[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': "Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing 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']"}]