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Simplify and improve Series.sum #1362

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Merged
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Sep 10, 2025
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Simplify and improve Series.sum #1362

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31 changes: 9 additions & 22 deletions pandas-stubs/core/series.pyi
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Original file line number Diff line number Diff line change
Expand Up @@ -26,11 +26,13 @@ from typing import (
Generic,
Literal,
NoReturn,
Protocol,
final,
overload,
type_check_only,
)

from _typeshed import SupportsGetItem
from matplotlib.axes import (
Axes as PlotAxes,
SubplotBase,
Expand Down Expand Up @@ -187,6 +189,7 @@ from pandas._typing import (
ValueKeyFunc,
VoidDtypeArg,
WriteBuffer,
_T_co,
np_1darray,
np_ndarray_anyint,
np_ndarray_bool,
Expand All @@ -203,6 +206,10 @@ from pandas.core.dtypes.dtypes import CategoricalDtype

from pandas.plotting import PlotAccessor

@type_check_only
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I just realized that you added the use of type_check_only in a previous PR, but now I'm wondering if it makes sense in a stub file, because if you're using a stub file, doesn't that mean that its contents are only for type checking?

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@hamdanal hamdanal Sep 9, 2025 •
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This decorator is meant to be used with functions/classes that exist at type checking time but not at runtime. It is not strictly needed but it tells type checkers to flag any misuse of the decorated object at runtime. It is part of the typing module but it only exists at type checking time. The API documentation is here https://docs.python.org/3/library/typing.html#typing.type_check_only and the user guide is here https://typing.python.org/en/latest/guides/writing_stubs.html#stub-only-objects

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OK - thanks for the explanation. We probably should be using that more in the pandas stubs

class _SupportsAdd(Protocol[_T_co]):
def __add__(self, value: Self, /) -> _T_co: ...

class _iLocIndexerSeries(_iLocIndexer, Generic[S1]):
# get item
@overload
Expand Down Expand Up @@ -3751,34 +3758,14 @@ class Series(IndexOpsMixin[S1], NDFrame):
numeric_only: _bool = False,
**kwargs: Any,
) -> float: ...
@overload
def sum(
self: Series[Never],
self: SupportsGetItem[Scalar, _SupportsAdd[_T]],
axis: AxisIndex | None = 0,
skipna: _bool | None = ...,
numeric_only: _bool = ...,
min_count: int = ...,
**kwargs: Any,
) -> Any: ...
# between `Series[bool]` and `Series[int]`.
@overload
def sum(
self: Series[bool],
axis: AxisIndex | None = 0,
skipna: _bool | None = ...,
numeric_only: _bool = ...,
min_count: int = ...,
**kwargs: Any,
) -> int: ...
@overload
def sum(
self: Series[S1],
axis: AxisIndex | None = 0,
skipna: _bool | None = ...,
numeric_only: _bool = ...,
min_count: int = ...,
**kwargs: Any,
) -> S1: ...
) -> _T: ...
def to_list(self) -> list[S1]: ...
def tolist(self) -> list[S1]: ...
def var(
Expand Down
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