Open-Source Internship opportunity by OpenGenus for programmers. Apply now.

In this article, we have explored the Random module in Python in depth and explored the different functions like seed, getrandbits, randrange, choice and much more with Python code examples.

Table of contents:

- Introduction to Random module in Python
- seed
- getrandbits
- randrange
- randint
- choice
- choices
- shuffle
- sample
- random
- uniform
- triangular

## Introduction to Random module in Python

A module in python is a file with group of statements and definitions.It can contain variables, classes and functions.

**random** module in python is used to generate random values.

**random** module can be imported in our program using the following syntax

```
import random
```

If a specific function(for example, choice) needs to be imported,then the following syntax can be used

```
from random import choice
```

If all the functions are to be imported,then the following syntax can be used

```
from random import *
```

There are several in-built functions in this module.Let's go through the functions frequently used one by one.

# 1) seed

A seed value **n** is used to generate a random number sequence.

**Syntax:**

```
random.seed(n)
```

Setting the seed value to 5

```
import random
random.seed(5)
print(random.random())
```

```
0.6229016948897019
```

Each time the same seed value is passed the same sequence of random numbers is generated.

```
import random
random.seed(5)
print(random.random())
print(random.random())
random.seed(5)
print(random.random())
print(random.random())
```

```
0.6229016948897019
0.7417869892607294
0.6229016948897019
0.7417869892607294
```

# 2) getrandbits

The getrandbits function takes in number of bits **n** as argument and returns a random integer of **n** bits.

**Syntax:**

```
random.getrandbits(n)
```

Here the value passed is n=4.Hence,it returns a 4-bit random integer.

```
import random
print(random.getrandbits(4))
```

```
13
```

# 3) randrange

The randrange takes in two integers **a** and **b** as arguments and returns a random integer in-between the specified values **a** and **b**.

**Syntax:**

```
random.randrange(a,b)
```

Here, a is **inclusive** and b is **exclusive** meaning the interval is [a,b-1]

Taking a=2,b=9

```
import random
print(random.randrange(2,9))
```

```
3
```

# 4) randint

The randint function is a simple modification of randrange.

**Syntax:**

```
random.randint(a,b)
```

Here, a and b both are **inclusive** meaning the interval is [a,b]

If a,b are the input parameters

*randrange(a,b)* generates a random value from the interval **[a,b-1]**

whereas

*randint(a,b)* generates a random value from the interval **[a,b]**

Hence,**randint(a,b)** is same as **randrange(a,b+1)**

Taking a=2,b=9

```
import random
print(random.randint(2,9))
```

```
6
```

# 5) choice

The choice function returns a random item from a specified sequence **sequence**.

**Syntax:**

```
random.choice(sequence)
```

The sequence can be string, list or tuple.

Let a list be passed as argument.

```
import random
sequence=["one","two","three","four","five"]
print(random.choice(sequence))
```

```
four
```

Now, passing a string as argument.

```
import random
sequence="opengenus"
print(random.choice(sequence))
```

```
u
```

# 6) choices

The choices function takes in a sequence **sequence** and an integer **k** as arguments and returns a randomly generated sequence of size **k** containing values from the specified sequence **sequence**.

**Syntax:**

```
random.choices(sequence,k)
```

Passing a tuple and k=10 as arguments

```
import random
sequence=("google","amazon","apple")
print(random.choices(sequence,k=10))
```

```
['google', 'google', 'amazon', 'amazon', 'google', 'apple', 'apple', 'apple', 'apple', 'google']
```

Each item in the sequence can be assigned weights **weights** so that the item with highest weight has the highest probability of being chosen and so on.

**Syntax:**

```
random.choices(sequence,weights,k)
```

```
import random
sequence=("google","amazon","apple")
weights=[1,2,20]
print(random.choices(sequence,weights,k=10))
```

```
['google', 'amazon', 'google', 'apple', 'google', 'apple', 'apple', 'apple', 'apple', 'apple']
```

As said the element "apple" was with the highest weight and it occured the most number of times.

# 7) shuffle

The shuffle function takes in sequence **sequence** as an argument and shuffles the list in random order.

**Syntax:**

```
random.shuffle(sequence)
```

The shuffle function doesn't return a new list rather it modifies the exisiting list.

The sequence must be mutable(for example,list) as it will be modified.

Passing a list as argument

```
import random
sequence=["a","b","c","d","e","f"]
random.shuffle(sequence)
print(sequence)
```

```
['c', 'f', 'b', 'a', 'e', 'd']
```

# 8) sample

The sample function takes a sequence **sequence** and an integer **k** as arguments and returns list of size **k** containing items randomly chosen from the specified **sequence**.

**Syntax:**

```
random.sample(sequence,k)
```

k must be less than or equal to the size of the specified sequence

```
import random
sequence=[1,2,3,4,5]
print(random.sample(sequence,k=4))
```

```
[3, 2, 1, 5]
```

# 9) random

The random function generates a random floating point number between 0 and 1.

**Syntax:**

```
random.random()
```

Here 0 is **inclusive** and 1 is **exclusive** meaning the interval is [0.0,1.0)

```
import random
print(random.random())
```

```
0.28979005625006504
```

# 10) uniform

The uniform function takes in two integers **a** and **b** as parameters and returns a random floating point number in between **a** and **b**.

**Syntax:**

```
random.uniform(a,b)
```

Here, a and b both are **inclusive** meaning the interval is [a,b]

```
import random
print(random.uniform(25,70))
```

```
35.014086669240854
```

# 11) triangular

The triangular function takes in two integers **a** and **b** as parameters and returns a random floating point number in between **a** and **b**.

**Syntax:**

```
random.triangular(a,b)
```

Here, a and b both are **inclusive** meaning the interval is [a,b]

```
import random
print(random.triangular(7,77))
```

```
39.2308642640529
```

The above case works similar to *uniform*.

In addition, a **mode** parameter can be set.The **mode** lets us generate a random floating point number close to one of the given parameter values.

**Syntax:**

```
random.triangular(a,b,mode)
```

By default the **mode** value is the midpoint of the specified parameters.Hence,the probability of number being generated is not favoured for any parameter.

```
import random
print(random.triangular(6,93,10))
```

```
9.420306033285689
```

It can be seen that 6,93 were passed as arguments and mode value is set to 10.The floating point number generated is close to 6.

With this article at OpenGenus, you must have the complete idea of Random module in Python.