ENH: pd.NamedAgg forwards *args and **kwargs to aggfunc

doc/source/whatsnew/v3.0.0.rst

@@ -156,6 +156,8 @@ All warnings for upcoming changes in pandas will have the base class :class:`pan
 
 Other enhancements
 ^^^^^^^^^^^^^^^^^^
+- :class:`pandas.NamedAgg` now supports passing ``*args`` and ``**kwargs``
+  to calls of ``aggfunc`` (:issue:`58283`)
 - :func:`pandas.merge` propagates the ``attrs`` attribute to the result if all
   inputs have identical ``attrs``, as has so far already been the case for
   :func:`pandas.concat`.

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 +1772,22 @@ def reconstruct_func(
         # "Callable[..., Any] | str | list[Callable[..., Any] | str] |
         # MutableMapping[Hashable, Callable[..., Any] | str | list[Callable[..., Any] |
         # str]] | None")
+        converted_kwargs = {}
+        for key, val in kwargs.items():
+            if isinstance(val, NamedAgg):
+                aggfunc = val.aggfunc
+                if val.args or val.kwargs:
+                    aggfunc = lambda x, func=aggfunc, a=val.args, kw=val.kwargs: func(
+                        x, *a, **kw
+                    )
+                converted_kwargs[key] = (val.column, aggfunc)
+            else:
+                converted_kwargs[key] = val
+
         func, columns, order = normalize_keyword_aggregation(  # type: ignore[assignment]
-            kwargs
+            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,
-    NamedTuple,
     TypeAlias,
     TypeVar,
     cast,
@@ -113,19 +113,20 @@
 
 
 @set_module("pandas")
-class NamedAgg(NamedTuple):
+@dataclasses.dataclass
+class NamedAgg:
     """
     Helper for column specific aggregation with control over output column names.
 
-    Subclass of typing.NamedTuple.
-
     Parameters
     ----------
     column : Hashable
         Column label in the DataFrame to apply aggfunc.
     aggfunc : function or str
         Function to apply to the provided column. If string, the name of a built-in
         pandas function.
+    *args, **kwargs : Any
+        Optional positional and keyword arguments passed to ``aggfunc``.
 
     See Also
     --------
@@ -137,14 +138,57 @@ class NamedAgg(NamedTuple):
     >>> agg_a = pd.NamedAgg(column="a", aggfunc="min")
     >>> agg_1 = pd.NamedAgg(column=1, aggfunc=lambda x: np.mean(x))
     >>> df.groupby("key").agg(result_a=agg_a, result_1=agg_1)
-         result_a  result_1
+        result_a  result_1
     key
     1          -1      10.5
     2           1      12.0
+
+    >>> def n_between(ser, low, high, **kwargs):
+    ...     return ser.between(low, high, **kwargs).sum()
+
+    >>> agg_between = pd.NamedAgg("a", n_between, 0, 1)
+    >>> df.groupby("key").agg(count_between=agg_between)
+        count_between
+    key
+    1               1
+    2               1
+
+    >>> agg_between_kw = pd.NamedAgg("a", n_between, 0, 1, inclusive="both")
+    >>> df.groupby("key").agg(count_between_kw=agg_between_kw)
+        count_between_kw
+    key
+    1                   1
+    2                   1
     """
 
     column: Hashable
     aggfunc: AggScalar
+    args: tuple[Any, ...] = ()
+    kwargs: dict[str, Any] = dataclasses.field(default_factory=dict)
+
+    def __init__(
+        self,
+        column: Hashable,
+        aggfunc: Callable[..., Any] | str,
+        *args: Any,
+        **kwargs: Any,
+    ) -> 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")

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -866,6 +866,64 @@ def test_agg_namedtuple(self):
         expected = df.groupby("A").agg(b=("B", "sum"), c=("B", "count"))
         tm.assert_frame_equal(result, expected)
 
+    def n_between(self, ser, low, high, **kwargs):
+        return ser.between(low, high, **kwargs).sum()
+
+    def test_namedagg_args(self):
+        # https://github.com/pandas-dev/pandas/issues/58283
+        df = DataFrame({"A": [0, 0, 1, 1], "B": [-1, 0, 1, 2]})
+
+        result = df.groupby("A").agg(
+            count_between=pd.NamedAgg("B", self.n_between, 0, 1)
+        )
+        expected = DataFrame({"count_between": [1, 1]}, index=Index([0, 1], name="A"))
+        tm.assert_frame_equal(result, expected)
+
+    def test_namedagg_kwargs(self):
+        # https://github.com/pandas-dev/pandas/issues/58283
+        df = DataFrame({"A": [0, 0, 1, 1], "B": [-1, 0, 1, 2]})
+
+        result = df.groupby("A").agg(
+            count_between_kw=pd.NamedAgg("B", self.n_between, 0, 1, inclusive="both")
+        )
+        expected = DataFrame(
+            {"count_between_kw": [1, 1]}, index=Index([0, 1], name="A")
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_namedagg_args_and_kwargs(self):
+        # https://github.com/pandas-dev/pandas/issues/58283
+        df = DataFrame({"A": [0, 0, 1, 1], "B": [-1, 0, 1, 2]})
+
+        result = df.groupby("A").agg(
+            count_between_mix=pd.NamedAgg(
+                "B", self.n_between, 0, 1, inclusive="neither"
+            )
+        )
+        expected = DataFrame(
+            {"count_between_mix": [0, 0]}, index=Index([0, 1], name="A")
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_multiple_named_agg_with_args_and_kwargs(self):
+        # https://github.com/pandas-dev/pandas/issues/58283
+        df = DataFrame({"A": [0, 1, 2, 3], "B": [1, 2, 3, 4]})
+
+        result = df.groupby("A").agg(
+            n_between01=pd.NamedAgg("B", self.n_between, 0, 1),
+            n_between13=pd.NamedAgg("B", self.n_between, 1, 3),
+            n_between02=pd.NamedAgg("B", self.n_between, 0, 2),
+        )
+        expected = DataFrame(
+            {
+                "n_between01": [1, 0, 0, 0],
+                "n_between13": [1, 1, 1, 0],
+                "n_between02": [1, 1, 0, 0],
+            },
+            index=Index([0, 1, 2, 3], name="A"),
+        )
+        tm.assert_frame_equal(result, expected)
+
     def test_mangled(self):
         df = DataFrame({"A": [0, 1], "B": [1, 2], "C": [3, 4]})
         result = df.groupby("A").agg(b=("B", lambda x: 0), c=("C", lambda x: 1))