# Matplotlib

matplotlib: plotting with Python

Repository: https://github.com/matplotlib/matplotlib
Canonical: https://ross.abutalabs.com/products/matplotlib
Homepage: https://matplotlib.org/stable/
Language: Python
License Family: other
Topics: matplotlib, data-visualization, data-science, python, qt, wx, gtk, plotting, tk
Last push: 2026-08-25T14:43:10+00:00
Link (homepage): https://matplotlib.org/stable/
Link (site_page): https://matplotlib.org/stable/users/release_notes.html
Link (site_page): https://matplotlib.org/stable/install/dependencies.html
Link (site_page): https://matplotlib.org/stable/install/index.html
Link (site_page): https://matplotlib.org/stable/users/faq.html

## Health v2 (maintenance only)
Score: 93/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 81, longevity 100
- inputs: {"age_days": 5674, "days_push": 8, "days_rel": 46, "gap_med": 39, "n_releases_24m": 12}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 23110, forks 8452 (observed 2026-08-28T04:11:34.432023+00:00)

## What it is
Matplotlib is a comprehensive Python library for creating static, animated, and interactive visualizations, producing publication-quality figures in many hardcopy formats and GUI backends. The ecosystem also includes ipympl, a Jupyter widget extension for interactive matplotlib figures in notebooks.

## Use cases
- plot line charts and scatter plots in python
- create publication-quality figures for a paper
- make animated plots in python
- embed interactive charts in a jupyter notebook
- export plots as pdf, svg, or png
- visualize numpy arrays and dataframes
- build dashboards with qt or tk plot windows

## When to choose
- you need fine-grained control over every element of a plot
- you want publication-quality static figures in many formats
- you need a mature, widely-documented plotting library for python
- you want interactive plots inside jupyter notebooks via ipympl

## When to avoid
- you want declarative, web-native interactive dashboards (consider plotly or bokeh)
- you need GPU-accelerated rendering of massive point clouds
- you want a high-level statistical plotting API with minimal code (consider seaborn)

## Facets
- artifact type: library
- maturity: stable
- function: data-visualization, charts, gui
- domain: data-visualization, data-science, developer-tools
- platform: python, cross-platform, windows
- tags: plotting, publication-quality-figures, jupyter, animation, numpy, linux, macos

## Member repositories
- matplotlib/matplotlib (main) score 93
- matplotlib/cheatsheets (docs) score 75
- matplotlib/ipympl (plugin) score 82

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:34.432023+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 2026-08-29T16:56:47.702281+00:00, confidence not recorded.
  - readme: https://github.com/matplotlib/matplotlib (fetched 2026-08-28T04:11:34.432023+00:00, sha 8bc0cde40dc0)
  - homepage: https://matplotlib.org/stable/ (fetched 2026-08-29T07:54:47.052479+00:00, sha f2ff25eb0fc6)
  - site_page: https://matplotlib.org/stable/install/dependencies.html (fetched 2026-08-29T07:54:47.063663+00:00, sha c381ef25850f)
  - site_page: https://matplotlib.org/stable/install/index.html (fetched 2026-08-29T07:54:47.066016+00:00, sha 5ce747243ed0)
  - registry_pypi: https://pypi.org/pypi/ipympl/json (fetched 2026-08-29T07:54:47.069866+00:00, sha d814da6242ef)
  - site_page: https://matplotlib.org/stable/users/release_notes.html (fetched 2026-08-29T07:54:47.061697+00:00, sha 44136fa355b3)
  - site_page: https://matplotlib.org/stable/users/faq.html (fetched 2026-08-29T07:54:47.068044+00:00, sha b779d8205e0d)
- Data as of 2026-08-30T08:39:29.467469+00:00.