Using a previous answer (merci Booboo),
The code idea is:
from multiprocessing import Pool def worker_1(x, y, z): ... t = zip(list_of_Polygon,list_of_Point,column_Point) return t def collected_result(t): x, y, z = t # unpack save_shp("polys.shp",x) save_shp("point.shp",y,z) if __name__ == '__main__': pool = Pool() pool.map(worker_1, zip(MultiPolygon, lat, lon)) pool.close() pool.join()
But the geodataframe (Polygon,Point) is not iterable so I can’t use pool, any suggestions to parallelize?
How to compress the (geodataframe) outputs in worker_1 and then save them independently (or multiple layers in a shapefile), its better to use global parameters? … because zip only saves lists (right*)?
Source: Python Questions