yield vs return

import memory_profiler
import time


start = time.time()
print("Before {}Mb".format(memory_profiler.memory_usage()))

def calculation(para):
    result = []
    for i in para:
        result.append(i)
    return result


''' 
return  [52.0] Mb  [56.25390625]Mb           1.644s
yield [51.86328125]Mb  [51.87109375]Mb  0.202s 
'''
res = calculation(range(10000000))
print("After {}Mb".format(memory_profiler.memory_usage()))
print(time.time()-start)

Obviously, generator can boost the performance by saving your memory. It's not holding all of the results in memory. But once you use the list() function, you'll lose this advantage.