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 d3 package


The d3 package has data access functionality. In this case, we will read from a tab-separated file and compute an average. Note the use of the underscore variable name for lodash. Variable names starting with an underscore are assumed to be private. However, in this case, it is just a play on the name of the package we are using, which is lodash, or underscore. lodash is also a widely used a utility package.

For this script to execute, I had to do the following:

  • Install d3
  • Install lodash
  • Install isomorphic-fetch (npm install --save isomorphic-fetch es6-promise)
  • Import isomorphic-fetch

The script we will use is as follows:

var fs = require("fs");
var d3 = require("d3");
var _ = require("lodash");
var _ = require("isomorphic-fetch");

//read and parse the animals file
console.log("Animal\tWeight");
d3.csv("http://www.dantoomeysoftware.com/data/animals.csv", function(data) {
    console.log(data.name + '\t' + data.avg_weight);
});

This assumes that we have previously loaded the fs...