Using cProfile shows that the for loop having to call append for each item of the list is what takes up all the time(I think).
import cProfile ordinary_loop = ''' lst = range(10000000) l = [] for i in lst: l.append(int(i)) ''' list_comp = ''' lst = range(10000000) [int(i) for i in lst] ''' _map = ''' lst = range(10000000) list(map(int, lst)) ''' cProfile.run(ordinary_loop) cProfile.run(list_comp) cProfile.run(_map)Output
10000003 function calls in 6.797 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 5.728 5.728 6.797 6.797 <string>:2(<module>)
1 0.000 0.000 6.797 6.797 {built-in method exec}
10000000 1.068 0.000 1.068 0.000 {method 'append' of 'list' objects}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
4 function calls in 3.155 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.220 0.220 3.155 3.155 <string>:2(<module>)
1 2.935 2.935 2.935 2.935 <string>:3(<listcomp>)
1 0.000 0.000 3.155 3.155 {built-in method exec}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
3 function calls in 2.321 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 2.321 2.321 2.321 2.321 <string>:2(<module>)
1 0.000 0.000 2.321 2.321 {built-in method exec}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}