Pandas Data Series: Compute the Euclidean distance between two given series

Pandas: Data Series Exercise-31 with Solution

Write a Pandas program to compute the Euclidean distance between two given series.

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.

Sample Solution :

Python Code :

import pandas as pd
import numpy as np
x = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
y = pd.Series([11, 8, 7, 5, 6, 5, 3, 4, 7, 1])
print("Original series:")
print(x)
print(y)
print("\nEuclidean distance between two said series:")
print(np.linalg.norm(x-y))

Sample Output:

Original series:
0     1
1     2
2     3
3     4
4     5
5     6
6     7
7     8
8     9
9    10
dtype: int64
0    11
1     8
2     7
3     5
4     6
5     5
6     3
7     4
8     7
9     1
dtype: int64

Euclidean distance between two said series:
16.492422502470642

Explanation:

x = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
y = pd.Series([11, 8, 7, 5, 6, 5, 3, 4, 7, 1])

This above code creates two Pandas Series objects 'x' and 'y', each containing ten integers.

print(np.linalg.norm(x-y)): This line computes the Euclidean distance between the two Series objects using the np.linalg.norm() function from the NumPy library. The norm() function calculates the Euclidean distance between the two vectors formed by the values of 'x' and 'y'. The Euclidean distance is the length of the shortest path connecting two points in a n-dimensional space.

The resulting output is a single float value representing the Euclidean distance between the two Series objects 'x' and 'y'.

Python-Pandas Code Editor:

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