BUG: aggregations were getting overwritten if they had the same name

doc/source/whatsnew/v1.1.0.rst

@@ -327,6 +327,7 @@ Reshaping
 - Bug in :func:`crosstab` when inputs are two Series and have tuple names, the output will keep dummy MultiIndex as columns. (:issue:`18321`)
 - :meth:`DataFrame.pivot` can now take lists for ``index`` and ``columns`` arguments (:issue:`21425`)
 - Bug in :func:`concat` where the resulting indices are not copied when ``copy=True`` (:issue:`29879`)
+- Bug in :meth:`SeriesGroupBy.aggregate` was resulting in aggregations being overwritten when they shared the same name (:issue:`30092`)
 - :meth:`DataFrame.replace` and :meth:`Series.replace` will raise a ``TypeError`` if ``to_replace`` is not an expected type. Previously the ``replace`` would fail silently (:issue:`18634`)
 
 

pandas/core/groupby/generic.py

@@ -311,8 +311,8 @@ def _aggregate_multiple_funcs(self, arg):
 
             arg = zip(columns, arg)
 
-        results = {}
-        for name, func in arg:
+        results: Mapping[base.OutputKey, Union[Series, DataFrame]] = {}
+        for idx, (name, func) in enumerate(arg):
             obj = self
 
             # reset the cache so that we
@@ -321,13 +321,12 @@ def _aggregate_multiple_funcs(self, arg):
                 obj = copy.copy(obj)
                 obj._reset_cache()
                 obj._selection = name
-            results[name] = obj.aggregate(func)
+            results[base.OutputKey(label=name, position=idx)] = obj.aggregate(func)
 
         if any(isinstance(x, DataFrame) for x in results.values()):
             # let higher level handle
-            return results
-
-        return DataFrame(results, columns=columns)
+            return {key.label: value for key, value in results.items()}
+        return DataFrame(self._wrap_aggregated_output(results), columns=columns)
 
     def _wrap_series_output(
         self, output: Mapping[base.OutputKey, Union[Series, np.ndarray]], index: Index
@@ -358,8 +357,10 @@ def _wrap_series_output(
         if len(output) > 1:
             result = DataFrame(indexed_output, index=index)
             result.columns = columns
-        else:
+        elif not columns.empty:
             result = Series(indexed_output[0], index=index, name=columns[0])
+        else:
+            result = DataFrame()
 
         return result
 

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -250,6 +250,24 @@ def test_agg_multiple_functions_maintain_order(df):
     tm.assert_index_equal(result.columns, exp_cols)
 
 
+def test_agg_multiple_functions_same_name(df):
+    # GH 30880
+    np.random.seed(1)
+    df = tm.makeTimeDataFrame()
+    result = df.resample("3D").agg(
+        {"A": [functools.partial(np.std, ddof=0), functools.partial(np.std, ddof=1)]}
+    )
+    expected_index = pd.date_range("2000-01-03", "2000-02-11", freq="3D")
+    expected_columns = pd.MultiIndex.from_tuples([("A", "std"), ("A", "std")])
+    expected_values = np.array(
+        [df.resample("3D").A.std(ddof=i).values for i in range(2)]
+    ).T
+    expected = pd.DataFrame(
+        expected_values, columns=expected_columns, index=expected_index
+    )
+    tm.assert_frame_equal(result, expected)
+
+
 def test_multiple_functions_tuples_and_non_tuples(df):
     # #1359
     funcs = [("foo", "mean"), "std"]