sample.py

import numpy as np
import scipy.stats as stats

total_numbers = []

for i in range(0,30):
    total_numbers.append(np.random.randint(0,100))

print('total_numbers are:\n ', total_numbers,'\n')

#lets take success probability from population (num >= 60 is success)
num_greater_than_59 = list(filter(lambda x:x > 60, total_numbers))
prob_getting_success = len(num_greater_than_59) / len(total_numbers)

print('Numbers greater than 59 are: %i \n '% len(num_greater_than_59));

print('Probability of success from previous data = %.2f \n\n'%prob_getting_success)


#Choose 7 random values from total list
seven_random_values = []
for n in range(0,7):
    seven_random_values.append(total_numbers[np.random.randint(0,6)])
seven_random_values

#lets create binomial distribution
n = 7
k = np.arange(0, n+1)
p = prob_getting_success

binom = stats.binom.pmf(k, n, p)

mean = n * p

#Q > What is mean of binomial
print('Mean by binomial : ',mean)

print('Mean of 7 random values ',np.array(seven_random_values).mean())

print('Mean of population ',np.array(total_numbers).mean())