diff --git a/doc/source/v0.15.0.txt b/doc/source/v0.15.0.txt
index 6b2d2b6aad490..9b3f79836141b 100644
--- a/doc/source/v0.15.0.txt
+++ b/doc/source/v0.15.0.txt
@@ -946,7 +946,7 @@ Bug Fixes
 - Bug in ``is_superperiod`` and ``is_subperiod`` cannot handle higher frequencies than ``S`` (:issue:`7760`, :issue:`7772`, :issue:`7803`)
 - Bug in 32-bit platforms with ``Series.shift`` (:issue:`8129`)
 - Bug in ``PeriodIndex.unique`` returns int64 ``np.ndarray`` (:issue:`7540`)
-
+- Bug in groupby ``.apply`` with a non-affecting mutation in the function (:issue:`8467`)
 - Bug in ``DataFrame.reset_index`` which has ``MultiIndex`` contains ``PeriodIndex`` or ``DatetimeIndex`` with tz raises ``ValueError`` (:issue:`7746`, :issue:`7793`)
 
 
diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py
index 2e107e0b0e935..d63bc554c771a 100644
--- a/pandas/core/groupby.py
+++ b/pandas/core/groupby.py
@@ -2851,8 +2851,9 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False):
                             return concat(values)
 
                     if not all_indexed_same:
+                        # GH 8467
                         return self._concat_objects(
-                            keys, values, not_indexed_same=not_indexed_same
+                            keys, values, not_indexed_same=True,
                         )
 
                 try:
diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py
index 09763d53c017d..0e8b5d68e3fd7 100644
--- a/pandas/tests/test_groupby.py
+++ b/pandas/tests/test_groupby.py
@@ -2112,6 +2112,18 @@ def f_no_copy(x):
         grpby_no_copy = mydf.groupby('cat1').apply(f_no_copy)
         assert_series_equal(grpby_copy,grpby_no_copy)
 
+    def test_no_mutate_but_looks_like(self):
+
+        # GH 8467
+        # first show's mutation indicator
+        # second does not, but should yield the same results
+        df = DataFrame({'key': [1, 1, 1, 2, 2, 2, 3, 3, 3],
+                        'value': range(9)})
+
+        result1 = df.groupby('key', group_keys=True).apply(lambda x: x[:].key)
+        result2 = df.groupby('key', group_keys=True).apply(lambda x: x.key)
+        assert_series_equal(result1, result2)
+
     def test_apply_chunk_view(self):
         # Low level tinkering could be unsafe, make sure not
         df = DataFrame({'key': [1, 1, 1, 2, 2, 2, 3, 3, 3],