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

Profiling Python code using built-in Python functions


Python comes with two profiling options, profile and cProfile, which share the same interface but differ in their impact on the profiled program's performance. The profile module is pure Python but adds significant overhead to the software being profiled and, therefore, isn't well suited for long running programs. The cProfile profiling option is a C extension and has much lower overhead, thus impacting program execution times to a lesser degree. As a result, we will use cProfile.

Getting ready

Part of the beauty of Python is its batteries included nature. The cProfile and profile profiling options both come built in to the Python distribution that you are using.

How to do it…

Perform the following steps to benchmark the code using cProfile:

  1. To benchmark the preceding asa code, we need to be at the command line in the directory of the source code.

  2. Type the following into the command line:

    python -m cProfile asa.py '1R0R.pdb'
    
  3. You should see...