Installing

Note

If you wish to contribute to the project, it's recommended you :ref:`install the latest development version<install_from_source>`.

Contents

Installing an official release
- macOS
- Test data
Third-party distributions of Matplotlib
- Scientific Python Distributions
- Linux: using your package manager
Installing from source

Installing an official release

Matplotlib and its dependencies are available as wheel packages for macOS, Windows and Linux distributions:

python -mpip install -U pip
python -mpip install -U matplotlib

Note

The following backends work out of the box: Agg, ps, pdf, svg and TkAgg.

For support of other GUI frameworks, LaTeX rendering, saving animations and a larger selection of file formats, you may need to install :ref:`additional dependencies <install_requirements>`.

Although not required, we suggest also installing IPython for interactive use. To easily install a complete Scientific Python stack, see :ref:`install_scipy_dists` below.

macOS

To use the native OSX backend you will need :ref:`a framework build <osxframework-faq>` build of Python.

Test data

The wheels (:file:`*.whl`) on the PyPI download page do not contain test data or example code.

If you want to try the many demos that come in the Matplotlib source distribution, download the :file:`*.tar.gz` file and look in the :file:`examples` subdirectory.

To run the test suite:

extract the :file:`lib/matplotlib/tests` or :file:`lib/mpl_toolkits/tests` directories from the source distribution;
install test dependencies: pytest, Pillow, MiKTeX, GhostScript, ffmpeg, avconv, ImageMagick, and Inkscape;
run python -mpytest.

Third-party distributions of Matplotlib

Scientific Python Distributions

Anaconda and Canopy and ActiveState are excellent choices that "just work" out of the box for Windows, macOS and common Linux platforms. WinPython is an option for Windows users. All of these distributions include Matplotlib and lots of other useful (data) science tools.

Linux: using your package manager

If you are on Linux, you might prefer to use your package manager. Matplotlib is packaged for almost every major Linux distribution.

Debian / Ubuntu: sudo apt-get install python3-matplotlib
Fedora: sudo dnf install python3-matplotlib
Red Hat: sudo yum install python3-matplotlib
Arch: sudo pacman -S python-matplotlib

Installing from source

If you are interested in contributing to Matplotlib development, running the latest source code, or just like to build everything yourself, it is not difficult to build Matplotlib from source. Grab the latest tar.gz release file from the PyPI files page, or if you want to develop Matplotlib or just need the latest bugfixed version, grab the latest git version, and see :ref:`install-from-git`.

The standard environment variables CC, CXX, PKG_CONFIG are respected. This means you can set them if your toolchain is prefixed. This may be used for cross compiling.

export CC=x86_64-pc-linux-gnu-gcc
export CXX=x86_64-pc-linux-gnu-g++
export PKG_CONFIG=x86_64-pc-linux-gnu-pkg-config

Once you have satisfied the requirements detailed below (mainly Python, NumPy, libpng and FreeType), you can build Matplotlib.

cd matplotlib
python -mpip install .

We provide a setup.cfg file which you can use to customize the build process. For example, which default backend to use, whether some of the optional libraries that Matplotlib ships with are installed, and so on. This file will be particularly useful to those packaging Matplotlib.

If you have installed prerequisites to nonstandard places and need to inform Matplotlib where they are, edit setupext.py and add the base dirs to the basedir dictionary entry for your sys.platform; e.g., if the header of some required library is in /some/path/include/someheader.h, put /some/path in the basedir list for your platform.

Dependencies

Matplotlib requires the following dependencies:

Python (>= 3.5)
FreeType (>= 2.3)
libpng (>= 1.2)
NumPy (>= 1.10.0)
setuptools
cycler (>= 0.10.0)
dateutil (>= 2.1)
kiwisolver (>= 1.0.0)
pyparsing

Optionally, you can also install a number of packages to enable better user interface toolkits. See :ref:`what-is-a-backend` for more details on the optional Matplotlib backends and the capabilities they provide.

