How time-consuming statistical functions run in Python

The examples in this paper describe how time-consuming statistical functions run in Python. Share to everyone for your reference. The implementation method is as follows:

Import Timedef Time_me (FN):  def _wrapper (*args, **kwargs):    start = Time.clock ()    fn (*args, **kwargs)    Print "%s cost%s second"% (fn.__name__, Time.clock ()-start)  return _wrapper# This adorner can be a time-consuming way to easily count function runs. #用来分析脚本的性能是最好不过了. #这样用: @time_medef Test (x, y):  time.sleep (0.1) @time_medef test2 (x):  time.sleep (0.2) test (1, 2) test2 (2) #输出: # Test cost 0.1001529524 second#test2 cost 0.199968431742 Second

Another higher-level version is:

Import Timeimport functoolsdef Time_me (info= "used"):  def _time_me (FN):    @functools. Wraps (FN)    def _ Wrapper (*args, **kwargs):      start = Time.clock ()      fn (*args, **kwargs)      print "%s%s%s"% (fn.__name__, info, Time.clock ()-start), "second"    return _wrapper  return _time_me@time_me () def test (x, y):  time.sleep ( 0.1) @time_me ("cost") def test2 (x):  time.sleep (0.2) test (1, 2) test2 (2)

Hopefully this article will help you with Python programming.

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