{"id":1096321,"date":"2025-01-08T15:00:07","date_gmt":"2025-01-08T07:00:07","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1096321.html"},"modified":"2025-01-08T15:00:10","modified_gmt":"2025-01-08T07:00:10","slug":"%e7%bb%99%e5%87%ba%e5%9c%b0%e7%90%86%e5%9d%90%e6%a0%87python%e4%b8%ad%e5%a6%82%e4%bd%95%e5%ae%9a%e4%bd%8d-3","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1096321.html","title":{"rendered":"\u7ed9\u51fa\u5730\u7406\u5750\u6807python\u4e2d\u5982\u4f55\u5b9a\u4f4d"},"content":{"rendered":"

\"\u7ed9\u51fa\u5730\u7406\u5750\u6807python\u4e2d\u5982\u4f55\u5b9a\u4f4d\"<\/p>\n

\u5f00\u5934\u6bb5\u843d\uff1a<\/h3>\n<\/p>\n

\u5229\u7528Python\u7684\u5730\u7406\u5750\u6807\u5b9a\u4f4d\uff0c\u53ef\u4ee5\u5b9e\u73b0\u5730\u7406\u6570\u636e\u7684\u5904\u7406\u548c\u5206\u6790\u3001\u5730\u7406\u7f16\u7801\u548c\u53cd\u5411\u5730\u7406\u7f16\u7801\u3001\u53ef\u89c6\u5316\u5730\u7406\u4fe1\u606f\u3002<\/strong>\u5728\u8fd9\u4e9b\u5e94\u7528\u4e2d\uff0c\u5730\u7406\u7f16\u7801\u548c\u53cd\u5411\u5730\u7406\u7f16\u7801\u5c24\u4e3a\u91cd\u8981\u3002\u5730\u7406\u7f16\u7801\u662f\u5c06\u5730\u5740\u8f6c\u6362\u4e3a\u5730\u7406\u5750\u6807\u7684\u8fc7\u7a0b\uff0c\u800c\u53cd\u5411\u5730\u7406\u7f16\u7801\u5219\u662f\u5c06\u5730\u7406\u5750\u6807\u8f6c\u6362\u4e3a\u5730\u5740\u7684\u8fc7\u7a0b\u3002\u63a5\u4e0b\u6765\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u5728Python\u4e2d\u5982\u4f55\u5b9e\u73b0\u8fd9\u4e9b\u529f\u80fd\uff0c\u5e76\u4e14\u5c55\u793a\u4e00\u4e9b\u5b9e\u9645\u7684\u4ee3\u7801\u793a\u4f8b\u3002<\/p>\n<\/p>\n

\u6b63\u6587\uff1a<\/h3>\n<\/p>\n

\u4e00\u3001\u5730\u7406\u6570\u636e\u7684\u5904\u7406\u548c\u5206\u6790<\/h4>\n<\/p>\n

\u5730\u7406\u6570\u636e\u7684\u5904\u7406\u548c\u5206\u6790\u5728\u8bb8\u591a\u9886\u57df\u4e2d\u662f\u81f3\u5173\u91cd\u8981\u7684\u3002Python\u63d0\u4f9b\u4e86\u591a\u79cd\u5e93\u6765\u5904\u7406\u548c\u5206\u6790\u5730\u7406\u6570\u636e\uff0c\u5982Geopandas\u3001Shapely\u548cFiona\u7b49\u3002\u8fd9\u4e9b\u5e93\u4f7f\u5f97\u6211\u4eec\u80fd\u591f\u8f7b\u677e\u5730\u8bfb\u53d6\u3001\u5904\u7406\u548c\u53ef\u89c6\u5316\u5730\u7406\u6570\u636e\u3002<\/p>\n<\/p>\n

