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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

Preparing your flights dataset


For this flights sample scenario, we will make use of two sets of data:

  • Airline On-Time Performance and Causes of Flight Delays: [http://bit.ly/2ccJPPM] This dataset contains scheduled and actual departure and arrival times, and delay causes as reported by US air carriers. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

  • Open Flights: Airports and airline data: [http://openflights.org/data.html] This dataset contains the list of US airport data including the IATA code, airport name, and airport location.

We will create two DataFrames – airports and departureDelays–which will make up our vertices and edges of our GraphFrame, respectively. We will be creating this flights sample application using Python.

As we are using a Databricks notebook for our example, we can make use of the /databricks-datasets/location, which contains numerous sample datasets. You can also download the data from:

  • depa rtureDelays.csv...

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