python numpy euclidean distance calculation between matrices of row vectors

Question

I am new to Numpy and I would like to ask you how to calculate euclidean distance between points stored in a vector.

Let's assume that we have a numpy.array each row is a vector and a single numpy.array. I would like to know if it is possible to calculate the euclidean distance between all the points and this single point and store them in one numpy.array.

Here is an interface:

points #2d list of row-vectors singlePoint #one row-vector  listOfDistances= procedure( points,singlePoint) 

Can we have something like this? Or is it possible to have one command to have the single point as a list of other points and at the end we get a matrix of distances?

Thanks

Answer 1

To get the distance you can use the norm method of the linalg module in numpy:

np.linalg.norm(x - y)