[{'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:def process_features(df):\n    \n    # Get month, day of month, and time of day. \n    months = [\'January\', \'February\', \'March\', \'April\', \'May\', \'June\', \'July\',\n              \'August\', \'September\', \'October\', \'November\', \'December\']\n    df[\'month\'] = df.datetime.apply(lambda x: months[int(x[5:7]) - 1])\n    df[\'day\'] = df.datetime.apply(lambda x: x[8:10]).astype(int)\n    df[\'hour\'] = df.datetime.apply(lambda x: x[11:13]).astype(int)\n    \n    def get_season(m):\n        if m in [\'January\', \'February\', \'December\']:\n            return \'Winter\'\n        elif m in [ \'March\', \'April\', \'May\']:\n            return \'Spring\'\n        elif m in [\'June\', \'July\',\'August\']:\n            return \'Summer\'\n        else:\n            return \'Fall\'\n        \n    df[\'real_seasons\'] = df.month.apply(lambda x: get_season(x))\n    \n    # Change "feels like" temperature to deviation from the mean of 24, which is a comfortable temperature. \n    median_temp = df.atemp.median()\n    df[\'temp_dev\'] = df.atemp.apply(lambda x: x - median_temp)\n    \n    # Create a date object and use it to extract day of week. \n    df[\'date\'] = df.datetime.apply(lambda x: dt.strptime(x, "%Y-%m-%d %H:%M:%S").date())\n    weekdays = [\'Monday\',\'Tuesday\',\'Wednesday\',\'Thursday\',\'Friday\',\'Saturday\',\'Sunday\']\n    df[\'day_of_week\'] = df.date.apply(lambda x: weekdays[x.weekday()])\n    df[\'weekend\'] = df.day_of_week.apply(lambda x: 1 if x in [\'Saturday\', \'Sunday\'] else 0)\n    \n    df = df.drop([\'date\', \'datetime\'], axis=1)\n    \n    print(df.columns)\n    \n    return df\''}, {'role': 'assistant', 'content': "['data_preprocessing']"}]