As I continue my journey through data structures, I recently explored a simple yet powerful concept — the queue. Queues are everywhere in computing, from scheduling processes in an operating system to managing background tasks in web servers.

In this post, I’ll share what I learned about queues, their key operations (enqueue and dequeue), and a brief look at priority queues, all through hands-on Python examples.

🧠 What is a Queue?

A queue is a linear data structure that follows the FIFO (First In, First Out) principle. The element that is inserted first is the one removed first — like people lining up at a counter.

📌 Queue Operations

➕ Enqueue Operation (Insert)

To enqueue means to add an element to the rear (end) of the queue.

"""
To enqueue is to add/insert elements to the rear/back of the queue

----STEPS----
    1. Check if the queue is full
    2. Insert the element 
    3. Update the pointer to the current back element
"""             

queue = []

queue.append(20)
queue.append(30)
queue.append(40)

print(queue)  # Output: [20, 30, 40]

In Python, we use append() to simulate the enqueue operation. Since Python lists are dynamic, we usually don’t check for "full" unless implementing with a fixed size.

➖ Dequeue Operation (Remove)

To dequeue means to remove an element from the front of the queue.

"""
To dequeue is to remove elements from the front of the queue

----STEPS----
    1. Check if the queue is empty
    2. Remove the front element 
    3. Update the pointer to the current front element
"""

queue = [20, 30, 40]

queue.pop(0)

print(queue)  # Output: [30, 40]

Here, we use pop(0) to remove the first element — this simulates removing the front of the queue.

🧱 Queue Types

There are multiple types of queues, but first i focused on one special type, the Priority queue

🔺 Priority Queue

In a priority queue, elements are served based on their priority — not just their arrival order. Think of a hospital ER where critical patients are treated before others.

import heapq

priority_queue = []

heapq.heappush(priority_queue, (1, 'low_priority'))
heapq.heappush(priority_queue, (0, 'high_priority'))

result = heapq.heappop(priority_queue)

print(result)  # Output: (0, 'high_priority')

We use Python’s heapq module which allows us to treat a list as a min-heap (smallest number = highest priority). So, (0, 'high_priority') is dequeued before (1, 'low_priority').

🌍 Real-World Applications of Queues

Understanding queues isn’t just theoretical — they’re used everywhere:

✅ Enqueue/Dequeue Applications:

Print Queue: Jobs sent to a printer are printed in order.
Call Center System: Calls are answered in the order received.
Breadth-First Search (BFS): Used in graph algorithms and AI.

✅ Priority Queue Applications:

Task Scheduling: Operating systems schedule tasks by priority.
Navigation Systems: Dijkstra's algorithm (shortest path).
Event-driven Systems: Handling events based on urgency.

🎯 Final Thoughts

This exercise helped me better understand queues — both conceptually and practically. Writing and running these examples in Python really solidified how enqueueing and dequeueing work under the hood. Learning about priority queues gave me a glimpse of how data can be managed more efficiently in time-critical systems.

Understanding Queues in Python: Enqueue, Dequeue, and Priority Queues