[{'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:country_names = {\'Bahamas, The\': \'Bahamas\',\n\'Brunei Darussalam\' : \'Brunei\',\n\'DR Congo\' :  \'Congo (Kinshasa)\',\n"Cte d\'Ivoire" : "Cote d\'Ivoire",\n\'Congo\' :  \'Congo (Brazzaville)\',                 \n\'Congo, Rep.\': \'Congo (Brazzaville)\',\n\'Congo, Dem. Rep.\': \'Congo (Kinshasa)\',\n\'Czech Republic (Czechia)\' : \'Czechia\',\n\'Czech Republic\': \'Czechia\',\n\'Diamond Princess\': \'Diamond Princess\',\n\'Egypt, Arab Rep.\': \'Egypt\',\n\'Gambia, The\': \'Gambia\',\n\'Holy See\': \'Holy See\',\n\'Iran, Islamic Rep.\': \'Iran\',\n\'Korea, Rep.\': \'Korea, South\',\n\'South Korea\':\'Korea, South\',\n\'Kyrgyz Republic\': \'Kyrgyzstan\',\n\'Lao PDR\': \'Laos\',\n\'Russian Federation\': \'Russia\',\n\'St. Kitts and Nevis\': \'Saint Kitts and Nevis\',\n\'Saint Kitts & Nevis\' : \'Saint Kitts and Nevis\',\n\'St. Lucia\': \'Saint Lucia\',\n\'St. Vincent and the Grenadines\': \'Saint Vincent and the Grenadines\',\n\'St. Vincent & Grenadines\':\'Saint Vincent and the Grenadines\',\n\'Serbia\': \'Serbia\', \n\'Slovak Republic\': \'Slovakia\', \n\'Syrian Arab Republic\': \'Syria\',\n\'Taiwan\': \'Taiwan*\', \n\'United States\': \'US\', \n\'Venezuela, RB\': \'Venezuela\'\n}\n\ndf_population2.Country.replace(country_names, inplace=True)\ndf_hospital_beds.Country.replace(country_names, inplace=True)\ndf_environment_pm2.Country.replace(country_names, inplace=True)\n\ndf_train = pd.merge(df_train, df_population2, on="Country", how="left")\ndf_test = pd.merge(df_test, df_population2, on="Country", how="left")\n\ndf_hospital_beds.rename(columns={\'2011\':\'HospitalBeds\'}, inplace=True)\ndf_train = pd.merge(df_train, df_hospital_beds[["Country", "HospitalBeds"]], on="Country", how="left")\ndf_test = pd.merge(df_test, df_hospital_beds[["Country", "HospitalBeds"]], on="Country", how="left")\n\ndf_environment_pm2.rename(columns={\'2017\':\'PM25\'}, inplace=True)\ndf_train = pd.merge(df_train, df_environment_pm2[["Country", "PM25"]], on="Country", how="left")\ndf_test = pd.merge(df_test, df_environment_pm2[["Country", "PM25"]], on="Country", how="left")\n\''}, {'role': 'assistant', 'content': 'data_preprocessing'}]