DEPR: deprecate sortlevel in favor of sort_index

xref pandas-dev#14220

Author: jreback

doc/source/api.rst

@@ -418,7 +418,6 @@ Reshaping, sorting
    Series.reorder_levels
    Series.sort_values
    Series.sort_index
-   Series.sortlevel
    Series.swaplevel
    Series.unstack
    Series.searchsorted
@@ -931,7 +930,6 @@ Reshaping, sorting, transposing
    DataFrame.reorder_levels
    DataFrame.sort_values
    DataFrame.sort_index
-   DataFrame.sortlevel
    DataFrame.nlargest
    DataFrame.nsmallest
    DataFrame.swaplevel

doc/source/whatsnew/v0.20.0.txt

@@ -261,7 +261,7 @@ Deprecations
 - ``Categorical.searchsorted()`` and ``Series.searchsorted()`` have deprecated the ``v`` parameter in favor of ``value`` (:issue:`12662`)
 - ``TimedeltaIndex.searchsorted()``, ``DatetimeIndex.searchsorted()``, and ``PeriodIndex.searchsorted()`` have deprecated the ``key`` parameter in favor of ``value`` (:issue:`12662`)
 - ``DataFrame.astype()`` has deprecated the ``raise_on_error`` parameter in favor of ``errors`` (:issue:`14878`)
-
+- ``Series.sortlevel`` and ``DataFrame.sortlevel`` have been deprecated in favor of ``Series.sort_index`` and ``DataFrame.sort_index`` (:issue:``)
 
 
 .. _whatsnew_0200.prior_deprecations:

pandas/core/frame.py

@@ -1274,7 +1274,7 @@ def to_panel(self):
 
         # minor axis must be sorted
         if self.index.lexsort_depth < 2:
-            selfsorted = self.sortlevel(0)
+            selfsorted = self.sort_index(level=0)
         else:
             selfsorted = self
 
@@ -3337,6 +3337,8 @@ def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
     def sortlevel(self, level=0, axis=0, ascending=True, inplace=False,
                   sort_remaining=True):
         """
+        DEPRECATED: use :meth:`DataFrame.sort_index`
+
         Sort multilevel index by chosen axis and primary level. Data will be
         lexicographically sorted by the chosen level followed by the other
         levels (in order)
@@ -3360,6 +3362,8 @@ def sortlevel(self, level=0, axis=0, ascending=True, inplace=False,
         DataFrame.sort_index(level=...)
 
         """
+        warnings.warn("sortlevel is deprecated, use sort_index(...)",
+                      FutureWarning, stacklevel=2)
         return self.sort_index(level=level, axis=axis, ascending=ascending,
                                inplace=inplace, sort_remaining=sort_remaining)
 

pandas/core/reshape.py

@@ -375,7 +375,7 @@ def pivot_simple(index, columns, values):
 
     hindex = MultiIndex.from_arrays([index, columns])
     series = Series(values.ravel(), index=hindex)
-    series = series.sortlevel(0)
+    series = series.sort_index(level=0)
     return series.unstack()
 
 
@@ -596,7 +596,7 @@ def _convert_level_number(level_num, columns):
         # which interferes with trying to sort based on the first
         # level
         level_to_sort = _convert_level_number(0, this.columns)
-        this = this.sortlevel(level_to_sort, axis=1)
+        this = this.sort_index(level=level_to_sort, axis=1)
 
     # tuple list excluding level for grouping columns
     if len(frame.columns.levels) > 2:

pandas/core/series.py

@@ -1988,6 +1988,8 @@ def nsmallest(self, n=5, keep='first'):
 
     def sortlevel(self, level=0, ascending=True, sort_remaining=True):
         """
+        DEPRECATED: use :meth:`Series.sort_index`
+
         Sort Series with MultiIndex by chosen level. Data will be
         lexicographically sorted by the chosen level followed by the other
         levels (in order)
@@ -2006,6 +2008,8 @@ def sortlevel(self, level=0, ascending=True, sort_remaining=True):
         Series.sort_index(level=...)
 
