BUG: get_group fails when multi-grouping with a categorical

Author: evanpw

doc/source/whatsnew/v0.17.0.txt

@@ -57,3 +57,13 @@ Performance Improvements
 
 Bug Fixes
 ~~~~~~~~~
+
+- Bug in ``Categorical`` repr with ``display.width`` of ``None`` in Python 3 (:issue:`10087`)
+
+
+- Bug in ``Timestamp``'s' ``microsecond``, ``quarter``, ``dayofyear``, ``week`` and ``daysinmonth`` properties return ``np.int`` type, not built-in ``int``. (:issue:`10050`)
+- Bug in ``GroupBy.get_group`` when grouping on multiple keys, one of which is categorical. (:issue:`10132`)
+- Bug in ``NaT`` raises ``AttributeError`` when accessing to ``daysinmonth``, ``dayofweek`` properties. (:issue:`10096`)
+
+
+

pandas/core/groupby.py

@@ -1301,11 +1301,18 @@ def apply(self, f, data, axis=0):
     @cache_readonly
     def indices(self):
         """ dict {group name -> group indices} """
+
+        def extract_values(x):
+            if isinstance(x, CategoricalIndex):
+                return x.values.get_values()
+            else:
+                return _values_from_object(x)
+
         if len(self.groupings) == 1:
             return self.groupings[0].indices
         else:
             label_list = [ping.labels for ping in self.groupings]
-            keys = [_values_from_object(ping.group_index) for ping in self.groupings]
+            keys = [extract_values(ping.group_index) for ping in self.groupings]
             return _get_indices_dict(label_list, keys)
 
     @property

pandas/tests/test_groupby.py

@@ -5140,6 +5140,13 @@ def test_groupby_categorical_two_columns(self):
                          "ints": [1,2,1,2,1,2]}).set_index(["cat","ints"])
         tm.assert_frame_equal(res, exp)
 
+        # GH 10132
+        for key in [('a', 1), ('b', 2), ('b', 1), ('a', 2)]:
+            c, i = key
+            result = groups_double_key.get_group(key)
+            expected = test.query('cat == @c & ints == @i')
+            assert_frame_equal(result, expected)
+
         d = {'C1': [3, 3, 4, 5], 'C2': [1, 2, 3, 4], 'C3': [10, 100, 200, 34]}
         test = pd.DataFrame(d)
         values = pd.cut(test['C1'], [1, 2, 3, 6])