A **linked list** is a data structure consisting of a sequence of elements called **nodes**, where each node contains data and a **reference** to the next node in the sequence.     
Unlike arrays, linked lists **do not have a fixed size in memory** and **do not store elements in contiguous memory locations**. Instead, they use pointers to connect nodes, allowing for dynamic memory allocation and efficient insertion and deletion of elements.

The next section of the code illustrates this concept.

<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Linked List vs Array Comparison</title>
<style>
    table {
        width: 80%;
        margin: 20px auto;
        border-collapse: collapse;
        border: 1px solid #ddd;
    }

    th,
    td {
        padding: 10px;
        text-align: left;
    }

    th {
        background-color: #f2f2f2;
    }

    tr:nth-child(even) {
        background-color: #f2f2f2;
    }

    tr:hover {
        background-color: #ddd;
    }

    th strong {
        font-weight: bold;
    }

    td strong {
        font-weight: bold;
    }
</style>
</head>
<body>

<table>
    <tr>
        <th><strong>Feature</strong></th>
        <th><strong>Linked List</strong></th>
        <th><strong>Array</strong></th>
    </tr>
    <tr>
        <td><strong>Dynamic Size</strong></td>
        <td>Linked lists can dynamically grow or shrink in size by allocating or deallocating memory for nodes.</td>
        <td>Arrays have a fixed size defined at the time of declaration and cannot easily change size during runtime.</td>
    </tr>
    <tr>
        <td><strong>Memory Allocation</strong></td>
        <td>Each node in a linked list occupies memory dynamically, allowing for efficient memory usage.</td>
        <td>Arrays store elements in contiguous memory locations, which may lead to memory wastage or insufficient memory for large arrays.</td>
    </tr>
    <tr>
        <td><strong>Insertion/Deletion</strong></td>
        <td>Linked lists offer efficient insertion and deletion operations by adjusting pointers.</td>
        <td>Arrays may require shifting elements to accommodate insertions or deletions in the middle, resulting in less efficient operations.</td>
    </tr>
    <tr>
        <td><strong>Random Access</strong></td>
        <td>Linked lists do not support efficient random access to elements, requiring traversal from the beginning to locate an element.</td>
        <td>Arrays support efficient random access to elements using indexing, allowing direct access to any element in constant time.</td>
    </tr>
    <tr>
        <td><strong>Traversal</strong></td>
        <td>Linked lists require traversal from the beginning to the desired position, which may result in slower access time for large lists.</td>
        <td>Arrays allow for fast traversal using indexing, providing efficient access to elements at any position.</td>
    </tr>
</table>

</body>
</html>


from lolviz import *
from IPython.display import display_png

class Node:

  def __init__(self, data):
    self.value = data
    self.next = None

# Let's create some nodes
node1 = Node(1)
node2 = Node(2)
node3 = Node(3)

# Then let's couple them into a linked list
node1.next = node2
node2.next = node3

display_png(objviz(node1))

>Note
>
>Pay attention that the built-in `list` Python structure is not a list conceptually - the `list` structure is implemented as a **dynamic array** that can hold elements of various data types. It is similar to an array but with additional functionalities and optimizations.

In contrast to an array where we have direct access to any item, in the linked list, we have **direct access only to the first item**, and we can access any other item only by the chain of references. 

In a linked list, what is the purpose of the "next" pointer?

The understanding of data structures and algorithms: this is what sets advanced professionals apart from the rest. In the modern world of growing data, it is very important to be able to process large amounts of data efficiently. 

In this course, you will study the most basic data structures that help us to deal with huge amounts of data as well as implement the most basic and crucial concepts on your own so you’ll get a deeper understanding of how each data structure works and you'll be able to decide what data structure best suited for your task.

When we start learning something new, it is very important to get familiar with the subject of our study first. So let's dive deeper into what we will learn about in this course.

Arrays and lists serve as foundational data structures in programming, with Python employing them extensively. This section provides an overview of these structures, delving into their concepts and highlighting their implementation nuances within the Python programming language.

There are many applied tasks in which it is necessary to operate with large amounts of structured data and simple data structures such as an array or a linked list may no longer be enough for these purposes. In this section, we'll look at the concept of an abstract data type and how different ADTs allow us to efficiently manage large amounts of data.

In the real world, one often has to deal with tasks in which it is necessary to model complex dependencies between various nodes of a system or domain. Graphs can help us with this. In this section, we will learn what a graph is and how to represent it in computer memory efficiently.

Linked List Data Structure

Linked list vs Array

Linked list implementation

Awesome!

Linked List Data Structure

Linked list vs Array

Linked list implementation