1. Geopandas\u7684\u4f7f\u7528<\/strong>\uff1a<\/p>\n<\/p>\n

Geopandas\u662f\u4e00\u4e2a\u57fa\u4e8ePandas\u7684\u6269\u5c55\u5e93\uff0c\u4e13\u95e8\u7528\u4e8e\u5904\u7406\u5730\u7406\u6570\u636e\u3002\u5b83\u4f7f\u5f97\u5730\u7406\u6570\u636e\u7684\u64cd\u4f5c\u50cf\u5904\u7406Pandas DataFrame\u4e00\u6837\u7b80\u5355\u3002<\/p>\n<\/p>\n

import geopandas as gpd<\/p>\n
\u8bfb\u53d6Shapefile<\/strong><\/h2>\n
gdf = gpd.read_file("path\/to\/shapefile.shp")<\/p>\n
\u6253\u5370\u524d\u51e0\u884c\u6570\u636e<\/strong><\/h2>\n
print(gdf.head())<\/p>\n
\u8ba1\u7b97\u6bcf\u4e2a\u51e0\u4f55\u5bf9\u8c61\u7684\u9762\u79ef<\/strong><\/h2>\n
gdf['area'] = gdf['geometry'].area<\/p>\n
\u8ba1\u7b97\u6bcf\u4e2a\u51e0\u4f55\u5bf9\u8c61\u7684\u5468\u957f<\/strong><\/h2>\n
gdf['perimeter'] = gdf['geometry'].length<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. Shapely\u7684\u4f7f\u7528<\/strong>\uff1a<\/p>\n<\/p>\n
Shapely\u662f\u4e00\u4e2a\u7528\u4e8e\u64cd\u4f5c\u548c\u5206\u6790\u51e0\u4f55\u5bf9\u8c61\u7684\u5e93\u3002\u5b83\u53ef\u4ee5\u7528\u4e8e\u521b\u5efa\u548c\u64cd\u4f5c\u70b9\u3001\u7ebf\u3001\u591a\u8fb9\u5f62\u7b49\u51e0\u4f55\u5bf9\u8c61\u3002<\/p>\n<\/p>\n
from shapely.geometry import Point, LineString, Polygon<\/p>\n
\u521b\u5efa\u70b9\u5bf9\u8c61<\/strong><\/h2>\n
point = Point(1, 1)<\/p>\n
\u521b\u5efa\u7ebf\u5bf9\u8c61<\/strong><\/h2>\n
line = LineString([(0, 0), (1, 1), (1, 2)])<\/p>\n
\u521b\u5efa\u591a\u8fb9\u5f62\u5bf9\u8c61<\/strong><\/h2>\n
polygon = Polygon([(0, 0), (1, 1), (1, 0)])<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u5730\u7406\u7f16\u7801\u548c\u53cd\u5411\u5730\u7406\u7f16\u7801<\/h4>\n<\/p>\n
\u5730\u7406\u7f16\u7801\u662f\u5c06\u5730\u5740\u8f6c\u6362\u4e3a\u5730\u7406\u5750\u6807\u7684\u8fc7\u7a0b\uff0c\u800c\u53cd\u5411\u5730\u7406\u7f16\u7801\u5219\u662f\u5c06\u5730\u7406\u5750\u6807\u8f6c\u6362\u4e3a\u5730\u5740\u7684\u8fc7\u7a0b\u3002Python\u63d0\u4f9b\u4e86\u591a\u4e2a\u5e93\u6765\u5b9e\u73b0\u5730\u7406\u7f16\u7801\u548c\u53cd\u5411\u5730\u7406\u7f16\u7801\uff0c\u5982Geopy\u3001Geocoder\u548cGoogle Maps API\u7b49\u3002<\/p>\n<\/p>\n
1. Geopy\u7684\u4f7f\u7528<\/strong>\uff1a<\/p>\n<\/p>\n
Geopy\u662f\u4e00\u4e2aPython\u5e93\uff0c\u7528\u4e8e\u8fdb\u884c\u5730\u7406\u7f16\u7801\u548c\u53cd\u5411\u5730\u7406\u7f16\u7801\u3002\u5b83\u652f\u6301\u591a\u4e2a\u5730\u7406\u7f16\u7801\u670d\u52a1\uff0c\u5982Nominatim\u3001Google Maps\u3001Bing Maps\u7b49\u3002<\/p>\n<\/p>\n
from geopy.geocoders import Nominatim<\/p>\n
geolocator = Nominatim(user_agent="geoapi")<\/p>\n
\u5730\u7406\u7f16\u7801<\/strong><\/h2>\n
