# arogozhnikov/python3_with_pleasure

A short guide on features of Python 3 with examples

Repository: https://github.com/arogozhnikov/python3_with_pleasure
Canonical: https://ross.abutalabs.com/products/python3_with_pleasure
License Family: other
Last push: 2021-05-03T06:04:40+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": 3181, "days_push": 1948, "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 3595, forks 188 (observed 2026-08-28T04:08:11.320909+00:00)

## What it is
A short tutorial-style guide showcasing useful Python 3 features (pathlib, type hinting, f-strings, etc.) aimed at data scientists migrating from Python 2. It is a collection of explanatory text and code examples rather than installable software.

## Use cases
- migrate codebase from python 2 to python 3
- learn new python 3 features with examples
- understand pathlib and type hinting in python 3
- guide for data scientists switching to python 3
- find python 3 idioms for data pipelines

## When to choose
- you are a data scientist still on Python 2 planning a migration
- you want a concise, example-driven tour of Python 3 improvements
- you need quick explanations of pathlib, type hints, or f-strings

## When to avoid
- you need a runnable library or tool rather than documentation
- you already write modern Python 3 daily
- you need an exhaustive or up-to-date language reference

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation, developer-tools
- domain: programming-languages, tutorials, data-science, education
- platform: python, cross-platform
- tags: python3, migration-guide, python2-to-python3, data-science, examples

## Member repositories
- arogozhnikov/python3_with_pleasure (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:11.320909+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 2026-08-29T18:33:47.996481+00:00, confidence not recorded.
  - readme: https://github.com/arogozhnikov/python3_with_pleasure (fetched 2026-08-28T04:08:11.320909+00:00, sha 90923097d715)
- Data as of 2026-08-30T08:39:29.467469+00:00.