{"id":945690,"date":"2024-12-26T23:28:19","date_gmt":"2024-12-26T15:28:19","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/945690.html"},"modified":"2024-12-26T23:28:20","modified_gmt":"2024-12-26T15:28:20","slug":"python%e5%a6%82%e4%bd%95%e7%9c%8b%e5%9c%b0%e7%90%83","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/945690.html","title":{"rendered":"python\u5982\u4f55\u770b\u5730\u7403"},"content":{"rendered":"

\"python\u5982\u4f55\u770b\u5730\u7403\"<\/p>\n

\u4f7f\u7528Python\u67e5\u770b\u5730\u7403\u7684\u5e38\u89c1\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528\u5730\u7406\u4fe1\u606f\u7cfb\u7edf\uff08GIS\uff09\u5e93\u3001\u536b\u661f\u5f71\u50cf\u5904\u7406\u5e93\u3001\u865a\u62df\u5730\u7403\u5e93\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u4f7f\u7528\u5730\u7406\u4fe1\u606f\u7cfb\u7edf\u5e93\u4e2d\u7684GeoPandas\u6765\u5904\u7406\u7a7a\u95f4\u6570\u636e\u3002<\/strong><\/p>\n<\/p>\n

GeoPandas\u662f\u4e00\u4e2a\u5f3a\u5927\u7684Python\u5e93\uff0c\u53ef\u4ee5\u5904\u7406\u548c\u5206\u6790\u5730\u7406\u7a7a\u95f4\u6570\u636e\u3002\u5b83\u662fPandas\u5e93\u7684\u6269\u5c55\uff0c\u4e13\u95e8\u7528\u4e8e\u7a7a\u95f4\u6570\u636e\u7684\u5904\u7406\u3002\u901a\u8fc7GeoPandas\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u8fdb\u884c\u7a7a\u95f4\u5206\u6790\u3001\u5730\u56fe\u7ed8\u5236\u548c\u6570\u636e\u53ef\u89c6\u5316\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528GeoPandas\u67e5\u770b\u548c\u5206\u6790\u5730\u7403\u7684\u5730\u7406\u6570\u636e\u3002<\/p>\n<\/p>\n

\u4e00\u3001GEO\u6570\u636e\u5904\u7406<\/p>\n<\/p>\n

GeoPandas\u662f\u4e00\u4e2a\u7528\u4e8e\u5904\u7406\u5730\u7406\u7a7a\u95f4\u6570\u636e\u7684\u5f00\u6e90\u5e93\u3002\u5b83\u6269\u5c55\u4e86Pandas\u7684\u529f\u80fd\uff0c\u4f7f\u5f97\u7528\u6237\u53ef\u4ee5\u8f7b\u677e\u5904\u7406\u5730\u7406\u6570\u636e\u3002GeoPandas\u652f\u6301\u5404\u79cd\u5e38\u89c1\u7684\u5730\u7406\u6570\u636e\u683c\u5f0f\uff0c\u5982Shapefile\u3001GeoJSON\u3001KML\u7b49\u3002\u8981\u4f7f\u7528GeoPandas\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u5b83\uff1a<\/p>\n<\/p>\n

pip install geopandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
    \n
  1. \u52a0\u8f7d\u548c\u5904\u7406\u5730\u7406\u6570\u636e<\/strong><\/li>\n<\/ol>\n
    \u52a0\u8f7d\u5730\u7406\u6570\u636e\u662f\u4f7f\u7528GeoPandas\u7684\u7b2c\u4e00\u6b65\u3002\u7528\u6237\u53ef\u4ee5\u4f7f\u7528gpd.read_file()<\/code>\u51fd\u6570\u52a0\u8f7d\u5404\u79cd\u5730\u7406\u6570\u636e\u6587\u4ef6\uff0c\u4f8b\u5982Shapefile\u3001GeoJSON\u7b49\u3002<\/p>\n<\/p>\n
    import geopandas as gpd<\/p>\n
    \u52a0\u8f7dShapefile\u6587\u4ef6<\/strong><\/h2>\n
