mean_1 = sample_distribution(1, "means_1.png")
print(mean_1)
mean_2 = sample_distribution(2, "means_2.png")
print(mean_2)
mean_5 = sample_distribution(5, "means_5.png")
print(mean_5)
mean_10 = sample_distribution(10, "means_10.png")
print(mean_10)
mean_20 = sample_distribution(20, "means_20.png")
print(mean_20)
mean_30 = sample_distribution(30, "means_30.png")
print(mean_30)
mean_40 = sample_distribution(40, "means_40.png")
print(mean_40)
mean_50 = sample_distribution(50, "means_50.png")
print(mean_50)

Please note that we can also verify that the population is not normally distributed by using the following code:

s = [random.randint(1, 6) for _ in range(100000)]
pyplot.hist(s)
pyplot.savefig("distribution_of_population.png")

And in this case, the distribution of the population will be like the following:

So, the whole Python code for verifying the Central Limit Theorem is as follows: …