BUG: stabilize sort_values algorithms for Series and time-like Indices

pandas/core/algorithms.py

@@ -822,9 +822,19 @@ def value_counts_arraylike(values, dropna: bool):
 
         mask = isna(values)
         if not dropna and mask.any():
+            # GH 35922. Series.sort_values is stable now, so need to
+            # append NaN counts or move to the end to make sure they are
+            # sorted toward the end when calling value_counts
             if not isna(keys).any():
-                keys = np.insert(keys, 0, np.NaN)
-                counts = np.insert(counts, 0, mask.sum())
+                keys = np.append(keys, np.NaN)
+                counts = np.append(counts, mask.sum())
+            else:
+                nan_pos = np.where(np.isnan(keys))
+                keys[nan_pos] = keys[-1]
+                keys[-1] = np.NaN
+                tmp = counts[nan_pos]
+                counts[nan_pos] = counts[-1]
+                counts[-1] = tmp
 
     keys = _reconstruct_data(keys, original.dtype, original)
 

pandas/core/indexes/base.py

@@ -4510,9 +4510,7 @@ def sort_values(
 
         # GH 35584. Sort missing values according to na_position kwarg
         # ignore na_position for MultiIndex
-        if not isinstance(
-            self, (ABCMultiIndex, ABCDatetimeIndex, ABCTimedeltaIndex, ABCPeriodIndex)
-        ):
+        if not isinstance(self, ABCMultiIndex):
             _as = nargsort(
                 items=idx, ascending=ascending, na_position=na_position, key=key
             )

pandas/core/series.py

@@ -92,7 +92,7 @@
 from pandas.core.indexing import check_bool_indexer
 from pandas.core.internals import SingleBlockManager
 from pandas.core.shared_docs import _shared_docs
-from pandas.core.sorting import ensure_key_mapped
+from pandas.core.sorting import nargsort
 from pandas.core.strings import StringMethods
 from pandas.core.tools.datetimes import to_datetime
 
@@ -3274,52 +3274,10 @@ def sort_values(
                 "sort in-place you must create a copy"
             )
 
-        def _try_kind_sort(arr):
-            arr = ensure_key_mapped(arr, key)
-            arr = getattr(arr, "_values", arr)
-
-            # easier to ask forgiveness than permission
-            try:
-                # if kind==mergesort, it can fail for object dtype
-                return arr.argsort(kind=kind)
-            except TypeError:
-                # stable sort not available for object dtype
-                # uses the argsort default quicksort
-                return arr.argsort(kind="quicksort")
-
         arr = self._values
-        sorted_index = np.empty(len(self), dtype=np.int32)
-
-        bad = isna(arr)
 
-        good = ~bad
-        idx = ibase.default_index(len(self))
-
-        argsorted = _try_kind_sort(self[good])
-
-        if is_list_like(ascending):
-            if len(ascending) != 1:
-                raise ValueError(
-                    f"Length of ascending ({len(ascending)}) must be 1 for Series"
-                )
-            ascending = ascending[0]
-
-        if not is_bool(ascending):
-            raise ValueError("ascending must be boolean")
-
-        if not ascending:
-            argsorted = argsorted[::-1]
-
-        if na_position == "last":
-            n = good.sum()
-            sorted_index[:n] = idx[good][argsorted]
-            sorted_index[n:] = idx[bad]
-        elif na_position == "first":
-            n = bad.sum()
-            sorted_index[n:] = idx[good][argsorted]
-            sorted_index[:n] = idx[bad]
-        else:
-            raise ValueError(f"invalid na_position: {na_position}")
+        # GH 35922. Make sorting stable by leveraging nargsort
+        sorted_index = nargsort(arr, kind, ascending, na_position, key)
 
         result = self._constructor(arr[sorted_index], index=self.index[sorted_index])
 

pandas/core/sorting.py

@@ -374,7 +374,16 @@ def nargsort(
     if not ascending:
         non_nans = non_nans[::-1]
         non_nan_idx = non_nan_idx[::-1]
-    indexer = non_nan_idx[non_nans.argsort(kind=kind)]
+
+    # GH 35922. Move support for object sort here from Series.sort_values
+    try:
+        # if kind==mergesort, it can fail for object dtype
+        indexer = non_nan_idx[non_nans.argsort(kind=kind)]
+    except TypeError:
+        # stable sort not available for object dtype
+        # uses the argsort default quicksort
+        indexer = non_nan_idx[non_nans.argsort(kind="quicksort")]
+
     if not ascending:
         indexer = indexer[::-1]
     # Finally, place the NaNs at the end or the beginning according to