Sort np array based on summed selected values of each row-CodePudding

I have a 2D numpy array, filled with floats. I want to take a selected chunk of each row (say item 2nd to 3rd), sum these values and sort all the rows based on that sum in a descending order.

For example:

array([[0.80372444, 0.35468653, 0.9081662 , 0.69995566],
       [0.53712474, 0.90619077, 0.69068265, 0.73794143],
       [0.14056974, 0.34685164, 0.87505744, 0.56927803]])

Here's what I tried:

a = np.array(sorted(a, key = sum))

But that just sums all values from each row, rather that, say, only 2nd to 6th element.

CodePudding user response：

You can start by using take to get elements at indices [1,2] from each row (axis = 1). Then sum across those element for each row (again axis = 1), and use argsort to get the order of the sums. This gives a set of row indices, which you can use to slice the array in the desired order.

import numpy as np

a = np.array([[0.80372444, 0.35468653, 0.9081662 , 0.69995566],
              [0.53712474, 0.90619077, 0.69068265, 0.73794143],
              [0.14056974, 0.34685164, 0.87505744, 0.56927803]])

a[a.take([1, 2], axis=1).sum(axis=1).argsort()]
# returns:
array([[0.14056974, 0.34685164, 0.87505744, 0.56927803],
       [0.80372444, 0.35468653, 0.9081662 , 0.69995566],
       [0.53712474, 0.90619077, 0.69068265, 0.73794143]])

CodePudding user response：

Replace key with the function you actually want:

a = np.array(sorted(d, key = lambda v : sum(v[1:3])))