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Building Data-Driven Applications with Danfo.js

You're reading from   Building Data-Driven Applications with Danfo.js A practical guide to data analysis and machine learning using JavaScript

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Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781801070850
Length 476 pages
Edition 1st Edition
Languages
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Authors (2):
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Stephen Oni Stephen Oni
Author Profile Icon Stephen Oni
Stephen Oni
Rising Odegua Rising Odegua
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Rising Odegua
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Toc

Table of Contents (18) Chapters Close

Preface 1. Section 1: The Basics
2. Chapter 1: An Overview of Modern JavaScript FREE CHAPTER 3. Section 2: Data Analysis and Manipulation with Danfo.js and Dnotebook
4. Chapter 2: Dnotebook - An Interactive Computing Environment for JavaScript 5. Chapter 3: Getting Started with Danfo.js 6. Chapter 4: Data Analysis, Wrangling, and Transformation 7. Chapter 5: Data Visualization with Plotly.js 8. Chapter 6: Data Visualization with Danfo.js 9. Chapter 7: Data Aggregation and Group Operations 10. Section 3: Building Data-Driven Applications
11. Chapter 8: Creating a No-Code Data Analysis/Handling System 12. Chapter 9: Basics of Machine Learning 13. Chapter 10: Introduction to TensorFlow.js 14. Chapter 11: Building a Recommendation System with Danfo.js and TensorFlow.js 15. Chapter 12: Building a Twitter Analysis Dashboard 16. Chapter 13: Appendix: Essential JavaScript Concepts 17. Other Books You May Enjoy

Summary

In this chapter, we went through why Danfo.js is needed, then looked into what Series and DataFrames actually are. We also discussed some of the essential functionality available in Danfo.js and implemented it in DataFrames and Series.

We also saw how we can use DataFrames and Series methods to handle and preprocess data. We saw how to filter a DataFrame based on column values. We also sorted a DataFrame by row index and column values. This chapter equips us to perform day-to-day data operations such as reading files in different formats, converting formats, and saving DataFrames after preprocessing into a desirable file format.

In the next chapter, we will look into data analysis, wrangling, and transformation. We will discuss data handling and preprocessing further and see how to handle missing numbers and how to deal with string and time series data.

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