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

Understanding spectrograms


A spectrogram is a time-varying spectral representation that shows how the spectral density of a signal varies with time.

It represents a spectrum of frequencies of the sound or other signal in a visual manner. It is used in various science fields, from sound fingerprinting like voice recognition to radar engineering and seismology.

Usually spectrogram layout is as following: x-axis represents time, y-axis represents frequency, and the third dimension is amplitude of a frequency-time pair, which is color coded. This is three-dimensional data, therefore, we can also create 3D plot where the intensity is represented as height on the z-axis. The problem with 3D charts is that humans are bad at understanding and comparing them. Also, they tend to take more space than 2D charts.

Getting ready

For serious signal processing, we would go into low level details to be able to detect patterns and auto fingerprint certain specific, but for this data visualization recipe we, will...