.. note::

This page has been moved to <https://matplotlib.org/mpl-third-party/>,

where you will find an up-to-date list of packages.

Third party packages

Several external packages that extend or build on Matplotlib functionality are

listed below. You can find more packages at `PyPI <https://pypi.org/search/?q=&o=&c=Framework+%3A%3A+Matplotlib>`_.

They are maintained and distributed separately from Matplotlib,

and thus need to be installed individually.

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 ``Framework :: Matplotlib``

in the classifier list in the ``setup.py`` file for your package. We are also

happy to host third party packages within the `Matplotlib GitHub Organization

<https://github.com/matplotlib>`_.

Mapping toolkits

Basemap

`Basemap <https://matplotlib.org/basemap/>`_ plots data on map projections,

with continental and political boundaries.

.. image:: /_static/basemap_contour1.png

:height: 400px

Cartopy

`Cartopy <https://scitools.org.uk/cartopy/docs/latest/>`_ 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 `Cartopy gallery

<https://scitools.org.uk/cartopy/docs/latest/gallery/index.html>`_:

.. image:: /_static/cartopy_hurricane_katrina_01_00.png

:height: 400px

Geoplot

`Geoplot <https://residentmario.github.io/geoplot/index.html>`_ builds on top

of Matplotlib and Cartopy to provide a "standard library" of simple, powerful,

and customizable plot types. An example plot from the `Geoplot gallery

<https://residentmario.github.io/geoplot/index.html>`_:

.. image:: /_static/geoplot_nyc_traffic_tickets.png

:height: 400px

Ridge Map

`ridge_map <https://github.com/ColCarroll/ridge_map>`_ uses Matplotlib,

SRTM.py, NumPy, and scikit-image to make ridge plots of your favorite

ridges.

.. image:: /_static/ridge_map_white_mountains.png

:height: 364px

Declarative libraries

ggplot

`ggplot <https://github.com/yhat/ggplot>`_ is a port of the R ggplot2 package

to python based on Matplotlib.

.. image:: /_static/ggplot.png

:height: 195px

holoviews

`holoviews <http://holoviews.org>`_ makes it easier to visualize data

interactively, especially in a `Jupyter notebook <https://jupyter.org>`_, by

providing a set of declarative plotting objects that store your data and

associated metadata. Your data is then immediately visualizable alongside or

overlaid with other data, either statically or with automatically provided

widgets for parameter exploration.

.. image:: /_static/holoviews.png

:height: 354px

plotnine

`plotnine <https://plotnine.readthedocs.io/en/stable/>`_ implements a grammar

of graphics, similar to R's `ggplot2 <https://ggplot2.tidyverse.org/>`_.

The grammar allows users to compose plots by explicitly mapping data to the

visual objects that make up the plot.

.. image:: /_static/plotnine.png

Specialty plots

Broken Axes

`brokenaxes <https://github.com/bendichter/brokenaxes>`_ supplies an axes

class that can have a visual break to indicate a discontinuous range.

.. image:: /_static/brokenaxes.png

DeCiDa

`DeCiDa <https://pypi.org/project/DeCiDa/>`_ is a library of functions

and classes for electron device characterization, electronic circuit design and

general data visualization and analysis.

matplotlib-scalebar

`matplotlib-scalebar <https://github.com/ppinard/matplotlib-scalebar>`_ provides a new artist to display a scale bar, aka micron bar.

It is particularly useful when displaying calibrated images plotted using ``plt.imshow(...)``.

.. image:: /_static/gold_on_carbon.jpg

Matplotlib-Venn

`Matplotlib-Venn <https://github.com/konstantint/matplotlib-venn>`_ provides a

set of functions for plotting 2- and 3-set area-weighted (or unweighted) Venn

diagrams.

mpl-probscale

`mpl-probscale <https://matplotlib.org/mpl-probscale/>`_ is a small extension

that allows Matplotlib users to specify probability scales. Simply importing the

``probscale`` module registers the scale with Matplotlib, making it accessible

via e.g., ``ax.set_xscale('prob')`` or ``plt.yscale('prob')``.

.. image:: /_static/probscale_demo.png

mpl-scatter-density

`mpl-scatter-density <https://github.com/astrofrog/mpl-scatter-density>`_ is a

small package that makes it easy to make scatter plots of large numbers

of points using a density map. The following example contains around 13 million

points and the plotting (excluding reading in the data) took less than a

second on an average laptop:

.. image:: /_static/mpl-scatter-density.png

:height: 400px

When used in interactive mode, the density map is downsampled on-the-fly while

panning/zooming in order to provide a smooth interactive experience.

mplstereonet

`mplstereonet <https://github.com/joferkington/mplstereonet>`_ provides

stereonets for plotting and analyzing orientation data in Matplotlib.

Natgrid

`mpl_toolkits.natgrid <https://github.com/matplotlib/natgrid>`_ is an interface

to the natgrid C library for gridding irregularly spaced data.

pyUpSet

`pyUpSet <https://github.com/ImSoErgodic/py-upset>`_ is a

static Python implementation of the `UpSet suite by Lex et al.

<http://caleydo.org/tools/upset/>`_ to explore complex intersections of

sets and data frames.

seaborn

`seaborn <http://seaborn.pydata.org/>`_ 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.

