seaborn
Statistical data visualization in Python observed · 2026-08-28
Health v2 · maintenance only
64/100
- Activity 91
- Release rhythm 8
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 5189
- days_rel: n/a
- days_push: 59
- n_releases_24m: 0
Adoption not part of the score
14005 stars · 2126 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
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
library · maturity stable
data-visualization charts data-visualization data-science analytics python cross-platform matplotlib pandas statistical-graphics plotting
6 sources
- readme: https://github.com/mwaskom/seaborn · fetched 2026-08-28 · 99b5e7134005
- homepage: https://seaborn.pydata.org · fetched 2026-08-29 · 9b72b69e6987
- site_page: https://seaborn.pydata.org/installing.html · fetched 2026-08-29 · 53447c5e654b
- registry_pypi: https://pypi.org/pypi/seaborn/json · fetched 2026-08-29 · 8c481108ca09
- site_page: https://seaborn.pydata.org/whatsnew/index.html · fetched 2026-08-29 · 73740c596f78
- site_page: https://seaborn.pydata.org/faq.html · fetched 2026-08-29 · a783c957ebd4
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| mwaskom/seaborn | main | 64 |
| mwaskom/seaborn-data | docs | 32 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem