# Generate Sample of Normal Distribution in Python NumPy

This tutorial shows how to generate a sample of normal distrubution using NumPy in Python. The following shows syntax of two methods.

Method 1: It can change the default values (Default: mu=0 and sd=1).

`np.random.normal(mu=0, sigma=1, size)`

Method 2: It can only generate numbers of standard normal (mu=0 and sd=1). But, it can have different shapes by changing (d0, d1, …, dn).

`np.random.randn(d0, d1, …, dn)`

## Example for Method 1

The following uses np.random.normal() to generate a sample of normal distribution using Numpy. The Python code sets mean `mu = 5` and standard variance `sigma = 1`.

``````# import numpy
import numpy as np

# set mean and standard deviation
mu, sigma = 5, 1

# set seed
np.random.seed(123)

# generate a set of 10 numbers using Numpy function
y = np.random.normal(mu, sigma, 10)

# print out these 10 numbers
print("Normal Distribution (mu=5, sigma=1) array: \n", y)``````

The following is the print out of these 10 numbers that following normal distribution N(mu=5, sigma=1).

```Normal Distribution (mu=5, sigma=1) array:
[3.9143694  5.99734545 5.2829785  3.49370529 4.42139975 6.65143654
2.57332076 4.57108737 6.26593626 4.1332596 ]```

## Example for Method 2

The following uses np.random.randn (d0, d1, …, dn) to generate a sample of standard normal distribution using Numpy. The Python code sets `d0= 5` and `d1 = 3`. Thus, it will generate an array of 5×3.

``````# import numpy
import numpy as np

# set seed
np.random.seed(123)

# Set size of 5x3 array
Array_2D_standard_normal_distribution = np.random.randn(5,3)
print("Standard Normal Distribution array (5x3): \n", Array_2D_standard_normal_distribution)``````

The following is the output, which shows an array of 5×3. All the numbers in this array follows the standard normal distribution (i.e., mu=0, and sigma =1).

```Standard Normal Distribution array (5x3):
[[-1.0856306   0.99734545  0.2829785 ]
[-1.50629471 -0.57860025  1.65143654]
[-2.42667924 -0.42891263  1.26593626]
[-0.8667404  -0.67888615 -0.09470897]
[ 1.49138963 -0.638902   -0.44398196]]```