#Priority queue python 3 full#
#Priority queue python 3 series#

In cases where several tasks have the same priority, they are returned in the order of insertion. Our priority queue returns the task with the highest priority first. In this article, we used a heapq to implement a priority queue.

#Priority queue python 3 full#

The full implementationīelow is the full implementation and some test cases: Conclusion This method has a constant time complexity O(1). The size method returns the length of the priority queue. As with pull, we need to return None if the priority queue is empty to avoid an Inde圎rror. The peek method returns the task with the highest priority. This method has a logarithmic time complexity O(log n). To avoid getting an Inde圎rror when pulling from an empty priority queue, we first check if it is empty and return None. The pull method returns and removes the task with the highest priority using the heappop method from heapq. We use the insertion count to decide which task to return when several tasks have the same priority. We increase the insertion count after every insert. The item added is a tuple that contains the priority, the insertion count, and the task. The insert method adds an item to the priority queue, using the heappush method from heapq. The is_empty method returns True if the priority queue is empty. At the top of the module, we must import heapq: pq.is_empty(): This data structure can be used to implement priority queue algorithms and maintainers. It generalizes the singly linked list to allow for multiple elements with different access times. We use this attribute to keep track of the insertion order. The PriorityQueue, or heapq module in Python, is a data structure for dynamic priority queues with O (1) lookup time. The class has an insertion_count attribute. We define a PriorityQueue class with a constructor that instantiates an empty list and a _str_ method that returns a readable representation of the priority queue object. Let’s go over our priority queue implementation. The reason for using heapq instead of other data types is that you can push and pop in O(log n) time while keeping the underlying data in order of priority. We’ll use the heapq available from the Lib/heapq.py module in Python for our priority queue implementation.

pq.is_empty(): returns True if the priority queue is empty.

pq.size(): returns the number of task in the priority queue.

pq.peek(): returns the highest priority task from the priority queue.

pq.pull(): returns and removes the highest priority task from the priority queue.

pq.insert(task, priority): adds a task with a given priority to the priority queue.

Most priority queue implementations have the following methods: They are also used in bandwidth management to prioritize important data packets.Ĭertain foundational algorithms, such as Dijkstra’s algorithm, also rely on priority queues. Operating systems use priority queues to select the following process to run, load balancing and interrupt handling.

In a max-priority queue, you assign a higher priority to a task with a higher priority number.

In a min-priority queue, you assign a higher priority to a task with a lower priority number.

While the next element in a queue is the first element inserted and in a stack is the last element inserted, in a priority queue, the next element to be retrieve is the one with the highest priority. Priority queues differ from queues and stack in the way your retrieve the elements. In computer science, a priority queue is an abstract data type in which each element has a priority associated with it.

A Priority Queue Implementation in Python (this article).

#Priority queue python 3 series#

I based the series on my notes while studying for a Python technical interview.įor quick access, here is the list of posts on this series: This post is the third in a miniseries exploring the implementation of linear data structures. Raise KeyError if the queue is empty.In this article, I’ll explore the priority queue, a linear data structure we can easily implement in Python. '''Remove the item with the lowest priority from the queue and return Present in the queue then its priority is updated.Įntry = '''Add item to the queue with the given priority. # break ties in case the items are not orderable. # Iterable generating unique sequence numbers that are used to Self._entry_finder = # mapping of items to entries Not going to reinvent the wheel so i use the the python's heapq implementation class priorityQ(): I'm trying to incorporate the advice found in in order to make a priority queue implementation (see corresponding section) in a class priorityQ.