    gdf = gpd.read_file('path_to_your_shapefile.shp')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u52a0\u8f7d\u6570\u636e\u540e\uff0c\u7528\u6237\u53ef\u4ee5\u50cf\u4f7f\u7528Pandas\u5904\u7406DataFrame\u4e00\u6837\u5904\u7406GeoDataFrame\u3002GeoDataFrame\u5305\u542b\u4e00\u4e2a\u51e0\u4f55\u5217\uff0c\u5176\u4e2d\u5b58\u50a8\u7a7a\u95f4\u4fe1\u606f\uff0c\u5982\u70b9\u3001\u7ebf\u6216\u591a\u8fb9\u5f62\u3002<\/p>\n<\/p>\n
      \n
    1. \u6570\u636e\u64cd\u4f5c<\/strong><\/li>\n<\/ol>\n
      GeoPandas\u63d0\u4f9b\u4e86\u8bb8\u591a\u7a7a\u95f4\u6570\u636e\u64cd\u4f5c\u529f\u80fd\uff0c\u4f8b\u5982\u7f13\u51b2\u533a\u521b\u5efa\u3001\u7a7a\u95f4\u8fde\u63a5\u548c\u53e0\u52a0\u5206\u6790\u7b49\u3002<\/p>\n<\/p>\n
        \n
      • \u7f13\u51b2\u533a\u521b\u5efa<\/strong>\uff1a\u53ef\u4ee5\u4f7f\u7528buffer()<\/code>\u51fd\u6570\u521b\u5efa\u51e0\u4f55\u7f13\u51b2\u533a\u3002<\/li>\n<\/ul>\n
        # \u521b\u5efa\u51e0\u4f55\u7f13\u51b2\u533a<\/p>\n
        gdf['geometry'] = gdf['geometry'].buffer(0.01)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        • \u7a7a\u95f4\u8fde\u63a5<\/strong>\uff1a\u53ef\u4ee5\u4f7f\u7528spatial_join()<\/code>\u51fd\u6570\u8fdb\u884c\u7a7a\u95f4\u8fde\u63a5\u3002<\/li>\n<\/ul>\n
          # \u8fdb\u884c\u7a7a\u95f4\u8fde\u63a5<\/p>\n
          joined_gdf = gpd.sjoin(gdf1, gdf2, how='inner', op='intersects')<\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          • \u53e0\u52a0\u5206\u6790<\/strong>\uff1a\u53ef\u4ee5\u4f7f\u7528overlay()<\/code>\u51fd\u6570\u8fdb\u884c\u51e0\u4f55\u53e0\u52a0\u5206\u6790\u3002<\/li>\n<\/ul>\n
            # \u8fdb\u884c\u53e0\u52a0\u5206\u6790<\/p>\n
            overlay_gdf = gpd.overlay(gdf1, gdf2, how='union')<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u4e8c\u3001\u6570\u636e\u53ef\u89c6\u5316<\/p>\n<\/p>\n
            GeoPandas\u4e0d\u4ec5\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u529f\u80fd\uff0c\u8fd8\u652f\u6301\u5730\u56fe\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
              \n
            1. \u57fa\u672c\u7ed8\u56fe<\/strong><\/li>\n<\/ol>\n
              GeoPandas\u7684plot()<\/code>\u65b9\u6cd5\u53ef\u4ee5\u5feb\u901f\u7ed8\u5236\u5730\u7406\u6570\u636e\u3002<\/p>\n<\/p>\n
              # \u57fa\u672c\u7ed8\u56fe<\/p>\n
              gdf.plot()<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u53c2\u6570\u6765\u81ea\u5b9a\u4e49\u5730\u56fe\u7684\u6837\u5f0f\uff0c\u5982\u989c\u8272\u3001\u900f\u660e\u5ea6\u3001\u56fe\u4f8b\u7b49\u3002<\/p>\n<\/p>\n
                \n
              1. \u9ad8\u7ea7\u7ed8\u56fe<\/strong><\/li>\n<\/ol>\n
                \u7ed3\u5408Matplotlib\uff0c\u7528\u6237\u53ef\u4ee5\u521b\u5efa\u66f4\u590d\u6742\u7684\u5730\u56fe\u3002<\/p>\n<\/p>\n
                import matplotlib.pyplot as plt<\/p>\n
                fig, ax = plt.subplots()<\/p>\n
