Parallel for Loop in Python

Parallelizing a for loop in Python means spreading the process over all the cores on a multicore system. Each loop can run in parallel on a different core, speeding up the overall processing time.


This tutorial will cover how to run for loops in parallel in Python.


Parallelize for Loop in Python Using the multiprocesssing Package

To parallelize a loop we can use the .Pool() object from the multiprocessing package. The example below demonstrates looping over a function 10 times using a multiprocessing.Pool() object.


import multiprocessing

def multiply(v):
   return v * 10

pool_obj = multiprocessing.Pool()


res =,range(0,10))


Use the joblib Package to Parallelize for Loop in Python

Here is another example using the joblib package to call a function 10 times:


from joblib import Parallel, delayed
def process(i):
   return i * i
results = Parallel(n_jobs=2)(delayed(process)(i) for i in range(10))

Related Tutorials


How to Use For Loops in Python

September 02, 2020

The Python Sleep Function

October 01, 2020

How to Enumerate in Python

September 17, 2020