![\"python\u5982\u4f55\u6839\u636e\u6570\u636e\u753b\u51fa\u5730\u7406\u4fe1\u606f\u56fe\"](\"https:\/\/cdn-docs.pingcode.com\/wp-content\/uploads\/2024\/12\/58f5e6c4-6783-4060-8d43-c5cf72d8c0f2.webp?x-oss-process=image\/auto-orient,1\/format,webp\")
<\/p>\n<\/p>\n
Python\u7ed8\u5236\u5730\u7406\u4fe1\u606f\u56fe\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528Geopandas\u8fdb\u884c\u6570\u636e\u5904\u7406\u3001\u4f7f\u7528Matplotlib\u8fdb\u884c\u53ef\u89c6\u5316\u3001\u4f7f\u7528Folium\u8fdb\u884c\u4ea4\u4e92\u5f0f\u5730\u56fe\u7ed8\u5236\u3002<\/strong>\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5de5\u5177\u6765\u7ed8\u5236\u5730\u7406\u4fe1\u606f\u56fe\uff0c\u5e2e\u52a9\u4f60\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528\u8fd9\u4e9b\u6280\u672f\u3002<\/p>\n<\/p>\n Geopandas\u662f\u4e00\u4e2a\u5f00\u6e90\u7684Python\u5e93\uff0c\u6269\u5c55\u4e86pandas\u4ee5\u652f\u6301\u5730\u7406\u6570\u636e\u3002\u5b83\u4f7f\u5f97\u5728Python\u4e2d\u5904\u7406\u5730\u7406\u6570\u636e\u66f4\u52a0\u7b80\u5355\u548c\u9ad8\u6548\u3002\u901a\u8fc7Geopandas\uff0c\u4f60\u53ef\u4ee5\u8bfb\u53d6\u3001\u64cd\u4f5c\u548c\u4fdd\u5b58\u5730\u7406\u6570\u636e\u3002<\/p>\n<\/p>\n \u9996\u5148\uff0c\u4f60\u9700\u8981\u5b89\u88c5Geopandas\u3002\u53ef\u4ee5\u901a\u8fc7pip\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n <\/code><\/pre>\n<\/p>\n Geopandas\u652f\u6301\u591a\u79cd\u5730\u7406\u6570\u636e\u683c\u5f0f\uff0c\u5982Shapefile\u3001GeoJSON\u7b49\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528 gdf = gpd.read_file('path_to_your_shapefile.shp')<\/p>\n <\/code><\/pre>\n<\/p>\n Geopandas\u63d0\u4f9b\u4e86\u8bb8\u591a\u65b9\u6cd5\u6765\u64cd\u4f5c\u5730\u7406\u6570\u636e\u3002\u4f60\u53ef\u4ee5\u8fdb\u884c\u6570\u636e\u8fc7\u6ee4\u3001\u5408\u5e76\u3001\u7a7a\u95f4\u64cd\u4f5c\u7b49\u3002\u4f8b\u5982\uff0c\u8fc7\u6ee4\u67d0\u4e2a\u533a\u57df\u7684\u6570\u636e\uff1a<\/p>\n<\/p>\n filtered_gdf = gdf[gdf['region'] == 'specific_region']<\/p>\n <\/code><\/pre>\n<\/p>\n Matplotlib\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u53ef\u89c6\u5316\u5e93\u4e4b\u4e00\u3002\u7ed3\u5408Geopandas\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528Matplotlib\u6765\u7ed8\u5236\u9759\u6001\u5730\u7406\u4fe1\u606f\u56fe\u3002<\/p>\n<\/p>\n \u53ef\u4ee5\u901a\u8fc7pip\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n <\/code><\/pre>\n<\/p>\n \u4f7f\u7528Geopandas\u548cMatplotlib\uff0c\u4f60\u53ef\u4ee5\u8f7b\u677e\u7ed8\u5236\u5730\u7406\u4fe1\u606f\u56fe\u3002\u4f8b\u5982\uff0c\u7ed8\u5236\u4e00\u4e2a\u7b80\u5355\u7684\u5730\u56fe\uff1a<\/p>\n<\/p>\n fig, ax = plt.subplots()<\/p>\n