Built In
builtin.com › data-science › priority-queues-in-python
Introduction to Priority Queues in Python | Built In
The PriorityQueue class uses the same heapq implementation from the previous example internally, so it has the same time complexity of O(log n). However, it’s different in two key ways.
Medium
medium.com › @yankuan › time-complexity-of-creating-a-heap-or-priority-queue-fd23bcaefb83
Time Complexity of Creating a Heap (or Priority Queue) | by Yankuan Zhang | Medium
May 16, 2022 - Priority Queue · Time Complexity · Yankuan Zhang · 2 min read · ·May 16, 2022 · -- 1 · Listen · Share · There are two ways to create a heap of n elements: heapify an existing array of n elements: O(n) of time complexity; create an empty ...
python - What's the time complexity of functions in heapq library - Stack Overflow
My question is from the solution in leetcode below, I can't understand why it is O(k+(n-k)log(k)). Supplement: Maybe the complexity isn't that, in fact I don't know the time complexity of heappush... More on stackoverflow.com
Is it so that a priority queue is faster than a hash set?
You should read more about what the data structures are and how they're implemented. Having that understanding will answer those questions. The main difference is that for a hashmap/hashset it's O(1) for insert and retrieval and is unordered. Priority Queue is ordered and takes O(1) to get the min/max (depending on how you define the priority) but O(logn) for insert and removal. Applications for sets tend to be when you need to know if a single value exists, or counting unique values since a set cannot have duplicates, and a contains() call is O(1). Maps are when you need to store key-value pairs, which is too useful to really enumerate. PQ is really good when you need to do something like find top K things, or a sorted order is important but you don't want to or can't build an O(n) storage and run an O(nlogn) sort on it. More on reddit.com
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November 11, 2022dynamic priority queue?
In c++, the elements in a priority queue are not dynamic (i.e. cannot be modified or else they give wrong results). This sentence makes me believe you are mixing up two distinct concepts. The implementation of priority queue in C++'s STD library does not allow you to change the priority of an element. However, this is not a characteristic of the concept of priority queue as a data structure. It simply is a characteristic of one implementation. You can implement your own priority queue where you do have access to methods that change the priority of elements and reorganize the queue accordingly while still maintaining the same theoretical worst-case asymptotic time complexity. As others pointed out, you can easily achieve that using a min-heap with either pointers or a straight out array-based heap. The underlying implementation of your priority queue may also depend on your use case. As for Dijkstra: adding duplicate elements to the queue is again one possible implementation of the algorithm. You can implement Dijkstra without duplicating elements and rather updating priorities of existing ones. Similarly, you can implement Dijkstra without a priority queue altogether and live with a O(n2) algorithm. Again: this is all implementation characteristics. More on reddit.com
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September 1, 2022Real-Time Priority Queue
For now this function would only allow an element to have the highest priority. This is going to be a bit more involved than a single function. Reading through your description of what you want to happen, I'm picturing a flow like this: +--------------------------------+ | PriorityQueue | +--------------------------------+ | - elements: int[] | | - priorities: int[] | | - capacity: int | | - size: int | +--------------------------------+ | + createPriorityQueue(capacity)| | + destroyPriorityQueue() | | + insert(element, priority) | | + removeMax() | | + updatePriority(element, newPriority) | +--------------------------------+ PriorityQueue class (priority queue data structure) elements: int[] attribute stores the elements of the queue. priorities: int[] attribute stores the priorities associated with each element. capacity: int attribute represents the maximum capacity of the queue. size: int attribute keeps track of the current number of elements in the queue. createPriorityQueue(capacity) method creates a new instance of the priority queue with the specified capacity. destroyPriorityQueue() method releases the allocated memory and destroys the priority queue. insert(element, priority) method inserts an element with its associated priority into the queue. removeMax() method removes and returns the element with the highest priority from the queue. updatePriority(element, newPriority) method updates the priority of a given element in the queue. You'll need a bit of data architecture before you can make your queue act correctly, but it's doable. There are existing Priority Queues you can take inspiration from, my favorites are in the BSD network stack, and a smattering in the Linux Kernel. Cheers. More on reddit.com
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June 15, 2023Q2.) What is the difference between a Priority Queue and a min heap?
