BUG: Fix droped result column in groupby with as_index False

doc/source/whatsnew/v1.1.0.rst

@@ -403,6 +403,7 @@ Groupby/resample/rolling
 - Bug in :meth:`GroupBy.apply` raises ``ValueError`` when the ``by`` axis is not sorted and has duplicates and the applied ``func`` does not mutate passed in objects (:issue:`30667`)
 - Bug in :meth:`DataFrameGroupby.transform` produces incorrect result with transformation functions (:issue:`30918`)
 - Bug in :meth:`DataFrame.groupby` and :meth:`Series.groupby` produces inconsistent type when aggregating Boolean series (:issue:`32894`)
+- Bug in :meth:`DataFrame.groupby` lost results, when ``as_index`` option was set to ``False``. The result values were replaced with the index values (:issue:`32240`).
 
 
 Reshaping

pandas/core/groupby/generic.py

@@ -963,16 +963,16 @@ def aggregate(self, func=None, *args, **kwargs):
                         [self._selected_obj.columns.name] * result.columns.nlevels
                     ).droplevel(-1)
 
-        if not self.as_index:
-            self._insert_inaxis_grouper_inplace(result)
-            result.index = np.arange(len(result))
-
         if relabeling:
 
             # used reordered index of columns
             result = result.iloc[:, order]
             result.columns = columns
 
+        if not self.as_index:
+            self._insert_inaxis_grouper_inplace(result)
+            result.index = np.arange(len(result))
+
         return result._convert(datetime=True)
 
     agg = aggregate

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -822,6 +822,41 @@ def test_groupby_aggregate_period_frame(func):
     tm.assert_frame_equal(result, expected)
 
 
+def test_grouby_agg_loses_results_with_as_index_false_relabel():
+    # GH 32240: When the aggregate function relabels column names and
+    # as_index=False is specified, the results are dropped.
+
+    df = pd.DataFrame(
+        {"key": ["x", "y", "z", "x", "y", "z"], "val": [1.0, 0.8, 2.0, 3.0, 3.6, 0.75]}
+    )
+
+    grouped = df.groupby("key", as_index=False)
+    result = grouped.agg(min_val=pd.NamedAgg(column="val", aggfunc="min"))
+    expected = pd.DataFrame({"key": ["x", "y", "z"], "min_val": [1.0, 0.8, 0.75]})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_grouby_agg_loses_results_with_as_index_false_relabel_multiindex():
+    # GH 32240: When the aggregate function relabels column names and
+    # as_index=False is specified, the results are dropped. Check if
+    # multiindex is returned in the right order
+
+    df = pd.DataFrame(
+        {
+            "key": ["x", "y", "x", "y", "x", "x"],
+            "key1": ["a", "b", "c", "b", "a", "c"],
+            "val": [1.0, 0.8, 2.0, 3.0, 3.6, 0.75],
+        }
+    )
+
+    grouped = df.groupby(["key", "key1"], as_index=False)
+    result = grouped.agg(min_val=pd.NamedAgg(column="val", aggfunc="min"))
+    expected = pd.DataFrame(
+        {"key": ["x", "x", "y"], "key1": ["a", "c", "b"], "min_val": [1.0, 0.75, 0.8]}
+    )
+    tm.assert_frame_equal(result, expected)
+
+
 class TestLambdaMangling:
     def test_basic(self):
         df = pd.DataFrame({"A": [0, 0, 1, 1], "B": [1, 2, 3, 4]})