How can the Euclidean distance be calculated with NumPy?

I have two points in 3D space:
a = (ax, ay, az)
b = (bx, by, bz)


I want to calculate the distance between them:
dist = sqrt((ax-bx)^2 + (ay-by)^2 + (az-bz)^2)


How do I do this with NumPy? I have:
import numpy
a = numpy.array((ax, ay, az))
b = numpy.array((bx, by, bz))


Use numpy.linalg.norm:

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

This works because the Euclidean distance is the l2 norm, and the default value of the ord parameter in numpy.linalg.norm is 2.
For more theory, see Introduction to Data Mining:

Stack Overflow Q#1401712, score: 1354