import numpy as np

# %% Number of trials:

num_trials = 100000

# %% Params of np.random.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]

# %% Initializing counter of event A:

count_A = 0

# %% For loop simulating the n events:

for k in range(0, num_trials):
    
    # Sample number k (event number k):
    sample_k = np.random.choice(
        sample_space, 
        sample_size, 
        replace, 
        prob)

    # Updating counter if event A has occured:
    if ((5 in sample_k) or (6 in sample_k)): count_A += 1

# %% Calculating and printing probability estimate:

prob_estimate_A = count_A/num_trials
print('Estimate of prob of event A =', prob_estimate_A)