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Scala for Data Science

You're reading from   Scala for Data Science Leverage the power of Scala with different tools to build scalable, robust data science applications

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Product type Paperback
Published in Jan 2016
Publisher
ISBN-13 9781785281372
Length 416 pages
Edition 1st Edition
Languages
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Author (1):
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Pascal Bugnion Pascal Bugnion
Author Profile Icon Pascal Bugnion
Pascal Bugnion
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Table of Contents (17) Chapters Close

Preface 1. Scala and Data Science FREE CHAPTER 2. Manipulating Data with Breeze 3. Plotting with breeze-viz 4. Parallel Collections and Futures 5. Scala and SQL through JDBC 6. Slick – A Functional Interface for SQL 7. Web APIs 8. Scala and MongoDB 9. Concurrency with Akka 10. Distributed Batch Processing with Spark 11. Spark SQL and DataFrames 12. Distributed Machine Learning with MLlib 13. Web APIs with Play 14. Visualization with D3 and the Play Framework A. Pattern Matching and Extractors Index

A whirlwind tour of JSON


JSON is a format for transferring structured data. It is flexible, easy for computers to generate and parse, and relatively readable for humans. It has become very common as a means of persisting program data structures and transferring data between programs.

JSON has four basic types: Numbers, Strings, Booleans, and null, and two compound types: Arrays and Objects. Objects are unordered collections of key-value pairs, where the key is always a string and the value can be any simple or compound type. We have already seen a JSON object: the data returned by the API call api.github.com/users/odersky.

Arrays are ordered lists of simple or compound types. For instance, type api.github.com/users/odersky/repos in your browser to get an array of objects, each representing a GitHub repository:

[
  {
    "id": 17335228,
    "name": "dotty",
    "full_name": "odersky/dotty",
    ...
  },
  {
    "id": 15053153,
    "name": "frontend",
    "full_name": "odersky/frontend",
   ...
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