# Parallel processing with Pool.apply_async()

​

import multiprocessing as mp

pool = mp.Pool(mp.cpu_count())

​

results = []

​

# Step 1: Redefine, to accept `i`, the iteration number

def howmany_within_range2(i, row, minimum, maximum):

    """Returns how many numbers lie within `maximum` and `minimum` in a given `row`"""

    count = 0

    for n in row:

        if minimum <= n <= maximum:

            count = count + 1

    return (i, count)

​

​

# Step 2: Define callback function to collect the output in `results`

def collect_result(result):

    global results

    results.append(result)

​

​

# Step 3: Use loop to parallelize

for i, row in enumerate(data):

    pool.apply_async(howmany_within_range2, args=(i, row, 4, 8), callback=collect_result)

​

# Step 4: Close Pool and let all the processes complete    

pool.close()

pool.join()  # postpones the execution of next line of code until all processes in the queue are done.

​

# Step 5: Sort results [OPTIONAL]

results.sort(key=lambda x: x[0])

results_final = [r for i, r in results]

​

print(results_final[:10])

#> [3, 1, 4, 4, 4, 2, 1, 1, 3, 3]