BUG: fix SparseSeries reindex by using Series implementation

doc/source/whatsnew/v0.20.0.txt

@@ -542,6 +542,10 @@ Bug Fixes
 - Bug in ``Rolling.quantile`` function that caused a segmentation fault when called with a quantile value outside of the range [0, 1] (:issue:`15463`)
 
 
+- Bug in ``SparseSeries.reindex`` on single level with list of length 1 (:issue:`15447`)
+
+
+
 - Bug in the display of ``.info()`` where a qualifier (+) would always be displayed with a ``MultiIndex`` that contains only non-strings (:issue:`15245`)
 - Bug in ``pd.read_msgpack()`` in which ``Series`` categoricals were being improperly processed (:issue:`14901`)
 - Bug in ``Series.ffill()`` with mixed dtypes containing tz-aware datetimes. (:issue:`14956`)

pandas/sparse/series.py

@@ -32,7 +32,7 @@
                                         _coo_to_sparse_series)
 
 
-_shared_doc_kwargs = dict(klass='SparseSeries',
+_shared_doc_kwargs = dict(axes='index', klass='SparseSeries',
                           axes_single_arg="{0, 'index'}")
 
 # -----------------------------------------------------------------------------
@@ -570,27 +570,13 @@ def copy(self, deep=True):
         return self._constructor(new_data, sparse_index=self.sp_index,
                                  fill_value=self.fill_value).__finalize__(self)
 
+    @Appender(generic._shared_docs['reindex'] % _shared_doc_kwargs)
     def reindex(self, index=None, method=None, copy=True, limit=None,
                 **kwargs):
-        """
-        Conform SparseSeries to new Index
-
-        See Series.reindex docstring for general behavior
 
-        Returns
-        -------
-        reindexed : SparseSeries
-        """
-        new_index = _ensure_index(index)
-
-        if self.index.equals(new_index):
-            if copy:
-                return self.copy()
-            else:
-                return self
-        return self._constructor(self._data.reindex(new_index, method=method,
-                                                    limit=limit, copy=copy),
-                                 index=new_index).__finalize__(self)
+        return super(SparseSeries, self).reindex(index=index, method=method,
+                                                 copy=copy, limit=limit,
+                                                 **kwargs)
 
     def sparse_reindex(self, new_index):
         """

pandas/tests/sparse/test_indexing.py

@@ -366,7 +366,7 @@ def test_reindex(self):
         exp = orig.reindex(['A', 'E', 'C', 'D']).to_sparse()
         tm.assert_sp_series_equal(res, exp)
 
-    def test_reindex_fill_value(self):
+    def test_fill_value_reindex(self):
         orig = pd.Series([1, np.nan, 0, 3, 0], index=list('ABCDE'))
         sparse = orig.to_sparse(fill_value=0)
 
@@ -397,6 +397,23 @@ def test_reindex_fill_value(self):
         exp = orig.reindex(['A', 'E', 'C', 'D']).to_sparse(fill_value=0)
         tm.assert_sp_series_equal(res, exp)
 
+    def test_reindex_fill_value(self):
+        floats = pd.Series([1., 2., 3.]).to_sparse()
+        result = floats.reindex([1, 2, 3], fill_value=0)
+        expected = pd.Series([2., 3., 0], index=[1, 2, 3]).to_sparse()
+        tm.assert_sp_series_equal(result, expected)
+
+    def test_reindex_nearest(self):
+        s = pd.Series(np.arange(10, dtype='float64')).to_sparse()
+        target = [0.1, 0.9, 1.5, 2.0]
+        actual = s.reindex(target, method='nearest')
+        expected = pd.Series(np.around(target), target).to_sparse()
+        tm.assert_sp_series_equal(expected, actual)
+
+        actual = s.reindex(target, method='nearest', tolerance=0.2)
+        expected = pd.Series([0, 1, np.nan, 2], target).to_sparse()
+        tm.assert_sp_series_equal(expected, actual)
+
     def tests_indexing_with_sparse(self):
         # GH 13985
 
@@ -504,6 +521,11 @@ def test_loc(self):
         exp = orig.loc[[1, 3, 4, 5]].to_sparse()
         tm.assert_sp_series_equal(result, exp)
 
+        # single element list (GH 15447)
+        result = sparse.loc[['A']]
+        exp = orig.loc[['A']].to_sparse()
+        tm.assert_sp_series_equal(result, exp)
+
         # dense array
         result = sparse.loc[orig % 2 == 1]
         exp = orig.loc[orig % 2 == 1].to_sparse()
@@ -537,6 +559,35 @@ def test_loc_slice(self):
                                   orig.loc['A':'B'].to_sparse())
         tm.assert_sp_series_equal(sparse.loc[:'B'], orig.loc[:'B'].to_sparse())
 
+    def test_reindex(self):
+        # GH 15447
+        orig = self.orig
+        sparse = self.sparse
+
+        res = sparse.reindex([('A', 0), ('C', 1)])
+        exp = orig.reindex([('A', 0), ('C', 1)]).to_sparse()
+        tm.assert_sp_series_equal(res, exp)
+
+        # On specific level:
+        res = sparse.reindex(['A', 'C', 'B'], level=0)
+        exp = orig.reindex(['A', 'C', 'B'], level=0).to_sparse()
+        tm.assert_sp_series_equal(res, exp)
+
+        # single element list (GH 15447)
+        res = sparse.reindex(['A'], level=0)
+        exp = orig.reindex(['A'], level=0).to_sparse()
+        tm.assert_sp_series_equal(res, exp)
+
+        with tm.assertRaises(TypeError):
+            # Incomplete keys are not accepted for reindexing:
+            sparse.reindex(['A', 'C'])
+
+        # "copy" argument:
+        res = sparse.reindex(sparse.index, copy=True)
+        exp = orig.reindex(orig.index, copy=True).to_sparse()
+        tm.assert_sp_series_equal(res, exp)
+        self.assertIsNot(sparse, res)
+
 
 class TestSparseDataFrameIndexing(tm.TestCase):