<divid="unreleased-message"> You are reading an old version of the documentation (v3.4.2). For the latest version see <ahref="https://matplotlib.org/stable/thirdpartypackages/index.html">https://matplotlib.org/stable/thirdpartypackages/index.html</a></div>
<spanid="thirdparty-index"></span><h1>Third party packages<aclass="headerlink" href="#third-party-packages" title="Permalink to this headline">¶</a></h1>
<p>Several external packages that extend or build on Matplotlib functionality are
listed below. You can find more packages at <aclass="reference external" href="https://pypi.org/search/?q=&o=&c=Framework+%3A%3A+Matplotlib">PyPI</a>.
They are maintained and distributed separately from Matplotlib,
and thus need to be installed individually.</p>
<p>If you have a created a package that extends or builds on Matplotlib
and would like to have your package listed on this page, please submit
an issue or pull request on GitHub. The pull request should include a short
description of the library and an image demonstrating the functionality.
To be included in the PyPI listing, please include <codeclass="docutils literal notranslate"><spanclass="pre">Framework</span><spanclass="pre">::</span><spanclass="pre">Matplotlib</span></code>
in the classifier list in the <codeclass="docutils literal notranslate"><spanclass="pre">setup.py</span></code> file for your package. We are also
happy to host third party packages within the <aclass="reference external" href="https://github.com/matplotlib">Matplotlib GitHub Organization</a>.</p>
<divclass="section" id="mapping-toolkits">
<h2>Mapping toolkits<aclass="headerlink" href="#mapping-toolkits" title="Permalink to this headline">¶</a></h2>
<divclass="section" id="basemap">
<h3>Basemap<aclass="headerlink" href="#basemap" title="Permalink to this headline">¶</a></h3>
<p><aclass="reference external" href="https://matplotlib.org/basemap/">Basemap</a> plots data on map projections,
<h3>Cartopy<aclass="headerlink" href="#cartopy" title="Permalink to this headline">¶</a></h3>
<p><aclass="reference external" href="https://scitools.org.uk/cartopy/docs/latest/">Cartopy</a> builds on top
of Matplotlib to provide object oriented map projection definitions
and close integration with Shapely for powerful yet easy-to-use vector
data processing tools. An example plot from the <aclass="reference external" href="https://scitools.org.uk/cartopy/docs/latest/gallery/index.html">Cartopy gallery</a>:</p>
<h3>Geoplot<aclass="headerlink" href="#geoplot" title="Permalink to this headline">¶</a></h3>
<p><aclass="reference external" href="https://residentmario.github.io/geoplot/index.html">Geoplot</a> builds on top
of Matplotlib and Cartopy to provide a "standard library" of simple, powerful,
and customizable plot types. An example plot from the <aclass="reference external" href="https://residentmario.github.io/geoplot/index.html">Geoplot gallery</a>:</p>
<h3>DeCiDa<aclass="headerlink" href="#decida" title="Permalink to this headline">¶</a></h3>
<p><aclass="reference external" href="https://pypi.org/project/DeCiDa/">DeCiDa</a> is a library of functions
and classes for electron device characterization, electronic circuit design and
general data visualization and analysis.</p>
</div>
<divclass="section" id="matplotlib-scalebar">
<h3>matplotlib-scalebar<aclass="headerlink" href="#matplotlib-scalebar" title="Permalink to this headline">¶</a></h3>
<p><aclass="reference external" href="https://github.com/ppinard/matplotlib-scalebar">matplotlib-scalebar</a> provides a new artist to display a scale bar, aka micron bar.
