WIP: Use default in TypeVar so Series defaults to Series[Any], and Index to Index[Any]

README.md

@@ -45,7 +45,7 @@ mypy round.py
 we get the following error message:
 
 ```text
-round.py:6: error: Argument "decimals" to "round" of "DataFrame" has incompatible type "DataFrame"; expected "Union[int, Dict[Any, Any], Series[Any]]"  [arg-type]
+round.py:6: error: Argument "decimals" to "round" of "DataFrame" has incompatible type "DataFrame"; expected "Union[int, Dict[Any, Any], Series]"  [arg-type]
 Found 1 error in 1 file (checked 1 source file)
 ```
 

docs/philosophy.md

@@ -29,7 +29,7 @@ lt = s < 3
 
 In the pandas source, `lt` is a `Series` with a `dtype` of `bool`.  In the pandas-stubs,
 the type of `lt` is `Series[bool]`.  This allows further type checking to occur in other
-pandas methods.  Note that in the above example, `s` is typed as `Series[Any]` because
+pandas methods.  Note that in the above example, `s` is typed as `Series` because
 its type cannot be statically inferred.
 
 This also allows type checking for operations on series that contain date/time data.  Consider

pandas-stubs/_libs/tslibs/timestamps.pyi

@@ -19,9 +19,9 @@ from typing import (
 import numpy as np
 from pandas import (
     DatetimeIndex,
+    Index,
     TimedeltaIndex,
 )
-from pandas.core.indexes.base import UnknownIndex
 from pandas.core.series import (
     Series,
     TimedeltaSeries,
@@ -236,15 +236,15 @@ class Timestamp(datetime, SupportsIndex):
     @overload
     def __eq__(self, other: TimestampSeries) -> Series[bool]: ...  # type: ignore[overload-overlap]
     @overload
-    def __eq__(self, other: npt.NDArray[np.datetime64] | UnknownIndex) -> np_ndarray_bool: ...  # type: ignore[overload-overlap]
+    def __eq__(self, other: npt.NDArray[np.datetime64] | Index) -> np_ndarray_bool: ...  # type: ignore[overload-overlap]
     @overload
     def __eq__(self, other: object) -> Literal[False]: ...
     @overload
     def __ne__(self, other: Timestamp | datetime | np.datetime64) -> bool: ...  # type: ignore[overload-overlap] # pyright: ignore[reportOverlappingOverload]
     @overload
     def __ne__(self, other: TimestampSeries) -> Series[bool]: ...  # type: ignore[overload-overlap]
     @overload
-    def __ne__(self, other: npt.NDArray[np.datetime64] | UnknownIndex) -> np_ndarray_bool: ...  # type: ignore[overload-overlap]
+    def __ne__(self, other: npt.NDArray[np.datetime64] | Index) -> np_ndarray_bool: ...  # type: ignore[overload-overlap]
     @overload
     def __ne__(self, other: object) -> Literal[True]: ...
     def __hash__(self) -> int: ...

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
@@ -65,7 +65,7 @@ HashableT5 = TypeVar("HashableT5", bound=Hashable)
 # array-like
 
 ArrayLike: TypeAlias = ExtensionArray | np.ndarray
-AnyArrayLike: TypeAlias = ArrayLike | Index[Any] | Series[Any]
+AnyArrayLike: TypeAlias = ArrayLike | Index | Series
 
 # list-like
 
@@ -801,7 +801,7 @@ DtypeNp = TypeVar("DtypeNp", bound=np.dtype[np.generic])
 KeysArgType: TypeAlias = Any
 ListLikeT = TypeVar("ListLikeT", bound=ListLike)
 ListLikeExceptSeriesAndStr: TypeAlias = (
-    MutableSequence[Any] | np.ndarray | tuple[Any, ...] | Index[Any]
+    MutableSequence[Any] | np.ndarray | tuple[Any, ...] | Index
 )
 ListLikeU: TypeAlias = Sequence | np.ndarray | Series | Index
 ListLikeHashable: TypeAlias = (
@@ -842,6 +842,7 @@ S1 = TypeVar(
     | CategoricalDtype
     | BaseOffset
     | list[str],
+    default=Any,
 )
 
