[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': 'Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:logmeans_prod = (x_train.groupby(["product_id"]))["log_demand"].mean().to_dict()\nlogmeans_prod_store = (x_train.groupby(["product_id", "store_id"]))["log_demand"].mean().to_dict()\nlogmeans_prod_depot = (x_train.groupby(["product_id", "depot_id"]))["log_demand"].mean().to_dict()\nlogmeans_prod_chan = (x_train.groupby(["product_id", "channel_id"]))["log_demand"].mean().to_dict()\n\'\'\'logmeans_prod_store_depot = (x_train.groupby(["product_id", "store_id", "depot_id"]))["log_demand"].mean().to_dict()\nlogmeans_prod_store_chan = (x_train.groupby(["product_id", "store_id", "channel_id"]))["log_demand"].mean().to_dict()\'\'\'\nlogmeans_prod_depot_store = (x_train.groupby(["product_id", "depot_id", "store_id"]))["log_demand"].mean().to_dict()\nlogmeans_prod_chan_store = (x_train.groupby(["product_id", "channel_id", "store_id"]))["log_demand"].mean().to_dict()\n\'\'\'logmeans_prod_store_depot_chan = (x_train.groupby(["product_id", "store_id", "depot_id",\n"channel_id"]))["log_demand"].mean().to_dict()\nlogmeans_prod_store_chan_depot = (x_train.groupby(["product_id", "store_id", "channel_id",\n"depot_id"]))["log_demand"].mean().to_dict()\nlogmeans_prod_depot_store_chan = (x_train.groupby(["product_id", "depot_id", "store_id",\n"channel_id"]))["log_demand"].mean().to_dict()\nlogmeans_prod_depot_chan_store = (x_train.groupby(["product_id", "depot_id", "channel_id",\n"store_id"]))["log_demand"].mean().to_dict()\nlogmeans_prod_chan_depot_store = (x_train.groupby(["product_id", "channel_id", "depot_id",\n"store_id"]))["log_demand"].mean().to_dict()\nlogmeans_prod_chan_store_depot = (x_train.groupby(["product_id", "channel_id", "store_id",\n"depot_id"]))["log_demand"].mean().to_dict()\'\'\''}, {'role': 'assistant', 'content': 'data_preprocessing'}]