Book Image

Data Cleaning with Power BI

By : Gus Frazer
Book Image

Data Cleaning with Power BI

By: Gus Frazer

Overview of this book

Microsoft Power BI offers a range of powerful data cleaning and preparation options through tools such as DAX, Power Query, and the M language. However, despite its user-friendly interface, mastering it can be challenging. Whether you're a seasoned analyst or a novice exploring the potential of Power BI, this comprehensive guide equips you with techniques to transform raw data into a reliable foundation for insightful analysis and visualization. This book serves as a comprehensive guide to data cleaning, starting with data quality, common data challenges, and best practices for handling data. You’ll learn how to import and clean data with Query Editor and transform data using the M query language. As you advance, you’ll explore Power BI’s data modeling capabilities for efficient cleaning and establishing relationships. Later chapters cover best practices for using Power Automate for data cleaning and task automation. Finally, you’ll discover how OpenAI and ChatGPT can make data cleaning in Power BI easier. By the end of the book, you will have a comprehensive understanding of data cleaning concepts, techniques, and how to use Power BI and its tools for effective data preparation.
Table of Contents (23 chapters)
Free Chapter
1
Part 1 – Introduction and Fundamentals
6
Part 2 – Data Import and Query Editor
11
Part 3 – Advanced Data Cleaning and Optimizations
16
Part 4 – Paginated Reports, Automations, and OpenAI

Filtering and sorting data with M

Filtering and sorting are essential data transformation tasks that help you extract relevant information from large datasets and organize it in a meaningful way.

Power Query’s M language offers a range of functions to efficiently filter rows based on conditions and sort data according to specific criteria. In this section, we’ll explore how to filter and sort data using M, accompanied by step-by-step examples and explanations of key functions.

First of all, filtering data from your analysis typically involves selecting certain rows from a dataset based on certain conditions. M has a function named Table.SelectRows for this exact purpose. As it suggests, it allows you to specify a condition within the argument that determines which rows should be retained.

Following on from the Products table we connected our M to earlier, we can add an additional step to help us filter the data for analysis.

For example, suppose for our analysis...