simplify check

pandas/core/indexing.py

@@ -50,12 +50,7 @@
  ABCDataFrame,
  ABCSeries,
 )
-from pandas.core.dtypes.missing import (
- infer_fill_value,
- is_valid_na_for_dtype,
- isna,
- na_value_for_dtype,
-)
+from pandas.core.dtypes.missing import infer_fill_value
 
 from pandas.core import algorithms as algos
 import pandas.core.common as com
@@ -2091,25 +2086,14 @@ def _setitem_with_indexer_missing(self, indexer, value):
  return self._setitem_with_indexer(new_indexer, value, "loc")
 
  # this preserves dtype of the value and of the object
+ new_dtype = None
  if is_list_like(value):
- new_dtype = None
-
- elif is_valid_na_for_dtype(value, self.obj.dtype):
- if not is_object_dtype(self.obj.dtype):
- # Every NA value is suitable for object, no conversion needed
- value = na_value_for_dtype(self.obj.dtype, compat=False)
-
- new_dtype = maybe_promote(self.obj.dtype, value)[0]
-
- elif isna(value):
- new_dtype = None
+ pass
  elif not self.obj.empty and not is_object_dtype(self.obj.dtype):
  # We should not cast, if we have object dtype because we can
  # set timedeltas into object series
  curr_dtype = self.obj.dtype
  new_dtype, value = maybe_promote(curr_dtype, value)
- else:
- new_dtype = None
 
  new_values = Series([value], dtype=new_dtype)._values
 

pandas/tests/series/indexing/test_setitem.py

@@ -570,7 +570,11 @@ def test_setitem_enlargement_object_none(self, nulls_fixture):
  ser[3] = nulls_fixture
  expected = Series(["a", "b", nulls_fixture], index=[0, 1, 3])
  tm.assert_series_equal(ser, expected)
- assert ser[3] is nulls_fixture
+ if isinstance(nulls_fixture, float):
+ # We retain the same type, but maybe not the same _object_
+ assert np.isnan(ser[3])
+ else:
+ assert ser[3] is nulls_fixture
 
 
 def test_setitem_scalar_into_readonly_backing_data():