![]() ![]() This is a guide to Priority Queues in Python. But PriorityQueue is a good default choice because it has a nice object-oriented interface. There are multiple ways to implement priority queues in python, as explained above. Priority queues are also used in Process Scheduling, where a high priority task is assigned to the CPU before a low priority task.Ī priority queue is a modified version of basic queues where the priority of tasks is considered rather than their insertion order. Operating System: It is also used in the OS for load balancing and Interrupt handling.The priority queue is used to keep track of unexplored routes the one which has a lower bound on the total length is smallest is given the highest priority. Artificial Intelligence: A* search algorithm finds the shortest path between two vertices of a weighted graph, trying out the most promising routes first.Data Compression: It is used in Huffman codes which are used to compress data.Graph algorithms: The priority queues are used in Graph algorithms like Dijkstra’s Shortest path and Prim’s Minimum spanning trees.Time Complexity using the queue.PriorityQueue Class Here, we have inserted a tuple-> task name along with its priority. Below is the example of a priority queue that can store any object in addition to a basic built-in primitive.Ĭode: # Implementing priority queue using Queue.PriorityQueue class Python uses a binary heap to implement priority queues.Ģ. Python provides a built-in implementation of the priority queue data structure. This is an example of priority queues using the queue.PriorityQueue class. ![]() It can be clearly depicted in the output that the order in which the elements are entered (5 -> 1 -> 10) is different from the dequeuing order (1 -> 5 -> 10). While loop is then used to pop elements out of the list. Using the heappush() method of the heapq module, we have inserted the elements into the list. Print("Elements will be dequeued according to their prorities")Įxplanation: First, we’ve imported the heapq module then created an empty list. Heapq.heappush(que, (10, 'low priority task')) Heapq.heappush(que, (1, 'High priority task')) Heapq.heappush(que, (5, 'Medium Priority task')) Min heap: A complete binary tree where the key at the root must be minimum among all the keys present in the Binary heap.Ĭode: # Implementing Priority queue using heapq module But heapq only provides a min-heap implementation. A binary heap is often used to implement priority queues. This is an example of priority queues using the heapq module. Therefore, sorted lists are suitable only when there are few insertions. The main drawback is that inserting a new element into the list is a slow operation. Pros and Cons of this Approach: It is best suitable to identify and delete the smallest or largest element quickly. This is a manual method of implementing priority queues. While the loop is used to retrieve elements from the list using the pop() method. The list is then sorted in ascending order. #list is sorted evertime a new element is insertedĮxplanation: First, we have declared an empty list into which elements are inserted using the append() method of the List class. Code: #Implementing Priority Queues with Sorted list ![]()
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