Backport PR pandas-dev#43410: REGR: Resampler.aggregate fails when used with column selection (pandas-dev#43444)

Co-authored-by: Richard Shadrach <[email protected]>

Author: meeseeksmachine

doc/source/whatsnew/v1.3.3.rst

@@ -22,6 +22,7 @@ Fixed regressions
 - Fixed regression in :meth:`RangeIndex.where` and :meth:`RangeIndex.putmask` raising ``AssertionError`` when result did not represent a :class:`RangeIndex` (:issue:`43240`)
 - Fixed regression in :meth:`read_parquet` where the ``fastparquet`` engine would not work properly with fastparquet 0.7.0 (:issue:`43075`)
 - Fixed regression in :func:`is_list_like` where objects with ``__iter__`` set to ``None`` would be identified as iterable (:issue:`43373`)
+- Fixed regression in :meth:`.Resampler.aggregate` when used after column selection would raise if ``func`` is a list of aggregation functions (:issue:`42905`)
 - Fixed regression in :meth:`DataFrame.corr` where Kendall correlation would produce incorrect results for columns with repeated values (:issue:`43401`)
 
 .. ---------------------------------------------------------------------------

pandas/core/resample.py

@@ -24,6 +24,7 @@
 )
 from pandas._typing import (
     FrameOrSeries,
+    IndexLabel,
     T,
     TimedeltaConvertibleTypes,
     TimestampConvertibleTypes,
@@ -1016,6 +1017,7 @@ class _GroupByMixin(PandasObject):
     """
 
     _attributes: list[str]  # in practice the same as Resampler._attributes
+    _selection: IndexLabel | None = None
 
     def __init__(self, obj, parent=None, groupby=None, **kwargs):
         # reached via ._gotitem and _get_resampler_for_grouping
@@ -1027,6 +1029,7 @@ def __init__(self, obj, parent=None, groupby=None, **kwargs):
         # the resampler attributes
         for attr in self._attributes:
             setattr(self, attr, kwargs.get(attr, getattr(parent, attr)))
+        self._selection = kwargs.get("selection")
 
         self.binner = parent.binner
 

pandas/tests/resample/test_resampler_grouper.py

@@ -9,6 +9,7 @@
 import pandas as pd
 from pandas import (
     DataFrame,
+    Index,
     Series,
     TimedeltaIndex,
     Timestamp,
@@ -405,6 +406,20 @@ def test_resample_groupby_agg():
     tm.assert_frame_equal(result, expected)
 
 
+def test_resample_groupby_agg_listlike():
+    # GH 42905
+    ts = Timestamp("2021-02-28 00:00:00")
+    df = DataFrame({"class": ["beta"], "value": [69]}, index=Index([ts], name="date"))
+    resampled = df.groupby("class").resample("M")["value"]
+    result = resampled.agg(["sum", "size"])
+    expected = DataFrame(
+        [[69, 1]],
+        index=pd.MultiIndex.from_tuples([("beta", ts)], names=["class", "date"]),
+        columns=["sum", "size"],
+    )
+    tm.assert_frame_equal(result, expected)
+
+
 @pytest.mark.parametrize("keys", [["a"], ["a", "b"]])
 def test_empty(keys):
     # GH 26411