Fixes pydata#4276

Pass 0d dask arrays through for indexing.

xarray/core/indexing.py

@@ -366,7 +366,7 @@ def __init__(self, key):
                     raise TypeError(
                         f"invalid indexer array, does not have integer dtype: {k!r}"
                     )
-                if k.ndim != 1:
+                if k.ndim > 1:
                     raise TypeError(
                         f"invalid indexer array for {type(self).__name__}; must have "
                         f"exactly 1 dimension: {k!r}"

xarray/core/variable.py

@@ -601,11 +601,12 @@ def _broadcast_indexes(self, key):
         key = self._item_key_to_tuple(key)  # key is a tuple
         # key is a tuple of full size
         key = indexing.expanded_indexer(key, self.ndim)
-        # Convert a scalar Variable to an integer
+        # Convert a scalar Variable to a 0d-array
         key = tuple(
-            k.data.item() if isinstance(k, Variable) and k.ndim == 0 else k for k in key
+            k.data if isinstance(k, Variable) and k.ndim == 0 else k for k in key
         )
-        # Convert a 0d-array to an integer
+        # Convert a 0d numpy arrays to an integer
+        # dask 0d arrays are passed through
         key = tuple(
             k.item() if isinstance(k, np.ndarray) and k.ndim == 0 else k for k in key
         )
@@ -681,10 +682,11 @@ def _validate_indexers(self, key):
                         )
 
     def _broadcast_indexes_outer(self, key):
+        # drop dim if k is integer or if k is a 0d dask array
         dims = tuple(
             k.dims[0] if isinstance(k, Variable) else dim
             for k, dim in zip(key, self.dims)
-            if not isinstance(k, integer_types)
+            if (not isinstance(k, integer_types) and k.ndim > 0)
         )
 
         new_key = []

xarray/tests/test_indexing.py

@@ -846,7 +846,6 @@ def test_indexing_dask_array():
     assert_identical(actual, expected)
 
 
-@pytest.mark.xfail
 @requires_dask
 def test_indexing_dask_array_scalar():
     # GH4276
@@ -856,7 +855,7 @@ def test_indexing_dask_array_scalar():
     da = DataArray(a, dims="x")
     x_selector = da.argmax(dim=...)
     actual = da.isel(x_selector)
-    expected = da.isel(x=1)
+    expected = da.isel(x=-1)
     assert_identical(actual, expected)