Queue Data Structure.

Queue follows the FIFO approach, i.e first in first out approach. This is similar to a queue in the banking hall where the first customer in the queue is the first one to be served.

Queue Operations.

• Enqueue - Adding elements to the end of the queue.
• Dequeue - Removing elements from the Front of the queue.
• Peek - Returning the element in Front of the Queue without removing it.
• isEmpty - Check if the Queue is empty.
• isFull - Check if the Queue is Full.

Enqueue Operation

• Sent FRONT AND REAR pointer to track first and last elements.
• check if the queue is full using isFull.
• Set the FRONT pointer to 0 for the first element.
• Increase the REAR pointer by 1.
• Point the REAR index to the new element.

Dequeue Operations.

• Use the isEmpty operation to check if the queue is empty.
• Remove the element pointed by the FRONT index.
• Whenever an element is removed from the FRONT index should increase by 1.
• When removing the last element from the queue reset the FRONT and REAR pointer to -1.

Queue Implementation Using an Array.

class Queue:

    def __init__(self):
        self.queue = []

    def enqueue(self, item):
        return self.queue.append(item)

    def dequeue(self):
        if len(self.queue) < 1:
            return None
        return self.queue.pop()

    def size(self):
        return len(self.queue)

    def display(self):
        return (self.queue)


q = Queue()

q.enqueue(10)
q.enqueue(20)
q.enqueue(30)
q.enqueue(40)


print(q.display())

When using an array to implement the queue data structure, the complexity of the enqueue and dequeue operations isO(1).

Applications of the Queue Data Structure.

• Handling Interrupts in real-time systems.

• Used in the call center to hold people calling in an order.

• Handling Interrupts in real-time systems.

• Scheduling tasks in CPUs and Disks.

Isaac Tonyloi