gdf.plot(ax=ax, color='blue')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u4f60\u53ef\u4ee5\u4f7f\u7528Matplotlib\u7684\u529f\u80fd\u6765\u589e\u5f3a\u56fe\u5f62\u3002\u4f8b\u5982\uff0c\u6dfb\u52a0\u6807\u9898\u3001\u56fe\u4f8b\u3001\u989c\u8272\u6620\u5c04\u7b49\uff1a<\/p>\n<\/p>\n fig, ax = plt.subplots()<\/p>\n gdf.plot(ax=ax, column='population', cmap='OrRd', legend=True)<\/p>\n plt.title('Population Distribution')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n Folium\u662f\u4e00\u4e2a\u7528\u4e8e\u5728Python\u4e2d\u521b\u5efa\u4ea4\u4e92\u5f0f\u5730\u56fe\u7684\u5e93\u3002\u5b83\u57fa\u4e8eLeaflet.js\uff0c\u53ef\u4ee5\u751f\u6210\u53ef\u5d4c\u5165\u5728\u7f51\u9875\u4e2d\u7684\u5730\u56fe\u3002<\/p>\n<\/p>\n \u53ef\u4ee5\u901a\u8fc7pip\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n <\/code><\/pre>\n<\/p>\n \u4f7f\u7528Folium\uff0c\u4f60\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u5730\u56fe\uff0c\u5e76\u6dfb\u52a0\u5404\u79cd\u56fe\u5c42\u548c\u6807\u8bb0\u3002\u4f8b\u5982\uff0c\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u4ea4\u4e92\u5f0f\u5730\u56fe\uff1a<\/p>\n<\/p>\n m = folium.Map(location=[latitude, longitude], zoom_start=10)<\/p>\n m<\/p>\n <\/code><\/pre>\n<\/p>\n \u4f60\u53ef\u4ee5\u5728\u5730\u56fe\u4e0a\u6dfb\u52a0\u5404\u79cd\u56fe\u5c42\u548c\u6807\u8bb0\uff0c\u4f8b\u5982\uff0c\u6dfb\u52a0\u4e00\u4e2aMarker\u6807\u8bb0\uff1a<\/p>\n<\/p>\n folium.Marker([latitude, longitude], popup='Location').add_to(m)<\/p>\n m<\/p>\n <\/code><\/pre>\n<\/p>\n \u901a\u8fc7\u4ee5\u4e0a\u4ecb\u7ecd\uff0c\u4f60\u5df2\u7ecf\u4e86\u89e3\u4e86\u5982\u4f55\u4f7f\u7528Geopandas\u3001Matplotlib\u548cFolium\u6765\u5904\u7406\u548c\u53ef\u89c6\u5316\u5730\u7406\u6570\u636e\u3002\u4e0b\u9762\u6211\u4eec\u5c06\u7efc\u5408\u5e94\u7528\u8fd9\u4e9b\u5de5\u5177\uff0c\u5c55\u793a\u4e00\u4e2a\u5b8c\u6574\u7684\u5b9e\u4f8b\u3002<\/p>\n<\/p>\n \u9996\u5148\uff0c\u8bfb\u53d6\u4e00\u4e2a\u5730\u7406\u6570\u636e\u6587\u4ef6\uff0c\u4f8b\u5982\u4e00\u4e2a\u5305\u542b\u4e16\u754c\u5404\u56fd\u8fb9\u754c\u7684Shapefile\u6587\u4ef6\uff1a<\/p>\n<\/p>\n world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))<\/p>\n <\/code><\/pre>\n<\/p>\n \u5bf9\u6570\u636e\u8fdb\u884c\u5904\u7406\uff0c\u4f8b\u5982\u8fc7\u6ee4\u51fa\u67d0\u4e2a\u5927\u6d32\u7684\u6570\u636e\uff1a<\/p>\n<\/p>\n asia = world[world['continent'] == 'Asia']<\/p>\n <\/code><\/pre>\n<\/p>\n \u4f7f\u7528Matplotlib\u7ed8\u5236\u4e9a\u6d32\u56fd\u5bb6\u7684\u5730\u56fe\uff1a<\/p>\n<\/p>\n fig, ax = plt.subplots(figsize=(10, 10))<\/p>\n asia.plot(ax=ax, color='lightblue', edgecolor='black')<\/p>\n plt.title('Map of Asia')<\/p>\n plt.show()<\/p>\n <\/code><\/pre>\n<\/p>\n \u4f7f\u7528Folium\u521b\u5efa\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u5730\u56fe\uff0c\u5e76\u6dfb\u52a0\u4e9a\u6d32\u5404\u56fd\u7684Marker\u6807\u8bb0\uff1a<\/p>\n<\/p>\n m = folium.Map(location=[20, 