BUG GH23744 ufuncs on DataFrame keeps dtype sparseness

doc/source/whatsnew/v0.24.0.rst

@@ -1463,6 +1463,7 @@ Sparse
 - Bug in unary inversion operator (``~``) on a ``SparseSeries`` with boolean values. The performance of this has also been improved (:issue:`22835`)
 - Bug in :meth:`SparseArary.unique` not returning the unique values (:issue:`19595`)
 - Bug in :meth:`SparseArray.nonzero` and :meth:`SparseDataFrame.dropna` returning shifted/incorrect results (:issue:`21172`)
+- Bug in :meth:`DataFrame.apply` where dtypes would lose sparseness (:issue:`23744`)
 
 Build Changes
 ^^^^^^^^^^^^^
@@ -1489,4 +1490,3 @@ Contributors
 ~~~~~~~~~~~~
 
 .. contributors:: v0.23.4..HEAD
-

pandas/core/apply.py

@@ -132,7 +132,7 @@ def get_result(self):
         # ufunc
         elif isinstance(self.f, np.ufunc):
             with np.errstate(all='ignore'):
-                results = self.f(self.values)
+                results = self.obj._data.apply('apply', func=self.f)
             return self.obj._constructor(data=results, index=self.index,
                                          columns=self.columns, copy=False)
 

pandas/tests/sparse/frame/test_apply.py

@@ -91,3 +91,14 @@ def test_applymap(frame):
     # just test that it works
     result = frame.applymap(lambda x: x * 2)
     assert isinstance(result, SparseDataFrame)
+
+
+def test_apply_keep_sparse_dtype():
+    # GH 23744
+    sdf = SparseDataFrame(np.array([[0, 1, 0], [0, 0, 0], [0, 0, 1]]),
+                          columns=['b', 'a', 'c'], default_fill_value=1)
+    df = DataFrame(sdf)
+
+    expected = sdf.apply(np.exp)
+    result = df.apply(np.exp)
+    tm.assert_frame_equal(expected, result)