Illustration with Python Central Limit Theorem | standardize

standardize.py

## standardize part
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stats

# Step 1
# use last sampling
from diffNumSampling import meansample
sm = meansample[len(meansample)-1]

# Step 2
# calculate start deviation
std = np.std(sm)
# set population mean
mean = np.mean(sm)
# list of standarded sample

# Step 3
zn = []
# for each sample subtract with mean and devided by standard deviation
for i in sm:
    zn.append((i-mean)/std)
    
# Step 4
# plot hist
plt.figure(figsize=(20,10))
plt.hist(zn, 200, density=True)
# compare with standard normal disrtibution line
mu = 0
sigma = 1
x = np.linspace(mu - 3*sigma, mu + 3*sigma, 100)
# draw standard normal disrtibution line
plt.plot(x, stats.norm.pdf(x, mu, sigma),linewidth = 5, color='red')
plt.show()