BUG: Fix groupby over a CategoricalIndex in axis=1 (#18525)

(cherry picked from commit 5da3759)

Author: ekisslinger

doc/source/whatsnew/v0.21.1.txt

@@ -139,6 +139,7 @@ Categorical
   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 in ``DataFrame.groupby(axis=1)`` with a ``CategoricalIndex`` (:issue:`18432`)
 
 String
 ^^^^^^

pandas/core/groupby.py

@@ -2931,9 +2931,11 @@ 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 ({len_gpr}) and axis ({len_axis})"
+                 " must be same length"
+                 .format(len_gpr=len(gpr), len_axis=obj.shape[axis])))
 
         # create the Grouping
         # allow us to passing the actual Grouping as the gpr

pandas/tests/groupby/test_groupby.py

@@ -10,7 +10,7 @@
 
 from pandas import (date_range, bdate_range, Timestamp,
                     Index, MultiIndex, DataFrame, Series,
-                    concat, Panel, DatetimeIndex)
+                    concat, Panel, DatetimeIndex, CategoricalIndex)
 from pandas.errors import UnsupportedFunctionCall, PerformanceWarning
 from pandas.util.testing import (assert_panel_equal, assert_frame_equal,
                                  assert_series_equal, assert_almost_equal,
@@ -262,6 +262,29 @@ def test_grouper_column_and_index(self):
         expected = df_single.reset_index().groupby(['inner', 'B']).mean()
         assert_frame_equal(result, expected)
 
+    def test_groupby_categorical_index_and_columns(self):
+        # GH18432
+        columns = ['A', 'B', 'A', 'B']
+        categories = ['B', 'A']
+        data = np.ones((5, 4), int)
+        cat_columns = CategoricalIndex(columns,
+                                       categories=categories,
+                                       ordered=True)
+        df = DataFrame(data=data, columns=cat_columns)
+        result = df.groupby(axis=1, level=0).sum()
+        expected_data = 2 * np.ones((5, 2), int)
+        expected_columns = CategoricalIndex(categories,
+                                            categories=categories,
+                                            ordered=True)
+        expected = DataFrame(data=expected_data, columns=expected_columns)
+        assert_frame_equal(result, expected)
+
+        # test transposed version
+        df = DataFrame(data.T, index=cat_columns)
+        result = df.groupby(axis=0, level=0).sum()
+        expected = DataFrame(data=expected_data.T, index=expected_columns)
+        assert_frame_equal(result, expected)
+
     def test_grouper_getting_correct_binner(self):
 
         # GH 10063