我有一个Python命令行程序,需要一段时间才能完成。我想知道完成跑步所需的确切时间。
我看过timeit模块,但它似乎只适用于小代码片段。我想给整个节目计时。
我有一个Python命令行程序,需要一段时间才能完成。我想知道完成跑步所需的确切时间。
我看过timeit模块,但它似乎只适用于小代码片段。我想给整个节目计时。
当前回答
以下代码段以可读的<HH:MM:SS>格式打印经过的时间。
import time
from datetime import timedelta
start_time = time.time()
#
# Perform lots of computations.
#
elapsed_time_secs = time.time() - start_time
msg = "Execution took: %s secs (Wall clock time)" % timedelta(seconds=round(elapsed_time_secs))
print(msg)
其他回答
我认为这是最好和最简单的方法:
from time import monotonic
start_time = monotonic()
# something
print(f"Run time {monotonic() - start_time} seconds")
或与装饰师一起:
from time import monotonic
def record_time(function):
def wrap(*args, **kwargs):
start_time = monotonic()
function_return = function(*args, **kwargs)
print(f"Run time {monotonic() - start_time} seconds")
return function_return
return wrap
@record_time
def your_function():
# something
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
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))]
对于使用Jupyter笔记本的数据人员
在单元格中,可以使用Jupyter的%%time魔术命令来测量执行时间:
%%time
[ x**2 for x in range(10000)]
输出
CPU times: user 4.54 ms, sys: 0 ns, total: 4.54 ms
Wall time: 4.12 ms
这将仅捕获特定单元的执行时间。如果您想捕获整个笔记本(即程序)的执行时间,可以在同一目录中创建一个新笔记本,并在新笔记本中执行所有单元格:
假设上面的笔记本名为example_notebook.ipynb。在同一目录中的新笔记本中:
# Convert your notebook to a .py script:
!jupyter nbconvert --to script example_notebook.ipynb
# Run the example_notebook with -t flag for time
%run -t example_notebook
输出
IPython CPU timings (estimated):
User : 0.00 s.
System : 0.00 s.
Wall time: 0.00 s.
我使用来自ttictoc的tic和toc。
pip install ttictoc
然后可以在脚本中使用:
from ttictoc import tic,toc
tic()
# foo()
print(toc())