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keon/algorithms

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25,269 stars·4,729 forks·Python·mit·0 viewskeon.kim/algorithms↗

Algorithms

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 Implementations - Python 100.0%
  • 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.
  • Idiomatic Implementations - 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.

    • 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.