Python: how to calculate the Euclidean distance between two Numpy arrays

Question

I want to calculate the distance between two NumPy arrays using the following formula.

d = (sum[(xi - yi)2])^1/2

Is there any Numpy function for the distance?

E.g.

x=np.array([2,4,6,8,10,12])

y=np.array([4,8,12,10,16,18])

d = 11.49

Answer 1

You can use the Numpy sum() and square() functions to calculate the distance between two Numpy arrays. You can also use euclidean() function of scipy.

Here is an example:

>>> import numpy as np
>>> x=np.array([2,4,6,8,10,12])
>>> y=np.array([4,8,12,10,16,18])
>>> d = np.sqrt(np.sum(np.square(x-y)))
>>> d
11.489125293076057

>>> from scipy.spatial.distance import euclidean
>>> euclidean(x,y)
11.489125293076057

If you just want to use the absolute value of the difference instead of square, you can use the following code:

>>> import numpy as np
>>> x=np.array([2,4,6,8,10,12])
>>> y=np.array([4,8,12,10,16,18])
>>> d = np.sum(np.abs(x-y))
>>> d
26