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

Series data accessors

Danfo.js provides data type-specific methods under various accessors. Accessors are namespaces within the Series object that can only be applied/called on specific data types. Two accessors are currently provided for string and date-time Series, and in this section, we'll discuss each and provide some examples for clarity.

String accessors

String columns in DataFrames or a Series with a dtype string can be accessed under the str accessor. Calling the str accessor on such an object exposes numerous string functions for manipulating the data. We will present some examples in this section.

Let's assume we have a Series that contains the following fields:

data = ['lower boy', 'capitals', 'sentence', 'swApCaSe']
sf = new dfd.Series(data)
table(sf)

Printing this Series results in the following diagram:

Figure 4.50 – Result of applying concat to three Series along the column...