Better name, tests

pandas/core/dtypes/base.py

@@ -392,7 +392,7 @@ def _can_hold_na(self) -> bool:
  """
  return True
 
- def _maybe_promote(self, item: Any) -> tuple[DtypeObj, Any]:
+ def _find_compatible_dtype(self, item: Any) -> tuple[DtypeObj, Any]:
  """
  Find the minimal dtype that we need to upcast to in order to hold
  the given item.

pandas/core/dtypes/cast.py

@@ -612,7 +612,7 @@ def _maybe_promote(dtype: DtypeObj, fill_value=np.nan):
  # a cached version.
 
  if not isinstance(dtype, np.dtype):
- return dtype._maybe_promote(fill_value)
+ return dtype._find_compatible_dtype(fill_value)
 
  if not is_scalar(fill_value):
  # with object dtype there is nothing to promote, and the user can

pandas/core/dtypes/dtypes.py

@@ -671,7 +671,7 @@ def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
 
  return find_common_type(non_cat_dtypes)
 
- def _maybe_promote(self, item) -> tuple[DtypeObj, Any]:
+ def _find_compatible_dtype(self, item) -> tuple[DtypeObj, Any]:
  from pandas.core.dtypes.missing import is_valid_na_for_dtype
 
  if item in self.categories or is_valid_na_for_dtype(
@@ -1569,7 +1569,7 @@ def _get_common_dtype(self, dtypes: list[DtypeObj]) -> DtypeObj | None:
  except (KeyError, NotImplementedError):
  return None
 
- def _maybe_promote(self, item) -> tuple[DtypeObj, Any]:
+ def _find_compatible_dtype(self, item) -> tuple[DtypeObj, Any]:
  from pandas.core.dtypes.cast import maybe_promote
  from pandas.core.dtypes.missing import is_valid_na_for_dtype
 
@@ -2316,7 +2316,7 @@ def __from_arrow__(self, array: pa.Array | pa.ChunkedArray):
  arr = array.cast(self.pyarrow_dtype, safe=True)
  return array_class(arr)
 
- def _maybe_promote(self, item: Any) -> tuple[DtypeObj, Any]:
+ def _find_compatible_dtype(self, item: Any) -> tuple[DtypeObj, Any]:
  if isinstance(item, pa.Scalar):
  if not item.is_valid:
  # TODO: ask joris for help making these checks more robust

pandas/tests/indexing/test_loc.py

@@ -1956,6 +1956,28 @@ def test_loc_drops_level(self):
 
 
 class TestLocSetitemWithExpansion:
+ def test_series_loc_setitem_with_expansion_categorical(self):
+ # NA value that can't be held in integer categories
+ ser = Series([1, 2, 3], dtype="category")
+ ser.loc[3] = pd.NaT
+ assert ser.dtype == object
+
+ def test_series_loc_setitem_with_expansion_interval(self):
+ idx = pd.interval_range(1, 3)
+ ser2 = Series(idx)
+ ser2.loc[2] = np.nan
+ assert ser2.dtype == "interval[float64, right]"
+
+ ser2.loc[3] = pd.NaT
+ assert ser2.dtype == object
+
+ def test_series_loc_setitem_with_expansion_list_object(self):
+ ser3 = Series(range(3))
+ ser3.loc[3] = []
+ assert ser3.dtype == object
+ item = ser3.loc[3]
+ assert isinstance(item, list) and len(item) == 0
+
  @pytest.mark.slow
  def test_loc_setitem_with_expansion_large_dataframe(self):
  # GH#10692