Concatenate and sort data in python

Question

I have a few numpy arrays like so:

import numpy as np
a = np.array([[1, 2, 3, 4, 5], [14, 16, 17, 27, 38]])
b = np.array([[1, 2, 3, 4, 5], [.4, .2, .5, .1, .6]])

I'd like to be able to 1.Copy these arrays into a new single array and 2. Sort the data so that the result is as follows:

data = [[1, 1, 2, 2, 3, 3, 4, 4, 5, 5], [14, .4, 16, .2, 17, .5, 27, .1, 38, .6]]

Or, in other words, I need all columns from the original array to be the same, just in an ascending order. I tried this:

data = np.hstack((a,b))

Which gave me the appended data, but I'm not sure how to sort it. I tried np.sort() but it didn't keep the columns the same. Thanks!

You convert [a, b, c], [d, e, f] into [a, d, b, e, c, f], not sorted([a, d, b, e, c, f]). You perform sorting on pairs of zip([a, b, c], [d, e, f]). — enedil
– enedil, Commented Jun 13, 2017 at 12:37
Yes, but that was my question -- how to go from [a, d, b, e, c, f] to [a, b, c, d, e, f]. Is that not sorting my array? — imnotaprogrammer
– imnotaprogrammer, Commented Jun 15, 2017 at 14:20

Divakar · Accepted Answer · 2017-06-12 20:55:17Z

4

Stack those horizontally (as you already did), then get argsort indices for sorting first row and use those to sort all columns in the stacked array.

Thus, we need to add one more step, like so -

ab = np.hstack((a,b))
out = ab[:,ab[0].argsort()]

Sample run -

In [370]: a
Out[370]: 
array([[ 1,  2,  3,  4,  5],
       [14, 16, 17, 27, 38]])

In [371]: b
Out[371]: 
array([[ 1. ,  2. ,  3. ,  4. ,  5. ],
       [ 0.4,  0.2,  0.5,  0.1,  0.6]])

In [372]: ab = np.hstack((a,b))

In [373]: print ab[:,ab[0].argsort()]
[[  1.    1.    2.    2.    3.    3.    4.    4.    5.    5. ]
 [ 14.    0.4  16.    0.2  17.    0.5  27.    0.1  38.    0.6]]

Please note that to keep the order for identical elements, we need to use to use kind='mergesort' with argsort as described in the docs.

edited Jun 12, 2017 at 20:55

answered Jun 12, 2017 at 20:46

Divakar

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digitake · Accepted Answer · 2017-06-12 21:32:10Z

0

If you like something short.

np.array(zip(*sorted(zip(*np.hstack((a,b))))))

>>> array([[  1. ,   1. ,   2. ,   2. ,   3. ,   3. ,   4. ,   4. ,   5. ,   5. ],
   [  0.4,  14. ,   0.2,  16. ,   0.5,  17. ,   0.1,  27. ,   0.6,  38. ]])

Version that preserve second element order:

np.array(zip(*sorted(zip(*np.hstack((a,b))),key=lambda x:x[0])))

>>>array([[  1. ,   1. ,   2. ,   2. ,   3. ,   3. ,   4. ,   4. ,   5. ,   5. ],
          [ 14. ,   0.4,  16. ,   0.2,  17. ,   0.5,  27. ,   0.1,  38. ,0.6]])

edited Jun 12, 2017 at 21:32

answered Jun 12, 2017 at 21:13

digitake

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