default Series to Series[Any]

Author: MarcoGorelli

pandas-stubs/_typing.pyi

@@ -18,7 +18,6 @@ from typing import (
     Protocol,
     SupportsIndex,
     TypedDict,
-    TypeVar,
     overload,
 )
 
@@ -35,6 +34,7 @@ from pandas.core.tools.datetimes import FulldatetimeDict
 from typing_extensions import (
     ParamSpec,
     TypeAlias,
+    TypeVar,
 )
 
 from pandas._libs.interval import Interval
@@ -842,6 +842,7 @@ S1 = TypeVar(
     | CategoricalDtype
     | BaseOffset
     | list[str],
+    default=Any,
 )
 
 S2 = TypeVar(

pandas-stubs/core/frame.pyi

@@ -75,7 +75,7 @@ from pandas._libs.tslibs import BaseOffset
 from pandas._libs.tslibs.nattype import NaTType
 from pandas._libs.tslibs.offsets import DateOffset
 from pandas._typing import (
-    S1,
+    S2,
     AggFuncTypeBase,
     AggFuncTypeDictFrame,
     AggFuncTypeDictSeries,
@@ -1392,13 +1392,13 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
     @overload
     def apply(
         self,
-        f: Callable[..., S1 | NAType],
+        f: Callable[..., S2 | NAType],
         axis: AxisIndex = ...,
         raw: _bool = ...,
         result_type: None = ...,
         args: Any = ...,
         **kwargs: Any,
-    ) -> Series[S1]: ...
+    ) -> Series[S2]: ...
     # Since non-scalar type T is not supported in Series[T],
     # we separate this overload from the above one
     @overload
@@ -1416,14 +1416,14 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
     @overload
     def apply(
         self,
-        f: Callable[..., S1 | NAType],
+        f: Callable[..., S2 | NAType],
         axis: Axis = ...,
         raw: _bool = ...,
         args: Any = ...,
         *,
         result_type: Literal["expand", "reduce"],
         **kwargs: Any,
-    ) -> Series[S1]: ...
+    ) -> Series[S2]: ...
     @overload
     def apply(
         self,
@@ -1477,14 +1477,14 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
     @overload
     def apply(
         self,
-        f: Callable[..., S1 | NAType],
+        f: Callable[..., S2 | NAType],
         raw: _bool = ...,
         result_type: None = ...,
         args: Any = ...,
         *,
         axis: AxisColumn,
         **kwargs: Any,
-    ) -> Series[S1]: ...
+    ) -> Series[S2]: ...
     @overload
     def apply(
         self,

pandas-stubs/core/groupby/generic.pyi

@@ -30,7 +30,7 @@ from typing_extensions import (
 
 from pandas._libs.tslibs.timestamps import Timestamp
 from pandas._typing import (
-    S1,
+    S2,
     AggFuncTypeBase,
     AggFuncTypeFrame,
     ByT,
@@ -52,7 +52,7 @@ class NamedAgg(NamedTuple):
     column: str
     aggfunc: AggScalar
 
-class SeriesGroupBy(GroupBy[Series[S1]], Generic[S1, ByT]):
+class SeriesGroupBy(GroupBy[Series[S2]], Generic[S2, ByT]):
     @overload
     def aggregate(
         self,
@@ -114,7 +114,7 @@ class SeriesGroupBy(GroupBy[Series[S1]], Generic[S1, ByT]):
         self,
         indices: TakeIndexer,
         **kwargs,
-    ) -> Series[S1]: ...
+    ) -> Series[S2]: ...
     def skew(
         self,
         skipna: bool = ...,
@@ -125,10 +125,10 @@ class SeriesGroupBy(GroupBy[Series[S1]], Generic[S1, ByT]):
     def plot(self) -> GroupByPlot[Self]: ...
     def nlargest(
         self, n: int = ..., keep: NsmallestNlargestKeep = ...
-    ) -> Series[S1]: ...
+    ) -> Series[S2]: ...
     def nsmallest(
         self, n: int = ..., keep: NsmallestNlargestKeep = ...
-    ) -> Series[S1]: ...
+    ) -> Series[S2]: ...
     def idxmin(self, skipna: bool = ...) -> Series: ...
     def idxmax(self, skipna: bool = ...) -> Series: ...
     def corr(
@@ -166,7 +166,7 @@ class SeriesGroupBy(GroupBy[Series[S1]], Generic[S1, ByT]):
     @final  # type: ignore[misc]
     def __iter__(  # pyright: ignore[reportIncompatibleMethodOverride]
         self,
-    ) -> Iterator[tuple[ByT, Series[S1]]]: ...
+    ) -> Iterator[tuple[ByT, Series[S2]]]: ...
 
 _TT = TypeVar("_TT", bound=Literal[True, False])