enable reportMissingTypeArgument

Author: cmp0xff

pandas-stubs/_libs/interval.pyi

@@ -26,7 +26,9 @@ VALID_CLOSED: frozenset[str]
 
 _OrderableScalarT = TypeVar("_OrderableScalarT", bound=int | float)
 _OrderableTimesT = TypeVar("_OrderableTimesT", bound=Timestamp | Timedelta)
-_OrderableT = TypeVar("_OrderableT", bound=int | float | Timestamp | Timedelta)
+_OrderableT = TypeVar(
+    "_OrderableT", bound=int | float | Timestamp | Timedelta, default=Any
+)
 
 @type_check_only
 class _LengthDescriptor:

pandas-stubs/_typing.pyi

@@ -77,6 +77,7 @@ from pandas.tseries.offsets import (
 P = ParamSpec("P")
 
 HashableT = TypeVar("HashableT", bound=Hashable)
+HashableT0 = TypeVar("HashableT0", bound=Hashable, default=Any)
 HashableT1 = TypeVar("HashableT1", bound=Hashable)
 HashableT2 = TypeVar("HashableT2", bound=Hashable)
 HashableT3 = TypeVar("HashableT3", bound=Hashable)
@@ -776,7 +777,7 @@ XMLParsers: TypeAlias = Literal["lxml", "etree"]
 HTMLFlavors: TypeAlias = Literal["lxml", "html5lib", "bs4"]
 
 # Interval closed type
-IntervalT = TypeVar("IntervalT", bound=Interval)
+IntervalT = TypeVar("IntervalT", bound=Interval, default=Interval)
 IntervalLeftRight: TypeAlias = Literal["left", "right"]
 IntervalClosedType: TypeAlias = IntervalLeftRight | Literal["both", "neither"]
 
@@ -874,7 +875,11 @@ ExcelWriterMergeCells: TypeAlias = bool | Literal["columns"]
 
 # read_csv: usecols
 UsecolsArgType: TypeAlias = (
-    SequenceNotStr[Hashable] | range | AnyArrayLike | Callable[[HashableT], bool] | None
+    SequenceNotStr[Hashable]
+    | range
+    | AnyArrayLike
+    | Callable[[HashableT0], bool]
+    | None
 )
 
 # maintain the sub-type of any hashable sequence
@@ -920,6 +925,7 @@ PyArrowNotStrDtypeArg: TypeAlias = (
 StrLike: TypeAlias = str | np.str_
 
 ScalarT = TypeVar("ScalarT", bound=Scalar)
+ScalarT0 = TypeVar("ScalarT0", bound=Scalar, default=Scalar)
 # Refine the definitions below in 3.9 to use the specialized type.
 np_num: TypeAlias = np.bool | np.integer | np.floating | np.complexfloating
 np_ndarray_intp: TypeAlias = npt.NDArray[np.intp]
@@ -974,7 +980,7 @@ ListLikeExceptSeriesAndStr: TypeAlias = (
 )
 ListLikeU: TypeAlias = Sequence[Any] | np_1darray | Series | Index
 ListLikeHashable: TypeAlias = (
-    MutableSequence[HashableT] | np_1darray | tuple[HashableT, ...] | range
+    MutableSequence[HashableT0] | np_1darray | tuple[HashableT0, ...] | range
 )
 
 class SupportsDType(Protocol[GenericT_co]):
@@ -1015,8 +1021,9 @@ SeriesDType: TypeAlias = (
     | datetime.datetime  # includes pd.Timestamp
     | datetime.timedelta  # includes pd.Timedelta
 )
+S0 = TypeVar("S0", bound=SeriesDType, default=Any)
 S1 = TypeVar("S1", bound=SeriesDType, default=Any)
-# Like S1, but without `default=Any`.
+# Like S0 and S1, but without `default=Any`.
 S2 = TypeVar("S2", bound=SeriesDType)
 S2_contra = TypeVar("S2_contra", bound=SeriesDType, contravariant=True)
 S2_NDT_contra = TypeVar(
@@ -1050,14 +1057,14 @@ IndexingInt: TypeAlias = (
 )
 
