Python pandas.dataframe.pivot\u table（）是更好的解决方案吗

下面的代码允许我确定美国每个地区最常见的主菜和最常见的主菜制作方法。它使用从“感恩节-2015-poll-data.csv”获得的数据，可在此处找到。我相信pivot_表可以提供一种更有效的方法来获取相同的信息，但我可以想出如何做到这一点。有人能提供一些见解吗？下面是我用来获取这些信息的代码，它很有效，但我觉得这不是最好（最快）的方法

    import pandas as pd

    data = pd.read_csv('thanksgiving-2015-poll-data.csv', encoding="Latin-1")
    regions = data['US Region'].value_counts().keys()
    main_dish = data['What is typically the main dish at your Thanksgiving dinner?']
    main_dish_prep = data['How is the main dish typically cooked?']
    regional_entire_meal_data_rows = []

    for region in regions:
        is_in_region = data['US Region'] == region
        most_common_regional_dish = main_dish[is_in_region].value_counts().keys().tolist()[0]
        is_region_and_most_common_dish = (is_in_region) & (main_dish == most_common_regional_dish)
        most_common_regional_dish_prep_type = main_dish_prep[is_region_and_most_common_dish].value_counts().keys().tolist()[0]
        regional_entire_meal_data_rows.append((region, most_common_regional_dish, most_common_regional_dish_prep_type))

    labels = ['US Region', 'Most Common Main Dish', 'Most Common Prep Type for Main Dish']
    regional_main_dish_data = pd.DataFrame(regional_entire_meal_data_rows, columns=labels)

    full_meal_message = '''\n\nThe table below shows a breakdown of the most common 
    full Thanksgiving meal broken down by region.\n'''
    print(full_meal_message)
    print(regional_main_dish_data)

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跑步需要多长时间？你的目标运行时间是多少？@John总运行时间是0.04688453674316406秒。没有明确的目标，只是想挑战自己，尽快实现。我想看看pivot_表是否能让事情加快一点，但不知道怎么做。在这种情况下，问题就属于这里：我投票结束这个问题，因为它已经被交叉发布到