BUG: groupby list of keys same length as index

This fix is for GH11185

Author: rinoc

doc/source/whatsnew/v0.17.0.txt

@@ -1184,3 +1184,4 @@ Bug Fixes
 - Bug in ``io.gbq`` when testing for minimum google api client version (:issue:`10652`)
 - Bug in ``DataFrame`` construction from nested ``dict`` with ``timedelta`` keys (:issue:`11129`)
 - Bug in ``.fillna`` against may raise ``TypeError`` when data contains datetime dtype (:issue:`7095`, :issue:`11153`)
+- Bug in ``.groupby`` when number of keys to group by is same as length of index (:issue:`11185`)

pandas/core/groupby.py

@@ -2111,6 +2111,7 @@ def _get_grouper(obj, key=None, axis=0, level=None, sort=True):
     # what are we after, exactly?
     match_axis_length = len(keys) == len(group_axis)
     any_callable = any(callable(g) or isinstance(g, dict) for g in keys)
+    any_groupers = any(isinstance(g, Grouper) for g in keys)
     any_arraylike = any(isinstance(g, (list, tuple, Series, Index, np.ndarray))
                         for g in keys)
 
@@ -2123,7 +2124,8 @@ def _get_grouper(obj, key=None, axis=0, level=None, sort=True):
         all_in_columns = False
 
     if (not any_callable and not all_in_columns
-        and not any_arraylike and match_axis_length
+        and not any_arraylike and not any_groupers
+            and match_axis_length
             and level is None):
         keys = [com._asarray_tuplesafe(keys)]
 

pandas/tests/test_groupby.py

@@ -2989,6 +2989,18 @@ def test_groupby_list_infer_array_like(self):
         result = df.groupby(['foo', 'bar']).mean()
         expected = df.groupby([df['foo'], df['bar']]).mean()[['val']]
 
+    def test_groupby_keys_same_size_as_index(self):
+        # GH 11185
+        freq = 's'
+        index = pd.date_range(start=np.datetime64(
+            '2015-09-29T11:34:44-0700'), periods=2, freq=freq)
+        df = pd.DataFrame([['A', 10], ['B', 15]], columns=[
+                          'metric', 'values'], index=index)
+        result = df.groupby([pd.Grouper(level=0, freq=freq), 'metric']).mean()
+        expected = df.set_index([df.index, 'metric'])
+
+        assert_frame_equal(result, expected)
+
     def test_groupby_nat_exclude(self):
         # GH 6992
         df = pd.DataFrame({'values': np.random.randn(8),