Matplotlib: Difference between revisions

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{{Short description|Library for creating static, animated, and interactive visualizations in Python.}}

{{Infobox software

| name = Matplotlib

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| website = {{URL|https://matplotlib.org}}

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'''Matplotlib''' (portmanteau of [[MATLAB]], plot, and library<bigref>M{{cite web | url=https://matplotlib.org/stable/project/history.html | title=History — Matplotlib 3.9.2 documentation }}</bigref>atplotlib''') is a [[Plotter|plotting]] [[Library (computer science)|library]] for the [[Python (programming language)|Python]] [[programming language]] and its [[Numerical analysis|numerical mathematics]] extension [[NumPy]]. It provides an [[Object-oriented programming|object-oriented]] [[API]] for embedding plots into applications using general-purpose [[GUI toolkit]]s like [[Tkinter]], [[wxPython]], [[Qt (software)|Qt]], or [[GTK]]. There is also a [[Procedural programming|procedural]] "pylab" interface based on a [[state machine]] (like [[OpenGL]]), designed to closely resemble that of [[MATLAB]], though its use is discouraged.<ref>{{cite web|title=API Overview|url=https://matplotlib.org/stable/api/index.html#module-pylab|publisher=matplotlib.org}}</ref> [[SciPy]] makes use of Matplotlib.

Matplotlib was originally written by [[John D. Hunter]]. Since then it has had an active development community<ref>{{cite web |url=https://matplotlib.org/users/github_stats.html |title=Matplotlib github stats |publisher=matplotlib.org }}</ref> and is distributed under a [[BSD licenses|BSD-style license]]. Michael Droettboom was nominated as matplotlib's lead developer shortly before John Hunter's death in August 2012<ref>{{cite web |url=http://matplotlib.1069221.n5.nabble.com/ANN-Michael-Droettboom-matplotlib-lead-developer-td5037.html |title=Announcing Michael Droettboom as the lead Matplotlib developer |publisher=matplotlib.org |access-date=2013-04-24 |archive-date=2020-10-27 |archive-url=https://web.archive.org/web/20201027122844/http://matplotlib.1069221.n5.nabble.com/ANN-Michael-Droettboom-matplotlib-lead-developer-td5037.html |url-status=dead }}</ref> and was further joined by Thomas Caswell.<ref>{{Cite news|url=https://www.numfocus.org/blog/matplotlib-lead-developer-explains-why-he-cant-fix-the-docs-but-you-can|title=Matplotlib Lead Developer Explains Why He Can't Fix the Docs—But You Can – NumFOCUS|date=2017-10-05|work=NumFOCUS|access-date=2018-04-11|language=en-US}}</ref><ref>{{Cite web|url=https://matplotlib.org/users/credits.html|title=Credits – Matplotlib 2.2.2 documentation|website=matplotlib.org|access-date=2018-04-11}}</ref> Matplotlib is a [[NumFOCUS]] fiscally sponsored project.<ref>{{cite web|title=NumFOCUS Sponsored Projects|url=https://numfocus.org/sponsored-projects|publisher=NumFOCUS|access-date=2021-10-25}}</ref>

==Comparison withUsage MATLAB==

Pyplot is a Matplotlib module that provides a MATLAB-like interface.<ref>{{Cite web|url=https://matplotlib.org/index.html|title=Matplotlib: Python plotting — Matplotlib 3.2.0 documentation|website=matplotlib.org|access-date=2020-03-14}}</ref> Matplotlib is designed to be as usable as MATLAB, with the ability to use Python, and the advantage of being free and [[Open source|open-source]].{{Citation needed|date=September 2023}}

Matplotlib is used in scientific research as a tool for data visualization. For example, the [[Event Horizon Telescope]] collaboration used Matplotlib to produce visualizations during the effort to create the first image of a black hole.<ref name="NumPyBlackHole">{{cite web |title=Case Study: First Image of a Black Hole |url=https://numpy.org/case-studies/blackhole-image/ |website=NumPy |access-date=22 April 2025}}</ref> Matplotlib also underpins the plotting functionality of many scientific [[Python (programming language)|Python]] libraries (for instance, [[pandas (software)|pandas]] uses Matplotlib as its default backend for plotting). Its importance to the scientific community has been acknowledged by institutions such as [[NASA]], which in 2024 awarded a grant to support Matplotlib’s continued development as part of an initiative to fund widely used open-source scientific software.<ref name="NASAOpenSource">{{cite web |title=NASA Funds Open-Source Software Underpinning Scientific Innovation |url=https://www.nasa.gov/news-release/nasa-funds-open-source-software-underpinning-scientific-innovation/ |website=NASA |access-date=22 April 2025}}</ref>

==Plot Types==

[[Matplotlib]] supports various types of 2 dimensional and 3 dimensional plots. The support for two dimensional plots is robust. The support for three dimensional plots was added later and while it is good, it is not as robust as 2 dimensional plots.

