Types of Data Structures in Python That Every Coder Should Know!

Lists, dictionaries, sets, and tuples are the core types of data structures in Python. Each one is designed for a specific kind of task. Lists store ordered collections. Dictionaries manage key-value pairs. Sets handle unique elements. Tuples create fixed, unchangeable sequences.

These built-in structures help you write clean and efficient Python code. Whether you are working on data analysis, web development, or automation, using the right data structure makes your programs easier to build and run faster.

This blog explains the various types of data structures in Python, their functionality, and when to use each one effectively.

Types of Data Structures in Python

The types of data structures in Python provide different ways to store, organize, and manage data efficiently. Choosing the right one helps improve performance and keeps your code clean and readable.

Python includes several core data structures, such as lists, tuples, sets, and dictionaries, along with specialized ones like arrays, stacks, queues, and heaps. Each serves a specific purpose depending on the task.

If you want to learn industry-relevant skills to understand different types of data structures in Python, the following courses can help you succeed.

• AI-Powered Full Stack Development Course by IIITB
• Professional Certificate Program in Cloud Computing and DevOps
• Executive Diploma in Machine Learning and AI with IIIT-B

Here’s a quick overview of the most commonly used types of data structures in Python:

Also Read: What is Mutable and Immutable in Python? Definitions, Data Types, Examples, and Key Differences

1. List Data Structure in Python

The list data structure in Python is an ordered, mutable collection used to store multiple items in a sequence. It allows indexing, slicing, and supports mixed data types. Lists are widely used for grouping and efficiently processing sequences of values. 

The list data structure in Python is flexible. It supports various built-in methods, such as append() for adding elements and remove() for deleting them, making it easy to manipulate and modify data.

Sample code:

# Creating and using a list in Python
fruits = ["apple", "banana", "cherry"]

# Adding an item
fruits.append("orange")

# Accessing elements
print(fruits[0])  # Output: apple

# Removing an item
fruits.remove("banana")

# Final list
print(fruits)

Code Explanation: This code creates a list data structure in Python named fruits with string elements. The append() method adds a new item, while remove() method deletes one. Indexing retrieves specific values. Lists are dynamic, easy to use, and perfect for storing ordered collections.

Expected output:

apple

['apple', 'cherry', 'orange']

2. Tuple Data Structure in Python

The tuple data structure in Python is one of the core types of data structures in Python. It is ordered like a list but immutable, meaning you can’t change its values once defined. Tuples support indexing and can store mixed data types just like lists.
Among the types of data structures in Python, tuples are ideal when you want a fixed collection of items that shouldn’t be modified.

Sample code:

# Creating and using a tuple in Python
dimensions = (10, 20, 30)

# Accessing elements
print(dimensions[1])  # Output: 20

# Length of tuple
print(len(dimensions))

Code Explanation: This example uses the tuple data structure in Python to store three numeric values. Since tuples are immutable, you can’t add or remove items after creation. Tuples are useful when working with fixed-size, read-only data.

Expected output:

20  

3

3. Set Data Structure in Python

The set data structure in Python is an unordered collection that stores unique elements. It is one of the most efficient types of data structures in Python for membership testing and removing duplicates. Sets do not allow duplicate values and do not maintain order.

Within the types of data structures in Python, sets are best when your focus is on uniqueness and fast operations.

Sample code:

# Creating and using a set in Python
unique_numbers = {1, 2, 3, 2, 1}

# Adding an item
unique_numbers.add(4)

# Removing an item
unique_numbers.discard(2)

# Final set
print(unique_numbers)

Code Explanation: This code defines a set data structure in Python that allows for duplicate values, which are automatically removed. The add() method inserts new items, while discard() removes them if present. Sets are perfect when you need fast lookups and no duplicates.

Expected output:

{1, 3, 4}

4. Dictionary Data Structure in Python

The dictionary data structure in Python is one of the most powerful types of data structures in Python. It stores data as key-value pairs, making it ideal for fast lookups and flexible data mapping. Each key in a dictionary is unique and used to access its associated value instantly.

Among the types of data structures in Python, dictionaries are widely used when you need structured, searchable data.

Sample code:

# Creating and using a dictionary in Python
student = {"name": "Alice", "age": 21, "course": "Python"}

# Accessing values
print(student["name"])

# Adding a new key-value pair
student["grade"] = "A"

# Final dictionary
print(student)

Code Explanation: This example shows the dictionary data structure in Python, where values are accessed by keys. The student dictionary stores multiple data types. You can easily add or retrieve data using keys. Dictionaries are efficient for storing related data with labeled access. 

Expected output:

Alice

{'name': 'Alice', 'age': 21, 'course': 'Python', 'grade': 'A'}

5. String Data Structure in Python

The string data structure in Python is a sequence of characters used to store and manipulate text. As one of the basic types of data structures in Python, strings are immutable and support slicing, concatenation, and formatting. You use the string data structure in Python whenever you're working with textual data.

Sample code:

# Creating and using a string in Python
message = "Hello, Python"

# Accessing characters
print(message[0])

# Concatenating strings
greeting = message + " learners!"
print(greeting)

Code Explanation: The message variable is a string data structure in Python that holds text. You access characters by index, and use + to combine strings. Though strings are immutable, Python offers many built-in string methods for formatting and analysis.

