Allow null values in dict columns

src/datasets/features/features.py

@@ -1937,7 +1937,7 @@ def encode_column(self, column, column_name: str):
             `list[Any]`
         """
         column = cast_to_python_objects(column)
-        return [encode_nested_example(self[column_name], obj) for obj in column]
+        return [encode_nested_example(self[column_name], obj, level=1) for obj in column]
 
     def encode_batch(self, batch):
         """
@@ -1955,7 +1955,7 @@ def encode_batch(self, batch):
             raise ValueError(f"Column mismatch between batch {set(batch)} and features {set(self)}")
         for key, column in batch.items():
             column = cast_to_python_objects(column)
-            encoded_batch[key] = [encode_nested_example(self[key], obj) for obj in column]
+            encoded_batch[key] = [encode_nested_example(self[key], obj, level=1) for obj in column]
         return encoded_batch
 
     def decode_example(self, example: dict, token_per_repo_id: Optional[Dict[str, Union[str, bool, None]]] = None):

tests/features/test_features.py

@@ -405,6 +405,16 @@ def test_encode_batch_with_example_with_empty_first_elem():
     assert encoded_batch == {"x": [[[0], [1]], [[], [1]]]}
 
 
+def test_encode_column_dict_with_none():
+    features = Features(
+        {
+            "x": {"a": ClassLabel(names=["a", "b"]), "b": Value("int32")},
+        }
+    )
+    encoded_column = features.encode_column([{"a": "a", "b": 1}, None], "x")
+    assert encoded_column == [{"a": 0, "b": 1}, None]
+
+
 @pytest.mark.parametrize(
     "feature",
     [