Sort numpy array on two columns without altering the result of the first

Question

I have an array

A = np.array([[   1. ,   -1. ,     0.],
              [  50. ,   10. ,    0.],
              [  10. ,   10. ,    2.],
              [-110. ,   -1. ,     1.],
              [ 120. ,   10. ,     1.],
              [-150. ,   -1. ,     2.]])

which I would like to sort FIRST in descending order based on the second column and THEN in ascending order based on the third column without altering the result from the sorting of the second column. So my result should look like:

array([[  50. ,   10. ,     0.],
       [ 120. ,   10. ,     1.],
       [  10. ,   10. ,     2.],
       [   1. ,   -1. ,     0.],
       [-110. ,   -1. ,     1.],
       [-150. ,   -1. ,     2.]])

This is what I could do best but this alters the sorting done on the second column:

ind = np.lexsort((-A[:,1],A[:,2]))
A = A[ind]

which produces the following:

array([[  50.,   10.,    0.],
       [   1.,   -1.,    0.],
       [ 120.,   10.,    1.],
       [-110.,   -1.,    1.],
       [  10.,   10.,    2.],
       [-150.,   -1.,    2.]])

Your help is highly appreciated. Thank you!

GLaw1300 · Accepted Answer · 2020-07-24 19:44:17Z

1

You can use python's built-in sorted for this:

A = np.array(sorted(A, key=lambda x: (x[1], -x[2]), reverse=True))

By negating the second sorting characteristic (x[2]) and setting reverse to True, it will sort first the second column in descending order, then the third in ascending order.

answered Jul 24, 2020 at 19:44

GLaw1300

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