# Best Practice to Calculate Cosine Distance Between Two Vectors in NumPy – NumPy Tutorial

By | September 15, 2019

Cosine distance between two vectors is defined as:

It is often used as evaluate the similarity of two vectors, the bigger the value is, the more similar between these two vectors.

Cosine distance is also can be defined as:

The smaller θ, the more similar x and y.

In this tutorial, we will introduce how to calculate the cosine distance between two vectors using numpy, you can refer to our example to learn how to do.

## Import library

import numpy as np

## Create two vectors

vector_1 = np.array([1, 5, 1, 4, 0, 0, 0, 0, 0])
vector_2 = np.array([2, 4, 1, 1, 1, 1, 0, 0, 0])

## Calculate cosine distance

def cos_sim(a, b):
"""Takes 2 vectors a, b and returns the cosine similarity
"""
dot_product = np.dot(a, b) # x.y
norm_a = np.linalg.norm(a) #|x|
norm_b = np.linalg.norm(b) #|y|
return dot_product / (norm_a * norm_b)

## How to use?

print(cos_sim(vector_1, vector_2))

The output is:

0.840473288592332