[{'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:# aggregate cases and fatalities\ndef do_aggregation(df, col, mean_range):\n    df_new = copy.deepcopy(df)\n    col_new = '{}_({}-{})'.format(col, mean_range[0], mean_range[1])\n    df_new[col_new] = 0\n    tmp = df_new[col].rolling(mean_range[1]-mean_range[0]+1).mean()\n    df_new[col_new][mean_range[0]:] = tmp[:-(mean_range[0])]\n    df_new[col_new][pd.isna(df_new[col_new])] = 0\n    return df_new[[col_new]].reset_index(drop=True)\n\ndef do_aggregations(df):\n    df = pd.concat([df, do_aggregation(df, 'cases/day', [1,1]).reset_index(drop=True)], axis=1)\n    df = pd.concat([df, do_aggregation(df, 'cases/day', [1,7]).reset_index(drop=True)], axis=1)\n    df = pd.concat([df, do_aggregation(df, 'cases/day', [8,14]).reset_index(drop=True)], axis=1)\n    df = pd.concat([df, do_aggregation(df, 'cases/day', [15,21]).reset_index(drop=True)], axis=1)\n    df = pd.concat([df, do_aggregation(df, 'fatal/day', [1,1]).reset_index(drop=True)], axis=1)\n    df = pd.concat([df, do_aggregation(df, 'fatal/day', [1,7]).reset_index(drop=True)], axis=1)\n    df = pd.concat([df, do_aggregation(df, 'fatal/day', [8,14]).reset_index(drop=True)], axis=1)\n    df = pd.concat([df, do_aggregation(df, 'fatal/day', [15,21]).reset_index(drop=True)], axis=1)\n    for threshold in [1, 10, 100]:\n        days_under_threshold = (df['ConfirmedCases']<threshold).sum()\n        tmp = df['day'].values - 22 - days_under_threshold\n        tmp[tmp<=0] = 0\n        df['days_since_{}cases'.format(threshold)] = tmp\n            \n    for threshold in [1, 10, 100]:\n        days_under_threshold = (df['Fatalities']<threshold).sum()\n        tmp = df['day'].values - 22 - days_under_threshold\n        tmp[tmp<=0] = 0\n        df['days_since_{}fatal'.format(threshold)] = tmp\n    \n    # process China/Hubei\n    if df['place_id'][0]=='China/Hubei':\n        df['days_since_1cases'] += 35 # 2019/12/8\n        df['days_since_10cases'] += 35-13 # 2019/12/8-2020/1/2 assume 2019/12/8+13\n        df['days_since_100cases'] += 4 # 2020/1/18\n        df['days_since_1fatal'] += 13 # 2020/1/9\n    return df"}, {'role': 'assistant', 'content': 'data_preprocessing'}]