What Is Data Structure in Python? Classify, Code & Apply

Summary: This blog simplifies what is data structure in Python, covering types, classifications, and real-life examples. Learn how Python structures like lists, stacks, and dictionaries help store and manage data efficiently. Discover practical use cases and best practices, and explore how they connect with modern data science applications.

Introduction

Ever wondered how your phone knows what message to show first or how apps store your data so neatly? That’s the magic of data structures! In this blog, we’ll break down what a data structure in Python is in the simplest way possible. 

Think of data structures as smart containers that help you organise and use your data better. And guess what? Python makes it super fun and easy to learn! 

By the end, you’ll know how to classify data structures, write code using them, and even apply them to real-world tasks. So, grab your curious brain—we’re diving in!

Key Takeaways

• Data structures help efficiently organise, manage, and store data in Python programs.
• Python offers built-in (list, tuple, set, dict) and user-defined (stack, queue) structures.
• Classify them as linear vs non-linear and mutable vs immutable for better understanding.
• Real-life uses include undo operations, quick lookups, and data organisation.
• Mastering Python data structures is essential for careers in data science and development.

What Are Data Structures?

Data structures are special ways to store and organise data in a computer so we can use it easily and quickly. Think of them like different containers — some are good for holding lists of items, some for pairing things together, and others for keeping things in order. 

In Python, data structures help us manage data smoothly when building programs, apps, or websites.

For example, a Python list can be used to make a shopping list. A dictionary can be used to look up a phone number using a name. Each data structure has its own purpose.

Why Are Data Structures Important?

They make your code cleaner, faster, and easier to understand. Choosing the right structure can save time and memory, especially when working with large data. It’s like picking the right tool for the job — using a hammer when you need a screwdriver won’t help.

Built-in vs. User-defined Data Structures

Python comes with built-in data structures like lists, tuples, sets, and dictionaries — these are ready to use right away. On the other hand, user-defined data structures like stacks, queues, or linked lists are created by programmers using Python code to meet specific needs.

Classification of Data Structures

It’s helpful to know the types of data structures, which fall into different categories based on how they manage and store information, to understand how data is stored and organised in Python. 

In this section, we’ll explore two important ways to classify them: Linear vs Non-linear and Mutable vs Immutable data structures. We’ll also look at simple examples to make things easy to understand.

Linear vs Non-linear Structures

Linear data structures organise data in a straight line. Think of it like a queue at a ticket counter—everyone is lined up one after the other. You can go from one element to the next in a specific order. Common examples include:

• List: A group of items where each item has a position (index). Example: [“apple”, “banana”, “cherry”]
• Stack: Think of a stack of plates—you can only add or remove the top plate. It follows the Last-In, First-Out (LIFO) rule.
• Queue: Like a line of people waiting. The first one to enter is the first one to leave—First-In, First-Out (FIFO) rule.

Non-linear data structures store data in a more complex way. Here, elements are connected like branches of a tree or nodes in a web. They don’t follow a single sequence.

• Tree: A structure where each element has a parent and can have multiple children. Think of a family tree.
• Graph: A collection of points (called nodes) connected by lines. These are great for showing networks like social media connections.

Mutable vs Immutable Types

The terms mutable and immutable tell us whether we can change the content of a data structure after creating it.

• Mutable means you can change, add, or remove elements.
  • Example: List – You can add more items to a shopping list.
  • Example: Dictionary – You can change the value for a specific key.
    
• Immutable means once created, you cannot change the data.
  • Example: Tuple – It’s like a frozen list. You can look at it but not edit it.
  • Example: String – Even though it looks like a list of characters, you can’t change individual letters.

Coding Core Python Data Structures

Understanding how to use Python’s basic data structures is an essential step for anyone learning programming. These structures—Lists, Tuples, Sets, and Dictionaries—help you store and organise data in simple and useful ways. Let’s explore each one with clear examples to help you learn how to use them in your own code.

