Added test to check if issue pandas-dev#16112 is fixed

pandas/tests/sparse/test_frame.py

@@ -1236,6 +1236,7 @@ def test_from_to_scipy_object(spmatrix, fill_value):
     tm.assert_sp_frame_equal(sdf_obj, expected)
     tm.assert_frame_equal(sdf_obj.to_dense(), expected.to_dense())
 
+
     # Assert spmatrices equal
     assert dict(sdf.to_coo().todok()) == dict(spm.todok())
 
@@ -1245,6 +1246,34 @@ def test_from_to_scipy_object(spmatrix, fill_value):
     assert sdf.to_coo().dtype == res_dtype
 
 
+def test_from_scipy_object_fillna(spmatrix):
+    columns = list('cd')
+    index = list('ab')
+    tm.skip_if_no_package('scipy', max_version='0.19.0')
+
+    # Explicitly convert one zero to np.nan
+    arr = np.eye(2)
+    arr[1, 0] = np.nan
+    try:
+        spm = spmatrix(arr)
+        assert spm.dtype == arr.dtype
+    except (TypeError, AssertionError):
+        # If conversion to sparse fails for this spmatrix type and arr.dtype,
+        # then the combination is not currently supported in NumPy, so we
+        # can just skip testing it thoroughly
+        return
+
+    sdf = pd.SparseDataFrame(spm, index=index, columns=columns).fillna(-1.0)
+
+    # Returning frame should fill all nan values with -1
+    expected = pd.SparseDataFrame({"c": {"a": 1.0, "b": -1.0}, "d": {"a": -1.0, "b": 1.0}})
+
+    # Assert frame is as expected
+    sdf_obj = sdf.astype(object)
+    tm.assert_sp_frame_equal(sdf_obj, expected)
+    tm.assert_frame_equal(sdf_obj.to_dense(), expected.to_dense())
+
+
 class TestSparseDataFrameArithmetic(tm.TestCase):
 
     def test_numeric_op_scalar(self):