.. image:: /_static/seaborn.png

:height: 157px

WCSAxes

The `Astropy <https://www.astropy.org/>`_ core package includes a submodule

called WCSAxes (available at `astropy.visualization.wcsaxes

<http://docs.astropy.org/en/stable/visualization/wcsaxes/index.html>`_) 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:

.. image:: /_static/wcsaxes.jpg

:height: 400px

Windrose

`Windrose <https://github.com/scls19fr/windrose>`_ 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.

Yellowbrick

`Yellowbrick <https://www.scikit-yb.org/>`_ 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 ``Visualizer``, which wraps both sklearn models and matplotlib Axes. ``Visualizer`` objects fit neatly into the machine learning workflow allowing data scientists to integrate visual diagnostic and model interpretation tools into experimentation without extra steps.

.. image:: /_static/yellowbrick.png

:height: 400px

Animations

animatplot

`animatplot <https://animatplot.readthedocs.io/>`_ is a library for

producing interactive animated plots with the goal of making production of

animated plots almost as easy as static ones.

.. image:: /_static/animatplot.png

For an animated version of the above picture and more examples, see the

`animatplot gallery. <https://animatplot.readthedocs.io/en/stable/gallery.html>`_

gif

`gif <https://github.com/maxhumber/gif/>`_ is an ultra lightweight animated gif API.

.. image:: /_static/gif_attachment_example.png

numpngw

`numpngw <https://pypi.org/project/numpngw/>`_ provides functions for writing

NumPy arrays to PNG and animated PNG files. It also includes the class

``AnimatedPNGWriter`` that can be used to save a Matplotlib animation as an

animated PNG file. See the example on the PyPI page or at the ``numpngw``

`github repository <https://github.com/WarrenWeckesser/numpngw>`_.

.. image:: /_static/numpngw_animated_example.png

Interactivity

mplcursors

`mplcursors <https://mplcursors.readthedocs.io>`_ provides interactive data

cursors for Matplotlib.

MplDataCursor

`MplDataCursor <https://github.com/joferkington/mpldatacursor>`_ is a toolkit

written by Joe Kington to provide interactive "data cursors" (clickable

annotation boxes) for Matplotlib.

mpl_interactions

`mpl_interactions <https://mpl-interactions.readthedocs.io/en/latest/>`_

makes it easy to create interactive plots controlled by sliders and other

widgets. It also provides several handy capabilities such as manual

image segmentation, comparing cross-sections of arrays, and using the

scroll wheel to zoom.

.. image:: /_static/mpl-interactions-slider-animated.png

Rendering backends

mplcairo

`mplcairo <https://github.com/anntzer/mplcairo>`_ is a cairo backend for

Matplotlib, with faster and more accurate marker drawing, support for a wider

selection of font formats and complex text layout, and various other features.

gr

==

`gr <https://gr-framework.org/>`_ is a framework for cross-platform

visualisation applications, which can be used as a high-performance Matplotlib

backend.

GUI integration

wxmplot

`WXMPlot <https://pypi.org/project/wxmplot/>`_ provides advanced wxPython

widgets for plotting and image display of numerical data based on Matplotlib.

Miscellaneous

adjustText

`adjustText <https://github.com/Phlya/adjustText>`_ is a small library for

automatically adjusting text position in Matplotlib plots to minimize overlaps

between them, specified points and other objects.

.. image:: /_static/adjustText.png

iTerm2 terminal backend

`matplotlib_iterm2 <https://github.com/oselivanov/matplotlib_iterm2>`_ is an

external Matplotlib backend using the iTerm2 nightly build inline image display

feature.

.. image:: /_static/matplotlib_iterm2_demo.png

mpl-template

`mpl-template <https://austinorr.github.io/mpl-template/index.html>`_ provides

a customizable way to add engineering figure elements such as a title block,

border, and logo.

.. image:: /_static/mpl_template_example.png

:height: 330px

figpager

`figpager <https://pypi.org/project/figpager/>`_ provides customizable figure

elements such as text, lines and images and subplot layout control for single

or multi page output.

.. image:: /_static/figpager.png

blume

`blume <https://pypi.org/project/blume/>`_ provides a replacement for

the Matplotlib ``table`` module. It fixes a number of issues with the

existing table. See the `blume github repository

<https://github.com/swfiua/blume>`_ for more details.

.. image:: /_static/blume_table_example.png

highlight-text

`highlight-text <https://pypi.org/project/highlight-text/>`_ 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 `highlight-text github repository

<https://github.com/znstrider/highlight_text>`_ for more details and examples.

.. image:: /_static/highlight_text_examples.png

DNA Features Viewer

`DNA Features Viewer <https://github.com/Edinburgh-Genome-Foundry/DnaFeaturesViewer>`_

provides methods to plot annotated DNA sequence maps (possibly along other Matplotlib

plots) for Bioinformatics and Synthetic Biology applications.

.. image:: /_static/dna_features_viewer_screenshot.png

GUI applications

sviewgui

`sviewgui <https://pypi.org/project/sviewgui/>`_ is a PyQt-based GUI for

visualisation of data from csv files or `pandas.DataFrame`\s. Main features:

- Scatter, line, density, histogram, and box plot types

- Settings for the marker size, line width, number of bins of histogram,

colormap (from cmocean)

- Save figure as editable PDF

- Code of the plotted graph is available so that it can be reused and modified

outside of sviewgui

.. image:: /_static/sviewgui_sample.png