# wesm/pydata-book

Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media

Repository: https://github.com/wesm/pydata-book
Canonical: https://ross.abutalabs.com/products/pydata-book
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Last push: 2025-10-17T13:19:14+00:00

## Health v2 (maintenance only)
Score: 53/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 47, release rhythm 35, longevity 100
- inputs: {"age_days": 5177, "days_push": 320, "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 24857, forks 15680 (observed 2026-08-28T04:11:37.675488+00:00)

## What it is
Companion materials and IPython notebooks for Wes McKinney's book 'Python for Data Analysis' (3rd Edition), published by O'Reilly Media. It provides runnable Jupyter notebooks covering Python basics, NumPy, pandas, data wrangling, visualization, and time series analysis.

## Use cases
- learn pandas for data analysis
- follow along with Python for Data Analysis book exercises
- learn NumPy array basics and vectorized computation
- practice data cleaning and wrangling with notebooks
- learn time series analysis in Python
- set up a Jupyter environment for data analysis tutorials

## When to choose
- you are reading the book and want the accompanying notebooks
- you want structured, example-driven learning of pandas and NumPy
- you need a ready-to-run Jupyter setup for data analysis practice

## When to avoid
- you need production data pipeline code rather than educational material
- you want an exhaustive reference for advanced machine learning
- you need a maintained software library to depend on

## Facets
- artifact type: learning-resource
- maturity: active
- function: data-science, data-visualization, nlp
- domain: data-science, tutorials, education
- platform: python, cross-platform
- tags: pandas, numpy, jupyter-notebooks, book-materials, data-analysis

## Member repositories
- wesm/pydata-book (main) score 53

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:37.675488+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 2026-08-29T16:56:09.057444+00:00, confidence not recorded.
  - readme: https://github.com/wesm/pydata-book (fetched 2026-08-28T04:11:37.675488+00:00, sha 6e8c2f78e455)
- Data as of 2026-08-30T08:39:29.467469+00:00.