在Python中对多个列表的每个组合求和的最快方法

Question

I have 5 lists that contain some data:

a = [52, 265, 1, 98, 26]
b = [42, 85, 45, 2, 3, 8, 632]
c = [7, 731, 92, 65, 28, 62]
d = [3, 5, 44, 55]
e = [15, 35, 850, 6, 18, 41]

What I want to do is get the sum of the values of every possible combination, for example:

a[0] + b[0] + c[0] + d[0] + e[0] = **121**
...
a[4] + b[6] + c[5] + d[3] + e[5] = **816**

This needs to be done is the fastest way possible, because in my code the length of these lists is bigger, thus making the calculation of all possibilities extremely time demanding.

My first idea was using nested for loops, but that's not fast enough. I imagine some transformations and some magic could be done with NumPy to speed up this process.

Answer 1

the fastest way to actually sum all the combinations(you actually want the product, not the combinations) is to generate them and sum them

print([(x,sum(x)) for x in itertools.product(my_list1,my_list2,my_list3,...)])

but I suspect that the secret is to not check all the products, but instead to intelligently select specific values to narrow your search space

Answer 2

You want something like this ?

a = [52, 265, 1, 98, 26]
b = [42, 85, 45, 2, 3, 8, 632]
c = [7, 731, 92, 65, 28, 62]
d = [3, 5, 44, 55]
e = [15, 35, 850, 6, 18, 41]

import itertools

print(list(map(lambda x:sum(x),itertools.product(a,b,c,d,e))))

output:

119, 139, 954, 110, 122, 145, 121, 141, 956, 112, 124, 147, 160, 180, 995, 151, 163, 186, 171, 191, 1006, 162, 174, 197, 843, 863, 1678, 834, 846, 869, 845, 865, 1680, 836, 848, 871, 884, 904, 1719, 875, 887, 910, 895, 915, 1730, 886, 898, 921, 204, 224, 1039, 195, 207, 230, 206, 226, 1041, 197, 209, 232, 245, 265, 1080, 236, 248, 271, 256, 276, 1091, 247, 259, 282, 177, 197, 1012, 168, 180, 203, 179, 199, 1014, 170, 182, 205, 218, 238, 1053, 209, 221, 244, 229, ....