用python实现：两个excel表中分别存有两类地址的经纬度，调用高德API分别查出两类地址相互之间的距离

在Python中，你可以使用pandas库处理Excel数据，`geopy`库来计算地理坐标间的距离，以及`requests`库来发送HTTP请求调用高德地图API。以下是一个简单的步骤说明：

1. 首先，安装必要的库：

   pip install pandas geopy requests

2. 导入所需的库：

import pandas as pd
from geopy.distance import geodesic
import requests
from json import loads

3. 加载Excel文件：

df_address1 = pd.read_excel('address_file1.xlsx')
df_address2 = pd.read_excel('address_file2.xlsx')

4. 确保地址列是字符串格式，并提取经纬度信息：

df_address1['lat'] = df_address1['Address'].apply(lambda x: get_latitude(x)) # 自定义函数获取经纬度
df_address1['lng'] = df_address1['Address'].apply(lambda x: get_longitude(x))
df_address2['lat'] = df_address2['Address'].apply(lambda x: get_latitude(x))
df_address2['lng'] = df_address2['Address'].apply(lambda x: get_longitude(x))

def get_latitude(address):
    # 实现从地址获取经纬度的功能，可能需要高德API或其他服务
    pass

def get_longitude(address):
    # 类似地，这里也获取经度
    pass

5. 计算并保存距离：

df_distance = pd.DataFrame()
for index, row1 in df_address1.iterrows():
    for index2, row2 in df_address2.iterrows():
        distance = geodesic((row1.lat, row1.lng), (row2.lat, row2.lng)).miles  # 或者kilometers
        df_distance = df_distance.append({'Address1': row1['Address'], 'Address2': row2['Address'], 'Distance': distance}, ignore_index=True)

6. 调用高德地图API获取更精确的距离（如果直接计算经纬度不够准确）：

   def get_distance_by_amap(address1, address2):
       key = "your_amap_api_key"  # 替换为你实际的高德地图API密钥
       url = f"https://restapi.amap.com/v3/geocode/regeo?key={key}&location={address1}%2C{address2}"
       response = requests.get(url).json()
       if 'regeocode' in response and 'result' in response['regeocode']:
           location1 = (response['regeocode']['result']['location']['lat'], response['regeocode']['result']['location']['lng'])
           location2 = (row2['lat'], row2['lng'])
           precise_distance = geodesic(location1, location2).miles
           return precise_distance
       else:
           print(f"Failed to get accurate distance for {address1} and {address2}")
           return None

   df_distance['Precise_Distance'] = df_distance.apply(lambda x: get_distance_by_amap(x['Address1'], x['Address2']), axis=1)

7. 可能的情况与注意事项：
- 如果使用高德地图API频繁可能会超出免费额度，考虑购买相应服务或者优化访问频率。