Finding Probability on a biased die

I had to simulate a biased die such that 6 occurs more than 50% of the times. Which I was able to do using:

from random import randint,choice
def bdie():
    out = random.randint(0,2)
    if (out==1 or out == 2):
        return 6
    else:
        r = choice([i for i in range(1,7) if i not in [6]])
        return r

def bdthrow(n):
    output = []
    for i in range(0,n):
        if n>0 & n<=100000000:
            outcome = bdie()
            output.append(outcome)
        else:
            print("Invalid")
    print(output)

For which the output would be:

[6, 6, 6, 6, 6, 3, 6, 3, 5, 4]

Now on this biased die I am supposed to find the probability of top face being 5 and I need to find the average count of each face of the die.

Now solving the sum on a paper is easy where I can find the probability but I'm not sure how to implement this in python.

If I understand correctly you are looking for an unbiased estimator of getting 5. Such an estimator can be the number of 5 obtains when throwing the dice enough times. I.e # fives / n.

From memory perspective I would recommend using defaultdict. Also there is no need to check the value of n on every round.

from random import randint,choice
from collections import defaultdict

def bdie():
    out = randint(0,2)
    if (out==1 or out == 2):
        return 6
    else:
        r = choice([i for i in range(1,7) if i not in [6]])
        return r

def bdthrow(n):
    output = defaultdict(int)
    for i in range(0,n):
        outcome = bdie()
        output[outcome] +=1
    return (float(output[5])/n)

There are few other optimisations to the code but naively that should work.