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Learning PySpark

You're reading from Learning PySpark Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0

Product type Paperback

Published in Feb 2017

Publisher Packt

ISBN-13 9781786463708

Length 274 pages

Edition 1st Edition

Languages

Python

Tools

Apache Spark

Concepts

Data Processing

Authors (2):

Denny Lee

Tomasz Drabas

View More author details

Table of Contents (13) Chapters

Preface

1. Understanding Spark

2. Resilient Distributed Datasets FREE CHAPTER

3. DataFrames

4. Prepare Data for Modeling

5. Introducing MLlib

6. Introducing the ML Package

7. GraphFrames

8. TensorFrames

9. Polyglot Persistence with Blaze

10. Structured Streaming

11. Packaging Spark Applications

Index

Getting to know your data

In order to build a statistical model in an informed way, an intimate knowledge of the dataset is necessary. Without knowing the data it is possible to build a successful model, but it is then a much more arduous task, or it would require more technical resources to test all the possible combinations of features. Therefore, after spending the required 80% of the time cleaning the data, we spend the next 15% getting to know it!

Descriptive statistics

I normally start with descriptive statistics. Even though the DataFrames expose the .describe() method, since we are working with MLlib, we will use the .colStats(...) method.

Note

A word of warning: the .colStats(...) calculates the descriptive statistics based on a sample. For real world datasets this should not really matter but if your dataset has less than 100 observations you might get some strange results.

The method takes an RDD of data to calculate the descriptive statistics of and return a MultivariateStatisticalSummary...

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Authors (2)

Drabas

Drabas

Tomasz Drabas is a Data Scientist working for Microsoft and currently residing in the Seattle area. He has over 12 years' international experience in data analytics and data science in numerous fields: advanced technology, airlines, telecommunications, finance, and consulting. Tomasz started his career in 2003 with LOT Polish Airlines in Warsaw, Poland while finishing his Master's degree in strategy management. In 2007, he moved to Sydney to pursue a doctoral degree in operations research at the University of New South Wales, School of Aviation; his research crossed boundaries between discrete choice modeling and airline operations research. During his time in Sydney, he worked as a Data Analyst for Beyond Analysis Australia and as a Senior Data Analyst/Data Scientist for Vodafone Hutchison Australia among others. He has also published scientific papers, attended international conferences, and served as a reviewer for scientific journals. In 2015 he relocated to Seattle to begin his work for Microsoft. While there, he has worked on numerous projects involving solving problems in high-dimensional feature space.

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Lee

Lee

Joanna Lee has more than 8 years of experience in game development. She has designed and programmed various video games. She first started working with Unreals game engine in 2005 and is very excited to be able to author a book about the newest Unreal Engine 4. She has also worked with many other engines as well as reviewed books and videos on Cry Engine 4.

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