# Compute Euclidean distance using NumPy Arrays

In this practice question, you have to create a Euclidean distance function `euclidean_distance()` that takes two parameters of NumPy arrays and return euclidean distance.

The following equation gives the Euclidean distance formula: ```import numpy as np
def euclidean_distance(a, b):
return np.sqrt(np.sum((a - b)*(a - b)))

a = np.array([11, 12, 13, 14])
b = np.array([1, 2, 3, 4])
print(euclidean_distance(a, b))```

Output

`20.0`

In NumPy, operations are performed element by element. So, when `a - b` is computed, the 0th index element of array b is subtracted from the 0th index element of array a. In this way, other elements are subtracted.

`a - b` gives `[10, 10, 10, 10]`, when `a - b` is multiplied with itself, it gives the square of (a - b). After that NumPy `sum()` function is used to calculate the sum, and finally, NumPy `sqrt()` is used to find the square root.

You can also test your Euclidean distance function using these two NumPy arrays:

```A = np.array(range(100))
B = np.array(range(1, 101))
print(euclidean_distance(A, B))
Output
0
```