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Jupyter for Data Science

You're reading from   Jupyter for Data Science Exploratory analysis, statistical modeling, machine learning, and data visualization with Jupyter

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781785880070
Length 242 pages
Edition 1st Edition
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Author (1):
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 Toomey Toomey
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Toomey
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Table of Contents (17) Chapters Close

Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Jupyter and Data Science FREE CHAPTER 2. Working with Analytical Data on Jupyter 3. Data Visualization and Prediction 4. Data Mining and SQL Queries 5. R with Jupyter 6. Data Wrangling 7. Jupyter Dashboards 8. Statistical Modeling 9. Machine Learning Using Jupyter 10. Optimizing Jupyter Notebooks

Loading JSON into Spark


Spark can also access JSON data for manipulation. Here we have an example that:

  • Loads a JSON file into a Spark data frame
  • Examines the contents of the data frame and displays the apparent schema
  • Like the other preceding data frames, moves the data frame into the context for direct access by the Spark session
  • Shows an example of accessing the data frame in the Spark context

The listing is as follows:

Our standard includes for Spark:

from pyspark import SparkContextfrom pyspark.sql import SparkSession sc = SparkContext.getOrCreate()spark = SparkSession(sc)

Read in the JSON and display what we found:

#using some data from file from https://gist.github.com/marktyers/678711152b8dd33f6346df = spark.read.json("people.json")df.show()

I had a difficult time getting a standard JSON to load into Spark. Spark appears to expect one record of data per list of the JSON file versus most JSON I have seen pretty much formats the record layouts with indentation and the like.

Note

Notice the use...

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