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

Fundamentals of Plotly.js

One of the major advantages of using Plotly.js is the fact that it is easy to get started, and there are lots of configurations you can specify to make your plot better. In the section, we are going to cover some of the important configuration options available, and we'll also show you how to specify these options.

Before we go further, let's understand how to get data into Plotly.

Data format

To make a two-dimensional (2D) plot, which is the most common type of plot you'll be creating, you have to pass an object of arrays with x and y keys, as shown in the following code example:

const trace1 = { 
  x: [20, 30, 40],
  y: [2, 4, 6]
}

Note

A data point is normally called a trace in Plotly. This is because you can plot more than one data point in a single graph. An example of this is provided here:

var data = [trace1, trace2]

Plotly.newPlot("my_div", data);

The x and y arrays can contain...