对于函数，我建议使用我创建的这个简单的修饰符。

def timeit(method):
    def timed(*args, **kwargs):
        ts = time.time()
        result = method(*args, **kwargs)
        te = time.time()
        if 'log_time' in kwargs:
            name = kwargs.get('log_name', method.__name__.upper())
            kwargs['log_time'][name] = int((te - ts) * 1000)
        else:
            print('%r  %2.22f ms' % (method.__name__, (te - ts) * 1000))
        return result
    return timed

@timeit
def foo():
    do_some_work()

# foo()
# 'foo'  0.000953 ms

根据这个答案，创建了一个简单但方便的工具。

import time
from datetime import timedelta

def start_time_measure(message=None):
    if message:
        print(message)
    return time.monotonic()

def end_time_measure(start_time, print_prefix=None):
    end_time = time.monotonic()
    if print_prefix:
        print(print_prefix + str(timedelta(seconds=end_time - start_time)))
    return end_time

用法：

total_start_time = start_time_measure()    
start_time = start_time_measure('Doing something...')
# Do something
end_time_measure(start_time, 'Done in: ')
start_time = start_time_measure('Doing something else...')
# Do something else
end_time_measure(start_time, 'Done in: ')
end_time_measure(total_start_time, 'Total time: ')

输出：

Doing something...
Done in: 0:00:01.218000
Doing something else...
Done in: 0:00:01.313000
Total time: 0:00:02.672000

这是获取程序运行时间的最简单方法：

在程序末尾编写以下代码。

import time
print(time.clock())

我尝试使用以下脚本找到时间差。

import time

start_time = time.perf_counter()
[main code here]
print (time.perf_counter() - start_time, "seconds")

您可以使用Python分析器cProfile来测量CPU时间，以及每个函数内部花费的时间以及每个函数被调用的次数。如果您想在不知道从哪里开始的情况下提高脚本的性能，这非常有用。对另一个堆栈溢出问题的回答很好。查看文档总是很好的。

以下是如何从命令行使用cProfile评测脚本的示例：

$ python -m cProfile euler048.py

1007 function calls in 0.061 CPU seconds

Ordered by: standard name
ncalls  tottime  percall  cumtime  percall filename:lineno(function)
    1    0.000    0.000    0.061    0.061 <string>:1(<module>)
 1000    0.051    0.000    0.051    0.000 euler048.py:2(<lambda>)
    1    0.005    0.005    0.061    0.061 euler048.py:2(<module>)
    1    0.000    0.000    0.061    0.061 {execfile}
    1    0.002    0.002    0.053    0.053 {map}
    1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler objects}
    1    0.000    0.000    0.000    0.000 {range}
    1    0.003    0.003    0.003    0.003 {sum}

使用line_profiler。

line_profiler将描述单个代码行执行所需的时间。分析器通过Cython在C语言中实现，以减少分析开销。

from line_profiler import LineProfiler
import random

def do_stuff(numbers):
    s = sum(numbers)
    l = [numbers[i]/43 for i in range(len(numbers))]
    m = ['hello'+str(numbers[i]) for i in range(len(numbers))]

numbers = [random.randint(1,100) for i in range(1000)]
lp = LineProfiler()
lp_wrapper = lp(do_stuff)
lp_wrapper(numbers)
lp.print_stats()

结果将是：

Timer unit: 1e-06 s

Total time: 0.000649 s
File: <ipython-input-2-2e060b054fea>
Function: do_stuff at line 4

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
     4                                           def do_stuff(numbers):
     5         1           10     10.0      1.5      s = sum(numbers)
     6         1          186    186.0     28.7      l = [numbers[i]/43 for i in range(len(numbers))]
     7         1          453    453.0     69.8      m = ['hello'+str(numbers[i]) for i in range(len(numbers))]