CLN: convert argument in .take method (#27171)

* CLN: Convert argument in take

* Fix one test and whatsnew note

Author: mroeschke

doc/source/whatsnew/v0.25.0.rst

@@ -646,6 +646,7 @@ Removal of prior version deprecations/changes
 - Removed the previously deprecated ``ordered`` and ``categories`` keyword arguments in ``astype`` (:issue:`17742`)
 - Removed the previously deprecated ``cdate_range`` (:issue:`17691`)
 - Removed the previously deprecated ``True`` option for the ``dropna`` keyword argument in :func:`SeriesGroupBy.nth` (:issue:`17493`)
+- Removed the previously deprecated ``convert`` keyword argument in :meth:`Series.take` and :meth:`DataFrame.take`(:issue:`17352`)
 
 .. _whatsnew_0250.performance:
 

pandas/core/generic.py

@@ -3365,7 +3365,7 @@ def _take(self, indices, axis=0, is_copy=True):
 
         return result
 
-    def take(self, indices, axis=0, convert=None, is_copy=True, **kwargs):
+    def take(self, indices, axis=0, is_copy=True, **kwargs):
         """
         Return the elements in the given *positional* indices along an axis.
 
@@ -3380,15 +3380,6 @@ def take(self, indices, axis=0, convert=None, is_copy=True, **kwargs):
         axis : {0 or 'index', 1 or 'columns', None}, default 0
             The axis on which to select elements. ``0`` means that we are
             selecting rows, ``1`` means that we are selecting columns.
-        convert : bool, default True
-            Whether to convert negative indices into positive ones.
-            For example, ``-1`` would map to the ``len(axis) - 1``.
-            The conversions are similar to the behavior of indexing a
-            regular Python list.
-
-            .. deprecated:: 0.21.0
-               In the future, negative indices will always be converted.
-
         is_copy : bool, default True
             Whether to return a copy of the original object or not.
         **kwargs
@@ -3449,11 +3440,6 @@ class  max_speed
         1  monkey  mammal        NaN
         3    lion  mammal       80.5
         """
-        if convert is not None:
-            msg = ("The 'convert' parameter is deprecated "
-                   "and will be removed in a future version.")
-            warnings.warn(msg, FutureWarning, stacklevel=2)
-
         nv.validate_take(tuple(), kwargs)
         return self._take(indices, axis=axis, is_copy=is_copy)
 

pandas/tests/frame/test_axis_select_reindex.py

@@ -919,13 +919,8 @@ def test_take(self, float_frame):
             expected = df.reindex(df.index.take(order))
             assert_frame_equal(result, expected)
 
-            with tm.assert_produces_warning(FutureWarning):
-                result = df.take(order, convert=True, axis=0)
-                assert_frame_equal(result, expected)
-
-            with tm.assert_produces_warning(FutureWarning):
-                result = df.take(order, convert=False, axis=0)
-                assert_frame_equal(result, expected)
+            result = df.take(order, axis=0)
+            assert_frame_equal(result, expected)
 
             # axis = 1
             result = df.take(order, axis=1)

pandas/tests/series/indexing/test_indexing.py

@@ -801,9 +801,6 @@ def test_take():
     with pytest.raises(IndexError, match=msg.format(5)):
         s.take([2, 5])
 
-    with tm.assert_produces_warning(FutureWarning):
-        s.take([-1, 3, 4], convert=False)
-
 
 def test_take_categorical():
     # https://github.com/pandas-dev/pandas/issues/20664

pandas/tests/sparse/series/test_series.py

@@ -542,15 +542,6 @@ def _compare(idx):
         exp = pd.Series(np.repeat(nan, 5))
         tm.assert_series_equal(sp.take([0, 1, 2, 3, 4]), exp.to_sparse())
 
-        # multiple FutureWarnings, can't check stacklevel
-        with tm.assert_produces_warning(FutureWarning,
-                                        check_stacklevel=False):
-            sp.take([1, 5], convert=True)
-
-        with tm.assert_produces_warning(FutureWarning,
-                                        check_stacklevel=False):
-            sp.take([1, 5], convert=False)
-
     def test_numpy_take(self):
         sp = SparseSeries([1.0, 2.0, 3.0])
         indices = [1, 2]