How can the euclidean distance be calculated with numpy?

2018-06-18 01:34:32

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))

使用numpy.linalg.norm ：

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

There's a function for that in SciPy, it's called Euclidean

example:

from scipy.spatial import distance
a = (1,2,3)
b = (4,5,6)
dst = distance.euclidean(a,b)

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)

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