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Arslan Bajwa

The **numpy** library in Python allows us to compute Euclidean distance between two arrays.

**Euclidean distance** is defined in mathematics as the magnitude or length of the line segment between two points.

Euclidean distance formula

In this method, we first initialize two `numpy`

arrays. Then, we use ** linalg.norm()** of numpy to compute the Euclidean distance directly.

The details of the function can be found here.

#importing numpy import numpy as np #initializing two arrays array1 = np.array([1,2,3,4,5]) array2 = np.array([7,6,5,4,3]) #computing the Euclidan distance temp = array1 - array2 distance = np.linalg.norm(temp) print("Euclidean Distance: ", distance)

In this method, we first initialize two `numpy`

arrays. Then, we take the difference of the two arrays, compute the `dot`

product of the result, and transpose of the result. Then we take the square root of the answer. This is another way to implement Euclidean distance.

#importing numpy import numpy as np #initializing two arrays array1 = np.array([1,2,3,4,5]) array2 = np.array([7,6,5,4,3]) #computing the Euclidan distance temp = array1 - array2 distance = np.sqrt(np.dot(temp.T, temp)) print("Euclidean Distance: ", distance)

In this method, we first initialize two `numpy`

arrays. Then, we compute the difference of these arrays and take their square. We take the sum of the squared elements, and after that, we take the square root in the end. This is another way to implement Euclidean distance.

#importing numpy import numpy as np #initializing two arrays array1 = np.array([1,2,3,4,5]) array2 = np.array([7,6,5,4,3]) #computing the Euclidan distance temp = array1 - array2 distance = np.sqrt(np.sum(np.square(temp))) print("Euclidean Distance: ", distance)

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