geopandas

来源： https://stackoverflow.com/questions/61050942/make-geopandas-dataframe-from-points-then-project-the-gpd-got-error-cannot-tr

来源： https://stackoverflow.com/questions/61050942/make-geopandas-dataframe-from-points-then-project-the-gpd-got-error-cannot-tr

来源： https://stackoverflow.com/questions/39995839/gis-calculate-distance-between-point-and-polygon-border

来源： https://stackoverflow.com/questions/39995839/gis-calculate-distance-between-point-and-polygon-border

来源： https://stackoverflow.com/questions/58750837/set-centre-of-geopandas-map

来源： https://stackoverflow.com/questions/48874113/concat-multiple-shapefiles-via-geopandas

来源： https://stackoverflow.com/questions/48874113/concat-multiple-shapefiles-via-geopandas

问题 I have a geodataframe 'all_locations' with a geometry column and a column with the name of the point. Plotting the points on a map works just fine but I want to annotate the points with location name. ['location'] ['geometry'] BUITHVN8 POINT() (Actual dataframe is much larger of course) I have tried this (and other things): all_locations['coords'] = all_locations['geometry'].apply(lambda x: x.point.coords[:]) all_locations['coords'] = [coords[0] for coords in all_locations['coords']] all

问题 I am running a loop that computes a networkx.classes.multidigraph.MultiDiGraph for each row (neighbourhood) of a list of GeoDataFrames (cities). It then computes some statistics for each row and writes the file out to disk. The problem is that the loop is extremely long to compute because the graph is computed for each row. The way I want to quicken the loop is by computing the graph for the whole GeoDataFrame and then clipping the graph into each row (each row has a polygon). You can do this

问题 I am running a loop that computes a networkx.classes.multidigraph.MultiDiGraph for each row (neighbourhood) of a list of GeoDataFrames (cities). It then computes some statistics for each row and writes the file out to disk. The problem is that the loop is extremely long to compute because the graph is computed for each row. The way I want to quicken the loop is by computing the graph for the whole GeoDataFrame and then clipping the graph into each row (each row has a polygon). You can do this