BUG: allow missing values in Index when calling Index.sort_values

doc/source/whatsnew/v1.2.0.rst

@@ -267,8 +267,9 @@ Interval
 
 Indexing
 ^^^^^^^^
+
 - Bug in :meth:`PeriodIndex.get_loc` incorrectly raising ``ValueError`` on non-datelike strings instead of ``KeyError``, causing similar errors in :meth:`Series.__geitem__`, :meth:`Series.__contains__`, and :meth:`Series.loc.__getitem__` (:issue:`34240`)
--
+- Bug in :meth:`Index.sort_values` where, when empty values were passed, the method would break by trying to compare missing values instead of pushing them to the end of the sort order. (:issue:`35584`)
 -
 
 Missing

pandas/conftest.py

@@ -437,6 +437,29 @@ def index(request):
 index_fixture2 = index
 
 
+@pytest.fixture(params=indices_dict.keys())
+def index_with_missing(request):
+    """
+    Fixture for indices with missing values
+    """
+    if request.param in ["int", "uint", "range", "empty", "repeats"]:
+        pytest.xfail("missing values not supported")
+    # GH 35538. Use deep copy to avoid illusive bug on np-dev
+    # Azure pipeline that writes into indices_dict despite copy
+    ind = indices_dict[request.param].copy(deep=True)
+    vals = ind.values
+    if request.param in ["tuples", "mi-with-dt64tz-level", "multi"]:
+        # For setting missing values in the top level of MultiIndex
+        vals = ind.tolist()
+        vals[0] = tuple([None]) + vals[0][1:]
+        vals[-1] = tuple([None]) + vals[-1][1:]
+        return MultiIndex.from_tuples(vals)
+    else:
+        vals[0] = None
+        vals[-1] = None
+        return type(ind)(vals)
+
+
 # ----------------------------------------------------------------
 # Series'
 # ----------------------------------------------------------------

pandas/core/indexes/base.py

@@ -88,7 +88,7 @@
 import pandas.core.missing as missing
 from pandas.core.ops import get_op_result_name
 from pandas.core.ops.invalid import make_invalid_op
-from pandas.core.sorting import ensure_key_mapped
+from pandas.core.sorting import ensure_key_mapped, nargsort
 from pandas.core.strings import StringMethods
 
 from pandas.io.formats.printing import (
@@ -4443,7 +4443,11 @@ def asof_locs(self, where, mask):
         return result
 
     def sort_values(
-        self, return_indexer=False, ascending=True, key: Optional[Callable] = None
+        self,
+        return_indexer=False,
+        ascending=True,
+        na_position: str_t = "last",
+        key: Optional[Callable] = None,
     ):
         """
         Return a sorted copy of the index.
@@ -4457,6 +4461,12 @@ def sort_values(
             Should the indices that would sort the index be returned.
         ascending : bool, default True
             Should the index values be sorted in an ascending order.
+        na_position : {'first' or 'last'}, default 'last'
+            Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at
+            the end.
+
+            .. versionadded:: 1.2.0
+
         key : callable, optional
             If not None, apply the key function to the index values
             before sorting. This is similar to the `key` argument in the
@@ -4497,9 +4507,16 @@ def sort_values(
         """
         idx = ensure_key_mapped(self, key)
 
-        _as = idx.argsort()
-        if not ascending:
-            _as = _as[::-1]
+        # GH 35584. Sort missing values according to na_position kwarg
+        # ignore na_position for MutiIndex
+        if not isinstance(self, ABCMultiIndex):
+            _as = nargsort(
+                items=idx, ascending=ascending, na_position=na_position, key=key
+            )
+        else:
+            _as = idx.argsort()
+            if not ascending:
+                _as = _as[::-1]
 
         sorted_index = self.take(_as)
 

pandas/tests/indexes/interval/test_interval.py

@@ -618,7 +618,7 @@ def test_sort_values(self, closed):
         expected = IntervalIndex([Interval(0, 1), Interval(1, 2), np.nan])
         tm.assert_index_equal(result, expected)
 
