In Numpy, find Euclidean distance between each pair from two arrays
arrays, euclidean-distance, numpy, python, scipy
Solution
I'm not seeing a built-in, but you could do it yourself pretty easily.
distances = (a-b)**2
distances = distances.sum(axis=-1)
distances = np.sqrt(distances)
Problem
I have two arrays of 2D coordinate points (x,y) ``` a = [ (x1,y1), (x2,y2), ... (xN,yN) ] b = [ (X1,Y1), (X2,Y2), ... (XN,YN) ] ``` How can I find the Euclidean distances between each aligned pairs `(xi,yi) to (Xi,Yi)` in an `1xN` array? The `scipy.spatial.cdist` function gives me distances between all pairs in an `NxN` array. If I just use `norm` function to calculate the distance one by one it seems to be slow. Is there a built in function to do this?