keon/algorithms
Algorithms
This repository is a structured educational archive of classic computer science algorithms and data structures implemented in Python. It serves as a reference library designed for study and technical skill development, providing clean, readable examples of fundamental computational techniques rather than production-ready software components.
The project distinguishes itself through its idiomatic approach, utilizing native language features and standard library conventions to demonstrate algorithmic logic clearly. Each implementation is organized into a hierarchical directory structure that mirrors standard computer science categories, allowing users to navigate between topics like dynamic programming, graph traversal, and bit manipulation with ease.
The collection covers a broad spectrum of problem-solving patterns, including searching, sorting, and various data structure operations, which are useful for technical interview preparation and competitive programming training. Every algorithm is provided as a standalone, self-contained script that requires no external dependencies, making the codebase accessible for quick prototyping and independent exploration.
Features
- Algorithms - Algorithms — a named example documented in this learning resource.
- Educational Codebases - Structures the codebase as a curated collection of canonical implementations intended for study rather than production integration.
- Algorithm Collections - Minimal examples of data structures and algorithms in Python keon.kim/algorithms/ ### Topics python search tree algorithm data-structure algorithms graph competitive-programming sort ### Resources Readme ### License MIT
- Algorithm Implementations - Map — a named example documented in this learning resource.
- Algorithmic References - A structured archive of classic problem-solving techniques and data structures implemented in a high-level programming language.
- Computer Science Curricula - Learning the core principles of software engineering by exploring clean and readable implementations of classic computational problem solving techniques.
- Mathematical Algorithms - Math — a named example documented in this learning resource.
- Matrix Operations - Matrix — a named example documented in this learning resource.
- Queue Implementations - Queue — a named example documented in this learning resource.
- Search Algorithms - Searching — a named example documented in this learning resource.
- Python Tooling - Python 100.0%
- Awesome List - A community-curated directory that catalogs and links out to other open-source projects, rather than a standalone tool you run yourself.
- Algorithms and Patterns - Collection of data structures and algorithms.
- Python Development Resources - Data structures and algorithms implemented in Python 3.
- Algorithms and Data Structures - Collection of common data structures and algorithmic implementations.
- Computer Science Tutorials - A curated set of examples covering core programming topics intended to help developers understand and master technical fundamentals.
- Interview Preparation Guides - Studying fundamental data structures and algorithmic patterns to build confidence and proficiency for coding assessments and technical job interviews.
- Bit Manipulation Tutorials - Provides structured learning materials and examples for understanding and implementing bitwise operations.
- Compression Algorithms - Provides educational explanations and code examples for various data compression algorithms.
- Data Structure Tutorials - Data Structures — a named example documented in this learning resource.
- Graph Algorithms - Graph — a named example documented in this learning resource.
- Greedy Algorithms - Greedy — a named example documented in this learning resource.
- Heap Algorithms - Heap — a named example documented in this learning resource.
- Linked List Algorithms - Linked List — a named example documented in this learning resource.
- Language Features - Utilizes native language features and standard library conventions to demonstrate algorithmic logic in a readable and concise manner.
- Algorithm Prototypes - Referencing verified implementations of complex logic to quickly integrate efficient data handling and processing into custom software projects.
