numpy vectorized operation for a large array

Question

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.

eg

 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, eg 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 !

Answer 1

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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