NumPy: Calculate the Euclidean distance

NumPy: Array Object Exercise-103 with Solution

Write a NumPy program to calculate the Euclidean distance.

From Wikipedia: In mathematics, the Euclidean distance or Euclidean metric is the "ordinary" straight-line distance between two points in Euclidean space. With this distance, Euclidean space becomes a metric space. The associated norm is called the Euclidean norm. Older literature refers to the metric as the Pythagorean metric

Sample Solution:

Python Code:

# Importing the 'distance' module from 'scipy.spatial'
from scipy.spatial import distance

# Defining the coordinates for point p1 and point p2 in three dimensions
p1 = (1, 2, 3)
p2 = (4, 5, 6)

# Calculating the Euclidean distance between points p1 and p2 using 'distance.euclidean()'
d = distance.euclidean(p1, p2)

# Printing the calculated Euclidean distance between the two points
print("Euclidean distance: ", d) 

Sample Output:

Euclidean distance:  5.196152422706632

Explanation:

In the above code –

• from scipy.spatial import distance: Import the distance module from the scipy.spatial package.
• p1 = (1, 2, 3): Define point p1 with coordinates (1, 2, 3).
• p2 = (4, 5, 6): Define point p2 with coordinates (4, 5, 6).
• d = distance.euclidean(p1, p2): Calculate the Euclidean distance between point p1 and point p2 using the euclidean() function from the distance module. The Euclidean distance is the straight-line distance between two points in a space.
• Finally print() function prints the calculated Euclidean distance.

Python-Numpy Code Editor:

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