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)
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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))
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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))
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Output:
2.23606797749979