Matplotlib
Matplotlib is a Python 2D plotting library that produces publication-quality figures in a variety of hardcopy formats and interactive environments.
Profile
Matplotlib is an open-source Python library for creating static, animated, and interactive visualizations, ranging from simple line plots to complex publication-quality figures.
Matplotlib is a Python 2D plotting library that produces publication-quality figures in a variety of hardcopy formats and interactive environments. It was originally created by John D. Hunter in 2003 as an open-source alternative to MATLAB's plotting capabilities.
The project is now stewarded by the NumFOCUS nonprofit organization and maintained by a volunteer community of over 1,000 contributors. As of June 2026, the GitHub repository has 22,900 stars, 8,300 forks, and 54,638 commits. The library is distributed under a BSD-compatible license and is a core component of the scientific Python ecosystem, alongside NumPy, SciPy, and pandas.
Matplotlib does not generate revenue directly; it is funded through donations via NumFOCUS and GitHub Sponsors. The project has no paid employees, no venture capital funding, and no corporate ownership. Its development is driven entirely by volunteer effort, with occasional grant support from organizations like the Chan Zuckerberg Initiative and the Gordon and Betty Moore Foundation.
The library's most recent major release, version 3.10, shipped in early 2026 with improvements to text rendering, color mapping, and interactive backends. Matplotlib faces ongoing competition from newer visualization libraries such as Plotly, Seaborn (which is built on top of Matplotlib), and Bokeh, as well as from web-native tools like Observable Plot and Vega-Lite. Despite this, Matplotlib remains the most widely used plotting library in Python, with an estimated 50 million users according to Anaconda's 2025 metrics. The project's governance model remains informal, with no formal board or executive leadership, which has led to occasional delays in decision-making and feature prioritization.
Who buys this
- Data scientists and machine learning engineers using Python for exploratory data analysis and model evaluation
- Academic researchers in physics, biology, economics, and other quantitative fields who need publication-ready figures
- Educators teaching data science, statistics, and scientific computing at universities and online platforms
- Enterprise data teams building internal dashboards and reporting tools within Jupyter notebooks and Python scripts
- Open-source developers integrating plotting capabilities into larger Python frameworks and applications
Strengths and what to watch
Strengths
- Unmatched ecosystem integration: Matplotlib is a foundational dependency of nearly every major Python data science library, including pandas, Seaborn, scikit-learn, and PyTorch, ensuring near-universal compatibility and adoption.
- Mature and stable API: With over two decades of development, Matplotlib offers a comprehensive set of plotting primitives and customization options that are well-documented and widely understood by the Python community.
- Strong institutional backing: As a NumFOCUS fiscally sponsored project, Matplotlib has a legal and financial framework that ensures long-term sustainability and independence from any single corporate entity.
Watch for
- Volunteer-maintained project with no paid staff: All development, maintenance, and support is done by unpaid contributors, leading to potential burnout and slow response times for bug fixes and feature requests.
- Growing competition from modern visualization libraries: Plotly, Bokeh, and Altair offer more interactive and web-native capabilities, potentially eroding Matplotlib's user base among newer Python developers.
- No formal governance or decision-making structure: The project lacks a steering council or technical committee, which can lead to stalled discussions, unclear roadmaps, and difficulty resolving contentious design decisions.
Key Information
- Industry
- Open Source Infra - Visualizations
- Founded
- 2003
Frequently Asked Questions
What is Matplotlib and what is it used for?
Matplotlib is an open-source Python library for creating static, animated, and interactive visualizations. It produces publication-quality figures in hardcopy formats and interactive environments, ranging from simple line plots to complex charts, and is widely used in data science and research.
Who created Matplotlib and who maintains it now?
Matplotlib was created by John D. Hunter in 2003 as an open-source alternative to MATLAB's plotting. It is now stewarded by the NumFOCUS nonprofit organization and maintained by a volunteer community of over 1,000 contributors, with no paid employees or corporate ownership.
Is Matplotlib free to use and how is it funded?
Yes, Matplotlib is free and distributed under a BSD-compatible license. It does not generate revenue directly; funding comes through donations via NumFOCUS and GitHub Sponsors, with occasional grant support from organizations like the Chan Zuckerberg Initiative and the Gordon and Betty Moore Foundation.
What are the main strengths of Matplotlib compared to other libraries?
Matplotlib offers unmatched ecosystem integration as a foundational dependency for pandas, Seaborn, scikit-learn, and PyTorch. It has a mature, stable API with two decades of development, strong institutional backing from NumFOCUS, and is the most widely used Python plotting library with an estimated 50 million users.
What challenges does Matplotlib face as a volunteer-maintained project?
Matplotlib is entirely volunteer-maintained with no paid staff, leading to potential burnout and slow bug fixes. It faces growing competition from modern libraries like Plotly and Bokeh, and lacks formal governance, which can cause stalled decisions and unclear roadmaps.
Who typically uses Matplotlib and for what purposes?
Matplotlib is used by data scientists and machine learning engineers for exploratory analysis, academic researchers for publication-ready figures, educators teaching data science, enterprise teams for internal dashboards, and open-source developers integrating plotting into larger Python frameworks.
Sources
- github.com — Repository statistics: 22,900 stars, 8,300 forks, 54,638 commits, BSD license, NumFOCUS sponsorship
- www.anaconda.com — Anaconda's claim of 50 million users and 95% Fortune 500 adoption, contextualizing Matplotlib's reach as a core dependency
- siliconangle.com — Independent reporting on Anaconda's Series C, corroborating user numbers and enterprise adoption claims
- news.crunchbase.com — Context on venture funding landscape for Python ecosystem tools, though Matplotlib itself has no VC funding