# cosmologicon/pywat

Python wats

Repository: https://github.com/cosmologicon/pywat
Canonical: https://ross.abutalabs.com/products/pywat
License Family: other
Last push: 2019-03-17T17:44:53+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": 4099, "days_push": 2726, "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 1216, forks 62 (observed 2026-08-28T04:04:01.194891+00:00)

## What it is
A curated collection of 'wat' code snippets demonstrating counterintuitive edge cases in Python, inspired by Gary Bernhardt's famous WAT talk. It includes explanations of why each behavior occurs and a quiz for testing your understanding of Python's quirks.

## Use cases
- learn surprising Python edge cases
- prepare for Python interview trick questions
- understand why Python behaves unexpectedly with operators and types
- quiz myself on Python language internals
- find funny examples of Python gotchas to share
- deepen my understanding of Python semantics

## When to choose
- you want a fun, example-driven way to learn Python's edge cases
- you are teaching Python and want memorable gotcha examples
- you want to test your Python knowledge with a quiz

## When to avoid
- you need a reference manual or comprehensive language documentation
- you want production tooling or libraries
- you are learning Python from scratch and need structured tutorials

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: developer-tools
- domain: programming-languages, tutorials, education
- platform: python
- tags: python-wats, language-quirks, edge-cases, quiz, gotchas, education

## Member repositories
- cosmologicon/pywat (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:01.194891+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 2026-08-30T06:17:10.622522+00:00, confidence not recorded.
  - readme: https://github.com/cosmologicon/pywat (fetched 2026-08-28T04:04:01.194891+00:00, sha b73bf04bfc1d)
- Data as of 2026-08-30T08:39:29.467469+00:00.