From 033694b779c86e0ecd7fe215abb4463fe0be1d2e Mon Sep 17 00:00:00 2001
From: dcherian <deepak@cherian.net>
Date: Mon, 17 Apr 2023 10:49:42 -0600
Subject: [PATCH] Fix binning by unsorted array

Closes #7759
---
 xarray/core/groupby.py       |  2 +-
 xarray/tests/test_groupby.py | 23 ++++++++++++++++++-----
 2 files changed, 19 insertions(+), 6 deletions(-)

diff --git a/xarray/core/groupby.py b/xarray/core/groupby.py
index 8df1e5fedec..b786fa60af9 100644
--- a/xarray/core/groupby.py
+++ b/xarray/core/groupby.py
@@ -338,7 +338,7 @@ def _factorize_bins(
     if (codes == -1).all():
         raise ValueError(f"None of the data falls within bins with edges {bins!r}")
     full_index = binned.categories
-    unique_values = binned.unique().dropna()
+    unique_values = np.sort(binned.unique().dropna())
     group_indices = [g for g in _codes_to_groups(codes, len(full_index)) if g]
 
     if len(group_indices) == 0:
diff --git a/xarray/tests/test_groupby.py b/xarray/tests/test_groupby.py
index 52c1af97fbf..5d72b800877 100644
--- a/xarray/tests/test_groupby.py
+++ b/xarray/tests/test_groupby.py
@@ -1371,17 +1371,30 @@ def test_groupby_multidim_map(self):
         )
         assert_identical(expected, actual)
 
-    def test_groupby_bins(self):
-        array = DataArray(np.arange(4), dims="dim_0")
+    @pytest.mark.parametrize("coords", [np.arange(4), np.arange(4)[::-1], [2, 0, 3, 1]])
+    def test_groupby_bins(self, coords):
+        array = DataArray(
+            np.arange(4), dims="dim_0", coords={"dim_0": coords}, name="a"
+        )
         # the first value should not be part of any group ("right" binning)
         array[0] = 99
         # bins follow conventions for pandas.cut
         # http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html
         bins = [0, 1.5, 5]
-        bin_coords = pd.cut(array["dim_0"], bins).categories
-        expected = DataArray(
-            [1, 5], dims="dim_0_bins", coords={"dim_0_bins": bin_coords}
+
+        df = array.to_dataframe()
+        df["dim_0_bins"] = pd.cut(array["dim_0"], bins)
+
+        expected_df = df.groupby("dim_0_bins").sum()
+        # TODO: can't convert df with IntervalIndex to Xarray
+
+        expected = (
+            expected_df.reset_index(drop=True)
+            .to_xarray()
+            .assign_coords(index=np.array(expected_df.index))
+            .rename({"index": "dim_0_bins"})["a"]
         )
+
         actual = array.groupby_bins("dim_0", bins=bins).sum()
         assert_identical(expected, actual)