Book Image

Python Data Visualization Cookbook

By : Igor Milovanovic
Book Image

Python Data Visualization Cookbook

By: Igor Milovanovic

Overview of this book

Today, data visualization is a hot topic as a direct result of the vast amount of data created every second. Transforming that data into information is a complex task for data visualization professionals, who, at the same time, try to understand the data and objectively transfer that understanding to others. This book is a set of practical recipes that strive to help the reader get a firm grasp of the area of data visualization using Python and its popular visualization and data libraries. 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 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 you 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. During the book, we go from simple plots and charts to more advanced ones, thoroughly explaining why we used them and how not to use them. As we go through the book, we will also discuss 3D diagrams. We will peep into animations just to show you what it takes to go into that area. Maps are irreplaceable for displaying geo-spatial data, so we also show you how to build them. In the last chapter, we show you how to incorporate matplotlib into different environments, such as a writing system, LaTeX, or how to create Gantt charts using Python. This book will help those who already know how to program in Python to explore a new field – one of data visualization. As this book is all about recipes that explain how to do something, code samples are abundant, and they are followed by visual diagrams and charts to help you understand the logic and compare your own results with what is explained in the book.
Table of Contents (15 chapters)
Python Data Visualization Cookbook
Credits
About the Author
About the Reviewers
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 easier way is by installing prepackaged Python environments such as EPD, Anaconda 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 as 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://code.google.com/p/pythonxy/) for Windows 32-bit system that contains all dependencies resolved, and is an easy (and free!) way of installing matplotlib on Windows. Because 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. First, we download and install standard Python using official MSI Installer for our platform (x86 or x86-64). After that, download official binaries for NumPy and SciPy and install them first. 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.