It is particularly useful when displaying calibrated images plotted using <codeclass="docutils literal notranslate"><spanclass="pre">plt.imshow(...)</span></code>.</p>
<h3>Matplotlib-Venn<aclass="headerlink" href="#matplotlib-venn" title="Permalink to this headline">¶</a></h3>
<p><aclass="reference external" href="https://github.com/konstantint/matplotlib-venn">Matplotlib-Venn</a> provides a
set of functions for plotting 2- and 3-set area-weighted (or unweighted) Venn
diagrams.</p>
</div>
<divclass="section" id="mpl-probscale">
<h3>mpl-probscale<aclass="headerlink" href="#mpl-probscale" title="Permalink to this headline">¶</a></h3>
<p><aclass="reference external" href="https://matplotlib.org/mpl-probscale/">mpl-probscale</a> is a small extension
that allows Matplotlib users to specify probability scales. Simply importing the
<codeclass="docutils literal notranslate"><spanclass="pre">probscale</span></code> module registers the scale with Matplotlib, making it accessible
via e.g., <codeclass="docutils literal notranslate"><spanclass="pre">ax.set_xscale('prob')</span></code> or <codeclass="docutils literal notranslate"><spanclass="pre">plt.yscale('prob')</span></code>.</p>
stereonets for plotting and analyzing orientation data in Matplotlib.</p>
</div>
<divclass="section" id="natgrid">
<h3>Natgrid<aclass="headerlink" href="#natgrid" title="Permalink to this headline">¶</a></h3>
<p><aclass="reference external" href="https://github.com/matplotlib/natgrid">mpl_toolkits.natgrid</a> is an interface
to the natgrid C library for gridding irregularly spaced data.</p>
</div>
<divclass="section" id="pyupset">
<h3>pyUpSet<aclass="headerlink" href="#pyupset" title="Permalink to this headline">¶</a></h3>
<p><aclass="reference external" href="https://github.com/ImSoErgodic/py-upset">pyUpSet</a> is a
static Python implementation of the <aclass="reference external" href="http://www.caleydo.org/tools/upset/">UpSet suite by Lex et al.</a> to explore complex intersections of
sets and data frames.</p>
</div>
<divclass="section" id="seaborn">
<h3>seaborn<aclass="headerlink" href="#seaborn" title="Permalink to this headline">¶</a></h3>
<p><aclass="reference external" href="http://seaborn.pydata.org/">seaborn</a> is a high level interface for drawing
statistical graphics with Matplotlib. It aims to make visualization a central
part of exploring and understanding complex datasets.</p>
<h3>WCSAxes<aclass="headerlink" href="#wcsaxes" title="Permalink to this headline">¶</a></h3>
<p>The <aclass="reference external" href="http://www.astropy.org">Astropy</a> core package includes a submodule
called WCSAxes (available at <aclass="reference external" href="http://docs.astropy.org/en/stable/visualization/wcsaxes/index.html">astropy.visualization.wcsaxes</a>) which
adds Matplotlib projections for Astronomical image data. The following is an
example of a plot made with WCSAxes which includes the original coordinate
system of the image and an overlay of a different coordinate system:</p>
<h3>Windrose<aclass="headerlink" href="#windrose" title="Permalink to this headline">¶</a></h3>
<p><aclass="reference external" href="https://github.com/scls19fr/windrose">Windrose</a> is a Python Matplotlib,
Numpy library to manage wind data, draw windroses (also known as polar rose
plots), draw probability density functions and fit Weibull distributions.</p>
</div>
<divclass="section" id="yellowbrick">
<h3>Yellowbrick<aclass="headerlink" href="#yellowbrick" title="Permalink to this headline">¶</a></h3>
<p><aclass="reference external" href="https://www.scikit-yb.org/">Yellowbrick</a> is a suite of visual diagnostic tools for machine learning that enables human steering of the model selection process. Yellowbrick combines scikit-learn with matplotlib using an estimator-based API called the <codeclass="docutils literal notranslate"><spanclass="pre">Visualizer</span></code>, which wraps both sklearn models and matplotlib Axes. <codeclass="docutils literal notranslate"><spanclass="pre">Visualizer</span></code> objects fit neatly into the machine learning workflow allowing data scientists to integrate visual diagnostic and model interpretation tools into experimentation without extra steps.</p>
<h3>highlight-text<aclass="headerlink" href="#highlight-text" title="Permalink to this headline">¶</a></h3>
<p><aclass="reference external" href="https://pypi.org/project/highlight-text/">highlight-text</a> is a small library
that provides an easy way to effectively annotate plots by highlighting
substrings with the font properties of your choice.
See the <aclass="reference external" href="https://github.com/znstrider/highlight_text">highlight-text github repository</a> for more details and examples.</p>
<h2>GUI applications<aclass="headerlink" href="#gui-applications" title="Permalink to this headline">¶</a></h2>
<divclass="section" id="sviewgui">
<h3>sviewgui<aclass="headerlink" href="#sviewgui" title="Permalink to this headline">¶</a></h3>
<p><aclass="reference external" href="https://pypi.org/project/sviewgui/">sviewgui</a> is a PyQt-based GUI for
visualisation of data from csv files or <aclass="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v1.2.4)"><codeclass="xref py py-obj docutils literal notranslate"><spanclass="pre">pandas.DataFrame</span></code></a>s. Main features:</p>
<ulclass="simple">
<li>Scatter, line, density, histogram, and box plot types</li>
<li>Settings for the marker size, line width, number of bins of histogram,
colormap (from cmocean)</li>
<li>Save figure as editable PDF</li>
<li>Code of the plotted graph is available so that it can be reused and modified