ENH: Support NamedAggs in kwargs in Rolling/Expanding/EWM agg method

doc/source/whatsnew/v3.0.0.rst

@@ -56,6 +56,7 @@ Other enhancements
 - :meth:`DataFrame.plot.scatter` argument ``c`` now accepts a column of strings, where rows with the same string are colored identically (:issue:`16827` and :issue:`16485`)
 - :func:`read_parquet` accepts ``to_pandas_kwargs`` which are forwarded to :meth:`pyarrow.Table.to_pandas` which enables passing additional keywords to customize the conversion to pandas, such as ``maps_as_pydicts`` to read the Parquet map data type as python dictionaries (:issue:`56842`)
 - :meth:`DataFrameGroupBy.transform`, :meth:`SeriesGroupBy.transform`, :meth:`DataFrameGroupBy.agg`, :meth:`SeriesGroupBy.agg`, :meth:`RollingGroupby.apply`, :meth:`ExpandingGroupby.apply`, :meth:`Rolling.apply`, :meth:`Expanding.apply`, :meth:`DataFrame.apply` with ``engine="numba"`` now supports positional arguments passed as kwargs (:issue:`58995`)
+- :meth:`Rolling.agg`, :meth:`Expanding.agg` and :meth:`ExponentialMovingWindow.agg` now accept :class:`NamedAgg` aggregations through ``**kwargs`` (:issue:`28333`)
 - :meth:`Series.map` can now accept kwargs to pass on to func (:issue:`59814`)
 - :meth:`pandas.concat` will raise a ``ValueError`` when ``ignore_index=True`` and ``keys`` is not ``None`` (:issue:`59274`)
 - :meth:`str.get_dummies` now accepts a  ``dtype`` parameter to specify the dtype of the resulting DataFrame (:issue:`47872`)

pandas/core/window/ewm.py

@@ -490,7 +490,7 @@ def online(
         klass="Series/Dataframe",
         axis="",
     )
-    def aggregate(self, func, *args, **kwargs):
+    def aggregate(self, func=None, *args, **kwargs):
         return super().aggregate(func, *args, **kwargs)
 
     agg = aggregate
@@ -981,7 +981,7 @@ def reset(self) -> None:
         """
         self._mean.reset()
 
-    def aggregate(self, func, *args, **kwargs):
+    def aggregate(self, func=None, *args, **kwargs):
         raise NotImplementedError("aggregate is not implemented.")
 
     def std(self, bias: bool = False, *args, **kwargs):

pandas/core/window/expanding.py

@@ -167,7 +167,7 @@ def _get_window_indexer(self) -> BaseIndexer:
         klass="Series/Dataframe",
         axis="",
     )
-    def aggregate(self, func, *args, **kwargs):
+    def aggregate(self, func=None, *args, **kwargs):
         return super().aggregate(func, *args, **kwargs)
 
     agg = aggregate

pandas/core/window/rolling.py

@@ -44,7 +44,10 @@
 
 from pandas.core._numba import executor
 from pandas.core.algorithms import factorize
-from pandas.core.apply import ResamplerWindowApply
+from pandas.core.apply import (
+    ResamplerWindowApply,
+    reconstruct_func,
+)
 from pandas.core.arrays import ExtensionArray
 from pandas.core.base import SelectionMixin
 import pandas.core.common as com
@@ -646,8 +649,12 @@ def _numba_apply(
             out = obj._constructor(result, index=index, columns=columns)
             return self._resolve_output(out, obj)
 
-    def aggregate(self, func, *args, **kwargs):
+    def aggregate(self, func=None, *args, **kwargs):
+        relabeling, func, columns, order = reconstruct_func(func, **kwargs)
         result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
+        if (result is not None) and isinstance(result, ABCDataFrame) and relabeling:
+            result = result.iloc[:, order]
+            result.columns = columns
         if result is None:
             return self.apply(func, raw=False, args=args, kwargs=kwargs)
         return result
@@ -1239,7 +1246,7 @@ def calc(x):
         klass="Series/DataFrame",
         axis="",
     )
-    def aggregate(self, func, *args, **kwargs):
+    def aggregate(self, func=None, *args, **kwargs):
         result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
         if result is None:
             # these must apply directly
@@ -1951,7 +1958,7 @@ def _raise_monotonic_error(self, msg: str):
         klass="Series/Dataframe",
         axis="",
     )
-    def aggregate(self, func, *args, **kwargs):
+    def aggregate(self, func=None, *args, **kwargs):
         return super().aggregate(func, *args, **kwargs)
 
     agg = aggregate

pandas/tests/window/test_groupby.py

@@ -6,6 +6,7 @@
     DatetimeIndex,
     Index,
     MultiIndex,
+    NamedAgg,
     Series,
     Timestamp,
     date_range,
@@ -489,6 +490,36 @@ def test_groupby_rolling_subset_with_closed(self):
         )
         tm.assert_series_equal(result, expected)
 
