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Mastering Python Data Visualization

You're reading from   Mastering Python Data Visualization Generate effective results in a variety of visually appealing charts using the plotting packages in Python

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Product type Paperback
Published in Oct 2015
Publisher
ISBN-13 9781783988327
Length 372 pages
Edition 1st Edition
Languages
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Toc

Table of Contents (11) Chapters Close

Preface 1. A Conceptual Framework for Data Visualization 2. Data Analysis and Visualization FREE CHAPTER 3. Getting Started with the Python IDE 4. Numerical Computing and Interactive Plotting 5. Financial and Statistical Models 6. Statistical and Machine Learning 7. Bioinformatics, Genetics, and Network Models 8. Advanced Visualization A. Go Forth and Explore Visualization Index

Analysis of social networks

Accessing data from social networks, such as LinkedIn, Facebook, or Twitter, used to be much simpler and easier several years ago. Now, most of the APIs have restrictions. Also, the accessing methods are a little bit more involved. First, one has to get authentication (which used to be the case even earlier) and then use methods that access either friends or connections. We have only chosen Twitter for demonstrating the analysis of social network data here, but you can also find other social media data in a similar way.

In order to access Twitter data, as we noticed from the previous chapters (when we discussed word clouds), you have to get authentication keys to access their APIs. There are four keys: CONSUMER_KEY, CONSUMER_SECRET, ACCESS_TOKEN_KEYS, and ACCESS_TOKEN_SECRET. Once these credentials are verified via Python successfully, you can call GetFriends() and GetFollowers() to get the list of friends and followers. There are many packages available in Python...

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