Probability calculator results around 0.17


I’m working through the probability calculator project for the Scientific Computing in Python course and I’m stumped on the probability values I’m getting.

I’ve seen several posts about values being around 0.17 instead of the expected 0.272 but can’t work out what I’m doing wrong here.

I’ve used print statements to check the evaluation of expected balls and balls picked and it looks like my code is picking them up OK.

I’ve looked through my ‘draw’ function but can’t see any cause for bias in there.
Any tips would be appreciated!

Your code so far

import copy
import random
# Consider using the modules imported above.

class Hat:
    def __init__(self, **kwargs):
        self.contents = []
        ball_dict = {}
        for key, value in kwargs.items():
            i = value

            while i > 0:


                i -= 1
    def draw(self, num_balls):
        temp = copy.copy(self.contents)
        # set up variables to use
        i = num_balls
        draw_list = []
        # for the num of times indicated, select and random and pop (remove + return) a 'ball'
        while i > 0:
            sel_range = len(self.contents)
            # if the number of balls exceeds the number available, continue and return what is there
            if i > sel_range:
                self.contents = temp
                selector = random.randint(0, sel_range-1)
            i -= 1

        return draw_list

def experiment(hat, expected_balls, num_balls_drawn, num_experiments):
    target_hat = hat
    expected_balls = expected_balls
    num_balls_drawn = num_balls_drawn
    num_experiments = num_experiments
    M = 0
    N = num_experiments

    # turn expected_balls into a list
    expected_list = []
    expected_dict = dict(expected_balls)

    for key, value in expected_dict.items():
        i = value
        while i > 0:
            i -= 1

    # running experiment for i (num of experiments)
    i = num_experiments
    while i > 0:
        # make copy of hat for test
        test_hat = copy.deepcopy(target_hat)
        # finds and sorts result
        result = test_hat.draw(num_balls_drawn)
        check = 0

        for key, value in expected_dict.items():
            if result.count(key) >= value:
                check += 1
        if check >= len(expected_dict):
           M += 1
        i -= 1

    return M/N

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Challenge: Probability Calculator

Link to the challenge:

Did you run the tests? I just did on your code and it passed.

The runner program does two things. First it runs a 3000 trial experiment and prints the results, which are around 0.17-0.18. Second, it runs the unit tests, one of which has an expected value of 0.272. Since your code passes, it must be computing that value correctly.

So in comparison to my output and the unit tests, everything is correct.