                gdf.plot(ax=ax, color='blue', edgecolor='black')<\/p>\n
                plt.show()<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u4e09\u3001\u7a7a\u95f4\u5206\u6790<\/p>\n<\/p>\n
                GeoPandas\u8fd8\u652f\u6301\u5404\u79cd\u7a7a\u95f4\u5206\u6790\u64cd\u4f5c\uff0c\u4f8b\u5982\u8ba1\u7b97\u51e0\u4f55\u9762\u79ef\u3001\u957f\u5ea6\u548c\u8ddd\u79bb\u7b49\u3002<\/p>\n<\/p>\n
                  \n
                1. \u8ba1\u7b97\u9762\u79ef\u548c\u957f\u5ea6<\/strong><\/li>\n<\/ol>\n
                  # \u8ba1\u7b97\u591a\u8fb9\u5f62\u9762\u79ef<\/p>\n
                  gdf['area'] = gdf['geometry'].area<\/p>\n
                  \u8ba1\u7b97\u7ebf\u957f\u5ea6<\/strong><\/h2>\n
                  gdf['length'] = gdf['geometry'].length<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                    \n
                  1. \u8ba1\u7b97\u8ddd\u79bb<\/strong><\/li>\n<\/ol>\n
                    GeoPandas\u53ef\u4ee5\u8ba1\u7b97\u51e0\u4f55\u5bf9\u8c61\u4e4b\u95f4\u7684\u8ddd\u79bb\u3002<\/p>\n<\/p>\n
                    # \u8ba1\u7b97\u70b9\u4e4b\u95f4\u7684\u8ddd\u79bb<\/p>\n
                    distance = gdf1['geometry'].distance(gdf2['geometry'])<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                    \u56db\u3001\u96c6\u6210\u5176\u4ed6GIS\u5de5\u5177<\/p>\n<\/p>\n
                    GeoPandas\u53ef\u4ee5\u4e0e\u5176\u4ed6GIS\u5de5\u5177\u548c\u5e93\u96c6\u6210\uff0c\u5982Shapely\u3001Fiona\u548cPyproj\uff0c\u4ee5\u6269\u5c55\u5176\u529f\u80fd\u3002<\/p>\n<\/p>\n
                      \n
                    1. Shapely<\/strong><\/li>\n<\/ol>\n
                      Shapely\u662f\u4e00\u4e2a\u7528\u4e8e\u64cd\u4f5c\u548c\u5206\u6790\u51e0\u4f55\u5bf9\u8c61\u7684\u5e93\u3002GeoPandas\u7684\u51e0\u4f55\u64cd\u4f5c\u4f9d\u8d56\u4e8eShapely\u3002<\/p>\n<\/p>\n
                      from shapely.geometry import Point, LineString<\/p>\n
                      \u521b\u5efa\u51e0\u4f55\u5bf9\u8c61<\/strong><\/h2>\n
                      point = Point(1, 2)<\/p>\n
                      line = LineString([(0, 0), (1, 1), (2, 2)])<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                        \n
                      1. Fiona<\/strong><\/li>\n<\/ol>\n
                        Fiona\u662f\u4e00\u4e2a\u7528\u4e8e\u8bfb\u53d6\u548c\u5199\u5165\u5730\u7406\u6587\u4ef6\u7684\u5e93\u3002GeoPandas\u7684read_file()<\/code>\u548cto_file()<\/code>\u65b9\u6cd5\u4f9d\u8d56\u4e8eFiona\u3002<\/p>\n<\/p>\n
                        import fiona<\/p>\n
                        \u4f7f\u7528Fiona\u8bfb\u53d6\u6587\u4ef6<\/strong><\/h2>\n
                        with fiona.open('path_to_your_shapefile.shp') as src:<\/p>\n
                            for feature in src:<\/p>\n
