BUG Series.sort_index does not accept parameters kind and na_position

Author: brandonmburroughs

doc/source/whatsnew/v0.19.1.txt

@@ -48,3 +48,4 @@ Bug Fixes
 - Bug in ``MultiIndex.set_levels`` where illegal level values were still set after raising an error (:issue:`13754`)
 - Bug in ``DataFrame.to_json`` where ``lines=True`` and a value contained a ``}`` character (:issue:`14391`)
 - Bug in ``df.groupby`` causing an ``AttributeError`` when grouping a single index frame by a column and the index level (:issue`14327`)
+- Bug in ``Series.sort_index`` where parameters ``kind`` and ``na_position`` did not exist (:issue:`13589` & :issue:`14444`)

pandas/core/frame.py

@@ -3323,16 +3323,6 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
         else:
             from pandas.core.groupby import _nargsort
 
-            # GH11080 - Check monotonic-ness before sort an index
-            # if monotonic (already sorted), return None or copy() according
-            # to 'inplace'
-            if ((ascending and labels.is_monotonic_increasing) or
-                    (not ascending and labels.is_monotonic_decreasing)):
-                if inplace:
-                    return
-                else:
-                    return self.copy()
-
             indexer = _nargsort(labels, kind=kind, ascending=ascending,
                                 na_position=na_position)
 

pandas/core/groupby.py

@@ -4280,6 +4280,11 @@ def _nargsort(items, kind='quicksort', ascending=True, na_position='last'):
     if is_categorical_dtype(items):
         return items.argsort(ascending=ascending)
 
+    # # GH11080 - Check monotonic-ness before sort an index
+    if ((ascending and all(x <= y for x, y in zip(items, items[1:]))) or
+            (not ascending and all(x >= y for x, y in zip(items, items[1:])))):
+        return np.arange(len(items))
+
     items = np.asanyarray(items)
     idx = np.arange(len(items))
     mask = isnull(items)

pandas/core/series.py

@@ -1773,7 +1773,7 @@ def _try_kind_sort(arr):
 
     @Appender(generic._shared_docs['sort_index'] % _shared_doc_kwargs)
     def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
-                   sort_remaining=True):
+                   kind='quicksort', na_position='last', sort_remaining=True):
 
         axis = self._get_axis_number(axis)
         index = self.index
@@ -1786,8 +1786,11 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
             indexer = _ensure_platform_int(indexer)
             new_index = index.take(indexer)
         else:
-            new_index, indexer = index.sort_values(return_indexer=True,
-                                                   ascending=ascending)
+            from pandas.core.groupby import _nargsort
+
+            indexer = _nargsort(index, kind=kind, ascending=ascending,
+                                na_position=na_position)
+            new_index = index.take(indexer)
 
         new_values = self._values.take(indexer)
         result = self._constructor(new_values, index=new_index)

pandas/tests/series/test_sorting.py

@@ -144,3 +144,28 @@ def test_sort_index_multiindex(self):
         # rows share same level='A': sort has no effect without remaining lvls
         res = s.sort_index(level='A', sort_remaining=False)
         assert_series_equal(s, res)
+
+    # GH #14444 & #13589:  Add support for sort algo choosing
+    def test_sort_index_kind(self):
+        series = Series(index=[3, 2, 1, 4, 3])
+        expected_series = Series(index=[1, 2, 3, 3, 4])
+
+        index_sorted_series = series.sort_index(kind='mergesort')
+        assert_series_equal(expected_series, index_sorted_series)
+
+        index_sorted_series = series.sort_index(kind='quicksort')
+        assert_series_equal(expected_series, index_sorted_series)
+
+        index_sorted_series = series.sort_index(kind='heapsort')
+        assert_series_equal(expected_series, index_sorted_series)
+
+    def test_sort_index_na_position(self):
+        series = Series(index=[3, 2, 1, 4, 3, np.nan])
+
+        expected_series_first = Series(index=[np.nan, 1, 2, 3, 3, 4])
+        index_sorted_series = series.sort_index(na_position='first')
+        assert_series_equal(expected_series_first, index_sorted_series)
+
+        expected_series_last = Series(index=[1, 2, 3, 3, 4, np.nan])
+        index_sorted_series = series.sort_index(na_position='last')
+        assert_series_equal(expected_series_last, index_sorted_series)