sort out dataframe.to_numpy too

narwhals/_pandas_like/dataframe.py

@@ -701,18 +701,32 @@ def to_numpy(self, dtype: Any = None, copy: bool | None = None) -> Any:
         if dtype is not None:
             return self._native_frame.to_numpy(dtype=dtype, copy=copy)
 
+        convert = set()
+        for key, val in self.schema.items():
+            if val == self._dtypes.Datetime and val.time_zone is not None:  # type: ignore[attr-defined]
+                convert.add(key)
+        df = self.with_columns(
+            *[
+                self.__narwhals_namespace__()
+                .col(x)
+                .dt.convert_time_zone("UTC")
+                .dt.replace_time_zone(None)
+                for x in convert
+            ]
+        )._native_frame
+
         # pandas return `object` dtype for nullable dtypes if dtype=None,
         # so we cast each Series to numpy and let numpy find a common dtype.
         # If there aren't any dtypes where `to_numpy()` is "broken" (i.e. it
         # returns Object) then we just call `to_numpy()` on the DataFrame.
-        for col_dtype in self._native_frame.dtypes:
+        for col_dtype in df.dtypes:
             if str(col_dtype) in PANDAS_TO_NUMPY_DTYPE_MISSING:
                 import numpy as np  # ignore-banned-import
 
                 return np.hstack(
                     [self[col].to_numpy(copy=copy)[:, None] for col in self.columns]
                 )
-        return self._native_frame.to_numpy(copy=copy)
+        return df.to_numpy(copy=copy)
 
     def to_pandas(self) -> Any:
         if self._implementation is Implementation.PANDAS:

tests/frame/to_numpy_test.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+from datetime import datetime
 from typing import TYPE_CHECKING
 
 import numpy as np
@@ -18,3 +19,21 @@ def test_to_numpy(constructor_eager: ConstructorEager) -> None:
     expected = np.array([[1, 3, 2], [4, 4, 6], [7.1, 8, 9]]).T
     np.testing.assert_array_equal(result, expected)
     assert result.dtype == "float64"
+
+
+def test_to_numpy_tz_aware(constructor_eager: ConstructorEager) -> None:
+    df = nw.from_native(
+        constructor_eager({"a": [datetime(2020, 1, 1), datetime(2020, 1, 2)]}),
+        eager_only=True,
+    )
+    df = df.select(nw.col("a").dt.replace_time_zone("Asia/Kathmandu"))
+    result = df.to_numpy()
+    # for some reason, NumPy uses 'M' for datetimes
+    assert result.dtype.kind == "M"
+    assert (
+        result
+        == np.array(
+            [["2019-12-31T18:15:00.000000"], ["2020-01-01T18:15:00.000000"]],
+            dtype=result.dtype,
+        )
+    ).all()

tests/series_only/to_numpy_test.py

@@ -8,6 +8,7 @@
 from numpy.testing import assert_array_equal
 
 import narwhals.stable.v1 as nw
+from tests.utils import PYARROW_VERSION
 
 if TYPE_CHECKING:
     from tests.utils import ConstructorEager
@@ -33,7 +34,11 @@ def test_to_numpy(
     assert_array_equal(s.to_numpy(), np.array(data, dtype=float))
 
 
-def test_to_numpy_tz_aware(constructor_eager: ConstructorEager) -> None:
+def test_to_numpy_tz_aware(
+    constructor_eager: ConstructorEager, request: pytest.FixtureRequest
+) -> None:
+    if "pyarrow_table" in str(constructor_eager) and PYARROW_VERSION < (12,):
+        request.applymarker(pytest.mark.xfail)
     df = nw.from_native(
         constructor_eager({"a": [datetime(2020, 1, 1), datetime(2020, 1, 2)]}),
         eager_only=True,