keon/algorithms

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Mindmap

mindmap
  root((repo))
    What it does
      Data structures
      Algorithm implementations
      Complexity analysis
    Topics covered
      Sorting and searching
      Graphs and trees
      Dynamic programming
      Backtracking
      String matching
    How to use it
      Learn from code
      Interview prep
      Import as package
    Why it works
      Self-contained files
      Type annotations
      Clear explanations

mindmap root((repo)) What it does Data structures Algorithm implementations Complexity analysis Topics covered Sorting and searching Graphs and trees Dynamic programming Backtracking String matching How to use it Learn from code Interview prep Import as package Why it works Self-contained files Type annotations Clear explanations

Things people build with this

USE CASE 1

Study classic algorithms and data structures with clean, readable Python implementations.

USE CASE 2

Prepare for technical job interviews by reviewing sorting, searching, graphs, and dynamic programming.

USE CASE 3

Import individual algorithms into your own Python projects as a reference library.

Tech stack

Python 3

Getting it running

Difficulty · easy Time to first run · 5min

Use freely for any purpose, including commercial use, as long as you keep the copyright notice.

In plain English

This repository is a clean, well-documented collection of data structures and algorithms implemented in Python 3. Data structures are ways of organizing information in memory (like lists, trees, or graphs), and algorithms are step-by-step procedures for solving specific computing problems (like sorting a list, finding the shortest path between two points, or searching for a value efficiently). The project is designed to be read and learned from, not just run. Every file is self-contained with explanations, type annotations (labels that say what kind of data a function expects), and notes on complexity (how fast or slow the solution is as input grows). It covers a broad range of topics: sorting and searching, graphs, trees, dynamic programming (breaking large problems into smaller solved subproblems), backtracking (exploring options and undoing bad choices), string matching, mathematical algorithms, and more. You would use this if you are a student learning computer science fundamentals, a developer preparing for technical job interviews, or someone who wants a reference implementation of a classic algorithm written in clean, readable Python. It is also installable as a package via pip, so you can import individual algorithms directly into your own Python projects. The tech stack is Python 3.

Copy-paste prompts

Prompt 1

Show me how to implement a binary search tree in Python with insert, delete, and search methods.

Prompt 2

I need to understand dynamic programming, walk me through the longest common subsequence algorithm from this repo.

Prompt 3

How do I use the graph algorithms from this repo to find the shortest path between two nodes?

Prompt 4

Explain the backtracking approach used in this repo for solving the N-queens problem.

Prompt 5

I'm interviewing soon, which sorting algorithms in this repo should I memorize and why?

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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.