Update Array in Parallel?

Thanks to @roganjosh's suggestion, I was able to find an answer:

import numpy as np
from multiprocessing import Pool

arr = ['a','b','c','d','e','f','g']

def edit_array(i):
    return arr[i] + ' hello!'

if __name__=='__main__':
    pool = Pool(processes=4)
    list_start_vals = range(len(arr))
    array_2D = pool.map(edit_array, list_start_vals)
    pool.close()
    print array_2D

Here's one, relatively simple, way to do it using the multiprocessing module:

import functools
import multiprocessing

def func(arr, i):
    arr[i] = arr[i] + ' hello!'

if __name__ == '__main__':
    manager = multiprocessing.Manager()  # Create a manager to handle shared object(s).
    xyz = manager.list(['a','b','c','d','e'])  # Create a proxy for the shared list object.

    p = multiprocessing.Pool(processes=4)  # Create a pool of worker processes.

    # Create a single arg function with the first positional argument (arr) supplied.
    # (This is necessary because Pool.map() only works with functions of one argument.)
    mono_arg_func = functools.partial(func, xyz)

    p.map(mono_arg_func, range(len(xyz)))  # Run func in parallel until finished

    for i in xyz:
         print(i)

Output:

a hello!
b hello!
c hello!
d hello!
e hello!

Note this is not going to very fast if the list is huge because sharing access to large objects requires a lot of overhead between separate tasks (which run in different memory spaces).

A better approach would use a multiprocessing.Queue which is implemented "using a pipe and a few locks/semaphores" according to the documentation (as opposed to a shared list object whose entire contents will have to be pickled and unpickled multiple times).

Getting from the question that you are wanting to replace the current value of an item in the list with the new value:

for position, value in enumerate(xyz):
    xyz[position] = '%s hello!' % value

Gives: ['a hello!', 'b hello!', 'c hello!', 'd hello!', 'e hello!']