Hello guys. How are you all? I hope you all fine. In this tutorial we will learn about **how to cosine similarity in python**. It calculate the cosine angle between the two vector lists. so without wasting time lets learn about of this.

## How to cosine similarity in python

**cosine similarity in python**to cosine similarity in python just

**Use numpy**.By using numpy you can cosine similarity in python. Lets learn about of this by given below example:Output :`from numpy import dot from numpy.linalg import norm List1 = [8,54,6,9] List2 = [9,8,7,5] result = dot(List1, List2)/(norm(List1)*norm(List2)) print(result)`

**0.717620473957404****How to cosine similarity in python**to cosine similarity in python just

**Use numpy.norm()**.By using numpy.norm() you can cosine similarity in python. Lets learn about of this by given below example:Output :`import numpy as np List1 =np.array([[8,54,6,9], [9,8,7,5]]) List2=np.array([ 42, 4, 3, 5]) result = List1.dot(List2)/ (np.linalg.norm(List1, axis=1) * np.linalg.norm(List2)) print(result)`

**[0.25946924 0.72347603]****python cosine similarity**to cosine similarity in python just

**Use numpy.norm()**.By using numpy.norm() you can cosine similarity in python. Lets learn about of this by given below example:Output :**[0.25946924 0.72347603]**

## Method 1: Use numpy

Just Use numpy. Lets learn about of this by given below example:

```
from numpy import dot
from numpy.linalg import norm
List1 = [8,54,6,9]
List2 = [9,8,7,5]
result = dot(List1, List2)/(norm(List1)*norm(List2))
print(result)
```

Output :

`0.717620473957404`

## Method 2: Use numpy.norm()

By using numpy.norm() you can cosine similarity. Lets learn about of this by given below example:

```
import numpy as np
List1 =np.array([[8,54,6,9],
[9,8,7,5]])
List2=np.array([ 42, 4, 3, 5])
result = List1.dot(List2)/ (np.linalg.norm(List1, axis=1) * np.linalg.norm(List2))
print(result)
```

Output :

`[0.25946924 0.72347603]`

**Conclusion**

It’s all About this Tutorial. Hope all methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which method worked for you?

**Also, Read**