The Algorithms - Python

All algorithms implemented in Python - for education 📚

Implementations are for learning purposes only. They may be less efficient than the implementations in the Python standard library. Use them at your discretion.

• About
• Features
• Getting Started
  • Installation
  • Usage
• Algorithm Categories
• Community Channels
• Contributing
• License
• List of Algorithms

This repository contains Python implementations of various algorithms and data structures for educational purposes. Whether you're a student learning algorithms, a developer preparing for technical interviews, or someone interested in computer science fundamentals, this collection provides clear and well-documented examples.

This repository includes implementations of algorithms across multiple categories:

• Sorting Algorithms: Bubble sort, Quick sort, Merge sort, Heap sort, and more
• Searching Algorithms: Binary search, Linear search, Jump search, Interpolation search
• Data Structures: Linked lists, Stacks, Queues, Trees, Graphs, Hash tables
• Graph Algorithms: BFS, DFS, Dijkstra's algorithm, Floyd-Warshall, Bellman-Ford
• Dynamic Programming: Knapsack, Longest Common Subsequence, Edit Distance
• Machine Learning: Neural networks, Linear regression, K-means clustering
• Mathematical Algorithms: Prime number algorithms, GCD, LCM, Number theory
• String Algorithms: Pattern matching, String manipulation, Parsing
• Cryptography: Various cipher implementations
• Computer Vision: Image processing algorithms
• And many more!

All implementations include:

• Clear documentation and explanations
• Type hints for better code readability
• Doctests for validation
• Educational comments

1. Clone the repository

   git clone https://github.com/TheAlgorithms/Python.git
   cd Python

2. Set up a virtual environment (recommended)

   python -m venv venv
   
   # On Windows
   venv\Scripts\activate
   
   # On macOS/Linux
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

Each algorithm is self-contained in its own file. You can run any algorithm directly or import it into your own projects.

Example 1: Running an algorithm directly

python sorts/quick_sort.py

Example 2: Importing and using an algorithm

from sorts.quick_sort import quick_sort

# Sort a list
numbers = [64, 34, 25, 12, 22, 11, 90]
sorted_numbers = quick_sort(numbers)
print(sorted_numbers)  # Output: [11, 12, 22, 25, 34, 64, 90]

Example 3: Running doctests

python -m doctest -v sorts/bubble_sort.py

For a complete list of all implemented algorithms organized by category, see our DIRECTORY.md file.

We are on Discord and Gitter! Community channels are a great way for you to ask questions and get help. Please join us!

We welcome contributions from the community! Before contributing:

1. 📋 Read through our Contribution Guidelines
2. 🔍 Check existing implementations to avoid duplicates
3. ✅ Ensure your code follows our coding standards
4. 🧪 Include doctests and proper documentation
5. 🎯 Make sure all tests pass before submitting

Quick Start for Contributors:

# Install pre-commit hooks
pip install pre-commit
pre-commit install

# Run tests
python -m pytest

# Format code
pip install ruff
ruff check

Contributions that are most welcome:

• New algorithm implementations
• Improvements to existing algorithms
• Better documentation and explanations
• Bug fixes
• Test coverage improvements

This project is licensed under the MIT License - see the LICENSE.md file for details.

This means you are free to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the software.

See our directory for easier navigation and a better overview of the project.