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Building Data-Driven Applications with Danfo.js

Building Data-Driven Applications with Danfo.js

By : Rising Odegua, Stephen Oni
3.8 (4)
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Building Data-Driven Applications with Danfo.js

Building Data-Driven Applications with Danfo.js

3.8 (4)
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)
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1
Section 1: The Basics
3
Section 2: Data Analysis and Manipulation with Danfo.js and Dnotebook
10
Section 3: Building Data-Driven Applications

Combining datasets

DataFrames and Series can be combined using built-in functions in Danfo.js. Methods such as danfo.merge and danfo.concat exist that, depending on the configurations, can help you combine datasets in different forms using familiar database-like joins.

In this section, we'll briefly talk about these join types, starting with the merge function.

DataFrame merge

The merge operation is similar to the database Join operation in that it performs join operations on columns or indexes found in the object. The signature of the merge operation is as follows:

danfo.merge({left, right, on, how}) 

Let's understand what each parameter entails:

  • left: The left-hand side DataFrame/Series you want to merge to.
  • right: The right-hand side DataFrame/Series you want to merge to.
  • on: The name(s) of the column(s) to join. These column(s) must be found in both the left and right DataFrames.
  • how: The how parameter specifies how the merge should...
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