BUG: Fix lost precision with common type of uint64/int64

doc/source/whatsnew/v3.0.0.rst

@@ -404,6 +404,7 @@ Other API changes
 - Index set operations (like union or intersection) will now ignore the dtype of
   an empty ``RangeIndex`` or empty ``Index`` with object dtype when determining
   the dtype of the resulting Index (:issue:`60797`)
+- ``np_find_common_type`` will now return ``object`` for mixed ``int64`` and ``uint64`` dtypes to avoid precision lost (:issue:`61676`, :issue:`61688`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_300.deprecations:

pandas/core/algorithms.py

@@ -47,15 +47,13 @@
     is_bool_dtype,
     is_complex_dtype,
     is_dict_like,
-    is_dtype_equal,
     is_extension_array_dtype,
     is_float,
     is_float_dtype,
     is_integer,
     is_integer_dtype,
     is_list_like,
     is_object_dtype,
-    is_signed_integer_dtype,
     needs_i8_conversion,
 )
 from pandas.core.dtypes.concat import concat_compat
@@ -508,16 +506,6 @@ def isin(comps: ListLike, values: ListLike) -> npt.NDArray[np.bool_]:
         orig_values = list(values)
         values = _ensure_arraylike(orig_values, func_name="isin-targets")
 
-        if (
-            len(values) > 0
-            and values.dtype.kind in "iufcb"
-            and not is_signed_integer_dtype(comps)
-            and not is_dtype_equal(values, comps)
-        ):
-            # GH#46485 Use object to avoid upcast to float64 later
-            # TODO: Share with _find_common_type_compat
-            values = construct_1d_object_array_from_listlike(orig_values)
-
     elif isinstance(values, ABCMultiIndex):
         # Avoid raising in extract_array
         values = np.array(values)

pandas/core/dtypes/cast.py

@@ -1418,6 +1418,13 @@ def np_find_common_type(*dtypes: np.dtype) -> np.dtype:
             # so fall back to object (find_common_dtype did unless there
             # was only one dtype)
             common_dtype = np.dtype("O")
+        elif (
+            # Some precision is lost with float64 when handling uint64/int64
+            # Use object instead for the common type
+            all(np.dtype(x).kind in "iu" and np.dtype(x).itemsize == 8 for x in dtypes)
+            and common_dtype == np.float64
+        ):
+            common_dtype = np.dtype("O")
 
     except TypeError:
         common_dtype = np.dtype("O")

pandas/tests/arrays/integer/test_concat.py

@@ -14,7 +14,7 @@
         (["Int8", "Int16"], "Int16"),
         (["UInt8", "Int8"], "Int16"),
         (["Int32", "UInt32"], "Int64"),
-        (["Int64", "UInt64"], "Float64"),
+        (["Int64", "UInt64"], "object"),
         (["Int64", "boolean"], "object"),
         (["UInt8", "boolean"], "object"),
     ],
@@ -48,7 +48,7 @@ def test_concat_series(to_concat_dtypes, result_dtype):
         (["Int8", "int16"], "Int16"),
         (["UInt8", "int8"], "Int16"),
         (["Int32", "uint32"], "Int64"),
-        (["Int64", "uint64"], "Float64"),
+        (["Int64", "uint64"], "object"),
         (["Int64", "bool"], "object"),
         (["UInt8", "bool"], "object"),
     ],

pandas/tests/dtypes/cast/test_find_common_type.py

@@ -31,7 +31,7 @@
         ((np.float16, np.float32), np.float32),
         ((np.float16, np.int16), np.float32),
         ((np.float32, np.int16), np.float32),
-        ((np.uint64, np.int64), np.float64),
+        ((np.uint64, np.int64), object),
         ((np.int16, np.float64), np.float64),
         ((np.float16, np.int64), np.float64),
         # Into others.
@@ -155,9 +155,16 @@ def test_interval_dtype(left, right):
     elif left.subtype.kind in ["i", "u", "f"]:
         # i.e. numeric
         if right.subtype.kind in ["i", "u", "f"]:
-            # both numeric -> common numeric subtype
-            expected = IntervalDtype(np.float64, "right")
-            assert result == expected
+            if (
+                left.subtype.kind in ["i", "u"]
+                and right.subtype.kind in ["i", "u"]
+                and left.subtype.kind != right.subtype.kind
+            ):
+                assert result == object
+            else:
+                # both numeric -> common numeric subtype
+                expected = IntervalDtype(np.float64, "right")
+                assert result == expected
         else:
             assert result == object
 

pandas/tests/indexes/interval/test_setops.py

@@ -55,6 +55,7 @@ def test_union_empty_result(self, closed, sort):
         tm.assert_index_equal(result, expected)
 
         other = empty_index(dtype="uint64", closed=closed)
+        expected = Index([], dtype="object")
         result = index.union(other, sort=sort)
         tm.assert_index_equal(result, expected)
 
@@ -117,6 +118,7 @@ def test_intersection_empty_result(self, closed, sort):
         tm.assert_index_equal(result, expected)
 
         other = monotonic_index(300, 314, dtype="uint64", closed=closed)
+        expected = Index([], dtype="object")
         result = index.intersection(other, sort=sort)
         tm.assert_index_equal(result, expected)
 

pandas/tests/test_algos.py

@@ -1197,6 +1197,13 @@ def test_isin_unsigned_dtype(self):
         expected = Series(False)
         tm.assert_series_equal(result, expected)
 
+    def test_isin_unsigned_dtype_other_side(self):
+        # GH#46485
+        ser = Series([1378774140726870442], dtype=np.int64)
+        result = ser.isin([np.uint64(1378774140726870528)])
+        expected = Series(False)
+        tm.assert_series_equal(result, expected)
+
 
 class TestValueCounts:
     def test_value_counts(self):