100], zoom_start=3)<\/p>\n for idx, row in asia.iterrows():<\/p>\n folium.Marker([row['geometry'].centroid.y, row['geometry'].centroid.x], <\/p>\n popup=row['name']).add_to(m)<\/p>\n m<\/p>\n <\/code><\/pre>\n<\/p>\n \u672c\u6587\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5982\u4f55\u4f7f\u7528Python\u7ed8\u5236\u5730\u7406\u4fe1\u606f\u56fe\uff0c\u4e3b\u8981\u6db5\u76d6\u4e86Geopandas\u6570\u636e\u5904\u7406\u3001Matplotlib\u9759\u6001\u5730\u56fe\u7ed8\u5236\u548cFolium\u4ea4\u4e92\u5f0f\u5730\u56fe\u521b\u5efa\u3002\u901a\u8fc7\u7efc\u5408\u5e94\u7528\u8fd9\u4e9b\u5de5\u5177\uff0c\u4f60\u53ef\u4ee5\u8f7b\u677e\u5904\u7406\u548c\u53ef\u89c6\u5316\u5730\u7406\u6570\u636e\uff0c\u6ee1\u8db3\u5404\u79cd\u5730\u7406\u4fe1\u606f\u56fe\u7684\u9700\u6c42\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\uff0c\u80fd\u591f\u5728\u5b9e\u9645\u9879\u76ee\u4e2d\u7075\u6d3b\u5e94\u7528\u8fd9\u4e9b\u6280\u672f\u3002<\/p>\n<\/p>\n \u5982\u4f55\u9009\u62e9\u5408\u9002\u7684Python\u5e93\u6765\u7ed8\u5236\u5730\u7406\u4fe1\u606f\u56fe\uff1f<\/strong> \u7ed8\u5236\u5730\u7406\u4fe1\u606f\u56fe\u65f6\uff0c\u5982\u4f55\u51c6\u5907\u548c\u5904\u7406\u6570\u636e\uff1f<\/strong> \u5982\u4f55\u5728Python\u4e2d\u5b9e\u73b0\u4ea4\u4e92\u5f0f\u5730\u7406\u4fe1\u606f\u56fe\u7684\u7ed8\u5236\uff1f<\/strong>\u4e00\u3001Geopandas\u6570\u636e\u5904\u7406<\/h3>\n<\/p>\n
1\u3001\u5b89\u88c5Geopandas<\/h4>\n<\/p>\n
pip install geopandas<\/p>\n2\u3001\u8bfb\u53d6\u5730\u7406\u6570\u636e<\/h4>\n<\/p>\n
read_file<\/code>\u65b9\u6cd5\u6765\u8bfb\u53d6\u8fd9\u4e9b\u6570\u636e\u3002\u4f8b\u5982\uff0c\u8bfb\u53d6\u4e00\u4e2aShapefile\u6587\u4ef6\uff1a<\/p>\n<\/p>\nimport geopandas as gpd<\/p>\n\u8bfb\u53d6Shapefile\u6587\u4ef6<\/strong><\/h2>\n
3\u3001\u6570\u636e\u64cd\u4f5c<\/h4>\n<\/p>\n
# \u8fc7\u6ee4\u67d0\u4e2a\u533a\u57df\u7684\u6570\u636e<\/p>\n\u4e8c\u3001Matplotlib\u53ef\u89c6\u5316<\/h3>\n<\/p>\n
1\u3001\u5b89\u88c5Matplotlib<\/h4>\n<\/p>\n
pip install matplotlib<\/p>\n2\u3001\u7ed8\u5236\u5730\u7406\u4fe1\u606f\u56fe<\/h4>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n\u521b\u5efa\u4e00\u4e2a\u56fe\u5f62\u548c\u5750\u6807\u8f74<\/strong><\/h2>\n
\u7ed8\u5236\u5730\u7406\u6570\u636e<\/strong><\/h2>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
3\u3001\u589e\u5f3a\u56fe\u5f62<\/h4>\n<\/p>\n
# \u7ed8\u5236\u5e26\u989c\u8272\u6620\u5c04\u7684\u5730\u56fe<\/p>\n\u6dfb\u52a0\u6807\u9898<\/strong><\/h2>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
\u4e09\u3001Folium\u4ea4\u4e92\u5f0f\u5730\u56fe<\/h3>\n<\/p>\n
1\u3001\u5b89\u88c5Folium<\/h4>\n<\/p>\n
pip install folium<\/p>\n2\u3001\u521b\u5efa\u4ea4\u4e92\u5f0f\u5730\u56fe<\/h4>\n<\/p>\n
import folium<\/p>\n\u521b\u5efa\u4e00\u4e2a\u5730\u56fe\u5bf9\u8c61<\/strong><\/h2>\n
