diff --git a/src/task/methods/lgc_ensemble_helpers/prepare_data.py b/src/task/methods/lgc_ensemble_helpers/prepare_data.py
index 4d948210..481f0a63 100644
--- a/src/task/methods/lgc_ensemble_helpers/prepare_data.py
+++ b/src/task/methods/lgc_ensemble_helpers/prepare_data.py
@@ -19,6 +19,7 @@ def prepare_data(par, paths):
     mean_sm_name = de_sm_name.groupby('sm_name').mean().reset_index()
     std_cell_type = de_cell_type.groupby('cell_type').std().reset_index()
     std_sm_name = de_sm_name.groupby('sm_name').std().reset_index()
+    std_sm_name_filled = std_sm_name.fillna(0)
     cell_types = de_cell_type.groupby('cell_type').quantile(0.1).reset_index()['cell_type'] # This is just to get cell types in the right order for the next line
     quantiles_cell_type = pd.concat([pd.DataFrame(cell_types)]+[de_cell_type.groupby('cell_type')[col]\
     .quantile([0.25, 0.50, 0.75], interpolation='linear').unstack().reset_index(drop=True) for col in list(de_train.columns)[5:]], axis=1)
@@ -30,7 +31,7 @@ def prepare_data(par, paths):
     mean_cell_type.to_csv(f'{paths["train_data_aug_dir"]}/mean_cell_type.csv', index=False)
     std_cell_type.to_csv(f'{paths["train_data_aug_dir"]}/std_cell_type.csv', index=False)
     mean_sm_name.to_csv(f'{paths["train_data_aug_dir"]}/mean_sm_name.csv', index=False)
-    std_sm_name.to_csv(f'{paths["train_data_aug_dir"]}/std_sm_name.csv', index=False)
+    std_sm_name_filled.to_csv(f'{paths["train_data_aug_dir"]}/std_sm_name.csv', index=False)
     quantiles_cell_type.to_csv(f'{paths["train_data_aug_dir"]}/quantiles_cell_type.csv', index=False)
     ## Create one hot encoding features
     one_hot_encode(de_train[["cell_type", "sm_name"]], id_map[["cell_type", "sm_name"]], out_dir=paths["train_data_aug_dir"])