CLN: enforce the deprecation of the Series.argsort NA behavior

doc/source/whatsnew/v3.0.0.rst

@@ -244,6 +244,7 @@ Removal of prior version deprecations/changes
 - Enforced deprecation of strings ``T``, ``L``, ``U``, and ``N`` denoting frequencies in :class:`Minute`, :class:`Second`, :class:`Milli`, :class:`Micro`, :class:`Nano` (:issue:`57627`)
 - Enforced deprecation of strings ``T``, ``L``, ``U``, and ``N`` denoting units in :class:`Timedelta` (:issue:`57627`)
 - Enforced deprecation of the behavior of :func:`concat` when ``len(keys) != len(objs)`` would truncate to the shorter of the two. Now this raises a ``ValueError`` (:issue:`43485`)
+- Enforced deprecation of the behavior of :meth:`Series.argsort` in the presence of NA values (:issue:`58232`)
 - Enforced deprecation of values "pad", "ffill", "bfill", and "backfill" for :meth:`Series.interpolate` and :meth:`DataFrame.interpolate` (:issue:`57869`)
 - Enforced deprecation removing :meth:`Categorical.to_list`, use ``obj.tolist()`` instead (:issue:`51254`)
 - Enforced silent-downcasting deprecation for :ref:`all relevant methods <whatsnew_220.silent_downcasting>` (:issue:`54710`)

pandas/core/series.py

@@ -3779,25 +3779,7 @@ def argsort(
             # GH#54257 We allow -1 here so that np.argsort(series) works
             self._get_axis_number(axis)
 
-        values = self._values
-        mask = isna(values)
-
-        if mask.any():
-            # TODO(3.0): once this deprecation is enforced we can call
-            #  self.array.argsort directly, which will close GH#43840 and
-            #  GH#12694
-            warnings.warn(
-                "The behavior of Series.argsort in the presence of NA values is "
-                "deprecated. In a future version, NA values will be ordered "
-                "last instead of set to -1.",
-                FutureWarning,
-                stacklevel=find_stack_level(),
-            )
-            result = np.full(len(self), -1, dtype=np.intp)
-            notmask = ~mask
-            result[notmask] = np.argsort(values[notmask], kind=kind)
-        else:
-            result = np.argsort(values, kind=kind)
+        result = self.array.argsort(kind=kind)
 
         res = self._constructor(
             result, index=self.index, name=self.name, dtype=np.intp, copy=False

pandas/tests/extension/base/methods.py

@@ -116,10 +116,8 @@ def test_argsort_missing_array(self, data_missing_for_sorting):
         tm.assert_numpy_array_equal(result, expected)
 
     def test_argsort_missing(self, data_missing_for_sorting):
-        msg = "The behavior of Series.argsort in the presence of NA values"
-        with tm.assert_produces_warning(FutureWarning, match=msg):
-            result = pd.Series(data_missing_for_sorting).argsort()
-        expected = pd.Series(np.array([1, -1, 0], dtype=np.intp))
+        result = pd.Series(data_missing_for_sorting).argsort()
+        expected = pd.Series(np.array([2, 0, 1], dtype=np.intp))
         tm.assert_series_equal(result, expected)
 
     def test_argmin_argmax(self, data_for_sorting, data_missing_for_sorting, na_value):

pandas/tests/series/methods/test_argsort.py

@@ -20,24 +20,10 @@ def test_argsort_axis(self):
 
     def test_argsort_numpy(self, datetime_series):
         ser = datetime_series
-
         res = np.argsort(ser).values
         expected = np.argsort(np.array(ser))
         tm.assert_numpy_array_equal(res, expected)
 
-        # with missing values
-        ts = ser.copy()
-        ts[::2] = np.nan
-
-        msg = "The behavior of Series.argsort in the presence of NA values"
-        with tm.assert_produces_warning(
-            FutureWarning, match=msg, check_stacklevel=False
-        ):
-            result = np.argsort(ts)[1::2]
-        expected = np.argsort(np.array(ts.dropna()))
-
-        tm.assert_numpy_array_equal(result.values, expected)
-
     def test_argsort(self, datetime_series):
         argsorted = datetime_series.argsort()
         assert issubclass(argsorted.dtype.type, np.integer)
@@ -56,10 +42,8 @@ def test_argsort_dt64(self, unit):
         expected = Series(range(5), dtype=np.intp)
         tm.assert_series_equal(result, expected)
 
-        msg = "The behavior of Series.argsort in the presence of NA values"
-        with tm.assert_produces_warning(FutureWarning, match=msg):
-            result = shifted.argsort()
-        expected = Series(list(range(4)) + [-1], dtype=np.intp)
+        result = shifted.argsort()
+        expected = Series(list(range(4)) + [4], dtype=np.intp)
         tm.assert_series_equal(result, expected)
 
     def test_argsort_stable(self):