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

Renaming columns

Renaming columns with more descriptive and meaningful names makes it easier to understand the content and purpose of each column. Clear and intuitive column names enhance the interpretability of the dataset, especially when sharing or collaborating with others.

To better understand all the concepts introduced in this chapter, we will use a scenario across the chapter. Let’s consider an e-commerce company that wants to analyze customer purchase data to optimize its marketing strategies. The dataset includes information about customer transactions, such as purchase amount, payment method, and timestamp of the transactions. However, the dataset is messy and requires cleaning and manipulation to derive meaningful insights.

The distribution of the features is presented in the following figure. To build the following statistic charts, execute the file at https://github.com/PacktPublishing/Python-Data-Cleaning-and-Preparation-Best-Practices/blob/main/chapter04...