Only exclude join keys that are indices from key columns

Previously, if any of the join keys were indices, we assumed that they
all were, and provided an empty set of key columns with matching names
in the left and right dataframe. This does the wrong thing for mixed
join keys (on a combination of index and normal columns), producing
more output columns than is correct. To avoid this, only skip matching
key names if they name indices.

Closes rapidsai#11550.

python/cudf/cudf/core/join/join.py

@@ -147,12 +147,14 @@ def __init__(
         self._key_columns_with_same_name = (
             set(_coerce_to_tuple(on))
             if on
-            else set()
-            if (self._using_left_index or self._using_right_index)
             else {
                 lkey.name
                 for lkey, rkey in zip(self._left_keys, self._right_keys)
                 if lkey.name == rkey.name
+                and not (
+                    isinstance(lkey, _IndexIndexer)
+                    or isinstance(rkey, _IndexIndexer)
+                )
             }
         )
 

python/cudf/cudf/tests/test_joining.py

@@ -1,5 +1,7 @@
 # Copyright (c) 2018-2022, NVIDIA CORPORATION.
 
+from itertools import combinations, product, repeat
+
 import numpy as np
 import pandas as pd
 import pytest
@@ -2106,6 +2108,28 @@ def test_string_join_values_nulls():
     assert_join_results_equal(expect, got, how="left")
 
 
+@pytest.mark.parametrize(
+    "left_on,right_on",
+    [
+        *product(["a", "b", "c"], ["a", "b"]),
+        *zip(combinations(["a", "b", "c"], 2), repeat(["a", "b"])),
+    ],
+)
+def test_merge_mixed_index_columns(left_on, right_on):
+    left = pd.DataFrame({"a": [1, 2, 1, 2], "b": [2, 3, 3, 4]}).set_index("a")
+    right = pd.DataFrame({"a": [1, 2, 1, 3], "b": [2, 30, 3, 4]}).set_index(
+        "a"
+    )
+
+    left["c"] = 10
+
+    expect = left.merge(right, left_on=left_on, right_on=right_on, how="outer")
+    cleft = cudf.from_pandas(left)
+    cright = cudf.from_pandas(right)
+    got = cleft.merge(cright, left_on=left_on, right_on=right_on, how="outer")
+    assert_join_results_equal(expect, got, how="outer")
+
+
 def test_merge_multiindex_columns():
     lhs = pd.DataFrame({"a": [1, 2, 3], "b": [2, 3, 4]})
     lhs.columns = pd.MultiIndex.from_tuples([("a", "x"), ("a", "y")])