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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

Why data quality is important

Allow me to unveil the reasons why data quality matters:

  • Accurate data can give you a competitive advantage: Organizations depend on data to determine patterns, trends, preferences, and other vital aspects governing their ecosystem. If your data quality is subpar, the resulting analysis and conclusions may be skewed, resulting in wrong moves that could jeopardize your entire business.
  • Complete data is the backbone of cost optimization: Data forms the foundation of automation and optimization, which can drive up productivity and lower expenses when executed properly. Incomplete or low-quality data can cause bottlenecks and increase costs. Imagine countless man-hours wasted on fixing errors that would have been avoided if only there had been higher standards set for data entry.
  • Top-notch data can lead to satisfied customers who stick around long term: The heartbeat of every business depends on satisfied clients, whose loyalty can ensure sustained...
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