在Python 2.7和Python 3.6中，我发现这个代码可以运行：
from cProfile import Profile; p = Profile(); p.enable()
...而这会引发异常：
from profile import Profile; p = Profile(); p.enable()
# --> AttributeError: 'Profile' object has no attribute 'enable'
我错过了什么？使用
profile和cProfile模块都提供以下函数:...
class profile.Profile(timer=None, timeunit=0.0, subcalls=True, builtins=True)...
enable()开始收集性能分析数据。
profile.Profile() 实例的正确方法是什么？enable() 和 disable() 仅适用于 cProfile。对于 profile.Profile，你需要使用 run() 调用而非 enable() 和 disable()。以下是每个示例：
cProfile.Profile:
# cProfile.Profile -- control with enable() and disable()
from datetime import datetime
import cProfile, pstats, io
sortby = 'cumulative'
pr1 = cProfile.Profile(lambda: int(datetime.now().timestamp()*1000000), 0.000001)
pr1.enable()
list(x for x in range(int(10*10*10*10/10*10+10)))
pr1.disable()
s1 = io.StringIO()
ps1 = pstats.Stats(pr1, stream=s1).sort_stats(sortby)
ps1.print_stats()
print(s1.getvalue())
profile.Profile:
# profile.Profile -- control with run()
from datetime import datetime
import profile, pstats, io
sortby = 'cumulative'
pr2 = profile.Profile(datetime.now().timestamp, 0.000001)
pr2.run('list(x for x in range(int(10*10*10*10/10*10+10)))')
s2 = io.StringIO()
ps2 = pstats.Stats(pr2, stream=s2).sort_stats(sortby)
ps2.print_stats()
print(s2.getvalue())
cProfile will push profiled functions' output to stdout, whereas profile will not.
The final report's filename:lineno(function) column will have slightly different output. cProfile generally will display built-in functions more readably.
profile's output and not in cProfile's -- but if using a named function, it is identifiable in both profilers' output (although the passed in arguments to the function are only visible from profile's output).Running identical functions thru both cProfile and profile provides slightly different results. It seems cProfile generates about two less function calls generally, and will provide more efficient times for the same function.
Using the profilers' optional custom timer and timeunit parameters can offer the benefit of gaining microsecond precision vs the default millisecond precision. However, it seems that attempting to use these comes with some caveats:
cProfile's timeunit function requires an integer, whereas profile expects a floating point number. This means you can't use datetime.now().timestamp() directly with cProfile, but must rather wrap it in a lambda to convert it to a full integer first.profile seems to not work correctly with datetime.now().timestamp() even though that's in the format it's expecting. It will print out negative time values -- I show this below. Switching to the lambda form to convert it to integer doesn't help, as profile expects the floating point number.Below is the output of profile.Profile with the datetime.now().timestamp() timer for microsecond provision.
print(s2.getvalue())
10015 function calls in -0.020 seconds`
Ordered by: cumulative time
ncalls tottime percall cumtime percall filename:lineno(function)
0 0.000 0.000 profile:0(profiler)
1 0.000 0.000 0.000 0.000 :0(setprofile)
10011 -0.010 -0.000 -0.010 -0.000 <string>:1(<genexpr>)
1 -0.010 -0.010 -0.020 -0.020 <string>:1(<module>)
1 -0.000 -0.000 -0.020 -0.020 :0(exec)
1 -0.000 -0.000 -0.020 -0.020 profile:0(list(x for x in range(int(10*10*10*10/10*10+10))))
However, this problem can be fixed by providing a custom timer function (which can even wrap around datetime.now() to provide the exact same output format as datetime.now().timestamp()), for reasons I can't explain.
def timenow():
now = datetime.now()
return (((now.year-1970) * 31557600) + (now.month * 2629800) +
(now.day * 86400) + (now.hour * 3600) + (now.minute * 60) +
now.second + (now.microsecond * 0.000001))
pr2 = profile.Profile(timenow, 0.000001)，这将输出以下（健康的）报告：>>> print(s2.getvalue())
10015 function calls in 0.041 seconds
Ordered by: cumulative time
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.006 0.006 0.041 0.041 profile:0(list(x for x in range(int(10*10*10*10/10*10+10))))
1 0.000 0.000 0.035 0.035 :0(exec)
1 0.017 0.017 0.035 0.035 <string>:1(<module>)
10011 0.018 0.000 0.018 0.000 <string>:1(<genexpr>)
1 0.001 0.001 0.001 0.001 :0(setprofile)
0 0.000 0.000 profile:0(profiler)
>>> print(s1.getvalue())
10013 function calls in 0.031 seconds
Ordered by: cumulative time
ncalls tottime percall cumtime percall filename:lineno(function)
10011 0.031 0.000 0.031 0.000 <stdin>:1(<genexpr>)
1 0.000 0.000 0.000 0.000 <stdin>:1(<module>)
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
datetime.now().timestamp()与profile不同，首先必须注意到timenow不提供datetime.now().timestamp()的计时。虽然它提供接近纪元时间，但是偏差很小（在同一系统上，增量将相同，不应影响正常的配置文件运行）。timenow中使用的手动转换肯定也不像datetime.now().timestamp()那样高效。我只能猜测后者过于高效，某种程度上导致profile源代码无法正确测量。
除非您有特定的原因，否则请使用cProfile。官方Python文档还指出，推荐使用模块是cProfile。虽然没有提供具体的理由，但该文档似乎暗示cProfile可能表现更好，并适用于大多数用户的目的（即使它是稍微冗长的语法）。
Profile.enable()方法的说明称它仅在cProfile中可用，这意味着您必须使用profile.Profile的一个run*()方法。这里有一些示例。 - martineau@为前缀的用户名，这样他们就会被通知到你的回复。 我在这种情况下仅仅是碰巧找到了你的回复（因为有时我会检查最近我自己所做的评论是否有任何回应）。 - martineau