explicitly cast the dtype of where's condition parameter to bool

pyproject.toml

@@ -344,6 +344,9 @@ filterwarnings = [
   "default:the `pandas.MultiIndex` object:FutureWarning:xarray.tests.test_variable",
   "default:Using a non-tuple sequence for multidimensional indexing is deprecated:FutureWarning",
   "default:Duplicate dimension names present:UserWarning:xarray.namedarray.core",
+  # TODO: numpy.bool was deprecated in older versions of numpy, but is in the Array API
+  # TODO: remove once we can drop numpy<2
+  "ignore:In the future `np.bool` will be defined as the corresponding NumPy scalar:FutureWarning",
   # TODO: this is raised for vlen-utf8, consolidated metadata, U1 dtype
   "ignore:is currently not part .* the Zarr version 3 specification.",
   # TODO: remove once we know how to deal with a changed signature in protocols

xarray/core/duck_array_ops.py

@@ -224,14 +224,17 @@ def empty_like(a, **kwargs):
     return xp.empty_like(a, **kwargs)
 
 
-def astype(data, dtype, **kwargs):
-    if hasattr(data, "__array_namespace__"):
+def astype(data, dtype, *, xp=None, **kwargs):
+    if not hasattr(data, "__array_namespace__") and xp is None:
+        return data.astype(dtype, **kwargs)
+
+    if xp is None:
         xp = get_array_namespace(data)
-        if xp == np:
-            # numpy currently doesn't have a astype:
-            return data.astype(dtype, **kwargs)
-        return xp.astype(data, dtype, **kwargs)
-    return data.astype(dtype, **kwargs)
+
+    if xp == np:
+        # numpy currently doesn't have a astype:
+        return data.astype(dtype, **kwargs)
+    return xp.astype(data, dtype, **kwargs)
 
 
 def asarray(data, xp=np, dtype=None):
@@ -373,6 +376,13 @@ def sum_where(data, axis=None, dtype=None, where=None):
 def where(condition, x, y):
     """Three argument where() with better dtype promotion rules."""
     xp = get_array_namespace(condition, x, y)
+
+    dtype = xp.bool_ if hasattr(xp, "bool_") else xp.bool
+    if not is_duck_array(condition):
+        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/groupby.py

@@ -524,7 +524,7 @@ def factorize(self) -> EncodedGroups:
         # Restore these after the raveling
         broadcasted_masks = broadcast(*masks)
         mask = functools.reduce(np.logical_or, broadcasted_masks)  # type: ignore[arg-type]
-        _flatcodes = where(mask, -1, _flatcodes)
+        _flatcodes = where(mask.data, -1, _flatcodes)
 
         full_index = pd.MultiIndex.from_product(
             (grouper.full_index.values for grouper in groupers),

xarray/tests/test_array_api.py

@@ -139,7 +139,6 @@ def test_unstack(arrays: tuple[xr.DataArray, xr.DataArray]) -> None:
     assert_equal(actual, expected)
 
 
-@pytest.mark.skip
 def test_where() -> None:
     np_arr = xr.DataArray(np.array([1, 0]), dims="x")
     xp_arr = xr.DataArray(xp.asarray([1, 0]), dims="x")