BUG: rolling aggregate() with a list of functions along axis 1 raises ValueError #46132

doc/source/whatsnew/v1.5.0.rst

@@ -629,6 +629,7 @@ Groupby/resample/rolling
 - Bug in :meth:`Rolling.var` and :meth:`Rolling.std` would give non-zero result with window of same values (:issue:`42064`)
 - Bug in :meth:`.Rolling.var` would segfault calculating weighted variance when window size was larger than data size (:issue:`46760`)
 - Bug in :meth:`Grouper.__repr__` where ``dropna`` was not included. Now it is (:issue:`46754`)
+- Bug in :meth:`Rolling.aggregate` raise ValueError when ``func`` was a list of functions and ``axis=1`` (:issue:`46132`)
 
 Reshaping
 ^^^^^^^^^

pandas/core/window/rolling.py

@@ -658,9 +658,22 @@ def _numba_apply(
             return self._resolve_output(out, obj)
 
     def aggregate(self, func, *args, **kwargs):
-        result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
+        # GH46132
+        _axis_modifed_flag = False
+        if self.axis == 1:
+            self.obj = self.obj.T
+            _axis_modifed_flag = True
+            self.axis = 0
+        try:
+            result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
+        finally:
+            if _axis_modifed_flag:
+                self.axis = 1
+                self.obj = self.obj.T
         if result is None:
             return self.apply(func, raw=False, args=args, kwargs=kwargs)
+        if _axis_modifed_flag:
+            result = result.T
         return result
 
     agg = aggregate

pandas/tests/window/test_rolling.py

@@ -1851,6 +1851,22 @@ def test_rolling_var_same_value_count_logic(values, window, min_periods, expecte
     tm.assert_series_equal(expected == 0, result_std == 0)
 
 
+def test_rolling_agg_on_columns():
+    # GH 46132
+
+    df = DataFrame({"a": [1, 3], "b": [2, 4]})
+    res = df.rolling(window=2, axis=1, min_periods=1).aggregate([np.sum, np.mean])
+    expected_val = np.array([[1, 3.0], [1, 1.5], [3, 7], [3, 3.5]])
+
+    expected_index = MultiIndex.from_tuples(
+        [(0, "sum"), (0, "mean"), (1, "sum"), (1, "mean")]
+    )
+
+    expected_frame = DataFrame(expected_val, index=expected_index, columns=["a", "b"])
+
+    tm.assert_frame_equal(expected_frame, res)
+
+
 def test_rolling_mean_sum_floating_artifacts():
     # GH 42064.
 
@@ -1860,3 +1876,18 @@ def test_rolling_mean_sum_floating_artifacts():
     assert (result[-3:] == 0).all()
     result = r.sum()
     assert (result[-3:] == 0).all()
+
+
+def test_rolling_agg_when_agg_fail():
+    # GH 46132
+    df = DataFrame({"a": [1, 3], "b": [2, 4]})
+    win = df.rolling(window=2, axis=1, min_periods=1)
+    try:
+        win.aggregate([np.log, np.sqrt])
+    except ValueError:
+        pass
+    tm.assert_frame_equal(win.obj, df)
+    # make sure if aggregate fails the attribute of Rolling/Window will not be change
+    assert win.axis == 1
+    win.aggregate([np.mean, np.sum])
+    assert win.axis == 1