[python] list結合の検証

code.py

# coding: utf-8

import profile
#from memory_profiler import profile as mprofile
from random import random
from itertools import chain

data = [[random() for j in xrange(1000)] for i in xrange(1000)]

def add(x,y):
    return x+y

#@mprofile
def profile_reduce_lambda():
    '''lambdaを使った場合'''
    return reduce(lambda x,y: x+y, data)

#@mprofile
def profile_reduce_add():
    '''lambdaを使わずadd()をあらかじめ定義'''
    return reduce(add, data)

#@mprofile
def profile_chain():
    return chain(*data)

#@mprofile
def profile_append():
    res = []
    for i in data:
        for j in i:
            res.append(j)
    return res

profile.run('profile_reduce_lambda()')
profile.run('profile_reduce_add()')
profile.run('profile_chain()')
profile.run('profile_append()')

#if __name__=="__main__":
#    profile_reduce_lambda()
#    profile_reduce_add()
#    profile_chain()
#    profile_append()

memory_profile_result

ilename: run.py

Line #    Mem usage    Increment   Line Contents
================================================
    13                             @mprofile
    14    41.012 MB     0.000 MB   def profile_reduce_lambda():
    15   170.035 MB   129.023 MB       return reduce(lambda x,y: x+y, data)


Filename: run.py

Line #    Mem usage    Increment   Line Contents
================================================
    17                             @mprofile
    18   162.555 MB     0.000 MB   def profile_reduce_add():
    19   170.184 MB     7.629 MB       return reduce(add, data)


Filename: run.py

Line #    Mem usage    Increment   Line Contents
================================================
    21                             @mprofile
    22   162.680 MB     0.000 MB   def profile_chain():
    23   162.680 MB     0.000 MB       return chain(*data)


Filename: run.py

Line #    Mem usage    Increment   Line Contents
================================================
    25                             @mprofile
    26   162.680 MB     0.000 MB   def profile_append():
    27   162.680 MB     0.000 MB       res = []
    28   162.727 MB     0.047 MB       for i in data:
    29   196.203 MB    33.477 MB           for j in i:
    30   187.859 MB    -8.344 MB               res.append(j)
    31   187.859 MB     0.000 MB       return res

profile_result

         1004 function calls in 7.695 seconds

   Ordered by: standard name

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    3.185    3.185    7.682    7.682 :0(reduce)
        1    0.006    0.006    0.006    0.006 :0(setprofile)
        1    0.006    0.006    7.689    7.689 <string>:1(<module>)
        1    0.000    0.000    7.695    7.695 profile:0(profile_reduce_lambda())
        0    0.000             0.000          profile:0(profiler)
        1    0.000    0.000    7.682    7.682 run.py:14(profile_reduce_lambda)
      999    4.497    0.005    4.497    0.005 run.py:15(<lambda>)


         1004 function calls in 7.631 seconds

   Ordered by: standard name

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    3.157    3.157    7.624    7.624 :0(reduce)
        1    0.000    0.000    0.000    0.000 :0(setprofile)
        1    0.006    0.006    7.631    7.631 <string>:1(<module>)
        1    0.000    0.000    7.631    7.631 profile:0(profile_reduce_add())
        0    0.000             0.000          profile:0(profiler)
      999    4.468    0.004    4.468    0.004 run.py:10(add)
        1    0.000    0.000    7.624    7.624 run.py:18(profile_reduce_add)


         4 function calls in 0.000 seconds

   Ordered by: standard name

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.000    0.000    0.000    0.000 :0(setprofile)
        1    0.000    0.000    0.000    0.000 <string>:1(<module>)
        1    0.000    0.000    0.000    0.000 profile:0(profile_chain())
        0    0.000             0.000          profile:0(profiler)
        1    0.000    0.000    0.000    0.000 run.py:22(profile_chain)


         1000004 function calls in 5.369 seconds

   Ordered by: standard name

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
  1000000    2.557    0.000    2.557    0.000 :0(append)
        1    0.000    0.000    0.000    0.000 :0(setprofile)
        1    0.009    0.009    5.369    5.369 <string>:1(<module>)
        1    0.000    0.000    5.369    5.369 profile:0(profile_append())
        0    0.000             0.000          profile:0(profiler)
        1    2.803    2.803    5.360    5.360 run.py:26(profile_append)