[{'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:%matplotlib inline\nimport pandas as pd\ndf_cen[\'W\']= df_cen[\'WA_MALE\']+df_cen[\'WA_FEMALE\']+df_cen[\'WAC_MALE\']+df_cen[\'WAC_FEMALE\']\ndf_cen[\'B\']= df_cen[\'BA_MALE\']+df_cen[\'BA_FEMALE\']+df_cen[\'BAC_MALE\']+df_cen[\'BAC_FEMALE\']\ndf_cen[\'A\']= df_cen[\'AA_MALE\']+df_cen[\'AA_FEMALE\']+df_cen[\'AAC_MALE\']+df_cen[\'AAC_FEMALE\']\ndf_cen[\'H\']= df_cen[\'H_MALE\']+df_cen[\'H_FEMALE\']+df_cen[\'HTOM_MALE\']+df_cen[\'HTOM_FEMALE\']\ndf_cen1 = df_cen[(df_cen[\'AGEGRP\']==0) & (df_cen[\'YEAR\']>2)]\ndf_cen1 = df_cen1[[\'FIPS\',\'YEAR\',\'TOT_POP\',\'W\',\'B\',\'A\',\'H\']]\ndf1 = df_cen1.pivot(index=\'FIPS\', columns = \'YEAR\')\ndf1 = df1.reset_index()\ndf1.columns = [\'FIPS\',\'T2010\',\'T2011\',\'T2012\',\'T2013\',\'T2014\',\'W2010\',\'W2011\',\'W2012\',\'W2013\',\'W2014\',\n\'B2010\',\'B2011\',\'B2012\',\'B2013\',\'B2014\',\'A2010\',\'A2011\',\'A2012\',\'A2013\',\'A2014\',\n\'H2010\',\'H2011\',\'H2012\',\'H2013\',\'H2014\']\ndf1[\'total_diff\'] = (df1[\'T2014\']-df1[\'T2010\'])/df1[\'T2010\']\ndf1[\'white_diff\'] = (df1[\'W2014\']-df1[\'W2010\'])/df1[\'W2010\']\ndf1[\'black_diff\'] = (df1[\'B2014\']-df1[\'B2010\'])/df1[\'B2010\']\ndf1[\'asian_diff\'] = (df1[\'A2014\']-df1[\'A2010\'])/df1[\'A2010\']\ndf1[\'hispanic_diff\'] = (df1[\'H2014\']-df1[\'H2010\'])/df1[\'H2010\']\ndf_z = pd.read_csv(\'County_Zhvi_AllHomes.csv\')\ndf_z[\'StateCodeFIPS\'] = df_z.StateCodeFIPS.map("{:02}".format)\ndf_z[\'MunicipalCodeFIPS\'] = df_z.MunicipalCodeFIPS.map("{:03}".format)\ndf_z[\'FIPS\'] = df_z[\'StateCodeFIPS\']+df_z[\'MunicipalCodeFIPS\']\ndf_zpi = df_z[[\'FIPS\',\'RegionName\']]\nfor yr in range(2000,2016):\nyr = str(yr)\ntmp_list = []\nfor header in list(df_z.columns.values):\nif yr == header.split(\'-\')[0]:\ntmp_list.append(header)\ndf_zpi[yr] = df_z[tmp_list].mean(axis=1)\ndf_zpi[\'price_diff\'] = (df_zpi[\'2014\']-df_zpi[\'2010\'])/df_zpi[\'2010\']\ndf = pd.merge(df1, df_zpi, on=\'FIPS\')\nprint(df.shape, df1.shape, df_zpi.shape)\ndf[[\'FIPS\',\'RegionName\',\'price_diff\',\'total_diff\',\'white_diff\', \'black_diff\', \'asian_diff\', \'hispanic_diff\']].hist()'}, {'role': 'assistant', 'content': 'data_preprocessing'}]