BUG: Series[bool].__setitem__(scalar, non_bool) (#39478)

Author: jbrockmendel

pandas/core/dtypes/cast.py

@@ -1904,12 +1904,15 @@ def validate_numeric_casting(dtype: np.dtype, value: Scalar) -> None:
     ):
         raise ValueError("Cannot assign nan to integer series")
 
-    if dtype.kind in ["i", "u", "f", "c"]:
+    elif dtype.kind in ["i", "u", "f", "c"]:
         if is_bool(value) or isinstance(value, np.timedelta64):
             # numpy will cast td64 to integer if we're not careful
             raise ValueError(
                 f"Cannot assign {type(value).__name__} to float/integer series"
             )
+    elif dtype.kind == "b":
+        if is_scalar(value) and not is_bool(value):
+            raise ValueError(f"Cannot assign {type(value).__name__} to bool series")
 
 
 def can_hold_element(dtype: np.dtype, element: Any) -> bool:

pandas/tests/indexing/test_coercion.py

@@ -161,34 +161,19 @@ def test_setitem_series_complex128(self, val, exp_dtype):
     @pytest.mark.parametrize(
         "val,exp_dtype",
         [
-            (1, np.int64),
-            (3, np.int64),
-            (1.1, np.float64),
-            (1 + 1j, np.complex128),
+            (1, object),
+            ("3", object),
+            (3, object),
+            (1.1, object),
+            (1 + 1j, object),
             (True, np.bool_),
         ],
     )
-    def test_setitem_series_bool(self, val, exp_dtype, request):
+    def test_setitem_series_bool(self, val, exp_dtype):
         obj = pd.Series([True, False, True, False])
         assert obj.dtype == np.bool_
 
-        mark = None
-        if exp_dtype is np.int64:
-            exp = pd.Series([True, True, True, False])
-            self._assert_setitem_series_conversion(obj, val, exp, np.bool_)
-            mark = pytest.mark.xfail(reason="TODO_GH12747 The result must be int")
-        elif exp_dtype is np.float64:
-            exp = pd.Series([True, True, True, False])
-            self._assert_setitem_series_conversion(obj, val, exp, np.bool_)
-            mark = pytest.mark.xfail(reason="TODO_GH12747 The result must be float")
-        elif exp_dtype is np.complex128:
-            exp = pd.Series([True, True, True, False])
-            self._assert_setitem_series_conversion(obj, val, exp, np.bool_)
-            mark = pytest.mark.xfail(reason="TODO_GH12747 The result must be complex")
-        if mark is not None:
-            request.node.add_marker(mark)
-
-        exp = pd.Series([True, val, True, False])
+        exp = pd.Series([True, val, True, False], dtype=exp_dtype)
         self._assert_setitem_series_conversion(obj, val, exp, exp_dtype)
 
     @pytest.mark.parametrize(

pandas/tests/series/indexing/test_setitem.py

@@ -281,6 +281,22 @@ def test_setitem_dt64_into_int_series(self, dtype):
         ser[:-1] = np.array([val, val])
         tm.assert_series_equal(ser, expected)
 
+    @pytest.mark.parametrize("unique", [True, False])
+    @pytest.mark.parametrize("val", [3, 3.0, "3"], ids=type)
+    def test_setitem_non_bool_into_bool(self, val, indexer_sli, unique):
+        # dont cast these 3-like values to bool
+        ser = Series([True, False])
+        if not unique:
+            ser.index = [1, 1]
+
+        indexer_sli(ser)[1] = val
+        assert type(ser.iloc[1]) == type(val)
+
+        expected = Series([True, val], dtype=object, index=ser.index)
+        if not unique and indexer_sli is not tm.iloc:
+            expected = Series([val, val], dtype=object, index=[1, 1])
+        tm.assert_series_equal(ser, expected)
+
 
 class SetitemCastingEquivalents:
     """