(fix): no fill_value on reindex

xarray/core/dtypes.py

@@ -4,6 +4,7 @@
 from typing import Any
 
 import numpy as np
+import pandas as pd
 from pandas.api.types import is_extension_array_dtype
 
 from xarray.compat import array_api_compat, npcompat
@@ -63,7 +64,9 @@ def maybe_promote(dtype: np.dtype) -> tuple[np.dtype, Any]:
     # N.B. these casting rules should match pandas
     dtype_: np.typing.DTypeLike
     fill_value: Any
-    if HAS_STRING_DTYPE and np.issubdtype(dtype, np.dtypes.StringDType()):
+    if pd.api.types.is_extension_array_dtype(dtype):
+        return dtype, pd.NA
+    elif HAS_STRING_DTYPE and np.issubdtype(dtype, np.dtypes.StringDType()):
         # for now, we always promote string dtypes to object for consistency with existing behavior
         # TODO: refactor this once we have a better way to handle numpy vlen-string dtypes
         dtype_ = object

xarray/core/duck_array_ops.py

@@ -250,7 +250,7 @@
 
     if xp == np:
         # numpy currently doesn't have a astype:
         return data.astype(dtype, **kwargs)
     return xp.astype(data, dtype, **kwargs)
 
 
@@ -272,18 +272,35 @@
         extension_array_types = [
             x.dtype for x in scalars_or_arrays if is_extension_array_dtype(x)
         ]
-        non_nans = [x for x in scalars_or_arrays if not isna(x)]
-        if len(extension_array_types) == len(non_nans) and all(
+        non_nans_or_scalar = [
+            x for x in scalars_or_arrays if not (isna(x) or np.isscalar(x))
+        ]
+        if len(extension_array_types) == len(non_nans_or_scalar) and all(
             isinstance(x, type(extension_array_types[0])) for x in extension_array_types
         ):
-            return [
+            # Get the extension array class of the first element, guaranteed to be the same
+            # as the others thanks to the anove check.
+            extension_array_class = type(
+                non_nans_or_scalar[0].array
+                if isinstance(non_nans_or_scalar[0], PandasExtensionArray)
+                else non_nans_or_scalar[0]
+            )
+            # Cast scalars/nans to extension array class
+            arrays_with_nan_to_sequence = [
                 x
-                if not isna(x)
-                else PandasExtensionArray(
-                    type(non_nans[0].array)._from_sequence([x], dtype=non_nans[0].dtype)
+                if not (isna(x) or np.isscalar(x))
+                else extension_array_class._from_sequence(
+                    [x], dtype=non_nans_or_scalar[0].dtype
                 )
                 for x in scalars_or_arrays
             ]
+            # Wrap the output if necessary
+            return [
+                PandasExtensionArray(x)
+                if not isinstance(x, PandasExtensionArray)
+                else x
+                for x in arrays_with_nan_to_sequence
+            ]
         raise ValueError(
             f"Cannot cast values to shared type, found values: {scalars_or_arrays}"
         )
@@ -406,7 +423,6 @@
         condition = asarray(condition, dtype=dtype, xp=xp)
     else:
         condition = astype(condition, dtype=dtype, xp=xp)
-
     return xp.where(condition, *as_shared_dtype([x, y], xp=xp))
 
 

xarray/core/extension_array.py

@@ -52,6 +52,17 @@ def __extension_duck_array__concatenate(
     return type(arrays[0])._concat_same_type(arrays)  # type: ignore[attr-defined]
 
 
+@implements(np.reshape)
+def __extension_duck_array__reshape(
+    arr: T_ExtensionArray, shape: tuple
+) -> T_ExtensionArray:
+    if (shape[0] == len(arr) and len(shape) == 1) or shape == (-1,):
+        return arr
+    raise NotImplementedError(
+        f"Cannot reshape 1d-only pandas extension array to: {shape}"
+    )
+
+
 @implements(np.where)
 def __extension_duck_array__where(
     condition: np.ndarray, x: T_ExtensionArray, y: T_ExtensionArray
@@ -134,6 +145,10 @@ def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
         return ufunc(*inputs, **kwargs)
 
