Kirthi Raman - Mastering Python Data Visualization

Kirthi Raman is currently working as a lead data engineer with Neustar Inc, based in Mclean, Virginia USA. Kirthi has worked on data visualization, with a focus on JavaScript, Python, R, and Java, and is a distinguished engineer. Previously, he worked as a principle architect, data analyst, and information retrieval specialist at Quotient, Inc. Kirthi has also worked as a technical lead and manager for a start-up.

He has taught discrete mathematics and computer science for several years. Kirthi has a graduate degree in mathematics and computer science from IIT Delhi and an MS in computer science from the University of Maryland. He has written several white papers on data analysis and big data.

I would like to thank my wife, Radhika, my son, Sid, and daughter, Niya, for putting up with my schedule even when I was on vacation. I would also like to thank my dad, Venkatraman, and my sisters, Vijaya and Meena, for their blessings.

Julian Quick is pursuing his bachelor's of science degree in environmental resources engineering at Humboldt State University with a specialization in energy resources and energy data analysis. He wrote Python code for the Earth Observing Laboratory, Canary Instruments, home energy monitoring, and the National Wind Technology Center.

I place on record my gratitude towards my family.

Hang (Harvey) Yu graduated from the University of Illinois at Urbana-Champaign with a PhD in computational biophysics and a master's in statistics. He has extensive experience in data mining, machine learning, and statistics. In the past, Harvey has worked on areas such as stochastic simulations and time series in C and Python as part of his academics. He was intrigued by algorithms and mathematical modeling and has been involved in data analytics since then.

Hang (Harvey) Yu is currently working as a data scientist in Silicon Valley. He is passionate about data science and has developed statistical/mathematical models based on techniques such as optimization and predictive modeling in R. Previously, Harvey has also worked as a computational science intern at ExxonMobil.

When Harvey is not coding, he plays soccer, reads fiction books, or listens to classical music. You can reach him at <.

www.PacktPub.com

Support files, eBooks, discount offers, and more

For support files and downloads related to your book, please visit www.PacktPub.com.

Did you know that Packt offers eBook versions of every book published, with PDF and ePub files available? You can upgrade to the eBook version at > for more details.

At www.PacktPub.com, you can also read a collection of free technical articles, sign up for a range of free newsletters and receive exclusive discounts and offers on Packt books and eBooks.

https://www2.packtpub.com/books/subscription/packtlib

Do you need instant solutions to your IT questions? PacktLib is Packt's online digital book library. Here, you can search, access, and read Packt's entire library of books.

Why subscribe?

• Fully searchable across every book published by Packt
• Copy and paste, print, and bookmark content
• On demand and accessible via a web browser

Free access for Packt account holders

If you have an account with Packt at www.PacktPub.com, you can use this to access PacktLib today and view 9 entirely free books. Simply use your login credentials for immediate access.

Preface

Data visualization is intended to provide information clearly and help the viewer understand them qualitatively. The well-known expression that a picture is worth a thousand words may be rephrased as "a picture tells a story as well as a large collection of words". Visualization is, therefore, a very precious tool that helps the viewer understand a concept quickly. However, data visualization is more of an art than a skill because if you try to overdo it, it could have a reverse effect.

We are currently faced with a plethora of data containing many insights that hold the key to success in the modern day. It is important to find the data, clean it, and use the right tool to visualize it. This book explains several different ways to visualize data using Python packages, along with very useful examples in many different areas such as numerical computing, financial models, statistical and machine learning, and genetics and networks.

This book presents an example code developed on Mac OS X 10.10.5 using Python 2.7, IPython 0.13.2, matplotlib 1.4.3, NumPy 1.9.2, SciPy 0.16.0, and conda build version 1.14.1.

What this book covers

, A Conceptual Framework for Data Visualization , expounds that data visualization should actually be referred to as "the visualization of information for knowledge inference". This chapter covers the framework, explaining the transition from data/information to knowledge and how meaningful representations (through logarithms, colormaps, scatterplots, correlations, and others) can make knowledge much easier to grasp.

, Data Analysis and Visualization , explains the importance of visualization and shows several steps in the visualization process, including several options of tools to choose from. Visualization methods have existed for a long time, and we are exposed to them very early; for instance, even young children can interpret bar charts. Interactive visualization has many strengths, and this chapter explains them with examples.