         """
+        warnings.warn("sortlevel is deprecated, use sort_index(...)",
+                      FutureWarning, stacklevel=2)
         return self.sort_index(level=level, ascending=ascending,
                                sort_remaining=sort_remaining)
 

pandas/sparse/frame.py

@@ -816,7 +816,7 @@ def stack_sparse_frame(frame):
 
     lp = DataFrame(stacked_values.reshape((nobs, 1)), index=index,
                    columns=['foo'])
-    return lp.sortlevel(level=0)
+    return lp.sort_index(level=0)
 
 
 def homogenize(series_dict):

pandas/sparse/tests/test_series.py

@@ -947,7 +947,7 @@ def setUp(self):
         micol = pd.MultiIndex.from_product(
             [['a', 'b', 'c'], ["1", "2"]], names=['col-foo', 'col-bar'])
         dense_multiindex_frame = pd.DataFrame(
-            index=miindex, columns=micol).sortlevel().sortlevel(axis=1)
+            index=miindex, columns=micol).sort_index().sort_index(axis=1)
         self.dense_multiindex_frame = dense_multiindex_frame.fillna(value=3.14)
 
     def test_to_sparse_preserve_multiindex_names_columns(self):

pandas/stats/plm.py

@@ -792,8 +792,8 @@ def _var_beta_panel(y, x, beta, xx, rmse, cluster_axis,
         resid = DataFrame(yv[:, None] - Xb, index=y.index, columns=['resid'])
 
         if cluster_axis == 1:
-            x = x.swaplevel(0, 1).sortlevel(0)
-            resid = resid.swaplevel(0, 1).sortlevel(0)
+            x = x.swaplevel(0, 1).sort_index(level=0)
+            resid = resid.swaplevel(0, 1).sort_index(level=0)
 
         m = _group_agg(x.values * resid.values, x.index._bounds,
                        lambda x: np.sum(x, axis=0))

pandas/tests/frame/test_misc_api.py

@@ -456,10 +456,6 @@ def _check_f(base, f):
         f = lambda x: x.sort_index(inplace=True)
         _check_f(data.copy(), f)
 
-        # sortlevel
-        f = lambda x: x.sortlevel(0, inplace=True)
-        _check_f(data.set_index(['a', 'b']), f)
-
         # fillna
         f = lambda x: x.fillna(0, inplace=True)
         _check_f(data.copy(), f)

pandas/tests/frame/test_operators.py

@@ -467,7 +467,7 @@ def test_binary_ops_align(self):
 
         df = DataFrame(np.arange(27 * 3).reshape(27, 3),
                        index=index,
-                       columns=['value1', 'value2', 'value3']).sortlevel()
+                       columns=['value1', 'value2', 'value3']).sort_index()
 
         idx = pd.IndexSlice
         for op in ['add', 'sub', 'mul', 'div', 'truediv']:
@@ -479,15 +479,15 @@ def test_binary_ops_align(self):
             result = getattr(df, op)(x, level='third', axis=0)
 
             expected = pd.concat([opa(df.loc[idx[:, :, i], :], v)
-                                  for i, v in x.iteritems()]).sortlevel()
+                                  for i, v in x.iteritems()]).sort_index()
             assert_frame_equal(result, expected)
 
             x = Series([1.0, 10.0], ['two', 'three'])
             result = getattr(df, op)(x, level='second', axis=0)
 
             expected = (pd.concat([opa(df.loc[idx[:, i], :], v)
                                    for i, v in x.iteritems()])
-                        .reindex_like(df).sortlevel())
+                        .reindex_like(df).sort_index())
             assert_frame_equal(result, expected)
 
         # GH9463 (alignment level of dataframe with series)

pandas/tests/frame/test_sorting.py

@@ -53,16 +53,6 @@ def test_sort_index_multiindex(self):
         mi = MultiIndex.from_tuples([[2, 1, 3], [1, 1, 1]], names=list('ABC'))
         df = DataFrame([[1, 2], [3, 4]], mi)
 
