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 arrayspoint1 = 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 distanceprint(dist) |
Output:
2.23606797749979
Method #2: Using dot()
# Python code to find Euclidean distance# using dot()import numpy as np# initializing points in# numpy arrayspoint1 = np.array((1, 2, 3))point2 = np.array((1, 1, 1))# subtracting vectortemp = point1 - point2# doing dot product# for finding# sum of the squaressum_sq = np.dot(temp.T, temp)# Doing squareroot and# printing Euclidean distanceprint(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 arrayspoint1 = np.array((1, 2, 3))point2 = np.array((1, 1, 1))# finding sum of squaressum_sq = np.sum(np.square(point1 - point2))# Doing squareroot and# printing Euclidean distanceprint(np.sqrt(sum_sq)) |
Output:
2.23606797749979
Last Updated on October 28, 2021 by admin