{"id":983536,"date":"2024-12-27T07:20:20","date_gmt":"2024-12-26T23:20:20","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/983536.html"},"modified":"2024-12-27T07:20:24","modified_gmt":"2024-12-26T23:20:24","slug":"python%e5%a6%82%e4%bd%95%e5%8a%a0%e5%85%a5%e7%a9%ba%e9%97%b4%e4%bf%a1%e6%81%af","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/983536.html","title":{"rendered":"python\u5982\u4f55\u52a0\u5165\u7a7a\u95f4\u4fe1\u606f"},"content":{"rendered":"

\"python\u5982\u4f55\u52a0\u5165\u7a7a\u95f4\u4fe1\u606f\"<\/p>\n

\u8981\u5728Python\u4e2d\u52a0\u5165\u7a7a\u95f4\u4fe1\u606f\uff0c\u53ef\u4ee5\u4f7f\u7528\u8bf8\u5982GeoPandas\u3001Shapely\u3001Fiona\u3001Pyproj\u3001Rasterio\u7b49\u5e93\u6765\u5904\u7406\u548c\u5206\u6790\u5730\u7406\u7a7a\u95f4\u6570\u636e\u3002\u9996\u5148\uff0c\u4f7f\u7528GeoPandas\u8bfb\u53d6\u548c\u5904\u7406\u77e2\u91cf\u6570\u636e\u3001Shapely\u7528\u4e8e\u51e0\u4f55\u64cd\u4f5c\u3001Fiona\u8bfb\u53d6\u548c\u5199\u5165\u5730\u7406\u6587\u4ef6\uff0cPyproj\u8fdb\u884c\u6295\u5f71\u53d8\u6362\u3001Rasterio\u7528\u4e8e\u5904\u7406\u6805\u683c\u6570\u636e\u3002GeoPandas\u662f\u6700\u4e3a\u63a8\u8350\u7684\u5e93\uff0c\u56e0\u4e3a\u5b83\u96c6\u6210\u4e86\u8bb8\u591a\u529f\u80fd\uff0c\u4fbf\u4e8e\u5904\u7406\u548c\u5206\u6790\u5730\u7406\u7a7a\u95f4\u6570\u636e\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001GEOPANDAS\u4ecb\u7ecd\u4e0e\u4f7f\u7528<\/p>\n<\/p>\n

GeoPandas\u662fPython\u4e2d\u4e00\u4e2a\u5f3a\u5927\u7684\u5730\u7406\u7a7a\u95f4\u6570\u636e\u5904\u7406\u5e93\u3002\u5b83\u5c06Pandas\u4e0eShapely\u3001Fiona\u7b49\u5e93\u7ed3\u5408\u5728\u4e00\u8d77\uff0c\u4f7f\u5f97\u7a7a\u95f4\u6570\u636e\u7684\u64cd\u4f5c\u66f4\u52a0\u7b80\u4fbf\u3002GeoPandas\u5141\u8bb8\u7528\u6237\u8f7b\u677e\u8bfb\u53d6\u3001\u64cd\u4f5c\u548c\u4fdd\u5b58\u5730\u7406\u7a7a\u95f4\u6570\u636e\u3002<\/p>\n<\/p>\n

    \n
  1. \u5b89\u88c5\u4e0e\u57fa\u672c\u4f7f\u7528<\/li>\n<\/ol>\n

    \u8981\u4f7f\u7528GeoPandas\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u5b83\uff0c\u53ef\u4ee5\u901a\u8fc7pip\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

    pip install geopandas<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u901a\u8fc7\u4ee5\u4e0b\u4ee3\u7801\u5bfc\u5165\u5e76\u8bfb\u53d6\u4e00\u4e2aShapefile\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
    import geopandas as gpd<\/p>\n
    \u8bfb\u53d6Shapefile\u6587\u4ef6<\/strong><\/h2>\n
    gdf = gpd.read_file('path\/to\/your\/shapefile.shp')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    GeoPandas\u4e2d\u7684GeoDataFrame\u4e0ePandas\u4e2d\u7684DataFrame\u7c7b\u4f3c\uff0c\u4f46\u589e\u52a0\u4e86\u5904\u7406\u5730\u7406\u7a7a\u95f4\u6570\u636e\u7684\u65b9\u6cd5\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528GeoPandas\u6765\u8fdb\u884c\u7a7a\u95f4\u64cd\u4f5c\uff0c\u5982\u8ba1\u7b97\u9762\u79ef\u3001\u957f\u5ea6\u3001\u8ddd\u79bb\u7b49\u3002<\/p>\n<\/p>\n
