# seaborn

Statistical data visualization in Python

Repository: https://github.com/mwaskom/seaborn
Canonical: https://ross.abutalabs.com/products/seaborn
Homepage: https://seaborn.pydata.org
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: python, data-visualization, data-science, matplotlib, pandas
Last push: 2026-07-06T02:11:55+00:00
Link (homepage): https://seaborn.pydata.org
Link (site_page): https://seaborn.pydata.org/installing.html
Link (site_page): https://seaborn.pydata.org/whatsnew/index.html
Link (site_page): https://seaborn.pydata.org/faq.html

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 91, release rhythm 8, longevity 100
- inputs: {"age_days": 5189, "days_push": 59, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 14005, forks 2126 (observed 2026-08-28T04:11:05.293061+00:00)

## What it is
Seaborn is a Python library for statistical data visualization built on top of matplotlib, offering a high-level interface for creating attractive and informative statistical graphics. It integrates closely with pandas DataFrames and supports relational, distributional, categorical, and regression plots as well as multi-plot grids and theming.

## Use cases
- plot statistical charts from a pandas dataframe
- create distribution and histogram plots in python
- make categorical box plots and violin plots
- visualize regression relationships with confidence intervals
- build a pairplot or correlation heatmap for exploratory data analysis
- style matplotlib figures with better themes and color palettes
- create multi-panel facet grids for comparing groups

## When to choose
- you need quick, attractive statistical plots from pandas data with minimal code
- you are doing exploratory data analysis in Jupyter notebooks
- you want sensible defaults for themes, palettes, and statistical estimation
- you need faceted multi-plot grids for comparing subsets of data

## When to avoid
- you need fully custom, pixel-level control over figure layout (use matplotlib directly)
- you need interactive or web-based dashboards (consider plotly or bokeh)
- you are plotting very large datasets where interactivity or WebGL rendering matters

## Facets
- artifact type: library
- maturity: stable
- function: data-visualization, charts
- domain: data-visualization, data-science, analytics
- platform: python, cross-platform
- tags: matplotlib, pandas, statistical-graphics, plotting

## Member repositories
- mwaskom/seaborn (main) score 64
- mwaskom/seaborn-data (docs) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:05.293061+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 2026-08-29T17:12:53.092205+00:00, confidence not recorded.
  - readme: https://github.com/mwaskom/seaborn (fetched 2026-08-28T04:11:05.293061+00:00, sha 99b5e7134005)
  - homepage: https://seaborn.pydata.org (fetched 2026-08-29T08:07:00.777867+00:00, sha 9b72b69e6987)
  - site_page: https://seaborn.pydata.org/installing.html (fetched 2026-08-29T08:07:00.787057+00:00, sha 53447c5e654b)
  - registry_pypi: https://pypi.org/pypi/seaborn/json (fetched 2026-08-29T08:07:00.793158+00:00, sha 8c481108ca09)
  - site_page: https://seaborn.pydata.org/whatsnew/index.html (fetched 2026-08-29T08:07:00.789196+00:00, sha 73740c596f78)
  - site_page: https://seaborn.pydata.org/faq.html (fetched 2026-08-29T08:07:00.790815+00:00, sha a783c957ebd4)
- Data as of 2026-08-30T08:39:29.467469+00:00.