-        result = df.sort_index(level='A', sort_remaining=False)
-        expected = df.sortlevel('A', sort_remaining=False)
-        assert_frame_equal(result, expected)
-
-        # sort columns by specified level of multi-index
-        df = df.T
-        result = df.sort_index(level='A', axis=1, sort_remaining=False)
-        expected = df.sortlevel('A', axis=1, sort_remaining=False)
-        assert_frame_equal(result, expected)
-
         # MI sort, but no level: sort_level has no effect
         mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC'))
         df = DataFrame([[1, 2], [3, 4]], mi)
@@ -453,13 +443,13 @@ def test_sort_index_duplicates(self):
         result = df.sort_values(by=('a', 1))
         assert_frame_equal(result, expected)
 
-    def test_sortlevel(self):
+    def test_sort_index_level(self):
         mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC'))
         df = DataFrame([[1, 2], [3, 4]], mi)
-        res = df.sortlevel('A', sort_remaining=False)
+        res = df.sort_index(level='A', sort_remaining=False)
         assert_frame_equal(df, res)
 
-        res = df.sortlevel(['A', 'B'], sort_remaining=False)
+        res = df.sort_index(level=['A', 'B'], sort_remaining=False)
         assert_frame_equal(df, res)
 
     def test_sort_datetimes(self):

pandas/tests/groupby/test_groupby.py

@@ -3427,7 +3427,7 @@ def test_int64_overflow(self):
         left = lg.sum()['values']
         right = rg.sum()['values']
 
-        exp_index, _ = left.index.sortlevel(0)
+        exp_index, _ = left.index.sortlevel()
         self.assert_index_equal(left.index, exp_index)
 
         exp_index, _ = right.index.sortlevel(0)
@@ -3708,7 +3708,7 @@ def test_more_flexible_frame_multi_function(self):
         exstd = grouped.agg(OrderedDict([['C', np.std], ['D', np.std]]))
 
         expected = concat([exmean, exstd], keys=['mean', 'std'], axis=1)
-        expected = expected.swaplevel(0, 1, axis=1).sortlevel(0, axis=1)
+        expected = expected.swaplevel(0, 1, axis=1).sort_index(level=0, axis=1)
 
         d = OrderedDict([['C', [np.mean, np.std]], ['D', [np.mean, np.std]]])
         result = grouped.aggregate(d)
@@ -4711,7 +4711,7 @@ def test_timegrouper_with_reg_groups(self):
             expected = df.groupby('user_id')[
                 'whole_cost'].resample(
                     freq).sum().dropna().reorder_levels(
-                        ['date', 'user_id']).sortlevel().astype('int64')
+                        ['date', 'user_id']).sort_index().astype('int64')
             expected.name = 'whole_cost'
 
             result1 = df.sort_index().groupby([pd.TimeGrouper(freq=freq),

pandas/tests/indexes/test_multi.py

@@ -440,7 +440,7 @@ def test_set_value_keeps_names(self):
             np.random.randn(6, 4),
             columns=['one', 'two', 'three', 'four'],
             index=idx)
-        df = df.sortlevel()
+        df = df.sort_index()
         self.assertIsNone(df.is_copy)
         self.assertEqual(df.index.names, ('Name', 'Number'))
         df = df.set_value(('grethe', '4'), 'one', 99.34)

pandas/tests/indexing/test_indexing.py

@@ -2104,7 +2104,7 @@ def test_ix_general(self):
             tm.assert_frame_equal(df.ix[key], df.iloc[2:])
 
