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Generate five random numbers from the normal distribution using NumPy
• Last Updated : 29 Aug, 2020

In Numpy we are provided with the module called random module that allows us to work with random numbers. The random module provides different methods for data distribution. In this article, we have to create an array of specified shape and fill it random numbers or values such that these values are part of a normal distribution or Gaussian distribution. This distribution is also called the Bell Curve this is because of its characteristics shape.

To generate five random numbers from the normal distribution we will use numpy.random.normal() method of the random module.

Syntax: numpy.random.normal(loc = 0.0, scale = 1.0, size = None)

Parameters:

loc: Mean of distribution

scale: Standard derivation

size: Resultant shape.
If size argument is empty then by default single value is returned.

Example 1:

## Python3

 `# importing module ` `import` `numpy as np ` ` `  ` `  `# numpy.random.normal() method ` `r ``=` `np.random.normal(size``=``5``) ` ` `  `# printing numbers ` `print``(r) `

Output :

```[ 0.27491897 -0.18001994 -0.01783066  1.07701319 -0.11356911]
```

Example 2:

## Python3

 `# importing module ` `import` `numpy as np ` ` `  ` `  `# numpy.random.normal() method ` `random_array ``=` `np.random.normal(``0.0``, ``1.0``, ``5``) ` ` `  `# printing 1D array with random numbers ` `print``(``"1D Array with random values : \n"``, random_array) `

Output :

```1D Array with random values :
[ 0.14559212  1.97263406  1.11170937 -0.88192442  0.8249291 ]
```

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