BUG: 43909 - check monoticity of rolling groupby

pandas/core/window/rolling.py

@@ -2513,8 +2513,9 @@ def _get_window_indexer(self) -> GroupbyIndexer:
     def _validate_monotonic(self):
         """
         Validate that on is monotonic;
-        in this case we have to check only for nans, because
-        monotonicity was already validated at a higher level.
         """
-        if self._on.hasnans:
+        if (
+            not (self._on.is_monotonic_increasing or self._on.is_monotonic_decreasing)
+            or self._on.hasnans
+        ):
             self._raise_monotonic_error()

pandas/tests/window/test_rolling.py

@@ -1417,6 +1417,17 @@ def test_groupby_rolling_nan_included():
     tm.assert_frame_equal(result, expected)
 
 
+def test_groupby_rolling_non_monotonic():
+    # GH 43909
+    shuffled = [3, 0, 1, 2]
+    sec = 1_000
+    df = DataFrame(
+        [{"t": Timestamp(2 * x * sec), "x": x + 1, "c": 42} for x in shuffled]
+    )
+    with pytest.raises(ValueError, match=r".* must be monotonic"):
+        df.groupby("c").rolling(on="t", window="3s")
+
+
 @pytest.mark.parametrize("method", ["skew", "kurt"])
 def test_rolling_skew_kurt_numerical_stability(method):
     # GH#6929

pandas/tests/window/test_timeseries_window.py

@@ -648,6 +648,8 @@ def test_groupby_monotonic(self):
         # GH 15130
         # we don't need to validate monotonicity when grouping
 
+        # GH 43909 we should raise an error here to match behaviour of non-groupby rolling.
+
         data = [
             ["David", "1/1/2015", 100],
             ["David", "1/5/2015", 500],
@@ -664,17 +666,14 @@ def test_groupby_monotonic(self):
         df = DataFrame(data=data, columns=["name", "date", "amount"])
         df["date"] = to_datetime(df["date"])
 
-        expected = (
-            df.set_index("date")
-            .groupby("name")
-            .apply(lambda x: x.rolling("180D")["amount"].sum())
-        )
-        result = df.groupby("name").rolling("180D", on="date")["amount"].sum()
-        tm.assert_series_equal(result, expected)
+        with pytest.raises(ValueError, match=r".* must be monotonic"):
+            df.groupby("name").rolling("180D", on="date")
 
-    def test_non_monotonic(self):
+    def test_non_monotonic_raises(self):
         # GH 13966 (similar to #15130, closed by #15175)
 
+        # superceded by 43909
+
         dates = date_range(start="2016-01-01 09:30:00", periods=20, freq="s")
         df = DataFrame(
             {
@@ -684,7 +683,7 @@ def test_non_monotonic(self):
             }
         )
 
-        result = df.groupby("A").rolling("4s", on="B").C.mean()
+        result = df.sort_values("B").groupby("A").rolling("4s", on="B").C.mean()
         expected = (
             df.set_index("B").groupby("A").apply(lambda x: x.rolling("4s")["C"].mean())
         )
@@ -694,6 +693,9 @@ def test_non_monotonic(self):
         result = df2.groupby("A").rolling("4s", on="B").C.mean()
         tm.assert_series_equal(result, expected)
 
+        with pytest.raises(ValueError, match=r".* must be monotonic"):
+            df.groupby("A").rolling("4s", on="B").C.mean()
+
     def test_rolling_cov_offset(self):
         # GH16058