How can the Euclidean distance be calculated with NumPy?

Question

I have two points in 3D:

(xa, ya, za)

(xb, yb, zb)

And I want to calculate the distance:

dist = sqrt((xa-xb)^2 + (ya-yb)^2 + (za-zb)^2)

What's the best way to do this with NumPy, or with Python in general? I have:

a = numpy.array((xa ,ya, za)

)

b = numpy.array((xb,

yb, zb))

Answer 1

To calculate  Euclidean distance with NumPy you can use numpy.linalg.norm:

numpy.linalg.norm(x, ord=None, axis=None, keepdims=False):-

It is a function which is able to return one of eight different matrix norms, or one of an infinite number of vector norms, depending on the value of the ord parameter.

You can use the following piece of code to calculate the distance:-

import numpy as np

from numpy import linalg as LA

a = (1, 2, 3)

b = (4, 5, 6)

dist = numpy.linalg.norm(a-b)

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