-        result = index.sort_values(ascending=False)
+        result = index.sort_values(ascending=False, na_position="first")
         expected = IntervalIndex([np.nan, Interval(1, 2), Interval(0, 1)])
         tm.assert_index_equal(result, expected)
 

pandas/tests/indexes/period/test_ops.py

@@ -174,9 +174,6 @@ def _check_freq(index, expected_index):
 
             ordered, indexer = idx.sort_values(return_indexer=True, ascending=False)
             tm.assert_index_equal(ordered, expected[::-1])
-
-            exp = np.array([2, 1, 3, 4, 0])
-            tm.assert_numpy_array_equal(indexer, exp, check_dtype=False)
             _check_freq(ordered, idx)
 
         pidx = PeriodIndex(["2011", "2013", "NaT", "2011"], name="pidx", freq="D")
@@ -333,3 +330,16 @@ def test_freq_setter_deprecated(self):
         # warning for setter
         with pytest.raises(AttributeError, match="can't set attribute"):
             idx.freq = pd.offsets.Day()
+
+
+@pytest.mark.xfail(reason="Datetime-like sort_values currently unstable (GH 35922)")
+def test_order_stability_compat():
+    # GH 35584. The new implementation of sort_values for Index.sort_values
+    # is stable when sorting in descending order. Datetime-like sort_values
+    # currently aren't stable. xfail should be removed after
+    # the implementations' behavior is synchronized (xref GH 35922)
+    pidx = PeriodIndex(["2011", "2013", "2015", "2012", "2011"], name="pidx", freq="A")
+    iidx = Index([2011, 2013, 2015, 2012, 2011], name="idx")
+    ordered1, indexer1 = pidx.sort_values(return_indexer=True, ascending=False)
+    ordered2, indexer2 = iidx.sort_values(return_indexer=True, ascending=False)
+    tm.assert_numpy_array_equal(indexer1, indexer2)

pandas/tests/indexes/test_common.py

@@ -13,7 +13,14 @@
 from pandas.core.dtypes.common import is_period_dtype, needs_i8_conversion
 
 import pandas as pd
-from pandas import CategoricalIndex, MultiIndex, RangeIndex
+from pandas import (
+    CategoricalIndex,
+    DatetimeIndex,
+    MultiIndex,
+    PeriodIndex,
+    RangeIndex,
+    TimedeltaIndex,
+)
 import pandas._testing as tm
 
 
@@ -391,3 +398,45 @@ def test_astype_preserves_name(self, index, dtype):
             assert result.names == index.names
         else:
             assert result.name == index.name
+
+
+@pytest.mark.parametrize("na_position", [None, "middle"])
+def test_sort_values_invalid_na_position(index_with_missing, na_position):
+    if type(index_with_missing) in [DatetimeIndex, PeriodIndex, TimedeltaIndex]:
+        # datetime-like indices will get na_position kwarg as part of
+        # synchronizing duplicate-sorting behavior, because we currently expect
+        # them, other indices, and Series to sort differently (xref 35922)
+        pytest.xfail("sort_values does not support na_position kwarg")
+    elif type(index_with_missing) in [CategoricalIndex, MultiIndex]:
+        pytest.xfail("missing value sorting order not defined for index type")
+
+    if na_position not in ["first", "last"]:
+        with pytest.raises(
+            ValueError, match=f"invalid na_position: {na_position}",
+        ):
+            index_with_missing.sort_values(na_position=na_position)
+
+
+@pytest.mark.parametrize("na_position", ["first", "last"])
+def test_sort_values_with_missing(index_with_missing, na_position):
+    # GH 35584. Test that sort_values works with missing values,
+    # sort non-missing and place missing according to na_position
+
+    if type(index_with_missing) in [DatetimeIndex, PeriodIndex, TimedeltaIndex]:
+        # datetime-like indices will get na_position kwarg as part of
+        # synchronizing duplicate-sorting behavior, because we currently expect
+        # them, other indices, and Series to sort differently (xref 35922)
+        pytest.xfail("sort_values does not support na_position kwarg")
+    elif type(index_with_missing) in [CategoricalIndex, MultiIndex]:
+        pytest.xfail("missing value sorting order not defined for index type")
+    missing_count = np.sum(index_with_missing.isna())
+    not_na_vals = index_with_missing[index_with_missing.notna()].values
+    sorted_values = np.sort(not_na_vals)
+    if na_position == "first":
+        sorted_values = np.concatenate([[None] * missing_count, sorted_values])
+    else:
+        sorted_values = np.concatenate([sorted_values, [None] * missing_count])
+    expected = type(index_with_missing)(sorted_values)
+
+    result = index_with_missing.sort_values(na_position=na_position)
+    tm.assert_index_equal(result, expected)