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Practical Data Analysis

You're reading from  Practical Data Analysis

Product type Book
Published in Oct 2013
Publisher Packt
ISBN-13 9781783280995
Pages 360 pages
Edition 1st Edition
Languages
Author (1):
Hector Cuesta Hector Cuesta
Profile icon Hector Cuesta
Toc

Table of Contents (24) Chapters close

Practical Data Analysis
Credits
Foreword
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started 2. Working with Data 3. Data 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 Disease with Cellular Automata 10. Working with Social Graphs 11. Sentiment Analysis of Twitter Data 12. Data Processing and Aggregation with MongoDB 13. Working with MapReduce 14. Online Data Analysis with IPython and Wakari Setting Up the Infrastructure Index

Group


An aggregation function is a type of function used in data processing by grouping values into categories, in order to find a significant meaning. The common aggregate functions include count, average, maximum , minimum , and sum. However, we may perform more complicated statistical functions such as mode or standard deviation. Typically the grouping is performed with the SQL GROUP BY statement, as shown in the following code, additionally we may use aggregation functions such as COUNT, MAX, MIN, SUM in order to retrieve summarized information:

SELECT sentiment, COUNT(*)
FROM Tweets
GROUP BY sentiment

In MongoDB, we may use the group function, which is similar to SQL Group By statement. However, the group function doesn't work in shared systems and the result size is limited to 10,000 documents (20,000 in the version 2.2 or newer). Due to this, the group function is not highly used. Nevertheless, it is an easy way to find aggregate information when we have only one MongoDB instance...

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