BUG: df.groupby().resample()[[cols]] without key columns raise KeyError

doc/source/whatsnew/v1.5.0.rst

@@ -996,6 +996,8 @@ Groupby/resample/rolling
 - Bug in :meth:`.DataFrameGroupBy.describe` and :meth:`.SeriesGroupBy.describe` produces inconsistent results for empty datasets (:issue:`41575`)
 - Bug in :meth:`DataFrame.resample` reduction methods when used with ``on`` would attempt to aggregate the provided column (:issue:`47079`)
 - Bug in :meth:`DataFrame.groupby` and :meth:`Series.groupby` would not respect ``dropna=False`` when the input DataFrame/Series had a NaN values in a :class:`MultiIndex` (:issue:`46783`)
+- Bug in :meth:`DataFrameGroupBy.resample` raises ``KeyError`` when getting the result from a key list which misses the resample key (:issue:`47362`)
+-
 
 Reshaping
 ^^^^^^^^^

pandas/core/resample.py

@@ -502,11 +502,11 @@ def _apply_loffset(self, result):
         self.loffset = None
         return result
 
-    def _get_resampler_for_grouping(self, groupby):
+    def _get_resampler_for_grouping(self, groupby, key=None):
         """
         Return the correct class for resampling with groupby.
         """
-        return self._resampler_for_grouping(self, groupby=groupby)
+        return self._resampler_for_grouping(self, groupby=groupby, key=key)
 
     def _wrap_result(self, result):
         """
@@ -1132,7 +1132,7 @@ 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) -> None:
+    def __init__(self, obj, parent=None, groupby=None, key=None, **kwargs) -> None:
         # reached via ._gotitem and _get_resampler_for_grouping
 
         if parent is None:
@@ -1145,6 +1145,7 @@ def __init__(self, obj, parent=None, groupby=None, **kwargs) -> None:
         self._selection = kwargs.get("selection")
 
         self.binner = parent.binner
+        self.key = key
 
         self._groupby = groupby
         self._groupby.mutated = True
@@ -1197,6 +1198,8 @@ def _gotitem(self, key, ndim, subset=None):
 
         # Try to select from a DataFrame, falling back to a Series
         try:
+            if isinstance(key, list) and self.key not in key:
+                key.append(self.key)
             groupby = self._groupby[key]
         except IndexError:
             groupby = self._groupby
@@ -1511,7 +1514,7 @@ def get_resampler_for_grouping(
     # .resample uses 'on' similar to how .groupby uses 'key'
     tg = TimeGrouper(freq=rule, key=on, **kwargs)
     resampler = tg._get_resampler(groupby.obj, kind=kind)
-    return resampler._get_resampler_for_grouping(groupby=groupby)
+    return resampler._get_resampler_for_grouping(groupby=groupby, key=tg.key)
 
 
 class TimeGrouper(Grouper):

pandas/tests/resample/test_resampler_grouper.py

@@ -470,3 +470,30 @@ def test_resample_groupby_agg_object_dtype_all_nan(consolidate):
         index=idx,
     )
     tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_resample_with_list_of_keys():
+    # GH 47362
+    df = DataFrame(
+        data={
+            "date": date_range(start="2016-01-01", periods=8),
+            "group": [0, 0, 0, 0, 1, 1, 1, 1],
+            "val": [1, 7, 5, 2, 3, 10, 5, 1],
+        }
+    )
+    result = df.groupby("group").resample("2D", on="date")[["val"]].mean()
+    expected = DataFrame(
+        data={
+            "val": [4.0, 3.5, 6.5, 3.0],
+        },
+        index=Index(
+            data=[
+                (0, Timestamp("2016-01-01")),
+                (0, Timestamp("2016-01-03")),
+                (1, Timestamp("2016-01-05")),
+                (1, Timestamp("2016-01-07")),
+            ],
+            name=("group", "date"),
+        ),
+    )
+    tm.assert_frame_equal(result, expected)