BUG: Series.__setitem__ positional raising ValuError (#45232)

Author: jbrockmendel

doc/source/whatsnew/v1.5.0.rst

@@ -151,7 +151,7 @@ Interval
 
 Indexing
 ^^^^^^^^
--
+- Bug in :meth:`Series.__setitem__` with a non-integer :class:`Index` when using an integer key to set a value that cannot be set inplace where a ``ValueError`` was raised insead of casting to a common dtype (:issue:`45070`)
 -
 
 Missing

pandas/core/series.py

@@ -1098,8 +1098,9 @@ def __setitem__(self, key, value) -> None:
                         FutureWarning,
                         stacklevel=find_stack_level(),
                     )
-                # this is equivalent to self._values[key] = value
-                self._mgr.setitem_inplace(key, value)
+                # can't use _mgr.setitem_inplace yet bc could have *both*
+                #  KeyError and then ValueError, xref GH#45070
+                self._set_values(key, value)
             else:
                 # GH#12862 adding a new key to the Series
                 self.loc[key] = value

pandas/tests/series/indexing/test_setitem.py

@@ -1350,15 +1350,25 @@ def test_6942(indexer_al):
     assert df.iloc[0, 0] == t2
 
 
-@pytest.mark.xfail(reason="Doesn't catch when numpy raises.")
-def test_45070():
+def test_setitem_positional_with_casting():
+    # GH#45070 case where in __setitem__ we get a KeyError, then when
+    #  we fallback we *also* get a ValueError if we try to set inplace.
     ser = Series([1, 2, 3], index=["a", "b", "c"])
 
     ser[0] = "X"
     expected = Series(["X", 2, 3], index=["a", "b", "c"], dtype=object)
     tm.assert_series_equal(ser, expected)
 
 
+def test_setitem_positional_float_into_int_coerces():
+    # Case where we hit a KeyError and then trying to set in-place incorrectly
+    #  casts a float to an int
+    ser = Series([1, 2, 3], index=["a", "b", "c"])
+    ser[0] = 1.5
+    expected = Series([1.5, 2, 3], index=["a", "b", "c"])
+    tm.assert_series_equal(ser, expected)
+
+
 @pytest.mark.xfail(reason="unwanted upcast")
 def test_15231():
     df = DataFrame([[1, 2], [3, 4]], columns=["a", "b"])