**A:** A Priority Queue is a heap implementation. Therefore, this implementation can be either a max heap or a min heap. If Priority Queue is implemented as a `max-heap`, it will be a max-priority queue. Similarly, if the implementation is a min-heap, then Priority Queue will be a min-priority queue.
scaler.com
scaler.com › home › topics › program for priority queue in python
Program for Priority Queue in Python - Scaler Topics
Q1.) Why shouldn't you keep a List?
Technically, a priority queue can be created using the Python list data structure. To do so, make a list and then sort it in ascending order.
scaler.com
scaler.com › home › topics › program for priority queue in python
Program for Priority Queue in Python - Scaler Topics
DigitalOcean
digitalocean.com › community › tutorials › priority-queue-python
How to Use a Priority Queue in Python | DigitalOcean
July 11, 2025 - heapq maintains the smallest tuple at index 0, ensuring efficient retrieval of the highest priority element. Each push and pop operation incurs a time complexity of O(log n), where n is the number of elements in the heap.
Linode
linode.com › docs › guides › python-priority-queue
What is the Python Priority Queue? | Linode Docs
June 17, 2022 - Insertions and deletions have a time complexity of O(log n) even when re-balancing activities are accounted for. This means the PriorityQueue class remains quite efficient even with large data sets.
iO Flood
ioflood.com › blog › python-priority-queue-practical-guide-with-examples
Python Priority Queue Examples | Best Practices and Usage
July 8, 2024 - In terms of time complexity, inserting an element into a PriorityQueue takes O(log n) time, while removing an element takes O(1) time. This efficiency makes PriorityQueue a robust choice for handling large data sets.
Brilliant
brilliant.org › wiki › priority-queues
Priority Queues | Brilliant Math & Science Wiki
The following code is an implementation of the priority queue in python. In an efficient implementation, you can expect to get a runtime of O(log n) for insert (by using binary search to find where to put it).
Wander In Dev
wanderin.dev › python-interview › a-priority-queue-implementation-in-python
A Priority Queue Implementation in Python – Wander In Dev
October 6, 2024 - This method has a constant time complexity O(1). Below is the full implementation and some test cases: In this article, we used a heapq to implement a priority queue. Our priority queue returns the task with the highest priority first.
Scaler
scaler.com › home › topics › program for priority queue in python
Program for Priority Queue in Python - Scaler Topics
December 13, 2022 - Priority Queue Python is an extension ... heap. Even when re-balancing actions are taken into account, insertions and deletions have a time complexity of O(log n)....
Top answer 1 of 6
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heapq is a binary heap, with O(log n) push and O(log n) pop. See the heapq source code.
The algorithm you show takes O(n log n) to push all the items onto the heap, and then O((n-k) log n) to find the kth largest element. So the complexity would be O(n log n). It also requires O(n) extra space.
You can do this in O(n log k), using O(k) extra space by modifying the algorithm slightly. I'm not a Python programmer, so you'll have to translate the pseudocode:
# create a new min-heap
# push the first k nums onto the heap
for the rest of the nums:
if num > heap.peek()
heap.pop()
heap.push(num)
# at this point, the k largest items are on the heap.
# The kth largest is the root:
return heap.pop()
The key here is that the heap contains just the largest items seen so far. If an item is smaller than the kth largest seen so far, it's never put onto the heap. The worst case is O(n log k).
Actually, heapq has a heapreplace method, so you could replace this:
if num > heap.peek()
heap.pop()
heap.push(num)
with
if num > heap.peek()
heap.replace(num)
Also, an alternative to pushing the first k items is to create a list of the first k items and call heapify. A more optimized (but still O(n log k)) algorithm is:
# create array of first `k` items
heap = heapify(array)
for remaining nums
if (num > heap.peek())
heap.replace(num)
return heap.pop()
You could also call heapify on the entire array, then pop the first n-k items, and then take the top:
heapify(nums)
for i = 0 to n-k
heapq.heappop(nums)
return heapq.heappop(nums)
That's simpler. Not sure if it's faster than my previous suggestion, but it modifies the original array. The complexity is O(n) to build the heap, then O((n-k) log n) for the pops. So it's be O((n-k) log n). Worst case O(n log n).
