# brandon-rhodes/pycon-pandas-tutorial

PyCon 2015 Pandas tutorial materials

Repository: https://github.com/brandon-rhodes/pycon-pandas-tutorial
Canonical: https://ross.abutalabs.com/products/pycon-pandas-tutorial
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2025-01-28T21:57:15+00:00

## Health v2 (maintenance only)
Score: 34/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 3, release rhythm 35, longevity 100
- inputs: {"age_days": 4194, "days_push": 582, "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 1076, forks 702 (observed 2026-08-28T04:03:29.503035+00:00)

## What it is
A set of Jupyter Notebook tutorial materials for learning the pandas data-analysis library, originally taught at PyCon 2015. It includes scripts to download and convert IMDB datasets into CSVs used by the exercises.

## Use cases
- learn pandas from scratch
- follow a pandas tutorial with real datasets
- practice data analysis in Jupyter notebooks
- teach a pandas workshop
- analyze IMDB movie data with pandas
- learn matplotlib plotting alongside pandas

## When to choose
- you want a structured, video-backed introduction to pandas
- you prefer hands-on notebooks with real-world data
- you are an instructor looking for ready-made tutorial materials

## When to avoid
- you need up-to-date pandas API coverage for the latest versions
- you want a reference manual rather than a guided tutorial
- you cannot download the legacy IMDB data files the build requires

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: data-science, data-visualization, developer-tools
- domain: data-science, education, tutorials, analytics
- platform: python, cross-platform, cli
- tags: pandas, jupyter-notebook, pycon-2015, tutorial-materials, imdb-dataset, matplotlib

## Member repositories
- brandon-rhodes/pycon-pandas-tutorial (main) score 34

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:29.503035+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 2026-08-30T06:53:21.302310+00:00, confidence not recorded.
  - readme: https://github.com/brandon-rhodes/pycon-pandas-tutorial (fetched 2026-08-28T04:03:29.503035+00:00, sha 071c7d592510)
- Data as of 2026-08-30T08:39:29.467469+00:00.