Там какой-либо простой путь состоит в том, чтобы сравнить сценария Python?

Have a look at timeit, the python profiler and pycallgraph.

timeit

def test():
    """Stupid test function"""
    lst = []
    for i in range(100):
        lst.append(i)

if __name__ == '__main__':
    import timeit
    print(timeit.timeit("test()", setup="from __main__ import test"))

Essentially, you can pass it python code as a string parameter, and it will run in the specified amount of times and prints the execution time. The important bits from the docs:

timeit.timeit(stmt='pass', setup='pass', timer=, number=1000000)

Create a Timer instance with the given statement, setup code and timer function and run its timeit method with number executions.

... and:

Timer.timeit(number=1000000)

Time number executions of the main statement. This executes the setup statement once, and then returns the time it takes to execute the main statement a number of times, measured in seconds as a float. The argument is the number of times through the loop, defaulting to one million. The main statement, the setup statement and the timer function to be used are passed to the constructor.

Note

By default, timeit temporarily turns off garbage collection during the timing. The advantage of this approach is that it makes independent timings more comparable. This disadvantage is that GC may be an important component of the performance of the function being measured. If so, GC can be re-enabled as the first statement in the setup string. For example:

timeit.Timer('for i in xrange(10): oct(i)', 'gc.enable()').timeit()

Profiling

Profiling will give you a much more detailed idea about what's going on. Here's the "instant example" from the official docs:

import cProfile
import re
cProfile.run('re.compile("foo|bar")')

Which will give you:

      197 function calls (192 primitive calls) in 0.002 seconds

Ordered by: standard name

ncalls  tottime  percall  cumtime  percall filename:lineno(function)
     1    0.000    0.000    0.001    0.001 <string>:1(<module>)
     1    0.000    0.000    0.001    0.001 re.py:212(compile)
     1    0.000    0.000    0.001    0.001 re.py:268(_compile)
     1    0.000    0.000    0.000    0.000 sre_compile.py:172(_compile_charset)
     1    0.000    0.000    0.000    0.000 sre_compile.py:201(_optimize_charset)
     4    0.000    0.000    0.000    0.000 sre_compile.py:25(_identityfunction)
   3/1    0.000    0.000    0.000    0.000 sre_compile.py:33(_compile)

Both of these modules should give you an idea about where to look for bottlenecks.

Also, to get to grips with the output of profile, have a look at this post

pycallgraph

This module uses graphviz to create callgraphs like the following:

You can easily see which paths used up the most time by colour. You can either create them using the pycallgraph API, or using a packaged script:

pycallgraph graphviz -- ./mypythonscript.py

The overhead is quite considerable though. So for already long-running processes, creating the graph can take some time.