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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

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781786463708
Length 274 pages
Edition 1st Edition
Languages
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Authors (2):
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Denny Lee Denny Lee
Author Profile Icon Denny Lee
Denny Lee
Tomasz Drabas Tomasz Drabas
Author Profile Icon Tomasz Drabas
Tomasz Drabas
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Toc

Table of Contents (13) Chapters Close

Preface 1. Understanding Spark FREE CHAPTER 2. Resilient Distributed Datasets 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

Loading and transforming the data


Even though MLlib is designed with RDDs and DStreams in focus, for ease of transforming the data we will read the data and convert it to a DataFrame.

Note

The DStreams are the basic data abstraction for Spark Streaming (see http://bit.ly/2jIDT2A)

Just like in the previous chapter, we first specify the schema of our dataset.

Note

Note that here (for brevity), we only present a handful of features. You should always check our GitHub account for this book for the latest version of the code: https://github.com/drabastomek/learningPySpark.

Here's the code:

import pyspark.sql.types as typ
labels = [
    ('INFANT_ALIVE_AT_REPORT', typ.StringType()),
    ('BIRTH_YEAR', typ.IntegerType()),
    ('BIRTH_MONTH', typ.IntegerType()),
    ('BIRTH_PLACE', typ.StringType()),
    ('MOTHER_AGE_YEARS', typ.IntegerType()),
    ('MOTHER_RACE_6CODE', typ.StringType()),
    ('MOTHER_EDUCATION', typ.StringType()),
    ('FATHER_COMBINED_AGE', typ.IntegerType()),
    ('FATHER_EDUCATION'...
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