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Data Cleaning with Power BI

Data Cleaning with Power BI

By : Frazer
5 (7)
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Data Cleaning with Power BI

Data Cleaning with Power BI

5 (7)
By: 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)
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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

Transforming data with M

Now, there are a plethora of transformation functions that can be used within Advanced Editor to transform your data. With the issue/error we faced when trying to apply the filter, there are a certain number of functions we will need to use, such as Table.TransformColumns and Table.RemoveLastN.

As mentioned earlier, the first issue we can see in the data that might prevent us from filtering is that the values for cost and price contain a $ character. This is leading Power BI to read this as a text value. So our first port of call should be to remove this value from the column.

Now, of course, you could use the Split column function in the Power Query UI but it’s important to understand what M code is created behind the scenes from using such buttons. Using M will also help reduce the steps you need to get to the desired goal. This will particularly help when you’re looking to script more complex queries in M later in your data journey.

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