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Practical Data Science Cookbook, Second Edition

You're reading from   Practical Data Science Cookbook, Second Edition Data pre-processing, analysis and visualization using R and Python

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781787129627
Length 434 pages
Edition 2nd Edition
Languages
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Authors (5):
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Anthony Ojeda Anthony Ojeda
Author Profile Icon Anthony Ojeda
Anthony Ojeda
Prabhanjan Narayanachar Tattar Prabhanjan Narayanachar Tattar
Author Profile Icon Prabhanjan Narayanachar Tattar
Prabhanjan Narayanachar Tattar
ABHIJIT DASGUPTA ABHIJIT DASGUPTA
Author Profile Icon ABHIJIT DASGUPTA
ABHIJIT DASGUPTA
Sean P Murphy Sean P Murphy
Author Profile Icon Sean P Murphy
Sean P Murphy
Bhushan Purushottam Joshi Bhushan Purushottam Joshi
Author Profile Icon Bhushan Purushottam Joshi
Bhushan Purushottam Joshi
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Table of Contents (12) Chapters Close

Preface 1. Preparing Your Data Science Environment FREE CHAPTER 2. Driving Visual Analysis with Automobile Data with R 3. Creating Application-Oriented Analyses Using Tax Data and Python 4. Modeling Stock Market Data 5. Visually Exploring Employment Data 6. Driving Visual Analyses with Automobile Data 7. Working with Social Graphs 8. Recommending Movies at Scale (Python) 9. Harvesting and Geolocating Twitter Data (Python) 10. Forecasting New Zealand Overseas Visitors 11. German Credit Data Analysis

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...
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