chore: Simplify is_ordered_categorical

narwhals/utils.py

@@ -42,8 +42,6 @@
     get_pyspark_connect,
     get_pyspark_sql,
     get_sqlframe,
-    is_cudf_series,
-    is_modin_series,
     is_narwhals_series,
     is_narwhals_series_int,
     is_numpy_array_1d,
@@ -62,6 +60,7 @@
     from typing import AbstractSet as Set
 
     import pandas as pd
+    import polars as pl
     import pyarrow as pa
     from typing_extensions import (
         Concatenate,
@@ -1147,7 +1146,7 @@ def scale_bytes(sz: int, unit: SizeUnit) -> int | float:
         raise ValueError(msg)
 
 
-def is_ordered_categorical(series: Series[Any]) -> bool:  # noqa: PLR0911
+def is_ordered_categorical(series: Series[Any]) -> bool:
     """Return whether indices of categories are semantically meaningful.
 
     This is a convenience function to accessing what would otherwise be
@@ -1193,29 +1192,27 @@ def is_ordered_categorical(series: Series[Any]) -> bool:  # noqa: PLR0911
 
     dtypes = series._compliant_series._version.dtypes
     compliant = series._compliant_series
+    # If it doesn't match any branches, let's just play it safe and return False.
+    result: bool = False
     if isinstance(compliant, InterchangeSeries) and isinstance(
         series.dtype, dtypes.Categorical
     ):
-        return compliant.native.describe_categorical["is_ordered"]
-    if series.dtype == dtypes.Enum:
-        return True
-    if series.dtype != dtypes.Categorical:
-        return False
-    native_series = series.to_native()
-    if is_polars_series(native_series):
-        return native_series.dtype.ordering == "physical"  # type: ignore[attr-defined]
-    if is_pandas_series(native_series):
-        return bool(native_series.cat.ordered)
-    if is_modin_series(native_series):  # pragma: no cover
-        return native_series.cat.ordered
-    if is_cudf_series(native_series):  # pragma: no cover
-        return native_series.cat.ordered
-    if is_pyarrow_chunked_array(native_series):
-        from narwhals._arrow.utils import is_dictionary
-
-        return is_dictionary(native_series.type) and native_series.type.ordered
-    # If it doesn't match any of the above, let's just play it safe and return False.
-    return False  # pragma: no cover
+        result = compliant.native.describe_categorical["is_ordered"]
+    elif series.dtype == dtypes.Enum:
+        result = True
+    elif series.dtype != dtypes.Categorical:
+        result = False
+    else:
+        native = series.to_native()
+        if is_polars_series(native):
+            result = cast("pl.Categorical", native.dtype).ordering == "physical"
+        elif is_pandas_like_series(native):
+            result = bool(native.cat.ordered)
+        elif is_pyarrow_chunked_array(native):
+            from narwhals._arrow.utils import is_dictionary
+
+            result = is_dictionary(native.type) and native.type.ordered
+    return result
 
 
 def generate_unique_token(

tests/series_only/is_ordered_categorical_test.py

@@ -5,12 +5,28 @@
 import pytest
 
 import narwhals as nw
+from narwhals.utils import Version
 
 if TYPE_CHECKING:
     from narwhals.typing import IntoSeries
     from tests.utils import ConstructorEager
 
 
+class MockCompliantSeries:
+    _version = Version.MAIN
+
+    def __narwhals_series__(self) -> Any:
+        return self
+
+    @property
+    def native(self) -> tuple[()]:
+        return ()
+
+    @property
+    def dtype(self) -> nw.Categorical:
+        return nw.Categorical()
+
+
 def test_is_ordered_categorical_polars() -> None:
     pytest.importorskip("polars")
     import polars as pl
@@ -62,3 +78,8 @@ def test_is_ordered_categorical_pyarrow() -> None:
     assert nw.is_ordered_categorical(
         nw.from_native(s, series_only=True)
     )  # pragma: no cover
+
+
+def test_is_ordered_categorical_unknown_series() -> None:
+    series: nw.Series[Any] = nw.Series(MockCompliantSeries(), level="full")
+    assert nw.is_ordered_categorical(series) is False