Advanced Data Structures in Python: Deque, Stacks, Queues, and Beyond

  1. Object-Oriented Programming (OOP) in Python
  2. Advanced Data Structures in Python: Deque, Stacks, Queues, and Beyond
  3. Lambda Functions and Map/Filter/Reduce: Unleashing Functional Programming in Python
  4. Generators and Iterators in Python: Mastering Lazy Evaluation
  5. Unveiling Python’s Advanced Features: Decorators and Metaprogramming
  6. Mastering Pattern Matching and Text Manipulation with Regular Expressions in Python
  7. Exploring Database Access with Python: Connecting, Querying, and Beyond
  8. Empowering Your Python Journey: An In-depth Introduction to Python Libraries
  9. Mastering Python: Debugging and Profiling Your Code
  10. Mastering Python: Advanced File Operations


As an intermediate Python programmer, you’ve already conquered the basics of Python’s foundational data structures like lists, tuples, and dictionaries. Now, it’s time to delve deeper into the world of advanced data structures. In this second installment of our intermediate Python programming series, we’ll explore advanced data structures that provide specialized features and efficiency improvements. We’ll cover deques, stacks, queues, and introduce you to other essential data structures, supported by practical code examples. 

Deque: Double-Ended Queue

A deque (short for “double-ended queue”) is a versatile data structure that allows you to add and remove elements efficiently from both ends. It combines the features of a stack and a queue. Python provides a built-in `deque` class in the `collections` module, making it easy to work with double-ended queues:

from collections import deque

Creating a deque
my_deque = deque([1, 2, 3])

Adding elements to the right end

Adding elements to the left end

Removing elements from the right end

Removing elements from the left end

print(my_deque)  Output: deque([1, 2, 3])

Deques are invaluable for tasks that involve managing a sliding window of data, implementing algorithms like breadth-first search (BFS), or efficiently simulating scenarios where you need to add or remove elements from both ends.

Stacks: Last-In, First-Out (LIFO)

A stack is a linear data structure that follows the Last-In, First-Out (LIFO) principle. In Python, you can implement a stack using a simple list:

Creating a stack
my_stack = []

Pushing elements onto the stack

Popping elements from the stack
popped_element = my_stack.pop()

print(popped_element)  Output: 3

Stacks are commonly used for tasks like tracking function calls in recursion, parsing expressions, and solving problems that require backtracking.

Queues: First-In, First-Out (FIFO)

A queue is another linear data structure, but it follows the First-In, First-Out (FIFO) principle. Python’s `queue` module provides a `Queue` class that simplifies working with queues:

from queue import Queue

Creating a queue
my_queue = Queue()

Enqueuing elements

Dequeuing elements
dequeued_element = my_queue.get()

print(dequeued_element)  Output: 1

Queues are essential for tasks like managing task scheduling, handling requests in web applications, and implementing breadth-first search algorithms.

Priority Queues: heapq

A priority queue is a variation of a queue where each element has a priority associated with it. Python’s `heapq` module provides a simple way to implement a priority queue using a list as the underlying data structure:

import heapq

Creating a priority queue
my_priority_queue = []

Enqueuing elements with priorities
heapq.heappush(my_priority_queue, (2, "Task 2"))
heapq.heappush(my_priority_queue, (1, "Task 1"))
heapq.heappush(my_priority_queue, (3, "Task 3"))

Dequeuing elements
priority, task = heapq.heappop(my_priority_queue)

print(task)  Output: "Task 1"

Priority queues are essential when you need to process tasks or events based on their urgency or importance.

Linked Lists

While Python doesn’t provide a built-in linked list class, you can create linked lists using custom classes. Linked lists are fundamental data structures for tasks like implementing dynamic data structures, memory-efficient storage, and manipulating large data sets.

Dictionaries and Sets

Python dictionaries (`dict`) and sets (`set`) are highly efficient data structures for managing collections of unique keys or elements. Dictionaries offer constant-time access to values based on their keys, while sets are excellent for performing set operations like union, intersection, and difference.


Python doesn’t include built-in tree data structures, but you can implement various tree types, such as binary search trees (BSTs), AVL trees, or other custom tree structures using classes. Trees are essential for tasks like searching, sorting, and hierarchical data representation.


In this comprehensive exploration of advanced data structures in Python, you’ve discovered how to harness the power of deques, stacks, queues, and other specialized data structures to tackle complex programming challenges with ease. By mastering these data structures, you’re better equipped to optimize your code, manage data efficiently, and craft elegant solutions to real-world problems.

As an intermediate Python programmer, you’ve now expanded your toolkit and gained the knowledge needed to excel in various programming scenarios. In the next article of our series, we will delve even deeper into advanced algorithms and data structures, equipping you with the skills to tackle complex problems effectively and efficiently. Stay tuned for more exciting insights into Python programming!