Book Image

Practical Data Science Cookbook

By : Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, Abhijit Dasgupta
Book Image

Practical Data Science Cookbook

By: Tony Ojeda, Sean Patrick Murphy, Benjamin Bengfort, Abhijit Dasgupta

Overview of this book

<p>As increasing amounts of data is generated each year, the need to analyze and operationalize it is more important than ever. Companies that know what to do with their data will have a competitive advantage over companies that don't, and this will drive a higher demand for knowledgeable and competent data professionals.</p> <p>Starting with the basics, this book will cover how to set up your numerical programming environment, introduce you to the data science pipeline (an iterative process by which data science projects are completed), and guide you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples in the two most popular programming languages for data analysis—R and Python.</p>
Table of Contents (18 chapters)
Practical Data Science Cookbook
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Reading through the code


The Python code to be optimized calculates the Accessible Surface Area (ASA) of a molecule. The ASA quantifies the surface area of a molecule that is open or available to a solvent and has many uses in biology and biochemistry. For the purposes of this recipe, a deeper background into the ASA is unnecessary. However, for those curious, I highly recommend that you read Bosco K. Ho's excellent post about both this code and the ASA. He is the author of the original code that was written for clarity and accuracy but not speed.

For the purpose of optimization, this code was going to be integrated into a web application that would compute the ASA for a molecule upon an upload by the user. As all calculations were computed synchronously, the longer the code takes, the longer the user waits for a result.

In this recipe, we are going to read through the critical portions of the code, which are contained primarily in the asa.py source file, in order to gain an understanding...