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Data Science with .NET and Polyglot Notebooks

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

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835882962
Length 404 pages
Edition 1st Edition
Languages
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Author (1):
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Matt Eland Matt Eland
Author Profile Icon Matt Eland
Matt Eland
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Table of Contents (22) Chapters Close

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 21. Other Books You May Enjoy

Performing univariate analysis with Plotly.NET

One of our jobs when performing data analysis work is to understand how data is distributed. We explored this earlier using descriptive statistics and our first box plot in ScottPlot.

In this section, we’re going to take our exploration of our market value up a notch by using Plotly.NET and its specialized charts. Before we do that, I want to put a label on the task we’re working on. When we’re exploring how data for a single column of values is distributed, that analysis process is called univariate analysis.

The goal of univariate analysis is to discover everything we can from just that one series of values – in this case, our market values. If this term doesn’t feel natural, that’s okay; just remember that univariate is a fancy word that just means one type of variable. Now that we’ve ironed out that bit of terminology, let’s get started with Plotly.NET.

Plotly and Plotly...

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