diff --git a/src/python/nimbusml/model_selection/cv.py b/src/python/nimbusml/model_selection/cv.py
index f2a0dd8b..746f8aee 100644
--- a/src/python/nimbusml/model_selection/cv.py
+++ b/src/python/nimbusml/model_selection/cv.py
@@ -8,7 +8,7 @@
 
 from pandas import DataFrame
 
-from .. import Pipeline
+from .. import Pipeline, FileDataStream
 from ..internal.entrypoints.models_crossvalidator import \
     models_crossvalidator
 from ..internal.entrypoints.transforms_manyheterogeneousmodelcombiner \
@@ -450,13 +450,22 @@ def fit(
         # Need to infer from group_id, bug 284886
         groups = groups or group_id
         if groups is not None:
-            if groups not in cv_aux_info[0]['data_import'][0].inputs[
-                    'CustomSchema']:
-                raise Exception(
-                    'Default stratification column: ' +
-                    str(groups) +
-                    ' cannot be found in the origin data, please specify '
-                    'groups in .fit() function.')
+            if isinstance(X, FileDataStream):
+                if groups not in cv_aux_info[0]['data_import'][0].inputs[
+                        'CustomSchema']:
+                    raise Exception(
+                        'Default stratification column: ' +
+                        str(groups) +
+                        ' cannot be found in the origin data, please specify '
+                        'groups in .fit() function.')
+            elif isinstance(X, DataFrame):
+                if groups not in X.columns:
+                    raise Exception(
+                        'Default stratification column: ' +
+                        str(groups) +
+                        ' cannot be found in the origin data, please specify '
+                        'groups in .fit() function.')
+
 
         split_index = self._process_split_start(split_start)
         graph_sections = cv_aux_info.graph_sections
diff --git a/src/python/nimbusml/tests/model_selection/test_cv.py b/src/python/nimbusml/tests/model_selection/test_cv.py
index f26f326a..3d7587e9 100644
--- a/src/python/nimbusml/tests/model_selection/test_cv.py
+++ b/src/python/nimbusml/tests/model_selection/test_cv.py
@@ -375,6 +375,12 @@ def data(self, label_name, group_id, features):
             data._set_role(Role.Label, label_name)
         return data
 
+    def data_pandas(self):
+        simpleinput_file = get_dataset("gen_tickettrain").as_filepath()
+        data = pd.read_csv(simpleinput_file)
+        data['group'] = data['group'].astype(str)
+        return data
+
     def data_wt_rename(self, label_name, group_id, features):
         simpleinput_file = get_dataset("gen_tickettrain").as_filepath()
         file_schema = 'sep=, col={label}:R4:0 col={group_id}:TX:1 ' \
@@ -402,6 +408,29 @@ def check_cv_with_defaults2(
         data = self.data_wt_rename(label_name, group_id, features)
         check_cv(pipeline=Pipeline(steps), X=data, **params)
 
+    @unittest.skipIf(os.name != "nt", "random crashes on linux")
+    def check_cv_with_defaults_df(
+            self,
+            label_name='rank',
+            group_id='group',
+            features=['price','Class','dep_day','nbr_stops','duration'],
+            **params):
+        steps = [
+            OneHotHashVectorizer(
+                output_kind='Key') << {
+                group_id: group_id},
+            LightGbmRanker(
+                min_data_per_leaf=1,
+                feature=features,
+                label='rank', group_id='group'
+            )]
+        data = self.data_pandas()
+        check_cv(pipeline=Pipeline(steps), X=data, **params)
+
+    @unittest.skipIf(os.name != "nt", "random crashes on linux")
+    def test_default_df(self):
+        self.check_cv_with_defaults_df()
+
     @unittest.skipIf(os.name != "nt", "random crashes on linux")
     def test_default_label2(self):
         self.check_cv_with_defaults2(split_start='try_all')