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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

Importance of Data Quality

Did you know that data serves as the backbone of many important business decisions? Without accurate, complete, and consistent information, companies risk making faulty judgments that could potentially damage their reputation, client relationships, and business overall. Consistency issues across different datasets can cause confusion and prevent meaningful analysis from happening. Irrelevant or outdated data can misguide the judgment of decision-makers, resulting in suboptimal choices. On the other hand, building high-quality data products serves as a powerful asset, empowering organizations to make informed decisions, uncover valuable insights, identify trends, mitigate risks, and gain a competitive edge.

In this chapter, we will dive deep into the following topics:

  • Why data quality is important
  • Different dimensions to measure data quality in your data products
  • The impact of data silos on data quality
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