import numpy as np

# %% Number of trials:

n = 10

# %% Params of choice():

sample_space = np.array([1, 2, 3, 4, 5, 6])
sample_size = 1
replace = True
prob = [1/6, 1/6, 1/6, 1/6, 1/6, 1/6]

# %% Array for storing the population:

x_pop = np.zeros(n)

# %% For loop realizing the n generated samples:

for k in range(0, n):
    
    # Sample number k:
    x_k = np.random.choice(sample_space,
                           sample_size, 
                           replace,
                           prob)
    # Storing sample_k:
    x_pop[k] = int(x_k)
    
# %% Displaying the data series:
    
# print('x_pop =', x_pop.astype(int))

# %% Statistical measures:

m_x = np.sum(x_pop)/n  # Mean

var_x = np.sum((x_pop - m_x)**2)/(n - 1)  # Variance

std_x = np.sqrt(var_x)  # Standard deviation

# %% Displaying results:

print('m_x =', f'{m_x:.2f}')
print('var_x =', f'{var_x:.2f}')
print('std_x =', f'{std_x:.2f}')