fix Rolling for multi-index and reversed index.

doc/source/whatsnew/v1.0.0.rst

@@ -176,8 +176,9 @@ Plotting
 Groupby/resample/rolling
 ^^^^^^^^^^^^^^^^^^^^^^^^
 
--
 - Bug in :meth:`DataFrame.rolling` not allowing for rolling over datetimes when ``axis=1`` (:issue: `28192`)
+- Bug in :meth:`DataFrame.rolling` not allowing rolling over multi-index levels (:issue: `15584`).
+- Bug in :meth:`DataFrame.rolling` not allowing rolling on monotonic decreasing time indexes (:issue: `19248`).
 - Bug in :meth:`DataFrame.groupby` not offering selection by column name when ``axis=1`` (:issue:`27614`)
 - Bug in :meth:`DataFrameGroupby.agg` not able to use lambda function with named aggregation (:issue:`27519`)
 

pandas/core/window/rolling.py

@@ -126,7 +126,7 @@ def _create_blocks(self):
         obj = self._selected_obj
 
         # filter out the on from the object
-        if self.on is not None:
+        if self.on is not None and not isinstance(self.on, Index):
             if obj.ndim == 2:
                 obj = obj.reindex(columns=obj.columns.difference([self.on]), copy=False)
         blocks = obj._to_dict_of_blocks(copy=False).values()
@@ -1651,18 +1651,19 @@ def is_datetimelike(self):
 
     @cache_readonly
     def _on(self):
-
         if self.on is None:
             if self.axis == 0:
                 return self.obj.index
             elif self.axis == 1:
                 return self.obj.columns
+        elif isinstance(self.on, Index):
+            return self.on
         elif isinstance(self.obj, ABCDataFrame) and self.on in self.obj.columns:
             return Index(self.obj[self.on])
         else:
             raise ValueError(
                 "invalid on specified as {0}, "
-                "must be a column (if DataFrame) "
+                "must be a column (of DataFrame), an Index "
                 "or None".format(self.on)
             )
 
@@ -1706,10 +1707,15 @@ def validate(self):
 
     def _validate_monotonic(self):
         """
-        Validate on is_monotonic.
+        Validate monotonic (increasing or decreasing).
         """
-        if not self._on.is_monotonic:
-            formatted = self.on or "index"
+        if (
+            not self._on.is_monotonic_increasing
+            and not self._on.is_monotonic_decreasing
+        ):
+            formatted = self.on
+            if self.on is None:
+                formatted = "index"
             raise ValueError("{0} must be monotonic".format(formatted))
 
     def _validate_freq(self):

pandas/tests/window/test_rolling.py

@@ -361,3 +361,32 @@ def test_rolling_datetime(self, axis_frame, tz_naive_fixture):
             }
         )
         tm.assert_frame_equal(result, expected)
+
+    def test_rolling_decreasing(self):
+        index = [
+            pd.Timestamp("20190101 09:00:00"),
+            pd.Timestamp("20190101 09:00:02"),
+            pd.Timestamp("20190101 09:00:03"),
+            pd.Timestamp("20190101 09:00:05"),
+            pd.Timestamp("20190101 09:00:06"),
+        ]
+
+        df = pd.DataFrame({"column": [3, 4, 4, 2, 1]}, index=reversed(index))
+        result = df.rolling("2s").min()
+        tm.assert_frame_equal(
+            result,
+            pd.DataFrame({"column": [3.0, 3.0, 3.0, 2.0, 1.0]}, index=reversed(index)),
+        )
+
+    def test_rolling_multi_index(self):
+        df = pd.DataFrame(
+            {"column": range(6)},
+            index=pd.MultiIndex.from_product(
+                [pd.date_range("20190101", periods=3), range(2)], names=["date", "seq"]
+            ),
+        )
+        result = df.rolling("10d", on=df.index.get_level_values("date")).sum()
+        tm.assert_frame_equal(
+            result,
+            pd.DataFrame({"column": [0.0, 1.0, 3.0, 6.0, 10.0, 15.0]}, index=df.index),
+        )

pandas/tests/window/test_timeseries_window.py

@@ -106,7 +106,9 @@ def test_monotonic_on(self):
         df.rolling("2s").sum()
 
         # non-monotonic
-        df.index = reversed(df.index.tolist())
+        non_monotonic_index = df.index.to_list()
+        non_monotonic_index[0] = non_monotonic_index[3]
+        df.index = non_monotonic_index
         assert not df.index.is_monotonic
 
         with pytest.raises(ValueError):