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Merged
merged 1 commit into from
Aug 29, 2025
Merged

type IndexOpsMixin #1357

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48 changes: 34 additions & 14 deletions pandas-stubs/core/base.pyi
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@ from typing import (
Any,
Generic,
Literal,
TypeAlias,
final,
overload,
)
Expand All @@ -22,18 +23,22 @@ from typing_extensions import Self

from pandas._typing import (
S1,
ArrayLike,
AxisIndex,
DropKeep,
DTypeLike,
GenericT,
GenericT_co,
NDFrameT,
Scalar,
SequenceNotStr,
SupportsDType,
np_1darray,
)
from pandas.util._decorators import cache_readonly

_ListLike: TypeAlias = ArrayLike | dict[str, np.ndarray] | SequenceNotStr[S1]
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not sure why this gets redefined in multiple files, but i've kept to the pattern

ok, or better to redefine in _typing?

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If you do want to use it here, then I think moving the definition to typing.pyi makes sense, but exercise care on the naming, because there is a ListLike in _typing that is used elsewhere and I'm not sure whether they should be the same or not. Maybe the name _ListLike is misleading.

Might be better to do the copying like you've done here, OR have series.pyi import _ListLike from core/base.pyi

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Might be better to do the copying like you've done here

sure, keeping like this for now then, thanks


class NoNewAttributesMixin:
def __setattr__(self, key: str, value: Any) -> None: ...

Expand All @@ -51,7 +56,7 @@ class IndexOpsMixin(OpsMixin, Generic[S1, GenericT_co]):
@property
def T(self) -> Self: ...
@property
def shape(self) -> tuple: ...
def shape(self) -> tuple[int, ...]: ...
@property
def ndim(self) -> int: ...
def item(self) -> S1: ...
Expand All @@ -67,41 +72,45 @@ class IndexOpsMixin(OpsMixin, Generic[S1, GenericT_co]):
dtype: None = None,
copy: bool = False,
na_value: Scalar = ...,
**kwargs,
**kwargs: Any,
) -> np_1darray[GenericT_co]: ...
@overload
def to_numpy(
self,
dtype: np.dtype[GenericT] | SupportsDType[GenericT] | type[GenericT],
copy: bool = False,
na_value: Scalar = ...,
**kwargs,
**kwargs: Any,
) -> np_1darray[GenericT]: ...
@overload
def to_numpy(
self,
dtype: DTypeLike,
copy: bool = False,
na_value: Scalar = ...,
**kwargs,
**kwargs: Any,
) -> np_1darray: ...
@property
def empty(self) -> bool: ...
def max(self, axis=..., skipna: bool = ..., **kwargs): ...
def min(self, axis=..., skipna: bool = ..., **kwargs): ...
def max(
self, axis: AxisIndex | None = ..., skipna: bool = ..., **kwargs: Any
) -> S1: ...
def min(
self, axis: AxisIndex | None = ..., skipna: bool = ..., **kwargs: Any
) -> S1: ...
def argmax(
self,
axis: AxisIndex | None = ...,
skipna: bool = True,
*args,
**kwargs,
*args: Any,
**kwargs: Any,
) -> np.int64: ...
def argmin(
self,
axis: AxisIndex | None = ...,
skipna: bool = True,
*args,
**kwargs,
*args: Any,
**kwargs: Any,
) -> np.int64: ...
def tolist(self) -> list[S1]: ...
def to_list(self) -> list[S1]: ...
Expand All @@ -114,7 +123,7 @@ class IndexOpsMixin(OpsMixin, Generic[S1, GenericT_co]):
normalize: Literal[False] = ...,
sort: bool = ...,
ascending: bool = ...,
bins=...,
bins: int | None = ...,
dropna: bool = ...,
) -> Series[int]: ...
@overload
Expand All @@ -123,7 +132,7 @@ class IndexOpsMixin(OpsMixin, Generic[S1, GenericT_co]):
normalize: Literal[True],
sort: bool = ...,
ascending: bool = ...,
bins=...,
bins: int | None = ...,
dropna: bool = ...,
) -> Series[float]: ...
def nunique(self, dropna: bool = True) -> int: ...
Expand All @@ -136,7 +145,18 @@ class IndexOpsMixin(OpsMixin, Generic[S1, GenericT_co]):
def factorize(
self, sort: bool = False, use_na_sentinel: bool = True
) -> tuple[np_1darray, np_1darray | Index | Categorical]: ...
@overload
def searchsorted(
self,
value: _ListLike,
side: Literal["left", "right"] = ...,
sorter: _ListLike | None = ...,
) -> np_1darray[np.intp]: ...
@overload
def searchsorted(
self, value, side: Literal["left", "right"] = ..., sorter=...
) -> int | list[int]: ...
self,
value: Scalar,
side: Literal["left", "right"] = ...,
sorter: _ListLike | None = ...,
) -> np.intp: ...
def drop_duplicates(self, *, keep: DropKeep = ...) -> Self: ...
4 changes: 2 additions & 2 deletions pandas-stubs/core/series.pyi
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Expand Up @@ -851,14 +851,14 @@ class Series(IndexOpsMixin[S1], NDFrame):
value: _ListLike,
side: Literal["left", "right"] = ...,
sorter: _ListLike | None = ...,
) -> list[int]: ...
) -> np_1darray[np.intp]: ...
@overload
def searchsorted(
self,
value: Scalar,
side: Literal["left", "right"] = ...,
sorter: _ListLike | None = ...,
) -> int: ...
) -> np.intp: ...
@overload
def compare(
self,
Expand Down
14 changes: 11 additions & 3 deletions tests/indexes/test_indexes.py
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Expand Up @@ -19,9 +19,6 @@
assert_type,
)

if TYPE_CHECKING:
from tests import Dtype # noqa: F401

from tests import (
PD_LTE_23,
TYPE_CHECKING_INVALID_USAGE,
Expand All @@ -30,6 +27,9 @@
pytest_warns_bounded,
)

if TYPE_CHECKING:
from tests import Dtype # noqa: F401


def test_index_unique() -> None:
df = pd.DataFrame({"x": [1, 2, 3, 4]}, index=pd.Index([1, 2, 3, 2]))
Expand Down Expand Up @@ -1489,6 +1489,14 @@ def test_index_naming() -> None:
check(assert_type(df.index.names, list[Hashable | None]), list)


def test_index_searchsorted() -> None:
idx = pd.Index([1, 2, 3])
check(assert_type(idx.searchsorted(1), np.intp), np.intp)
check(assert_type(idx.searchsorted([1]), "np_1darray[np.intp]"), np_1darray)
check(assert_type(idx.searchsorted(1, side="left"), np.intp), np.intp)
check(assert_type(idx.searchsorted(1, sorter=[1, 0, 2]), np.intp), np.intp)


def test_period_index_constructor() -> None:
check(
assert_type(pd.PeriodIndex(["2000"], dtype="period[D]"), pd.PeriodIndex),
Expand Down
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