Type args and kwargs in frame pipe

Author: paw-lu

pandas-stubs/_typing.pyi

@@ -28,7 +28,10 @@ from pandas.core.generic import NDFrame
 from pandas.core.groupby.grouper import Grouper
 from pandas.core.indexes.base import Index
 from pandas.core.series import Series
-from typing_extensions import TypeAlias
+from typing_extensions import (
+    ParamSpec,
+    TypeAlias,
+)
 
 from pandas._libs.interval import Interval
 from pandas._libs.tslibs import (
@@ -446,6 +449,7 @@ JSONSerializable: TypeAlias = PythonScalar | list | dict
 Axes: TypeAlias = AnyArrayLike | list | dict | range | tuple
 Renamer: TypeAlias = Mapping[Any, Label] | Callable[[Any], Label]
 T = TypeVar("T")
+P = ParamSpec("P")
 FuncType: TypeAlias = Callable[..., Any]
 F = TypeVar("F", bound=FuncType)
 HashableT = TypeVar("HashableT", bound=Hashable)

pandas-stubs/core/frame.pyi

@@ -52,7 +52,10 @@ from pandas.core.window.rolling import (
     Rolling,
     Window,
 )
-from typing_extensions import Self
+from typing_extensions import (
+    Concatenate,
+    Self,
+)
 import xarray as xr
 
 from pandas._libs.missing import NAType
@@ -100,6 +103,7 @@ from pandas._typing import (
     MergeHow,
     NaPosition,
     NDFrameT,
+    P,
     ParquetEngine,
     QuantileInterpolation,
     RandomState,
@@ -1832,9 +1836,9 @@ class DataFrame(NDFrame, OpsMixin):
     ) -> DataFrame: ...
     def pipe(
         self,
-        func: Callable[..., TType] | tuple[Callable[..., TType], _str],
-        *args,
-        **kwargs,
+        func: Callable[Concatenate[Self, P], TType] | tuple[Callable[..., TType], _str],
+        *args: P.args,
+        **kwargs: P.kwargs,
     ) -> TType: ...
     def pop(self, item: _str) -> Series: ...
     def pow(

tests/test_frame.py

@@ -1386,6 +1386,27 @@ def baz(val: Styler) -> str:
 
     check(assert_type(pd.DataFrame({"a": [1], "b": [1]}).style.pipe(baz), str), str)
 
+    def qux(
+        df: pd.DataFrame,
+        positional_only: int,
+        /,
+        argument_1: list[float],
+        argument_2: str,
+        *,
+        keyword_only: tuple[int, int],
+    ) -> pd.DataFrame:
+        return pd.DataFrame(df)
+
+    check(
+        assert_type(
+            pd.DataFrame({"a": [1], "b": [1]}).pipe(
+                qux, 1, [1.0, 2.0], argument_2="hi", keyword_only=(1, 2)
+            ),
+            pd.DataFrame,
+        ),
+        pd.DataFrame,
+    )
+
 
 # set_flags() method added in 1.2.0 https://pandas.pydata.org/docs/whatsnew/v1.2.0.html
 def test_types_set_flags() -> None: