Change .groupby fastpath to work for monotonic increasing and decreasing

doc/whats-new.rst

@@ -22,6 +22,8 @@ v2024.06.1 (unreleased)
 
 New Features
 ~~~~~~~~~~~~
+- Use fastpath when grouping both montonically increasing and decreasing variable
+  in :py:class:`GroupBy` (:issue:`6220`, :pull:`7427`). By `Joel Jaeschke <https://github.com/joeljaeschke>`_.
 - Introduce new :py:class:`groupers.UniqueGrouper`, :py:class:`groupers.BinGrouper`, and
   :py:class:`groupers.TimeResampler` objects as a step towards supporting grouping by
   multiple variables. See the `docs <groupby.groupers_>` and the

xarray/core/groupers.py

@@ -123,7 +123,9 @@ def is_unique_and_monotonic(self) -> bool:
         if isinstance(self.group, _DummyGroup):
             return True
         index = self.group_as_index
-        return index.is_unique and index.is_monotonic_increasing
+        return index.is_unique and (
+            index.is_monotonic_increasing or index.is_monotonic_decreasing
+        )
 
     @property
     def group_as_index(self) -> pd.Index:
@@ -326,7 +328,7 @@ def _init_properties(self, group: T_Group) -> None:
 
         if not group_as_index.is_monotonic_increasing:
             # TODO: sort instead of raising an error
-            raise ValueError("index must be monotonic for resampling")
+            raise ValueError("Index must be monotonic for resampling")
 
         if isinstance(group_as_index, CFTimeIndex):
             from xarray.core.resample_cftime import CFTimeGrouper

xarray/tests/test_groupby.py

@@ -1792,6 +1792,23 @@ def test_groupby_fillna(self) -> None:
         actual = a.groupby("b").fillna(DataArray([0, 2], dims="b"))
         assert_identical(expected, actual)
 
+    @pytest.mark.parametrize("use_flox", [True, False])
+    def test_groupby_fastpath_for_monotonic(self, use_flox: bool) -> None:
+        # Fixes https://github.com/pydata/xarray/issues/6220
+        # Fixes https://github.com/pydata/xarray/issues/9279
+        index = [1, 2, 3, 4, 7, 9, 10]
+        array = DataArray(np.arange(len(index)), [("idx", index)])
+        array_rev = array.copy().assign_coords({"idx": index[::-1]})
+        fwd = array.groupby("idx", squeeze=False)
+        rev = array_rev.groupby("idx", squeeze=False)
+
+        for gb in [fwd, rev]:
+            assert all([isinstance(elem, slice) for elem in gb._group_indices])
+
+        with xr.set_options(use_flox=use_flox):
+            assert_identical(fwd.sum(), array)
+            assert_identical(rev.sum(), array_rev)
+
 
 class TestDataArrayResample:
     @pytest.mark.parametrize("use_cftime", [True, False])
@@ -1828,9 +1845,13 @@ def resample_as_pandas(array, *args, **kwargs):
         expected = resample_as_pandas(array, "24h", closed="right")
         assert_identical(expected, actual)
 
-        with pytest.raises(ValueError, match=r"index must be monotonic"):
+        with pytest.raises(ValueError, match=r"Index must be monotonic"):
             array[[2, 0, 1]].resample(time="1D")
 
+        reverse = array.isel(time=slice(-1, None, -1))
+        with pytest.raises(ValueError):
+            reverse.resample(time="1D").mean()
+
     @pytest.mark.parametrize("use_cftime", [True, False])
     def test_resample_doctest(self, use_cftime: bool) -> None:
         # run the doctest example here so we are not surprised