How can I “zip sort” parallel numpy arrays?

Question

If I have two parallel lists and want to sort them by the order of the elements in the first, it\'s very easy:

>>> a = [2, 3, 1]
>>> b = [4         


        

Answer 1

b[a.argsort()] should do the trick.

Here's how it works. First you need to find a permutation that sorts a. argsort is a method that computes this:

>>> a = numpy.array([2, 3, 1])
>>> p = a.argsort()
>>> p
[2, 0, 1]

You can easily check that this is right:

>>> a[p]
array([1, 2, 3])

Now apply the same permutation to b.

>>> b = numpy.array([4, 6, 7])
>>> b[p]
array([7, 4, 6])

Answer 2

Here's an approach that creates no intermediate Python lists, though it does require a NumPy "record array" to use for the sorting. If your two input arrays are actually related (like columns in a spreadsheet) then this might open up an advantageous way of dealing with your data in general, rather than keeping two distinct arrays around all the time, in which case you'd already have a record array and your original problem would be answered merely by calling sort() on your array.

This does an in-place sort after packing both arrays into a record array:

>>> from numpy import array, rec
>>> a = array([2, 3, 1])
>>> b = array([4, 6, 7])
>>> c = rec.fromarrays([a, b])
>>> c.sort()
>>> c.f1   # fromarrays adds field names beginning with f0 automatically
array([7, 4, 6])

Edited to use rec.fromarrays() for simplicity, skip redundant dtype, use default sort key, use default field names instead of specifying (based on this example).

Answer 3

Like @Peter Hansen's answer, this makes a copy of the arrays before it sorts them. But it is simple, does the main sort in-place, uses the second array for auxiliary sorting, and should be very fast:

a = np.array([2, 3, 1])
b = np.array([4, 6, 2])
# combine, sort and break apart
a, b = np.sort(np.array([a, b]))

Update: The code above doesn't actually work, as pointed out in a comment. Below is some better code. This should be fairly efficient—e.g., it avoids explicitly making extra copies of the arrays. It's hard to say how efficient it will be, because the documentation doesn't give any details on the numpy.lexsort algorithm. But it should work pretty well, since this is exactly the job lexsort was written for.

a = np.array([5, 3, 1])
b = np.array([4, 6, 7])
new_order = np.lexsort([b, a])
a = a[new_order]
b = b[new_order]
print(a, b)
# (array([1, 3, 5]), array([7, 6, 4]))

Answer 4

This might the simplest and most general way to do what you want. (I used three arrays here, but this will work on arrays of any shape, whether two columns or two hundred).

import numpy as NP
fnx = lambda : NP.random.randint(0, 10, 6)
a, b, c = fnx(), fnx(), fnx()
abc = NP.column_stack((a, b, c))
keys = (abc[:,0], abc[:,1])          # sort on 2nd column, resolve ties using 1st col
indices = NP.lexsort(keys)        # create index array
ab_sorted = NP.take(abc, indices, axis=0)

One quirk w/ lexsort is that you have to specify the keys in reverse order, i.e., put your primary key second and your secondary key first. In my example, i want to sort using the 2nd column as the primary key so i list it second; the 1st column resolves ties only, but it is listed first).