Greetings, this is Chad Adams, and welcome to Learning Python Data Visualization. In this book, we will cover the basics of generating dynamic charts and general graphics with code using the Python programming language. We will use the pygal library, a simple yet powerful graphing library written for Python, to explore the different types of charts we can create for various kinds of data.
We will also review the Python language itself and discuss working with file I/O and cover topics on working with data. We will then parse that data into a chart to create a dynamic charting application. We will also touch on more popular (and more advanced) libraries such as matplotlib and Plotly and build charts using these libraries and explore their features.
With this book, we will explore and build data visualizations using the basic toolsets used in many popular charting applications for the scientific, financial, medical, and pharmaceutical industries.
Chapter 1, Setting Up Your Development Environment, will discuss the installation process for Python on Windows, Mac, and Ubuntu. We will review the easy_install and pip package managers for Python and discuss common issues when installing third-party libraries for Python.
Chapter 2, Python Refresher, will quickly review the Python language and common libraries found in most Python developers' tool belts. We will also ease into building charts by creating custom graphics with nothing but code and learn about saving files to the filesystem.
Chapter 3, Getting Started with pygal, will cover the basics of the pygal library, a simple charting library that generates charts in HTML5-ready SVG files. We will build some basic charts using the library, some of which include line charts, bar charts, and scatter plots.
Chapter 4, Advanced Charts, will cover more complex charts in the pygal library such as box plots, radar charts, and worldmap charts.
Chapter 5, Tweaking pygal, will discuss the optional settings we can give our pygal charts such as adjusting the font size and the positioning of labels and legends. We will also cover the French country map chart in the pygal library using it as an example.
Chapter 6, Importing Dynamic Data, will go over the finer points of pulling data from the Web using the Python language and its built-in libraries and cover parsing XML, JSON, and JSONP data.
Chapter 7, Putting It All Together, will build a simple chart that takes what we learned from the past chapters and builds a dynamic pygal-based chart using data from the Web.
Chapter 8, Further Resources, will review some very popular charting libraries such as matplotlib and Plotly, go over building sample charts for each library, and cover resources for further reading.
Appendix, References and Resources, will list some popular data visualization libraries for Python as well as some helpful utilities.