Python自带数据结构的运行效率

1.List:

def test1():
l = []
for i in range(1000):
l = l + [i]
def test2():
l = []
for i in range(1000):
l.append(i)
def test3():
l = [i for i in range(1000)]
def test4():
l = list(range(1000))

import timeit
from timeit import Timer
if __name__=="__main__":
t1 = Timer("test1()", "from __main__ import test1")
print("concat ",t1.timeit(number=1000), "milliseconds")
t2 = Timer("test2()", "from __main__ import test2")
print("append ",t2.timeit(number=1000), "milliseconds")
t3 = Timer("test3()", "from __main__ import test3")
print("comprehension ",t3.timeit(number=1000), "milliseconds")
t4 = Timer("test4()", "from __main__ import test4")
print("list range ",t4.timeit(number=1000), "milliseconds")

运行结果：
>>>
RESTART: D:\Program Files\Python\test\algorithms\data structure and problem solving by python\2-List.py
concat 1.5390356234120435 milliseconds
append 0.16744655181905288 milliseconds
comprehension 0.04742712544752048 milliseconds
list range 0.022613714719721845 milliseconds
>>>



pop_zero = Timer("x.pop(0)","from __main__ import x")
pop_end = Timer("x.pop()","from __main__ import x")
x = list(range(2000000))
print(pop_zero.timeit(number=1000))
x = list(range(2000000))
print(pop_end.timeit(number=1000))

1.5236582787413726
0.00023699800044640895

2.Dictionaries