Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK. There is also a procedural "pylab" interface based on a state machine (like OpenGL), designed to closely resemble that of MATLAB, though its use is discouraged.[3] SciPy makes use of Matplotlib.

Matplotlib was originally written by John D. Hunter. Since then it has had an active development community[4] and is distributed under a BSD-style license. Michael Droettboom was nominated as matplotlib's lead developer shortly before John Hunter's death in August 2012[5] and was further joined by Thomas Caswell.[6][7] Matplotlib is a NumFOCUS fiscally sponsored project.[8]

Comparison with MATLAB

Pyplot is a Matplotlib module that provides a MATLAB-like interface.[9] Matplotlib is designed to be as usable as MATLAB, with the ability to use Python, and the advantage of being free and open-source.[citation needed]



Several toolkits are available which extend Matplotlib functionality. Some are separate downloads, others ship with the Matplotlib source code but have external dependencies.[10]

  • Basemap: map plotting with various map projections, coastlines, and political boundaries[11]
  • Cartopy: a mapping library featuring object-oriented map projection definitions, and arbitrary point, line, polygon and image transformation capabilities.[12] (Matplotlib v1.2 and above)
  • Excel tools: utilities for exchanging data with Microsoft Excel
  • GTK tools: interface to the GTK library
  • Qt interface
  • Mplot3d: 3-D plots
  • Natgrid: interface to the natgrid library for gridding irregularly spaced data.
  • tikzplotlib: export to Pgfplots for smooth integration into LaTeX documents (formerly known as matplotlib2tikz)[13]
  • Seaborn: provides an API on top of Matplotlib that offers sane choices for plot style and color defaults, defines simple high-level functions for common statistical plot types, and integrates with the functionality provided by Pandas

Related projects

  • Biggles[14]
  • Chaco[15]
  • GNU Octave
  • gnuplotlib – plotting for numpy with a gnuplot backend
  • Gnuplot-py[16]
  • PLplot – Python bindings available
  • SageMath – uses Matplotlib to draw plots
  • SciPy (modules plt and gplt)
  • Plotly – for interactive, online Matplotlib and Python graphs
  • Bokeh[17] – Python interactive visualization library that targets modern web browsers for presentation


  1. ^ "Copyright Policy".
  2. ^ "Release 3.8.0". 15 September 2023. Retrieved 18 September 2023.
  3. ^ "API Overview".{{cite web}}: CS1 maint: url-status (link)
  4. ^ "Matplotlib github stats".
  5. ^ "Announcing Michael Droettboom as the lead Matplotlib developer". Archived from the original on 2020-10-27. Retrieved 2013-04-24.
  6. ^ "Matplotlib Lead Developer Explains Why He Can't Fix the Docs—But You Can – NumFOCUS". NumFOCUS. 2017-10-05. Retrieved 2018-04-11.
  7. ^ "Credits – Matplotlib 2.2.2 documentation". Retrieved 2018-04-11.
  8. ^ "NumFOCUS Sponsored Projects". NumFOCUS. Retrieved 2021-10-25.
  9. ^ "Matplotlib: Python plotting — Matplotlib 3.2.0 documentation". Retrieved 2020-03-14.
  10. ^ "Toolkits".
  11. ^ Whitaker, Jeffrey. "The Matplotlib Basemap Toolkit User's Guide (v. 1.0.5)". Matplotlib Basemap Toolkit documentation. Retrieved 24 April 2013.
  12. ^ Elson, Philip. "Cartopy". Retrieved 24 April 2013.
  13. ^ Schlömer, Nico. "tikzplotlib". GitHub. Retrieved 7 November 2016.
  14. ^ "Bigglessimple, elegant python plotting". Retrieved 24 November 2010.
  15. ^ "Chaco".
  16. ^ " on". Retrieved 24 November 2010.
  17. ^ "Bokeh 2.0.0 Documentation". Retrieved 2020-03-14.

External links