如何修改代码基于城市数据集为每个城市生成距离列？

问题背景 #

我之前使用haversine_distance函数计算数据集中的坐标与单个指定点（start_lat, start_lon = 40.6976637, -74.1197643）的距离，代码已成功生成Distance列。现在需要修改代码，改用包含多个城市坐标的数据集，为每个城市生成以城市名为列名的距离列。

原始城市坐标数组如下：

[['Nanaimo' -123.9364 49.1642]
 ['Prince Rupert' -130.3271 54.3122]
 ['Vancouver' -123.1386 49.2636]
 ['Victoria' -123.3673 48.4275]
 ['Edmonton' -113.4909 53.5445]
 ['Winnipeg' -97.1392 49.8994]
 ['Sarnia' -82.4065 42.9746]
 ['Sarnia' -82.4065 42.9746]
 ['North York' -79.4112 43.7598]
 ['Kingston' -76.4812 44.2305]
 ['St. Catharines' -79.2333 43.1833]
 ['Thunder Bay' -89.2461 48.3822]
 ['Gaspé' -64.4833 48.8333]
 ['Cap-aux-Meules' -61.8607 47.3801]
 ['Kangiqsujuaq' -71.9667 61.6]
 ['Montreal' -73.5534 45.5091]
 ['Quebec City' -71.2074 46.8142]
 ['Rimouski' -68.524 48.4489]
 ['Sept-Îles' -66.3833 50.2167]
 ['Bathurst' -65.6497 47.6186]
 ['Charlottetown' -63.1399 46.24]
 ['Corner Brook' -57.9711 48.9411]
 ['Dartmouth' -63.5714 44.6715]
 ['Lewisporte' -55.0667 49.2333]
 ['Port Hawkesbury' -61.3642 45.6153]
 ['Saint John' -66.0628 45.2796]
 ["St. John's" -52.7072 47.5675]
 ['Sydney' -60.1947 46.1381]
 ['Yarmouth' -66.1175 43.8361]]

解决方案 #

以下是修改后的代码，已优化效率并实现需求：

import numpy as np
import pandas as pd

# 保留原有的Haversine距离计算函数
def haversine_distance(lat1, lon1, lat2, lon2):
    r = 6371  # 地球半径（公里）
    phi1 = np.radians(lat1)
    phi2 = np.radians(lat2)
    delta_phi = np.radians(lat2 - lat1)
    delta_lambda = np.radians(lon2 - lon1)
    a = np.sin(delta_phi / 2)**2 + np.cos(phi1) * np.cos(phi2) * np.sin(delta_lambda / 2)**2
    res = r * (2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a)))
    return np.round(res, 2)

# 整理城市数据：转成元组列表并去重重复的Sarnia条目
cities = [
    ('Nanaimo', -123.9364, 49.1642),
    ('Prince Rupert', -130.3271, 54.3122),
    ('Vancouver', -123.1386, 49.2636),
    ('Victoria', -123.3673, 48.4275),
    ('Edmonton', -113.4909, 53.5445),
    ('Winnipeg', -97.1392, 49.8994),
    ('Sarnia', -82.4065, 42.9746),
    ('North York', -79.4112, 43.7598),
    ('Kingston', -76.4812, 44.2305),
    ('St. Catharines', -79.2333, 43.1833),
    ('Thunder Bay', -89.2461, 48.3822),
    ('Gaspé', -64.4833, 48.8333),
    ('Cap-aux-Meules', -61.8607, 47.3801),
    ('Kangiqsujuaq', -71.9667, 61.6),
    ('Montreal', -73.5534, 45.5091),
    ('Quebec City', -71.2074, 46.8142),
    ('Rimouski', -68.524, 48.4489),
    ('Sept-Îles', -66.3833, 50.2167),
    ('Bathurst', -65.6497, 47.6186),
    ('Charlottetown', -63.1399, 46.24),
    ('Corner Brook', -57.9711, 48.9411),
    ('Dartmouth', -63.5714, 44.6715),
    ('Lewisporte', -55.0667, 49.2333),
    ('Port Hawkesbury', -61.3642, 45.6153),
    ('Saint John', -66.0628, 45.2796),
    ("St. John's", -52.7072, 47.5675),
    ('Sydney', -60.1947, 46.1381),
    ('Yarmouth', -66.1175, 43.8361)
]

# 遍历每个城市，生成对应距离列
for city_name, city_lon, city_lat in cities:
    # 使用pandas向量化计算，比逐行循环效率更高
    pandas_df[city_name] = haversine_distance(
        pandas_df['lat'], pandas_df['lon'],
        city_lat, city_lon
    )

# 查看处理后的数据集
print(pandas_df)

关键说明 #

• 对原始城市数组做了整理：转成元组列表并移除重复的Sarnia条目，避免生成重复列
• 改用pandas向量化操作替代itertuples逐行循环，大幅提升计算效率（尤其适合大数据集）
• 直接以城市名称作为新列的列名，完全匹配需求

内容的提问来源于stack exchange，提问作者avxeesh