diff --git a/metagpt/tools/functions/libs/data_preprocess.py b/metagpt/tools/functions/libs/data_preprocess.py
index ec3580889..8c70462ee 100644
--- a/metagpt/tools/functions/libs/data_preprocess.py
+++ b/metagpt/tools/functions/libs/data_preprocess.py
@@ -151,53 +151,3 @@ def get_column_info(df: pd.DataFrame) -> dict:
         columns=["Column_name", "Data_type", "NaN_Frequency(%)", "N_unique"],
     )
     return samples.to_dict(orient='list')
-#
-#
-# if __name__ == '__main__':
-#     def run():
-#         V = {
-#             'a': [-1, 2, 3, 6, 5, 4],
-#             'b': [1.1, 2.2, 3.3, 6.6, 5.5, 4.4],
-#             'c': ['aa', 'bb', 'cc', 'dd', 'ee', 'ff'],
-#             'd': [1, None, 3, None, 5, 4],
-#             'e': [1.1, np.NAN, 3.3, None, 5.5, 4.4],
-#             'f': ['aa', np.NAN, 'cc', None, '', 'ff'],
-#
-#         }
-#
-#         df = pd.DataFrame(V)
-#         print(df.dtypes)
-#
-#         numeric_features = ['a', 'b', 'd', 'e']
-#         numeric_features_wo_miss = ['a', 'b', ]
-#         categorial_features = ['c', 'f']
-#
-#         df_ = fill_missing_value(df.copy(), numeric_features)
-#         print(df_)
-#         df_ = fill_missing_value(df.copy(), categorial_features, strategy='constant', fill_value='hehe')
-#         print(df_)
-#
-#         df_ = fill_missing_value(df.copy(), numeric_features, strategy='constant', fill_value=999)
-#         print(df_)
-#
-#         # df_ = label_encode(df.copy(), numeric_features + categorial_features, )
-#         # print(df_)
-#
-#         df_ = split_bins(df.copy(), numeric_features_wo_miss, strategy='quantile')
-#         print(df_)
-#
-#         df_ = min_max_scale(df.copy(), numeric_features, )
-#         print(df_)
-#
-#         df_ = standard_scale(df.copy(), numeric_features, )
-#         print(df_)
-#
-#         df_ = log_transform(df.copy(), numeric_features, )
-#         print(df_)
-#
-#         df_ = max_abs_scale(df.copy(), numeric_features, )
-#         print(df_)
-#
-#         df_ = robust_scale(df.copy(), numeric_features, )
-#         print(df_)
-#     run()
\ No newline at end of file