                                print(feature)<\/p>\n
                        <\/code><\/pre>\n<\/p>\n
                          \n
                        1. Pyproj<\/strong><\/li>\n<\/ol>\n
                          Pyproj\u662f\u4e00\u4e2a\u7528\u4e8e\u5750\u6807\u7cfb\u8f6c\u6362\u7684\u5e93\u3002GeoPandas\u53ef\u4ee5\u4f7f\u7528Pyproj\u8fdb\u884c\u5750\u6807\u7cfb\u8f6c\u6362\u3002<\/p>\n<\/p>\n
                          from pyproj import CRS, Transformer<\/p>\n
                          \u521b\u5efa\u5750\u6807\u7cfb\u8f6c\u6362\u5668<\/strong><\/h2>\n
                          transformer = Transformer.from_crs("EPSG:4326", "EPSG:3857")<\/p>\n
                          \u8f6c\u6362\u5750\u6807<\/strong><\/h2>\n
                          x, y = transformer.transform(45.0, 45.0)<\/p>\n
                          <\/code><\/pre>\n<\/p>\n
                          \u4e94\u3001\u5e94\u7528\u5b9e\u4f8b<\/p>\n<\/p>\n
                          \u4e3a\u4e86\u66f4\u597d\u5730\u7406\u89e3GeoPandas\u7684\u4f7f\u7528\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u4e00\u4e2a\u7b80\u5355\u7684\u5e94\u7528\u5b9e\u4f8b\u6765\u5c55\u793a\u5176\u529f\u80fd\u3002<\/p>\n<\/p>\n
                          \u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4e2a\u5305\u542b\u591a\u4e2a\u57ce\u5e02\u5750\u6807\u7684CSV\u6587\u4ef6\uff0c\u6211\u4eec\u5e0c\u671b\u5c06\u8fd9\u4e9b\u5750\u6807\u7ed8\u5236\u5728\u5730\u56fe\u4e0a\uff0c\u5e76\u8ba1\u7b97\u5b83\u4eec\u4e4b\u95f4\u7684\u8ddd\u79bb\u3002<\/p>\n<\/p>\n
                            \n
                          1. \u52a0\u8f7d\u6570\u636e<\/strong><\/li>\n<\/ol>\n
                            \u9996\u5148\uff0c\u6211\u4eec\u5c06\u4f7f\u7528Pandas\u52a0\u8f7dCSV\u6587\u4ef6\uff0c\u5e76\u5c06\u5176\u8f6c\u6362\u4e3aGeoDataFrame\u3002<\/p>\n<\/p>\n
                            import pandas as pd<\/p>\n
                            \u52a0\u8f7dCSV\u6587\u4ef6<\/strong><\/h2>\n
                            df = pd.read_csv('cities.csv')<\/p>\n
                            \u521b\u5efaGeoDataFrame<\/strong><\/h2>\n
                            gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.longitude, df.latitude))<\/p>\n
                            <\/code><\/pre>\n<\/p>\n
                              \n
                            1. \u7ed8\u5236\u5730\u56fe<\/strong><\/li>\n<\/ol>\n
                              \u4f7f\u7528GeoPandas\u7ed8\u5236\u57ce\u5e02\u5730\u56fe\u3002<\/p>\n<\/p>\n
                              # \u7ed8\u5236\u5730\u56fe<\/p>\n
                              gdf.plot(marker='o', color='red', markersize=5)<\/p>\n
                              plt.show()<\/p>\n
                              <\/code><\/pre>\n<\/p>\n
                                \n
