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Python Data Analysis, Second Edition

You're reading from   Python Data Analysis, Second Edition Data manipulation and complex data analysis with Python

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781787127487
Length 330 pages
Edition 2nd Edition
Languages
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Table of Contents (16) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. The Pandas Primer 4. Statistics and Linear Algebra 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources

Performing MapReduce with Jug

Jug is a distributed computing framework that uses tasks as central parallelization units. Jug uses filesystems or the Redis server as backends. The Redis server was discussed in Chapter 8, Working with Databases. Install Jug with the following command:

$ pip3 install jug

MapReduce (see http://en.wikipedia.org/wiki/MapReduce) is a distributed algorithm used to process large datasets with a cluster of computers. The algorithm consists of a Map and a Reduce phase. During the Map phase, data is processed in a parallel fashion. The data is split up into parts, and on each part, filtering or other operations are performed. In the Reduce phase, the results from the Map phase are aggregated, for instance, to create a statistics report.

If we have a list of text files, we can compute word counts for each file. This can be done during the Map phase. At the end, we can combine individual word counts into a corpus word frequency dictionary. Jug has MapReduce functionality...

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