[[File:Matplotlib in jupyter notebook.png|thumb|258x258px|A scatter plot created in Jupyter Notebook using Matplotlib]]

===Examples===

<gallery mode="packed">

File:Synchrotron Functions.svg|Line plot

File:Matplotlib histogram v.svg|Histogram

File:Matplotlib scatter v.svg|Scatter plot

File:Matplotlib 3d v.svg|3D plot

File:Mpl example qbo.svg|Image plot

File:Mpl example Helmoltz coils.svg|Contour plot

File:Weight Growth of RN First Rate Line-of-Battle Ships 1630-1875.svg|Scatter plot

File:Logarithmic Spiral Pylab.svg|Polar plot

File:Temp-sunspot-co2.svg|Line plot

File:Mpl example Rosenbrock function.svg|3-D plot

File:Mandelbrot set, plotted with Matplotlib.svg|Image plot

</gallery>

In education and data science, Matplotlib is frequently used to teach programming and data visualization. It integrates with [[Jupyter Notebook]], allowing students and instructors to generate inline plots and interactively explore data within a notebook environment.<ref name="JupyterNature">{{cite news |title=Interactive notebooks: Sharing the code |url=https://www.nature.com/articles/d41586-018-07196-1 |work=Nature |date=18 September 2018 |access-date=22 April 2025}}</ref> Many educational institutions incorporate Matplotlib into their curricula for teaching STEM concepts,<ref name="MatplotlibTutorial">{{cite web |title=Pyplot tutorial |url=https://matplotlib.org/stable/tutorials/introductory/pyplot.html |website=Matplotlib |access-date=22 April 2025}}</ref> and it is widely featured in tutorials, workshops, and open online courses as a primary plotting library.

==Animations==

Matplotlib-animation<ref>{{cite web|url=https://matplotlib.org/stable/users/explain/animations/animations.html|title=Animations using Matplotlib|publisher=matplotlib.org|accessdate=30 Aug 2024}}</ref> capabilities are intended for visualizing how certain data changes. However, one can use the functionality in any way required.

These animations are defined as a function of frame number (or time). In other words, one defines a function that takes a frame number as input and defines/updates the matplotlib-figure based on it.

<blockquote> The time at the beginning of a frame-number since the start of animation can be calculated as - <math>\text{time} = \frac{\text{frame-number}-1}{\text{FPS}}</math></blockquote>

==Toolkits==

Several toolkits are available which extend Matplotlib functionality. Some are separate [[Download|downloads]], others ship with the Matplotlib [[source code]] but have external dependencies.<ref>{{cite web |url=https://matplotlib.org/thirdpartypackages/index.html |title=Toolkits |publisher=matplotlib.org}}</ref>

*[[Web mapping|Basemap]]: map plotting with various [[Map projection|map projections]], [[Coast|coastlines]], and political boundaries<ref name=basemap>{{cite web|last=Whitaker|first=Jeffrey|title=The Matplotlib Basemap Toolkit User's Guide (v. 1.0.5)|url=https://matplotlib.org/basemap/index.html|work=Matplotlib Basemap Toolkit documentation|accessdate=24 April 2013}}</ref>

*Cartopy: a mapping library featuring object-oriented map projection definitions, and arbitrary point, line, [[polygon]] and image transformation capabilities.<ref name=cartopy>{{cite web|last=Elson|first=Philip|title=Cartopy|url=http://scitools.org.uk/cartopy/docs/latest/|accessdate=24 April 2013}}</ref> (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'')<ref name=tikzplotlib>{{cite web|last=Schlömer|first=Nico|title=tikzplotlib|website=[[GitHub]]|url=https://github.com/nschloe/tikzplotlib|accessdate=7 November 2016}}</ref>

* 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 (software)|Pandas]]

* GeoPandas:<ref>{{Cite web |title=GeoPandas 0.14.4 — GeoPandas 0.14.4+0.g60c9773.dirty documentation |url=https://geopandas.org/en/stable/ |access-date=2024-04-29 |website=geopandas.org}}</ref> simplifies geospatial work in Python without needing a spatial database like PostGIS<ref>{{Citation |last=Jordahl |first=Kelsey |title=geopandas: Geographic pandas extensions |url=https://pypi.org/project/geopandas/ |access-date=2024-04-29}}</ref>

* Cartopy: streamlines map creation in matplotlib by enabling users to specify a projection and add coastlines with a single line of code<ref>{{Cite web |title=Using cartopy with matplotlib — cartopy 0.15.0 documentation |url=https://scitools.org.uk/cartopy/docs/v0.15/matplotlib/intro.html |access-date=2024-04-30 |website=scitools.org.uk}}</ref>

==Related projects==

* Biggles<ref>{{cite web |url=http://biggles.sourceforge.net/ |title=Bigglessimple, elegant python plotting |publisher=biggles.sourceforge.net |accessdate=24 November 2010}}</ref>

* Chaco<ref>{{cite web |url=http://docs.enthought.com/chaco/ |title=Chaco |publisher=code.enthought.com}}</ref>

* [[DISLIN]]

* [[GNU Octave]]

* [[Plotly]] – for interactive, online Matplotlib and Python graphs

* [https://github.com/dkogan/gnuplotlib/ gnuplotlib] – plotting for numpy with a gnuplot backend

* [[Gnuplot]]-py<ref>{{cite web |url=http://gnuplot-py.sourceforge.net/ |title=Gnuplot.py on |publisher=gnuplot-py.sourceforge.net |accessdate=24 November 2010}}</ref>

* [[PLplot]] – Python bindings available

* [[SageMath]] – uses <code>Matplotlib</code> to draw plots

* SciPy (modules <code>plt</code> and <code>gplt</code>)

* [[Plotly]] – for interactive, online Matplotlib and Python graphs

* Bokeh<ref>{{Cite web|url=https://docs.bokeh.org/en/latest/|title=Bokeh 2.0.0 Documentation|website=docs.bokeh.org|access-date=2020-03-14}}</ref> – Python interactive visualization library that targets modern web browsers for presentation

==References==

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[[Category:Science software that uses GTK]]

[[Category:Science software that uses Qt]]

[[Category:Free data and information visualization software]]