Book Image

Matplotlib 2.x By Example

By : Allen Yu, Claire Chung, Aldrin Yim
Book Image

Matplotlib 2.x By Example

By: Allen Yu, Claire Chung, Aldrin Yim

Overview of this book

Big data analytics are driving innovations in scientific research, digital marketing, policy-making and much more. Matplotlib offers simple but powerful plotting interface, versatile plot types and robust customization. Matplotlib 2.x By Example illustrates the methods and applications of various plot types through real world examples. It begins by giving readers the basic know-how on how to create and customize plots by Matplotlib. It further covers how to plot different types of economic data in the form of 2D and 3D graphs, which give insights from a deluge of data from public repositories, such as Quandl Finance. You will learn to visualize geographical data on maps and implement interactive charts. By the end of this book, you will become well versed with Matplotlib in your day-to-day work to perform advanced data visualization. This book will guide you to prepare high quality figures for manuscripts and presentations. You will learn to create intuitive info-graphics and reshaping your message crisply understandable.
Table of Contents (15 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback

About the Reviewer

Nikhil Borkar holds a CQF designation and a postgraduate degree in quantitative finance. He also holds certified financial crime examiner and certified anti-money laundering professional qualifications. He is a registered research analyst with the securities and Exchange Board of India (SEBI) and has a keen grasp of laws and regulations pertaining to securities and investment. He is currently working as an independent FinTech and legal consultant. Prior to this, he worked with Morgan Stanley Capital International as a Global RFP project manager. He is self-motivated, intellectually curious, and hardworking. He loves to approach problems using a multi-disciplinary, holistic approach. Currently, he is actively working on machine learning, artificial intelligence, and deep learning projects. He has expertise in the following areas:

  • Quantitative investing: equities, futures and options, and derivatives engineering
  • Econometrics: time series analysis, statistical modeling
  • Algorithms: parametric, non-parametric, and ensemble machine learning algorithms
  • Code: R programming, Python, Scala, Excel VBA, SQL, and big data ecosystems.
  • Data analysis: Quandl and Quantopian
  • Strategies: trend following, mean reversion, cointegration, Monte-Carlo srimulations, Value at Risk, Credit Risk Modeling and Credit Rating
  • Data visualization : Tableau and Matplotlib