- directly use NumPy method

import numpy as nprv = np.random.normal(0,1)

2. use the central limit theorem

`rv = (np.mean(np.random.uniform(0,1,n))-0.5)*np.sqrt(12*n)`

3. use inverse-CDF

from scipy.stats import normiid = np.random.uniform(0,1,size)rv = norm.ppf(iid)

quick proof of inverse-CDF correctness [1]:

Let *F* be a continuous cumulative distribution function, and let F^-1…