Python - How to parallel consume and operate on fi

Current scenario: I have 900 files in a directory called directoryA. The files are named file0.txt through file 899.txt, each 15MB in size. I loop through each file sequentially in python. Each file I load as a list, do some operations, and write out an output file in directoryB. When the loop ends I have 900 files in directoryB. The files are named out0.csv through out899.csv.

Problem: The processing of each file takes 3 minutes, making the script run for more than 40 hours. I would like to run the process in a parallel manner as all the files are independent of each other (do not have any inter-dependencies). I have 12 cores in my machine.

The below script runs sequentially. Please help me run it parallel. I have looked at some of the parallel processing modules in python using related stackoverflow questions, but they are difficult for me to understand as I dont have much exposure to python. Thanks a billion.

Pseudo Script

    from os import listdir 
    import csv

    mypath = "some/path/"

    inputDir = mypath + 'dirA/'
    outputDir = mypath + 'dirB/'

    for files in listdir(inputDir):
        #load the text file as list using csv module 
        #run a bunch of operations
        #regex the int from the filename. for ex file1.txt returns 1, and file42.txt returns 42
        #write out a corresponsding csv file in dirB. For example input file file99.txt is written as out99.csv

To fully utilize your hardware core, it's better to use the multiprocessing library.

from multiprocessing import Pool

from os import listdir 
import csv

def process_file(file):
    #load the text file as list using csv module 
    #run a bunch of operations
    #regex the int from the filename. for ex file1.txt returns 1, and file42.txt returns 42
    #write out a corresponsding csv file in dirB. For example input file file99.txt is written as out99.csv

if __name__ == '__main__':
    mypath = "some/path/"

    inputDir = mypath + 'dirA/'
    outputDir = mypath + 'dirB/'

    p = Pool(12)
    p.map(process_file, listdir(inputDir))

Document of multiprocessing: https://docs.python.org/2/library/multiprocessing.html