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 different DataFrame operation components

In this section, we will create different DataFrame operation components and also implement the Side Plane for DataFrame operation components. Danfo.js contains a lot of DataFrame operations. If we were to design a component for each, it would be very stressful and redundant.

To prevent the creation of a component for each DataFrame method, we group each of the DataFrame operations based on their (keyword) argument, that is, based on the variable passed into them. For example, there are some DataFrame methods that take in only the axis of operation, hence we can group these types of methods together.

Here is a list of DataFrame operation components to be created and the DataFrame method grouped under them:

  • The Arithmetic component: This contains the DataFrame method whose argument is only the axis of operation, which can be either 1 or 0. The methods used to carry out arithmetic operations on DataFrame include min, max...