diff --git a/Orange/statistics/contingency.py b/Orange/statistics/contingency.py
index 9905a52eb43..47fd7e3ec38 100644
--- a/Orange/statistics/contingency.py
+++ b/Orange/statistics/contingency.py
@@ -225,6 +225,17 @@ def from_data(self, data, col_variable, row_variable=None):
                 "Fallback method for computation of contingencies is not implemented yet"
             )
 
+    @property
+    def array_with_unknowns(self):
+        """
+        This function returns the list of all items returned by __getitem__
+        with adding a row of row_unknowns together with values.
+        """
+        other_rows = [x for x in self]
+        ind = self.row_unknowns > 0
+        unknown_rows = np.vstack((self.values[ind], self.row_unknowns[ind]))
+        return other_rows + [unknown_rows]
+
     def __eq__(self, other):
         return (
             np.array_equal(self.values, other.values) and
diff --git a/Orange/tests/test_contingency.py b/Orange/tests/test_contingency.py
index 6f7ccfca2f3..2f64bbed698 100644
--- a/Orange/tests/test_contingency.py
+++ b/Orange/tests/test_contingency.py
@@ -165,6 +165,31 @@ def test_continuous_missing(self):
              3., 4., 2., 1., 1., 1., 1.])
         self.assertEqual(cont.unknowns, 1)
 
+    def test_continuous_array_with_unknowns(self):
+        """
+        Test array_with_unknowns function
+        """
+        d = data.Table("iris")
+        d.Y[:50] = np.zeros(50) * float("nan")
+        cont = contingency.Continuous(d, "sepal width")
+        correct_row_unknowns = [0., 0., 1., 0., 0., 0., 0., 0., 1., 6., 5., 5.,
+                                2., 9., 6., 2., 3., 4., 2., 1., 1., 1., 1.]
+        correct_row_unknowns_no_zero = [
+            c for c in correct_row_unknowns if c > 0]
+        correct_values_no_zero = [
+            v for v, c in zip(cont.values, correct_row_unknowns) if c > 0]
+
+        np.testing.assert_almost_equal(cont.row_unknowns, correct_row_unknowns)
+        arr_unknowns = cont.array_with_unknowns
+        np.testing.assert_almost_equal(
+            arr_unknowns[-1][1], correct_row_unknowns_no_zero)
+        np.testing.assert_almost_equal(
+            arr_unknowns[-1][0], correct_values_no_zero)
+
+        # check if other match to what we get with __getitem__
+        for v1, v2 in zip(arr_unknowns[:-1], cont):
+            np.testing.assert_almost_equal(v1, v2)
+
     def test_mixedtype_metas(self):
         import Orange
         zoo = Orange.data.Table("zoo")
@@ -212,7 +237,6 @@ def _construct_sparse():
         Y = np.array([[1, 2, 1, 0, 0]]).T
         return data.Table.from_numpy(domain, X, Y)
 
-
     def test_sparse(self):
         d = self._construct_sparse()
         cont = contingency.Discrete(d, 5)