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I am trying to do some computations for a numpy array by python3.

the array:

 c0 c1 c2 c3 r0 1 5 2 7 r1 3 9 4 6 r2 8 2 1 3

Here the "cx" and "rx" are column and row names.

I need to compute the difference of each element by row if the elements are not in a given column list.

e.g.

 given a column list [0, 2, 1] # they are column indices which means that for r0, we need to calculate the difference between the c0 and all other columns, so we have [1, 5-1, 2-1, 7-1] for r1, we need to calculate the difference between the c2 and all other columns, so we have [3-4, 9-4, 4, 6-4] for r2, we need to calculate the difference between the c1 and all other columns, so we have [8-2, 2, 1-2, 3-2]

so, the result should be

 1 4 1 6 -1 5 4 2 6 2 -1 1

Because the array could be very large, I would like to do the calculation by numpy vectorized operation, e.g. broadcasting.

BuT, I am not sure how to do it efficiently.

I have checked Vectorizing operation on numpy array, Vectorizing a Numpy slice operation, Vectorize large NumPy multiplication, Replace For Loop with Numpy Vectorized Operation, Vectorize numpy array for loop.

But, none of them work for me.

thanks for any help !

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Extract the values from the array first and then do subtraction:

import numpy as np a = np.array([[1, 5, 2, 7], [3, 9, 4, 6], [8, 2, 1, 3]]) cols = [0,2,1] # create the index for advanced indexing idx = np.arange(len(a)), cols # extract values vals = a[idx] # subtract array by the values a -= vals[:, None] # add original values back to corresponding position a[idx] += vals print(a) #[[ 1 4 1 6] # [-1 5 4 2] # [ 6 2 -1 1]]

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