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

Cleaning up data from outliers


This recipe describes how to deal with datasets coming from the real world and how to clean them before doing any visualization.

We will present a few techniques, which are different in essence but have the same goal, to get the data cleaned.

Cleaning, however, should not be fully automatic. We need to understand the data as given and be able to understand what the outliers are and what the data points represent before we apply any of the robust modern algorithms made to clean the data. This is not something that can be defined in a recipe because it relies on vast areas such as statistics, knowledge of the domain, and a good eye (and then some luck).

Getting ready

We will use the standard Python modules we already know about, so no additional installation is required.

In this recipe, I will introduce a new. Median absolute deviation (MAD) in statistics represents a measure of the variability of a univariate (possessing one variable) sample of quantitative data...