      \n
    1. \u7a7a\u95f4\u64cd\u4f5c\u793a\u4f8b<\/li>\n<\/ol>\n
      GeoPandas\u652f\u6301\u591a\u79cd\u7a7a\u95f4\u64cd\u4f5c\uff0c\u4f8b\u5982\u7f13\u51b2\u533a\u3001\u4ea4\u96c6\u3001\u5408\u5e76\u7b49\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u7528\u7684\u7a7a\u95f4\u64cd\u4f5c\uff1a<\/p>\n<\/p>\n
      # \u8ba1\u7b97\u51e0\u4f55\u5f62\u72b6\u7684\u9762\u79ef<\/p>\n
      gdf['area'] = gdf.area<\/p>\n
      \u8ba1\u7b97\u7f13\u51b2\u533a<\/strong><\/h2>\n
      gdf['buffer'] = gdf.buffer(100)<\/p>\n
      \u7a7a\u95f4\u4ea4\u96c6<\/strong><\/h2>\n
      intersection = gpd.overlay(gdf1, gdf2, how='intersection')<\/p>\n
      \u7a7a\u95f4\u5408\u5e76<\/strong><\/h2>\n
      merged = gpd.overlay(gdf1, gdf2, how='union')<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4e8c\u3001SHAPELY\u4e0e\u7a7a\u95f4\u51e0\u4f55<\/p>\n<\/p>\n
      Shapely\u662f\u4e00\u4e2a\u7528\u4e8e\u521b\u5efa\u548c\u64cd\u4f5c\u51e0\u4f55\u5bf9\u8c61\u7684Python\u5e93\u3002\u5b83\u652f\u6301\u70b9\u3001\u7ebf\u3001\u591a\u8fb9\u5f62\u7b49\u51e0\u4f55\u7c7b\u578b\u7684\u521b\u5efa\u548c\u64cd\u4f5c\u3002Shapely\u901a\u5e38\u4e0eGeoPandas\u4e00\u8d77\u4f7f\u7528\uff0c\u4ee5\u4fbf\u8fdb\u884c\u590d\u6742\u7684\u51e0\u4f55\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
        \n
      1. \u521b\u5efa\u51e0\u4f55\u5bf9\u8c61<\/li>\n<\/ol>\n
        Shapely\u63d0\u4f9b\u4e86\u7b80\u5355\u7684\u63a5\u53e3\u7528\u4e8e\u521b\u5efa\u51e0\u4f55\u5bf9\u8c61\uff1a<\/p>\n<\/p>\n
        from shapely.geometry import Point, LineString, Polygon<\/p>\n
        \u521b\u5efa\u70b9<\/strong><\/h2>\n
        point = Point(1.0, 2.0)<\/p>\n
        \u521b\u5efa\u7ebf<\/strong><\/h2>\n
        line = LineString([(0, 0), (1, 1), (1, 2)])<\/p>\n
        \u521b\u5efa\u591a\u8fb9\u5f62<\/strong><\/h2>\n
        polygon = Polygon([(0, 0), (1, 1), (1, 0)])<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u51e0\u4f55\u64cd\u4f5c<\/li>\n<\/ol>\n
          Shapely\u652f\u6301\u591a\u79cd\u51e0\u4f55\u64cd\u4f5c\uff0c\u5982\u8ba1\u7b97\u957f\u5ea6\u3001\u9762\u79ef\u3001\u4ea4\u96c6\u3001\u5e76\u96c6\u7b49\uff1a<\/p>\n<\/p>\n
          # \u8ba1\u7b97\u957f\u5ea6\u548c\u9762\u79ef<\/p>\n
          line_length = line.length<\/p>\n
          polygon_area = polygon.area<\/p>\n
          \u8ba1\u7b97\u4ea4\u96c6<\/strong><\/h2>\n
          intersection = line.intersection(polygon)<\/p>\n
          \u8ba1\u7b97\u5e76\u96c6<\/strong><\/h2>\n
          union = line.union(polygon)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4e09\u3001FIONA\u7528\u4e8e\u8bfb\u53d6\u4e0e\u5199\u5165\u5730\u7406\u6587\u4ef6<\/p>\n<\/p>\n
