BUG: Fixes pd.merge issue with columns of dtype numpy.uintc on windows

pandas/core/reshape/merge.py

@@ -123,7 +123,17 @@
 
 # See https://github.com/pandas-dev/pandas/issues/52451
 if np.intc is not np.int32:
-    _factorizers[np.intc] = libhashtable.Int64Factorizer
+    if np.dtype(np.intc).itemsize == 4:
+        _factorizers[np.intc] = libhashtable.Int32Factorizer
+    else:
+        _factorizers[np.intc] = libhashtable.Int64Factorizer
+
+if np.uintc is not np.uint32:
+    if np.dtype(np.uintc).itemsize == 4:
+        _factorizers[np.uintc] = libhashtable.UInt32Factorizer
+    else:
+        _factorizers[np.uintc] = libhashtable.UInt64Factorizer
+
 
 _known = (np.ndarray, ExtensionArray, Index, ABCSeries)
 

pandas/tests/reshape/merge/test_merge.py

@@ -1843,6 +1843,20 @@ def test_merge_empty(self, left_empty, how, exp):
 
         tm.assert_frame_equal(result, expected)
 
+    def test_merge_with_uintc_columns(dataframes_with_uintc):
+        """To test if pd.merge works with numpy.uintc on windows"""
+
+        df1 = pd.DataFrame({"a": ["foo", "bar"], "b": np.array([1, 2], dtype=np.uintc)})
+        df2 = pd.DataFrame({"a": ["foo", "baz"], "b": np.array([3, 4], dtype=np.uintc)})
+        result = df1.merge(df2, how="outer")
+        expected = pd.DataFrame(
+            {
+                "a": ["bar", "baz", "foo", "foo"],
+                "b": np.array([2, 4, 1, 3], dtype=np.uintc),
+            }
+        )
+        tm.assert_frame_equal(result.reset_index(drop=True), expected)
+
 
 @pytest.fixture
 def left():