BUG: Bug in groupby.transform(..) when axis=1 is specified with a non-monotonic ordered index

closes #12713

Author: jreback

doc/source/whatsnew/v0.18.1.txt

@@ -118,7 +118,7 @@ Performance Improvements
 Bug Fixes
 ~~~~~~~~~
 - ``usecols`` parameter in ``pd.read_csv`` is now respected even when the lines of a CSV file are not even (:issue:`12203`)
-
+- Bug in ``groupby.transform(..)`` when ``axis=1`` is specified with a non-monotonic ordered index (:issue:`12713`)
 - Bug in ``Period`` and ``PeriodIndex`` creation raises ``KeyError`` if ``freq="Minute"`` is specified. Note that "Minute" freq is deprecated in v0.17.0, and recommended to use ``freq="T"`` instead (:issue:`11854`)
 - Bug in printing data which contains ``Period`` with different ``freq`` raises ``ValueError`` (:issue:`12615`)
 - Bug in numpy compatibility of ``np.round()`` on a ``Series`` (:issue:`12600`)

pandas/core/groupby.py

@@ -457,19 +457,18 @@ def _set_selection_from_grouper(self):
                 self._group_selection = ax.difference(Index(groupers)).tolist()
 
     def _set_result_index_ordered(self, result):
-        # set the result index on the passed values object
-        # return the new object
-        # related 8046
+        # set the result index on the passed values object and
+        # return the new object, xref 8046
 
         # the values/counts are repeated according to the group index
-        # shortcut of we have an already ordered grouper
+        # shortcut if we have an already ordered grouper
         if not self.grouper.is_monotonic:
             index = Index(np.concatenate(
                 self._get_indices(self.grouper.result_index)))
-            result.index = index
-            result = result.sort_index()
+            result.set_axis(self.axis, index)
+            result = result.sort_index(axis=self.axis)
 
-        result.index = self.obj.index
+        result.set_axis(self.axis, self.obj._get_axis(self.axis))
         return result
 
     def _dir_additions(self):

pandas/tests/test_groupby.py

@@ -1090,6 +1090,46 @@ def test_transform_broadcast(self):
             for idx in gp.index:
                 assert_fp_equal(res.xs(idx), agged[idx])
 
+    def test_transform_axis(self):
+
+        # make sure that we are setting the axes
+        # correctly when on axis=0 or 1
+        # in the presence of a non-monotonic indexer
+        # GH12713
+
+        base = self.tsframe.iloc[0:5]
+        r = len(base.index)
+        c = len(base.columns)
+        tso = DataFrame(np.random.randn(r, c),
+                        index=base.index,
+                        columns=base.columns,
+                        dtype='float64')
+        # monotonic
+        ts = tso
+        grouped = ts.groupby(lambda x: x.weekday())
+        result = ts - grouped.transform('mean')
+        expected = grouped.apply(lambda x: x - x.mean())
+        assert_frame_equal(result, expected)
+
+        ts = ts.T
+        grouped = ts.groupby(lambda x: x.weekday(), axis=1)
+        result = ts - grouped.transform('mean')
+        expected = grouped.apply(lambda x: (x.T - x.mean(1)).T)
+        assert_frame_equal(result, expected)
+
+        # non-monotonic
+        ts = tso.iloc[[1, 0] + list(range(2, len(base)))]
+        grouped = ts.groupby(lambda x: x.weekday())
+        result = ts - grouped.transform('mean')
+        expected = grouped.apply(lambda x: x - x.mean())
+        assert_frame_equal(result, expected)
+
+        ts = ts.T
+        grouped = ts.groupby(lambda x: x.weekday(), axis=1)
+        result = ts - grouped.transform('mean')
+        expected = grouped.apply(lambda x: (x.T - x.mean(1)).T)
+        assert_frame_equal(result, expected)
+
     def test_transform_dtype(self):
         # GH 9807
         # Check transform dtype output is preserved