 S2 = TypeVar(
@@ -891,6 +892,7 @@ ByT = TypeVar(
     | Period
     | Interval[int | float | Timestamp | Timedelta]
     | tuple,
+    default=Any,
 )
 # Use a distinct SeriesByT when using groupby with Series of known dtype.
 # Essentially, an intersection between Series S1 TypeVar, and ByT TypeVar
@@ -949,7 +951,7 @@ ReplaceValue: TypeAlias = (
     | NAType
     | Sequence[Scalar | Pattern]
     | Mapping[HashableT, ScalarT]
-    | Series[Any]
+    | Series
     | None
 )
 

pandas-stubs/core/dtypes/missing.pyi

@@ -26,9 +26,9 @@ isneginf_scalar = ...
 @overload
 def isna(obj: DataFrame) -> DataFrame: ...
 @overload
-def isna(obj: Series[Any]) -> Series[bool]: ...
+def isna(obj: Series) -> Series[bool]: ...
 @overload
-def isna(obj: Index[Any] | list[Any] | ArrayLike) -> npt.NDArray[np.bool_]: ...
+def isna(obj: Index | list[Any] | ArrayLike) -> npt.NDArray[np.bool_]: ...
 @overload
 def isna(
     obj: Scalar | NaTType | NAType | None,
@@ -39,9 +39,9 @@ isnull = isna
 @overload
 def notna(obj: DataFrame) -> DataFrame: ...
 @overload
-def notna(obj: Series[Any]) -> Series[bool]: ...
+def notna(obj: Series) -> Series[bool]: ...
 @overload
-def notna(obj: Index[Any] | list[Any] | ArrayLike) -> npt.NDArray[np.bool_]: ...
+def notna(obj: Index | list[Any] | ArrayLike) -> npt.NDArray[np.bool_]: ...
 @overload
 def notna(obj: ScalarT | NaTType | NAType | None) -> TypeIs[ScalarT]: ...
 

pandas-stubs/core/frame.pyi

@@ -52,7 +52,6 @@ from pandas.core.reshape.pivot import (
 )
 from pandas.core.series import (
     Series,
-    UnknownSeries,
 )
 from pandas.core.window import (
     Expanding,
@@ -75,7 +74,7 @@ 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,
@@ -1318,11 +1317,11 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
     @overload
     def stack(
         self, level: Level | list[Level] = ..., dropna: _bool = ..., sort: _bool = ...
-    ) -> Self | Series[Any]: ...
+    ) -> Self | Series: ...
     @overload
     def stack(
         self, level: Level | list[Level] = ..., future_stack: _bool = ...
-    ) -> Self | Series[Any]: ...
+    ) -> Self | Series: ...
     def explode(
         self, column: Sequence[Hashable], ignore_index: _bool = ...
     ) -> Self: ...
@@ -1382,7 +1381,7 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
     @overload
     def apply(
         self,
-        f: Callable[..., ListLikeExceptSeriesAndStr | Series[Any]],
+        f: Callable[..., ListLikeExceptSeriesAndStr | Series],
         axis: AxisIndex = ...,
         raw: _bool = ...,
         result_type: None = ...,
@@ -1392,13 +1391,13 @@ 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
@@ -1410,24 +1409,24 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
         result_type: None = ...,
         args: Any = ...,
         **kwargs: Any,
-    ) -> Series[Any]: ...
+    ) -> Series: ...
 
     # apply() overloads with keyword result_type, and axis does not matter
     @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,
-        f: Callable[..., ListLikeExceptSeriesAndStr | Series[Any] | Mapping[Any, Any]],
+        f: Callable[..., ListLikeExceptSeriesAndStr | Series | Mapping[Any, Any]],
         axis: Axis = ...,
         raw: _bool = ...,
         args: Any = ...,
@@ -1445,12 +1444,12 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
         *,
         result_type: Literal["reduce"],
         **kwargs: Any,
-    ) -> Series[Any]: ...
+    ) -> Series: ...
     @overload
     def apply(
         self,
         f: Callable[
-            ..., ListLikeExceptSeriesAndStr | Series[Any] | Scalar | Mapping[Any, Any]
+            ..., ListLikeExceptSeriesAndStr | Series | Scalar | Mapping[Any, Any]
         ],
         axis: Axis = ...,
         raw: _bool = ...,
@@ -1464,27 +1463,27 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
     @overload
     def apply(
         self,
-        f: Callable[..., Series[Any]],
+        f: Callable[..., Series],
         axis: AxisIndex = ...,
         raw: _bool = ...,
         args: Any = ...,
         *,
         result_type: Literal["reduce"],
         **kwargs: Any,
-    ) -> Series[Any]: ...
+    ) -> Series: ...
 
