review comments

Author: sreeja97

pandas/core/apply.py

@@ -1745,7 +1745,13 @@ def reconstruct_func(
     >>> reconstruct_func("min")
     (False, 'min', None, None)
     """
-    relabeling = func is None and is_multi_agg_with_relabel(**kwargs)
+    from pandas.core.groupby.generic import NamedAgg
+
+    relabeling = func is None and (
+        is_multi_agg_with_relabel(**kwargs)
+        or any(isinstance(v, NamedAgg) for v in kwargs.values())
+    )
+
     columns: tuple[str, ...] | None = None
     order: npt.NDArray[np.intp] | None = None
 
@@ -1766,9 +1772,20 @@ def reconstruct_func(
         # "Callable[..., Any] | str | list[Callable[..., Any] | str] |
         # MutableMapping[Hashable, Callable[..., Any] | str | list[Callable[..., Any] |
         # str]] | None")
-        func, columns, order = normalize_keyword_aggregation(  # type: ignore[assignment]
-            kwargs
-        )
+        converted_kwargs = {}
+        for key, val in kwargs.items():
+            if isinstance(val, NamedAgg):
+                aggfunc = val.aggfunc
+                if getattr(val, "args", ()) or getattr(val, "kwargs", {}):
+                    a = getattr(val, "args", ())
+                    kw = getattr(val, "kwargs", {})
+                    aggfunc = lambda x, func=aggfunc, a=a, kw=kw: func(x, *a, **kw)
+                converted_kwargs[key] = (val.column, aggfunc)
+            else:
+                converted_kwargs[key] = val
+
+        func, columns, order = normalize_keyword_aggregation(converted_kwargs)
+
     assert func is not None
 
     return relabeling, func, columns, order

pandas/core/groupby/generic.py

@@ -10,13 +10,13 @@
 
 from collections import abc
 from collections.abc import Callable
+import dataclasses
 from functools import partial
 from textwrap import dedent
 from typing import (
     TYPE_CHECKING,
     Any,
     Literal,
-    Self,
     TypeAlias,
     TypeVar,
     cast,
@@ -113,12 +113,11 @@
 
 
 @set_module("pandas")
-class NamedAgg(tuple):
+@dataclasses.dataclass
+class NamedAgg:
     """
     Helper for column specific aggregation with control over output column names.
 
-    Subclass of tuple.
-
     Parameters
     ----------
     column : Hashable
@@ -164,32 +163,32 @@ class NamedAgg(tuple):
 
     column: Hashable
     aggfunc: AggScalar
+    args: tuple[Any, ...] = dataclasses.field(default_factory=tuple)
+    kwargs: dict[str, Any] = dataclasses.field(default_factory=dict)
 
-    __slots__ = ()
-
-    def __new__(
-        cls,
+    def __init__(
+        self,
         column: Hashable,
         aggfunc: Callable[..., Any] | str,
         *args: Any,
         **kwargs: Any,
-    ) -> Self:
-        if (
-            callable(aggfunc)
-            and not getattr(aggfunc, "_is_wrapped", False)
-            and (args or kwargs)
-        ):
-            original_func = aggfunc
-
-            def wrapped(*call_args: Any, **call_kwargs: Any) -> Any:
-                series = call_args[0]
-                final_args = call_args[1:] + args
-                final_kwargs = {**kwargs, **call_kwargs}
-                return original_func(series, *final_args, **final_kwargs)
-
-            wrapped._is_wrapped = True  # type: ignore[attr-defined]
-            aggfunc = wrapped
-        return super().__new__(cls, (column, aggfunc))
+    ) -> None:
+        self.column = column
+        self.aggfunc = aggfunc
+        self.args = args
+        self.kwargs = kwargs
+
+    def __getitem__(self, key: int) -> Any:
+        """Provide backward-compatible tuple-style access."""
+        if key == 0:
+            return self.column
+        elif key == 1:
+            return self.aggfunc
+        elif key == 2:
+            return self.args
+        elif key == 3:
+            return self.kwargs
+        raise IndexError("index out of range")
 
 
 @set_module("pandas.api.typing")