Regarding exercises from data science

Can anyone tell me how to solve exercise from week 19 ,first video.

You can start with this code:

 fig, axs = plt.subplots(3, 1)
 num_samples = 1000
 i = 0
 for sample_size in [100, 1000, 10000]:
     #dice throw, uniform, outcome: 1, 2, 3, 4, 5, 6

     #Each element represent the face up on dice(R.V), sum along the columns will form the new R.V.
     samples = np.random.randint(1, 6, (num_samples, sample_size))
     x = samples.sum(axis=1)

     #see if CLT holds.
     axs[i].hist(x, bins=50)
     i = i + 1

Here, is my solution

import numpy as np
import pandas as pd
import random
import matplotlib.pyplot as plt

#build Uniform distribution as the population, you can replace the uniform distribution by Binomial, Normal, etc.

s = np.random.uniform(0,1,1000000)

take sample from polulation

no_sample = [10,100,1000,10000] 
mean_sampl = []
sampl_size = 250
for i in no_sample:
    each_s_m = []
    for j in range(0,i):
        rc = random.choices(s, k=sampl_size)
        each_s_m.append(sum(rc)/len(rc))
    mean_sampl.append(each_s_m)
#>  Plot the mean distribution
cols = 2
rows = 2
fig, ax = plt.subplots(rows, cols, figsize=(20,15))
n = 0
for i in range(0, rows):
    for j in range(0, cols):
        ax[i, j].hist(mean_sampl[n], 200, density=True)
        ax[i, j].set_title(label="number of sampling :" + str(no_sample[n]))
        n += 1
plt.show()