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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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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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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. Statistics and Linear Algebra 4. pandas Primer 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
Index

Performing MapReduce with Jug

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

$ pip 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 in 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.

Performing MapReduce with Jug

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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