[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': 'Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:##train_users[\'year_account_creation\'] = pd.DatetimeIndex(train_users[\'date_account_created\']).year\ntrain_users[\'timestamp_first_active\'] = train_users[\'timestamp_first_active\'].astype(str)\n\ntrain_users[\'date_account_created\'] = pd.to_datetime(train_users[\'date_account_created\'])\n\n#### converting the first active day to a date-time var\ntrain_users[\'timestamp_first_active_day\'] = train_users[\'timestamp_first_active\'].str[:8]\ntrain_users[\'timestamp_first_active_day\'] = pd.to_datetime(train_users[\'timestamp_first_active_day\'], format=\'%Y%m%d\')\n\n#### find the first active year\ntrain_users[\'timestamp_first_active_year\'] = train_users[\'timestamp_first_active\'].str[:4]\ntrain_users[\'timestamp_first_active_hour\'] = train_users[\'timestamp_first_active\'].str[8:10]\n\n#### create a var to see if they searched before joining\n#train_users[\'searched_before_joining\'] = (train_users[\'timestamp_first_active_day\'] < train_users[\'date_account_created\'])\n#train_users[\'searched_before_joining\'] = train_users[\'searched_before_joining\'] * 1\n\n#### did they do a previous trip? This appears to be a weird variable..\n##train_users[\'first_trip\'] = pd.isnull(train_users[\'date_first_booking\']) * 1\n\nmajor_languages = [\'en\']  \ntrain_users[\'language_bucket\'] = np.where(train_users[\'language\'].isin(major_languages), \'en\', \'other\')\n\n##### group up the age variable\nlabels = [1, 2, 3, 4, 5, 6, 7]\nbins = [0, 20, 30, 40, 50, 60, 9000, 100000]\ntrain_users[\'age\'].fillna(10000)\ntrain_users[\'age_group\'] = pd.cut(train_users[\'age\'], bins, right=False, labels=labels)\ntrain_users[\'age_group\'] = train_users[\'age_group\'] * 1\n\ntrain_users["signup_combo"] = train_users["signup_method"].map(str) + train_users["signup_flow"].map(str)\n\n##### let\'s group the affiliate_provider variable\n\nmajor_affiliate_providers = [\'direct\', \'google\', \'bing\', \'craigslist\', \'facebook\']\ntrain_users[\'affiliate_provider_grp\'] = np.where(train_users[\'affiliate_provider\'].isin(major_affiliate_providers), train_users[\'affiliate_provider\'], \'other\')\ntrain_users["affiliate_combined"] = train_users["affiliate_provider_grp"].map(str) + train_users["affiliate_channel"].map(str)\n\n\n\n\n\n\n###### adjust test so it matches the adjustments made to the train dataset\ntest_users[\'timestamp_first_active\'] = test_users[\'timestamp_first_active\'].astype(str)\ntest_users[\'date_account_created\'] = pd.to_datetime(test_users[\'date_account_created\'])\ntest_users[\'timestamp_first_active_day\'] = test_users[\'timestamp_first_active\'].str[:8]\ntest_users[\'timestamp_first_active_day\'] = pd.to_datetime(test_users[\'timestamp_first_active_day\'], format=\'%Y%m%d\')\ntest_users[\'timestamp_first_active_year\'] = test_users[\'timestamp_first_active\'].str[:4]\n#test_users[\'searched_before_joining\'] = (test_users[\'timestamp_first_active_day\'] < test_users[\'date_account_created\'])\n#test_users[\'searched_before_joining\'] = test_users[\'searched_before_joining\'] * 1\n##test_users[\'first_trip\'] = pd.isnull(test_users[\'date_first_booking\']) * 1\ntest_users[\'language_bucket\'] = np.where(test_users[\'language\'].isin(major_languages), \'en\', \'other\')\ntest_users[\'age\'].fillna(10000)\ntest_users[\'age_group\'] = pd.cut(test_users[\'age\'], bins, right=False, labels=labels)\ntest_users[\'age_group\'] = test_users[\'age_group\'] * 1\ntest_users[\'timestamp_first_active_day\'] = pd.to_datetime(test_users[\'timestamp_first_active_day\'], format=\'%Y%m%d\')\ntest_users["signup_combo"] = test_users["signup_method"].map(str) + test_users["signup_flow"].map(str)\ntest_users[\'timestamp_first_active_hour\'] = test_users[\'timestamp_first_active\'].str[8:10]\ntest_users[\'affiliate_provider_grp\'] = np.where(test_users[\'affiliate_provider\'].isin(major_affiliate_providers), test_users[\'affiliate_provider\'], \'other\')\ntest_users["affiliate_combined"] = test_users["affiliate_provider_grp"].map(str) + test_users["affiliate_channel"].map(str)\n\n\''}, {'role': 'assistant', 'content': 'data_preprocessing'}]