BUG: Allow merging on Index vectors

This behavior used to work in v0.19.0 and
is consistent with the documentation.

Closes gh-19038

Author: gfyoung

doc/source/whatsnew/v0.23.0.txt

@@ -375,7 +375,7 @@ Reshaping
 - Bug in :func:`Series.rank` where ``Series`` containing ``NaT`` modifies the ``Series`` inplace (:issue:`18521`)
 - Bug in :func:`cut` which fails when using readonly arrays (:issue:`18773`)
 - Bug in :func:`Dataframe.pivot_table` which fails when the ``aggfunc`` arg is of type string.  The behavior is now consistent with other methods like ``agg`` and ``apply`` (:issue:`18713`)
-
+- Bug in :func:`DataFrame.merge` in which merging using ``Index`` objects as vectors raised an Exception (:issue:`19038`)
 
 Numeric
 ^^^^^^^

pandas/core/reshape/merge.py

@@ -814,12 +814,12 @@ def _get_merge_keys(self):
         join_names = []
         right_drop = []
         left_drop = []
+
         left, right = self.left, self.right
+        list_types = (np.ndarray, Series, Index)
 
-        is_lkey = lambda x: isinstance(
-            x, (np.ndarray, Series)) and len(x) == len(left)
-        is_rkey = lambda x: isinstance(
-            x, (np.ndarray, Series)) and len(x) == len(right)
+        is_lkey = lambda x: isinstance(x, list_types) and len(x) == len(left)
+        is_rkey = lambda x: isinstance(x, list_types) and len(x) == len(right)
 
         # Note that pd.merge_asof() has separate 'on' and 'by' parameters. A
         # user could, for example, request 'left_index' and 'left_by'. In a

pandas/tests/reshape/merge/test_merge.py

@@ -1370,6 +1370,32 @@ def f():
             household.join(log_return, how='outer')
         pytest.raises(NotImplementedError, f)
 
+    @pytest.mark.parametrize("klass", [None, np.asarray, Series, Index])
+    def test_merge_datetime_index(self, klass):
+        # see gh-19038
+        df = DataFrame([1, 2, 3],
+                       ["2016-01-01", "2017-01-01", "2018-01-01"],
+                       columns=["a"])
+        df.index = pd.to_datetime(df.index)
+        on_vector = df.index.year
+
+        if klass is not None:
+            on_vector = klass(on_vector)
+
+        expected = DataFrame({"a": [1, 2, 3]})
+
+        if klass == np.asarray:
+            # The join key is added for ndarray.
+            expected["key_1"] = [2016, 2017, 2018]
+
+        result = df.merge(df, on=["a", on_vector], how="inner")
+        tm.assert_frame_equal(result, expected)
+
+        expected = DataFrame({"a_x": [1, 2, 3],
+                              "a_y": [1, 2, 3]})
+        result = df.merge(df, on=[df.index.year], how="inner")
+        tm.assert_frame_equal(result, expected)
+
 
 class TestMergeDtypes(object):