BUG: Fix groupby over a CategoricalIndex in axis=1

closes GH18432

Add multi-index columns test to test_groupby_categorical_columns_index()

Add whatsnew for GH18432 bug fix

Fix ValueError text for GH18432 bug fix

Author: Eric Kisslinger

doc/source/whatsnew/v0.21.1.txt

@@ -137,6 +137,7 @@ Categorical
 - Error messages in the testing module have been improved when items have different ``CategoricalDtype`` (:issue:`18069`)
 - ``CategoricalIndex`` can now correctly take a ``pd.api.types.CategoricalDtype`` as its dtype (:issue:`18116`)
 - Bug in ``Categorical.unique()`` returning read-only ``codes``  array when all categories were ``NaN`` (:issue:`18051`)
+- Bug when grouping over a ``CategoricalIndex`` in axis=1 (:issue:`18432`)
 
 String
 ^^^^^^

pandas/core/groupby.py

@@ -2859,9 +2859,10 @@ def is_in_obj(gpr):
         else:
             in_axis, name = False, None
 
-        if is_categorical_dtype(gpr) and len(gpr) != len(obj):
-            raise ValueError("Categorical dtype grouper must "
-                             "have len(grouper) == len(data)")
+        if is_categorical_dtype(gpr) and len(gpr) != obj.shape[axis]:
+            raise ValueError(
+                ("Length of grouper ({0}) and axis ({1}) must be same length"
+                 .format(len(gpr), obj.shape[axis])))
 
         # create the Grouping
         # allow us to passing the actual Grouping as the gpr

pandas/tests/groupby/test_categorical.py

@@ -191,6 +191,45 @@ def test_groupby_categorical_index(self):
                 [0, 1, 2, 3], levels, ordered=True), name='cats')
         assert_frame_equal(result, expected)
 
+    def test_groupby_categorical_columns_index(self):
+        # GH18432
+        s = np.random.RandomState(0)
+        columns = ['A', 'B', 'A', 'B']
+        categories = ['B', 'A']
+        cat_columns = CategoricalIndex(columns,
+                                       categories=categories,
+                                       ordered=True)
+        data = s.rand(5, len(columns))
+        df = DataFrame(data, columns=cat_columns)
+        result = df.groupby(axis=1, level=0).sum()
+        df = DataFrame(data, columns=columns)
+        expected = df.groupby(axis=1, level=0).sum()
+        expected_cat_columns = CategoricalIndex(expected.columns,
+                                                categories=categories,
+                                                ordered=True)
+        expected.columns = expected_cat_columns
+        expected = expected.sort_index(axis=1)
+        assert_frame_equal(result, expected)
+
+        # test multi-index version
+        levels = [['a', 'b', 'c', 'd'],
+                  Categorical(['A', 'B'], categories=categories, ordered=True)]
+        mixed_multi_index = MultiIndex.from_product(levels)
+        data = s.rand(5, len(mixed_multi_index))
+        df = DataFrame(data=data, columns=mixed_multi_index)
+        result = df.groupby(axis=1, level=1).sum()
+        vanilla_multi_index = MultiIndex.from_product([
+            levels[0],
+            levels[1].get_values()])
+        df = DataFrame(data=data, columns=vanilla_multi_index)
+        expected = df.groupby(axis=1, level=1).sum()
+        expected_cat_index = CategoricalIndex(expected.columns,
+                                              categories=levels[1],
+                                              ordered=True)
+        expected.columns = expected_cat_index
+        expected = expected.sort_index(axis=1)
+        assert_frame_equal(result, expected)
+
     def test_groupby_describe_categorical_columns(self):
         # GH 11558
         cats = pd.CategoricalIndex(['qux', 'foo', 'baz', 'bar'],