time.time vs. timeit.timeit [英] time.time vs. timeit.timeit

问题描述

有时，我喜欢计算我的部分代码运行所需的时间.我已经检查了很多在线站点，并且大致上看到了两种主要的方法来做到这一点.一种是使用 time.time，另一种是使用 timeit.timeit.

Sometimes, I like to time how long it takes parts of my code to run. I've checked a lot of online sites and have seen, at large, two main ways to do this. One is using time.time and the other is using timeit.timeit.

所以，我写了一个非常简单的脚本来比较两者:

So, I wrote a very simple script to compare the two:

from timeit import timeit
from time import time
start = time()
for i in range(100): print('ABC')
print(time()-start, timeit("for i in range(100): print('ABC')", number=1))

基本上，它乘以在 for 循环中打印 "ABC" 100 次所需的时间.左边的数字是 time.time 的结果，右边的数字是 timeit.timeit 的结果:

Basically, it times how long it takes to print "ABC" 100 times in a for-loop. The number on the left is the results for time.time and the number on the right is for timeit.timeit:

# First run
0.0 0.012654680972022981
# Second run
0.031000137329101562 0.012747430190149865
# Another run
0.0 0.011262325239660349
# Another run
0.016000032424926758 0.012740166697164025
# Another run
0.016000032424926758 0.0440628627381413

如您所见，有时 time.time 更快，有时更慢.哪种方法更好(更准确)?

As you can see, sometimes, time.time is faster and sometimes it's slower. Which is the better way (more accurate)?

推荐答案

timeit 更准确，原因有以下三个:

timeit is more accurate, for three reasons:

• 它会多次重复测试以消除其他任务对您机器的影响，例如磁盘刷新和操作系统调度.
• 它通过在不合时宜的时刻安排收集运行来禁用垃圾收集器，以防止该进程歪曲结果.
• 它为您的操作系统选择最准确的计时器，time.time 或 time.clock 在 Python 2 和 time.perf_counter() 在 Python 3 上.参见 timeit.default_timer.

• it repeats the tests many times to eliminate the influence of other tasks on your machine, such as disk flushing and OS scheduling.
• it disables the garbage collector to prevent that process from skewing the results by scheduling a collection run at an inopportune moment.
• it picks the most accurate timer for your OS, time.time or time.clock in Python 2 and time.perf_counter() on Python 3. See timeit.default_timer.

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