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

doc/source/whatsnew/v3.0.0.rst

@@ -739,6 +739,7 @@ Reshaping
 - Bug in :meth:`DataFrame.join` when a :class:`DataFrame` with a :class:`MultiIndex` would raise an ``AssertionError`` when :attr:`MultiIndex.names` contained ``None``. (:issue:`58721`)
 - Bug in :meth:`DataFrame.merge` where merging on a column containing only ``NaN`` values resulted in an out-of-bounds array access (:issue:`59421`)
 - Bug in :meth:`DataFrame.unstack` producing incorrect results when ``sort=False`` (:issue:`54987`, :issue:`55516`)
+- Bug in :meth:`DataFrame.merge` when merging two :class:`DataFrame` on ``intc`` or ``uintc`` types on Windows (:issue:`60091`, :issue:`58713`)
 - Bug in :meth:`DataFrame.pivot_table` incorrectly subaggregating results when called without an ``index`` argument (:issue:`58722`)
 - Bug in :meth:`DataFrame.unstack` producing incorrect results when manipulating empty :class:`DataFrame` with an :class:`ExtentionDtype` (:issue:`59123`)
 

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 +1843,41 @@ def test_merge_empty(self, left_empty, how, exp):
 
         tm.assert_frame_equal(result, expected)
 
+    def test_merge_with_uintc_columns(self):
+        df1 = DataFrame({"a": ["foo", "bar"], "b": np.array([1, 2], dtype=np.uintc)})
+        df2 = DataFrame({"a": ["foo", "baz"], "b": np.array([3, 4], dtype=np.uintc)})
+        result = df1.merge(df2, how="outer")
+        expected = 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)
+
+    def test_merge_with_intc_columns(self):
+        df1 = DataFrame({"a": ["foo", "bar"], "b": np.array([1, 2], dtype=np.intc)})
+        df2 = DataFrame({"a": ["foo", "baz"], "b": np.array([3, 4], dtype=np.intc)})
+        result = df1.merge(df2, how="outer")
+        expected = DataFrame(
+            {
+                "a": ["bar", "baz", "foo", "foo"],
+                "b": np.array([2, 4, 1, 3], dtype=np.intc),
+            }
+        )
+        tm.assert_frame_equal(result.reset_index(drop=True), expected)
+
+    def test_merge_intc_non_monotonic(self):
+        df = DataFrame({"join_key": Series([0, 2, 1], dtype=np.intc)})
+        df_details = DataFrame(
+            {"join_key": Series([0, 1, 2], dtype=np.intc), "value": ["a", "b", "c"]}
+        )
+        merged = df.merge(df_details, on="join_key", how="left")
+        expected = DataFrame(
+            {"join_key": np.array([0, 2, 1], dtype=np.intc), "value": ["a", "c", "b"]}
+        )
+        tm.assert_frame_equal(merged.reset_index(drop=True), expected)
+
 
 @pytest.fixture
 def left():