Merge branch 'main' into h5netcdf-new-features

.github/workflows/pypi-release.yaml

@@ -88,7 +88,7 @@ jobs:
           path: dist
       - name: Publish package to TestPyPI
         if: github.event_name == 'push'
-        uses: pypa/[email protected].1
+        uses: pypa/[email protected].2
         with:
           repository_url: https://test.pypi.org/legacy/
           verbose: true
@@ -111,6 +111,6 @@ jobs:
           name: releases
           path: dist
       - name: Publish package to PyPI
-        uses: pypa/[email protected].1
+        uses: pypa/[email protected].2
         with:
           verbose: true

xarray/core/dataset.py

@@ -10627,7 +10627,7 @@ def rolling(
         --------
         Dataset.cumulative
         DataArray.rolling
-        core.rolling.DatasetRolling
+        DataArray.rolling_exp
         """
         from xarray.core.rolling import DatasetRolling
 
@@ -10657,9 +10657,9 @@ def cumulative(
 
         See Also
         --------
-        Dataset.rolling
         DataArray.cumulative
-        core.rolling.DatasetRolling
+        Dataset.rolling
+        Dataset.rolling_exp
         """
         from xarray.core.rolling import DatasetRolling
 

xarray/core/variable.py

@@ -320,12 +320,14 @@ def convert_non_numpy_type(data):
         else:
             data = np.asarray(data)
 
-    if not isinstance(data, np.ndarray) and (
+    # immediately return array-like types except `numpy.ndarray` subclasses and `numpy` scalars
+    if not isinstance(data, np.ndarray | np.generic) and (
         hasattr(data, "__array_function__") or hasattr(data, "__array_namespace__")
     ):
         return cast("T_DuckArray", data)
 
-    # validate whether the data is valid data types.
+    # validate whether the data is valid data types. Also, explicitly cast `numpy`
+    # subclasses and `numpy` scalars to `numpy.ndarray`
     data = np.asarray(data)
 
     if data.dtype.kind in "OMm":

xarray/tests/test_variable.py

@@ -2585,7 +2585,12 @@ def test_unchanged_types(self):
                 assert source_ndarray(x) is source_ndarray(as_compatible_data(x))
 
     def test_converted_types(self):
-        for input_array in [[[0, 1, 2]], pd.DataFrame([[0, 1, 2]])]:
+        for input_array in [
+            [[0, 1, 2]],
+            pd.DataFrame([[0, 1, 2]]),
+            np.float64(1.4),
+            np.str_("abc"),
+        ]:
             actual = as_compatible_data(input_array)
             assert_array_equal(np.asarray(input_array), actual)
             assert np.ndarray is type(actual)