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

Creating scatter plots with Danfo.js

We can easily make scatter plots by specifying the plot type to be scatter. For example, using the code from the preceding section, Creating line charts with Danfo.js, we can just change the plot type from line to scatter, and we get a scatter plot of the selected columns, as demonstrated in the following code block:

var layout = {
  title: "Time series plot of AAPL open and close points",
  width: 1000,
  yaxis: {
    title: 'AAPL open points',
  },
  xaxis: {
    title: 'Date',
  }
}
var config = {
  columns: ["AAPL.Open", "AAPL.Close"],
  layout
}
new_df.plot(this_div()).scatter(config)

Running the preceding code cell gives the following output:

Figure 6.7 – A scatter plot on two columns

If you need to make a scatter plot between two specific...