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

Introducing Series and DataFrames

Danfo.js exposes two main data structures, Series and DataFrames, to which all data manipulation can be done easily. DataFrames and Series provide a general representation for different types of data, hence the same data handling process can be applied to datasets with different formats.

In this section, we will look at different means of creating Series and DataFrames. We will see how to handle data in DataFrame and Series format. We will also look into different DataFrame and Series methods for data handling.

We will start this section by looking at how to handle data in a Series data structure.

Series

A Series provides an entry point to handling one-dimensional data, such as a single array with a sequence of values of the same data type.

In this section, we will get familiar with Series data structures with the following Series methods:

 *  table() and print() method:let sdata= new dfd.Series([1,3,5,7,9,11])
table( sdata...