+    def test_groupby_rolling_agg_namedagg(self):
+        # GH#28333
+        df = DataFrame(
+            {
+                "kind": ["cat", "dog", "cat", "dog", "cat", "dog"],
+                "height": [9.1, 6.0, 9.5, 34.0, 12.0, 8.0],
+                "weight": [7.9, 7.5, 9.9, 198.0, 10.0, 42.0],
+            }
+        )
+        result = (
+            df.groupby("kind")
+            .rolling(2)
+            .agg(
+                total_weight=NamedAgg(column="weight", aggfunc=sum),
+                min_height=NamedAgg(column="height", aggfunc=min),
+            )
+        )
+        expected = DataFrame(
+            {
+                "total_weight": [np.nan, 17.8, 19.9, np.nan, 205.5, 240.0],
+                "min_height": [np.nan, 9.1, 9.5, np.nan, 6.0, 8.0],
+            },
+            index=MultiIndex(
+                [["cat", "dog"], [0, 1, 2, 3, 4, 5]],
+                [[0, 0, 0, 1, 1, 1], [0, 2, 4, 1, 3, 5]],
+                names=["kind", None],
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
     def test_groupby_subset_rolling_subset_with_closed(self):
         # GH 35549
         df = DataFrame(
@@ -1134,6 +1165,36 @@ def test_expanding_apply(self, raw, frame):
         expected.index = expected_index
         tm.assert_frame_equal(result, expected)
 
+    def test_groupby_expanding_agg_namedagg(self):
+        # GH#28333
+        df = DataFrame(
+            {
+                "kind": ["cat", "dog", "cat", "dog", "cat", "dog"],
+                "height": [9.1, 6.0, 9.5, 34.0, 12.0, 8.0],
+                "weight": [7.9, 7.5, 9.9, 198.0, 10.0, 42.0],
+            }
+        )
+        result = (
+            df.groupby("kind")
+            .expanding(1)
+            .agg(
+                total_weight=NamedAgg(column="weight", aggfunc=sum),
+                min_height=NamedAgg(column="height", aggfunc=min),
+            )
+        )
+        expected = DataFrame(
+            {
+                "total_weight": [7.9, 17.8, 27.8, 7.5, 205.5, 247.5],
+                "min_height": [9.1, 9.1, 9.1, 6.0, 6.0, 6.0],
+            },
+            index=MultiIndex(
+                [["cat", "dog"], [0, 1, 2, 3, 4, 5]],
+                [[0, 0, 0, 1, 1, 1], [0, 2, 4, 1, 3, 5]],
+                names=["kind", None],
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
 
 class TestEWM:
     @pytest.mark.parametrize(
@@ -1162,6 +1223,41 @@ def test_methods(self, method, expected_data):
         )
         tm.assert_frame_equal(result, expected)
 
+    def test_groupby_ewm_agg_namedagg(self):
+        # GH#28333
+        df = DataFrame({"A": ["a"] * 4, "B": range(4)})
+        result = (
+            df.groupby("A")
+            .ewm(com=1.0)
+            .agg(
+                B_mean=NamedAgg(column="B", aggfunc="mean"),
+                B_std=NamedAgg(column="B", aggfunc="std"),
+                B_var=NamedAgg(column="B", aggfunc="var"),
+            )
+        )
+        expected = DataFrame(
+            {
+                "B_mean": [
+                    0.0,
+                    0.6666666666666666,
+                    1.4285714285714286,
+                    2.2666666666666666,
+                ],
+                "B_std": [np.nan, 0.707107, 0.963624, 1.177164],
+                "B_var": [np.nan, 0.5, 0.9285714285714286, 1.3857142857142857],
+            },
+            index=MultiIndex.from_tuples(
+                [
+                    ("a", 0),
+                    ("a", 1),
+                    ("a", 2),
+                    ("a", 3),
+                ],
+                names=["A", None],
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
     @pytest.mark.parametrize(
         "method, expected_data",
         [["corr", [np.nan, 1.0, 1.0, 1]], ["cov", [np.nan, 0.5, 0.928571, 1.385714]]],