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

Julia unit testing

As a full language, Julia has unit testing abilities to make sure your code is performing as expected. The unit tests usually reside in the tests folder.

Two of the standard functions available for unit testing in Julia are FactCheck and Base.Test. They both do the same thing, but react differently to failed tests. FactCheck will generate an error message that will not stop processing on a failure. If you provide an error handler, that error handler can take control of the test.

Base.Test will throw an exception and stop processing on the first test failure. In that regard, it is probably not useful as a unit testing function so much as a runtime test that you may put in place to make sure parameters are within reason, or otherwise, just stop processing before something bad happens.

Both packages are built-in to the standard Julia distributions.

As an example, we can create a unit tests notebook that does the same tests and see the resulting, different responses for errors...