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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

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

View More author details

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

Why subscribe?

18. Other Books You May Enjoy

Summary

Throughout this chapter, we focused on the components of designing and optimizing data write operations. We discussed how to choose the right data sink technology, how file formats significantly impact storage efficiency and query performance, and why it matters to choose the right one for your use case. Finally, we discussed why data partitioning is crucial for optimizing query performance and resource utilization.

In the next chapter, we will start transforming the data that’s been written on the data sink to better prepare it for downstream analytics by detecting and handling outliers and missing values.

The rest of the chapter is locked

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Authors (1)

Maria Zervou

Maria Zervou

Maria Zervou is a Generative AI and machine learning expert, dedicated to making advanced technologies accessible. With over a decade of experience, she has led impactful AI projects across industries and mentored teams on cutting-edge advancements. As a machine learning specialist at Databricks, Maria drives innovative AI solutions and industry adoption. Beyond her role, she democratizes knowledge through her YouTube channel, featuring experts on AI topics. A recognized thought leader and finalist in the Women in Tech Excellence Awards, Maria advocates for responsible AI use and contributes to open source projects, fostering collaboration and empowering future AI leaders.

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