 # AxesData is used for data for Index
-AxesData: TypeAlias = Mapping[S3, Any] | Axes | KeysView[S3]
+AxesData: TypeAlias = Mapping[S0, Any] | Axes | KeysView[S0]
 
 # Any plain Python or numpy function
 Function: TypeAlias = np.ufunc | Callable[..., Any]
 # Use a distinct HashableT in shared types to avoid conflicts with
 # shared HashableT and HashableT#. This one can be used if the identical
 # type is need in a function that uses GroupByObjectNonScalar
-_HashableTa = TypeVar("_HashableTa", bound=Hashable)
+_HashableTa = TypeVar("_HashableTa", bound=Hashable, default=Any)
 if TYPE_CHECKING:  # noqa: PYI002
     ByT = TypeVar(
         "ByT",
@@ -1075,7 +1082,7 @@ if TYPE_CHECKING:  # noqa: PYI002
         | Scalar
         | Period
         | Interval[int | float | Timestamp | Timedelta]
-        | tuple,
+        | tuple[Any, ...],
     )
     # Use a distinct SeriesByT when using groupby with Series of known dtype.
     # Essentially, an intersection between Series S1 TypeVar, and ByT TypeVar
@@ -1093,21 +1100,23 @@ if TYPE_CHECKING:  # noqa: PYI002
         | Period
         | Interval[int | float | Timestamp | Timedelta],
     )
-GroupByObjectNonScalar: TypeAlias = (
-    tuple[_HashableTa, ...]
-    | list[_HashableTa]
-    | Function
-    | list[Function]
-    | list[Series]
-    | np_ndarray
-    | list[np_ndarray]
-    | Mapping[Label, Any]
-    | list[Mapping[Label, Any]]
-    | list[Index]
-    | Grouper
-    | list[Grouper]
-)
-GroupByObject: TypeAlias = Scalar | Index | GroupByObjectNonScalar | Series
+    GroupByObjectNonScalar: TypeAlias = (
+        tuple[_HashableTa, ...]
+        | list[_HashableTa]
+        | Function
+        | list[Function]
+        | list[Series]
+        | np_ndarray
+        | list[np_ndarray]
+        | Mapping[Label, Any]
+        | list[Mapping[Label, Any]]
+        | list[Index]
+        | Grouper
+        | list[Grouper]
+    )
+    GroupByObject: TypeAlias = (
+        Scalar | Index | GroupByObjectNonScalar[_HashableTa] | Series
+    )
 
 StataDateFormat: TypeAlias = Literal[
     "tc",
@@ -1130,10 +1139,10 @@ StataDateFormat: TypeAlias = Literal[
 # `DataFrame.replace` also accepts mappings of these.
 ReplaceValue: TypeAlias = (
     Scalar
-    | Pattern
+    | Pattern[Any]
     | NAType
-    | Sequence[Scalar | Pattern]
-    | Mapping[HashableT, ScalarT]
+    | Sequence[Scalar | Pattern[Any]]
+    | Mapping[HashableT0, ScalarT0]
     | Series
     | None
 )

pandas-stubs/core/frame.pyi

@@ -58,7 +58,7 @@ from pandas.core.indexing import (
     _LocIndexer,
 )
 from pandas.core.reshape.pivot import (
-    _PivotAggFunc,
+    _PivotAggFuncTypes,
     _PivotTableColumnsTypes,
     _PivotTableIndexTypes,
     _PivotTableValuesTypes,
@@ -178,7 +178,7 @@ from pandas.plotting import PlotAccessor
 from pandas.plotting._core import _BoxPlotT
 
 _T_MUTABLE_MAPPING_co = TypeVar(
-    "_T_MUTABLE_MAPPING_co", bound=MutableMapping, covariant=True
+    "_T_MUTABLE_MAPPING_co", bound=MutableMapping[Any, Any], covariant=True
 )
 
