We assume that you already have Linux (preferably Debian/Ubuntu or RedHat/SciLinux) installed and Python installed on it. Usually, Python is already installed on the mentioned Linux distributions and, if not, it is easily installable through standard means. We assume that Python 2.7+ Version is installed on your workstation.

We also assume that you know how to use your OS package manager in order to install software packages and know how to use a terminal.

The build requirements must be satisfied before matplotlib can be built.

matplotlib requires NumPy, libpng, and freetype as build dependencies. In order to be able to build matplotlib from source, we must have installed NumPy. Here's how to do it:

Install NumPy (1.5+ if you want to use it with Python 3) from http://www.numpy.org/

NumPy will provide us with data structures and mathematical functions for using it with large datasets. Python's default data structures such as tuples, lists, or dictionaries are great for insertions, deletions, and concatenation. NumPy's data structures support "vectorized" operations and are very efficient for use and for executions. They are implemented with big data in mind and rely on C implementations that allow efficient execution time.

Perform the following steps for installing NumPy:

There are many ways one can install matplotlib and its dependencies: from source, precompiled binaries, OS package manager, and with prepackaged Python distributions with built-in matplotlib.

Most probably the easiest way is to use your distribution's package manager. For Ubuntu that should be:

# in your terminal, type:
$ sudo apt-get install python-numpy python-matplotlib python-scipy

If you want to be on the bleeding edge, the best option is to install from source. This path comprises a few steps: get the source code, build requirements, and configure, compile, and install.

Download the latest source from code host SourceForge by following these steps:

$ cd ~/Downloads/
$ wget https://downloads.sourceforge.net/project/matplotlib/matplotlib/matplotlib-1.3.1/matplotlib-1.3.1.tar.gz
$ tar xzf matplotlib-1.4.3.tar.gz
$ cd matplotlib-1.4.3
$ python setup.py build
$ sudo python setup.py install

We use standard Python Distribution Utilities, known as Distutils, to install matplotlib from the source code. This procedure requires us to previously install dependencies, as we already explained in the Getting ready section of this recipe. The dependencies are installed using the standard Linux packaging tools.

There are more optional packages that you might want to install depending on what your data visualization projects are about.

No matter what project you are working on, we recommend installing IPython—an Interactive Python shell where you already have matplotlib and related packages, such as NumPy and SciPy, imported and ready to play with. Please refer to IPython's official site on how to install it and use it—it is, though, very straightforward.

To install virtualenv, you must have a workable installation of Python and pip. Pip is a tool for installing and managing Python packages, and it is a replacement for easy_install. We will use pip through most of this book for package management. Pip is easily installed, as root executes the following line in your terminal:

# easy_install pip

virtualenv by itself is really useful, but with the help of virtualenvwrapper, all this becomes easy to do and also easy to organize many virtual environments. See all the features at http://virtualenvwrapper.readthedocs.org/en/latest/#features.

By performing the following steps, you can install the virtualenv and virtualenvwrapper tools:

A few useful and most frequently used commands are as follows:

pip not only provides you with a practical way of installing packages, but it also is a good solution for keeping track of the python packages installed on your system, as well as their version. The command pip freeze will print all the installed packages on your current environment, followed by their version number:

$ pip freeze
matplotlib==1.4.3
mock==1.0.1
nose==1.3.6
numpy==1.9.2
pyparsing==2.0.3
python-dateutil==2.4.2
pytz==2015.2
six==1.9.0
wsgiref==0.1.2

In this case, we see that even though we simply installed matplotlib, many other packages are also installed. Apart from wsgiref, which is used by pip itself, these are required dependencies of matplotlib which have been automatically installed.

When transferring a project from an environment (possibly a virtual environment) to another, the receiving environment needs to have all the necessary packages installed (in the same version as in the original environment) in order to be sure that the code can be properly run. This can be problematic as two different environments might not contain the same packages, and, worse, might contain different versions of the same package. This can lead to conflicts or unexpected behaviors in the execution of the program.

In order to avoid this problem, pip freeze can be used to save a copy of the current environment configuration. The command will save the output of the command to the file requirements.txt:

$ pip freeze > requirements.txt

In a new environment, this file can be used to install all the required libraries. Simply run:

$ pip install -r requirements.txt

All the necessary packages will automatically be installed in their specified version. That way, we ensure that the environment where the code is used is always the same. This is a good practice to have a virtual environment and a requirements.txt file for every project you are developing. Therefore, before installing the required packages, it is advised that you first create a new virtual environment to avoid conflicts with other projects.

The overall workflow from one machine to another is therefore:

We will use the Homebrew (you could also use MacPorts in the same way) project that eases the installation of all software that Apple did not install on your OS, including Python and matplotlib. Under the hood, Homebrew is a set of Ruby and Git that automate download and installation. Following these instructions should get the installation working. First, we will install Homebrew, and then Python, followed by tools such as virtualenv, then dependencies for matplotlib (NumPy and SciPy), and finally matplotlib. Hold on, here we go.