Book Image

Python Data Visualization Cookbook (Second Edition)

Book Image

Python Data Visualization Cookbook (Second Edition)

Overview of this book

Python Data Visualization Cookbook will progress the reader from the point of installing and setting up a Python environment for data manipulation and visualization all the way to 3D animations using Python libraries. Readers will benefit from over 60 precise and reproducible recipes that will guide the reader towards a better understanding of data concepts and the building blocks for subsequent and sometimes more advanced concepts. Python Data Visualization Cookbook starts by showing how to set up matplotlib and the related libraries that are required for most parts of the book, before moving on to discuss some of the lesser-used diagrams and charts such as Gantt Charts or Sankey diagrams. Initially it uses simple plots and charts to more advanced ones, to make it easy to understand for readers. As the readers will go through the book, they will get to know about the 3D diagrams and animations. Maps are irreplaceable for displaying geo-spatial data, so this book will also show how to build them. In the last chapter, it includes explanation on how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python.
Table of Contents (16 chapters)
Python Data Visualization Cookbook Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Index

Installing matplotlib on Windows


In this recipe, we will demonstrate how to install Python and start working with matplotlib installation. We assume Python was not previously installed.

Getting ready

There are two ways of installing matplotlib on Windows. The easiest way is by installing prepackaged Python environments, such as EPD, Anaconda, SageMath, and Python(x,y). This is the suggested way to install Python, especially for beginners.

The second way is to install everything using binaries of precompiled matplotlib and required dependencies. This is more difficult as you have to be careful about the versions of NumPy and SciPy you are installing, as not every version is compatible with the latest version of matplotlib binaries. The advantage in this is that you can even compile your particular versions of matplotlib or any library to have the latest features, even if they are not provided by authors.

How to do it...

The suggested way of installing free or commercial Python scientific distributions is as easy as following the steps provided on the project's website.

If you just want to start using matplotlib and don't want to be bothered with Python versions and dependencies, you may want to consider using the Enthought Python Distribution (EPD). EPD contains prepackaged libraries required to work with matplotlib and all the required dependencies (SciPy, NumPy, IPython, and more).

As usual, we download Windows installer (*.exe) that will install all the code we need to start using matplotlib and all recipes from this book.

There is also a free scientific project Python(x,y) (http://python-xy.github.io) for Windows 32-bit system that contains all dependencies resolved, and is an easy (and free!) way of installing matplotlib on Windows. Since Python(x,y) is compatible with Python modules installers, it can be easily extended with other Python libraries. No Python installation should be present on the system before installing Python(x,y).

Let me shortly explain how we would install matplotlib using precompiled Python, NumPy, SciPy, and matplotlib binaries:

  1. First, we download and install standard Python using the official .msi installer for our platform (x86 or x86-64).

  2. After that, download official binaries for NumPy and SciPy and install them first.

  3. When you are sure that NumPy and SciPy are properly installed. Then, we download the latest stable release binary for matplotlib and install it by following the official instructions.

There's more...

Note that many examples are not included in the Windows installer. If you want to try the demos, download the matplotlib source and look in the examples subdirectory.