from numpy.random import seed #seed is random pattern
from numpy.random import randint #randint(a,b,c), a is smallest value, b is biggest values, c is how much the random number is generated in list/array
from numpy import mean #to calculate mean in list/array
from matplotlib import pyplot #to build histogram

# Example 1
means = [mean(randint(1,7,50)) for _ in range(1000)] # calculate mean of (50 random number beetween 1-7) 1000 times
# print(type(means))
# print(means)
pyplot.hist(means)
pyplot.show()
pyplot.savefig('1.png')

# Example 2
# Make a data distribution
data = [
    1,
    2,2,
    3,3,3,
    4,4,4,4,
    5,5,5,5,5,
    6,6,6,6,6,6,
    7,7,7,7,7,7,7,
    8,8,8,8,8,8,8,8,
    9,9,9,9,9,9,9,9,9,
    10,10,10,10,10,10,10,10,10,10
]
val = []
hisMean = []

for u in range(500):
    for i in range(1000):
        new = data[randint(0,54)]
        val.append(new)
    rata2 = mean(val)
    hisMean.append(rata2)
    val = []

# print(type(hisMean))
# print(hisMean)
pyplot.hist(hisMean)
pyplot.show()
pyplot.savefig('2.png')