Skip to content

Create Min-Heap with heapq

Zero-dependency Python snippets for creating and using a min-heap with the standard library heapq module.

4 snippets available in this sub-category.

Simple

Heapify a list

heap heapq heapify min-heap data-structures

Convert a list into a min-heap in-place

import heapq

numbers = [5, 3, 8, 1, 2]
heapq.heapify(numbers)
print(numbers)  # [1, 2, 8, 5, 3] (heap order)

Notes

  • heapq.heapify() rearranges list into heap order
  • The smallest element is always at index 0

Push and pop from heap

heap heapq push pop min-heap data-structures

Add and remove elements from a min-heap

import heapq

heap = []
heapq.heappush(heap, 4)
heapq.heappush(heap, 1)
heapq.heappush(heap, 7)
print(heap)  # [1, 4, 7]

smallest = heapq.heappop(heap)
print(smallest)  # 1
print(heap)  # [4, 7]

Notes

  • heappush() adds, heappop() removes smallest

Complex

Peek at smallest element

heap peek min-heap data-structures

Access the smallest element without removing

import heapq

heap = [3, 1, 4]
heapq.heapify(heap)
smallest = heap[0]
print(smallest)  # 1

Notes

  • heap[0] is always the smallest

Heap sort (ascending)

heap sort heapq min-heap data-structures

Use heapq to sort a list in ascending order

import heapq


def heap_sort(iterable):
    h = list(iterable)
    heapq.heapify(h)
    return [heapq.heappop(h) for _ in range(len(h))]


numbers = [5, 3, 8, 1, 2]
sorted_numbers = heap_sort(numbers)
print(sorted_numbers)  # [1, 2, 3, 5, 8]

Notes

  • Heap sort is O(n log n)
  • Not stable, but memory efficient

🔗 Cross Reference

🏷️ Tags

heap, heapq, min-heap, heapify, push, pop, peek, sort, data-structures

📝 Notes

  • heapq only provides min-heap (use -x for max-heap)
  • Heaps are useful for priority queues, scheduling, and more