Numpy: Average of values corresponding to unique coordinate positions

Question

So, I have been browsing stackoverflow for quite some time now, but I can\'t seem to find the solution for my problem

Consider this

import numpy as n         


        

Answer 1

It is very likely going to be faster to flatten your indices, i.e.:

flat_index = coo[:, 0] * np.max(coo[:, 1]) + coo[:, 1]

then use np.unique on it:

unq, unq_idx, unq_inv, unq_cnt = np.unique(flat_index,
                                           return_index=True,
                                           return_inverse=True,
                                           return_counts=True)
unique_coo = coo[unq_idx]
unique_mean = np.bincount(unq_inv, values) / unq_cnt

than the similar approach using lexsort.

But under the hood the method is virtually the same.