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Practical Data Analysis

You're reading from   Practical Data Analysis Pandas, MongoDB, Apache Spark, and more

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Product type Paperback
Published in Sep 2016
Publisher
ISBN-13 9781785289712
Length 338 pages
Edition 2nd Edition
Languages
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Authors (2):
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Hector Cuesta Hector Cuesta
Author Profile Icon Hector Cuesta
Hector Cuesta
Dr. Sampath Kumar Dr. Sampath Kumar
Author Profile Icon Dr. Sampath Kumar
Dr. Sampath Kumar
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Toc

Table of Contents (16) Chapters Close

Preface 1. Getting Started 2. Preprocessing Data FREE CHAPTER 3. Getting to Grips with Visualization 4. Text Classification 5. Similarity-Based Image Retrieval 6. Simulation of Stock Prices 7. Predicting Gold Prices 8. Working with Support Vector Machines 9. Modeling Infectious Diseases with Cellular Automata 10. Working with Social Graphs 11. Working with Twitter Data 12. Data Processing and Aggregation with MongoDB 13. Working with MapReduce 14. Online Data Analysis with Jupyter and Wakari 15. Understanding Data Processing using Apache Spark

Data preparation

In Chapter 11, Working with Twitter Data, we explored how to create a bag of words from the Tweets Sentiment140 dataset. In this chapter, we will complement the example using MongoDB. First, we will prepare and transform the dataset from CSV into a JSON format in order to add it into a MongoDB collection.

Tip

We can download the Sentiment140 training and test data at http://help.sentiment140.com/for-students.

We will download and open the test data; the columns represent sentiment, id, date, and via, user, and text. The first five records will look similar to this:

4,1,Mon May 11 03:21:41 UTC 2009,kindle2,yamarama,@mikefish Fair enough. But i have the Kindle2 and I think it's perfect :)
4,2,Mon May 11 03:26:10 UTC 2009, jquery,dcostalis,Jquery is my new best friend.
4,3,Mon May 11 03:27:15 UTC 2009,twitter,PJ_King,Loves twitter
4,4,Mon May 11 03:29:20 UTC 2009,obama,mandanicole,how can you not love Obama? he makes jokes about himself.
4,5,Mon May 11 05:22:12 UTC 2009,lebron...
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