         # this is ok
-        df.sortlevel(inplace=True)
+        df.sort_index(inplace=True)
         res = df.ix[key]
         # col has float dtype, result should be Float64Index
         index = MultiIndex.from_arrays([[4.] * 3, [2012] * 3],
@@ -2137,7 +2137,7 @@ def test_xs_multiindex(self):
             [('a', 'foo'), ('a', 'bar'), ('b', 'hello'),
              ('b', 'world')], names=['lvl0', 'lvl1'])
         df = DataFrame(np.random.randn(4, 4), columns=columns)
-        df.sortlevel(axis=1, inplace=True)
+        df.sort_index(axis=1, inplace=True)
         result = df.xs('a', level='lvl0', axis=1)
         expected = df.iloc[:, 0:2].loc[:, 'a']
         tm.assert_frame_equal(result, expected)
@@ -2180,7 +2180,7 @@ def test_per_axis_per_level_getitem(self):
         df = DataFrame(
             np.arange(16, dtype='int64').reshape(
                 4, 4), index=index, columns=columns)
-        df = df.sortlevel(axis=0).sortlevel(axis=1)
+        df = df.sort_index(axis=0).sort_index(axis=1)
 
         # identity
         result = df.loc[(slice(None), slice(None)), :]
@@ -2249,7 +2249,7 @@ def f():
 
         # not lexsorted
         self.assertEqual(df.index.lexsort_depth, 2)
-        df = df.sortlevel(level=1, axis=0)
+        df = df.sort_index(level=1, axis=0)
         self.assertEqual(df.index.lexsort_depth, 0)
         with tm.assertRaisesRegexp(
                 UnsortedIndexError,
@@ -2265,11 +2265,11 @@ def test_multiindex_slicers_non_unique(self):
                              B=['a', 'a', 'a', 'a'],
                              C=[1, 2, 1, 3],
                              D=[1, 2, 3, 4]))
-              .set_index(['A', 'B', 'C']).sortlevel())
+              .set_index(['A', 'B', 'C']).sort_index())
         self.assertFalse(df.index.is_unique)
         expected = (DataFrame(dict(A=['foo', 'foo'], B=['a', 'a'],
                                    C=[1, 1], D=[1, 3]))
-                    .set_index(['A', 'B', 'C']).sortlevel())
+                    .set_index(['A', 'B', 'C']).sort_index())
         result = df.loc[(slice(None), slice(None), 1), :]
         tm.assert_frame_equal(result, expected)
 
@@ -2281,11 +2281,11 @@ def test_multiindex_slicers_non_unique(self):
                              B=['a', 'a', 'a', 'a'],
                              C=[1, 2, 1, 2],
                              D=[1, 2, 3, 4]))
-              .set_index(['A', 'B', 'C']).sortlevel())
+              .set_index(['A', 'B', 'C']).sort_index())
         self.assertFalse(df.index.is_unique)
         expected = (DataFrame(dict(A=['foo', 'foo'], B=['a', 'a'],
                                    C=[1, 1], D=[1, 3]))
-                    .set_index(['A', 'B', 'C']).sortlevel())
+                    .set_index(['A', 'B', 'C']).sort_index())
         result = df.loc[(slice(None), slice(None), 1), :]
         self.assertFalse(result.index.is_unique)
         tm.assert_frame_equal(result, expected)
@@ -2357,7 +2357,7 @@ def test_multiindex_slicers_edges(self):
 
         df['DATE'] = pd.to_datetime(df['DATE'])
         df1 = df.set_index(['A', 'B', 'DATE'])
-        df1 = df1.sortlevel()
+        df1 = df1.sort_index()
 
         # A1 - Get all values under "A0" and "A1"
         result = df1.loc[(slice('A1')), :]
@@ -2440,7 +2440,7 @@ def f():
             df.loc['A1', (slice(None), 'foo')]
 
         self.assertRaises(UnsortedIndexError, f)
-        df = df.sortlevel(axis=1)
+        df = df.sort_index(axis=1)
 
         # slicing
         df.loc['A1', (slice(None), 'foo')]
@@ -2459,7 +2459,7 @@ def test_loc_axis_arguments(self):
         df = DataFrame(np.arange(len(index) * len(columns), dtype='int64')
                        .reshape((len(index), len(columns))),
                        index=index,
-                       columns=columns).sortlevel().sortlevel(axis=1)
+                       columns=columns).sort_index().sort_index(axis=1)
 
