Included NammedAgg changes in whatsnew

Author: tomytp

doc/source/whatsnew/v3.0.0.rst

@@ -31,6 +31,7 @@ Other enhancements
 - :class:`pandas.api.typing.FrozenList` is available for typing the outputs of :attr:`MultiIndex.names`, :attr:`MultiIndex.codes` and :attr:`MultiIndex.levels` (:issue:`58237`)
 - :class:`pandas.api.typing.SASReader` is available for typing the output of :func:`read_sas` (:issue:`55689`)
 - :func:`DataFrame.to_excel` now raises an ``UserWarning`` when the character count in a cell exceeds Excel's limitation of 32767 characters (:issue:`56954`)
+- :func:`pandas.NamedAgg` now forwards any *args and **kwargs to calls of ``aggfunc`` (:issue:`58283`)
 - :func:`read_stata` now returns ``datetime64`` resolutions better matching those natively stored in the stata format (:issue:`55642`)
 - :meth:`Styler.set_tooltips` provides alternative method to storing tooltips by using title attribute of td elements. (:issue:`56981`)
 - Allow dictionaries to be passed to :meth:`pandas.Series.str.replace` via ``pat`` parameter (:issue:`51748`)
@@ -43,7 +44,7 @@ Other enhancements
 - :meth:`DataFrame.cummin`, :meth:`DataFrame.cummax`, :meth:`DataFrame.cumprod` and :meth:`DataFrame.cumsum` methods now have a ``numeric_only`` parameter (:issue:`53072`)
 - :meth:`DataFrame.fillna` and :meth:`Series.fillna` can now accept ``value=None``; for non-object dtype the corresponding NA value will be used (:issue:`57723`)
 - :meth:`Series.cummin` and :meth:`Series.cummax` now supports :class:`CategoricalDtype` (:issue:`52335`)
-- :meth:`Series.plot` now correctly handle the ``ylabel`` parameter for pie charts, allowing for explicit control over the y-axis label (:issue:`58239`)
+- :meth:`Series.plot` now correctly handle the ``ylabel`` parameter for pie chgit aarts, allowing for explicit control over the y-axis label (:issue:`58239`)
 - Support reading Stata 110-format (Stata 7) dta files (:issue:`47176`)
 -
 

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -827,6 +827,34 @@ def test_agg_namedtuple(self):
         expected = df.groupby("A").agg(b=("B", "sum"), c=("B", "count"))
         tm.assert_frame_equal(result, expected)
 
+    def test_single_named_agg_with_args_and_kwargs(self):
+        df = DataFrame({"A": [0, 1, 2, 3], "B": [1, 2, 3, 4]})
+
+        def n_between(ser, low, high):
+            return ser.between(low, high).sum()
+
+        result = df.groupby("A").agg(n_between=pd.NamedAgg("B", n_between, 0, high=2))
+        expected = df.groupby("A").agg(n_between=("B", lambda x: x.between(0, 2).sum()))
+        tm.assert_frame_equal(result, expected)
+
+    def test_multiple_named_agg_with_args_and_kwargs(self):
+        df = DataFrame({"A": [0, 1, 2, 3], "B": [1, 2, 3, 4]})
+
+        def n_between(ser, low, high):
+            return ser.between(low, high).sum()
+
+        result = df.groupby("A").agg(
+            n_between01=pd.NamedAgg("B", n_between, 0, 1),
+            n_between13=pd.NamedAgg("B", n_between, 1, 3),
+            n_between02=pd.NamedAgg("B", n_between, 0, 2),
+        )
+        expected = df.groupby("A").agg(
+            n_between01=("B", lambda x: x.between(0, 1).sum()),
+            n_between13=("B", lambda x: x.between(0, 3).sum()),
+            n_between02=("B", lambda x: x.between(0, 2).sum()),
+        )
+        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))