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

Smoothing the noise in real-world data


In this recipe, we introduce a few advanced algorithms to help with cleaning the data coming from real-world sources. These algorithms are well known in the signal processing world, and we will not go deep into mathematics but will just exemplify how and why they work and for what purposes they can be used.

Getting ready

Data that comes from different real-life sensors usually is not smooth and clean and contains some noise that we usually don't want to show on diagrams and plots. We want graphs and plots to be clear and to display information and cost viewers minimal efforts to interpret.

We don't need any new software installed because we are going to use some already familiar Python packages: NumPy, SciPy, and matplotlib.

How to do it...

The basic algorithm is based on using the rolling window (for example, convolution). This window rolls over the data and is used to compute the average over that window.

For our discrete data, we use NumPy's convolve...