# Calculate the Euclidean distance using NumPy

In simple terms, Euclidean distance is the shortest between the 2 points irrespective of the dimensions. In this article to find the Euclidean distance, we will use the NumPy library. This library used for manipulating multidimensional array in a very efficient way. Let’s discuss a few ways to find Euclidean distance by NumPy library.

Method #1: Using linalg.norm()

## Python3

 `# Python code to find Euclidean distance ` `# using linalg.norm() ` ` `  `import` `numpy as np ` ` `  `# intializing points in ` `# numpy arrays ` `point1 ``=` `np.array((``1``, ``2``, ``3``)) ` `point2 ``=` `np.array((``1``, ``1``, ``1``)) ` ` `  `# calculating Euclidean distance ` `# using linalg.norm() ` `dist ``=` `np.linalg.norm(point1 ``-` `point2) ` ` `  `# printing Euclidean distance ` `print``(dist) `

Output:

`2.23606797749979`

Method #2: Using dot()

## Python3

 `# Python code to find Euclidean distance ` `# using dot() ` ` `  `import` `numpy as np ` ` `  `# intializing points in ` `# numpy arrays ` `point1 ``=` `np.array((``1``, ``2``, ``3``)) ` `point2 ``=` `np.array((``1``, ``1``, ``1``)) ` ` `  `# subtracting vector ` `temp ``=` `point1 ``-` `point2 ` ` `  `# doing dot product ` `# for finding ` `# sum of the squares ` `sum_sq ``=` `np.dot(temp.T, temp) ` ` `  `# Doing squareroot and ` `# printing Euclidean distance ` `print``(np.sqrt(sum_sq)) `

Output:

`2.23606797749979`

Method #3: Using square() and sum()

## Python3

 `# Python code to find Euclidean distance ` `# using sum() and square() ` ` `  `import` `numpy as np ` ` `  `# intializing points in ` `# numpy arrays ` `point1 ``=` `np.array((``1``, ``2``, ``3``)) ` `point2 ``=` `np.array((``1``, ``1``, ``1``)) ` ` `  `# finding sum of squares ` `sum_sq ``=` `np.``sum``(np.square(point1 ``-` `point2)) ` ` `  `# Doing squareroot and ` `# printing Euclidean distance ` `print``(np.sqrt(sum_sq)) `

Output:

`2.23606797749979`

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