     # apply() overloads with default result_type of None, and keyword axis=1 matters
     @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,
@@ -1495,11 +1494,11 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
         *,
         axis: AxisColumn,
         **kwargs: Any,
-    ) -> Series[Any]: ...
+    ) -> Series: ...
     @overload
     def apply(
         self,
-        f: Callable[..., Series[Any]],
+        f: Callable[..., Series],
         raw: _bool = ...,
         result_type: None = ...,
         args: Any = ...,
@@ -1512,7 +1511,7 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
     @overload
     def apply(
         self,
-        f: Callable[..., Series[Any]],
+        f: Callable[..., Series],
         raw: _bool = ...,
         args: Any = ...,
         *,
@@ -1537,7 +1536,7 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
     ) -> Self: ...
     def merge(
         self,
-        right: DataFrame | Series[Any],
+        right: DataFrame | Series,
         how: MergeHow = ...,
         on: IndexLabel | AnyArrayLike | None = ...,
         left_on: IndexLabel | AnyArrayLike | None = ...,
@@ -2011,7 +2010,7 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
             | Callable[[DataFrame], DataFrame]
             | Callable[[Any], _bool]
         ),
-        other: Scalar | UnknownSeries | DataFrame | Callable | NAType | None = ...,
+        other: Scalar | Series | DataFrame | Callable | NAType | None = ...,
         *,
         inplace: Literal[True],
         axis: Axis | None = ...,
@@ -2027,7 +2026,7 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
             | Callable[[DataFrame], DataFrame]
             | Callable[[Any], _bool]
         ),
-        other: Scalar | UnknownSeries | DataFrame | Callable | NAType | None = ...,
+        other: Scalar | Series | DataFrame | Callable | NAType | None = ...,
         *,
         inplace: Literal[False] = ...,
         axis: Axis | None = ...,

pandas-stubs/core/generic.pyi

@@ -20,7 +20,7 @@ from pandas import Index
 import pandas.core.indexing as indexing
 from pandas.core.resample import DatetimeIndexResampler
 from pandas.core.series import (
-    UnknownSeries,
+    Series,
 )
 import sqlalchemy.engine
 from typing_extensions import (
@@ -82,7 +82,7 @@ class NDFrame(indexing.IndexingMixin):
     def ndim(self) -> int: ...
     @property
     def size(self) -> int: ...
-    def equals(self, other: UnknownSeries) -> _bool: ...
+    def equals(self, other: Series) -> _bool: ...
     def __neg__(self) -> Self: ...
     def __pos__(self) -> Self: ...
     def __nonzero__(self) -> None: ...
@@ -306,7 +306,7 @@ class NDFrame(indexing.IndexingMixin):
         labels: None = ...,
         *,
         axis: Axis = ...,
-        index: Hashable | Sequence[Hashable] | Index[Any] = ...,
+        index: Hashable | Sequence[Hashable] | Index = ...,
         columns: Hashable | Iterable[Hashable],
         level: Level | None = ...,
         inplace: Literal[True],
@@ -318,7 +318,7 @@ class NDFrame(indexing.IndexingMixin):
         labels: None = ...,
         *,
         axis: Axis = ...,
-        index: Hashable | Sequence[Hashable] | Index[Any],
+        index: Hashable | Sequence[Hashable] | Index,
         columns: Hashable | Iterable[Hashable] = ...,
         level: Level | None = ...,
         inplace: Literal[True],
@@ -327,7 +327,7 @@ class NDFrame(indexing.IndexingMixin):
     @overload
     def drop(
         self,
-        labels: Hashable | Sequence[Hashable] | Index[Any],
+        labels: Hashable | Sequence[Hashable] | Index,
         *,
         axis: Axis = ...,
         index: None = ...,
@@ -342,7 +342,7 @@ class NDFrame(indexing.IndexingMixin):
         labels: None = ...,
         *,
         axis: Axis = ...,
-        index: Hashable | Sequence[Hashable] | Index[Any] = ...,
+        index: Hashable | Sequence[Hashable] | Index = ...,
         columns: Hashable | Iterable[Hashable],
         level: Level | None = ...,
         inplace: Literal[False] = ...,
@@ -354,7 +354,7 @@ class NDFrame(indexing.IndexingMixin):
         labels: None = ...,
         *,
         axis: Axis = ...,
-        index: Hashable | Sequence[Hashable] | Index[Any],
+        index: Hashable | Sequence[Hashable] | Index,
         columns: Hashable | Iterable[Hashable] = ...,
         level: Level | None = ...,
         inplace: Literal[False] = ...,
@@ -363,7 +363,7 @@ class NDFrame(indexing.IndexingMixin):
     @overload
     def drop(
         self,
-        labels: Hashable | Sequence[Hashable] | Index[Any],
+        labels: Hashable | Sequence[Hashable] | Index,
         *,
         axis: Axis = ...,
         index: None = ...,