Book Image

Learning Jupyter

By : Dan Toomey
Book Image

Learning Jupyter

By: Dan Toomey

Overview of this book

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, machine learning, and much more. This book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next we’ll help you will learn to integrate Jupyter system with different programming languages such as R, Python, JavaScript, and Julia and explore the various versions and packages that are compatible with the Notebook system. Moving ahead, you master interactive widgets, namespaces, and working with Jupyter in a multiuser mode. Towards the end, you will use Jupyter with a big data set and will apply all the functionalities learned throughout the book.
Table of Contents (16 chapters)
Learning Jupyter
About the Author
About the Reviewer

Scala case classes

A case class is a simplified type that can be used without calling out new Classname(..). For example, we could have this script, which defines a case class and uses it:

case class Car(brand: String, model: String)
val buickLeSabre = Car("Buick", "LeSabre")

So, we have a case class called Car. We make an instance of that class called buickLeSabre.

Case classes are most useful for pattern matching since we can easily construct complex objects and examine their contents. Here's an example:

def carType(car: Car) = car match {
  case Car("Honda", "Accord") => "sedan"
  case Car("GM", "Denali") => "suv"
  case Car("Mercedes", "300") => "luxury"
  case Car("Buick", "LeSabre") => "sedan"
  case _ => "Car: is of unknown type"
val typeOfBuick = carType(buickLeSabre)

We define a pattern match block (as in the previous section of this chapter). In the match, we look at a Car object that has brand = GM, model = Denali, and so on. For each of the models of interest...