     def __getitem__(self, key) -> PandasExtensionArray[T_ExtensionArray]:
+        if (
+            isinstance(key, tuple) and len(key) == 1
+        ):  # pyarrow type arrays can't handle since-length tuples
+            key = key[0]
         item = self.array[key]
         if is_extension_array_dtype(item):
             return PandasExtensionArray(item)

xarray/tests/test_dataarray.py

@@ -53,6 +53,7 @@
     assert_no_warnings,
     has_dask,
     has_dask_ge_2025_1_0,
+    has_pyarrow,
     raise_if_dask_computes,
     requires_bottleneck,
     requires_cupy,
@@ -61,6 +62,7 @@
     requires_iris,
     requires_numexpr,
     requires_pint,
+    requires_pyarrow,
     requires_scipy,
     requires_sparse,
     source_ndarray,
@@ -3075,6 +3077,58 @@ def test_propagate_attrs(self, func) -> None:
         with set_options(keep_attrs=True):
             assert func(da).attrs == da.attrs
 
+    @pytest.mark.parametrize(
+        "fill_value,extension_array",
+        [
+            pytest.param("a", pd.Categorical([pd.NA, "a", "b"]), id="categorical"),
+        ]
+        + [
+            pytest.param(
+                0,
+                pd.array([pd.NA, 1, 1], dtype="int64[pyarrow]"),
+                id="int64[pyarrow]",
+            )
+        ]
+        if has_pyarrow
+        else [],
+    )
+    def test_fillna_extension_array(self, fill_value, extension_array) -> None:
+        srs: pd.Series = pd.Series(index=np.array([1, 2, 3]), data=extension_array)
+        da = srs.to_xarray()
+        filled = da.fillna(fill_value)
+        assert filled.dtype == srs.dtype
+        assert (filled.values == np.array([fill_value, *(srs.values[1:])])).all()
+
+    @requires_pyarrow
+    def test_fillna_extension_array_bad_val(self) -> None:
+        srs: pd.Series = pd.Series(
+            index=np.array([1, 2, 3]),
+            data=pd.array([pd.NA, 1, 1], dtype="int64[pyarrow]"),
+        )
+        da = srs.to_xarray()
+        with pytest.raises(ValueError):
+            da.fillna("a")
+
+    @pytest.mark.parametrize(
+        "extension_array",
+        [
+            pytest.param(pd.Categorical([pd.NA, "a", "b"]), id="categorical"),
+        ]
+        + [
+            pytest.param(
+                pd.array([pd.NA, 1, 1], dtype="int64[pyarrow]"), id="int64[pyarrow]"
+            )
+        ]
+        if has_pyarrow
+        else [],
+    )
+    def test_dropna_extension_array(self, extension_array) -> None:
+        srs: pd.Series = pd.Series(index=np.array([1, 2, 3]), data=extension_array)
+        da = srs.to_xarray()
+        filled = da.dropna("index")
+        assert filled.dtype == srs.dtype
+        assert (filled.values == srs.values[1:]).all()
+
     def test_fillna(self) -> None:
         a = DataArray([np.nan, 1, np.nan, 3], coords={"x": range(4)}, dims="x")
         actual = a.fillna(-1)

xarray/tests/test_dataset.py

@@ -70,6 +70,7 @@
     requires_dask,
     requires_numexpr,
     requires_pint,
+    requires_pyarrow,
     requires_scipy,
     requires_sparse,
     source_ndarray,
@@ -1802,28 +1803,48 @@ def test_categorical_index_reindex(self) -> None:
         actual = ds.reindex(cat=["foo"])["cat"].values
         assert (actual == np.array(["foo"])).all()
 
