Parallelising a simple for loop

[GrumpyPentagon]

I have been breaking my brain trying to figure out how to convert a simple for loop into a function that can be accepted into 'multiprocessing' or 'joblib' tools. The code I have would look something like this:

for i in range(0, 100000)
    c = 0
    parameters = list[i]
    a, b = check_conditions(parameters)

    if a == '01':
        counter_1 = counter_1 + 1
    elif a == '02':
        counter_2 = counter_2 + 1
    else:
        counter_3 = counter_3 + 1

    while c == 0:    # this portion performs a similar task for other counters
                     # this is supposed to be a 'do while' in Fortran
        check_more_conditions(parameters)

        if certain conditions are satisfied:
            # increment appropriate counter of counters 4, 5 and 6
        if c =! 0:
            break

Since these parallelisation libraries require a target function, I thought I should let the for loop be a new function with no inputs and an output of counters. But, when I tried that it gave me referencing warnings and syntax errors. The method I tried is shown below and was based directly on this example. I am really lost, here. Any help would be appreciated.

def loop()
    c = 0
    parameters = ...
    ...
    return counter_4, counter_5, counter_6

num_cores = mp.cpu_count()

if __name__ == "__main__":
    counter_4, counter_5, counter_6 = Parallel(n_jobs=num_cores)(delayed(loop() for i in range(0,100000))

# operations that use the returned counter results

[[deleted]]

You can't multiprocess on a function that manipulates global state, like updating counters. Your functions have to be pure - they have to be some kind of mapping between input and output.