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

View More author details

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

Why subscribe?

18. Other Books You May Enjoy

To get the most out of this book

To benefit fully from this book, you should have a good knowledge of Python and a grasp of data engineering and data science basics.

Software/hardware covered in the book	Operating system requirements
Python 3	Windows, macOS, or Linux
Visual Studio Code (or your preferred IDE)

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book’s GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

The GitHub repository follows the chapters of the book, and all the scripts are numbered according to the sections within each chapter. Each script is independent of the others, so you can move ahead without having to run all the scripts beforehand. However, it is critically recommended to follow the flow of the book so that you don’t miss any necessary information.

The rest of the chapter is locked

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

Table of Contents (19) Chapters

To get the most out of this book

Authors (1)

Personalised recommendations for you

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

Table of Contents (19) Chapters

To get the most out of this book

Unlock this book and the full library FREE for 7 days

Authors (1)

Personalised recommendations for you