欢迎您访问程序员文章站本站旨在为大家提供分享程序员计算机编程知识!
您现在的位置是: 首页

Python内置性能分析----timeit模块

程序员文章站 2024-02-27 12:32:09
...

timeit模块

timeit模块可以用来测试一小段Python代码的执行速度。

class timeit.Timer(stmt='pass', setup='pass', timer=<timer function>)

  • Timer是测量小段代码执行速度的类。

  • stmt参数是要测试的代码语句(statment);

  • setup参数是运行代码时需要的设置;

  • timer参数是一个定时器函数,与平台有关。

timeit.Timer.timeit(number=1000000)

Timer类中测试语句执行速度的对象方法。number参数是测试代码时的测试次数,默认为1000000次。方法返回执行代码的耗时,一个float类型的秒数。

 

list的操作测试

def t1():
   l = []
   for i in range(1000):
      l = l + [i]
def t2():
   l = []
   for i in range(1000):
      l.append(i)
def t3():
   l = [i for i in range(1000)]
def t4():
   l = list(range(1000))

from timeit import Timer

timer1 = Timer("t1()", "from __main__ import t1")
print("concat ",timer1.timeit(number=1000), "seconds")
timer2 = Timer("t2()", "from __main__ import t2")
print("append ",timer2.timeit(number=1000), "seconds")
timer3 = Timer("t3()", "from __main__ import t3")
print("comprehension ",timer3.timeit(number=1000), "seconds")
timer4 = Timer("t4()", "from __main__ import t4")
print("list range ",timer4.timeit(number=1000), "seconds")

# ('concat ', 1.7890608310699463, 'seconds')
# ('append ', 0.13796091079711914, 'seconds')
# ('comprehension ', 0.05671119689941406, 'seconds')
# ('list range ', 0.014147043228149414, 'seconds')

insert与append比较

def t2():
    li = []
    for i in range(10000):
        li.append(i)


def t5():
    li = []
    for i in range(10000):
        li.insert(0, i)

timer2 = Timer('t2()', 'from __main__ import t2')
print("append:", timer2.timeit(number=1000))

timer5 = Timer('t5()', 'from __main__ import t5')
print("insert:", timer5.timeit(number=1000))

# append: 0.9202240769991477
# insert: 21.039387496999552

从结果可以看出,append从尾端添加元素效率远远高于insert从顶端添加元素

list内置操作的时间复杂度

Python内置性能分析----timeit模块

dict内置操作的时间复杂度

Python内置性能分析----timeit模块

相关标签: Python timeit