-    @pytest.mark.parametrize("fill_value", [np.nan, pd.NA])
-    def test_extensionarray_negative_reindex(self, fill_value) -> None:
-        cat = pd.Categorical(
-            ["foo", "bar", "baz"],
-            categories=["foo", "bar", "baz", "qux", "quux", "corge"],
-        )
+    @pytest.mark.parametrize("fill_value", [np.nan, pd.NA, None])
+    @pytest.mark.parametrize(
+        "extension_array",
+        [
+            pytest.param(
+                pd.Categorical(
+                    ["foo", "bar", "baz"],
+                    categories=["foo", "bar", "baz", "qux"],
+                ),
+                id="categorical",
+            ),
+        ]
+        + [
+            pytest.param(
+                pd.array([1, 1, None], dtype="int64[pyarrow]"), id="int64[pyarrow]"
+            )
+        ]
+        if has_pyarrow
+        else [],
+    )
+    def test_extensionarray_negative_reindex(self, fill_value, extension_array) -> None:
         ds = xr.Dataset(
-            {"cat": ("index", cat)},
+            {"arr": ("index", extension_array)},
             coords={"index": ("index", np.arange(3))},
         )
+        kwargs = {}
+        if fill_value is not None:
+            kwargs["fill_value"] = fill_value
         reindexed_cat = cast(
             pd.api.extensions.ExtensionArray,
-            (
-                ds.reindex(index=[-1, 1, 1], fill_value=fill_value)["cat"]
-                .to_pandas()
-                .values
-            ),
+            (ds.reindex(index=[-1, 1, 1], **kwargs)["arr"].to_pandas().values),
+        )
+        assert reindexed_cat.equals(  # type: ignore[attr-defined]
+            pd.array(
+                [pd.NA, extension_array[1], extension_array[1]],
+                dtype=extension_array.dtype,
+            )
         )
-        assert reindexed_cat.equals(pd.array([pd.NA, "bar", "bar"], dtype=cat.dtype))  # type: ignore[attr-defined]
 
+    @requires_pyarrow
     def test_extension_array_reindex_same(self) -> None:
-        series = pd.Series([1, 2, pd.NA, 3], dtype=pd.Int32Dtype())
+        series = pd.Series([1, 2, pd.NA, 3], dtype="int32[pyarrow]")
         test = xr.Dataset({"test": series})
         res = test.reindex(dim_0=series.index)
         align(res, test, join="exact")
@@ -5473,6 +5494,51 @@ def test_dropna(self) -> None:
         with pytest.raises(TypeError, match=r"must specify how or thresh"):
             ds.dropna("a", how=None)  # type: ignore[arg-type]
 
+    @pytest.mark.parametrize(
+        "fill_value,extension_array",
+        [
+            pytest.param("a", pd.Categorical([pd.NA, "a", "b"]), id="category"),
+        ]
+        + [
+            pytest.param(
+                0,
+                pd.array([pd.NA, 1, 1], dtype="int64[pyarrow]"),
+                id="int64[pyarrow]",
+            )
+        ]
+        if has_pyarrow
+        else [],
+    )
+    def test_fillna_extension_array(self, fill_value, extension_array) -> None:
+        srs = pd.DataFrame({"data": extension_array}, index=np.array([1, 2, 3]))
+        ds = srs.to_xarray()
+        filled = ds.fillna(fill_value)
+        assert filled["data"].dtype == extension_array.dtype
+        assert (
+            filled["data"].values
+            == np.array([fill_value, *srs["data"].values[1:]], dtype="object")
+        ).all()
+
+    @pytest.mark.parametrize(
+        "extension_array",
+        [
+            pytest.param(pd.Categorical([pd.NA, "a", "b"]), id="category"),
+        ]
+        + [
+            pytest.param(
+                pd.array([pd.NA, 1, 1], dtype="int64[pyarrow]"), id="int64[pyarrow]"
+            )
+        ]
+        if has_pyarrow
+        else [],
+    )
+    def test_dropna_extension_array(self, extension_array) -> None:
+        srs = pd.DataFrame({"data": extension_array}, index=np.array([1, 2, 3]))
+        ds = srs.to_xarray()
+        dropped = ds.dropna("index")
+        assert dropped["data"].dtype == extension_array.dtype
+        assert (dropped["data"].values == srs["data"].values[1:]).all()
+
     def test_fillna(self) -> None:
         ds = Dataset({"a": ("x", [np.nan, 1, np.nan, 3])}, {"x": [0, 1, 2, 3]})