Python numpy.random.normal

Question

I generated random 20 numbers with mean 0 and variance 1 (np.random.normal). I calculated the variance twice ddof = 1 and 0.

My question is I am trying to add (mean 0 and variance 1) to (np.random.normal), However on there website is no mention for the variance 

https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.normal.html

loc : float Mean (“centre”) of the distribution. 

scale : float Standard deviation (spread or “width”) of the distribution. 

size : int or tuple of ints, optional

So can i just do it like this

mu, sigma = 0, math.sqrt(1) 

x = np.random.normal(mu, sigma, 20)

Because i have to perform the estimation in 90 times and 20 numbers each time and recount again

a = np.random.rand(90, x)

Here is the full code

import math 

import numpy as np 

import pandas as pd 

mu, sigma = 0, math.sqrt(1) 

x = np.random.normal(mu, sigma, 20) 

#caluclateing the unbiased_estimator and the biased_estimator 

unbiased_estimator = np.var(x, ddof=1) 

biased_estimator = np.var(x, ddof=0) 

print ("Unbiased_estimator : ",unbiased_estimator) 

print ("Biased_estimator : ", biased_estimator) 

a = np.random.rand(90, x) 

#caluclateing the unbiased_estimator and the biased_estimator 

unbiased_estimator_for_each_20 = np.var(a, ddof=1, axis=1) 

biased_estimator_for_each_20 = np.var(a, ddof=0, axis=1) 

print (unbiased_estimator_for_each_20 ) 

print(" ") 

print (biased_estimator_for_each_20 )

Answer 1

You can do something like this:-

import numpy as np 

mean = 0, 

variance = 1, 

np.random.normal(loc = mean, scale= np.sqrt(variance), 20) 

unbiased_estimator = np.var(x, ddof=1) 

biased_estimator = np.var(x, ddof=0) 

print ("Unbiased_estimator : ",unbiased_estimator) 

print ("Biased_estimator : ", biased_estimator)

Output:

Unbiased_estimator : 1.08318083742 

Biased_estimator : 1.02902179555

To know more about this you can have a look at the following video tutorial:-