          Fiona\u662f\u4e00\u4e2a\u7528\u4e8e\u8bfb\u53d6\u548c\u5199\u5165\u5730\u7406\u7a7a\u95f4\u6587\u4ef6\u7684Python\u5e93\u3002\u5b83\u652f\u6301\u591a\u79cd\u683c\u5f0f\uff0c\u5982Shapefile\u3001GeoJSON\u7b49\u3002Fiona\u4e0eGeoPandas\u7ed3\u5408\u4f7f\u7528\uff0c\u53ef\u4ee5\u8f7b\u677e\u8bfb\u53d6\u548c\u4fdd\u5b58\u7a7a\u95f4\u6570\u636e\u3002<\/p>\n<\/p>\n
            \n
          1. \u8bfb\u53d6\u5730\u7406\u6587\u4ef6<\/li>\n<\/ol>\n
            \u4f7f\u7528Fiona\u8bfb\u53d6\u5730\u7406\u6587\u4ef6\u975e\u5e38\u7b80\u5355\uff1a<\/p>\n<\/p>\n
            import fiona<\/p>\n
            \u8bfb\u53d6Shapefile\u6587\u4ef6<\/strong><\/h2>\n
            with fiona.open('path\/to\/your\/shapefile.shp', 'r') as source:<\/p>\n
                for feature in source:<\/p>\n
                    print(feature)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u5199\u5165\u5730\u7406\u6587\u4ef6<\/li>\n<\/ol>\n
              Fiona\u4e5f\u53ef\u4ee5\u7528\u4e8e\u5c06GeoDataFrame\u5199\u5165\u6587\u4ef6\uff1a<\/p>\n<\/p>\n
              from fiona import collection<\/p>\n
              \u5b9a\u4e49schema<\/strong><\/h2>\n
              schema = {<\/p>\n
                  'geometry': 'Point',<\/p>\n
                  'properties': {'name': 'str'}<\/p>\n
              }<\/p>\n
              \u5199\u5165GeoJSON\u6587\u4ef6<\/strong><\/h2>\n
              with collection(<\/p>\n
                  "path\/to\/your\/output.geojson", "w", "GeoJSON", schema) as output:<\/p>\n
                  for point in points:<\/p>\n
                      output.write({<\/p>\n
                          'properties': {<\/p>\n
                              'name': point['name']<\/p>\n
                          },<\/p>\n
                          'geometry': mapping(point['geometry'])<\/p>\n
                      })<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u56db\u3001PYPROJ\u7528\u4e8e\u6295\u5f71\u53d8\u6362<\/p>\n<\/p>\n
              Pyproj\u662f\u4e00\u4e2a\u7528\u4e8e\u6267\u884c\u6295\u5f71\u548c\u5750\u6807\u53d8\u6362\u7684Python\u5e93\u3002\u5b83\u57fa\u4e8eProj\u5e93\uff0c\u652f\u6301\u591a\u79cd\u6295\u5f71\u548c\u5750\u6807\u7cfb\u7684\u53d8\u6362\u3002<\/p>\n<\/p>\n
                \n
              1. \u5b89\u88c5\u4e0e\u57fa\u672c\u4f7f\u7528<\/li>\n<\/ol>\n
                \u53ef\u4ee5\u901a\u8fc7pip\u5b89\u88c5Pyproj\uff1a<\/p>\n<\/p>\n
                pip install pyproj<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u5b89\u88c5\u5b8c\u6210\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u8fdb\u884c\u5750\u6807\u53d8\u6362\uff1a<\/p>\n<\/p>\n
                from pyproj import Proj, transform<\/p>\n
                \u5b9a\u4e49\u6295\u5f71<\/strong><\/h2>\n
                proj_wgs84 = Proj(init='epsg:4326')  # WGS84<\/p>\n
                proj_utm = Proj(init='epsg:32633')  # UTM zone 33N<\/p>\n
                \u8f6c\u6362\u5750\u6807<\/strong><\/h2>\n
