This example shows you how to implement a process pool to perform a parallel application. We create a pool of four processes and then we use the pool's map method to perform a simple function:

1. Import the multiprocessing library:

import multiprocessing

2. The Pool method applies function_square to the input element to perform a simple calculation:

def function_square(data):
    result = data*data
    return result

if __name__ == '__main__':

3. The parameter inputs are a list of integers from 0 to 100:

    inputs = list(range(0,100))

4. The total number of parallel processes is 4:

    pool = multiprocessing.Pool(processes=4)

5. The pool.map method submits to the process pool as separate tasks:

    pool_outputs = pool.map(function_square, inputs)
    pool.close() 
    pool.join() 

6. The result of the calculation is stored in pool_outputs:

    print ('Pool    :', pool_outputs)

It is important to note that the result of the pool.map() method is equivalent to Python's built-in map() function, except that the processes run in parallel.