TL;DR â€“ The Python random module contains a set of functions for generating random numbers. The module allows you to control the types of random numbers that you create.

#### Contents

## Why use the Python random module?

The random module can perform a few different tasks: you can use it to generate random numbers, randomize lists, or choose elements from a list at random.

This module is perfect for generating passwords or producing test datasets. It can also be integrated into Python `for`

or `if`

loops to change the outcome of a function at random.

## Generating Python random integers

The most basic and common use of the random module is to generate Python random integers. To accomplish this, you will need to import the random module and then use the `randint()`

Python function:

This will output a random number between `0`

and `10`

, including end-points.

Alternatively, if you want a step size other than `1`

, you can use the `randrange()`

function. In this case, the syntax is:

**Example**

`random.randrange(start, stop[, step])`

The `random.randrange()`

function uses a `step`

value of `1`

by default. If you specify a `step`

, the range of potential outputs is determined using the Python `range()`

function.

## Generating random floating values

If you want to generate a random floating value rather than an integer, use the Python `random.random()`

function:

This will tell Python to generate a random number between `0`

and `1`

, excluding `1`

.

If you want a random float number between specific start and end values, use the `random.uniform()`

function:

**Example**

```
import numpy
uniform = numpy.random.uniform(0, 100, size = (3, 5))
print(uniform)
```

This will tell Python to generate a random float number between `0`

and `100`

, excluding `100`

.

**Pros**

- Easy to use with a learn-by-doing approach
- Offers quality content
- Gamified in-browser coding experience
- The price matches the quality
- Suitable for learners ranging from beginner to advanced

**Main Features**

- Free certificates of completion
- Focused on data science skills
- Flexible learning timetable

**Pros**

- Simplistic design (no unnecessary information)
- High-quality courses (even the free ones)
- Variety of features

**Main Features**

- Nanodegree programs
- Suitable for enterprises
- Paid Certificates of completion

**Pros**

- Easy to navigate
- No technical issues
- Seems to care about its users

**Main Features**

- Huge variety of courses
- 30-day refund policy
- Free certificates of completion

## Random functions for lists and sequences

If you have a list of numbers, values, or other elements, you can use the Python random module to randomly select one or more elements. To choose a single element at random, use the `random.choice()`

function:

**Example**

```
import random
myList = ["bmw", "volvo", "toyota", "chrysler"]
print(random.choice(myList))
```

If you want to pick more than one element from a list or sequence, use the `random.sample()`

function:

**Example**

```
import random
myList = ["bmw", "volvo", "toyota", "chrysler"]
print(random.sample(myList, 3))
```

In the case that you have a list or sequence and you want Python to randomize the order of elements in the list for you, use the `random.shuffle()`

function:

**Example**

```
import random
myList = ["bmw", "toyota", "volvo", "chrysler"]
random.shuffle(myList)
print(myList)
```

## Creating arrays of random numbers

If you need to create a test dataset, you can accomplish this using the `randn()`

Python function from the Numpy library. `randn()`

creates arrays filled with random numbers sampled from a normal (Gaussian) distribution between `0`

and `1`

.

The dimensions of the array created by the `randn()`

Python function depend on the number of inputs given. To create a 1-D array (that is, a list), enter only the length of the array desired. For example:

**Example**

```
import numpy
array = numpy.random.randn(3)
print(array)
```

Similarly, for 2-D and 3-D arrays, enter the length of each dimension of the desired array:

**Example**

```
import numpy
array = numpy.random.randn(3, 5, 2)
print(array)
```

It is possible to multiply the array generated by `randn()`

to get values outside of the default 0 to 1 range. Alternatively, you can use the `uniform()`

function to set upper and lower bounds on the random numbers generated:

**Example**

```
import numpy
array = numpy.random.uniform(0, 100, size = (3, 5))
print(array)
```

## Python random: useful tips

- When using
`random.random()`

to generate a random float number, you can multiply the result to generate a number outside the 0 to 1 range. - If you want to be able to generate the same random number in the future, check the internal state of the random number generator using the
`random.getstate()`

. You can reset the generator to the same state using the`random.setstate()`

.