[{'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:#Splitting out Datetime attribute in dataframe and dropping unwanted variables as per before analysis\nnew_df=train_df.copy(deep=True)\nnew_df[\'day\']=new_df[\'datetime\'].apply(lambda dateString : calendar.day_name[datetime.strptime(dateString,"%Y-%m-%d %H:%M:%S").weekday()])\nnew_df[\'datetime\'] = pd.to_datetime(new_df[\'datetime\'], format=\'%Y-%m-%d %H:%M:%S\')\nnew_df[\'month\']=new_df[\'datetime\'].apply(lambda x:x.month)\nnew_df[\'hour\']=new_df[\'datetime\'].apply(lambda x:x.hour)\nnew_df[\'year\']=new_df[\'datetime\'].apply(lambda x:x.year)\nfinal_df=new_df.copy(deep=True)\nfinal_df=new_df.drop([\'datetime\',\'temp\',\'casual\',\'registered\'], axis=1)\nfinal_df.head()\''}, {'role': 'assistant', 'content': "['data_preprocessing']"}]