Source code: Lib/heapq.py This module provides an implementation of the heap queue algorithm, also known as the priority queue algorithm. Min-heaps are binary trees for which every parent node has ...

June 11, 2026 - Example: The following example inserts elements into a heap using heappush(). ... Explanation: heapq.heappush() inserts each element into h and automatically rearranges the heap so that the smallest element remains at index 0.

October 21, 2023 - This process is optimized due to the logarithmic nature of the binary heap structure, resulting in the overall time complexity of O(log n) for both heappush and heappop operations. Let’s demonstrate the time complexities with Python examples. import heapq # Creating a simple heap heap = [] heapq.heappush(heap, 4) heapq.heappush(heap, 1) heapq.heappush(heap, 7) heapq.heappush(heap, 3) print("Heap after push operations:", heap)

Python Examples Python Compiler ... Q&A Python Bootcamp Python Training · ❮ Standard Library Modules · Maintain a min-heap and pop the smallest items: import heapq h = [] heapq.heappush(h, 3) heapq.heappush(h, 1) heapq.heappush(h, ...

The heapq module is an inbuilt module in python. The module offers APIs for different operations of the heap data structure. Also, it provides min heap implementation where the parent key is less than or equal to those of its children.

You then operate on a standard Python list. To add an element to the heap while maintaining the heap property, use heapq.heappush(heap, item).

>>> import heapq >>> # Convert list to heap in-place >>> h = [5, 1, 3, 2, 6] >>> heapq.heapify(h) >>> h[0] # smallest element at root 1 >>> # Push and pop >>> heapq.heappush(h, 0) >>> heapq.heappop(h) 0 >>> # Push and pop in one operation >>> heapq.heappushpop(h, 4) # push 4, then pop smallest 1 >>> # Pop and push in one operation >>> heapq.heapreplace(h, 0) # pop smallest, then push 0 2

June 17, 2024 - heapq.heappush(heap, item) — Function in Python adds an item to an existing heap while maintaining the heap property.

July 18, 2022 - The Python heapq module also includes heappush() for pushing an element to the heap while preserving the heap property.

The heappush() function from the heapq module of Python adds an element into an existing heap while maintaining the heap property. The Python example creates a heap, adds elements to it and prints the heap.

It uses a standard Python list to represent a min-heap, where the smallest element is always at the root (index 0). The heapq.heappush(heap, item) function pushes the value item onto the heap, maintaining the heap invariant.

Python offers a vast range of libraries, including a built-in module, heapq, which allows for the creation and manipulation of heaps with ease. import heapq heap = [] # Insert in heap heapq.heappush(heap, 4) heapq.heappush(heap, 9) heapq.heappush(heap, 6) print("Heap after insertion: ", heap) ...

September 19, 2024 - The heapq module provides functions to perform heap operations on a regular Python list. ... To create a heap, you start with an empty list and use the heapq.heappush() function to add elements:

May 4, 2023 - The following table lists the time complexity of some of heapq's most popular functions. Depending on the heap size, heapify can take up to O(n) time to complete. heappush is complex O(log n), where n is the number of elements in the heap.

Language: Python · >>> import heapq >>> nums = [5, 1, 3, 7, 8, 2] >>> heapq.heapify(nums) >>> heapq.heappush(nums, 4) >>> nums [1, 5, 2, 7, 8, 3, 4] Popping the smallest item off the heap: Language: Python · >>> import heapq >>> nums = [5, 1, 3, 7, 8, 2] >>> heapq.heapify(nums) >>> heapq.heappop(nums) 1 ·

December 15, 2025 - But don’t use .append() to add Jim to service_queue. Instead, let’s use heapq.heappush() to push an item onto the heap:

November 30, 2025 - Python’s heapq module provides a min-heap implementation using a binary heap structure. It offers seven key functions to work with priority queues, split into two categories: Four of them are primarily used for basic heap operations: heappush, heappop, heapify, and heapreplace.

This is more efficient than heappop() followed by heappush(), and can be more appropriate when using a fixed-size heap. Note that the value returned may be larger than item! That constrains reasonable uses of this routine unless written as part of a conditional replacement: ... >>> from heapq import heappush, heappop >>> heap = [] >>> data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0] >>> for item in data: ...

import heapq from heapq_showtree import show_tree from heapq_heapdata import data heap = [] print 'random :', data print for n in data: print 'add =:' % n heapq.heappush(heap, n) show_tree(heap) $ python heapq_heappush.py random : [19, 9, 4, 10, 11, 8, 2] add 19: 19 ------------------------------------ add 9: 9 19 ------------------------------------ add 4: 4 19 9 ------------------------------------ add 10: 4 10 9 19 ------------------------------------ add 11: 4 10 9 19 11 ------------------------------------ add 8: 4 10 8 19 11 9 ------------------------------------ add 2: 2 10 4 19 11 9 8 ------------------------------------ If the data is already in memory, it is more efficient to use heapify() to rearrange the items of the list in place.