Replace dicts with OrderedDicts in groupby aggregation functions

doc/source/whatsnew/v0.25.0.rst

@@ -253,6 +253,7 @@ Groupby/Resample/Rolling
 - Bug in :meth:`pandas.core.groupby.DataFrameGroupBy.nunique` in which the names of column levels were lost (:issue:`23222`)
 - Bug in :func:`pandas.core.groupby.GroupBy.agg` when applying a aggregation function to timezone aware data (:issue:`23683`)
 - Bug in :func:`pandas.core.groupby.GroupBy.first` and :func:`pandas.core.groupby.GroupBy.last` where timezone information would be dropped (:issue:`21603`)
+- Ensured that ordering of outputs in ``groupby`` aggregation functions is consistent across all versions of Python (:issue:`25692`)
 
 
 Reshaping

pandas/core/groupby/generic.py

@@ -219,7 +219,7 @@ def _aggregate_generic(self, func, *args, **kwargs):
         axis = self.axis
         obj = self._obj_with_exclusions
 
-        result = {}
+        result = collections.OrderedDict()
         if axis != obj._info_axis_number:
             try:
                 for name, data in self:
@@ -246,7 +246,7 @@ def _aggregate_item_by_item(self, func, *args, **kwargs):
         # only for axis==0
 
         obj = self._obj_with_exclusions
-        result = {}
+        result = collections.OrderedDict()
         cannot_agg = []
         errors = None
         for item in obj:
@@ -899,7 +899,7 @@ def _get_index():
                           name=self._selection_name)
 
     def _aggregate_named(self, func, *args, **kwargs):
-        result = {}
+        result = collections.OrderedDict()
 
         for name, group in self:
             group.name = name

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -303,3 +303,15 @@ def test_groupby_agg_coercing_bools():
     result = gp['c'].aggregate(lambda x: x.isnull().all())
     expected = Series([True, False], index=index, name='c')
     tm.assert_series_equal(result, expected)
+
+
+def test_order_aggregate_multiple_funcs():
+    # GH 25692
+    df = pd.DataFrame({'A': [1, 1, 2, 2], 'B': [1, 2, 3, 4]})
+
+    res = df.groupby('A').agg(['sum', 'max', 'mean', 'ohlc', 'min'])
+    result = res.columns.levels[1]
+
+    expected = pd.Index(['sum', 'max', 'mean', 'ohlc', 'min'])
+
+    tm.assert_index_equal(result, expected)