location = geolocator.geocode("1600 Amphitheatre Parkway, MountAI<\/a>n View, CA")<\/p>\n
print((location.latitude, location.longitude))<\/p>\n
\u53cd\u5411\u5730\u7406\u7f16\u7801<\/strong><\/h2>\n
location = geolocator.reverse("37.4219999, -122.0840575")<\/p>\n
print(location.address)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528Google Maps API<\/strong>\uff1a<\/p>\n<\/p>\n
Google Maps API\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u5730\u7406\u7f16\u7801\u548c\u53cd\u5411\u5730\u7406\u7f16\u7801\u529f\u80fd\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u7684googlemaps\u5e93\u6765\u8bbf\u95ee\u8fd9\u4e9b\u529f\u80fd\u3002<\/p>\n<\/p>\n
import googlemaps<\/p>\n
\u521d\u59cb\u5316Google Maps\u5ba2\u6237\u7aef<\/strong><\/h2>\n
gmaps = googlemaps.Client(key='YOUR_API_KEY')<\/p>\n
\u5730\u7406\u7f16\u7801<\/strong><\/h2>\n
geocode_result = gmaps.geocode('1600 Amphitheatre Parkway, Mountain View, CA')<\/p>\n
location = geocode_result[0]['geometry']['location']<\/p>\n
print((location['lat'], location['lng']))<\/p>\n
\u53cd\u5411\u5730\u7406\u7f16\u7801<\/strong><\/h2>\n
reverse_geocode_result = gmaps.reverse_geocode((37.4219999, -122.0840575))<\/p>\n
address = reverse_geocode_result[0]['formatted_address']<\/p>\n
print(address)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u53ef\u89c6\u5316\u5730\u7406\u4fe1\u606f<\/h4>\n<\/p>\n
\u53ef\u89c6\u5316\u5730\u7406\u4fe1\u606f\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u548c\u5206\u6790\u5730\u7406\u6570\u636e\u3002Python\u63d0\u4f9b\u4e86\u591a\u4e2a\u5e93\u6765\u5b9e\u73b0\u5730\u7406\u4fe1\u606f\u7684\u53ef\u89c6\u5316\uff0c\u5982Folium\u3001Matplotlib\u548cPlotly\u7b49\u3002<\/p>\n<\/p>\n
1. Folium\u7684\u4f7f\u7528<\/strong>\uff1a<\/p>\n<\/p>\n
Folium\u662f\u4e00\u4e2a\u57fa\u4e8eLeaflet\u7684Python\u5e93\uff0c\u7528\u4e8e\u521b\u5efa\u4ea4\u4e92\u5f0f\u5730\u56fe\u3002\u5b83\u975e\u5e38\u9002\u5408\u7528\u4e8e\u53ef\u89c6\u5316\u5730\u7406\u6570\u636e\u3002<\/p>\n<\/p>\n
import folium<\/p>\n
\u521b\u5efa\u5730\u56fe\u5bf9\u8c61<\/strong><\/h2>\n
m = folium.Map(location=[37.7749, -122.4194], zoom_start=13)<\/p>\n
\u6dfb\u52a0\u6807\u8bb0<\/strong><\/h2>\n
folium.Marker([37.7749, -122.4194], popup='San Francisco').add_to(m)<\/p>\n
\u4fdd\u5b58\u5730\u56fe\u5230HTML\u6587\u4ef6<\/strong><\/h2>\n
m.save('map.html')<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528Matplotlib\u8fdb\u884c\u5730\u7406\u53ef\u89c6\u5316<\/strong>\uff1a<\/p>\n<\/p>\n
