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Hands-On Big Data Analytics with PySpark

You're reading from   Hands-On Big Data Analytics with PySpark Analyze large datasets and discover techniques for testing, immunizing, and parallelizing Spark jobs

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781838644130
Length 182 pages
Edition 1st Edition
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Authors (3):
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James Cross James Cross
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James Cross
Bartłomiej Potaczek Bartłomiej Potaczek
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Bartłomiej Potaczek
Rudy Lai Rudy Lai
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Rudy Lai
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Table of Contents (15) Chapters Close

Preface 1. Installing Pyspark and Setting up Your Development Environment FREE CHAPTER 2. Getting Your Big Data into the Spark Environment Using RDDs 3. Big Data Cleaning and Wrangling with Spark Notebooks 4. Aggregating and Summarizing Data into Useful Reports 5. Powerful Exploratory Data Analysis with MLlib 6. Putting Structure on Your Big Data with SparkSQL 7. Transformations and Actions 8. Immutable Design 9. Avoiding Shuffle and Reducing Operational Expenses 10. Saving Data in the Correct Format 11. Working with the Spark Key/Value API 12. Testing Apache Spark Jobs 13. Leveraging the Spark GraphX API 14. Other Books You May Enjoy

Pivot tabling with key-value paired data points

Pivot tables are very simple and easy to use. What we are going to do is use big datasets, such as the KDD cup dataset, and group certain values by certain keys.

For example, we have a dataset of people and their favorite fruits. We want to know how many people have apple as their favorite fruit, so we will group the number of people, which is the value, against a key, which is the fruit. This is the simple concept of a pivot table.

We can use the map function to move the KDD datasets into a key-value pair paradigm. We map feature 41 of the dataset using a lambda function in the kv key value, and we append the value as follows:

kv = csv.map(lambda x: (x[41], x))
kv.take(1)

We use feature 41 as the key, and the value is the data point, which is x. We can use the take function to take one of these transformed rows to see how it looks...

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