![]() ![]() At the top of the module, we must import heapq: pq.is_empty(): We use this attribute to keep track of the insertion order. 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.
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