ENH: implement fill_value on SparseDataFrame._combine_frame, close #887

pandas/sparse/frame.py

@@ -412,16 +412,26 @@ def _combine_frame(self, other, func, fill_value=None, level=None):
                                  copy=False)
         new_index, new_columns = this.index, this.columns
 
-        if fill_value is not None or level is not None:
+        if level is not None:
             raise NotImplementedError
 
         if not self and not other:
             return SparseDataFrame(index=new_index)
 
         new_data = {}
-        for col in new_columns:
-            if col in this and col in other:
-                new_data[col] = func(this[col], other[col])
+        if fill_value is not None:
+            # TODO: be a bit more intelligent here
+            for col in new_columns:
+                if col in this and col in other:
+                    dleft = this[col].to_dense()
+                    dright = other[col].to_dense()
+                    result = dleft._binop(dright, func, fill_value=fill_value)
+                    result = result.to_sparse(fill_value=this[col].fill_value)
+                    new_data[col] = result
+        else:
+            for col in new_columns:
+                if col in this and col in other:
+                    new_data[col] = func(this[col], other[col])
 
         return self._constructor(data=new_data, index=new_index,
                                  columns=new_columns)

pandas/sparse/tests/test_sparse.py

@@ -1250,6 +1250,17 @@ def test_combine_first(self):
         assert_sp_frame_equal(result, result2)
         assert_sp_frame_equal(result, expected)
 
+    def test_combine_add(self):
+        df = self.frame.to_dense()
+        df2 = df.copy()
+        df2['C'][:3] = np.nan
+        df['A'][:3] = 5.7
+
+        result = df.to_sparse().add(df2.to_sparse(), fill_value=0)
+        expected = df.add(df2, fill_value=0).to_sparse()
+        assert_sp_frame_equal(result, expected)
+
+
 def _dense_series_compare(s, f):
     result = f(s)
     assert(isinstance(result, SparseSeries))