Python内置性能分析----timeit模块

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

dict内置操作的时间复杂度