calculate euclidean distance 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

Another instance of this problem solving method. As soon as I submitted the question I got it:

def dist(x,y):   
    return numpy.sqrt(numpy.sum((x-y)**2))

a = numpy.array((xa,ya,za))
b = numpy.array((xb,yb,zb))
dist_a_b = dist(a,b)

Answer 2

Use

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