 _iLocSetItemKey: TypeAlias = (
@@ -1279,7 +1279,7 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
         dropna: _bool = ...,
     ) -> DataFrameGroupBy[Period, Literal[False]]: ...
     @overload
-    def groupby(  # pyright: ignore reportOverlappingOverload
+    def groupby(
         self,
         by: IntervalIndex[IntervalT],
         level: IndexLabel | None = ...,
@@ -1292,7 +1292,7 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
     @overload
     def groupby(
         self,
-        by: IntervalIndex[IntervalT],
+        by: IntervalIndex,
         level: IndexLabel | None = ...,
         as_index: Literal[False] = False,
         sort: _bool = ...,
@@ -1378,9 +1378,7 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
         values: _PivotTableValuesTypes = None,
         index: _PivotTableIndexTypes = None,
         columns: _PivotTableColumnsTypes = None,
-        aggfunc: (
-            _PivotAggFunc | Sequence[_PivotAggFunc] | Mapping[Hashable, _PivotAggFunc]
-        ) = "mean",
+        aggfunc: _PivotAggFuncTypes[Scalar] = "mean",
         fill_value: Scalar | None = None,
         margins: _bool = False,
         dropna: _bool = True,
@@ -2761,8 +2759,12 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
     def __rfloordiv__(
         self, other: float | DataFrame | Series[int] | Series[float] | Sequence[float]
     ) -> Self: ...
-    def __truediv__(self, other: float | DataFrame | Series | Sequence) -> Self: ...
-    def __rtruediv__(self, other: float | DataFrame | Series | Sequence) -> Self: ...
+    def __truediv__(
+        self, other: float | DataFrame | Series | Sequence[Any]
+    ) -> Self: ...
+    def __rtruediv__(
+        self, other: float | DataFrame | Series | Sequence[Any]
+    ) -> Self: ...
     @final
     def __bool__(self) -> NoReturn: ...
 

pandas-stubs/core/indexes/accessors.pyi

@@ -5,6 +5,7 @@ from datetime import (
     tzinfo as _tzinfo,
 )
 from typing import (
+    Any,
     Generic,
     Literal,
     TypeVar,
@@ -377,7 +378,7 @@ class PeriodProperties(
         Series[Timestamp], Series[int], Series[str], DatetimeArray, PeriodArray
     ],
     _DatetimeFieldOps[Series[int]],
-    _IsLeapYearProperty,
+    _IsLeapYearProperty[_DTBoolOpsReturnType],
     _FreqProperty[BaseOffset],
 ): ...
 class CombinedDatetimelikeProperties(
@@ -393,7 +394,9 @@ class CombinedDatetimelikeProperties(
         Series[Period],
     ],
     _TimedeltaPropertiesNoRounding[Series[int], Series[float]],
-    _PeriodProperties,
+    _PeriodProperties[
+        Series[Timestamp], Series[int], Series[str], DatetimeArray, PeriodArray
+    ],
 ): ...
 
 @type_check_only
@@ -458,7 +461,7 @@ class DtDescriptor:
     @overload
     def __get__(
         self, instance: Series[Period], owner: type[Series]
-    ) -> PeriodProperties: ...
+    ) -> PeriodProperties[Any]: ...
 
 @type_check_only
 class ArrayDescriptor:

pandas-stubs/core/indexes/base.pyi

@@ -313,7 +313,7 @@ class Index(IndexOpsMixin[S1], ElementOpsMixin[S1]):
         copy: bool = ...,
         name: Hashable = ...,
         tupleize_cols: bool = ...,
-    ) -> IntervalIndex[Interval[Any]]: ...
+    ) -> IntervalIndex[Interval]: ...
     @overload
     def __new__(
         cls,

pandas-stubs/core/indexes/interval.pyi

@@ -243,7 +243,9 @@ class IntervalIndex(ExtensionIndex[IntervalT, np.object_], IntervalMixin):
     def __contains__(self, key: IntervalT) -> bool: ...  # type: ignore[overload-overlap] # pyright: ignore[reportOverlappingOverload]
     @overload
     def __contains__(self, key: object) -> Literal[False]: ...
-    def astype(self, dtype: DtypeArg, copy: bool = True) -> IntervalIndex: ...
+    def astype(  # type: ignore[override] # pyright: ignore[reportIncompatibleMethodOverride]
+        self, dtype: DtypeArg, copy: bool = True
+    ) -> IntervalIndex: ...
     @property
     def inferred_type(self) -> str: ...
     def memory_usage(self, deep: bool = False) -> int: ...

