BUG: Sparse misc fixes

Author: sinhrks

doc/source/whatsnew/v0.18.1.txt

@@ -81,6 +81,13 @@ API changes
 - ``CParserError`` is now a ``ValueError`` instead of just an ``Exception`` (:issue:`12551`)
 
 - ``pd.show_versions()`` now includes ``pandas_datareader`` version (:issue:`12740`)
+- ``SparseArray.take`` now returns scalar for scalar input, ``SparseArray`` for others (:issue:`10560`)
+
+.. ipython:: python
+
+    s = pd.SparseArray([np.nan, np.nan, 1, 2, 3, np.nan, 4, 5, np.nan, 6])
+    s.take(0)
+    s.take([1, 2, 3])
 
 .. _whatsnew_0181.apply_resample:
 
@@ -211,3 +218,9 @@ Bug Fixes
 - Bug in ``.describe()`` resets categorical columns information (:issue:`11558`)
 - Bug where ``loffset`` argument was not applied when calling ``resample().count()`` on a timeseries (:issue:`12725`)
 - ``pd.read_excel()`` now accepts path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path, in line with other ``read_*`` functions (:issue:`12655`)
+
+
+- Bug in ``SparseSeries.loc[]`` with list-like input raises ``TypeError`` (:issue:`10560`)
+- Bug in ``SparseSeries.iloc[]`` with scalar input may raise ``IndexError`` (:issue:`10560`)
+- Bug in ``SparseSeries.loc[]``, ``.iloc[]`` with ``slice`` returns ``SparseArray``, rather than ``SparseSeries`` (:issue:`10560`)
+- Bug in ``SparseSeries.__repr__`` raises ``TypeError`` when it is longer than ``max_rows`` (:issue:`10560`)

pandas/io/tests/test_pickle.py

@@ -10,7 +10,6 @@
 import pandas as pd
 from pandas import Index
 from pandas.compat import u
-from pandas.sparse.tests import test_sparse
 from pandas.util.misc import is_little_endian
 import pandas
 import pandas.util.testing as tm
@@ -46,7 +45,7 @@ def compare_element(self, result, expected, typ, version=None):
             return
 
         if typ.startswith('sp_'):
-            comparator = getattr(test_sparse, "assert_%s_equal" % typ)
+            comparator = getattr(tm, "assert_%s_equal" % typ)
             comparator(result, expected, exact_indices=False)
         else:
             comparator = getattr(tm, "assert_%s_equal" %

pandas/sparse/array.py

@@ -280,10 +280,7 @@ def __getitem__(self, key):
             if isinstance(key, SparseArray):
                 key = np.asarray(key)
             if hasattr(key, '__len__') and len(self) != len(key):
-                indices = self.sp_index
-                if hasattr(indices, 'to_int_index'):
-                    indices = indices.to_int_index()
-                data_slice = self.values.take(indices.indices)[key]
+                return self.take(key)
             else:
                 data_slice = self.values[key]
             return self._constructor(data_slice)
@@ -320,6 +317,11 @@ def take(self, indices, axis=0):
         """
         if axis:
             raise ValueError("axis must be 0, input was {0}".format(axis))
+
+        if com.is_integer(indices):
+            # return scalar
+            return self[indices]
+
         indices = np.atleast_1d(np.asarray(indices, dtype=int))
 
         # allow -1 to indicate missing values
@@ -344,7 +346,7 @@ def take(self, indices, axis=0):
             result = np.empty(len(indices))
             result.fill(self.fill_value)
 
-        return result
+        return self._constructor(result)
 
     def __setitem__(self, key, value):
         # if com.is_integer(key):

pandas/sparse/frame.py

@@ -543,9 +543,10 @@ def _reindex_index(self, index, method, copy, level, fill_value=np.nan,
                 continue
 
             values = series.values
+            # .take returns SparseArray
             new = values.take(indexer)
-
             if need_mask:
+                new = new.values
                 np.putmask(new, mask, fill_value)
 
             new_series[col] = new

pandas/sparse/series.py

@@ -354,10 +354,33 @@ def _set_subtyp(self, is_all_dates):
         else:
             object.__setattr__(self, '_subtyp', 'sparse_series')
 
+    def _ixs(self, i, axis=0):
+        """
+        Return the i-th value or values in the SparseSeries by location
+
+        Parameters
+        ----------
+        i : int, slice, or sequence of integers
+
+        Returns
+        -------
+        value : scalar (int) or Series (slice, sequence)
+        """
+        label = self.index[i]
+        if isinstance(label, Index):
+            return self.take(i, axis=axis, convert=True)
+        else:
+            return self._get_val_at(i)
+
     def _get_val_at(self, loc):
         """ forward to the array """
         return self.block.values._get_val_at(loc)
 
