[{'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:b"#find gennemsnit af forrige 4(=daybasecount) dage\\ngrouped=dftrain.groupby([\'Country_Region\',\'Province_State\']).tail(daybasecount*2)\\ngrouped=grouped.groupby([\'Country_Region\',\'Province_State\']).head(daybasecount)\\ngrouped.drop([\'FatalityBasis\'],axis=1,inplace=True)\\n\\nto_sum = [\'NewCases\',\'NewFatalities\']\\ngrouped1 = grouped.groupby([\'Country_Region\'])[to_sum].sum()\\n\\ngrouped1.rename(columns={\'NewCases\':\'NewCases1\',\'NewFatalities\':\'NewFatalities1\'}, inplace=True)\\n\\n# beregn grundlggende increase ud fra sidst og forrige 4(=daybasecount) dage\\ngrouped = pd.merge(grouped1, grouped2, on=[\'Country_Region\'])\\ngrouped[\'CasesIncreasePct\'] = 100*(grouped[\'NewCases2\']/grouped[\'NewCases1\']-1)\\nmask = grouped[\'CasesIncreasePct\'] > maxincrease\\ngrouped.loc[mask,\'CasesIncreasePct\'] = maxincrease\\nmask = grouped[\'CasesIncreasePct\'] < 0\\ngrouped.loc[mask,\'CasesIncreasePct\'] = 0\\nmask = grouped[\'CasesIncreasePct\'].isnull()\\ngrouped.loc[mask,\'CasesIncreasePct\'] = 0\\ngrouped[\'Factor\'] = (grouped[\'CasesIncreasePct\']/100+1)**exponent\\n\\ngrouped = pd.merge(grouped, ppp_tabel, on=[\'Country_Region\'])\\n#grouped[\'ppp\'].isnull().sum()\\n\\n# afgrns Fatality procent ud fra hndestimerede kurver med ppp \\ngrouped[\'ppp\'] = grouped[\'ppp\']/10000.\\nif False:\\n    mask = (grouped[\'FatalityPct2\'] > 9) & (grouped[\'ppp\'] <= 1)\\n    grouped.loc[mask,\'FatalityPct2\'] = 5\\n    mask = (grouped[\'FatalityPct2\'] < 5) & (grouped[\'ppp\'] <= 1)\\n    grouped.loc[mask,\'FatalityPct2\'] = 5\\n    mask = (grouped[\'FatalityPct2\'] > 6) & (grouped[\'ppp\'] >= 7)\\n    grouped.loc[mask,\'FatalityPct2\'] = 6\\n    mask = (grouped[\'FatalityPct2\'] < 1.5) & (grouped[\'ppp\'] >= 7)\\n    grouped.loc[mask,\'FatalityPct2\'] = 1.5\\n    mask = (grouped[\'FatalityPct2\'] > (9.5 - 0.43*grouped[\'ppp\'])) & (grouped[\'ppp\'] > 1) & (grouped[\'ppp\'] < 7)\\n    grouped.loc[mask,\'FatalityPct2\'] = (9.5 - 0.43*grouped[\'ppp\'])\\n    mask = (grouped[\'FatalityPct2\'] < (5.6 - 0.5*grouped[\'ppp\'])) & (grouped[\'ppp\'] > 1) & (grouped[\'ppp\'] < 7)\\n    grouped.loc[mask,\'FatalityPct2\'] = (5.6 - 0.5*grouped[\'ppp\'])\\n    mask = (grouped[\'FatalityPct2\'].isnull()) &  (grouped[\'ppp\'] <= 1)\\n    grouped.loc[mask,\'FatalityPct2\'] = 7\\n    mask = (grouped[\'FatalityPct2\'].isnull()) &  (grouped[\'ppp\'] >= 7)\\n    grouped.loc[mask,\'FatalityPct2\'] = 4\\n    mask = (grouped[\'FatalityPct2\'].isnull()) & (grouped[\'ppp\'] > 1) & (grouped[\'ppp\'] < 7)\\n    grouped.loc[mask,\'FatalityPct2\'] = (7.5 - 0.5*grouped[\'ppp\'])\\nelse:\\n    mask = (grouped[\'FatalityPct2\'] > 4) & (grouped[\'ppp\'] <= 1)\\n    grouped.loc[mask,\'FatalityPct2\'] = 3\\n    mask = (grouped[\'FatalityPct2\'] < 1) & (grouped[\'ppp\'] <= 1)\\n    grouped.loc[mask,\'FatalityPct2\'] = 2\\n    mask = (grouped[\'FatalityPct2\'] > 1.5) & (grouped[\'ppp\'] >= 7)\\n    grouped.loc[mask,\'FatalityPct2\'] = 1.5\\n    mask = (grouped[\'FatalityPct2\'] < 0.5) & (grouped[\'ppp\'] >= 7)\\n    grouped.loc[mask,\'FatalityPct2\'] = 0.5\\n    mask = (grouped[\'FatalityPct2\'] > (4.5 - 0.43*grouped[\'ppp\'])) & (grouped[\'ppp\'] > 1) & (grouped[\'ppp\'] < 7)\\n    grouped.loc[mask,\'FatalityPct2\'] = (4.5 - 0.43*grouped[\'ppp\'])\\n    mask = (grouped[\'FatalityPct2\'] < (1.1 - 0.1*grouped[\'ppp\'])) & (grouped[\'ppp\'] > 1) & (grouped[\'ppp\'] < 7)\\n    grouped.loc[mask,\'FatalityPct2\'] = (1.1 - 0.1*grouped[\'ppp\'])\\n    mask = (grouped[\'FatalityPct2\'].isnull()) &  (grouped[\'ppp\'] <= 1)\\n    grouped.loc[mask,\'FatalityPct2\'] = 3\\n    mask = (grouped[\'FatalityPct2\'].isnull()) &  (grouped[\'ppp\'] >= 7)\\n    grouped.loc[mask,\'FatalityPct2\'] = 1\\n    mask = (grouped[\'FatalityPct2\'].isnull()) & (grouped[\'ppp\'] > 1) & (grouped[\'ppp\'] < 7)\\n    grouped.loc[mask,\'FatalityPct2\'] = (2.6 - 0.23*grouped[\'ppp\'])"'}, {'role': 'assistant', 'content': 'data_preprocessing'}]