BUG: Fix unexpected sort in groupby

doc/source/whatsnew/v0.21.0.txt

@@ -625,6 +625,7 @@ Groupby/Resample/Rolling
 - Bug in ``.rolling(...).apply(...)`` with a ``DataFrame`` with a ``DatetimeIndex``, a ``window`` of a timedelta-convertible and ``min_periods >= 1` (:issue:`15305`)
 - Bug in ``DataFrame.groupby`` where index and column keys were not recognized correctly when the number of keys equaled the number of elements on the groupby axis (:issue:`16859`)
 - Bug in ``groupby.nunique()`` with ``TimeGrouper`` which cannot handle ``NaT`` correctly (:issue:`17575`)
+- Bug in ``DataFrame.groupby`` where a single level selection from a ``MultiIndex`` unexpectedly sorts (:issue:`17537`)
 
 Sparse
 ^^^^^^

pandas/core/generic.py

@@ -6631,7 +6631,7 @@ def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None,
         return rs
 
     def _agg_by_level(self, name, axis=0, level=0, skipna=True, **kwargs):
-        grouped = self.groupby(level=level, axis=axis)
+        grouped = self.groupby(level=level, axis=axis, sort=False)
         if hasattr(grouped, name) and skipna:
             return getattr(grouped, name)(**kwargs)
         axis = self._get_axis_number(axis)

pandas/core/groupby.py

@@ -2586,10 +2586,27 @@ def _get_grouper(obj, key=None, axis=0, level=None, sort=True,
     """
     group_axis = obj._get_axis(axis)
 
-    # validate that the passed level is compatible with the passed
+    # validate that the passed single level is compatible with the passed
     # axis of the object
     if level is not None:
-        if not isinstance(group_axis, MultiIndex):
+        # TODO: These if-block and else-block are almost same.
+        # MultiIndex instance check is removable, but it seems that there are
+        # some processes only for non-MultiIndex in else-block,
+        # eg. `obj.index.name != level`. We have to consider carefully whether
+        # these are applicable for MultiIndex. Even if these are applicable,
+        # we need to check if it makes no side effect to subsequent processes
+        # on the outside of this condition.
+        # (GH 17621)
+        if isinstance(group_axis, MultiIndex):
+            if is_list_like(level) and len(level) == 1:
+                level = level[0]
+
+            if key is None and is_scalar(level):
+                # Get the level values from group_axis
+                key = group_axis.get_level_values(level)
+                level = None
+
+        else:
             # allow level to be a length-one list-like object
             # (e.g., level=[0])
             # GH 13901
@@ -2611,6 +2628,8 @@ def _get_grouper(obj, key=None, axis=0, level=None, sort=True,
                 raise ValueError('level > 0 or level < -1 only valid with '
                                  ' MultiIndex')
 
+            # NOTE: `group_axis` and `group_axis.get_level_values(level)`
+            # are same in this section.
             level = None
             key = group_axis
 

pandas/tests/groupby/test_groupby.py

@@ -1791,18 +1791,20 @@ def aggfun(ser):
         agged2 = df.groupby(keys).aggregate(aggfun)
         assert len(agged2.columns) + 1 == len(df.columns)
 
-    def test_groupby_level(self):
+    @pytest.mark.parametrize('sort', [True, False])
+    def test_groupby_level(self, sort):
+        # GH 17537
         frame = self.mframe
         deleveled = frame.reset_index()
 
-        result0 = frame.groupby(level=0).sum()
-        result1 = frame.groupby(level=1).sum()
+        result0 = frame.groupby(level=0, sort=sort).sum()
+        result1 = frame.groupby(level=1, sort=sort).sum()
 
-        expected0 = frame.groupby(deleveled['first'].values).sum()
-        expected1 = frame.groupby(deleveled['second'].values).sum()
+        expected0 = frame.groupby(deleveled['first'].values, sort=sort).sum()
+        expected1 = frame.groupby(deleveled['second'].values, sort=sort).sum()
 
-        expected0 = expected0.reindex(frame.index.levels[0])
-        expected1 = expected1.reindex(frame.index.levels[1])
+        expected0.index.name = 'first'
+        expected1.index.name = 'second'
 
         assert result0.index.name == 'first'
         assert result1.index.name == 'second'
@@ -1813,15 +1815,15 @@ def test_groupby_level(self):
         assert result1.index.name == frame.index.names[1]
 
         # groupby level name
-        result0 = frame.groupby(level='first').sum()
-        result1 = frame.groupby(level='second').sum()
+        result0 = frame.groupby(level='first', sort=sort).sum()
+        result1 = frame.groupby(level='second', sort=sort).sum()
         assert_frame_equal(result0, expected0)
         assert_frame_equal(result1, expected1)
 
         # axis=1
 
-        result0 = frame.T.groupby(level=0, axis=1).sum()
-        result1 = frame.T.groupby(level=1, axis=1).sum()
+        result0 = frame.T.groupby(level=0, axis=1, sort=sort).sum()
+        result1 = frame.T.groupby(level=1, axis=1, sort=sort).sum()
         assert_frame_equal(result0, expected0.T)
         assert_frame_equal(result1, expected1.T)
 
@@ -1835,15 +1837,17 @@ def test_groupby_level_index_names(self):
         df.groupby(level='exp')
         pytest.raises(ValueError, df.groupby, level='foo')
 
-    def test_groupby_level_with_nas(self):
+    @pytest.mark.parametrize('sort', [True, False])
+    def test_groupby_level_with_nas(self, sort):
+        # GH 17537
         index = MultiIndex(levels=[[1, 0], [0, 1, 2, 3]],
                            labels=[[1, 1, 1, 1, 0, 0, 0, 0], [0, 1, 2, 3, 0, 1,
                                                               2, 3]])
 