Matplotlib\u662f\u4e00\u4e2a\u5e7f\u6cdb\u4f7f\u7528\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\u3002\u7ed3\u5408Basemap\u6216Cartopy\uff0c\u53ef\u4ee5\u7528\u4e8e\u521b\u5efa\u5730\u7406\u5730\u56fe\u3002<\/p>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
from mpl_toolkits.basemap import Basemap<\/p>\n
\u521b\u5efa\u5730\u56fe\u5bf9\u8c61<\/strong><\/h2>\n
m = Basemap(projection='mill', llcrnrlat=-60, urcrnrlat=90, llcrnrlon=-180, urcrnrlon=180)<\/p>\n
\u7ed8\u5236\u6d77\u5cb8\u7ebf\u548c\u56fd\u5bb6\u8fb9\u754c<\/strong><\/h2>\n
m.drawcoastlines()<\/p>\n
m.drawcountries()<\/p>\n
\u663e\u793a\u5730\u56fe<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u5b9e\u9645\u5e94\u7528\u6848\u4f8b<\/h4>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u5730\u7406\u5750\u6807\u5b9a\u4f4d\u548c\u5730\u7406\u6570\u636e\u5904\u7406\u5e7f\u6cdb\u5e94\u7528\u4e8e\u591a\u4e2a\u9886\u57df\uff0c\u5982\u7269\u6d41\u3001\u4ea4\u901a\u3001\u73af\u5883\u76d1\u6d4b\u548c\u57ce\u5e02\u89c4\u5212\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5177\u4f53\u7684\u5e94\u7528\u6848\u4f8b\u3002<\/p>\n<\/p>\n
1. \u7269\u6d41\u9886\u57df<\/strong>\uff1a<\/p>\n<\/p>\n
\u5728\u7269\u6d41\u9886\u57df\uff0c\u53ef\u4ee5\u4f7f\u7528\u5730\u7406\u7f16\u7801\u548c\u53cd\u5411\u5730\u7406\u7f16\u7801\u6765\u4f18\u5316\u914d\u9001\u8def\u7ebf\u548c\u63d0\u9ad8\u914d\u9001\u6548\u7387\u3002\u901a\u8fc7\u5206\u6790\u914d\u9001\u5730\u5740\u548c\u914d\u9001\u8def\u7ebf\uff0c\u53ef\u4ee5\u627e\u5230\u6700\u4f18\u7684\u914d\u9001\u8def\u5f84\uff0c\u4ece\u800c\u964d\u4f4e\u6210\u672c\u548c\u63d0\u9ad8\u6548\u7387\u3002<\/p>\n<\/p>\n
from geopy.distance import geodesic<\/p>\n
\u5b9a\u4e49\u914d\u9001\u70b9<\/strong><\/h2>\n
points = [(37.7749, -122.4194), (34.0522, -118.2437), (36.7783, -119.4179)]<\/p>\n
\u8ba1\u7b97\u914d\u9001\u70b9\u4e4b\u95f4\u7684\u8ddd\u79bb<\/strong><\/h2>\n
for i in range(len(points)):<\/p>\n
    for j in range(i + 1, len(points)):<\/p>\n
        distance = geodesic(points[i], points[j]).miles<\/p>\n
        print(f"Distance between {points[i]} and {points[j]}: {distance:.2f} miles")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u73af\u5883\u76d1\u6d4b<\/strong>\uff1a<\/p>\n<\/p>\n
\u5728\u73af\u5883\u76d1\u6d4b\u9886\u57df\uff0c\u53ef\u4ee5\u4f7f\u7528\u5730\u7406\u5750\u6807\u5b9a\u4f4d\u6765\u76d1\u6d4b\u6c61\u67d3\u6e90\u7684\u4f4d\u7f6e\u548c\u6c61\u67d3\u7269\u7684\u6269\u6563\u60c5\u51b5\u3002\u901a\u8fc7\u5206\u6790\u5730\u7406\u6570\u636e\uff0c\u53ef\u4ee5\u9884\u6d4b\u6c61\u67d3\u7269\u7684\u6269\u6563\u8def\u5f84\u548c\u5f71\u54cd\u8303\u56f4\uff0c\u4ece\u800c\u91c7\u53d6\u76f8\u5e94\u7684\u63aa\u65bd\u8fdb\u884c\u6cbb\u7406\u3002<\/p>\n<\/p>\n
