You're reading from Data Science with .NET and Polyglot Notebooks Programmer's guide to data science using ML.NET, OpenAI, and Semantic Kernel

Product type Paperback

Published in Aug 2024

Publisher Packt

ISBN-13 9781835882962

Length 404 pages

Edition 1st Edition

Languages

Tools

Codespaces

Concepts

Artificial Intelligence

Author (1):

Matt Eland

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Table of Contents (22) Chapters

Preface

1. Part 1: Data Analysis in Polyglot Notebooks

2. Chapter 1: Data Science, Notebooks, and Kernels FREE CHAPTER

3. Chapter 2: Exploring Polyglot Notebooks

4. Chapter 3: Getting Data and Code into Your Notebooks

5. Chapter 4: Working with Tabular Data and DataFrames

6. Chapter 5: Visualizing Data

7. Chapter 6: Variable Correlations

8. Part 2: Machine Learning with Polyglot Notebooks and ML.NET

9. Chapter 7: Classification Experiments with ML.NET AutoML

10. Chapter 8: Regression Experiments with ML.NET AutoML

11. Chapter 9: Beyond AutoML: Pipelines, Trainers, and Transforms

12. Chapter 10: Deploying Machine Learning Models

13. Part 3: Exploring Generative AI with Polyglot Notebooks

14. Chapter 11: Generative AI in Polyglot Notebooks

15. Chapter 12: AI Orchestration with Semantic Kernel

16. Part 4: Polyglot Notebooks in the Enterprise

17. Chapter 13: Enriching Documentation with Mermaid Diagrams

18. Chapter 14: Extending Polyglot Notebooks

19. Chapter 15: Adopting and Deploying Polyglot Notebooks

20. Index

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21. Other Books You May Enjoy

DataFrames in other languages

Much of what is written in this chapter was not possible just a short time ago before ML.NET 3.0 was released. The .NET DataFrame is a capable tool, but it is one that is still growing and improving its usability and feature set.

If you prefer, you can perform your data wrangling tasks using another language or DataFrame implementation.

First, you may want to investigate the Deedle Frame object, which has been the F# DataFrame of choice since its inception. The Deedle Frame object was built with functional programming in mind and may be preferable to Microsoft.Data.Analysis DataFrame if F# is your primary programming language.

Second, the gold standard in the data analysis world is the pandas library in Python. The pandas library is efficient, full-featured, and intuitive. If you find that the DataFrame class doesn’t fully meet your needs, I encourage you to investigate pandas to fulfill them.

Python and R in Polyglot Notebooks

It...