    expected = pl.DataFrame(
+        {"test": [["a", None], ["c"], [], ["a", "b", "c"]]},
+        schema={"test": pl.List(pl.Categorical)},
+    )
+    result = df.select(pl.col("test").list.eval(pl.element().cast(pl.Categorical)))
+    assert_frame_equal(result, expected)
+
+
 def test_list_eval_type_coercion() -> None:
     last_non_null_value = pl.element().fill_null(3).last()
     df = pl.DataFrame({"array_cols": [[1, None]]})