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

Calculating statistics

Danfo.js comes with some important statistical and mathematical functions. These functions can be used to generate a summary or descriptive statistics of entire DataFrames, as well as a single Series. In datasets, statistics are important because they can give us better insights into data.

In the following sections, we'll use the popular Titanic dataset, which you can download from the following GitHub repository: https://github.com/PacktPublishing/Building-Data-Driven-Applications-with-Danfo.js/blob/main/Chapter03/data/titanic.csv.

First, let's load the dataset into Dnotebook using the read_csv function, as shown in the following code:

var df //using var so df is available to all cells
load_csv("https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv")
.then((data)=>{
  df = data
})

The preceding code loads the Titanic dataset from the specified URL and persists it in the df variable.

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