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heapify() actually takes linear time because the approach is different than calling heapq.push() N times.
heapq.push()/heapq.pop() takes log n time because it adjust all the nodes at a given hight/level.
when you pass an array in heapify() it makes sure that the left and right children of the node are already maintaining the heap property whether it is a min heap or max heap.
you can see this video: https://www.youtube.com/watch?v=HqPJF2L5h9U
https://www.youtube.com/watch?v=B7hVxCmfPtM
Hope this would help.
Python
docs.python.org › 3 › library › heapq.html
heapq — Heap queue algorithm
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 ...
Progressive Robot
progressiverobot.com › home › python › how to use a priority queue in python
Priority Queue: Complete Guide - Progressive Robot
May 12, 2026 - Note: The time complexity for the entire process is O(n log n) due to the n insertions and one extraction operation. ... Not suitable for complex objects or non-numeric priorities.
Medium
medium.com › @kapilsharmax24 › what-is-a-priority-queue-21badf357301
What is a Priority Queue ?. A priority queue is a special type of… | by Shivam | Medium
September 21, 2024 - This ensures that items with the same priority are returned in the order they were added. Efficiency: The time complexity for push and pop operations in a priority queue implemented with a binary heap is O(log n), where n is the number of items ...
Verve AI
vervecopilot.com › interview-questions › can-priority-queue-implementation-python-be-the-secret-weapon-for-acing-your-next-interview
Can Priority Queue Implementation Python Be The Secret Weapon For Acing Your… · For Acing Your Next Interview · Interview Q&A | Verve AI
Regardless of the method chosen, ... to the queue with its associated priority. The time complexity for this operation is O(log n), where 'n' is the number of elements in the queue, due to the need to maintain the heap ...
GeeksforGeeks
geeksforgeeks.org › priority-queue-in-python
Priority Queue in Python - GeeksforGeeks
April 26, 2025 - Huffman Encoding (Data Compression) combines least frequent symbols using a priority queue to reduce data size. Merging Multiple Sorted Lists merges sorted lists by selecting the smallest element from each list. A Search Algorithm (Pathfinding) prioritizes nodes based on cost to find the shortest path in navigation or games. Let's understand the different types of priority queues because they define how elements are prioritized and dequeued based on their associated priority.
GeeksforGeeks
geeksforgeeks.org › dsa › priority-queue-set-1-introduction
Introduction to Priority Queue - GeeksforGeeks
June 4, 2026 - A Binary Heap is ideal for priority queue implementation as it offers better performance The largest key is at the top and can be removed in O(log n) time, with the heap property restored efficiently.
Real Python
realpython.com › queue-in-python
Python Stacks, Queues, and Priority Queues in Practice – Real Python
December 1, 2023 - Sorting the entire queue would be even more expensive, especially when exercised often. Python’s list.sort() method employs an algorithm called Timsort, which has O(n log(n)) worst-case time complexity.
dbader.org
dbader.org › blog › priority-queues-in-python
Priority Queues in Python – dbader.org
April 12, 2017 - This is a binary heap implementation usually backed by a plain list and it supports insertion and extraction of the smallest element in O(log n) time. This module is a good choice for implementing priority queues in Python.
APXML
apxml.com › courses › advanced-python-programming-ml › chapter-4-advanced-data-structures-algorithms-ml › priority-queues-heaps
Python Priority Queues & Heaps in ML
Adding an element (push) or removing the highest priority element (pop) takes ... nn is the number of elements in the heap. Accessing the highest priority element (without removing it) takes ... O(1)O(1) time. Building a heap from an existing collection of ... O(n)O(n) time.
Python Guides
pythonguides.com › priority-queue-in-python
Priority Queue in Python
December 12, 2025 - Use custom classes for complex data handling and better code organization. Forgetting that heapq does not automatically support max-heaps. Using mutable objects as priorities, which can cause unpredictable behavior. Not maintaining insertion order for items with equal priority (use an index). Mixing up heapq and queue.PriorityQueue — the latter is thread-safe but slower. Mastering priority queues in Python ...