Python Data Structure and Algorithm Tutorial – LinkedList Data Structure

LinkedList Data Structure


In this tutorial, you will learn about linked list data structure and it’s implementation in Python.

A linked list data structure includes a series of connected nodes. Here, each node store the data and the address of the next node. For example,

linkedlist data structure
Linked List Data Structure

You have to start somewhere, so we give the address of the first node a special name called HEAD.

Also, the last node in the linked list can be identified because its next portion points to NULL.

You might have played the game Treasure Hunt, where each clue includes the information about the next clue. That is how the linked list operates.

In just a few steps, we have created a simple linked list with three nodes.

representing linked list by connecting each node with next node using address of next node
LinkedList Representation

The power of LinkedList comes from the ability to break the chain and rejoin it. E.g. if you wanted to put an element 4 between 1 and 2, the steps would be:

  • Create a new struct node and allocate memory to it.
  • Add its data value as 4
  • Point its next pointer to the struct node containing 2 as the data value
  • Change the next pointer of “1” to the node we just created.


Doing something similar in an array would have required shifting the positions of all the subsequent elements.

In python and Java, the linked list can be implemented using classes as shown in the codes below.

Linked List Utility

Lists are one of the most popular and efficient data structures, with implementation in every programming language like C, C++, Python, Java, and C#.

Apart from that, linked lists are a great way to learn how pointers work. By practicing how to manipulate linked lists, you can prepare yourself to learn more advanced data structures like graphs and trees.

Linked List Implementations in Python Examples

/* Linked list implementation in Python */

class Node:
    /* Creating a node */
    def __init__(self, item):
        self.item = item = None

class LinkedList:

    def __init__(self):
        self.head = None

if __name__ == '__main__':

    linked_list = LinkedList()

    /* Assign item values */
    linked_list.head = Node(1)
    second = Node(2)
    third = Node(3)

    /* Connect nodes */ = second = third

    /* Print the linked list item */
    while linked_list.head != None:
        print(linked_list.head.item, end=" ")
        linked_list.head =

Linked List Complexity

Time Complexity

Worst case Average Case
Search O(n) O(n)
Insert O(1) O(1)
Deletion O(1) O(1)

Space Complexity: O(n)

Linked List Applications

  • Dynamic memory allocation
  • Implemented in stack and queue
  • In undo functionality of software
  • Hash tables, Graphs

Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes

Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!

Latest end-to-end Learn by Coding Recipes in Project-Based Learning:

Applied Statistics with R for Beginners and Business Professionals

Data Science and Machine Learning Projects in Python: Tabular Data Analytics

Data Science and Machine Learning Projects in R: Tabular Data Analytics

Python Machine Learning & Data Science Recipes: Learn by Coding

R Machine Learning & Data Science Recipes: Learn by Coding

Comparing Different Machine Learning Algorithms in Python for Classification (FREE)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.  

Google –> SETScholars