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Python Data Cleaning and Preparation Best Practices

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

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781837634743
Length 456 pages
Edition 1st Edition
Languages
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Author (1):
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Maria Zervou Maria Zervou
Author Profile Icon Maria Zervou
Maria Zervou
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Table of Contents (19) Chapters Close

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 18. Other Books You May Enjoy

Data Transformation – Merging and Concatenating

Understanding how to transform and manipulate data is crucial for unlocking valuable insights. Techniques such as joining, merging, and appending allow us to blend information from various sources and organize and analyze subsets of data. In this chapter, we’ll learn how to merge multiple datasets into a single dataset and explore the various techniques that we can use. We’ll understand how to avoid duplicate values while merging datasets and some tricks to improve the process of merging datasets.

In this chapter, we’ll cover the following topics:

  • Joining datasets
  • Handling duplicates when merging datasets
  • Performance tricks for merging
  • Concatenating DataFrames
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