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

Investigating the makes and models of automobiles with Python


To continue our investigation of this dataset, we are going to examine the makes and models of the various automobiles more closely, repeating many of the steps from the previous chapter while translating from R to Python.

Getting ready

If you've completed the previous recipe, you should have everything you need in order to continue.

How to do it…

The following steps will lead us through our investigation:

  1. Let's look at how makes and models of cars inform us about fuel efficiency over time. First, let's look at the frequency of makes and models of cars available in the U.S., concentrating on 4-cylinder cars. To select the 4-cylinder cars, we first make the cylinders variable unique to see what the possible values are:

    pd.unique(vehicles_non_hybrid.cylinders)
    array([4.0, 12.0, 8.0, 6.0, 5.0, 10.0, 2.0, 3.0, 16.0, 6, 8, 12, 4, 3, 5, 2, 10, 16, nan], dtype=object)
    

    Note

    Both 4.0 and 4 are listed as unique values; this fact should raise...