## Python heapq replace priority

I'm trying to implement Dijkstra's algorithm using Python's heapq. The algorithm requires changing a cell's value if a shorter path is discovered leading to it.

I'm doing that with this check:

if curr_cell[0] + val < prev_cell[0]: # value of new path is less than old value new_cell = (curr_cell[0] + val, prev_cell[1], curr_cell[1]) heap[index] = new_cell heapify(heap)

However, when running my program on a larger maze this is taking a long time, probably because of the `heapify()`

call.

What's a more efficient way of changing the priority of a heap's entry?

The heapq library doesn't support updating priorities efficiently because there is no efficient way to search the heap. If you search the heap in O(n) time and manually replace the element, you can then use _siftup() and _siftdown() to restore the heap invariant.

Alternatively, here is a compatible implementation I wrote which uses a dict to allow O(1) lookups of heap indexes.

https://github.com/elplatt/python-priorityq

**8.4. heapq — Heap queue algorithm,** The heap size doesn't change. If the heap is A priority queue is common use for a heap, and it presents several implementation challenges: Sort stability: how Heaps and priority queues are little-known but surprisingly useful data structures. For many problems that involve finding the best element in a dataset, they offer a solution that’s easy to use and highly effective. The Python heapq module is part of the standard library. It implements all the

A possible solution is to mark the entry as removed and add a new entry with the revised priority. The documentation provides an example implementation:

pq = [] # list of entries arranged in a heap entry_finder = {} # mapping of tasks to entries REMOVED = '<removed-task>' # placeholder for a removed task counter = itertools.count() # unique sequence count def add_task(task, priority=0): 'Add a new task or update the priority of an existing task' if task in entry_finder: remove_task(task) count = next(counter) entry = [priority, count, task] entry_finder[task] = entry heappush(pq, entry) def remove_task(task): 'Mark an existing task as REMOVED. Raise KeyError if not found.' entry = entry_finder.pop(task) entry[-1] = REMOVED def pop_task(): 'Remove and return the lowest priority task. Raise KeyError if empty.' while pq: priority, count, task = heappop(pq) if task is not REMOVED: del entry_finder[task] return task raise KeyError('pop from an empty priority queue'

**8.4. heapq — Heap queue algorithm,** The heap size doesn't change. If the heap is A priority queue is common use for a heap, and it presents several implementation challenges: Sort stability: how Removing the entry or changing its priority is more difficult because it would break the heap structure invariants. So, a possible solution is to mark the existing entry as removed and add a new entry with the revised priority:

Actually I wrote one just for this a couple of years ago that you might find useful!

https://pypi.org/project/priorityq/

**heapq — Heap queue algorithm,** The heap size doesn't change. If the heap is empty, IndexError Using a heap to insert items at the correct place in a priority queue: >>> heap = [] >>> data = [(1 Understanding the Python heapq Module. This module is a part of the standard library, so there’s no need to install it separately using pip. To import the heapq module, we can do the following: import heapq In the heapq module, we mainly require 3 methods which we need for building and manipulating our priority queue:

@elplatt's answer about using a dictionary and then using `_siftdown/_siftup`

is a good one. Here's another implementation: https://github.com/ActiveState/code/blob/master/recipes/Python/577892_Dijkstrshortest_path/recipe-577892.py

**Heap queue (or heapq) in Python,** Heap data structure is mainly used to represent a priority queue. In Python, it is available using “heapq” module. The property of this data structure in python is A priority queue is a powerful tool that can solve problems as varied as writing an email scheduler, finding the shortest path on a map, or merging log files. Programming is full of optimization problems in which the goal is to find the best element. Priority queues and the functions in the Python heapq module can often help with that.

heapify() routine provided by heapq takes linear time.

So instead of using heapify() which costs O(n) every time you change the priority, you should use data structure in which you can change the priority of that record and heapify occurs in O(logn).

You can use PriorityQueue provided by Queue.

**Priority dict: a priority queue with updatable priorities You may have ,** To change an item's priority, it is sufficient to do thedict[item]=new_priority. The priority queue is implemented as a dictionary, where keys are the items of the queue, and values are their The advantage over a standard heapq-based priority queue is http://mail.python.org/pipermail/python-ideas/2007-May/000744.html. The heapq implements a min-heap sort algorithm suitable for use with Python's lists. This module provides an implementation of the heap queue algorithm, also known as the priority queue algorithm. Heaps are binary trees for which every parent node has a value less than or equal to any of its children.

**Priority Queue with update-able values,** Here is my attempt to implement a minimum priority queue class in Python using the heapq module. I plan to use it in graph search algorithms Home › Python › The Python Heapq Module: Using Heaps and Priority Queues In this step-by-step tutorial, you’ll explore the heap and priority queue data structures. You’ll learn what kinds of problems heaps and priority queues are useful for and how you can use the Python heapq module to solve them.

**Priority Queue,** Python heapq is a minimum priority queue implementation. heapq The function heapify will change a list to a heap so that it can be used as heap later on. In this step-by-step tutorial, you'll explore the heap and priority queue data structures. You'll learn what kinds of problems heaps and priority queues are useful for and how you can use the Python heapq module to solve them. #python

**Heap queue (or heapq) in Python,** In python it is implemented using the heapq module. It is very useful is implementing priority queues where the queue item with higher weight the smallest data element from the heap. heapreplace – This function replaces