BUG: RollingGroupby MultiIndex levels dropped (pandas-dev#40701)

Author: mroeschke

doc/source/whatsnew/v1.3.0.rst

@@ -400,6 +400,38 @@ However, floating point artifacts may now exist in the results when rolling over
    s = pd.Series([7, 5, 5, 5])
    s.rolling(3).var()
 
+.. _whatsnew_130.notable_bug_fixes.rolling_groupby_multiindex:
+
+GroupBy.rolling with MultiIndex no longer drops levels in the result
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+:class:`core.window.rolling.RollingGroupby` will no longer drop levels of a :class:`DataFrame`
+with a :class:`MultiIndex` in the result. This can lead to a perceived duplication of levels in the resulting
+:class:`MultiIndex`, but this change restores the behavior that was present in version 1.1.3 (:issue:`38787`, :issue:`38523`).
+
+
+.. ipython:: python
+
+   index = pd.MultiIndex.from_tuples([('idx1', 'idx2')], names=['label1', 'label2'])
+   df = pd.DataFrame({'a': [1], 'b': [2]}, index=index)
+   df
+
+*Previous behavior*:
+
+.. code-block:: ipython
+
+    In [1]: df.groupby('label1').rolling(1).sum()
+    Out[1]:
+              a    b
+    label1
+    idx1    1.0  2.0
+
+*New behavior*:
+
+.. ipython:: python
+
+    df.groupby('label1').rolling(1).sum()
+
 
 .. _whatsnew_130.api_breaking.deps:
 

pandas/core/window/rolling.py

@@ -577,26 +577,23 @@ def _apply(
             numba_cache_key,
             **kwargs,
         )
-        # Reconstruct the resulting MultiIndex from tuples
+        # Reconstruct the resulting MultiIndex
         # 1st set of levels = group by labels
-        # 2nd set of levels = original index
-        # Ignore 2nd set of levels if a group by label include an index level
-        result_index_names = copy.copy(self._grouper.names)
-        grouped_object_index = None
+        # 2nd set of levels = original DataFrame/Series index
+        grouped_object_index = self.obj.index
+        grouped_index_name = [*grouped_object_index.names]
+        groupby_keys = copy.copy(self._grouper.names)
+        result_index_names = groupby_keys + grouped_index_name
 
-        column_keys = [
+        drop_columns = [
             key
-            for key in result_index_names
+            for key in self._grouper.names
             if key not in self.obj.index.names or key is None
         ]
 
-        if len(column_keys) == len(result_index_names):
-            grouped_object_index = self.obj.index
-            grouped_index_name = [*grouped_object_index.names]
-            result_index_names += grouped_index_name
-        else:
+        if len(drop_columns) != len(groupby_keys):
             # Our result will have still kept the column in the result
-            result = result.drop(columns=column_keys, errors="ignore")
+            result = result.drop(columns=drop_columns, errors="ignore")
 
         codes = self._grouper.codes
         levels = copy.copy(self._grouper.levels)

pandas/tests/window/test_groupby.py

@@ -588,23 +588,31 @@ def test_groupby_rolling_nans_in_index(self, rollings, key):
         with pytest.raises(ValueError, match=f"{key} must be monotonic"):
             df.groupby("c").rolling("60min", **rollings)
 
-    def test_groupby_rolling_group_keys(self):
+    @pytest.mark.parametrize("group_keys", [True, False])
+    def test_groupby_rolling_group_keys(self, group_keys):
         # GH 37641
+        # GH 38523: GH 37641 actually was not a bug.
+        # group_keys only applies to groupby.apply directly
         arrays = [["val1", "val1", "val2"], ["val1", "val1", "val2"]]
         index = MultiIndex.from_arrays(arrays, names=("idx1", "idx2"))
 
         s = Series([1, 2, 3], index=index)
-        result = s.groupby(["idx1", "idx2"], group_keys=False).rolling(1).mean()
+        result = s.groupby(["idx1", "idx2"], group_keys=group_keys).rolling(1).mean()
         expected = Series(
             [1.0, 2.0, 3.0],
             index=MultiIndex.from_tuples(
-                [("val1", "val1"), ("val1", "val1"), ("val2", "val2")],
-                names=["idx1", "idx2"],
+                [
+                    ("val1", "val1", "val1", "val1"),
+                    ("val1", "val1", "val1", "val1"),
+                    ("val2", "val2", "val2", "val2"),
+                ],
+                names=["idx1", "idx2", "idx1", "idx2"],
             ),
         )
         tm.assert_series_equal(result, expected)
 
     def test_groupby_rolling_index_level_and_column_label(self):
+        # The groupby keys should not appear as a resulting column
         arrays = [["val1", "val1", "val2"], ["val1", "val1", "val2"]]
         index = MultiIndex.from_arrays(arrays, names=("idx1", "idx2"))
 
@@ -613,7 +621,12 @@ def test_groupby_rolling_index_level_and_column_label(self):
         expected = DataFrame(
             {"B": [0.0, 1.0, 2.0]},
             index=MultiIndex.from_tuples(
-                [("val1", 1), ("val1", 1), ("val2", 2)], names=["idx1", "A"]
+                [
+                    ("val1", 1, "val1", "val1"),
+                    ("val1", 1, "val1", "val1"),
+                    ("val2", 2, "val2", "val2"),
+                ],
+                names=["idx1", "A", "idx1", "idx2"],
             ),
         )
         tm.assert_frame_equal(result, expected)
@@ -695,6 +708,30 @@ def test_by_column_not_in_values(self, columns):
         assert "A" not in result.columns
         tm.assert_frame_equal(g.obj, original_obj)
 
+    def test_groupby_level(self):
+        # GH 38523, 38787
+        arrays = [
+            ["Falcon", "Falcon", "Parrot", "Parrot"],
+            ["Captive", "Wild", "Captive", "Wild"],
+        ]
+        index = MultiIndex.from_arrays(arrays, names=("Animal", "Type"))
+        df = DataFrame({"Max Speed": [390.0, 350.0, 30.0, 20.0]}, index=index)
+        result = df.groupby(level=0)["Max Speed"].rolling(2).sum()
+        expected = Series(
+            [np.nan, 740.0, np.nan, 50.0],
+            index=MultiIndex.from_tuples(
+                [
+                    ("Falcon", "Falcon", "Captive"),
+                    ("Falcon", "Falcon", "Wild"),
+                    ("Parrot", "Parrot", "Captive"),
+                    ("Parrot", "Parrot", "Wild"),
+                ],
+                names=["Animal", "Animal", "Type"],
+            ),
+            name="Max Speed",
+        )
+        tm.assert_series_equal(result, expected)
+
 
 class TestExpanding:
     def setup_method(self):