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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

Using native M functions

As highlighted earlier in this book, M, the language behind Power Query, offers a rich library of native functions designed for data transformation. Leveraging these functions can often be more efficient than custom code.

For instance, let’s say you need to standardize product names by converting them to title case. Instead of writing custom code, you can utilize the Text.ToTitleCase function, making your query more concise and performant.

Here is an example of doing just this:

let
    Source = ... // Your data source
    StandardizedData = Table.TransformColumns(Source, {"ProductName", Text.ToTitleCase})
in
    StandardizedData

In this code, we use the Table.TransformColumns function along with the Text.ToTitleCase function to standardize product names. Native functions are highly optimized for their specific tasks, resulting in more efficient and faster queries.

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