🚀 Data Structures & Algorithms in Python

An organized, production-quality repository dedicated to learning and mastering Data Structures and Algorithms (DSA) using Python. Featuring clean implementations, step-by-step dry-run traces, mathematical analyses, and Big-O complexity breakdowns.

Curriculum • Repository Structure • Complexity Cheat Sheet • Getting Started • Contributing

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📌 Overview

This repository is designed as both a study companion and an engineering reference. Every algorithm is provided with:

• 💡 Clean, PEP 8 Compliant Python Code with type annotations and docstrings.
• 📝 In-depth Theoretical Notes breaking down intuition, edge cases, and dry-runs.
• ⏱️ Time & Space Complexity Proofs (Best, Average, and Worst cases).
• 🧪 Interactive Demonstrations ready to execute directly from the terminal.

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🗺️ Curriculum & Roadmap

1. Sorting Algorithms

• Bubble Sort (Code | Notes)
• Selection Sort (Code | Notes)
• Insertion Sort (Code | Notes)
• Merge Sort (Upcoming)
• Quick Sort (Upcoming)

2. Searching Algorithms

• Linear Search (Upcoming)
• Binary Search (Upcoming)

3. Complexity Analysis

• Asymptotic Notation ($\mathcal{O}$, $\Omega$, $\Theta$) (Notes)
• Iteration & Swap Calculation Formulas (Notes (Bubble Sort) | Notes (Selection Sort) | Notes (Insertion Sort))
• Time & Space Complexity Proofs & Common Patterns (Notes)

4. Linear Data Structures

• Arrays & Dynamic Arrays
• Stack (Array & Linked List implementations)
• Queue (Simple, Circular, & Deque)
• Linked List (Singly, Doubly, & Circular)

5. Non-Linear Data Structures & Advanced Topics

• Trees & Binary Search Trees (BST)
• Graphs & Traversals (BFS / DFS)
• Hashing & Hash Maps

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📊 Complexity Cheat Sheet

Algorithm	Best Case	Average Case	Worst Case	Space Complexity	Stable?	In-Place?
Bubble Sort	$\mathcal{O}(n)$	$\mathcal{O}(n^2)$	$\mathcal{O}(n^2)$	$\mathcal{O}(1)$	Yes	Yes
Selection Sort	$\mathcal{O}(n^2)$	$\mathcal{O}(n^2)$	$\mathcal{O}(n^2)$	$\mathcal{O}(1)$	No	Yes
Insertion Sort	$\mathcal{O}(n)$	$\mathcal{O}(n^2)$	$\mathcal{O}(n^2)$	$\mathcal{O}(1)$	Yes	Yes
Merge Sort	$\mathcal{O}(n \log n)$	$\mathcal{O}(n \log n)$	$\mathcal{O}(n \log n)$	$\mathcal{O}(n)$	Yes	No
Quick Sort	$\mathcal{O}(n \log n)$	$\mathcal{O}(n \log n)$	$\mathcal{O}(n^2)$	$\mathcal{O}(\log n)$	No	Yes

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📁 Repository Structure

DSA_Using_Python/
│
├── .gitignore              # Ignores bytecode, caches, and environment configs
├── BubbleSort.md           # Theoretical foundation, dry-runs & Bubble Sort notes
├── complexity.md           # Comprehensive Time & Space Complexity analysis guide
├── InsertionSort.md        # Insertion Sort theory, pass traces & complexity notes
├── LICENSE                 # MIT Open-Source License
├── README.md               # Repository documentation and navigation guide
├── SelectionSort.md        # Selection Sort theory, pass traces & complexity notes
│
└── Programs/               # Executable Python implementations
    ├── bubble_sort.py      # Bubble Sort (PEP 8 standard naming)
    ├── BubleSort.py        # Bubble Sort implementation
    ├── insertion_sort.py   # Insertion Sort (PEP 8 standard naming)
    ├── InsertionSort.py    # Insertion Sort implementation
    ├── selection_sort.py   # Selection Sort (PEP 8 standard naming)
    └── SelectionSort.py    # Selection Sort implementation

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⚡ Getting Started

Prerequisites

• Python 3.8 or higher installed on your system.

1. Clone the Repository

git clone https://github.com/AnilYadav17/DSA_Using_Python.git
cd DSA_Using_Python

2. Run an Algorithm

Each program contains an interactive demo mode:

# Run Bubble Sort demo
python3 Programs/bubble_sort.py

# Run Selection Sort demo
python3 Programs/selection_sort.py

# Run Insertion Sort demo
python3 Programs/insertion_sort.py

Example Run (Bubble Sort):

==================================================
               BUBBLE SORT DEMO
==================================================
Enter numbers separated by space (or press Enter for default demo): 64 34 25 12 22 11 90
Original Array : [64, 34, 25, 12, 22, 11, 90]
Sorted Array   : [11, 12, 22, 25, 34, 64, 90]

Example Run (Selection Sort):

==================================================
              SELECTION SORT DEMO
==================================================
Enter numbers separated by space (or press Enter for default demo): 10 5 8 2 1 3
Original Array : [10, 5, 8, 2, 1, 3]
Sorted Array   : [1, 2, 3, 5, 8, 10]

Example Run (Insertion Sort):

==================================================
              INSERTION SORT DEMO
==================================================
Enter numbers separated by space (or press Enter for default demo): 12 11 13 5 6
Original Array : [12, 11, 13, 5, 6]
Sorted Array   : [5, 6, 11, 12, 13]

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🛠️ Standards & Best Practices

All implementations in this repository follow strict coding principles:

• Type Hinting: Using Python's typing module for clear parameter and return types.
• Documentation: Google/Sphinx style docstrings describing time and space complexities.
• Modularity: Algorithms are encapsulated in reusable functions with __main__ entry points.

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🤝 Contributing

Contributions, issues, and feature requests are welcome!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/NewAlgorithm)
3. Commit your Changes (git commit -m 'Add Selection Sort algorithm')
4. Push to the Branch (git push origin feature/NewAlgorithm)
5. Open a Pull Request

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📜 License

Distributed under the MIT License. See LICENSE for more details.

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Author: Anil Yadav • Happy Coding! 💻