Add np.intc to _factorizers in pd.merge (#53175)

* Add np.intc to merge table

* Add whatsnew

* Updated unit test

* Fix lint

* Only add intc to _factorizers if not int32

* Mention that the problem is on Windows

* Update pandas/core/reshape/merge.py

Author: hoxbro

doc/source/whatsnew/v2.0.2.rst

@@ -14,11 +14,11 @@ including other versions of pandas.
 Fixed regressions
 ~~~~~~~~~~~~~~~~~
 - Fixed performance regression in :meth:`GroupBy.apply` (:issue:`53195`)
+- Fixed regression in :func:`merge` on Windows when dtype is ``np.intc`` (:issue:`52451`)
 - Fixed regression in :func:`read_sql` dropping columns with duplicated column names (:issue:`53117`)
 - Fixed regression in :meth:`DataFrame.loc` losing :class:`MultiIndex` name when enlarging object (:issue:`53053`)
 - Fixed regression in :meth:`DataFrame.to_string` printing a backslash at the end of the first row of data, instead of headers, when the DataFrame doesn't fit the line width (:issue:`53054`)
 - Fixed regression in :meth:`MultiIndex.join` returning levels in wrong order (:issue:`53093`)
--
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_202.bug_fixes:

pandas/core/reshape/merge.py

@@ -123,6 +123,10 @@
     np.object_: libhashtable.ObjectFactorizer,
 }
 
+# See https://github.com/pandas-dev/pandas/issues/52451
+if np.intc is not np.int32:
+    _factorizers[np.intc] = libhashtable.Int64Factorizer
+
 _known = (np.ndarray, ExtensionArray, Index, ABCSeries)
 
 

pandas/tests/reshape/merge/test_merge.py

@@ -1460,7 +1460,9 @@ def test_different(self, right_vals):
         result = merge(left, right, on="A")
         assert is_object_dtype(result.A.dtype)
 
-    @pytest.mark.parametrize("d1", [np.int64, np.int32, np.int16, np.int8, np.uint8])
+    @pytest.mark.parametrize(
+        "d1", [np.int64, np.int32, np.intc, np.int16, np.int8, np.uint8]
+    )
     @pytest.mark.parametrize("d2", [np.int64, np.float64, np.float32, np.float16])
     def test_join_multi_dtypes(self, d1, d2):
         dtype1 = np.dtype(d1)