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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
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Tomasz Drabas
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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

Building the graph


Now that we've imported our data, let's build our graph. To do this, we're going to build the structure for our vertices and edges. At the time of writing, GraphFrames requires a specific naming convention for vertices and edges:

  • The column representing the vertices needs to have the name ofid. In our case, the vertices of our flight data are the airports. Therefore, we will need to rename the IATA airport code to id in our airports DataFrame.

  • The columns representing the edges need to have a source (src) and destination (dst). For our flight data, the edges are the flights, therefore the src and dst are the origin and destination columns from the departureDelays_geo DataFrame.

To simplify the edges for our graph, we will create the tripEdges DataFrame with a subset of the columns available within the departureDelays_Geo DataFrame. As well, we created a tripVertices DataFrame that simply renames the IATA column to id to match the GraphFrame naming convention:

# Note, ensure...
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