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

Exploring the geographic information available in profiles


The Twitter users' profiles contain two different, potential sources of geographic information: the profile itself and the most recently tweeted status update. We will utilize both options in this recipe with an eye towards usability in constructing a geographic visualization of our friends.

Getting ready

You will need the harvested friends' and/or followers' profiles from Twitter, as directed in the previous recipes.

How to do it...

Perform the following steps to extract the geographic data that we need to visualize the approximate locations of our connections:

  1. We start this exercise in IPython or your favorite Python REPL. Load your friends' profiles from the file:
In[1]: fname = 'test_friends_profiles.json' 
In[2]: load_json(fname)
  1. Next, we build lists from all of the values of the geo_enabled field in the user profiles' data structures for our friends. Then, we use the count method to find the number of user profiles that have the...