Bug: groupby multiindex levels equals rows

doc/source/whatsnew/v0.21.0.txt

@@ -361,7 +361,7 @@ Groupby/Resample/Rolling
 - Bug in ``groupby.transform()`` that would coerce boolean dtypes back to float (:issue:`16875`)
 - Bug in ``Series.resample(...).apply()`` where an empty ``Series`` modified the source index and did not return the name of a ``Series`` (:issue:`14313`)
 - Bug in ``.rolling(...).apply(...)`` with a ``DataFrame`` with a ``DatetimeIndex``, a ``window`` of a timedelta-convertible and ``min_periods >= 1` (:issue:`15305`)
-
+- Bug in ``DataFrame.groupby`` where index and column keys were not recognized correctly when the number of keys equaled the number of elements on the groupby axis (:issue:`16859`)
 
 Sparse
 ^^^^^^

pandas/core/groupby.py

@@ -2629,13 +2629,14 @@ def _get_grouper(obj, key=None, axis=0, level=None, sort=True,
 
     try:
         if isinstance(obj, DataFrame):
-            all_in_columns = all(g in obj.columns for g in keys)
+            all_in_columns_index = all(g in obj.columns or g in obj.index.names
+                                       for g in keys)
         else:
-            all_in_columns = False
+            all_in_columns_index = False
     except Exception:
-        all_in_columns = False
+        all_in_columns_index = False
 
-    if not any_callable and not all_in_columns and \
+    if not any_callable and not all_in_columns_index and \
        not any_arraylike and not any_groupers and \
        match_axis_length and level is None:
         keys = [com._asarray_tuplesafe(keys)]

pandas/tests/groupby/test_groupby.py

@@ -3891,6 +3891,19 @@ def predictions(tool):
         result = df2.groupby('Key').apply(predictions).p1
         tm.assert_series_equal(expected, result)
 
+    def test_gb_key_len_equal_axis_len(self):
+            # GH16843
+            # test ensures that index and column keys are recognized correctly
+            # when number of keys equals axis length of groupby
+            df = pd.DataFrame([['foo', 'bar', 'B', 1],
+                               ['foo', 'bar', 'B', 2],
+                               ['foo', 'baz', 'C', 3]],
+                              columns=['first', 'second', 'third', 'one'])
+            df = df.set_index(['first', 'second'])
+            df = df.groupby(['first', 'second', 'third']).size()
+            assert df.loc[('foo', 'bar', 'B')] == 2
+            assert df.loc[('foo', 'baz', 'C')] == 1
+
 
 def _check_groupby(df, result, keys, field, f=lambda x: x.sum()):
     tups = lmap(tuple, df[keys].values)