Calculate the Euclidean distance using NumPy

In simple terms, Euclidean distance is the shortest between the 2 points irrespective of the dimensions. In this article to find the Euclidean distance, we will use the NumPy library. This library used for manipulating multidimensional array in a very efficient way. Let’s discuss a few ways to find Euclidean distance by NumPy library.

Method #1: Using linalg.norm()

Python3

# Python code to find Euclidean distance
# using linalg.norm()

import numpy as np

# initializing points in
# numpy arrays
point1 = np.array((1, 2, 3))
point2 = np.array((1, 1, 1))

# calculating Euclidean distance
# using linalg.norm()
dist = np.linalg.norm(point1 - point2)

# printing Euclidean distance
print(dist)

Output:

2.23606797749979

Method #2: Using dot()

Python3

# Python code to find Euclidean distance
# using dot()

import numpy as np

# initializing points in
# numpy arrays
point1 = np.array((1, 2, 3))
point2 = np.array((1, 1, 1))

# subtracting vector
temp = point1 - point2

# doing dot product
# for finding
# sum of the squares
sum_sq = np.dot(temp.T, temp)

# Doing squareroot and
# printing Euclidean distance
print(np.sqrt(sum_sq))

Output:

2.23606797749979

Method #3: Using square() and sum()

Python3

# Python code to find Euclidean distance
# using sum() and square()

import numpy as np

# initializing points in
# numpy arrays
point1 = np.array((1, 2, 3))
point2 = np.array((1, 1, 1))

# finding sum of squares
sum_sq = np.sum(np.square(point1 - point2))

# Doing squareroot and
# printing Euclidean distance
print(np.sqrt(sum_sq))

Output:

2.23606797749979