                              1. \u8ba1\u7b97\u8ddd\u79bb<\/strong><\/li>\n<\/ol>\n
                                \u8ba1\u7b97\u57ce\u5e02\u4e4b\u95f4\u7684\u8ddd\u79bb\u3002<\/p>\n<\/p>\n
                                from shapely.geometry import Point<\/p>\n
                                \u8ba1\u7b97\u8ddd\u79bb<\/strong><\/h2>\n
                                gdf['distance'] = gdf['geometry'].apply(lambda x: x.distance(Point(0, 0)))<\/p>\n
                                <\/code><\/pre>\n<\/p>\n
                                \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u4f7f\u7528GeoPandas\u52a0\u8f7d\u3001\u5904\u7406\u548c\u53ef\u89c6\u5316\u5730\u7406\u6570\u636e\u3002<\/p>\n<\/p>\n
                                \u603b\u7ed3\uff1a<\/p>\n<\/p>\n
                                GeoPandas\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684Python\u5e93\uff0c\u53ef\u4ee5\u5e2e\u52a9\u7528\u6237\u8f7b\u677e\u5904\u7406\u548c\u5206\u6790\u5730\u7406\u7a7a\u95f4\u6570\u636e\u3002\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u6211\u4eec\u4e86\u89e3\u4e86GeoPandas\u7684\u57fa\u672c\u4f7f\u7528\u65b9\u6cd5\uff0c\u5305\u62ec\u6570\u636e\u52a0\u8f7d\u3001\u5904\u7406\u3001\u53ef\u89c6\u5316\u548c\u7a7a\u95f4\u5206\u6790\u7b49\u3002\u7ed3\u5408\u5176\u4ed6GIS\u5de5\u5177\uff0cGeoPandas\u53ef\u4ee5\u6269\u5c55\u5176\u529f\u80fd\uff0c\u4e3a\u7528\u6237\u63d0\u4f9b\u66f4\u5f3a\u5927\u7684\u5730\u7406\u6570\u636e\u5904\u7406\u80fd\u529b\u3002\u65e0\u8bba\u662f\u7b80\u5355\u7684\u5730\u56fe\u7ed8\u5236\u8fd8\u662f\u590d\u6742\u7684\u7a7a\u95f4\u5206\u6790\uff0cGeoPandas\u90fd\u662f\u4e00\u4e2a\u503c\u5f97\u63a8\u8350\u7684\u5de5\u5177\u3002<\/p>\n<\/p>\n
                                \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                                 \u5982\u4f55\u4f7f\u7528Python\u83b7\u53d6\u5730\u7403\u7684\u5b9e\u65f6\u6570\u636e\uff1f<\/strong>
                                Python\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u5e93\u548cAPI\u83b7\u53d6\u5730\u7403\u7684\u5b9e\u65f6\u6570\u636e\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528requests<\/code>\u5e93\u4ece\u5f00\u653e\u7684\u5730\u7403\u89c2\u6d4bAPI\u83b7\u53d6\u6c14\u8c61\u6570\u636e\u3001\u5730\u9707\u4fe1\u606f\u6216\u5176\u4ed6\u5730\u7406\u6570\u636e\u3002\u6b64\u5916\uff0cgeopandas<\/code>\u548cmatplotlib<\/code>\u7b49\u5e93\u53ef\u4ee5\u5e2e\u52a9\u53ef\u89c6\u5316\u8fd9\u4e9b\u6570\u636e\uff0c\u63d0\u4f9b\u66f4\u52a0\u76f4\u89c2\u7684\u5730\u7403\u89c6\u56fe\u3002<\/p>\n
                                Python\u4e2d\u6709\u54ea\u4e9b\u5e93\u53ef\u4ee5\u7528\u4e8e\u5730\u7406\u4fe1\u606f\u7cfb\u7edf\uff08GIS\uff09\uff1f<\/strong>
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