BUG: groupby agg raising when column was selected twice

doc/source/whatsnew/v1.4.0.rst

@@ -794,6 +794,7 @@ Groupby/resample/rolling
 - Bug in :meth:`GroupBy.mean` failing with ``complex`` dtype (:issue:`43701`)
 - Fixed bug in :meth:`Series.rolling` and :meth:`DataFrame.rolling` not calculating window bounds correctly for the first row when ``center=True`` and index is decreasing (:issue:`43927`)
 - Fixed bug in :meth:`Series.rolling` and :meth:`DataFrame.rolling` for centered datetimelike windows with uneven nanosecond (:issue:`43997`)
+- Bug in :meth:`GroupBy.mean` raising ``KeyError`` when column was selected at least twice (:issue:`44924`)
 - Bug in :meth:`GroupBy.nth` failing on ``axis=1`` (:issue:`43926`)
 - Fixed bug in :meth:`Series.rolling` and :meth:`DataFrame.rolling` not respecting right bound on centered datetime-like windows, if the index contain duplicates (:issue:`3944`)
 - Bug in :meth:`Series.rolling` and :meth:`DataFrame.rolling` when using a :class:`pandas.api.indexers.BaseIndexer` subclass that returned unequal start and end arrays would segfault instead of raising a ``ValueError`` (:issue:`44470`)

pandas/core/base.py

@@ -235,7 +235,7 @@ def __getitem__(self, key):
             raise IndexError(f"Column(s) {self._selection} already selected")
 
         if isinstance(key, (list, tuple, ABCSeries, ABCIndex, np.ndarray)):
-            if len(self.obj.columns.intersection(key)) != len(key):
+            if len(self.obj.columns.intersection(key)) != len(set(key)):
                 bad_keys = list(set(key).difference(self.obj.columns))
                 raise KeyError(f"Columns not found: {str(bad_keys)[1:-1]}")
             return self._gotitem(list(key), ndim=2)

pandas/tests/groupby/test_indexing.py

@@ -2,6 +2,7 @@
 
 import random
 
+import numpy as np
 import pytest
 
 import pandas as pd
@@ -285,3 +286,20 @@ def test_column_axis(column_group_df):
     expected = column_group_df.iloc[:, [1, 3]]
 
     tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("func", [list, pd.Index, pd.Series, np.array])
+def test_groupby_duplicated_columns(func):
+    # GH#44924
+    df = pd.DataFrame(
+        {
+            "A": [1, 2],
+            "B": [3, 3],
+            "C": ["G", "G"],
+        }
+    )
+    result = df.groupby("C")[func(["A", "B", "A"])].mean()
+    expected = pd.DataFrame(
+        [[1.5, 3.0, 1.5]], columns=["A", "B", "A"], index=pd.Index(["G"], name="C")
+    )
+    tm.assert_frame_equal(result, expected)