         # axis 0
         result = df.loc(axis=0)['A1':'A3', :, ['C1', 'C3']]
@@ -2551,7 +2551,7 @@ def test_per_axis_per_level_setitem(self):
         df_orig = DataFrame(
             np.arange(16, dtype='int64').reshape(
                 4, 4), index=index, columns=columns)
-        df_orig = df_orig.sortlevel(axis=0).sortlevel(axis=1)
+        df_orig = df_orig.sort_index(axis=0).sort_index(axis=1)
 
         # identity
         df = df_orig.copy()
@@ -2764,12 +2764,12 @@ def f():
         idx = pd.MultiIndex.from_product([['A', 'B', 'C'],
                                           ['foo', 'bar', 'baz']],
                                          names=['one', 'two'])
-        s = pd.Series(np.arange(9, dtype='int64'), index=idx).sortlevel()
+        s = pd.Series(np.arange(9, dtype='int64'), index=idx).sort_index()
 
         exp_idx = pd.MultiIndex.from_product([['A'], ['foo', 'bar', 'baz']],
                                              names=['one', 'two'])
         expected = pd.Series(np.arange(3, dtype='int64'),
-                             index=exp_idx).sortlevel()
+                             index=exp_idx).sort_index()
 
         result = s.loc[['A']]
         tm.assert_series_equal(result, expected)
@@ -2786,7 +2786,7 @@ def f():
 
         idx = pd.IndexSlice
         expected = pd.Series([0, 3, 6], index=pd.MultiIndex.from_product(
-            [['A', 'B', 'C'], ['foo']], names=['one', 'two'])).sortlevel()
+            [['A', 'B', 'C'], ['foo']], names=['one', 'two'])).sort_index()
 
         result = s.loc[idx[:, ['foo']]]
         tm.assert_series_equal(result, expected)
@@ -2799,7 +2799,7 @@ def f():
                                                   ['alpha', 'beta']))
         df = DataFrame(
             np.random.randn(5, 6), index=range(5), columns=multi_index)
-        df = df.sortlevel(0, axis=1)
+        df = df.sort_index(level=0, axis=1)
 
         expected = DataFrame(index=range(5),
                              columns=multi_index.reindex([])[0])

pandas/tests/series/test_analytics.py

@@ -1595,21 +1595,21 @@ def test_nsmallest_nlargest(self):
         expected = s.sort_values().head(3)
         assert_series_equal(result, expected)
 
-    def test_sortlevel(self):
+    def test_sort_index_level(self):
         mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list('ABC'))
         s = Series([1, 2], mi)
         backwards = s.iloc[[1, 0]]
 
-        res = s.sortlevel('A')
+        res = s.sort_index(level='A')
         assert_series_equal(backwards, res)
 
-        res = s.sortlevel(['A', 'B'])
+        res = s.sort_index(level=['A', 'B'])
         assert_series_equal(backwards, res)
 
-        res = s.sortlevel('A', sort_remaining=False)
+        res = s.sort_index(level='A', sort_remaining=False)
         assert_series_equal(s, res)
 
-        res = s.sortlevel(['A', 'B'], sort_remaining=False)
+        res = s.sort_index(level=['A', 'B'], sort_remaining=False)
         assert_series_equal(s, res)
 
     def test_apply_categorical(self):
@@ -1738,7 +1738,8 @@ def test_unstack(self):
         s = Series(np.random.randn(6), index=index)
         exp_index = MultiIndex(levels=[['one', 'two', 'three'], [0, 1]],
                                labels=[[0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]])
-        expected = DataFrame({'bar': s.values}, index=exp_index).sortlevel(0)
+        expected = DataFrame({'bar': s.values},
+                             index=exp_index).sort_index(level=0)
         unstacked = s.unstack(0)
         assert_frame_equal(unstacked, expected)