# Calculate the Euclidean distance using NumPy

## 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()

 `# Python code to find Euclidean distance` `# using linalg.norm()` `import` `numpy as np` `# initializing 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()

 `# Python code to find Euclidean distance` `# using dot()` `import` `numpy as np` `# initializing 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()

 `# Python code to find Euclidean distance` `# using sum() and square()` `import` `numpy as np` `# initializing 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`

Last Updated on October 28, 2021 by admin

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