                x, y = transform(proj_wgs84, proj_utm, lon, lat)<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u6295\u5f71\u53d8\u6362\u793a\u4f8b<\/li>\n<\/ol>\n
                  Pyproj\u652f\u6301\u590d\u6742\u7684\u6295\u5f71\u548c\u5750\u6807\u53d8\u6362\uff1a<\/p>\n<\/p>\n
                  from pyproj import CRS, Transformer<\/p>\n
                  \u5b9a\u4e49\u5750\u6807\u7cfb<\/strong><\/h2>\n
                  crs_wgs84 = CRS.from_epsg(4326)<\/p>\n
                  crs_utm = CRS.from_epsg(32633)<\/p>\n
                  \u521b\u5efaTransformer\u5bf9\u8c61<\/strong><\/h2>\n
                  transformer = Transformer.from_crs(crs_wgs84, crs_utm)<\/p>\n
                  \u8fdb\u884c\u5750\u6807\u53d8\u6362<\/strong><\/h2>\n
                  x, y = transformer.transform(lat, lon)<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u4e94\u3001RASTERIO\u7528\u4e8e\u6805\u683c\u6570\u636e\u5904\u7406<\/p>\n<\/p>\n
                  Rasterio\u662f\u4e00\u4e2a\u7528\u4e8e\u8bfb\u53d6\u548c\u5199\u5165\u6805\u683c\u6570\u636e\u7684Python\u5e93\u3002\u5b83\u652f\u6301\u591a\u79cd\u683c\u5f0f\uff0c\u5982GeoTIFF\u3001JPEG\u3001PNG\u7b49\uff0c\u5e7f\u6cdb\u7528\u4e8e\u5904\u7406\u9065\u611f\u5f71\u50cf\u548c\u5730\u7406\u6805\u683c\u6570\u636e\u3002<\/p>\n<\/p>\n
                    \n
                  1. \u5b89\u88c5\u4e0e\u57fa\u672c\u4f7f\u7528<\/li>\n<\/ol>\n
                    \u53ef\u4ee5\u901a\u8fc7pip\u5b89\u88c5Rasterio\uff1a<\/p>\n<\/p>\n
                    pip install rasterio<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                    \u4f7f\u7528Rasterio\u8bfb\u53d6\u6805\u683c\u6570\u636e\u5982\u4e0b\uff1a<\/p>\n<\/p>\n
                    import rasterio<\/p>\n
                    \u6253\u5f00\u6805\u683c\u6587\u4ef6<\/strong><\/h2>\n
                    with rasterio.open('path\/to\/your\/raster.tif') as src:<\/p>\n
                        # \u8bfb\u53d6\u6805\u683c\u6570\u636e<\/p>\n
                        array = src.read(1)<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                      \n
                    1. \u6805\u683c\u6570\u636e\u64cd\u4f5c<\/li>\n<\/ol>\n
                      Rasterio\u652f\u6301\u591a\u79cd\u6805\u683c\u6570\u636e\u64cd\u4f5c\uff0c\u5982\u88c1\u526a\u3001\u91cd\u91c7\u6837\u3001\u5199\u5165\u7b49\uff1a<\/p>\n<\/p>\n
                      from rasterio.mask import mask<\/p>\n
                      from rasterio.warp import calculate_default_transform, reproject, Resampling<\/p>\n
                      \u88c1\u526a\u6805\u683c\u6570\u636e<\/strong><\/h2>\n
                      with rasterio.open('path\/to\/your\/raster.tif') as src:<\/p>\n
                          out_image, out_transform = mask(src, shapes, crop=True)<\/p>\n
                      \u91cd\u91c7\u6837\u6805\u683c\u6570\u636e<\/strong><\/h2>\n
                      transform, width, height = calculate_default_transform(<\/p>\n
