In each case imagine we want to sort a list of peoples names by their ages.

Method 1

Zip the lists together, making sure that the one to sort by is passed first to zip(). The result is a list of tuples. When you sort a list of tuples, it sorts using the first item in each tuple. Then use the zip* trick to unzip the now sorted tuples into separate variables.

people = ['Jim', 'Pam', 'Micheal', 'Dwight']
ages = [27, 25, 4, 9]

agesAndPeople = zip(ages, people)
agesAndPeople.sort()
sortedAges, sortedPeople = zip(*agesAndPeople)

Note that if you want to sort in reverse, you can use agesAndPeople.sort(reverse=True).

Method 2

This method uses NumPy, and you don’t have to convert the lists into arrays. The argsort() function doesn’t return the sorted ages . . . instead, it returns the indices that each item would if it were in an already sorted array (try it to see what I mean). take() is a way of using useful NumPy indexing on a list. See the next example for something that might be more straigtforward for Matlab users.

people = ['Jim', 'Pam', 'Micheal', 'Dwight']
ages = [27, 25, 4, 9]

import numpy
inds = numpy.argsort(ages)
sortedPeople = numpy.take(people, inds)

Method 3

This method also uses NumPy, but first it converts the lists into arrays. Then it uses the argsort() of one to index into the other.

people = ['Jim', 'Pam', 'Micheal', 'Dwight']
ages = [27, 25, 4, 9]

import numpy
people = numpy.array(people)
ages = numpy.array(ages)
inds = ages.argsort()
sortedPeople = people[inds]