BUG: avoid float upcast when mixing signed/unsigned ints in isin

pandas/core/algorithms.py

@@ -522,11 +522,18 @@ def isin(comps: ListLike, values: ListLike) -> npt.NDArray[np.bool_]:
         if (
             len(values) > 0
             and values.dtype.kind in "iufcb"
-            and not is_signed_integer_dtype(comps)
-            and not is_dtype_equal(values, comps)
+            # If the dtypes differ and either side is unsigned integer,
+            # prefer object dtype to avoid unsafe upcast to float64 that
+            # can lose precision for large 64-bit integers.
+            and (not is_dtype_equal(values, comps))
+            and (
+                (not is_signed_integer_dtype(comps))
+                or (not is_signed_integer_dtype(values))
+            )
         ):
-            # GH#46485 Use object to avoid upcast to float64 later
-            # TODO: Share with _find_common_type_compat
+            # GH#46485: Use object to avoid upcast to float64 later
+            # Ensure symmetric behavior when mixing signed and unsigned
+            # integer dtypes.
             values = construct_1d_object_array_from_listlike(orig_values)
 
     elif isinstance(values, ABCMultiIndex):

pandas/tests/series/methods/test_isin.py

@@ -267,3 +267,16 @@ def test_isin_filtering_on_iterable(data, isin):
     expected_result = Series([True, True, False])
 
     tm.assert_series_equal(result, expected_result)
+
+
+def test_isin_int64_vs_uint64_mismatch():
+    # Regression test for mixing signed int64 Series with uint64 values
+    # Ensure we do not implicitly upcast to float64 and return incorrect True
+    # related to GH# (user report)
+    ser = Series([1378774140726870442], dtype=np.int64)
+    vals = [np.uint64(1378774140726870528)]
+
+    res = ser.isin(vals)
+    # different values -> should be False
+    expected = Series([False])
+    tm.assert_series_equal(res, expected)