🐛 aggregations were getting overwritten if they had the same name

Author: Marco Gorelli

doc/source/whatsnew/v1.0.0.rst

@@ -1017,6 +1017,7 @@ Groupby/resample/rolling
 - Bug in :meth:`DataFrame.groupby` when using nunique on axis=1 (:issue:`30253`)
 - Bug in :meth:`GroupBy.quantile` with multiple list-like q value and integer column names (:issue:`30289`)
 - Bug in :meth:`GroupBy.pct_change` and :meth:`core.groupby.SeriesGroupBy.pct_change` causes ``TypeError`` when ``fill_method`` is ``None`` (:issue:`30463`)
+- Bug in :meth:`SeriesGroupBy._aggregate_multiple_funcs` was resulting in aggregations being overwritten when they shared the same name (:issue:`30092`)
 
 Reshaping
 ^^^^^^^^^

pandas/core/groupby/generic.py

@@ -309,7 +309,7 @@ def _aggregate_multiple_funcs(self, arg):
             arg = zip(columns, arg)
 
         results = {}
-        for name, func in arg:
+        for idx, (name, func) in enumerate(arg):
             obj = self
 
             # reset the cache so that we
@@ -318,13 +318,16 @@ 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)
+            oldresults[name] = obj.aggregate(func)
 
         if any(isinstance(x, DataFrame) for x in results.values()):
             # let higher level handle
-            return results
+            return {key.label: value for key, value in results.items()}
 
-        return DataFrame(results, columns=columns)
+        if results:
+            return DataFrame(self._wrap_aggregated_output(results), columns=columns)
+        return DataFrame(columns=columns)
 
     def _wrap_series_output(
         self, output: Mapping[base.OutputKey, Union[Series, np.ndarray]], index: Index

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -239,6 +239,55 @@ def test_agg_multiple_functions_maintain_order(df):
     tm.assert_index_equal(result.columns, exp_cols)
 
 
+def test_agg_multiple_functions_same_name(df):
+    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.DatetimeIndex(
+        [
+            "2000-01-03",
+            "2000-01-06",
+            "2000-01-09",
+            "2000-01-12",
+            "2000-01-15",
+            "2000-01-18",
+            "2000-01-21",
+            "2000-01-24",
+            "2000-01-27",
+            "2000-01-30",
+            "2000-02-02",
+            "2000-02-05",
+            "2000-02-08",
+            "2000-02-11",
+        ],
+        dtype="datetime64[ns]",
+        freq="3D",
+    )
+    expected_columns = pd.MultiIndex.from_tuples([("A", "std"), ("A", "std")])
+    expected_values = [
+        [1.03497007, 1.26757429],
+        [0.96918813, 1.37063899],
+        [2.02317523, 2.86120185],
+        [0.44121013, 0.54036984],
+        [0.0, np.nan],
+        [0.80503606, 0.98596379],
+        [0.0, np.nan],
+        [0.3954067, 0.48427232],
+        [0.27030073, 0.38226296],
+        [1.12267144, 1.58769718],
+        [0.18796289, 0.23020659],
+        [0.0, np.nan],
+        [0.3540029, 0.43356324],
+        [0.0, np.nan],
+    ]
+    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"]