Python Geopy库地理编码和地理距离计算案例展示

pip install geopy

from geopy.geocoders import Nominatim
# 初始化地理编码器
geolocator = Nominatim(user_agent="geoapiExercises")
# 地理编码
location = geolocator.geocode("1600 Amphitheatre Parkway, Mountain View, CA")
print((location.latitude, location.longitude))

from geopy.geocoders import Nominatim
# 初始化地理编码器
geolocator = Nominatim(user_agent="geoapiExercises")
# 反向地理编码
location = geolocator.reverse("37.4219999, -122.0840575")
print(location.address)

from geopy.distance import geodesic
# 定义两个地理坐标
coords_1 = (37.4219999, -122.0840575)
coords_2 = (40.712776, -74.005974)
# 计算距离
distance = geodesic(coords_1, coords_2).miles
print(f"Distance: {distance} miles")

from geopy.geocoders import GoogleV3
# 初始化地理编码器，提供API密钥
geolocator = GoogleV3(api_key='YOUR_API_KEY')
# 地理编码
location = geolocator.geocode("1600 Amphitheatre Parkway, Mountain View, CA")
print((location.latitude, location.longitude))
# 反向地理编码
location = geolocator.reverse("37.4219999, -122.0840575")
print(location.address)

from geopy.geocoders import Nominatim
import pandas as pd
# 初始化地理编码器
geolocator = Nominatim(user_agent="geoapiExercises")
# 创建示例地址列表
addresses = ["1600 Amphitheatre Parkway, Mountain View, CA",
             "1 Infinite Loop, Cupertino, CA",
             "500 Terry A Francois Blvd, San Francisco, CA"]
# 批量地理编码
locations = [geolocator.geocode(address) for address in addresses]
coords = [(location.latitude, location.longitude) for location in locations]
# 创建DataFrame
df = pd.DataFrame(coords, columns=["Latitude", "Longitude"], index=addresses)
print(df)

from geopy.geocoders import Nominatim
# 初始化地理编码器
geolocator = Nominatim(user_agent="geoapiExercises")
# 定义地理编码函数
def geocode_address(address):
    try:
        location = geolocator.geocode(address)
        return (location.latitude, location.longitude)
    except Exception as e:
        print(f"Error geocoding {address}: {e}")
        return (None, None)
# 测试地理编码函数
address = "1600 Amphitheatre Parkway, Mountain View, CA"
coords = geocode_address(address)
print(coords)

from geopy.distance import geodesic, great_circle
# 定义两个地理坐标
coords_1 = (37.4219999, -122.0840575)
coords_2 = (40.712776, -74.005974)
# 使用不同的距离计算方法
geodesic_distance = geodesic(coords_1, coords_2).miles
great_circle_distance = great_circle(coords_1, coords_2).miles
print(f"Geodesic Distance: {geodesic_distance} miles")
print(f"Great Circle Distance: {great_circle_distance} miles")

import pandas as pd
import folium
from geopy.geocoders import Nominatim
# 初始化地理编码器
geolocator = Nominatim(user_agent="geoapiExercises")
# 创建示例地址列表
addresses = ["1600 Amphitheatre Parkway, Mountain View, CA",
             "1 Infinite Loop, Cupertino, CA",
             "500 Terry A Francois Blvd, San Francisco, CA"]
# 批量地理编码
locations = [geolocator.geocode(address) for address in addresses]
coords = [(location.latitude, location.longitude) for location in locations]
# 创建DataFrame
df = pd.DataFrame(coords, columns=["Latitude", "Longitude"], index=addresses)
# 创建地图
m = folium.Map(location=[37.7749, -122.4194], zoom_start=10)
# 添加标记
for idx, row in df.iterrows():
    folium.Marker([row["Latitude"], row["Longitude"]], popup=idx).add_to(m)
# 保存地图
m.save("map.html")

from geopy.distance import geodesic
import itertools
# 定义多个地理坐标
locations = {
    "Location1": (37.4219999, -122.0840575),
    "Location2": (40.712776, -74.005974),
    "Location3": (34.052235, -118.243683),
    "Location4": (51.507351, -0.127758)
}
# 计算所有地点对之间的距离
distances = {}
for (loc1, coord1), (loc2, coord2) in itertools.combinations(locations.items(), 2):
    distance = geodesic(coord1, coord2).miles
    distances[f"{loc1} to {loc2}"] = distance
# 输出距离
for route, distance in distances.items():
    print(f"{route}: {distance} miles")

from geopy.distance import geodesic
from geopy.geocoders import Nominatim
# 初始化地理编码器
geolocator = Nominatim(user_agent="geoapiExercises")
# 定义餐馆列表
restaurants = {
    "Restaurant1": "1600 Amphitheatre Parkway, Mountain View, CA",
    "Restaurant2": "1 Infinite Loop, Cupertino, CA",
    "Restaurant3": "500 Terry A Francois Blvd, San Francisco, CA"
}
# 用户当前位置
user_location = "37.7749, -122.4194"
# 获取用户坐标
user_coords = tuple(map(float, user_location.split(", ")))
# 计算用户与每个餐馆的距离
distances = {}
for name, address in restaurants.items():
    restaurant_coords = geolocator.geocode(address)
    distance = geodesic(user_coords, (restaurant_coords.latitude, restaurant_coords.longitude)).miles
    distances[name] = distance
# 推荐最近的餐馆
closest_restaurant = min(distances, key=distances.get)
print(f"The closest restaurant is {closest_restaurant}, {distances[closest_restaurant]:.2f} miles away.")