[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:dac = np.vstack(df_all.date_account_created.astype(str).apply(lambda x: list(map(int, x.split(\'-\')))).values)\ndf_all[\'dac_year\'] = dac[:,0]\ndf_all[\'dac_month\'] = dac[:,1]\ndf_all[\'dac_day\'] = dac[:,2]\ntfa = np.vstack(df_all.timestamp_first_active.astype(str).apply(lambda x: list(map(int, [x[:4],x[4:6],x[6:8],x[8:10],x[10:12],x[12:14]]))).values)\ndf_all[\'tfa_year\'] = tfa[:,0]\ndf_all[\'tfa_month\'] = tfa[:,1]\ndf_all[\'tfa_day\'] = tfa[:,2]\nav = df_all.age.values\ndf_all[\'age\'] = np.where(np.logical_or(av<14, av>100), -1, av)\nohe_feats = [\'gender\', \'signup_method\', \'signup_flow\', \'language\', \'affiliate_channel\', \'affiliate_provider\', \'first_affiliate_tracked\', \'signup_app\', \'first_device_type\', \'first_browser\']\nfor f in ohe_feats:\ndf_all_dummy = pd.get_dummies(df_all[f], prefix=f)\ndf_all = df_all.drop([f], axis=1)\ndf_all = pd.concat((df_all, df_all_dummy), axis=1)\ndf_all = df_all.drop([\'id\', \'date_first_booking\',\'country_destination\',\'date_account_created\',\n\'timestamp_first_active\',], axis=1)\nprint("new columns created")'}, {'role': 'assistant', 'content': "['data_preprocessing']"}]