:term:`tk` (>= 8.3, != 8.6.0 or 8.6.1): for the Tk-based backends;
PyQt4 (>= 4.6) or PySide (>= 1.0.3): for the Qt4-based backends;
PyQt5: for the Qt5-based backends;
PyGObject or pgi (>= 0.0.11.2): for the GTK3-based backends;
:term:`wxpython` (>= 4): for the WX-based backends;
cairocffi (>= 0.8) or pycairo: for the cairo-based backends;
Tornado: for the WebAgg backend;

For better support of animation output format and image file formats, LaTeX, etc., you can install the following:

ffmpeg/avconv: for saving movies;
ImageMagick: for saving animated gifs;
Pillow (>= 3.4): for a larger selection of image file formats: JPEG, BMP, and TIFF image files;
LaTeX and GhostScript (for rendering text with LaTeX).

Note

Matplotlib depends on non-Python libraries. pkg-config can be used to find required non-Python libraries and thus make the install go more smoothly if the libraries and headers are not in the expected locations.

Note

The following libraries are shipped with Matplotlib:

Agg: the Anti-Grain Geometry C++ rendering engine;
qhull: to compute Delaunay triangulation;
ttconv: a TrueType font utility.

Building on Linux

It is easiest to use your system package manager to install the dependencies.

If you are on Debian/Ubuntu, you can get all the dependencies required to build Matplotlib with:

sudo apt-get build-dep python-matplotlib

If you are on Fedora, you can get all the dependencies required to build Matplotlib with:

sudo dnf builddep python-matplotlib

If you are on RedHat, you can get all the dependencies required to build Matplotlib by first installing yum-builddep and then running:

su -c "yum-builddep python-matplotlib"

These commands do not build Matplotlib, but instead get and install the build dependencies, which will make building from source easier.

Building on macOS

The build situation on macOS is complicated by the various places one can get the libpng and FreeType requirements (MacPorts, Fink, /usr/X11R6), the different architectures (e.g., x86, ppc, universal), and the different macOS versions (e.g., 10.4 and 10.5). We recommend that you build the way we do for the macOS release: get the source from the tarball or the git repository and install the required dependencies through a third-party package manager. Two widely used package managers are Homebrew, and MacPorts. The following example illustrates how to install libpng and FreeType using brew:

brew install libpng freetype pkg-config

If you are using MacPorts, execute the following instead:

port install libpng freetype pkgconfig

After installing the above requirements, install Matplotlib from source by executing:

python -mpip install .

Note that your environment is somewhat important. Some conda users have found that, to run the tests, their PYTHONPATH must include /path/to/anaconda/.../site-packages and their DYLD_FALLBACK_LIBRARY_PATH must include /path/to/anaconda/lib.

Building on Windows

The Python shipped from https://www.python.org is compiled with Visual Studio 2015 for 3.5+. Python extensions should be compiled with the same compiler, see e.g. https://packaging.python.org/guides/packaging-binary-extensions/#setting-up-a-build-environment-on-windows for how to set up a build environment.

Since there is no canonical Windows package manager, the methods for building FreeType, zlib, and libpng from source code are documented as a build script at matplotlib-winbuild.

There are a few possibilities to build Matplotlib on Windows:

Wheels via matplotlib-winbuild
Wheels by using conda packages
Conda packages

Wheel builds using conda packages

This is a wheel build, but we use conda packages to get all the requirements. The binary requirements (png, FreeType,...) are statically linked and therefore not needed during the wheel install.

# create a new environment with the required packages
conda create -n "matplotlib_build" python=3.5 numpy python-dateutil pyparsing pytz tornado cycler tk libpng zlib freetype
activate matplotlib_build
# if you want a qt backend, you also have to install pyqt (be aware that pyqt doesn't mix well if
# you have created the environment with conda-forge already activated...)
conda install pyqt
# this package is only available in the conda-forge channel
conda install -c conda-forge msinttypes

# copy the libs which have "wrong" names
set LIBRARY_LIB=%CONDA_PREFIX%\Library\lib
mkdir lib || cmd /c "exit /b 0"
copy %LIBRARY_LIB%\zlibstatic.lib lib\z.lib
copy %LIBRARY_LIB%\libpng_static.lib lib\png.lib

# Make the header files and the rest of the static libs available during the build
# CONDA_DEFAULT_ENV is a env variable which is set to the currently active environment path
set MPLBASEDIRLIST=%CONDA_PREFIX%\Library\;.

# build the wheel
python setup.py bdist_wheel

The build_alllocal.cmd script in the root folder automates these steps if you have already created and activated the conda environment.

Conda packages

The conda packaging scripts for Matplotlib are available at https://github.com/conda-forge/matplotlib-feedstock.

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