我正在尝试使用该itertools.product函数使我的代码段（在同位素模式模拟器中）更易于阅读并且希望也更快（文档指出没有创建中间结果），但是我已经针对这两个版本的代码进行了测试彼此使用该cProfiling库，并注意到该方法itertools.product比我的嵌套for循环要慢得多。

用于测试的示例值：

carbons = [(0.0, 0.004613223957020534), (1.00335, 0.02494768843632857), (2.0067, 0.0673219412049374), (3.0100499999999997, 0.12087054681917497), (4.0134, 0.16243239687902825), (5.01675, 0.17427700732161705), (6.020099999999999, 0.15550695260604208), (7.0234499999999995, 0.11869556397525197), (8.0268, 0.07911287899598853), (9.030149999999999, 0.04677626606764402)]
hydrogens = [(0.0, 0.9417611429667746), (1.00628, 0.05651245007201512)]
nitrogens = [(0.0, 0.16148864310897554), (0.99703, 0.2949830688288726), (1.99406, 0.26887643366755537), (2.99109, 0.16305943261399866), (3.98812, 0.0740163089529218), (4.98515, 0.026824040474519875), (5.98218, 0.008084687617425748)]
oxygens17 = [(0.0, 0.8269292736927519), (1.00422, 0.15717628899143962), (2.00844, 0.014907548827832968)]
oxygens18 = [(0.0, 0.3584191873916266), (2.00425, 0.36813434247849824), (4.0085, 0.18867830334103902), (6.01275, 0.06433912182670033), (8.017, 0.016421642936302827)]
sulfurs33 = [(0.0, 0.02204843659673093), (0.99939, 0.08442569434459646), (1.99878, 0.16131398792444965), (2.99817, 0.2050722764666321), (3.99756, 0.1951327596407101), (4.99695, 0.14824112268069747), (5.99634, 0.09365899226198841), (6.99573, 0.050618028523695714), (7.99512, 0.023888506307006133), (8.99451, 0.010000884811585533)]
sulfurs34 = [(0.0, 3.0106350597190195e-10), (1.9958, 6.747270089956428e-09), (3.9916, 7.54568412614702e-08), (5.9874, 5.614443102700176e-07), (7.9832, 3.1268212758750728e-06), (9.979, 1.3903197959791067e-05), (11.9748, 5.141248916434075e-05), (13.970600000000001, 0.0001626288218672788), (15.9664, 0.00044921518047309414), (17.9622, 0.0011007203440032396)]
sulfurs36 = [(0.0, 0.904828368500412), (3.99501, 0.0905009370374487)]

演示嵌套循环的代码段：

totals = []
for i in carbons:
    for j in hydrogens:
        for k in nitrogens:
            for l in oxygens17:
                for m in oxygens18:
                    for n in sulfurs33:
                        for o in sulfurs34:
                            for p in sulfurs36:
                                totals.append((i[0]+j[0]+k[0]+l[0]+m[0]+n[0]+o[0]+p[0], i[1]*j[1]*k[1]*l[1]*m[1]*n[1]*o[1]*p[1]))

演示使用的代码段itertools.product：

totals = []
for i in itertools.product(carbons,hydrogens,nitrogens,oxygens17,oxygens18,sulfurs33,sulfurs34,sulfurs36):
    massDiff = i[0][0]
    chance = i[0][1]
    for j in i[1:]:
        massDiff += j[0]
        chance = chance * j[1]
    totals.append((massDiff,chance))

剖析的结果（基于每种方法10次运行）对于嵌套循环法而言平均约为0.8秒，对于方法而言约为1.3秒itertools.product。因此，我的问题是，我是否itertools.product错误地使用了函数，还是应该坚持嵌套的for循环？

-更新-

我列出了两个cProfile结果：

# ITERTOOLS.PRODUCT APPROACH 
420003 function calls in 1.306 seconds

   Ordered by: standard name

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.018    0.018    1.306    1.306 <string>:1(<module>)
        1    1.246    1.246    1.289    1.289 IsotopeBas.py:64(option1)
   420000    0.042    0.000    0.042    0.000 {method 'append' of 'list' objects}
        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}

和：

# NESTED FOR LOOP APPROACH
420003 function calls in 0.830 seconds

   Ordered by: standard name

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.019    0.019    0.830    0.830 <string>:1(<module>)
        1    0.769    0.769    0.811    0.811 IsotopeBas.py:78(option2)
   420000    0.042    0.000    0.042    0.000 {method 'append' of 'list' objects}
        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}