﻿ Pandas Data Series: Compute the Euclidean distance between two given series - w3resource

# Python Pandas: Compute the Euclidean distance between two given series

## Python 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
```

Python Code Editor:

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## Python: Tips of the Day

Python: Cache results with decorators

There is a great way to cache functions with decorators in Python. Caching will help save time and precious resources when there is an expensive function at hand.

Implementation is easy, just import lru_cache from functools library and decorate your function using @lru_cache.

```from functools import lru_cache

@lru_cache(maxsize=None)
def fibo(a):
if a <= 1:
return a
else:
return fibo(a-1) + fibo(a-2)

for i in range(20):
print(fibo(i), end="|")

print("\n\n", fibo.cache_info())
```

Output:

```0|1|1|2|3|5|8|13|21|34|55|89|144|233|377|610|987|1597|2584|4181|

CacheInfo(hits=36, misses=20, maxsize=None, currsize=20)```