                          src.crs, dst_crs, src.width, src.height, *src.bounds)<\/p>\n
                      kwargs = src.meta.copy()<\/p>\n
                      kwargs.update({<\/p>\n
                          'crs': dst_crs,<\/p>\n
                          'transform': transform,<\/p>\n
                          'width': width,<\/p>\n
                          'height': height<\/p>\n
                      })<\/p>\n
                      with rasterio.open('path\/to\/your\/reprojected.tif', 'w', kwargs) as dst:<\/p>\n
                          for i in range(1, src.count + 1):<\/p>\n
                              reproject(<\/p>\n
                                  source=rasterio.band(src, i),<\/p>\n
                                  destination=rasterio.band(dst, i),<\/p>\n
                                  src_transform=src.transform,<\/p>\n
                                  src_crs=src.crs,<\/p>\n
                                  dst_transform=transform,<\/p>\n
                                  dst_crs=dst_crs,<\/p>\n
                                  resampling=Resampling.nearest)<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                      \u901a\u8fc7\u4ee5\u4e0a\u51e0\u4e2aPython\u5e93\u7684\u4ecb\u7ecd\u548c\u4f7f\u7528\u793a\u4f8b\uff0c\u53ef\u4ee5\u770b\u5230\u5728Python\u4e2d\u52a0\u5165\u7a7a\u95f4\u4fe1\u606f\u662f\u975e\u5e38\u7075\u6d3b\u548c\u5f3a\u5927\u7684\u3002\u65e0\u8bba\u662f\u77e2\u91cf\u6570\u636e\u8fd8\u662f\u6805\u683c\u6570\u636e\uff0c\u65e0\u8bba\u662f\u7b80\u5355\u7684\u51e0\u4f55\u64cd\u4f5c\u8fd8\u662f\u590d\u6742\u7684\u6295\u5f71\u53d8\u6362\uff0c\u8fd9\u4e9b\u5e93\u90fd\u80fd\u63d0\u4f9b\u6709\u6548\u7684\u89e3\u51b3\u65b9\u6848\u3002\u5229\u7528\u8fd9\u4e9b\u5de5\u5177\uff0c\u60a8\u53ef\u4ee5\u5728Python\u4e2d\u5b9e\u73b0\u4ece\u7a7a\u95f4\u6570\u636e\u7684\u83b7\u53d6\u3001\u5904\u7406\u5230\u5206\u6790\u548c\u8f93\u51fa\u7684\u5b8c\u6574\u5de5\u4f5c\u6d41\u7a0b\u3002<\/p>\n<\/p>\n
                      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                       \u5982\u4f55\u5728Python\u4e2d\u5904\u7406\u7a7a\u95f4\u6570\u636e\uff1f<\/strong>
                      \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u4e2a\u5e93\u6765\u5904\u7406\u7a7a\u95f4\u6570\u636e\uff0c\u4f8b\u5982Geopandas\u3001Shapely\u548cFiona\u7b49\u3002Geopandas\u662f\u4e00\u4e2a\u6269\u5c55Pandas\u7684\u5e93\uff0c\u80fd\u591f\u8f7b\u677e\u8bfb\u53d6\u548c\u5904\u7406\u5730\u7406\u6570\u636e\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7Geopandas\u8bfb\u53d6Shapefile\u3001GeoJSON\u7b49\u683c\u5f0f\uff0c\u5e76\u8fdb\u884c\u7a7a\u95f4\u5206\u6790\u3002<\/p>\n
                      Python\u4e2d\u7684\u7a7a\u95f4\u4fe1\u606f\u5982\u4f55\u53ef\u89c6\u5316\uff1f<\/strong>
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