Estimated reading time: 3 minutes

You may have a number of machine learning projects where you need a way to generate random integers to test your model assumptions.

There are plenty of ways to achieve this, here we will take you through a number of different steps.

To demonstrate this we take you through a number of different scenarios, but before we do that:

## What are the uses of using one or more of the below?

- Computer simulation – look to generate random numbers as part of a simulation in a computer program.
- Statistcial sampling – If you are conducting some analysis on a set of data, you use the below to generate samples in a range.
- Introduce randomness – Helps with testing in different scenarios to ensure that does not allow any bias be introduced.
- Cryptography – It can be used as part of of security testing to make data secure in transmission or on a database.
- Stress testing – Pick extreme values to pass to a program and see how it reacts and if it returns an error.

## You can use approach of the randrange function

In this function, this creates a randomly selected value that you need, in a range you define:

```
from random import randrange
print(randrange(1,10,1))
Result = prints a random number starting at 1, ending at 10, in increments of 1
```

## Use the randint function?

```
import random
print(random.randint(0,9))
Result = This produces a random number integer value between 0 and 9
```

## What about the concept of randbelow function?

```
from secrets import randbelow
print(randbelow(9))
Result = Returns a random number between 0 and 9
```

## As a result you could try the numpy random.randit functionality as it is also popular

import numpy as np print(np.random.randint(10, size=(1, 10))) Result = prints ten integer values, anything between 1 and 10 Sample Output: [[6 8 3 9 4 1 5 3 2 7]]

## Using the range function in a for loop is an option

```
n={} # Creates a dictionary of key value pairs
for i in range(10):
n[i]=i
print(n)
Result = Iterates and creates a dictionary as it moves through each step.
Sampele Output:
{0: 0}
{0: 0, 1: 1}
{0: 0, 1: 1, 2: 2}
{0: 0, 1: 1, 2: 2, 3: 3}
{0: 0, 1: 1, 2: 2, 3: 3, 4: 4}
{0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5}
{0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6}
{0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7}
{0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8}
{0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9}
```

## Use random.randrange in a for loop

```
from random import randrange
for i in range(10):
print(random.randrange(0, 10))
Result = Loops ten times to create ten random numbers
Sample Output:
9
1
8
1
7
7
6
3
9
7
```

**In this instance, w hile the focus of this post is on integers, we have two examples using floating points**.

## You can generate random floating points using random.uniform

```
import random
print(random.uniform(0.0, 9.0))
Result = Returns a random floating point number.
Sample Output:
4.436111014119316
```

## An alternative is to generate random floating point values in a range

import random for i in range(10): print(random.random()) Result = Returns ten random floating point numbers. Sample Output: 8.80776539498274 0.8975905667079721 0.8467133530607382 0.757433819303854 0.88431126761758 0.9077189321042094 0.4826489161220854 0.7689417823093723 0.505382717614604 0.3102908090040003 0.01832993383665016

So in summary there are various different ways to create random numbers either a single one or a group of random numbers.

As a result of this, it is a very handy way to assist a programmer test a machine learning project.

On the other hand, if you need to try and break your program with values that are at the extreme of a range it can be accommodated this way as well.