Python line_profiler 代码示例
我正在尝试使用Python的line_profiler来获取逐行执行时间，想要的格式可以参考这个问题的回答。
我已经安装了这个模块，并且像下面这样调用它的LineProfiler对象，但我得到的输出只是一个总时间，而不是逐行的总结。
有没有什么建议？另外，我该如何获取在任何函数外部的numbers = [random.randint(1,100) for i in range(1000)]这一行的执行时间呢？
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)]
profile = LineProfiler(do_stuff(numbers))
profile.print_stats()
[] Timer unit: 3.20721e-07 s
2 个回答
8
在文档中提到：
在你的脚本中，你可以用 @profile 来装饰任何你想要分析的函数。
你想要用 @profile 来装饰你的 do_stuff 函数，然后运行
kernprof -v -l script_to_profile.py
这样就可以在终端上看到带注释的输出。这个分析结果还会被写入 script_to_profile.py.lprof 文件，你可以稍后用
python -m line_profiler script_to_profile.py.lprof
来重新生成输出。
51
line_profiler 的测试案例（可以在 GitHub 上找到）展示了如何在 Python 脚本中生成性能分析数据。你需要把想要分析的函数包裹起来，然后调用这个包裹的函数，并传入你想要的参数。
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()
输出结果：
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))]
添加更多要分析的函数
你还可以添加其他函数进行分析。例如，如果你有一个第二个被调用的函数，而你只包裹了调用的函数，那么你只会看到调用函数的分析结果。
from line_profiler import LineProfiler
import random
def do_other_stuff(numbers):
s = sum(numbers)
def do_stuff(numbers):
do_other_stuff(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()
上面的操作只会为调用函数生成以下的分析输出：
Timer unit: 1e-06 s
Total time: 0.000773 s
File: <ipython-input-3-ec0394d0a501>
Function: do_stuff at line 7
Line # Hits Time Per Hit % Time Line Contents
==============================================================
7 def do_stuff(numbers):
8 1 11 11.0 1.4 do_other_stuff(numbers)
9 1 236 236.0 30.5 l = [numbers[i]/43 for i in range(len(numbers))]
10 1 526 526.0 68.0 m = ['hello'+str(numbers[i]) for i in range(len(numbers))]
在这种情况下，你可以像这样添加额外的被调用函数进行分析：
from line_profiler import LineProfiler
import random
def do_other_stuff(numbers):
s = sum(numbers)
def do_stuff(numbers):
do_other_stuff(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.add_function(do_other_stuff) # add additional function to profile
lp_wrapper = lp(do_stuff)
lp_wrapper(numbers)
lp.print_stats()
输出结果：
Timer unit: 1e-06 s
Total time: 9e-06 s
File: <ipython-input-4-dae73707787c>
Function: do_other_stuff at line 4
Line # Hits Time Per Hit % Time Line Contents
==============================================================
4 def do_other_stuff(numbers):
5 1 9 9.0 100.0 s = sum(numbers)
Total time: 0.000694 s
File: <ipython-input-4-dae73707787c>
Function: do_stuff at line 7
Line # Hits Time Per Hit % Time Line Contents
==============================================================
7 def do_stuff(numbers):
8 1 12 12.0 1.7 do_other_stuff(numbers)
9 1 208 208.0 30.0 l = [numbers[i]/43 for i in range(len(numbers))]
10 1 474 474.0 68.3 m = ['hello'+str(numbers[i]) for i in range(len(numbers))]
注意：以这种方式添加要分析的函数不需要修改被分析的代码（也就是说，不需要添加 @profile 装饰器）。