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Data Cleaning with Power BI

You're reading from   Data Cleaning with Power BI The definitive guide to transforming dirty data into actionable insights

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Product type Paperback
Published in Feb 2024
Publisher
ISBN-13 9781805126409
Length 340 pages
Edition 1st Edition
Languages
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Author (1):
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Gus Frazer Gus Frazer
Author Profile Icon Gus Frazer
Gus Frazer
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Table of Contents (23) Chapters Close

Preface 1. Part 1 – Introduction and Fundamentals FREE CHAPTER
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 22. Other Books You May Enjoy

Using row groups/column groups

Row groups and column groups in paginated reports are essential features that provide a structured way to organize and present data. They play a crucial role in preparing and analyzing data for reporting.

Let’s explore how row groups and column groups help users in this process.

Organizing and structuring data

It’s important to understand why you should use row groups and column groups when creating reports in Power BI Report Builder. Let’s dive deeper into more information on why they are important and the use cases for them:

  • Row groups:
    • Importance:
      • Hierarchical structure: Row groups allow users to create a hierarchical structure in the report based on the values in a specific column. This is particularly useful for representing data in a nested or grouped manner.
      • Logical organization: By grouping rows based on certain criteria (for example, category and date range), users can logically organize data, making it easier...
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