[{'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:#Creating a DataFrame with train+test data\ndf_all = pd.concat((df_train, df_test), axis=0, ignore_index=True)\n#Removing id and date_first_booking\ndf_all = df_all.drop(['id', 'date_first_booking'], axis=1)\n#Filling nan\ndf_all = df_all.fillna(-1)\n\n#####Feature engineering#######\n#date_account_created\ndf_all.date_account_created = pd.to_datetime(df_all.date_account_created)\ndf_all['dac_year'] = df_all.date_account_created.apply(lambda x: x.year)\ndf_all['dac_month'] = df_all.date_account_created.apply(lambda x: x.month)\ndf_all['dac_day'] = df_all.date_account_created.apply(lambda x: x.day)\ndf_all['dac_weekday'] = df_all.date_account_created.apply(lambda x: x.weekday())\ndf_all['dac_week'] = df_all.date_account_created.apply(lambda x: x.week)\ndf_all['dac_log_elapsed'] = np.log((datetime.date(2016, 1, 1) - df_all.date_account_created).astype('timedelta64[D]'))\ndf_all = df_all.drop(['date_account_created'], axis=1)\n\n#timestamp_first_active\ndf_all.timestamp_first_active = pd.to_datetime(df_all.timestamp_first_active, format='%Y%m%d%H%M%S')\ndf_all['tfa_year'] = df_all.timestamp_first_active.apply(lambda x: x.year)\ndf_all['tfa_month'] = df_all.timestamp_first_active.apply(lambda x: x.month)\ndf_all['tfa_day'] = df_all.timestamp_first_active.apply(lambda x: x.day)\ndf_all['tfa_weekday'] = df_all.timestamp_first_active.apply(lambda x: x.weekday())\ndf_all['tfa_week'] = df_all.timestamp_first_active.apply(lambda x: x.week)\ndf_all['tfa_log_elapsed'] = np.log((datetime.date(2016, 1, 1) - df_all.timestamp_first_active).astype('timedelta64[D]'))\ndf_all = df_all.drop(['timestamp_first_active'], axis=1)\n\n#Age\nav = df_all.age.values\ndf_all['age'] = np.where(np.logical_or(av<14, av>90), -1, av)\ndf_all['age_year'] = np.where(av > 1900, -1, av)\n\n#One-hot-encoding features\nohe_feats = ['gender', 'signup_method', 'signup_flow', 'language', 'affiliate_channel',\n             'affiliate_provider', 'first_affiliate_tracked', 'signup_app', 'first_device_type',\n             'first_browser']\nfor f in ohe_feats:\n    df_all_dummy = pd.get_dummies(df_all[f], prefix=f)\n    df_all = df_all.drop([f], axis=1)\n    df_all = pd.concat((df_all, df_all_dummy), axis=1)    "}, {'role': 'assistant', 'content': "['data_preprocessing']"}]