         # factorizing doesn't confuse things
         s = Series(np.arange(8.), index=index)
-        result = s.groupby(level=0).sum()
-        expected = Series([22., 6.], index=[1, 0])
+        result = s.groupby(level=0, sort=sort).sum()
+        expected = Series([6., 22.], index=[0, 1])
         assert_series_equal(result, expected)
 
         index = MultiIndex(levels=[[1, 0], [0, 1, 2, 3]],
@@ -1852,8 +1856,8 @@ def test_groupby_level_with_nas(self):
 
         # factorizing doesn't confuse things
         s = Series(np.arange(8.), index=index)
-        result = s.groupby(level=0).sum()
-        expected = Series([18., 6.], index=[1, 0])
+        result = s.groupby(level=0, sort=sort).sum()
+        expected = Series([6., 18.], index=[0.0, 1.0])
         assert_series_equal(result, expected)
 
     def test_groupby_level_apply(self):
@@ -1936,9 +1940,14 @@ def test_groupby_complex(self):
         result = a.sum(level=0)
         assert_series_equal(result, expected)
 
-    def test_level_preserve_order(self):
-        grouped = self.mframe.groupby(level=0)
-        exp_labels = np.array([0, 0, 0, 1, 1, 2, 2, 3, 3, 3], np.intp)
+    @pytest.mark.parametrize('sort,labels', [
+        [True, [2, 2, 2, 0, 0, 1, 1, 3, 3, 3]],
+        [False, [0, 0, 0, 1, 1, 2, 2, 3, 3, 3]]
+    ])
+    def test_level_preserve_order(self, sort, labels):
+        # GH 17537
+        grouped = self.mframe.groupby(level=0, sort=sort)
+        exp_labels = np.array(labels, np.intp)
         assert_almost_equal(grouped.grouper.labels[0], exp_labels)
 
     def test_grouping_labels(self):

pandas/tests/groupby/test_whitelist.py

@@ -174,12 +174,16 @@ def raw_frame():
 
 
 @pytest.mark.parametrize(
-    "op, level, axis, skipna",
+    "op, level, axis, skipna, sort",
     product(AGG_FUNCTIONS,
             lrange(2), lrange(2),
+            [True, False],
             [True, False]))
-def test_regression_whitelist_methods(raw_frame, op, level, axis, skipna):
+def test_regression_whitelist_methods(
+        raw_frame, op, level,
+        axis, skipna, sort):
     # GH6944
+    # GH 17537
     # explicity test the whitelest methods
 
     if axis == 0:
@@ -188,15 +192,19 @@ def test_regression_whitelist_methods(raw_frame, op, level, axis, skipna):
         frame = raw_frame.T
 
     if op in AGG_FUNCTIONS_WITH_SKIPNA:
-        grouped = frame.groupby(level=level, axis=axis)
+        grouped = frame.groupby(level=level, axis=axis, sort=sort)
         result = getattr(grouped, op)(skipna=skipna)
         expected = getattr(frame, op)(level=level, axis=axis,
                                       skipna=skipna)
+        if sort:
+            expected = expected.sort_index(axis=axis, level=level)
         tm.assert_frame_equal(result, expected)
     else:
-        grouped = frame.groupby(level=level, axis=axis)
+        grouped = frame.groupby(level=level, axis=axis, sort=sort)
         result = getattr(grouped, op)()
         expected = getattr(frame, op)(level=level, axis=axis)
+        if sort:
+            expected = expected.sort_index(axis=axis, level=level)
         tm.assert_frame_equal(result, expected)
 
 

pandas/tests/test_multilevel.py

@@ -1392,17 +1392,23 @@ def test_count(self):
     AGG_FUNCTIONS = ['sum', 'prod', 'min', 'max', 'median', 'mean', 'skew',
                      'mad', 'std', 'var', 'sem']
 
-    def test_series_group_min_max(self):
+    @pytest.mark.parametrize('sort', [True, False])
+    def test_series_group_min_max(self, sort):
+        # GH 17537
         for op, level, skipna in cart_product(self.AGG_FUNCTIONS, lrange(2),
                                               [False, True]):
-            grouped = self.series.groupby(level=level)
+            grouped = self.series.groupby(level=level, sort=sort)
             aggf = lambda x: getattr(x, op)(skipna=skipna)
             # skipna=True
             leftside = grouped.agg(aggf)
             rightside = getattr(self.series, op)(level=level, skipna=skipna)
+            if sort:
+                rightside = rightside.sort_index(level=level)
             tm.assert_series_equal(leftside, rightside)
 
-    def test_frame_group_ops(self):
+    @pytest.mark.parametrize('sort', [True, False])
+    def test_frame_group_ops(self, sort):
+        # GH 17537
         self.frame.iloc[1, [1, 2]] = np.nan
         self.frame.iloc[7, [0, 1]] = np.nan
 
@@ -1415,7 +1421,7 @@ def test_frame_group_ops(self):
             else:
                 frame = self.frame.T
 
-            grouped = frame.groupby(level=level, axis=axis)
+            grouped = frame.groupby(level=level, axis=axis, sort=sort)
 
             pieces = []
 
@@ -1426,6 +1432,9 @@ def aggf(x):
             leftside = grouped.agg(aggf)
             rightside = getattr(frame, op)(level=level, axis=axis,
                                            skipna=skipna)
+            if sort:
+                rightside = rightside.sort_index(level=level, axis=axis)
+                frame = frame.sort_index(level=level, axis=axis)
 
             # for good measure, groupby detail
             level_index = frame._get_axis(axis).levels[level]