[{'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:combined.drop('race',axis = 1, inplace =True)\ncombined = combined.dropna(axis=0)\ncombined.reset_index(drop = True, inplace = True)\ncombined.name = combined.name.map(lambda name: name.split('@')[0].strip() if(name.find('@')!=-1) else name.strip())\ncombined.name = combined.name.map(lambda name: name.strip() if(name.find('.')==-1) else name.split('.')[1].strip() if(len(name.split('.')[0].strip())<=5) else name.strip())\ndef checkTitle1(name):\ntitle = ['j ','dr ','ku ','ku- ','k ','km ','km- ','kum ','km0 ','sant ','st ','mo ','gen ','smt ','ms ','mis ','shri ','sri ','sh ','shi ','p ','md ','gd ','m ','sk ','so ','mohd ','mho ','dd ','ed ','ct ','na ', 'miss ', 'lc ', 'smt- ', 'smts ','smt-', 'smt,','1-smt ','mo- ','gs-1957975 ','mrs ','shrimati ','a ','b ']\nk = any(name.find(i)==0 for i in title)\nreturn k\ndef checkTitle2(name):\ntitle = ['ku  ','kum  ','shri  ','md  ','mohd  ','smt  ','km  ']\nk = any(name.find(i)==0 for i in title)\nreturn k\ndef checkTitle3(name):\ntitle = ['s p ','kum a ','ct b ']\nk = any(name.find(i)==0 for i in title)\nreturn k\ncombined.name = combined.name.map(lambda name: name.split('  ')[1].strip() if(checkTitle2(name)==True) else name.split(' ')[2].strip() if (checkTitle3(name)==True) else name.split(' ')[1].strip() if(checkTitle1(name)==True) else name.strip())\ncombined.name = combined.name.map(lambda name: name if(name.find(' ')==-1) else name.split()[0].strip())\ncombined.name = combined.name.map(lambda name: ''.join(i for i in name if i.isalpha()).strip())\ncombined = combined.dropna(axis=0)\nfor index,row in combined.iterrows():\ntry:\nrow['name'].encode('ascii')\nexcept UnicodeEncodeError:\ncombined.drop(index,axis=0,inplace=True)\ncombined.reset_index(drop = True, inplace = True)\ncombined.reset_index(drop = True, inplace = True)\ncombined.to_csv('CleanedData.csv', index=False)"}, {'role': 'assistant', 'content': 'data_preprocessing'}]