central limit theorem simulator

#Write a function sample that simulates N sets of coin flips and
#returns a list of the proportion of heads in each set of N flips
#It may help to use the flip and mean functions that you wrote before

import random
from math import sqrt
from plotting import *

def mean(data):
    return float(sum(data))/len(data)

def variance(data):
    mu=mean(data)
    return sum([(x-mu)**2 for x in data])/len(data)

def stddev(data):
    return sqrt(variance(data))
    

def flip(N):
    return [random.random()>0.5 for x in range(N)]
    
def sample(N):
    #Insert your code here
    return [mean(flip(N)) for i in range(N)]

N=1000
outcomes=sample(N)
histplot(outcomes,nbins=30)

print mean(outcomes)
print stddev(outcomes)