BUG: multi-type sparse slicing fixes and improvements

Types were incorrectly determined when slicing SparseDataFrames with
multiple dtypes (such as float and object). Also enables type inference
for SparseArrays by default.

Author: sstanovnik

pandas/core/internals.py

@@ -4439,7 +4439,10 @@ def _lcd_dtype(l):
         """ find the lowest dtype that can accomodate the given types """
         m = l[0].dtype
         for x in l[1:]:
-            if x.dtype.itemsize > m.itemsize:
+            # the new dtype must either be wider or a strict subtype
+            if (x.dtype.itemsize > m.itemsize or
+                    (np.issubdtype(m, x.dtype) and
+                     not np.issubdtype(x.dtype, m))):
                 m = x.dtype
         return m
 

pandas/tests/frame/test_indexing.py

@@ -2712,6 +2712,14 @@ def test_type_error_multiindex(self):
         result = dg['x', 0]
         assert_series_equal(result, expected)
 
+    def test_sparse_indexing_single_multitype(self):
+        from pandas import SparseDataFrame, SparseSeries
+        sdf = SparseDataFrame([[1, 2, 'a'], [4, 5, 'b']])
+        tm.assert_sp_series_equal(sdf.iloc[0],
+                                  SparseSeries([1, 2, 'a'], name=0))
+        tm.assert_sp_series_equal(sdf.iloc[1],
+                                  SparseSeries([4, 5, 'b'], name=1))
+
 
 class TestDataFrameIndexingDatetimeWithTZ(tm.TestCase, TestData):