diff --git a/doc/source/whatsnew/v0.16.2.txt b/doc/source/whatsnew/v0.16.2.txt
index b571aab0b19a5..1a8fc90b9683f 100644
--- a/doc/source/whatsnew/v0.16.2.txt
+++ b/doc/source/whatsnew/v0.16.2.txt
@@ -55,7 +55,7 @@ Bug Fixes
   multi-indexed (:issue:`7212`)
 - Bug in ``Categorical`` repr with ``display.width`` of ``None`` in Python 3 (:issue:`10087`)
 
-
+- Bug in groupby.apply aggregation for Categorical not preserving categories (:issue:`10138`)
 - Bug in ``mean()`` where integer dtypes can overflow (:issue:`10172`)
 - Bug where Panel.from_dict does not set dtype when specified (:issue:`10058`)
 - Bug in ``Index.union`` raises ``AttributeError`` when passing array-likes. (:issue:`10149`)
diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py
index 51674bad60f5b..4abdd1112c721 100644
--- a/pandas/core/groupby.py
+++ b/pandas/core/groupby.py
@@ -2944,7 +2944,8 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False):
                     cd = 'coerce'
                 else:
                     cd = True
-                return result.convert_objects(convert_dates=cd)
+                result = result.convert_objects(convert_dates=cd)
+                return self._reindex_output(result)
 
             else:
                 # only coerce dates if we find at least 1 datetime
diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py
index 0789e20df3945..ab78bd63a7c94 100644
--- a/pandas/tests/test_groupby.py
+++ b/pandas/tests/test_groupby.py
@@ -2595,6 +2595,35 @@ def get_stats(group):
         result = self.df.groupby(cats).D.apply(get_stats)
         self.assertEqual(result.index.names[0], 'C')
 
+    def test_apply_categorical_data(self):
+        # GH 10138
+        for ordered in [True, False]:
+            dense = Categorical(list('abc'), ordered=ordered)
+            # 'b' is in the categories but not in the list
+            missing = Categorical(list('aaa'), categories=['a', 'b'], ordered=ordered)
+            values = np.arange(len(dense))
+            df = DataFrame({'missing': missing,
+                            'dense': dense,
+                            'values': values})
+            grouped = df.groupby(['missing', 'dense'])
+
+            # missing category 'b' should still exist in the output index
+            idx = MultiIndex.from_product([['a', 'b'], ['a', 'b', 'c']],
+                                          names=['missing', 'dense'])
+            expected = DataFrame([0, 1, 2, np.nan, np.nan, np.nan],
+                                 index=idx,
+                                 columns=['values'])
+
+            assert_frame_equal(grouped.apply(lambda x: np.mean(x)), expected)
+            assert_frame_equal(grouped.mean(), expected)
+            assert_frame_equal(grouped.agg(np.mean), expected)
+
+            # but for transform we should still get back the original index
+            idx = MultiIndex.from_product([['a'], ['a', 'b', 'c']],
+                                          names=['missing', 'dense'])
+            expected = Series(1, index=idx)
+            assert_series_equal(grouped.apply(lambda x: 1), expected)
+
     def test_apply_corner_cases(self):
         # #535, can't use sliding iterator