[{'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:ownership_by_type = pluto['OwnerType'].value_counts(dropna=False)\nownership_by_type['P'] += ownership_by_type[ownership_by_type.isnull().values[0]]\nownership_by_type = ownership_by_type[1:]\nownership_type_map = {  'P': 'Private',\n'C': 'Public',\n'M': 'Mixed',\n'O': 'Public',\n'X': 'Mixed'\n}\nownership_by_type.index = [ownership_type_map[x] for x in ownership_by_type.index]\nownership_by_type = ownership_by_type.groupby([ownership_by_type.index]).sum().sort_values(ascending=False)\nownership_by_type = ownership_by_type.map(lambda x: x / 859464 * 100)\nownership_by_type.name = '%'\nownership_by_type.index.name = 'Use'"}, {'role': 'assistant', 'content': "['data_preprocessing']"}]