# coells/100days

100 days of algorithms

Repository: https://github.com/coells/100days
Canonical: https://ross.abutalabs.com/products/100days
Language: Jupyter Notebook
License Family: other
Last push: 2018-05-11T17:11:45+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3449, "days_push": 3036, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 7475, forks 1159 (observed 2026-08-28T04:10:00.163831+00:00)

## What it is
A collection of Jupyter notebooks accompanying the '100 days of algorithms' Medium challenge, implementing one algorithm per day in Python. It serves as an educational resource with live, runnable code rather than production-quality software.

## Use cases
- learn algorithms with runnable python notebooks
- study implementations of classic algorithms
- follow a 100-day algorithm learning challenge
- find example code for sorting, graphs, and math algorithms
- use notebooks as teaching material for algorithms

## When to choose
- you want hands-on, runnable notebook examples of many algorithms
- you are learning algorithms and prefer code over theory
- you want a broad survey of algorithm implementations in Python

## When to avoid
- you need production-ready, bug-free, or optimized implementations
- you need a maintained library with a license for commercial use
- you need up-to-date compatibility with modern Python versions

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: developer-tools, data-visualization
- domain: tutorials, education
- platform: python, cross-platform
- tags: jupyter-notebooks, algorithms, learning-challenge, educational

## Member repositories
- coells/100days (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:00.163831+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 2026-08-29T17:37:36.868178+00:00, confidence not recorded.
  - readme: https://github.com/coells/100days (fetched 2026-08-28T04:10:00.163831+00:00, sha 07f5549a96f2)
- Data as of 2026-08-30T08:39:29.467469+00:00.