\u663e\u793a\u5730\u56fe<\/strong><\/h2>\n
3\u3001\u6dfb\u52a0\u56fe\u5c42\u548c\u6807\u8bb0<\/h4>\n<\/p>\n
# \u6dfb\u52a0\u4e00\u4e2aMarker\u6807\u8bb0<\/p>\n\u663e\u793a\u5730\u56fe<\/strong><\/h2>\n
\u56db\u3001\u7efc\u5408\u5e94\u7528\u5b9e\u4f8b<\/h3>\n<\/p>\n
1\u3001\u8bfb\u53d6\u6570\u636e<\/h4>\n<\/p>\n
import geopandas as gpd<\/p>\n\u8bfb\u53d6\u4e16\u754c\u5404\u56fd\u8fb9\u754c\u6570\u636e<\/strong><\/h2>\n
2\u3001\u6570\u636e\u5904\u7406<\/h4>\n<\/p>\n
# \u8fc7\u6ee4\u51fa\u4e9a\u6d32\u7684\u6570\u636e<\/p>\n3\u3001\u4f7f\u7528Matplotlib\u7ed8\u5236\u5730\u56fe<\/h4>\n<\/p>\n
import matplotlib.pyplot as plt<\/p>\n\u521b\u5efa\u4e00\u4e2a\u56fe\u5f62\u548c\u5750\u6807\u8f74<\/strong><\/h2>\n
\u7ed8\u5236\u4e9a\u6d32\u56fd\u5bb6<\/strong><\/h2>\n
\u6dfb\u52a0\u6807\u9898<\/strong><\/h2>\n
\u663e\u793a\u56fe\u5f62<\/strong><\/h2>\n
4\u3001\u4f7f\u7528Folium\u521b\u5efa\u4ea4\u4e92\u5f0f\u5730\u56fe<\/h4>\n<\/p>\n
import folium<\/p>\n\u521b\u5efa\u4e00\u4e2a\u5730\u56fe\u5bf9\u8c61<\/strong><\/h2>\n
\u6dfb\u52a0\u4e9a\u6d32\u5404\u56fd\u7684Marker\u6807\u8bb0<\/strong><\/h2>\n
\u663e\u793a\u5730\u56fe<\/strong><\/h2>\n
\u4e94\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
\u5728Python\u4e2d\uff0c\u6709\u51e0\u4e2a\u6d41\u884c\u7684\u5e93\u53ef\u4ee5\u7528\u6765\u7ed8\u5236\u5730\u7406\u4fe1\u606f\u56fe\uff0c\u4f8b\u5982Matplotlib\u3001Basemap\u3001Geopandas\u548cFolium\u7b49\u3002\u6839\u636e\u9879\u76ee\u7684\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u5e93\u975e\u5e38\u91cd\u8981\u3002\u5982\u679c\u9700\u8981\u8fdb\u884c\u9759\u6001\u5730\u56fe\u7684\u7ed8\u5236\uff0cMatplotlib\u7ed3\u5408Basemap\u53ef\u80fd\u662f\u4e0d\u9519\u7684\u9009\u62e9\uff1b\u800c\u5bf9\u4e8e\u4ea4\u4e92\u5f0f\u5730\u56fe\uff0cFolium\u662f\u4e00\u4e2a\u975e\u5e38\u597d\u7684\u9009\u62e9\u3002Geopandas\u5219\u9002\u5408\u5904\u7406\u5730\u7406\u6570\u636e\uff0c\u5e76\u80fd\u8f7b\u677e\u7ed8\u5236\u5730\u56fe\u3002<\/p>\n
\u5728\u7ed8\u5236\u5730\u7406\u4fe1\u606f\u56fe\u4e4b\u524d\uff0c\u786e\u4fdd\u6570\u636e\u7684\u683c\u5f0f\u6b63\u786e\u81f3\u5173\u91cd\u8981\u3002\u901a\u5e38\uff0c\u5730\u7406\u6570\u636e\u9700\u8981\u5305\u542b\u5750\u6807\u4fe1\u606f\uff08\u5982\u7ecf\u7eac\u5ea6\uff09\u4ee5\u53ca\u5176\u4ed6\u76f8\u5173\u5c5e\u6027\u3002\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u6765\u5904\u7406\u6570\u636e\uff0c\u786e\u4fdd\u6570\u636e\u6e05\u6d17\u3001\u683c\u5f0f\u8f6c\u6362\u548c\u7f3a\u5931\u503c\u5904\u7406\u7b49\u6b65\u9aa4\u5df2\u7ecf\u5b8c\u6210\u3002\u540c\u65f6\uff0cGeoPandas\u53ef\u4ee5\u5e2e\u52a9\u5c06\u6570\u636e\u8f6c\u6362\u4e3a\u5730\u7406\u6570\u636e\u6846\u67b6\uff0c\u4ee5\u4fbf\u4e8e\u540e\u7eed\u7ed8\u56fe\u3002<\/p>\n
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