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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

Designing an online retail data platform

An online retailer wants to create an analytics platform to collect and analyze all the data generated by their e-commerce website. This platform aims to provide capabilities that allow for real-time data processing and analytics to improve customer experiences, optimize business operations, and drive strategic decision-making for the online retail business.

After long discussions with the team, we identified four main requirements to consider:

  • Handle large volumes of transaction data: The platform needs to efficiently ingest and transform large volumes of transaction data. This needs to be done by accounting for scalability, high throughput, and cost-effectiveness.
  • Provide real-time insights: Business analysts require immediate access to real-time insights derived from transaction data. The platform should support real-time data processing and analytics to enable timely decision-making.
  • There’s a need to combine batch...
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