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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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Table of Contents (13) Chapters Close

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

Checking for duplicates, missing observations, and outliers

Until you have fully tested the data and proven it worthy of your time, you should neither trust it nor use it. In this section, we will show you how to deal with duplicates, missing observations, and outliers.

Duplicates

Duplicates are observations that appear as distinct rows in your dataset, but which, upon closer inspection, look the same. That is, if you looked at them side by side, all the features in these two (or more) rows would have exactly the same values.

On the other hand, if your data has some form of an ID to distinguish between records (or associate them with certain users, for example), then what might initially appear as a duplicate may not be; sometimes systems fail and produce erroneous IDs. In such a situation, you need to either check whether the same ID is a real duplicate, or you need to come up with a new ID system.

Consider the following example:

df = spark.createDataFrame([
        (1, 144.5, 5.9, 33, &apos...
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