Book Image

Building Data-Driven Applications with Danfo.js

By : Rising Odegua, Stephen Oni
Book Image

Building Data-Driven Applications with Danfo.js

By: Rising Odegua, Stephen Oni

Overview of this book

Most data analysts use Python and pandas for data processing for the convenience and performance these libraries provide. However, JavaScript developers have always wanted to use machine learning in the browser as well. This book focuses on how Danfo.js brings data processing, analysis, and ML tools to JavaScript developers and how to make the most of this library to build data-driven applications. Starting with an overview of modern JavaScript, you’ll cover data analysis and transformation with Danfo.js and Dnotebook. The book then shows you how to load different datasets, combine and analyze them by performing operations such as handling missing values and string manipulations. You’ll also get to grips with data plotting, visualization, aggregation, and group operations by combining Danfo.js with Plotly. As you advance, you’ll create a no-code data analysis and handling system and create-react-app, react-table, react-chart, Draggable.js, and tailwindcss, and understand how to use TensorFlow.js and Danfo.js to build a recommendation system. Finally, you’ll build a Twitter analytics dashboard powered by Danfo.js, Next.js, node-nlp, and Twit.js. By the end of this app development book, you’ll be able to build and embed data analytics, visualization, and ML capabilities into any JavaScript app in server-side Node.js or the browser.
Table of Contents (18 chapters)
1
Section 1: The Basics
3
Section 2: Data Analysis and Manipulation with Danfo.js and Dnotebook
10
Section 3: Building Data-Driven Applications

Writing interactive code

In this section, we'll highlight some important things to know when writing interactive code in Dnotebook.

Loading external packages

Importing external packages into your notebook is very important when writing JavaScript, and as such, Dnotebook has an inbuilt function called load_package for doing this.

The load_package method helps you to easily add external packages/libraries to your notebook via their CDN links. For instance, to load Tensorflow.js and Plotly.js, you can pass their CDN links to the load_package function, as shown in the following code:

load_package(["https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]/dist/tf.min.js","https://cdn.plot.ly/plotly-latest.min.js"])

This loads the packages and adds them to the notebook state so they can be accessed from any cell. In the following section, we use the Plotly library we just imported.

Add the following code to a new cell in your notebook:

trace1 = {...