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

Summary

In this chapter, we extensively discussed the groupby operation as implemented in Danfo.js. We discussed grouping data and mentioned that at the moment, Danfo.js only supports grouping by single and double columns; there is a plan to make this more flexible in coming versions of Danfo.js. We also showed how to iterate through grouped data and access group keys and their associated grouped data. We looked at how to obtain grouped data associated with a group key without looping.

We also saw how the .apply method gives us the ability to create custom data aggregation functions for our grouped data, and finally, we demonstrated how to perform different aggregation functions on different columns of grouped data at the same time.

This chapter equipped us with the knowledge of grouping our data, and more essentially, it introduced us to the internals of Danfo.js. With this, we can reshape the groupby method to our desired taste and have the ability to contribute to Danfo.js...