Rolling on DataFrameGroupBy duplicated index column when part of the grouping cols is from index

doc/source/whatsnew/v1.2.0.rst

@@ -404,6 +404,7 @@ Groupby/resample/rolling
 - Bug in :meth:`DataFrame.groupby` does not always maintain column index name for ``any``, ``all``, ``bfill``, ``ffill``, ``shift`` (:issue:`29764`)
 - Bug in :meth:`DataFrameGroupBy.apply` raising error with ``np.nan`` group(s) when ``dropna=False`` (:issue:`35889`)
 - Bug in :meth:`Rolling.sum()` returned wrong values when dtypes where mixed between float and integer and axis was equal to one (:issue:`20649`, :issue:`35596`)
+- Bug in :meth:`DataFrameGroupBy.rolling` duplicates index columns when a part of the grouping columns was from the index (:issue:`36794`)
 
 Reshaping
 ^^^^^^^^^

pandas/core/window/rolling.py

@@ -2209,6 +2209,20 @@ def _apply(
         grouped_index_name = [*grouped_object_index.names]
         groupby_keys = [grouping.name for grouping in self._groupby.grouper._groupings]
         result_index_names = groupby_keys + grouped_index_name
+        drop_levels = []
+        obj = self._groupby._selected_obj
+        # We have to handle a unnamed Series different, because we can not say, if
+        # grouping column was index or Series column.
+        if (
+            isinstance(obj, ABCDataFrame)
+            or obj.name is not None
+            or obj.index.names != [None]
+        ):
+            drop_levels = [
+                i for i, name in enumerate(groupby_keys) if name in grouped_index_name
+            ]
+        elif self._groupby.level is not None:
+            drop_levels = com.maybe_make_list(self._groupby.level)
 
         result_index_data = []
         for key, values in self._groupby.grouper.indices.items():
@@ -2222,6 +2236,7 @@ def _apply(
         result_index = MultiIndex.from_tuples(
             result_index_data, names=result_index_names
         )
+        result_index = result_index.droplevel(drop_levels)
         result.index = result_index
         return result
 

pandas/tests/window/test_grouper.py

@@ -416,3 +416,41 @@ def test_groupby_rolling_empty_frame(self):
         result = expected.groupby(["s1", "s2"]).rolling(window=1).sum()
         expected.index = pd.MultiIndex.from_tuples([], names=["s1", "s2", None])
         tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("groupings", [{"level": 0}, {"by": "groupby_col"}])
+    def test_groupby_rolling_index_in_by_columns(self, groupings):
+        # GH: 36794
+        df = pd.DataFrame(
+            {"groupby_col": [1, 1, 1, 2, 2, 2], "agg_col": [1, 1, 0, 0, 1, 0]}
+        ).set_index("groupby_col")
+        result = df.groupby(**groupings).rolling(2).sum()
+        expected = pd.DataFrame(
+            {
+                "groupby_col": [1, 1, 1, 2, 2, 2],
+                "agg_col": [np.nan, 2.0, 1.0, np.nan, 1.0, 1.0],
+            }
+        ).set_index("groupby_col")
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("index_name", ["index", None])
+    def test_groupby_rolling_unnamed_series(self, index_name):
+        # GH: 36794
+        ds = pd.Series([1, 2, 3, 4], index=pd.Index([0, 1, 2, 3], name=index_name))
+        result = ds.groupby(ds).rolling(2).sum()
+        expected = pd.Series(
+            [np.nan] * 4,
+            index=pd.MultiIndex.from_tuples(
+                [(1, 0), (2, 1), (3, 2), (4, 3)], names=[None, index_name]
+            ),
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("groupings", [{"level": 0}, {"by": "index"}])
+    def test_groupby_rolling_index_of_unnamed_series(self, groupings):
+        # GH: 36794
+        ds = pd.Series([1, 2, 3, 4], index=pd.Index([0, 0, 1, 1], name="index"))
+        result = ds.groupby(**groupings).rolling(2).sum()
+        expected = pd.Series(
+            [np.nan, 3.0, np.nan, 7.0], index=pd.Index([0, 0, 1, 1], name="index")
+        )
+        tm.assert_series_equal(result, expected)

pandas/tests/window/test_rolling.py

@@ -675,9 +675,7 @@ def test_iter_rolling_datetime(expected, expected_index, window):
     [
         (
             {"level": 0},
-            pd.MultiIndex.from_tuples(
-                [(0, 0), (0, 0), (1, 1), (1, 1), (1, 1)], names=[None, None]
-            ),
+            pd.Index([0, 0, 1, 1, 1]),
         ),
         (
             {"by": "X"},