Extract required rows from a numpy array

Question

I have a given numpy array as follows.

import numpy as np

data = np.array([[4,6,8,9,3,2,4,4,1], # no of 0s == 0
                  [4,6,8,9,3,0,0,4,0], # no of 0s == 3
                  [4,6,0,9,0,2,0,4,0], # no of 0s == 4
                  [4,6,8,0,3,0,0,0,0], # no of 0s == 5
                  [4,6,8,9,3,2,0,4,0]]) # no of 0s == 2

From the given array, data , I have to extract 3 rows which contain the least 0s. So, the expected are, 1st, last, and second rows.

res = np.array([[4,6,8,9,3,2,4,4,1], # no of 0s == 0
                      [4,6,8,9,3,0,0,4,0], # no of 0s == 3                      
                      [4,6,8,9,3,2,0,4,0]]) # no of 0s == 2

How can I do it guys?

user3483203 · Accepted Answer · 2020-02-04 16:04:02Z

1

Sum on your condition and partition.

n = 3
c = (data == 0).sum(1)
mn = np.argpartition(c, n)[:n]
data[mn]

array([[4, 6, 8, 9, 3, 2, 4, 4, 1],
       [4, 6, 8, 9, 3, 2, 0, 4, 0],
       [4, 6, 8, 9, 3, 0, 0, 4, 0]])

If you need the rows sorted by original index value and not number of zeros, replace the last line with:

data[np.sort(mn)]

answered Feb 4, 2020 at 16:04

user3483203

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