Book Image

Python Data Analysis - Second Edition

By : Ivan Idris
Book Image

Python Data Analysis - Second Edition

By: Ivan Idris

Overview of this book

Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.
Table of Contents (22 chapters)
Python Data Analysis - Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Key Concepts
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...