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Learning Data Mining with Python

You're reading from   Learning Data Mining with Python Use Python to manipulate data and build predictive models

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787126787
Length 358 pages
Edition 2nd Edition
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Table of Contents (14) Chapters Close

Preface 1. Getting Started with Data Mining FREE CHAPTER 2. Classifying with scikit-learn Estimators 3. Predicting Sports Winners with Decision Trees 4. Recommending Movies Using Affinity Analysis 5. Features and scikit-learn Transformers 6. Social Media Insight using Naive Bayes 7. Follow Recommendations Using Graph Mining 8. Beating CAPTCHAs with Neural Networks 9. Authorship Attribution 10. Clustering News Articles 11. Object Detection in Images using Deep Neural Networks 12. Working with Big Data 13. Next Steps...

Getting follower information from Twitter


With our initial set of users, we now need to get the friends of each of these users. A friend is a person whom the user is following. The API for this is called friends/ids, and it has both good and bad points. The good news is that it returns up to 5,000 friend IDs in a single API call. The bad news is that you can only make 15 calls every 15 minutes, which means it will take you at least 1 minute per user to get all followers—more if they have more than 5,000 friends (which happens more often than you may think).

The code is similar to the code from our previous API usage (obtaining tweets). We will package it as a function, as we will use this code in the next two sections. Our function takes a twitter user's ID value, and returns their friends. While it may be surprising to some, many Twitter users have more than 5,000 friends. Due to this we will need to use Twitter's pagination function, which lets Twitter return multiple pages of data through...

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