update Scalar to align with pandas-stubs

Author: Dr-Irv

doc/source/reference/aliases.rst

@@ -72,7 +72,7 @@ Alias                                Meaning
 :py:type:`ReadCsvBuffer`             Additional argument type corresponding to buffers for :meth:`pandas.read_csv`
 :py:type:`ReadPickleBuffer`          Additional argument type corresponding to buffers for :meth:`pandas.read_pickle`
 :py:type:`ReindexMethod`             Argument type for ``reindex`` in :meth:`reindex`
-:py:type:`Scalar`                    Basic type that can be stored in :class:`Series`
+:py:type:`Scalar`                    Regular non-object types that can be stored in :class:`Series`
 :py:type:`SequenceNotStr`            Used for arguments that require sequences, but not plain strings
 :py:type:`SliceType`                 Argument types for ``start`` and ``end`` in :meth:`Index.slice_locs`
 :py:type:`SortKind`                  Argument type for ``kind`` in :meth:`sort_index` and :meth:`sort_values`

pandas/_typing.py

@@ -133,8 +133,27 @@ def __reversed__(self) -> Iterator[_T_co]: ...
 
 PythonScalar: TypeAlias = str | float | bool
 DatetimeLikeScalar: TypeAlias = Union["Period", "Timestamp", "Timedelta"]
-PandasScalar: TypeAlias = Union["Period", "Timestamp", "Timedelta", "Interval"]
-Scalar: TypeAlias = PythonScalar | PandasScalar | np.datetime64 | np.timedelta64 | date
+
+# aligned with pandas-stubs - typical scalars found in Series.  Explicitly leaves
+# out object
+_IndexIterScalar: TypeAlias = Union[
+    str,
+    bytes,
+    date,
+    datetime,
+    timedelta,
+    np.datetime64,
+    np.timedelta64,
+    bool,
+    int,
+    float,
+    "Timestamp",
+    "Timedelta",
+]
+Scalar: TypeAlias = Union[
+    _IndexIterScalar, "Interval", complex, np.integer, np.floating, np.complexfloating
+]
+
 IntStrT = TypeVar("IntStrT", bound=int | str)
 
 # timestamp and timedelta convertible types

pandas/core/dtypes/cast.py

@@ -599,7 +599,7 @@ def _maybe_promote(dtype: np.dtype, fill_value=np.nan):
             dtype = np.dtype(np.object_)
 
         elif issubclass(dtype.type, np.integer):
-            if not np_can_cast_scalar(fill_value, dtype):  # type: ignore[arg-type]
+            if not np_can_cast_scalar(fill_value, dtype):
                 # upcast to prevent overflow
                 mst = np.min_scalar_type(fill_value)
                 dtype = np.promote_types(dtype, mst)