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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

Aggregation framework

The MongoDB aggregation framework is an easy way to get aggregated values and works fine with sharding without having to use MapReduce (see Chapter 13, Working with MapReduce). Aggregation framework is flexible, functional, and simple to implement operation pipelines and computational expressions. Aggregation Framework uses a declarative JSON format implemented in C++ instead of JavaScript, which improves the performance. The aggregate method prototype is shown here:

db.collection.aggregate( [<pipeline>] )

In the following code, we can see a simple counting by grouping the sentiment field with the aggregate method. In this case, the pipeline is only using the $group operator:

from pymongo import MongoClientcon = MongoClient()
db = con.Corpustweets = db.tweets
results = tweets.aggregate([         
         {"$group": {"_id": "$sentiment", "count": {"$sum": 1}}}     ])
for doc in results["result"]:...
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