pandas-stubs/core/indexes/period.pyi

@@ -1,7 +1,6 @@
 from collections.abc import Hashable
 import datetime
 from typing import (
-    Any,
     overload,
 )
 
@@ -30,7 +29,7 @@ from pandas._typing import (
 class PeriodIndex(DatetimeIndexOpsMixin[pd.Period, np.object_], PeriodIndexFieldOps):
     def __new__(
         cls,
-        data: AxesData[Any] | None = None,
+        data: AxesData | None = None,
         freq: Frequency | None = None,
         dtype: Dtype | None = None,
         copy: bool = False,

pandas-stubs/core/reshape/pivot.pyi

@@ -61,7 +61,7 @@ _PivotTableColumnsTypes: TypeAlias = (
 _PivotTableValuesTypes: TypeAlias = Label | Sequence[Hashable] | None
 
 _ExtendedAnyArrayLike: TypeAlias = AnyArrayLike | ArrayLike
-_Values: TypeAlias = SequenceNotStr[Any] | _ExtendedAnyArrayLike
+_CrossTabValues: TypeAlias = SequenceNotStr[Any] | _ExtendedAnyArrayLike
 
 @overload
 def pivot_table(
@@ -118,9 +118,9 @@ def pivot(
 ) -> DataFrame: ...
 @overload
 def crosstab(
-    index: _Values | list[_Values],
-    columns: _Values | list[_Values],
-    values: _Values,
+    index: _CrossTabValues | list[_CrossTabValues],
+    columns: _CrossTabValues | list[_CrossTabValues],
+    values: _CrossTabValues,
     rownames: SequenceNotStr[Hashable] | None = None,
     colnames: SequenceNotStr[Hashable] | None = None,
     *,
@@ -132,8 +132,8 @@ def crosstab(
 ) -> DataFrame: ...
 @overload
 def crosstab(
-    index: _Values | list[_Values],
-    columns: _Values | list[_Values],
+    index: _CrossTabValues | list[_CrossTabValues],
+    columns: _CrossTabValues | list[_CrossTabValues],
     values: None = None,
     rownames: SequenceNotStr[Hashable] | None = None,
     colnames: SequenceNotStr[Hashable] | None = None,

pandas-stubs/core/series.pyi

@@ -446,7 +446,7 @@ class Series(IndexOpsMixin[S1], ElementOpsMixin[S1], NDFrame):
             IntervalIndex[Interval[_OrderableT]]
             | Interval[_OrderableT]
             | Sequence[Interval[_OrderableT]]
-            | dict[HashableT1, Interval[_OrderableT]]
+            | dict[Hashable, Interval[_OrderableT]]
         ),
         index: AxesData | None = None,
         dtype: Literal["Interval"] = ...,
@@ -534,7 +534,7 @@ class Series(IndexOpsMixin[S1], ElementOpsMixin[S1], NDFrame):
             Scalar
             | _DataLike
             | Mapping[HashableT1, Any]
-            | BaseGroupBy
+            | BaseGroupBy[Any]
             | NaTType
             | NAType
             | None
@@ -1105,7 +1105,7 @@ class Series(IndexOpsMixin[S1], ElementOpsMixin[S1], NDFrame):
     def swaplevel(
         self, i: Level = -2, j: Level = -1, copy: _bool = True
     ) -> Series[S1]: ...
-    def reorder_levels(self, order: list[Any]) -> Series[S1]: ...
+    def reorder_levels(self, order: Sequence[int | np.integer]) -> Series[S1]: ...
     def explode(self, ignore_index: _bool = ...) -> Series[S1]: ...
     def unstack(
         self,

pandas-stubs/io/html.pyi

@@ -29,7 +29,7 @@ from pandas._typing import (
 def read_html(
     io: FilePath | ReadBuffer[str],
     *,
-    match: str | Pattern = ...,
+    match: str | Pattern[str] = ...,
     flavor: HTMLFlavors | Sequence[HTMLFlavors] | None = ...,
     header: int | Sequence[int] | None = ...,
     index_col: int | Sequence[int] | list[HashableT1] | None = ...,