Expected output:

H

Hello, Python learners!

6. Array Data Structure in Python

The array data structure in Python is used to store elements of the same type in a compact and memory-efficient way. Unlike lists, arrays require all elements to be of the same data type. Among the types of data structures in Python, arrays are ideal when working with large sets of numeric data.

The array data structure in Python comes from the array module and is helpful for mathematical operations and optimizations.

Sample code:

import array

# Creating and using an array in Python
numbers = array.array('i', [10, 20, 30, 40])

# Accessing elements
print(numbers[2])

# Appending an item
numbers.append(50)

# Final array
print(numbers)

Code Explanation: This shows the array data structure in Python using the array module. The 'i' type code specifies integer values. Arrays are efficient for performance-critical operations where type consistency matters.

Expected output:

30

array('i', [10, 20, 30, 40, 50])

7. Stack Data Structure in Python

The stack data structure in Python is a linear structure based on the Last-In, First-Out (LIFO) principle. You add and remove elements from the same end, known as the "top" of the stack. It is one of the key types of data structures in Python for tasks like backtracking, undo operations, and parsing expressions.
Python implements stacks using lists, with built-in methods such as append() for pushing elements and pop() for removing them.

Sample code:

# Implementing a stack in Python
stack = []

# Pushing elements
stack.append(10)
stack.append(20)
stack.append(30)

# Popping the top element
print(stack.pop())

# Final stack
print(stack)

Code Explanation: This code uses a list to create a stack data structure in Python. The append() method adds elements to the top, while pop() removes the last added item. Among the types of data structures in Python, stack is ideal when the most recent item needs to be accessed first.

Expected output:

30  

[10, 20]

8. Queue Data Structure in Python

The queue data structure in Python follows the First-In, First-Out (FIFO) principle. Elements are added at the rear and removed from the front. This is one of the most practical types of data structures in Python, often used in scheduling, task processing, and buffering.
Python offers queues using lists or the collections.deque module for better performance.

Sample code:

from collections import deque

# Creating a queue
queue = deque()

# Enqueuing elements
queue.append("Task1")
queue.append("Task2")
queue.append("Task3")

# Dequeuing the front element
print(queue.popleft())

# Final queue
print(queue)

Code Explanation: This code creates a queue data structure in Python using the deque module. The append() method enqueues tasks, and popleft() dequeues the oldest one. Among the types of data structures in Python, a queue is perfect when task order matters.

Expected output:

Task1  

deque(['Task2', 'Task3'])

9. Heap Data Structure in Python

The heap data structure in Python is a binary tree-based structure used to maintain a priority queue. It ensures the smallest element is always at the root. This is one of the advanced types of data structures in Python, used in algorithms like Dijkstra’s or for implementing schedulers.
Python provides the heapq module to create min-heaps efficiently.

Sample code:

import heapq

# Creating a heap
heap = []

# Pushing elements
heapq.heappush(heap, 25)
heapq.heappush(heap, 15)
heapq.heappush(heap, 30)

# Accessing and removing the smallest element
print(heapq.heappop(heap))

# Final heap
print(heap)

Code Explanation: This code demonstrates a heap data structure in Python using the heapq module. heappush() adds elements while maintaining heap order, and heappop() removes the smallest item. Of all types of data structures in Python, a heap is ideal when priority matters.

Expected output:

15  

[25, 30]

Also read: 

Top 25+ Python Projects on GitHub for Every Skill Level: Beginner to Pro

Top 10 Python Framework for Web Development

How upGrad Advances Your Expertise in Python Programming?

The types of data structures in Python, list, tuple, set, and dictionary, each serve distinct technical purposes based on mutability, indexing, and hashing behavior. Lists are best for dynamic sequences, tuples for fixed immutable data, sets for enforcing uniqueness, and dictionaries for structured key-value mapping. Choose based on required access time, memory constraints, and whether your workload needs constant-time lookup, ordered output, or safe concurrent read operations.

If you are interested in learning fundamentals in Python that can help you deploy scalable projects and applications. These are some of the additional courses that can help you understand Python for enterprise-level applications. 

• Master of Design in User Experience
• Master’s Degree in Artificial Intelligence and Data Science
• Data Structures & Algorithms

Curious which courses can help you gain proficiency in Python? Contact upGrad for personalized counseling and valuable insights. For more details, you can also visit your nearest upGrad offline center.

Unlock the power of data with our popular Data Science courses, designed to make you proficient in analytics, machine learning, and big data!

Explore our Popular Data Science Courses

Elevate your career by learning essential Data Science skills such as statistical modeling, big data processing, predictive analytics, and SQL!

Top Data Science Skills to Learn

Stay informed and inspired  with our popular Data Science articles, offering expert insights, trends, and practical tips for aspiring data professionals!

Read our popular Data Science Articles

Reference: 
https://content.techgig.com/technology/python-dominates-2025-programming-landscape-with-unprecedented-popularity/articleshow/121134781.cms