### Introduction to Priority Queue A Priority Queue is an abstract data structure that is used to store and manage a collection of elements with each element being associated with a priority. It supports the following operations: 1. Insertion: Inserts an element in the priority queue with an associated priority. 2. Deletion: Deletes the element with the highest priority in the priority queue. 3. Extract-Max/Extract-Min: Returns the element with the highest/lowest priority in the priority queue. Priority Queues are used in various applications such as task scheduling, graph algorithms, and data compression. In task scheduling, the element with the highest priority is given the highest priority to be executed first. In graph algorithms, the element with the highest priority is used to traverse the graph. In data compression, the element with the highest priority is used to compress the data more effectively. Priority queues are implemented using various data structures such as an array, linked list, binary heap, etc. Depending on the data structure used, the time complexity of the operations can vary. For example, the time complexity of insertion and deletion operations using an array is O(1).

A Priority Queue is a data structure used to store and manage data elements in an ordered sequence according to their priority. It is similar to a regular queue, but instead of being organized in a FIFO (First-in, First-out) manner, it is organized so that the element with the highest priority is always at the front of the queue. Essentially, a priority queue is a collection of data elements such that each element has a priority associated with it. When an element is added to the Priority Queue, it is inserted in the correct position based on its priority. Higher priority elements are placed in the front of the queue, while lower priority elements are placed in the back. Below is a sample code for a Priority Queue in Python: class PriorityQueue: def __init__(self): self.queue = [] def __str__(self): return ' '.join([str(i) for i in self.queue]) def isEmpty(self): return self.queue == [] def insert(self, data): self.queue.append(data) def delete(self): try: max = 0 for i in range(len(self.queue)): if self.queue[i] > self.queue[max]: max = i item = self.queue[max] del self.queue[max] return item except IndexError: print() exit() q = PriorityQueue() q.insert(12) q.insert(1) q.insert(14) q.insert(7) print(q) while not q.isEmpty(): print(q.delete()) # Output: 14 12 7 1