You're reading from Python Data Cleaning and Preparation Best Practices A practical guide to organizing and handling data from various sources and formats using Python

Product type Paperback

Published in Sep 2024

Publisher Packt

ISBN-13 9781837634743

Length 456 pages

Edition 1st Edition

Languages

Python

Concepts

Data Analysis

Author (1):

Maria Zervou

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Table of Contents (19) Chapters

Preface

1. Part 1: Upstream Data Ingestion and Cleaning

2. Chapter 1: Data Ingestion Techniques FREE CHAPTER

3. Chapter 2: Importance of Data Quality

4. Chapter 3: Data Profiling – Understanding Data Structure, Quality, and Distribution

5. Chapter 4: Cleaning Messy Data and Data Manipulation

6. Chapter 5: Data Transformation – Merging and Concatenating

7. Chapter 6: Data Grouping, Aggregation, Filtering, and Applying Functions

8. Chapter 7: Data Sinks

9. Part 2: Downstream Data Cleaning – Consuming Structured Data

10. Chapter 8: Detecting and Handling Missing Values and Outliers

11. Chapter 9: Normalization and Standardization

12. Chapter 10: Handling Categorical Features

13. Chapter 11: Consuming Time Series Data

14. Part 3: Downstream Data Cleaning – Consuming Unstructured Data

15. Chapter 12: Text Preprocessing in the Era of LLMs

16. Chapter 13: Image and Audio Preprocessing with LLMs

17. Index

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18. Other Books You May Enjoy

This concludes the book! You did it!

I want to express my sincere gratitude for your dedication to finishing this book. I’ve aimed to share the insights from my experience, with a focus on ML and AI, as these fields have been central to my career. I find them incredibly fascinating and transformative, though I might be a bit biased.

As you’ve seen in the later chapters, I believe LLMs are poised to revolutionize the field and the way we process data. That’s why I dedicated the last chapters to building a foundation and showcasing how effortlessly different types of data can be transformed and manipulated using LLMs.

Please take my advice and spend some time diving into the code examples provided. Implement these techniques in your daily tasks and projects. If there’s anything you find yourself doing manually or redoing frequently, code it up to streamline your process. This hands-on practice will help reinforce your learning. Experiment with the techniques...