DSA with Python

Welcome to DSA with Python.

This book is written for people who want to actually understand data structures and algorithms, not just memorize code. The focus is simple: clear thinking, solid fundamentals, and enough practice to make concepts stick.

You are expected to know basic Python syntax. Everything else is built from scratch, step by step, with explanations that connect ideas instead of throwing formulas at you.

Syllabus Tracker¶

Arrays and Lists¶

• Arrays: operations and time complexity

• Lists: 1D operations

• Lists: 2D operations

• Arrays vs Lists

Practice Problems

• Largest element

• Count occurrences

• Remove duplicates

• Reverse a list

• Where arrays and lists are used in real problems

Strings¶

• Introduction and properties

• Use cases

• Common string functions

• String manipulation and comparison

Practice Problems

• Count vowels and consonants

• Character frequency

• Palindrome check

• Anagram detection

• How strings appear in algorithmic problems

Linked Lists¶

• Introduction and memory model

• Linked List vs Array

Types of Linked Lists

• Singly Linked List

• Circular Singly Linked List

• Doubly Linked List

• Circular Doubly Linked List

Operations

• Insertion at beginning

• Insertion in middle

• Insertion at end

• Deletion at beginning

• Deletion in middle

• Deletion at end

• Searching

• Complexity analysis

Practice Problems

• Find middle element

• Count nodes

• Sum of nodes

• Minimum and maximum element

Stacks¶

• Stack concepts and operations

Implementations

• Using list

• Using collections

• Using linked list

Practice Problems

• Reverse a string

• Balanced parentheses

• Undo operation

• Stack usage in real systems

Queues¶

• Queue concepts and operations

Implementations

• Using list

• Using linked list

• Using collections module

• Queue using stacks

Practice Problems

• Reverse first K elements

• Ticket counter simulation

• Queue usage in real systems

Searching¶

• Linear Search

• Binary Search

• Exponential Search

• Choosing the right search strategy

Sorting¶

• Bubble Sort

• Selection Sort

• Insertion Sort

• Merge Sort

• Quick Sort

• Counting Sort

• Complexity and stability comparison

• Sorting in real applications

Recursion¶

• Introduction and internal working

• Recursion vs iteration

• Common mistakes

Practice Problems

• Factorial

• Fibonacci

• Print N to 1

• Sum of N numbers

• Count digits

• Power function

• When recursion is the right tool

Hashing¶

• Hash functions and hash tables

• Collision Handling:

  • Chaining

  • Linear probing

  • Quadratic probing

  • Load factor and complexity

  • Hashing with dictionaries and sets

Practice Problems

• Character count

• First non-repeating character

• Two-sum problem

• Hashing in real-world systems

Problem Solving Patterns¶

• Sliding Window

• Two Pointers

• Fast and Slow Pointers

• Prefix Sum

• Hashing Pattern

• Binary Search on Answer

• Greedy Approach

Practice Problems

• Maximum sum subarray

• Longest substring without repeating characters

• Anagram problems

• Cycle detection

• Subarray sum equals K

• Top K frequent elements

• Aggressive cows

• Activity selection

• Jump game

Trees¶

• Tree terminology and properties

Traversals

• Preorder

• Inorder

• Postorder

• Level order

Binary Search Tree

• Insertion

• Search

• Deletion

• N-ary Trees

• Dynamic Programming on Trees

Practice Problems

• Tree depth and height

• Validate BST

• Lowest Common Ancestor

• Kth smallest element

• Maximum path sum

• Tree-based problem solving

Graphs¶

• Graph types and representations

• BFS and DFS

• Multi-source BFS

• 0-1 BFS

Shortest Path Algorithms

• Dijkstra

• Bellman-Ford

• Floyd-Warshall

Advanced Algorithms

• Disjoint Set Union

• Prim’s Minimum Spanning Tree

• Kruskal’s Minimum Spanning Tree

DAG Algorithms

• Kahn’s Algorithm

• Shortest paths in DAG

• Longest paths in DAG

State Space Graphs

• Rat in a Maze

• 8-Puzzle

Practice Problems

• Number of islands

• Cycle detection

• Course scheduling

• Network delay time

• Cheapest flights

• Rotten oranges

How to Use This Book¶

Read slowly. Implement everything yourself. If you can explain a solution without looking at code, you understand it. If not, revisit the topic.

DSA is not about speed at first. It is about clarity. Speed comes later.