Changes requested by @jreback

Author: Jean-Mathieu Deschenes

doc/source/whatsnew/v0.21.0.txt

@@ -180,7 +180,6 @@ Bug Fixes
 
 - Fixes regression in 0.20, :func:`Series.aggregate` and :func:`DataFrame.aggregate` allow dictionaries as return values again (:issue:`16741`)
 - Fixes bug where indexing with ``np.inf`` caused an ``OverflowError`` to be raised (:issue:`16957`)
-- Fixes regression when sorting by multiple columns on a datetime array with NaT values (:issue:`16836`)
 
 Conversion
 ^^^^^^^^^^
@@ -223,6 +222,7 @@ Sparse
 Reshaping
 ^^^^^^^^^
 - Joining/Merging with a non unique ``PeriodIndex`` raised a TypeError (:issue:`16871`)
+- Fixes regression when sorting by multiple columns on a ``datetime64`` dtype ``Series`` with ``NaT`` values (:issue:`16836`)
 
 
 Numeric

pandas/tests/frame/test_sorting.py

@@ -89,22 +89,6 @@ def test_sort_values(self):
  with tm.assert_raises_regex(ValueError, msg):
  frame.sort_values(by=['A', 'B'], axis=0, ascending=[True] * 5)
 
- # GH 16836
-
- d1 = [Timestamp(x) for x in ['2016-01-01', '2015-01-01',
- np.nan, '2016-01-01']]
- d2 = [Timestamp(x) for x in ['2017-01-01', '2014-01-01',
- '2016-01-01', '2015-01-01']]
- df = pd.DataFrame({'a': d1, 'b': d2}, index=[0, 1, 2, 3])
-
- d3 = [Timestamp(x) for x in ['2015-01-01', '2016-01-01',
- '2016-01-01', np.nan]]
- d4 = [Timestamp(x) for x in ['2014-01-01', '2015-01-01',
- '2017-01-01', '2016-01-01']]
- expected = pd.DataFrame({'a': d3, 'b': d4}, index=[1, 3, 0, 2])
- sorted_df = df.sort_values(by=['a', 'b'], )
- tm.assert_frame_equal(sorted_df, expected)
-
  def test_sort_values_inplace(self):
  frame = DataFrame(np.random.randn(4, 4), index=[1, 2, 3, 4],
  columns=['A', 'B', 'C', 'D'])
@@ -348,6 +332,22 @@ def test_sort_nat_values_in_int_column(self):
  df_sorted = df.sort_values(["datetime", "float"], ascending=False)
  assert_frame_equal(df_sorted, df)
 
+ # GH 16836
+
+ d1 = [Timestamp(x) for x in ['2016-01-01', '2015-01-01',
+ np.nan, '2016-01-01']]
+ d2 = [Timestamp(x) for x in ['2017-01-01', '2014-01-01',
+ '2016-01-01', '2015-01-01']]
+ df = pd.DataFrame({'a': d1, 'b': d2}, index=[0, 1, 2, 3])
+
+ d3 = [Timestamp(x) for x in ['2015-01-01', '2016-01-01',
+ '2016-01-01', np.nan]]
+ d4 = [Timestamp(x) for x in ['2014-01-01', '2015-01-01',
+ '2017-01-01', '2016-01-01']]
+ expected = pd.DataFrame({'a': d3, 'b': d4}, index=[1, 3, 0, 2])
+ sorted_df = df.sort_values(by=['a', 'b'], )
+ tm.assert_frame_equal(sorted_df, expected)
+
 
 class TestDataFrameSortIndexKinds(TestData):