# jvns/pandas-cookbook

Recipes for using Python's pandas library

Repository: https://github.com/jvns/pandas-cookbook
Canonical: https://ross.abutalabs.com/products/pandas-cookbook
Language: Jupyter Notebook
License Family: other
Last push: 2024-10-24T02:01:47+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4638, "days_push": 679, "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 7108, forks 2343 (observed 2026-08-28T04:09:55.960455+00:00)

## What it is
A collection of Jupyter Notebook tutorials ('recipes') teaching Python's pandas library through real-world datasets like NYC 311 calls and Montréal bike path and weather data. It is designed for beginners who find the official pandas docs hard to get started with.

## Use cases
- learn pandas for data analysis
- get started with pandas dataframes
- find pandas examples with real-world data
- learn groupby and aggregation in pandas
- learn to read CSV files with pandas
- practice exploratory data analysis in Jupyter

## When to choose
- you are new to pandas and want concrete, runnable examples
- you prefer learning from notebooks with included datasets
- you want a quick tour of Jupyter plus pandas basics

## When to avoid
- you need comprehensive pandas API reference documentation
- you need an actively developed tool rather than a tutorial
- you need advanced or up-to-date pandas features

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: data-science, developer-tools
- domain: data-science, tutorials, education
- platform: python, cross-platform
- tags: pandas, jupyter-notebook, cookbook, data-analysis, tutorial

## Member repositories
- jvns/pandas-cookbook (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:55.960455+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 2026-08-29T17:40:07.779540+00:00, confidence not recorded.
  - readme: https://github.com/jvns/pandas-cookbook (fetched 2026-08-28T04:09:55.960455+00:00, sha 190297594270)
- Data as of 2026-08-30T08:39:29.467469+00:00.