cast numpy scalars to arrays in as_compatible_data (#9403)

* also call `np.asarray` on numpy scalars

* check that numpy scalars are properly casted to arrays

* don't allow `numpy.ndarray` subclasses

* comment on the purpose of the explicit isinstance and `np.asarray`

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)