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n Largest and n Smallest with heapq

Zero-dependency Python snippets for finding the n largest and n smallest elements using the standard library heapq module.

4 snippets available in this sub-category.

Simple

Find n largest elements

heap heapq nlargest max data-structures

Get the n largest elements from a list

import heapq

numbers = [5, 1, 8, 3, 7, 2]
largest = heapq.nlargest(3, numbers)
print(largest)  # [8, 7, 5]

Notes

  • Returns a sorted list of n largest elements

Find n smallest elements

heap heapq nsmallest min data-structures

Get the n smallest elements from a list

import heapq

numbers = [5, 1, 8, 3, 7, 2]
smallest = heapq.nsmallest(3, numbers)
print(smallest)  # [1, 2, 3]

Notes

  • Returns a sorted list of n smallest elements

Complex

nlargest/nsmallest with custom key

heap heapq nlargest nsmallest key custom data-structures

Use key function to customize selection

import heapq

words = ["apple", "banana", "pear", "grape"]
longest = heapq.nlargest(2, words, key=len)
print(longest)  # ['banana', 'apple']

shortest = heapq.nsmallest(2, words, key=len)
print(shortest)  # ['pear', 'grape']

Notes

  • key argument works like in sorted()
  • Useful for objects, custom criteria

Edge cases: n > len(list), n = 0

heap heapq nlargest nsmallest edge-case data-structures

Handles cases where n is too large or zero

import heapq

numbers = [1, 2]
print(heapq.nlargest(5, numbers))  # [2, 1]
print(heapq.nsmallest(0, numbers))  # []

Notes

  • If n > len(list), returns all elements sorted
  • If n = 0, returns empty list

🔗 Cross Reference

🏷️ Tags

heap, heapq, nlargest, nsmallest, key, data-structures

📝 Notes

  • nlargest/nsmallest are efficient for small n (O(n log k))
  • For full sort, use sorted()