+    def _slice(self, slobj, axis=0, kind=None):
+        slobj = self.index._convert_slice_indexer(slobj,
+                                                  kind=kind or 'getitem')
+        return self._get_values(slobj)
+
     def __getitem__(self, key):
         """
 
@@ -382,6 +405,13 @@ def __getitem__(self, key):
         new_index = Index(self.index.view(ndarray)[key])
         return self._constructor(dataSlice, index=new_index).__finalize__(self)
 
+    def _get_values(self, indexer):
+        try:
+            return self._constructor(self._data.get_slice(indexer),
+                                     fastpath=True).__finalize__(self)
+        except Exception:
+            return self[indexer]
+
     def _set_with_engine(self, key, value):
         return self.set_value(key, value)
 
@@ -517,7 +547,8 @@ def copy(self, deep=True):
         return self._constructor(new_data, sparse_index=self.sp_index,
                                  fill_value=self.fill_value).__finalize__(self)
 
-    def reindex(self, index=None, method=None, copy=True, limit=None):
+    def reindex(self, index=None, method=None, copy=True, limit=None,
+                **kwargs):
         """
         Conform SparseSeries to new Index
 

pandas/sparse/tests/test_array.py

@@ -11,15 +11,6 @@
 import pandas.util.testing as tm
 
 
-def assert_sp_array_equal(left, right):
-    assert_almost_equal(left.sp_values, right.sp_values)
-    assert (left.sp_index.equals(right.sp_index))
-    if np.isnan(left.fill_value):
-        assert (np.isnan(right.fill_value))
-    else:
-        assert (left.fill_value == right.fill_value)
-
-
 class TestSparseArray(tm.TestCase):
     _multiprocess_can_split_ = True
 
@@ -29,11 +20,32 @@ def setUp(self):
         self.zarr = SparseArray([0, 0, 1, 2, 3, 0, 4, 5, 0, 6], fill_value=0)
 
     def test_get_item(self):
+
+        self.assertTrue(np.isnan(self.arr[1]))
+        self.assertEqual(self.arr[2], 1)
+        self.assertEqual(self.arr[7], 5)
+
+        self.assertEqual(self.zarr[0], 0)
+        self.assertEqual(self.zarr[2], 1)
+        self.assertEqual(self.zarr[7], 5)
+
         errmsg = re.compile("bounds")
         assertRaisesRegexp(IndexError, errmsg, lambda: self.arr[11])
         assertRaisesRegexp(IndexError, errmsg, lambda: self.arr[-11])
         self.assertEqual(self.arr[-1], self.arr[len(self.arr) - 1])
 
+    def test_take(self):
+        self.assertTrue(np.isnan(self.arr.take(0)))
+        self.assertTrue(np.isscalar(self.arr.take(2)))
+        self.assertEqual(self.arr.take(2), np.take(self.arr_data, 2))
+        self.assertEqual(self.arr.take(6), np.take(self.arr_data, 6))
+
+        tm.assert_sp_array_equal(self.arr.take([2, 3]),
+                                 SparseArray(np.take(self.arr_data, [2, 3])))
+        tm.assert_sp_array_equal(self.arr.take([0, 1, 2]),
+                                 SparseArray(np.take(self.arr_data,
+                                                     [0, 1, 2])))
+
     def test_bad_take(self):
         assertRaisesRegexp(IndexError, "bounds", lambda: self.arr.take(11))
         self.assertRaises(IndexError, lambda: self.arr.take(-11))
@@ -96,20 +108,20 @@ def _checkit(i):
     def test_getslice(self):
         result = self.arr[:-3]
         exp = SparseArray(self.arr.values[:-3])
-        assert_sp_array_equal(result, exp)
+        tm.assert_sp_array_equal(result, exp)
 
         result = self.arr[-4:]
         exp = SparseArray(self.arr.values[-4:])
-        assert_sp_array_equal(result, exp)
+        tm.assert_sp_array_equal(result, exp)
 
