: print(i)execution_time = timeit.timeit(number = 50)print("运行时长：",execution_time)使用 datetime 模块使用 Python 中的 datetime 模块的 datetime.now() 函数记录开始和结束的时间戳，并计算差值来获取代码执行时间。from datetime import datetimestart_time = datetime.now()for i in range(...
execution_time = end_time - start_time print(f"代码执行时间:{execution_time} 秒") time.perf_counter() time.perf_counter()函数返回一个高精度的性能计数器,通常用于测量较小代码块的执行时间。 import time start_time = time.perf_counter() # 执行你的代码 end_time = time.perf_counter() execut...
等待10秒end_time=time.time()# 获取当前时间戳execution_time=end_time-start_time# 计算函数执行时间ifexecution_time>5:# 判断函数执行时间是否超过5秒raiseTimeoutError("Function execution timed out")# 函数正常执行完毕return"Function executed successfully"try:result...
importsignal# 定义超时处理函数deftimeout_handler(signum,frame):raiseTimeoutError("Function execution timeout")# 设置超时时间为5秒defset_timeout(func,timeout):signal.signal(signal.SIGALRM,timeout_handler)signal.alarm(timeout)func()signal.alarm(0)# 重置定时器# 需要设置超时时间的函数deflong_running...
It’s designed precisely to measure the execution time of small code snippets. While you can import and call timeit.timeit() from Python as a regular function, it’s usually more convenient to use the command-line interface. You can time the two variants as follows: Shell $ python -m ...
end_time=time.time()execution_time=end_time-start_timeprint(f"代码执行时间:{execution_time} 秒") 1. 2. 3. 4. 5. 6. 7. 8. 9. time.perf_counter() time.perf_counter()函数返回一个高精度的性能计数器,通常用于测量较小代码块的执行时间。
sleep(5) return "Function completed" try: result = limit_execution_time(my_function, 2) print(result) except Exception as e: print(e) 在这个示例中,我们定义了一个handler函数,当SIGALRM信号被触发时,它会引发一个Exception。然后,我们定义了一个limit_execution_time函数,它接受一个函数和一个时间限制...
Toolformeasuring execution timeofsmall code snippets. Thismoduleavoids a numberofcommon trapsformeasuring execution times. See also Tim Peters' introduction to the Algorithms chapter inthe Python Cookbook, publishedbyO'Reilly.Library usage: see the Timerclass. ...
"""timer=timeit.Timer(stmt=code_to_measure)execution_time=timer.timeit(number=1000)# 执行代码1000次print(f"代码执行平均时间:{execution_time/1000}秒") 3. 使用cProfile模块进行性能分析 Python 的cProfile模块用于执行代码的性能分析。它会生成一个分析报告,显示函数调用次数、执行时间和内存占用等信息。
importtimedefcalculate_time(func):defwrapper(*args,**kwargs):start_time=time.time()result=func(*args,**kwargs)end_time=time.time()execution_time=end_time-start_timeprint(f"函数{func.__name__}运行时间为:{execution_time}秒")returnresultreturnwrapper@calculate_timedefmy_function(param1,param...