Python Data Visualization Cookbook - Second Edition: Over 70 recipes to get you started with popular Python libraries based on the principal concepts of data visualization - Tapa blanda

Reseña del editor

Over 70 recipes to get you started with popular Python libraries based on the principal concepts of data visualization

About This Book

• Learn how to set up an optimal Python environment for data visualization
• Understand how to import, clean and organize your data
• Determine different approaches to data visualization and how to choose the most appropriate for your needs

Who This Book Is For

If you already know about Python programming and want to understand data, data formats, data visualization, and how to use Python to visualize data then this book is for you.

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

In Detail

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.

Style and approach

A step-by-step recipe based approach to data visualization. The topics are explained sequentially as cookbook recipes consisting of a code snippet and the resulting visualization.

Biografía del autor

Igor Milovanovic is an experienced developer, with strong background in Linux system knowledge and software engineering education, he is skilled in building scalable data-driven distributed software rich systems. Evangelist for high-quality systems design who holds strong interests in software architecture and development methodologies, Igor is always persistent on advocating methodologies which promote high-quality software, such as test-driven development, one-step builds and continuous integration. He also possesses solid knowledge of product development. Having field experience and official training, he is capable of transferring knowledge and communication flow from business to developers and vice versa. Igor is most grateful to his girlfriend for letting him spent hours on the work instead with her and being avid listener to his endless book monologues. He thanks his brother for being the strongest supporter. He is thankful to his parents to let him develop in various ways and become a person he is today. Dimitry Foures is a data scientist with a background in applied mathematics and theoretical physics. After completing his undergraduate studies in physics at ENS Lyon (France), he studied fluid mechanics at Ecole Polytechnique in Paris where he obtained a first class master's. He holds a PhD in applied mathematics from the University of Cambridge. He currently works as a data scientist for a smart-energy startup in Cambridge, in close collaboration with the university. Giuseppe Vettigli is a data scientist who has worked in the research industry and academia for many years. His work is focused on the development of machine learning models and applications to use information from structured and unstructured data. He also writes about scientific computing and data visualization in Python on his blog at http://glowingpython.blogspot.com.