Lists: Store Items in Order

A list is like a shopping list. It keeps items in the order you add them and allows duplicates.

Syntax:

Common Operations:

• Add an item: fruits.append(‘orange’)
• Access an item: fruits[1] (gives ‘banana’)
• Remove an item: fruits.remove(‘apple’)

Lists are changeable, so you can update or delete items easily.

Tuples: Fixed and Ordered Data

A tuple is like a list, but it cannot be changed after creation. Use it when your data should stay the same.

Syntax:

Operations:

• Access an item: coordinates[0] (gives 10)
• Count items: coordinates.count(10)

Tuples are faster and secure, great for fixed data like days of the week or coordinates.

Sets: Unique Items Only

A set is like a bag of different things where no item repeats. It’s great for removing duplicates.

Syntax:

Common Operations:

• Add an item: colors.add(‘yellow’)
• Remove an item: colors.discard(‘red’)
• Check common items: colors1 & colors2

Sets are unordered, and they automatically ignore repeated values.

Dictionaries: Key-Value Pairs

A dictionary is like a real dictionary. Each word (key) has a meaning (value). It’s great for labeled data.

Syntax:

Common Operations:

• Access a value: person[‘name’] (gives ‘Alice’)
• Add or change a value: person[‘age’] = 26
• Remove a key: del person[‘age’]

Dictionaries are powerful for storing and retrieving data quickly.

Real-World Applications and Use Cases

Data structures are not just concepts in books—they are tools used every day by developers to build apps, websites, and software that we all use. Whether it’s managing your social media feed or saving your progress in a game, data structures make it all work smoothly behind the scenes.

Organising Data in Real Life

Imagine you run a small store and want to keep track of the items you sell. Use a list to write down the names of all products. In the same way, programmers use lists in Python to store and organise data like names, numbers, or files. 

This helps sort, filter, and manage large amounts of information quickly.

Fast Lookups with Dictionaries

Think of a dictionary in Python like a contact list on your phone—each name is linked to a number. In the tech world, developers use Python dictionaries to store key-value pairs. 

For example, a developer might store cities as keys and temperatures as values when building a weather app. This makes it fast and easy to look up any city’s weather.

Undo Options Using Stacks

Have you ever pressed “undo” in a text editor? Behind the scenes, a stack data structure remembers your recent actions. It works like a stack of plates—whatever goes in last comes out first. Developers use this to build features like undo, redo, or even backtracking in games and navigation apps.

Best Practices for Choosing a Data Structure

To choose the right data structure, consider your goal. Do you need to store things in order? Use a list. Need quick lookups? Use a dictionary. Want to track actions in reverse? Pick a stack. Matching the right tool to the task makes apps faster, wiser, and more reliable.

Wrapping Up

Understanding what is data structure in Python is key to writing efficient, readable, and scalable code—skills every programmer and data scientist needs. Whether building a to-do app or working on a machine learning project, choosing the right data structure shapes your program’s performance. 

Python’s built-in and user-defined data structures make it easier to manage complex datasets, a vital skill in data science. Want to sharpen your skills further? Join Pickl.AI’s data science courses and learn how to use data structures effectively while working on real-world data problems. Start your journey toward becoming a data expert today!

Frequently Asked Questions

What is a data structure in Python used for?

A data structure in Python stores, organises, and manages data efficiently. It helps with tasks like searching, sorting, and updating data. These structures are essential for developing fast and optimised programs, especially in data analysis and real-time applications.

What are the types of data structures in Python?

Python supports both built-in and user-defined data structures. Built-in types include lists, tuples, sets, and dictionaries. User-defined structures, such as stacks, queues, linked lists, trees, and graphs, are created using classes and are used to solve specific data handling problems.

How are data structures used in data science?

In data science, data structures organise and process large datasets efficiently. Lists and dictionaries help with data manipulation, while stacks and queues manage tasks and memory. They are crucial for building algorithms, machine learning models, and effectively managing structured and unstructured data.