Milovanovic I., Foures D., Vettigli G. - Python Data Visualization Cookbook (Second Edition) [2015, PDF/EPUB/MOBI, ENG]

Python Data Visualization Cookbook (Second Edition)

Год издания: 2015
Автор: Milovanovic I., Foures D., Vettigli G.
Жанр или тематика: Программирование

Издательство: Packt Publishing
ISBN: 9781784396695
Язык: Английский

Формат: PDF/EPUB/MOBI
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 302

Описание: 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.

What You Will Learn

- Introduce yourself to the essential tooling to set up your working environment
- Explore your data using the capabilities of standard Python Data Library and Panda Library
- Draw your first chart and customize it
- Use the most popular data visualization Python libraries
- Make 3D visualizations mainly using mplot3d
- Create charts with images and maps
- Understand the most appropriate charts to describe your data
- Know the matplotlib hidden gems
- Use plot.ly to share your visualization online