我不确定您所说的“同时”是什么意思:
如果顺序处理很好,这
Results = list(map(Function, Values))
print(Results)
或更多pythonic列表理解
Results = [Function(value) for value in Values]
print(Results)
为您提供以下输出
Function Running With Values 1 and 2 At timestamp : 1605276375.4642859
Function Running With Values 2 and 3 At timestamp : 1605276375.4645345
Function Running With Values 4 and 5 At timestamp : 1605276375.4647174
Function Running With Values 1 and 4 At timestamp : 1605276375.4648669
[3, 5, 9, 5]
如果你真的想要多处理,那么这个
import multiprocessing as mp
with mp.Pool() as p:
Results = list(p.map(Function, Values))
print(Results)
或者这个
from concurrent.futures import ProcessPoolExecutor
with ProcessPoolExecutor() as p:
Results = list(p.map(Function, Values))
print(Results)
给你输出像
Function Running With Values 1 and 2 At timestamp : 1605276375.4532914
Function Running With Values 4 and 5 At timestamp : 1605276375.4547572
Function Running With Values 2 and 3 At timestamp : 1605276375.4549458
Function Running With Values 1 and 4 At timestamp : 1605276375.456188
[3, 5, 9, 5]
如果您想要多处理,那么您应该更深入地研究它以确保没有任何问题并且处理确实更快。但是您的示例是一个经典的MapReduce场景,应该可以正常工作。
那是你要找的吗?