type IndexOpsMixin

Author: MarcoGorelli

pandas-stubs/core/base.pyi

@@ -22,11 +22,13 @@ from typing_extensions import Self
 
 from pandas._typing import (
     S1,
+    AnyArrayLike,
     AxisIndex,
     DropKeep,
     DTypeLike,
     GenericT,
     GenericT_co,
+    ListLike,
     NDFrameT,
     Scalar,
     SupportsDType,
@@ -51,7 +53,7 @@ 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: ...
@@ -67,41 +69,45 @@ 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]: ...
@@ -114,7 +120,7 @@ class IndexOpsMixin(OpsMixin, Generic[S1, GenericT_co]):
         normalize: Literal[False] = ...,
         sort: bool = ...,
         ascending: bool = ...,
-        bins=...,
+        bins: int | None = ...,
         dropna: bool = ...,
     ) -> Series[int]: ...
     @overload
@@ -123,7 +129,7 @@ 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: ...
@@ -136,7 +142,18 @@ 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, side: Literal["left", "right"] = ..., sorter=...
-    ) -> int | list[int]: ...
+        self,
+        value: Scalar,
+        side: Literal["left", "right"] = ...,
+        sorter: AnyArrayLike = ...,
+    ) -> np.intp: ...
+    @overload
+    def searchsorted(
+        self,
+        value: ListLike,
+        side: Literal["left", "right"] = ...,
+        sorter: AnyArrayLike = ...,
+    ) -> np_1darray[np.intp]: ...
     def drop_duplicates(self, *, keep: DropKeep = ...) -> Self: ...

tests/indexes/test_indexes.py

@@ -19,14 +19,15 @@
     assert_type,
 )
 
+from tests import np_1darray
+
 if TYPE_CHECKING:
     from tests import Dtype  # noqa: F401
 
 from tests import (
     PD_LTE_23,
     TYPE_CHECKING_INVALID_USAGE,
     check,
-    np_1darray,
     pytest_warns_bounded,
 )
 
@@ -1489,6 +1490,16 @@ 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=pd.Series([1, 0, 2])), np.intp), np.intp
+    )
+
+
 def test_period_index_constructor() -> None:
     check(
         assert_type(pd.PeriodIndex(["2000"], dtype="period[D]"), pd.PeriodIndex),