Book Image

Practical Data Science Cookbook, Second Edition - Second Edition

By : Prabhanjan Narayanachar Tattar, Bhushan Purushottam Joshi, Sean Patrick Murphy, ABHIJIT DASGUPTA, Anthony Ojeda
Book Image

Practical Data Science Cookbook, Second Edition - Second Edition

By: Prabhanjan Narayanachar Tattar, Bhushan Purushottam Joshi, Sean Patrick Murphy, ABHIJIT DASGUPTA, Anthony Ojeda

Overview of this book

As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don’t. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides 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 using the two most popular programming languages for data analysis—R and Python.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface

Analyzing and visualizing the top income data of the US


Now that we've imported and explored the top incomes dataset a bit, let's drill down on a specific country and conduct some analyses on their income distribution data. In particular, the United States has excellent data relating to the top incomes by percentile, so we'll use the data of the United States in the following exercises. If you choose other countries to leverage their datasets, beware that you may need to use different fields to get the same analyses.

Getting ready

In order to conduct our analyses, we're going to create a few helper methods that we will use continually throughout this chapter. Application-oriented analyses typically produce reusable code that performs singular tasks in order to adapt quickly to changing data or analysis requirements. In particular, let's create two helper functions: one that extracts data by a particular country and one that creates a time series from a set of particular rows:

In [22]: def...