--- python-feather-format-0.3.1+dfsg1.orig/feather/api.py
+++ python-feather-format-0.3.1+dfsg1/feather/api.py
@@ -39,9 +39,11 @@ def write_dataframe(df, path):
     # TODO(wesm): pipeline conversion to Arrow memory layout
     for i, name in enumerate(df.columns):
         col = df.iloc[:, i]
+        if pandas.api.types.is_sparse(col):
+            col = col.sparse.to_dense()

         if pdapi.is_object_dtype(col):
-            inferred_type = pandas.api.types.infer_dtype(col)
+            inferred_type = pandas.api.types.infer_dtype(col, skipna=False)
             msg = ("cannot serialize column {n} "
                    "named {name} with dtype {dtype}".format(
                        n=i, name=name, dtype=inferred_type))
--- python-feather-format-0.3.1+dfsg1.orig/feather/tests/test_reader.py
+++ python-feather-format-0.3.1+dfsg1/feather/tests/test_reader.py
@@ -343,8 +343,8 @@ class TestFeatherReader(unittest.TestCas
         # GH #221
         data = {'A': [0,1,2],
                 'B': [1,0,1]}
-        df = pd.DataFrame(data).to_sparse(fill_value=1)
-        expected = df.to_dense()
+        df = pd.DataFrame(data).astype(pd.SparseDtype(int, fill_value=1))
+        expected = df.sparse.to_dense()
         self._check_pandas_roundtrip(df, expected)

     def test_duplicate_columns(self):