更专业的性能分析软件一般有两类方法论:event-based profiling和statistical profiling 。 Event-based Profiling 并不是所有编程语言都支持这类性能分析,支持这类分析的语言主要有: Java:JVMTI(JVM 吕海峰 2018/04/03 1.4K0 python性能分析之cProfile模块 pythonfunctionlineprofileprofiler -s cumulative开关告诉c...
首先,讨论最基础的Profiling工具:%timeit,time.time() 和time装饰器。然后介绍cProfiler,这个内置包可以帮助我们查看程序中不同函数占用的时间,帮助发现瓶颈。接下来,line_profiler 可以对选中的瓶颈函数进行逐行profile,比如每一个行被调用多少次,每一行花掉时间的百分比等等。最后,我们还需要提一下memory_profiler,它...
print("Something is happening after the function is called.") return wrapper @simple_decorator def say_hello(): print("Hello!") say_hello() 输出结果: Something is happening before the function is called. Hello! Something is happening after the function is called.1.2 装饰器的语法糖 @ Python...
$ python -m line_profiler script_to_profile.py.lprof For example, here are the results of profiling a single function from a decorated version of the pystone.py benchmark (the first two lines are output from pystone.py, not kernprof): ...
我们将使用https://github.com/pyutils/line_profiler上。使用行剖析器非常简单:只需在 get_distance 中添加注解即可: @profiledefget_distance(p1, p2): 这是因为我们将使用line_profiler软件包中的便捷脚本kernprof来: kernprof -l lprofile_distance_cache.py ...
python性能分析器:line_profiler 代码: import line_profiler import sys def test(): for i in range(0, 10): print( i**2 ) print("End of the function") prof = line_profiler.LineProfiler(test) #pass in the function to profile prof.enable() #start profiling...
importrequestssetattr(requests,'get', simple_profiling(requests.get)) 如果你想对requests模块下所有的公有API计时,我们可以写一个patch_module辅助函数 importtypesimportrequestsdefpatch_module(module): methods = [mformindir(module)ifnotm.startswith('_')andisinstance(getattr(module, m), types.FunctionTyp...
$ python -m line_profiler script_to_profile.py.lprof For example, here are the results of profiling a single function from a decorated version of the pystone.py benchmark (the first two lines are output from pystone.py, not kernprof): Pystone(1.1) time for 50000 passes = 2.48 This ma...
If your function is very fast or slow, then adjust that number as needed to get an accurate measure. When you run timeit in the command line or use the %timeit magic command in a Jupyter Notebook, then it’ll show you the best runtime of the code snippet that you’ve given it: ...
如今，有了pandas-profiling库，我们一行代码就可以生成一份超详细的数据分析报告~from bokeh.plotting ...