You're reading from Data Cleaning with Power BI

Product type Book

Published in Feb 2024

Publisher Packt

ISBN-13 9781805126409

Pages 340 pages

Edition 1st Edition

Languages

Concepts

Data Analysis

Author (1):

Gus Frazer

Table of Contents (23) Chapters

Preface

1. Part 1 – Introduction and Fundamentals

2. Chapter 1: Introduction to Power BI Data Cleaning

3. Chapter 2: Understanding Data Quality and Why Data Cleaning is Important

4. Chapter 3: Data Cleaning Fundamentals and Principles

5. Chapter 4: The Most Common Data Cleaning Operations

6. Part 2 – Data Import and Query Editor

7. Chapter 5: Importing Data into Power BI

8. Chapter 6: Cleaning Data with Query Editor

9. Chapter 7: Transforming Data with the M Language

10. Chapter 8: Using Data Profiling for Exploratory Data Analysis (EDA)

11. Part 3 – Advanced Data Cleaning and Optimizations

12. Chapter 9: Advanced Data Cleaning Techniques

13. Chapter 10: Creating Custom Functions in Power Query

14. Chapter 11: M Query Optimization

15. Chapter 12: Data Modeling and Managing Relationships

16. Part 4 – Paginated Reports, Automations, and OpenAI

17. Chapter 13: Preparing Data for Paginated Reporting

18. Chapter 14: Automating Data Cleaning Tasks with Power Automate

19. Chapter 15: Making Life Easier with OpenAI

20. Assessments

21. Index

Why subscribe?

22. Other Books You May Enjoy

Reviewing column quality, distribution, and profile

As mentioned earlier, Column distribution, Column quality, and Column profile views are the fundamental features of Power BI and Power Query, and they provide comprehensive insights into your dataset. These views are essential for EDA and help you understand the characteristics of individual columns in your data.

In the following sections, we will explore each view and how they report information.

Column distribution

The Column distribution view in Power BI and Power Query allows you to explore the distribution of values within a specific column. It provides visualization and statistics that help you understand how data is spread across different ranges or categories.

Let’s see an example from the dataset provided in this book’s GitHub repository. Here, we have only selected Column distribution and have then zoomed in on a particular value called OrderQuantity.