📚 Advanced Data Structures and Algorithms (2023)

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📚 Data Structures with Python (2023)

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📚 Data Structures the Fun Way (2022)

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📚 Linked Data (2023)

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Topic: Python List vs Tuple — Differences and Use Cases

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Key Differences

• Lists are mutable — you can change, add, or remove elements.

• Tuples are immutable — once created, they cannot be changed.

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Creating Lists and Tuples

my_list = [1, 2, 3]
my_tuple = (1, 2, 3)

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When to Use Each

• Use lists when you need a collection that can change over time.

• Use tuples when the collection should remain constant, providing safer and faster data handling.

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Common Tuple Uses

• Returning multiple values from a function.

def get_coordinates():
    return (10, 20)

x, y = get_coordinates()

• Using as keys in dictionaries (since tuples are hashable, lists are not).

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Converting Between Lists and Tuples

list_to_tuple = tuple(my_list)
tuple_to_list = list(my_tuple)

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Performance Considerations

• Tuples are slightly faster than lists due to immutability.

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Summary

• Lists: mutable, dynamic collections.

• Tuples: immutable, fixed collections.

• Choose based on whether data should change or stay constant.

---

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Topic: Linked Lists in Python – Part 1: Introduction and Singly Linked List Basics

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What is a Linked List?

• A linked list is a linear data structure where each element (called a node) contains:

• The data value.
• A pointer (or reference) to the next node.

• Unlike arrays, linked lists don’t require contiguous memory and can grow dynamically.

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Why Use Linked Lists?

• Efficient insertions/deletions at the beginning or middle.
• No need for pre-defining size, unlike arrays.
• Used in memory-efficient applications like OS kernels, compilers, and real-time systems.

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Types of Linked Lists

• Singly Linked List – Each node points to the next.
• Doubly Linked List – Nodes have next and previous pointers.
• Circular Linked List – The last node points back to the head.

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Basic Structure of a Node

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

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Building a Singly Linked List

class LinkedList:
    def __init__(self):
        self.head = None

    def append(self, data):
        new_node = Node(data)

        if not self.head:
            self.head = new_node
            return

        current = self.head
        while current.next:
            current = current.next
        current.next = new_node

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Traversing the List

def display(self):
    current = self.head
    while current:
        print(current.data, end=" -> ")
        current = current.next
    print("None")

Usage:

ll = LinkedList()
ll.append(10)
ll.append(20)
ll.append(30)
ll.display()  # Output: 10 -> 20 -> 30 -> None

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Inserting at the Beginning

def insert_at_beginning(self, data):
    new_node = Node(data)
    new_node.next = self.head
    self.head = new_node

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Summary

• A singly linked list stores data as a sequence of nodes linked by references.

• Supports dynamic memory usage, fast insertions, and flexible resizing.

• The key is managing node connections safely and efficiently.

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Exercise

• Implement a method length() that returns the number of nodes in the list.

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