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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

Determining the most popular non-stop flights


Expanding upon our tripGraph GraphFrame, the following query will allow us to find the most popular non-stop flights in the US (for this dataset):

# Determine the most popular non-stop flights
import pyspark.sql.functions as func
topTrips = tripGraph \
  .edges \
  .groupBy("src", "dst") \
  .agg(func.count("delay").alias("trips"))

# Show the top 20 most popular flights (single city hops)
display(topTrips.orderBy(topTrips.trips.desc()).limit(20))

Note, while we are using the delay column, we're just actually doing a count of the number of trips. Here's the output:

As can be observed from this query, the two most frequent non-stop flights are between LAX (Los Angeles) and SFO (San Francisco). The fact that these flights are so frequent indicates their importance in the airline market. As noted in the New York Times article from April 4, 2016, Alaska Air Sees Virgin America as Key to West Coast (http://nyti.ms/2ea1uZR), acquiring slots at these two...

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