BUG: make Series.sort_values(ascending=[False]) behave as ascending=False (pandas-dev#15604)

Author: manu

doc/source/whatsnew/v0.20.0.txt

@@ -228,7 +228,7 @@ Other enhancements
 - ``pd.TimedeltaIndex`` now has a custom datetick formatter specifically designed for nanosecond level precision (:issue:`8711`)
 - ``pd.types.concat.union_categoricals`` gained the ``ignore_ordered`` argument to allow ignoring the ordered attribute of unioned categoricals (:issue:`13410`). See the :ref:`categorical union docs <categorical.union>` for more information.
 - ``pandas.io.json.json_normalize()`` with an empty ``list`` will return an empty ``DataFrame`` (:issue:`15534`)
-
+- ``Series.sort_values`` accepts a one element list of bool for consistency with the behavior of ``DataFrame.sort_values`` (:issue:`15604`)
 .. _ISO 8601 duration: https://en.wikipedia.org/wiki/ISO_8601#Durations
 
 

pandas/core/series.py

@@ -21,6 +21,7 @@
                                  is_datetime64tz_dtype,
                                  is_timedelta64_dtype,
                                  is_list_like,
+                                 is_sequence,
                                  is_hashable,
                                  is_iterator,
                                  is_dict_like,
@@ -1722,6 +1723,12 @@ def _try_kind_sort(arr):
 
         argsorted = _try_kind_sort(arr[good])
 
+        if is_sequence(ascending):
+            if len(ascending) > 1:
+                raise ValueError('Length of ascending (%d) cannot be larger '
+                                 'than 1 for Series' % (len(ascending)))
+            ascending = ascending[0]
+
         if not ascending:
             argsorted = argsorted[::-1]
 

pandas/tests/series/test_sorting.py

@@ -64,6 +64,13 @@ def test_sort_values(self):
         ordered = ts.sort_values(ascending=False, na_position='first')
         assert_almost_equal(expected, ordered.valid().values)
 
+        # ascending=[False] should behave the same as ascending=False
+        ordered = ts.sort_values(ascending=[False])
+        expected = np.sort(ts.valid().values)[::-1]
+        assert_almost_equal(expected, ordered.valid().values)
+        ordered = ts.sort_values(ascending=[False], na_position='first')
+        assert_almost_equal(expected, ordered.valid().values)
+
         # inplace=True
         ts = self.ts.copy()
         ts.sort_values(ascending=False, inplace=True)