How can I "zip sort" parallel numpy arrays?

Solution 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])

Solution 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).