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 higher-order functions

A higher-order function either takes other functions as arguments or returns a function as its result.

We can use this example script:

def squared(x: Int): Int = x * x
def cubed(x: Int): Int = x * x * x
def process(a: Int, processor: Int => Int): Int = {processor(a) }
val fiveSquared = process(5, squared)
val sevenCubed = process(7, cubed)

We define two functions; one squares the number passed and the other cubes the number passed.

Next, we define the higher-order function that takes the number to work on and the processor to apply.

Lastly, we call each one. For example, we call process() with 5 and the squared() function. The process() function passes the 5 to the squared() function and returns the result:

We take advantage of the Scala's engine automatically printing out variable values to see the result expected.