TST: Check merging on equivalent CategoricalDtype (pandas-dev#25898)

Closes pandas-devgh-22501

Author: gfyoung

doc/source/whatsnew/v0.25.0.rst

@@ -382,6 +382,7 @@ Reshaping
 - :func:`to_records` now accepts dtypes to its `column_dtypes` parameter (:issue:`24895`)
 - Bug in :func:`concat` where order of ``OrderedDict`` (and ``dict`` in Python 3.6+) is not respected, when passed in as  ``objs`` argument (:issue:`21510`)
 - Bug in :func:`concat` where the resulting ``freq`` of two :class:`DatetimeIndex` with the same ``freq`` would be dropped (:issue:`3232`).
+- Bug in :func:`merge` where merging with equivalent Categorical dtypes was raising an error (:issue:`22501`)
 
 Sparse
 ^^^^^^

pandas/tests/reshape/merge/test_merge.py

@@ -1666,3 +1666,68 @@ def test_merge_suffix_none_error(col1, col2, suffixes):
     msg = "iterable"
     with pytest.raises(TypeError, match=msg):
         pd.merge(a, b, left_index=True, right_index=True, suffixes=suffixes)
+
+
+@pytest.mark.parametrize("cat_dtype", ["one", "two"])
+@pytest.mark.parametrize("reverse", [True, False])
+def test_merge_equal_cat_dtypes(cat_dtype, reverse):
+    # see gh-22501
+    cat_dtypes = {
+        "one": CategoricalDtype(categories=["a", "b", "c"], ordered=False),
+        "two": CategoricalDtype(categories=["a", "b", "c"], ordered=False),
+    }
+
+    df1 = DataFrame({
+        "foo": Series(["a", "b", "c"]).astype(cat_dtypes["one"]),
+        "left": [1, 2, 3],
+    }).set_index("foo")
+
+    data_foo = ["a", "b", "c"]
+    data_right = [1, 2, 3]
+
+    if reverse:
+        data_foo.reverse()
+        data_right.reverse()
+
+    df2 = DataFrame({
+        "foo": Series(data_foo).astype(cat_dtypes[cat_dtype]),
+        "right": data_right
+    }).set_index("foo")
+
+    result = df1.merge(df2, left_index=True, right_index=True)
+
+    expected = DataFrame({
+        "left": [1, 2, 3],
+        "right": [1, 2, 3],
+        "foo": Series(["a", "b", "c"]).astype(cat_dtypes["one"]),
+    }).set_index("foo")
+
+    # Categorical is unordered, so don't check ordering.
+    tm.assert_frame_equal(result, expected, check_categorical=False)
+
+
+def test_merge_equal_cat_dtypes2():
+    # see gh-22501
+    cat_dtype = CategoricalDtype(categories=["a", "b", "c"], ordered=False)
+
+    # Test Data
+    df1 = DataFrame({
+        "foo": Series(["a", "b"]).astype(cat_dtype),
+        "left": [1, 2],
+    }).set_index("foo")
+
+    df2 = DataFrame({
+        "foo": Series(["a", "b", "c"]).astype(cat_dtype),
+        "right": [3, 2, 1],
+    }).set_index("foo")
+
+    result = df1.merge(df2, left_index=True, right_index=True)
+
+    expected = DataFrame({
+        "left": [1, 2],
+        "right": [3, 2],
+        "foo": Series(["a", "b"]).astype(cat_dtype),
+    }).set_index("foo")
+
+    # Categorical is unordered, so don't check ordering.
+    tm.assert_frame_equal(result, expected, check_categorical=False)