         # two corner cases from Series
         result = self.arr[-12:]
         exp = SparseArray(self.arr)
-        assert_sp_array_equal(result, exp)
+        tm.assert_sp_array_equal(result, exp)
 
         result = self.arr[:-12]
         exp = SparseArray(self.arr.values[:0])
-        assert_sp_array_equal(result, exp)
+        tm.assert_sp_array_equal(result, exp)
 
     def test_binary_operators(self):
         data1 = np.random.randn(20)
@@ -134,11 +146,11 @@ def _check_op(op, first, second):
 
             res2 = op(first, second.values)
             tm.assertIsInstance(res2, SparseArray)
-            assert_sp_array_equal(res, res2)
+            tm.assert_sp_array_equal(res, res2)
 
             res3 = op(first.values, second)
             tm.assertIsInstance(res3, SparseArray)
-            assert_sp_array_equal(res, res3)
+            tm.assert_sp_array_equal(res, res3)
 
             res4 = op(first, 4)
             tm.assertIsInstance(res4, SparseArray)
@@ -169,7 +181,7 @@ def _check_inplace_op(op):
     def test_pickle(self):
         def _check_roundtrip(obj):
             unpickled = self.round_trip_pickle(obj)
-            assert_sp_array_equal(unpickled, obj)
+            tm.assert_sp_array_equal(unpickled, obj)
 
         _check_roundtrip(self.arr)
         _check_roundtrip(self.zarr)

pandas/sparse/tests/test_indexing.py

@@ -0,0 +1,84 @@
+# pylint: disable-msg=E1101,W0612
+
+import nose  # noqa
+import numpy as np
+import pandas as pd
+import pandas.util.testing as tm
+
+
+class TestSparseSeriesIndexing(tm.TestCase):
+
+    _multiprocess_can_split_ = True
+
+    def test_loc(self):
+        orig = pd.Series([1, np.nan, np.nan, 3, np.nan])
+        sparse = orig.to_sparse()
+
+        self.assertEqual(sparse.loc[0], 1)
+        self.assertTrue(np.isnan(sparse.loc[1]))
+
+        result = sparse.loc[[1, 3, 4]]
+        exp = orig.loc[[1, 3, 4]].to_sparse()
+        tm.assert_sp_series_equal(result, exp)
+
+        # exceeds the bounds
+        result = sparse.loc[[1, 3, 4, 5]]
+        exp = orig.loc[[1, 3, 4, 5]].to_sparse()
+        tm.assert_sp_series_equal(result, exp)
+        # padded with NaN
+        self.assertTrue(np.isnan(result[-1]))
+
+        # dense array
+        result = sparse.loc[orig % 2 == 1]
+        exp = orig.loc[orig % 2 == 1].to_sparse()
+        tm.assert_sp_series_equal(result, exp)
+
+        # sparse array (actuary it coerces to normal Series)
+        result = sparse.loc[sparse % 2 == 1]
+        exp = orig.loc[orig % 2 == 1].to_sparse()
+        tm.assert_sp_series_equal(result, exp)
+
+    def test_loc_index(self):
+        orig = pd.Series([1, np.nan, np.nan, 3, np.nan], index=list('ABCDE'))
+        sparse = orig.to_sparse()
+
+        self.assertEqual(sparse.loc['A'], 1)
+        self.assertTrue(np.isnan(sparse.loc['B']))
+
+        result = sparse.loc[['A', 'C', 'D']]
+        exp = orig.loc[['A', 'C', 'D']].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()
+        tm.assert_sp_series_equal(result, exp)
+
+        # sparse array (actuary it coerces to normal Series)
+        result = sparse.loc[sparse % 2 == 1]
+        exp = orig.loc[orig % 2 == 1].to_sparse()
+        tm.assert_sp_series_equal(result, exp)
+
+    def test_loc_slice(self):
+        orig = pd.Series([1, np.nan, np.nan, 3, np.nan])
+        sparse = orig.to_sparse()
+        tm.assert_sp_series_equal(sparse.loc[2:], orig.loc[2:].to_sparse())
+
+    def test_iloc(self):
+        orig = pd.Series([1, np.nan, np.nan, 3, np.nan])
+        sparse = orig.to_sparse()
+
+        self.assertEqual(sparse.iloc[3], 3)
+        self.assertTrue(np.isnan(sparse.iloc[2]))
+
+        result = sparse.iloc[[1, 3, 4]]
+        exp = orig.iloc[[1, 3, 4]].to_sparse()
+        tm.assert_sp_series_equal(result, exp)
+
+        with tm.assertRaises(IndexError):
+            sparse.iloc[[1, 3, 5]]
+
+    def test_iloc_slice(self):
+        orig = pd.Series([1, np.nan, np.nan, 3, np.nan])
+        sparse = orig.to_sparse()
+        tm.assert_sp_series_equal(sparse.iloc[2:], orig.iloc[2:].to_sparse())