# TomAugspurger/effective-pandas

Source code for my collection of articles on using pandas.

Repository: https://github.com/TomAugspurger/effective-pandas
Canonical: https://ross.abutalabs.com/products/effective-pandas
Homepage: https://leanpub.com/effective-pandas
Language: Jupyter Notebook
License: CC-BY-4.0
License Family: other
Last push: 2022-12-14T09:14:25+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": 3762, "days_push": 1358, "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 1563, forks 390 (observed 2026-08-28T04:05:04.091968+00:00)

## What it is
A collection of Jupyter notebooks accompanying Tom Augspurger's 'Effective Pandas' article series and Leanpub book on writing idiomatic pandas code. It covers topics like method chaining, indexes, performance, tidy data, visualization, and time series analysis.

## Use cases
- learn to write idiomatic pandas code
- improve my pandas method chaining style
- understand pandas indexes better
- speed up slow pandas operations
- learn tidy data reshaping in pandas
- pandas time series tutorial
- out-of-core data processing with pandas

## When to choose
- you are an intermediate pandas user wanting to write cleaner, more effective code
- you prefer learning through runnable notebooks alongside a free book
- you want guidance on performance, method chaining, and tidy data idioms

## When to avoid
- you need a complete beginner's introduction to pandas or Python
- you need up-to-date coverage of the latest pandas APIs, as the material was last updated in 2022
- you need a reference manual rather than a tutorial series

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: data-science, developer-tools
- domain: data-science, tutorials, analytics
- platform: python, cross-platform
- tags: pandas, jupyter-notebooks, tutorial-series, idiomatic-python, data-analysis, ebook

## Member repositories
- TomAugspurger/effective-pandas (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:04.091968+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 2026-08-30T04:00:15.090579+00:00, confidence not recorded.
  - readme: https://github.com/TomAugspurger/effective-pandas (fetched 2026-08-28T04:05:04.091968+00:00, sha 2f4199013ccf)
  - homepage: https://leanpub.com/effective-pandas (fetched 2026-08-29T11:29:17.554588+00:00, sha dd6719220ad4)
  - site_page: https://leanpub.com/about (fetched 2026-08-29T11:29:17.570403+00:00, sha a3ffdd2e2a46)
  - site_page: https://leanpub.com/authors (fetched 2026-08-29T11:29:17.558855+00:00, sha 8047e75e1d4b)
  - site_page: https://help.leanpub.com/reader-help/what-formats-do-leanpub-books-come-in (fetched 2026-08-29T11:29:17.562589+00:00, sha d570a1b8b3ed)
  - site_page: https://help.leanpub.com/en/articles/4405350-leanpub-author-faq (fetched 2026-08-29T11:29:17.564830+00:00, sha 6798dddde50d)
  - site_page: https://leanpub.com/help/api (fetched 2026-08-29T11:29:17.566964+00:00, sha a3f97ef691e5)
  - site_page: https://help.leanpub.com/en/articles/3788293-the-leanpub-faq (fetched 2026-08-29T11:29:17.572594+00:00, sha bab4d516dc40)
- Data as of 2026-08-30T08:39:29.467469+00:00.