[{'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:from itertools import cycle, islice\ndiscrete_col = list(islice(cycle([\'orange\', \'r\', \'g\', \'k\', \'b\', \'c\', \'m\']), None, len(train_data_by_country_confirm.head(30))))\nplt.rcParams.update({\'font.size\': 22})\ntrain_data_by_country_confirm.head(20).plot(figsize=(20,15), kind=\'barh\', color=discrete_col)\nplt.legend(["Confirmed Cases", "Fatalities"]);\nplt.xlabel("Number of Covid-19 Affectees")\nplt.title("First 20 Countries with Highest Confirmed Cases")\nylocs, ylabs = plt.yticks()\nfor i, v in enumerate(train_data_by_country_confirm.head(20)["ConfirmedCases"][:]):\n    plt.text(v+0.01, ylocs[i]-0.25, str(int(v)), fontsize=12)\nfor i, v in enumerate(train_data_by_country_confirm.head(20)["Fatalities"][:]):\n    if v > 0: #disply for only >300 fatalities\n        plt.text(v+0.01,ylocs[i]+0.1,str(int(v)),fontsize=12)    \''}, {'role': 'assistant', 'content': 'result_visualization'}]