Another groupby.reduce bugfix. (#3403)

* Another groupby.reduce bugfix.

Fixes #3402

* Add whats-new.

* Use is_scalar instead

* bugfix

* fix whats-new

* Update xarray/core/groupby.py

Co-Authored-By: Maximilian Roos <5635139+max-sixty@users.noreply.github.com>

doc/whats-new.rst

@@ -55,6 +55,10 @@ Bug fixes
 - Sync with cftime by removing `dayofwk=-1` for cftime>=1.0.4. 
   By `Anderson Banihirwe <https://github.com/andersy005>`_.
 
+- Fix :py:meth:`xarray.core.groupby.DataArrayGroupBy.reduce` and
+  :py:meth:`xarray.core.groupby.DatasetGroupBy.reduce` when reducing over multiple dimensions.
+  (:issue:`3402`). By `Deepak Cherian <https://github.com/dcherian/>`_
+
 
 Documentation
 ~~~~~~~~~~~~~

xarray/core/groupby.py

@@ -15,13 +15,26 @@
 from .utils import (
     either_dict_or_kwargs,
     hashable,
+    is_scalar,
     maybe_wrap_array,
     peek_at,
     safe_cast_to_index,
 )
 from .variable import IndexVariable, Variable, as_variable
 
 
+def check_reduce_dims(reduce_dims, dimensions):
+
+    if reduce_dims is not ...:
+        if is_scalar(reduce_dims):
+            reduce_dims = [reduce_dims]
+        if any([dim not in dimensions for dim in reduce_dims]):
+            raise ValueError(
+                "cannot reduce over dimensions %r. expected either '...' to reduce over all dimensions or one or more of %r."
+                % (reduce_dims, dimensions)
+            )
+
+
 def unique_value_groups(ar, sort=True):
     """Group an array by its unique values.
 
@@ -794,15 +807,11 @@ def reduce(
         if keep_attrs is None:
             keep_attrs = _get_keep_attrs(default=False)
 
-        if dim is not ... and dim not in self.dims:
-            raise ValueError(
-                "cannot reduce over dimension %r. expected either '...' to reduce over all dimensions or one or more of %r."
-                % (dim, self.dims)
-            )
-
         def reduce_array(ar):
             return ar.reduce(func, dim, axis, keep_attrs=keep_attrs, **kwargs)
 
+        check_reduce_dims(dim, self.dims)
+
         return self.apply(reduce_array, shortcut=shortcut)
 
 
@@ -895,11 +904,7 @@ def reduce(self, func, dim=None, keep_attrs=None, **kwargs):
         def reduce_dataset(ds):
             return ds.reduce(func, dim, keep_attrs, **kwargs)
 
-        if dim is not ... and dim not in self.dims:
-            raise ValueError(
-                "cannot reduce over dimension %r. expected either '...' to reduce over all dimensions or one or more of %r."
-                % (dim, self.dims)
-            )
+        check_reduce_dims(dim, self.dims)
 
         return self.apply(reduce_dataset)
 

xarray/tests/test_dataarray.py

@@ -2560,15 +2560,6 @@ def change_metadata(x):
         expected = change_metadata(expected)
         assert_equal(expected, actual)
 
-    def test_groupby_reduce_dimension_error(self):
-        array = self.make_groupby_example_array()
-        grouped = array.groupby("y")
-        with raises_regex(ValueError, "cannot reduce over dimension 'y'"):
-            grouped.mean()
-
-        grouped = array.groupby("y", squeeze=False)
-        assert_identical(array, grouped.mean())
-
     def test_groupby_math(self):
         array = self.make_groupby_example_array()
         for squeeze in [True, False]:

xarray/tests/test_groupby.py

@@ -5,7 +5,23 @@
 import xarray as xr
 from xarray.core.groupby import _consolidate_slices
 
-from . import assert_identical, raises_regex
+from . import assert_allclose, assert_identical, raises_regex
+
+
+@pytest.fixture
+def dataset():
+    ds = xr.Dataset(
+        {"foo": (("x", "y", "z"), np.random.randn(3, 4, 2))},
+        {"x": ["a", "b", "c"], "y": [1, 2, 3, 4], "z": [1, 2]},
+    )
+    ds["boo"] = (("z", "y"), [["f", "g", "h", "j"]] * 2)
+
+    return ds
+
+
+@pytest.fixture
+def array(dataset):
+    return dataset["foo"]
 
 
 def test_consolidate_slices():
@@ -21,25 +37,17 @@ def test_consolidate_slices():
         _consolidate_slices([slice(3), 4])
 
 
-def test_groupby_dims_property():
-    ds = xr.Dataset(
-        {"foo": (("x", "y", "z"), np.random.randn(3, 4, 2))},
-        {"x": ["a", "bcd", "c"], "y": [1, 2, 3, 4], "z": [1, 2]},
-    )
+def test_groupby_dims_property(dataset):
+    assert dataset.groupby("x").dims == dataset.isel(x=1).dims
+    assert dataset.groupby("y").dims == dataset.isel(y=1).dims
 
-    assert ds.groupby("x").dims == ds.isel(x=1).dims
-    assert ds.groupby("y").dims == ds.isel(y=1).dims
-
-    stacked = ds.stack({"xy": ("x", "y")})
+    stacked = dataset.stack({"xy": ("x", "y")})
     assert stacked.groupby("xy").dims == stacked.isel(xy=0).dims
 
 
-def test_multi_index_groupby_apply():
+def test_multi_index_groupby_apply(dataset):
     # regression test for GH873
-    ds = xr.Dataset(
-        {"foo": (("x", "y"), np.random.randn(3, 4))},
-        {"x": ["a", "b", "c"], "y": [1, 2, 3, 4]},
-    )
+    ds = dataset.isel(z=1, drop=True)[["foo"]]
     doubled = 2 * ds
     group_doubled = (
         ds.stack(space=["x", "y"])
@@ -276,6 +284,24 @@ def test_groupby_grouping_errors():
         dataset.to_array().groupby(dataset.foo * np.nan)
 
 
+def test_groupby_reduce_dimension_error(array):
+    grouped = array.groupby("y")
+    with raises_regex(ValueError, "cannot reduce over dimensions"):
+        grouped.mean()
+
+    with raises_regex(ValueError, "cannot reduce over dimensions"):
+        grouped.mean("huh")
+
+    with raises_regex(ValueError, "cannot reduce over dimensions"):
+        grouped.mean(("x", "y", "asd"))
+
+    grouped = array.groupby("y", squeeze=False)
+    assert_identical(array, grouped.mean())
+
+    assert_identical(array.mean("x"), grouped.reduce(np.mean, "x"))
+    assert_allclose(array.mean(["x", "z"]), grouped.reduce(np.mean, ["x", "z"]))
+
+
 def test_groupby_bins_timeseries():
     ds = xr.Dataset()
     ds["time"] = xr.DataArray(