# joeyajames/Python

Python code for YouTube videos.

Repository: https://github.com/joeyajames/Python
Canonical: https://ross.abutalabs.com/products/joeyajames-python
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2024-05-15T05:21:53+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4098, "days_push": 840, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1883, forks 1389 (observed 2026-08-28T04:05:48.777776+00:00)

## What it is
A collection of Python 3 code samples accompanying Joe James's YouTube tutorial videos, covering algorithms, data structures, and other programming topics. It is intended purely for educational self-study rather than production use.

## Use cases
- learn python algorithms with example code
- study data structures in python
- follow along with youtube programming tutorials
- self-study python programming basics
- find reference implementations of common algorithms
- practice coding exercises for beginners

## When to choose
- you are a beginner learning Python through video tutorials
- you want simple, readable example implementations of algorithms and data structures
- you want MIT-licensed educational code to study and modify

## When to avoid
- you need production-ready, tested, or maintained libraries
- you need a specific algorithm implementation with performance guarantees
- you want a structured course rather than loose code files

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: developer-tools
- domain: tutorials, education, programming-languages
- platform: python
- tags: python-tutorials, youtube, algorithms, data-structures, self-study, educational-code

## Member repositories
- joeyajames/Python (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:48.777776+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:13:41.864003+00:00, confidence not recorded.
  - readme: https://github.com/joeyajames/Python (fetched 2026-08-28T04:05:48.777776+00:00, sha 2d8b0669d45d)
- Data as of 2026-08-30T08:39:29.467469+00:00.