[{'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:#print(os.listdir("../kaggle-Covid19/covid19-global-forecasting-week-2"))\ndftrain = pd.read_csv(\'../input/covid19-global-forecasting-week-3/train.csv\', parse_dates=[\'Date\']).sort_values(by=[\'Country_Region\', \'Date\'])\ndftest    = pd.read_csv(\'../input/covid19-global-forecasting-week-3/test.csv\', parse_dates=[\'Date\']).sort_values(by=[\'Country_Region\', \'Date\'])\n\n\n# CURVE SMOOTHING\n#Add averages\n#dftrain [\'Cases_m\'] =  dftrain.groupby([\'Country_Region\', \'Province_State\'])[[\'ConfirmedCases\']].transform(lambda x: x.shift(1)) \n#dftrain [\'Cases_p\']  = dftrain.groupby([\'Country_Region\', \'Province_State\'])[[\'ConfirmedCases\']].transform(lambda x: x.shift(-1)) \n#dftrain [\'Cases_ave\'] = 0.5*(dftrain[\'ConfirmedCases\']+0.5*(dftrain[\'Cases_p\']+dftrain[\'Cases_m\']))\n#case_cols = [\'ConfirmedCases\',\'Cases_m\',\'Cases_p\',\'Cases_ave\']\n\n#dftrain [\'Fatalities_m\'] =  dftrain.groupby([\'Country_Region\', \'Province_State\'])[[\'Fatalities\']].transform(lambda x: x.shift(1)) \n#dftrain [\'Fatalities_p\']  = dftrain.groupby([\'Country_Region\', \'Province_State\'])[[\'Fatalities\']].transform(lambda x: x.shift(-1)) \n#dftrain [\'Fatalities_ave\'] = 0.5*(dftrain[\'Fatalities\']+0.5*(dftrain[\'Fatalities_p\']+dftrain[\'Fatalities_m\']))\n#fata_cols = [\'Fatalities\',\'Fatalities_m\',\'Fatalities_p\',\'Fatalities_ave\']\n\n#date_max = dftrain["Date"].max()\n#mask = df["Date"]==date_max\n#dftrain.loc[mask,\'Cases_ave\']         = 0.75*dftrain.loc[mask,\'ConfirmedCases\']+0.25*dftrain.loc[mask,\'Cases_m\']\n#dftrain.loc[mask,\'Fatalities_ave\']    = 0.75*dftrain.loc[mask,\'Fatalities\']    +0.25*dftrain.loc[mask,\'Fatalities_m\']\n#dftrain.drop([\'Cases_m\', \'Cases_p\', \'Fatalities_m\',\'Fatalities_p\'],axis=1,inplace=True)\n#dftrain.drop([\'ConfirmedCases\',\'Fatalities\'],axis=1,inplace = True)\n#dftrain.rename(columns={\'Cases_ave\':\'ConfirmedCases\',\'Fatalities_ave\':\'Fatalities\'},inplace=True)\n#dftrain[\'ConfirmedCases\',\'Fatalities\'].fillna(0, inplace=True)\n\nppp_tabel = pd.read_csv(\'../input/country-ppp/Country_PPP.csv\', sep=\'\\\\s+\')#.sort_values(by=[\'Country\'])\nppp_tabel.drop(\'Id\', 1,inplace=True)\nppp_tabel = ppp_tabel.append({\'Country\' : \'Burma\' , \'ppp\' : 8000} , ignore_index=True)\nppp_tabel = ppp_tabel.append({\'Country\' : \'MS_Zaandam\' , \'ppp\' : 40000} , ignore_index=True)\nppp_tabel = ppp_tabel.append({\'Country\' : \'West_Bank_and_Gaza\' , \'ppp\' : 20000} , ignore_index=True)\n\nppp_tabel["Country"].replace( \'_\',\' \', regex=True,inplace=True)  # _ var indfrt for at f den til at lse\nppp_tabel["Country"].replace( \'United States\',\'US\', regex=True,inplace=True)  # _ var indfrt for at f den til at lse\nppp_tabel.rename(columns={\'Country\':\'Country_Region\'},inplace=True)\nppp_tabel.sort_values(\'Country_Region\',inplace=True)\''}, {'role': 'assistant', 'content': 'load_data'}]