Python中对NumPy数组内字典进行按值降序排序的实用教程

import csv
import numpy as np

city_dict = {'New Delhi': 2095, 'Samastipur': 4, 'Bombay': 210, 'Imphal': 603, 'Aizawl': 2, 'Amapur': 2, 'Raisikah': 1, 'Champhai': 1, 'Jamshedpur': 32, 'Chennai': 366, 'Chiaplant': 1, 'Tindol': 7, 'Calcutta': 57, 'Tirupattur': 6, 'Gauhati': 112, 'Jorhat': 3, 'Massad': 1, 'Chandigarh': 333, 'Jodhpur': 2, 'Amritsar': 768, 'Tipaimukh': 6, 'Guwahati': 822, 'Harchowal': 1, 'Mothan Wala': 2, 'Qadian': 7, 'Baloda Bazar': 10}

# 将字典放入NumPy数组
np_city = np.array(city_dict)
print("原始NumPy数组内容：")
print(np_city)
print("NumPy数组类型：", type(np_city))
print("NumPy数组元素类型：", np_city.dtype)

原始NumPy数组内容：
{'New Delhi': 2095, 'Samastipur': 4, 'Bombay': 210, 'Imphal': 603, 'Aizawl': 2, 'Amapur': 2, 'Raisikah': 1, 'Champhai': 1, 'Jamshedpur': 32, 'Chennai': 366, 'Chiaplant': 1, 'Tindol': 7, 'Calcutta': 57, 'Tirupattur': 6, 'Gauhati': 112, 'Jorhat': 3, 'Massad': 1, 'Chandigarh': 333, 'Jodhpur': 2, 'Amritsar': 768, 'Tipaimukh': 6, 'Guwahati': 822, 'Harchowal': 1, 'Mothan Wala': 2, 'Qadian': 7, 'Baloda Bazar': 10}
NumPy数组类型： <class 'numpy.ndarray'>
NumPy数组元素类型： object

# 提取NumPy数组中的字典
extracted_dict = np_city.item()
print("\n提取出的字典类型：", type(extracted_dict))
print("提取出的字典内容：", extracted_dict)

# 对字典的items进行排序
sorted_items = sorted(extracted_dict.items(), key=lambda item: item[1], reverse=True)
print("\n排序后的键值对列表：")
print(sorted_items)

# 使用字典推导式重构为有序字典
sorted_city_dict = {key: value for key, value in sorted_items}
print("\n最终按伤亡人数降序排列的城市字典：")
print(sorted_city_dict)

最终按伤亡人数降序排列的城市字典：
{'New Delhi': 2095, 'Guwahati': 822, 'Amritsar': 768, 'Imphal': 603, 'Chennai': 366, 'Chandigarh': 333, 'Bombay': 210, 'Gauhati': 112, 'Calcutta': 57, 'Jamshedpur': 32, 'Baloda Bazar': 10, 'Tindol': 7, 'Qadian': 7, 'Tirupattur': 6, 'Tipaimukh': 6, 'Samastipur': 4, 'Jorhat': 3, 'Aizawl': 2, 'Amapur': 2, 'Jodhpur': 2, 'Mothan Wala': 2, 'Raisikah': 1, 'Champhai': 1, 'Chiaplant': 1, 'Massad': 1, 'Harchowal': 1}

import csv
import numpy as np

def get_top_cities_by_casualties(csv_file_path, country='India', top_n=5):
    """
    从CSV文件中读取数据，计算指定国家各城市的总伤亡人数，
    并返回按伤亡人数降序排列的前N个城市。

    Args:
        csv_file_path (str): CSV文件路径。
        country (str): 要筛选的国家名称。
        top_n (int): 要返回的前N个城市。

    Returns:
        dict: 按伤亡人数降序排列的前N个城市字典。
    """
    city_casualties = {}
    try:
        with open(csv_file_path, 'r', encoding='utf-8') as file_obj:
            data_reader = csv.DictReader(file_obj, skipinitialspace=True)

            for row in data_reader:
                if row['Country'] == country:
                    # 处理空值并转换为整数
                    killed = int(float(row['Killed'])) if row['Killed'] else 0
                    wounded = int(float(row['Wounded'])) if row['Wounded'] else 0

                    total_casualty = killed + wounded
                    city = row['City']

                    if city and city != 'Unknown':            
                        city_casualties[city] = city_casualties.get(city, 0) + total_casualty
    except FileNotFoundError:
        print(f"错误: 文件 '{csv_file_path}' 未找到。")
        return {}
    except Exception as e:
        print(f"处理文件时发生错误: {e}")
        return {}

    # 将字典放入NumPy数组（虽然在此场景下并非最佳实践，但为了演示而保留）
    np_city_casualties = np.array(city_casualties)

    # 提取字典
    extracted_dict = np_city_casualties.item()

    # 对字典进行按值降序排序
    sorted_items = sorted(extracted_dict.items(), key=lambda item: item[1], reverse=True)

    # 重构为有序字典并获取前N个
    top_cities = {key: value for key, value in sorted_items[:top_n]}

    return top_cities

# 示例调用
csv_path = 'terrorismData.csv' # 替换为你的CSV文件路径
top_5_cities = get_top_cities_by_casualties(csv_path, top_n=5)
print(f"\n印度伤亡人数最多的前5个城市：")
print(top_5_cities)