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

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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

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In the world of modern data processing, crucial decisions about data management, storage, and processing will determine successful outcomes. In this chapter, we will deep dive into three important pillars that underpin effective data processing pipelines: selecting the right data sink, choosing the optimal file type, and mastering partitioning strategies. By discussing these critical elements and their real-world applications, this chapter will equip you with the insights and strategies needed to architect data solutions that optimize efficiency, scalability, and performance within the complicated landscape of data processing technologies.

In this chapter, we will discuss the following topics:

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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

Data Sinks

Authors (1)

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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

Data Sinks

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Authors (1)

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