DOC: Add the description for fix unexpected sort in groupby in whatsnew note

Author: Licht-T

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/tests/groupby/test_groupby.py

@@ -1793,6 +1793,7 @@ def aggfun(ser):
 
  @pytest.mark.parametrize('sort', [True, False])
  def test_groupby_level(self, sort):
+ # GH 17537
  frame = self.mframe
  deleveled = frame.reset_index()
 
@@ -1838,6 +1839,7 @@ def test_groupby_level_index_names(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]])
@@ -1943,6 +1945,7 @@ def test_groupby_complex(self):
  [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)

pandas/tests/groupby/test_whitelist.py

@@ -183,6 +183,7 @@ def test_regression_whitelist_methods(
  raw_frame, op, level,
  axis, skipna, sort):
  # GH6944
+ # GH 17537
  # explicity test the whitelest methods
 
  if axis == 0:

pandas/tests/test_multilevel.py

@@ -1394,6 +1394,7 @@ def test_count(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, sort=sort)
@@ -1407,6 +1408,7 @@ def test_series_group_min_max(self, sort):
 
  @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