[{'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:# create date parser\ndateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d')\n\n# create data type converters\ndtype_map_weather = dict(Station = 'str')\ndtype_map_test_train = dict(Block = 'str', Street = 'str')\n\n# read data into PANDAS DataFrames with date parsing\ntest = pd.read_csv('../input/test.csv', parse_dates=['Date'], date_parser=dateparse, dtype= dtype_map_test_train)\ntrain = pd.read_csv('../input/train.csv', parse_dates=['Date'], date_parser=dateparse, dtype= dtype_map_test_train)\nweather = pd.read_csv('../input/weather.csv', parse_dates=['Date'], date_parser=dateparse, dtype= dtype_map_weather)\nsample_sub = pd.read_csv('../input/sampleSubmission.csv')"}, {'role': 'assistant', 'content': "['load_data']"}]