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

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Detecting missing data

Missing data is a common and inevitable issue in real-world datasets. It occurs when one or more values are absent in a particular observation or record. This data gap can greatly impact the validity and reliability of any analysis or model built with those data. As we say in the data world: garbage in, garbage out, meaning that if your data is not correct, then the models or analysis created with that data will not be correct either.

In the following parts, we will use a scenario to demonstrate how to detect missing data and how the different imputation methods work. The scenario is the following:

Imagine you are analyzing a dataset containing information about students, including their ages and test scores. However, due to various reasons, some ages and test scores are missing.

The code for this section can be found at https://github.com/PacktPublishing/Python-Data-Cleaning-and-Preparation-Best-Practices/blob/main/chapter08/1.detect_missing_data.py...