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Monte Carlo multiprocessing TypeError: map() missing 1 required positional argument: 'iterable'

I'm trying to do a multiprocessed version of Monte Carlo Pi calculation.

However, I always get an error that says

TypeError: map() missing 1 required positional argument: 'iterable'

I read that the error can be fixed by using starmap but it doesn't work either for some reason.

%%timeit
import random
import math
import itertools
import multiprocessing
from multiprocessing import Pool, current_process
from timeit import default_timer as timer
import functools
def monteCarlo(total):
    inside = 0
    for i in range(0, total):
        x2 = random.random()**2
        y2 = random.random()**2
        if math.sqrt(x2 + y2) < 1.0:
            inside += 1
    pi = (float(inside) / total) * 4
    return pi
if __name__ == '__main__':
    pool = multiprocessing.Pool(processes=4)
    values = [100]
    data = sum(pool.map(functools.partial(monteCarlo, values)))
    print(data)
                pool.map requires two arguments: func, and iterable. You are passing only one argument: functools.partial(monteCarlo, values). Did you mean pool.map(monteCarlo, values)?
– mkrieger1
                Nov 26, 2020 at 14:18
                Yes. The "functools.partial" was to fix another error with the scope, that i had. But i get the same error without it.
– RazgiZtheGreat
                Nov 26, 2020 at 14:21
                You cannot get the same error because then you would pass two arguments and the iterable argument would not be missing.
– mkrieger1
                Nov 26, 2020 at 14:24
Don't need to call partial to get map value partial rather use just pool.map. The partial() is used for partial function application which “freezes” some portion of a function’s arguments and/or keywords resulting in a new object with a simplified signature. But you don't have need something freeze, if further need to use partial then you need to pass two argument to get the callable beahave. Get more about functools
import random
import math
import itertools
import multiprocessing
from multiprocessing import Pool, current_process
from timeit import default_timer as timer
import functools
def monteCarlo(total):
    inside = 0
    for i in range(0, total):
        x2 = random.random()**2
        y2 = random.random()**2
        if math.sqrt(x2 + y2) < 1.0:
            inside += 1
    pi = (float(inside) / total) * 4
    return pi
if __name__ == '__main__':
    pool = multiprocessing.Pool(processes=4)
    values = [100]
    data = sum(pool.map(monteCarlo, values))
    print(data)
        
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