diff --git a/qlib/__init__.py b/qlib/__init__.py
index ed6ae1543..3fecc85c3 100644
--- a/qlib/__init__.py
+++ b/qlib/__init__.py
@@ -182,7 +182,7 @@ def _mount_nfs_uri(C):
                 LOG.warning(f"{_remote_uri} on {_mount_path} is already mounted")
 
 
-def init_from_yaml_conf(conf_path):
+def init_from_yaml_conf(conf_path, **kwargs):
     """init_from_yaml_conf
 
     :param conf_path: A path to the qlib config in yml format
@@ -190,5 +190,6 @@ def init_from_yaml_conf(conf_path):
 
     with open(conf_path) as f:
         config = yaml.load(f, Loader=yaml.FullLoader)
+    config.update(kwargs)
     default_conf = config.pop("default_conf", "client")
     init(default_conf, **config)
diff --git a/qlib/contrib/data/handler.py b/qlib/contrib/data/handler.py
index 99a601b9e..8cce92907 100644
--- a/qlib/contrib/data/handler.py
+++ b/qlib/contrib/data/handler.py
@@ -23,7 +23,7 @@ class ALPHA360_Denoise(DataHandlerLP):
         }
 
         learn_processors = [
-            {"class": "DropnaLabel", "kwargs": {"group": "label"}},
+            {"class": "DropnaLabel", "kwargs": {"fields_group": "label"}},
             {"class": "CSZScoreNorm", "kwargs": {"fields_group": "label"}},
         ]
         infer_processors = [
@@ -96,7 +96,7 @@ class ALPHA360(DataHandlerLP):
         }
 
         learn_processors = [
-            {"class": "DropnaLabel", "kwargs": {"group": "label"}},
+            {"class": "DropnaLabel", "kwargs": {"fields_group": "label"}},
             {"class": "CSZScoreNorm", "kwargs": {"fields_group": "label"}},
         ]
         infer_processors = [
diff --git a/qlib/data/dataset/processor.py b/qlib/data/dataset/processor.py
index 3970c8a0a..5944db40b 100755
--- a/qlib/data/dataset/processor.py
+++ b/qlib/data/dataset/processor.py
@@ -74,16 +74,16 @@ class Processor(Serializable):
 
 
 class DropnaProcessor(Processor):
-    def __init__(self, group=None):
-        self.group = group
+    def __init__(self, fields_group=None):
+        self.fields_group = fields_group
 
     def __call__(self, df):
-        return df.dropna(subset=get_group_columns(df, self.group))
+        return df.dropna(subset=get_group_columns(df, self.fields_group))
 
 
 class DropnaLabel(DropnaProcessor):
-    def __init__(self, group="label"):
-        super().__init__(group=group)
+    def __init__(self, fields_group="label"):
+        super().__init__(fields_group=fields_group)
 
     def is_for_infer(self) -> bool:
         """The samples are dropped according to label. So it is not usable for inference"""