When no need to return anything:

from joblib import Parallel, delayed
import multiprocessing

# Number of cores available to use
num_cores = multiprocessing.cpu_count()

# If your function takes only 1 variable
def yourFunction(input):
    # anything in your loop
    return XXX

Parallel(n_jobs=num_cores)(delayed(yourFunction)(input) for input in list)


# If your function taking more than 1 variable
def yourFunction(input1, input2):
    # anything in your loop
    return XXX

Parallel(n_jobs=num_cores)(delayed(yourFunction)(input1, input2) for input1 in list1 for input2 in list2)

When need to return things, simply point it to a variable, it will be saved as a list:

results = Parallel(n_jobs=num_cores)(delayed(yourFunction)(input) for input in list)

When need to return data.frame and later concatenate together, using mp.Pool

import multiprocessing as mp
with mp.Pool(processes = num_cores-1) as pool:
    resultList = pool.map(yourFunction, argvList))

results_df = pd.concat(resultList)

Source:
https://stackoverflow.com/questions/9786102/how-do-i-parallelize-a-simple-python-loop
https://blog.dominodatalab.com/simple-parallelization/
https://stackoverflow.com/questions/36794433/python-using-multiprocessing-on-a-pandas-dataframe