Illustration with Python Central Limit Theorem | sample with different sample size

diffSampleSize.py

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

# build gamma distribution as population
shape, scale = 2., 2.  # mean=4, std=2*sqrt(2)
s = np.random.gamma(shape, scale, 1000000)

# Step 1
## sample with different sample size
# list of sample mean
meansample = []
# number of sampling
numofsample = 25000
# sample size
samplesize = [1,5,10,30,100,1000]
# for each sample size (1 to 1000)
for i in samplesize:
    # collect mean of each sample
    eachmeansample = []
    # for each sampling
    for j in range(0,numofsample):
        # sampling i sample from population
        rc = random.choices(s, k=i)
        # collect mean of each sample
        eachmeansample.append(sum(rc)/len(rc))
    # add mean of each sampling to the list
    meansample.append(eachmeansample)

# Step 2
# plot
cols = 2
rows = 3
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(meansample[n], 200, density=True)
        ax[i, j].set_title(label="sample size :" + str(samplesize[n]))
        n += 1
plt.show()