fix: sqlframe false positives in compliance checks

narwhals/translate.py

@@ -386,32 +386,8 @@ def _from_native_impl(  # noqa: PLR0915
         msg = "Invalid parameter combination: `eager_only=True` and `eager_or_interchange_only=True`"
         raise ValueError(msg)
 
-    # SQLFrame
-    # This one needs checking before extensions as `hasattr` always returns `True`.
-    if is_sqlframe_dataframe(native_object):  # pragma: no cover
-        from narwhals._spark_like.dataframe import SparkLikeLazyFrame
-
-        if series_only:
-            msg = "Cannot only use `series_only` with SQLFrame DataFrame"
-            raise TypeError(msg)
-        if eager_only or eager_or_interchange_only:
-            msg = "Cannot only use `eager_only` or `eager_or_interchange_only` with SQLFrame DataFrame"
-            raise TypeError(msg)
-        import sqlframe._version
-
-        backend_version = parse_version(sqlframe._version)
-        return LazyFrame(
-            SparkLikeLazyFrame(
-                native_object,
-                backend_version=backend_version,
-                version=version,
-                implementation=Implementation.SQLFRAME,
-            ),
-            level="lazy",
-        )
-
     # Extensions
-    elif is_compliant_dataframe(native_object):
+    if is_compliant_dataframe(native_object):
         if series_only:
             if not pass_through:
                 msg = "Cannot only use `series_only` with dataframe"
@@ -765,6 +741,28 @@ def _from_native_impl(  # noqa: PLR0915
             level="lazy",
         )
 
+    elif is_sqlframe_dataframe(native_object):  # pragma: no cover
+        from narwhals._spark_like.dataframe import SparkLikeLazyFrame
+
+        if series_only:
+            msg = "Cannot only use `series_only` with SQLFrame DataFrame"
+            raise TypeError(msg)
+        if eager_only or eager_or_interchange_only:
+            msg = "Cannot only use `eager_only` or `eager_or_interchange_only` with SQLFrame DataFrame"
+            raise TypeError(msg)
+        import sqlframe._version
+
+        backend_version = parse_version(sqlframe._version)
+        return LazyFrame(
+            SparkLikeLazyFrame(
+                native_object,
+                backend_version=backend_version,
+                version=version,
+                implementation=Implementation.SQLFRAME,
+            ),
+            level="lazy",
+        )
+
     # Interchange protocol
     elif _supports_dataframe_interchange(native_object):
         from narwhals._interchange.dataframe import InterchangeFrame

narwhals/utils.py

@@ -5,6 +5,7 @@
 from datetime import timezone
 from enum import Enum
 from enum import auto
+from inspect import getattr_static
 from secrets import token_hex
 from typing import TYPE_CHECKING
 from typing import Any
@@ -1275,16 +1276,24 @@ def dtype_matches_time_unit_and_time_zone(
     )
 
 
+def _hasattr_static(obj: Any, attr: str) -> bool:
+    try:
+        getattr_static(obj, attr)
+    except AttributeError:
+        return False
+    return True
+
+
 def is_compliant_dataframe(obj: Any) -> TypeIs[CompliantDataFrame]:
-    return hasattr(obj, "__narwhals_dataframe__")
+    return _hasattr_static(obj, "__narwhals_dataframe__")
 
 
 def is_compliant_lazyframe(obj: Any) -> TypeIs[CompliantLazyFrame]:
-    return hasattr(obj, "__narwhals_lazyframe__")
+    return _hasattr_static(obj, "__narwhals_lazyframe__")
 
 
 def is_compliant_series(obj: Any) -> TypeIs[CompliantSeries]:
-    return hasattr(obj, "__narwhals_series__")
+    return _hasattr_static(obj, "__narwhals_series__")
 
 
 def is_compliant_expr(

tests/frame/schema_test.py

@@ -205,20 +205,6 @@ def test_schema_object(method: str, expected: Any) -> None:
     assert getattr(schema, method)() == expected
 
 
-@pytest.mark.skipif(
-    PANDAS_VERSION < (2,),
-    reason="Before 2.0, pandas would raise on `drop_duplicates`",
-)
-def test_from_non_hashable_column_name() -> None:
-    # This is technically super-illegal
-    # BUT, it shows up in a scikit-learn test, so...
-    df = pd.DataFrame([[1, 2], [3, 4]], columns=["pizza", ["a", "b"]])
-
-    df = nw.from_native(df, eager_only=True)
-    assert df.columns == ["pizza", ["a", "b"]]
-    assert df["pizza"].dtype == nw.Int64
-
-
 def test_validate_not_duplicated_columns_pandas_like() -> None:
     df = pd.DataFrame([[1, 2], [4, 5]], columns=["a", "a"])
     with pytest.raises(

tests/translate/from_native_test.py

@@ -233,6 +233,40 @@ def test_eager_only_lazy_dask(eager_only: Any, context: Any) -> None:
         assert nw.from_native(dframe, eager_only=eager_only, strict=False) is dframe
 
 
+def test_series_only_sqlframe() -> None:  # pragma: no cover
+    pytest.importorskip("sqlframe")
+    from sqlframe.duckdb import DuckDBSession
+
+    session = DuckDBSession()
+    df = (  # type: ignore[no-any-return]
+        session.createDataFrame([*zip(*data.values())], schema=[*data.keys()])
+    )
+
+    with pytest.raises(TypeError, match="Cannot only use `series_only`"):
+        nw.from_native(df, series_only=True)
+
+
+@pytest.mark.parametrize(
+    ("eager_only", "context"),
+    [
+        (False, does_not_raise()),
+        (True, pytest.raises(TypeError, match="Cannot only use `eager_only`")),
+    ],
+)
+def test_eager_only_sqlframe(eager_only: Any, context: Any) -> None:  # pragma: no cover
+    pytest.importorskip("sqlframe")
+    from sqlframe.duckdb import DuckDBSession
+
+    session = DuckDBSession()
+    df = (  # type: ignore[no-any-return]
+        session.createDataFrame([*zip(*data.values())], schema=[*data.keys()])
+    )
+
+    with context:
+        res = nw.from_native(df, eager_only=eager_only)
+        assert isinstance(res, nw.LazyFrame)
+
+
 def test_from_native_strict_false_typing() -> None:
     df = pl.DataFrame()
     nw.from_native(df, strict=False)