Book Image

Learning Jupyter 5 - Second Edition

Book Image

Learning Jupyter 5 - Second Edition

Overview of this book

The Jupyter Notebook 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, and machine learning. Learning Jupyter 5 will help you get to grips with interactive computing using real-world examples. The book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next, you will learn to integrate the Jupyter system with different programming languages such as R, Python, Java, JavaScript, and Julia, and explore various versions and packages that are compatible with the Notebook system. Moving ahead, you will master interactive widgets and namespaces and work with Jupyter in a multi-user mode. By the end of this book, you will have used Jupyter with a big dataset and be able to apply all the functionalities you’ve explored throughout the book. You will also have learned all about the Jupyter Notebook and be able to start performing data transformation, numerical simulation, and data visualization.
Table of Contents (18 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Node.js stats-analysis package


The stats-analysis package has many of the common statistics that you may want to perform on your data. You will 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 by 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); 
 
//mean 
var my_mean = stats.mean(arr).toFixed(2); 
 
//median 
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...