how to efficiently get the k bigger elements of a

Question

What´s the most efficient, elegant and pythonic way of solving this problem?

Given a list (or set or whatever) of n elements, we want to get the k biggest ones. ( You can assume k<n/2 without loss of generality, I guess) For example, if the list were:

l = [9,1,6,4,2,8,3,7,5]

n = 9, and let's say k = 3. What's the most efficient algorithm for retrieving the 3 biggest ones? In this case we should get [9,8,7], in no particular order.

Thanks! Manuel

Answer 1

sorted(l, reverse=True)[:k]

Answer 2

Use nlargest from heapq module

from heapq import nlargest
lst = [9,1,6,4,2,8,3,7,5]
nlargest(3, lst) # Gives [9,8,7]

You can also give a key to nlargest in case you wanna change your criteria:

from heapq import nlargest
tags = [ ("python", 30), ("ruby", 25), ("c++", 50), ("lisp", 20) ]
nlargest(2, tags, key=lambda e:e[1]) # Gives [ ("c++", 50), ("python", 30) ]

Answer 3

The simple, O(n log n) way is to sort the list then get the last k elements.

The proper way is to use a selection algorithm, which runs in O(n + k log k) time.

Also, heapq.nlargest takes O(k log n) time on average, which may or may not be good enough.

(If k = O(n), then all 3 algorithms have the same complexity (i.e. don't bother). If k = O(log n), then the selection algorithm as described in Wikipedia is O(n) and heapq.nlargest is O(n log log n), but double logarithm is "constant enough" for most practical n that it doesn't matter.)

Answer 4

l = [9,1,6,4,2,8,3,7,5]

sorted(l)[-k:]

Answer 5

You can use the heapq module.

>>> from heapq import heapify, nlargest
>>> l = [9,1,6,4,2,8,3,7,5]
>>> heapify(l)
>>> nlargest(3, l)
[9, 8, 7]
>>>