Bugfix/groupby datetime issue

doc/source/whatsnew/v1.0.0.rst

@@ -182,7 +182,7 @@ Datetimelike
 - Bug in :class:`Series` and :class:`DataFrame` with integer dtype failing to raise ``TypeError`` when adding or subtracting a ``np.datetime64`` object (:issue:`28080`)
 - Bug in :class:`Week` with ``weekday`` incorrectly raising ``AttributeError`` instead of ``TypeError`` when adding or subtracting an invalid type (:issue:`28530`)
 - Bug in :class:`DataFrame` arithmetic operations when operating with a :class:`Series` with dtype `'timedelta64[ns]'` (:issue:`28049`)
--
+- Bug in :func:`pandas.core.groupby.generic._recast_datetimelike_result` causing ``KeyError`` in :func:`pandas.core.groupby.generic.SeriesGroupBy.apply` when a column index in the original DataFrame is datetimelike (:issue:`28247`)
 
 Timedelta
 ^^^^^^^^^

pandas/core/groupby/generic.py

@@ -1905,7 +1905,9 @@ def _recast_datetimelike_result(result: DataFrame) -> DataFrame:
     result = result.copy()
 
     obj_cols = [
-        idx for idx in range(len(result.columns)) if is_object_dtype(result.dtypes[idx])
+        idx
+        for idx in range(len(result.columns))
+        if is_object_dtype(result.dtypes.iloc[idx])
     ]
 
     # See GH#26285

pandas/tests/groupby/test_apply.py

@@ -657,3 +657,31 @@ def test_apply_with_mixed_types():
 
     result = g.apply(lambda x: x / x.sum())
     tm.assert_frame_equal(result, expected)
+
+
+def test_apply_datetime_issue():
+    # GH-28247
+
+    df = pd.DataFrame({"a": ["foo"], "b": [datetime.today()]})
+    result = df.groupby("a").apply(lambda x: pd.Series(["spam"], index=[42]))
+
+    expected = pd.DataFrame(
+        ["spam"], Index(["foo"], dtype="object", name="a"), columns=[42]
+    )
+    tm.assert_frame_equal(result, expected)
+
+    df = pd.DataFrame({"a": ["foo"], "b": [datetime.today().date()]})
+    result = df.groupby("a").apply(lambda x: pd.Series(["spam"], index=[42]))
+
+    expected = pd.DataFrame(
+        ["spam"], Index(["foo"], dtype="object", name="a"), columns=[42]
+    )
+    tm.assert_frame_equal(result, expected)
+
+    df = pd.DataFrame({"a": ["foo"], "b": [datetime.today().time()]})
+    result = df.groupby("a").apply(lambda x: pd.Series(["spam"], index=[42]))
+
+    expected = pd.DataFrame(
+        ["spam"], Index(["foo"], dtype="object", name="a"), columns=[42]
+    )
+    tm.assert_frame_equal(result, expected)