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

R forecasting

For this example, we will forecast the Fraser River levels given the data from!ds=22nm&display=line . I was not able to find a suitable source so I extracted the data by hand from the site into a local file.

We will be using the R forecast package. You have to add this package to your setup (as described at the start of this chapter).

The R script we will be using is as follows:

fraser <- scan("fraser.txt")
fraser.ts <- ts(fraser, frequency=12, start=c(1913,3))
fraser.stl = stl(fraser.ts, s.window="periodic")

The output of interest in this example are the three plots: simple plot, monthly, and computed seasonal.

The simple plot (using the R plot command) is like the following screenshot. There is no apparent organization or structure:

The monthly plot (using the monthplot command) is like the following screenshot. River flows...