import geopandas as gpd<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
\u8bfb\u53d6\u6c61\u67d3\u6e90\u6570\u636e<\/strong><\/h2>\n
pollution_sources = gpd.read_file("path\/to\/pollution_sources.shp")<\/p>\n
\u521b\u5efa\u5730\u56fe\u5bf9\u8c61<\/strong><\/h2>\n
ax = pollution_sources.plot(figsize=(10, 10), color='red')<\/p>\n
\u663e\u793a\u5730\u56fe<\/strong><\/h2>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u7ed3\u8bba<\/h4>\n<\/p>\n
\u901a\u8fc7\u672c\u6587\u7684\u5b66\u4e60\uff0c\u6211\u4eec\u4e86\u89e3\u4e86\u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528\u5730\u7406\u5750\u6807\u8fdb\u884c\u5b9a\u4f4d\u3002\u6211\u4eec\u63a2\u8ba8\u4e86\u5730\u7406\u6570\u636e\u7684\u5904\u7406\u548c\u5206\u6790\u3001\u5730\u7406\u7f16\u7801\u548c\u53cd\u5411\u5730\u7406\u7f16\u7801\u3001\u4ee5\u53ca\u5730\u7406\u4fe1\u606f\u7684\u53ef\u89c6\u5316\u3002\u901a\u8fc7\u5b9e\u9645\u7684\u4ee3\u7801\u793a\u4f8b\uff0c\u6211\u4eec\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528Geopandas\u3001Shapely\u3001Geopy\u3001Google Maps API\u3001Folium\u548cMatplotlib\u7b49\u5e93\u6765\u5b9e\u73b0\u8fd9\u4e9b\u529f\u80fd\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u5730\u7406\u5750\u6807\u5b9a\u4f4d\u548c\u5730\u7406\u6570\u636e\u5904\u7406\u5e7f\u6cdb\u5e94\u7528\u4e8e\u591a\u4e2a\u9886\u57df\uff0c\u5982\u7269\u6d41\u3001\u4ea4\u901a\u3001\u73af\u5883\u76d1\u6d4b\u548c\u57ce\u5e02\u89c4\u5212\u7b49\u3002\u5e0c\u671b\u901a\u8fc7\u672c\u6587\u7684\u5b66\u4e60\uff0c\u80fd\u591f\u5e2e\u52a9\u8bfb\u8005\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528\u5730\u7406\u5750\u6807\u5b9a\u4f4d\u6280\u672f\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u4f7f\u7528\u5730\u7406\u5750\u6807\u8fdb\u884c\u4f4d\u7f6e\u5b9a\u4f4d\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u591a\u4e2a\u5e93\u6765\u8fdb\u884c\u5730\u7406\u5750\u6807\u5b9a\u4f4d\uff0c\u6700\u5e38\u7528\u7684\u662fGeopy\u548cFolium\u3002Geopy\u53ef\u4ee5\u5e2e\u52a9\u60a8\u901a\u8fc7\u7ecf\u7eac\u5ea6\u83b7\u53d6\u5177\u4f53\u5730\u5740\uff0c\u800cFolium\u5219\u53ef\u4ee5\u7528\u6765\u5728\u5730\u56fe\u4e0a\u53ef\u89c6\u5316\u8fd9\u4e9b\u4f4d\u7f6e\u3002\u4f7f\u7528Geopy\uff0c\u60a8\u53ea\u9700\u8f93\u5165\u7ecf\u7eac\u5ea6\u5373\u53ef\u83b7\u53d6\u8be6\u7ec6\u7684\u5730\u5740\u4fe1\u606f\uff0c\u975e\u5e38\u9002\u5408\u5730\u7406\u6570\u636e\u5206\u6790\u3002<\/p>\n
\u5728Python\u4e2d\u83b7\u53d6\u5730\u7406\u5750\u6807\u7684\u6700\u4f73\u5e93\u6709\u54ea\u4e9b\uff1f<\/strong>
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