# zedr/clean-code-python

:bathtub: Clean Code concepts adapted for Python

Repository: https://github.com/zedr/clean-code-python
Canonical: https://ross.abutalabs.com/products/clean-code-python
Language: Python
License: MIT
License Family: permissive
Last push: 2023-06-10T14:49:36+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": 3284, "days_push": 1180, "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 4835, forks 823 (observed 2026-08-28T04:09:00.643437+00:00)

## What it is
A guide adapting Robert C. Martin's Clean Code principles to Python, covering variables, functions, classes, and SOLID design principles with good/bad code examples. It is a documentation-style learning resource, not a library or tool.

## Use cases
- learn clean code practices in python
- improve readability of my python code
- understand SOLID principles with python examples
- refactor messy python code
- python coding guidelines for my team
- naming conventions and function design tips for python

## When to choose
- you want concise, example-driven guidance on writing readable Python
- you are teaching or onboarding developers on Python best practices
- you want a Python adaptation of Clean Code concepts

## When to avoid
- you need an executable linter or formatter rather than prose guidelines
- you need an official PEP 8 style guide
- you need language-agnostic Clean Code material

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: developer-tools, documentation
- domain: developer-tools, tutorials, programming-languages
- platform: python
- tags: clean-code, best-practices, code-quality, software-craftsmanship, solid-principles, style-guide

## Member repositories
- zedr/clean-code-python (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:00.643437+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 2026-08-29T18:18:29.702826+00:00, confidence not recorded.
  - readme: https://github.com/zedr/clean-code-python (fetched 2026-08-28T04:09:00.643437+00:00, sha ab7bd93f7db6)
- Data as of 2026-08-30T08:39:29.467469+00:00.