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

Node.js stats-analysis package

The stats-analysis package has many of the common statistics you may want to perform on your data. You would have to install this package using npm as explained previously.

If we had a small set of people's temperatures to work with, we could get some of the statistics on the data readily, using this script:

const stats = require("stats-analysis");
var arr = [98, 98.6, 98.4, 98.8, 200, 120, 98.5];
//standard deviation
var my_stddev = stats.stdev(arr).toFixed(2);
var my_mean = stats.mean(arr).toFixed(2);
var my_median = stats.median(arr);
//median absolute deviation
var my_mad = stats.MAD(arr);
// Get the index locations of the outliers in the data set
var my_outliers = stats.indexOfOutliers(arr);
// Remove the outliers
var my_without_outliers = stats.filterOutliers(arr);
//display our stats
console.log("Raw data is ", arr);
console.log("Standard Deviation is ", my_stddev);
console.log("Mean is ", my_mean);
console.log("Median is ", my_median...