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Power Query Cookbook

Power Query Cookbook

By : Janicijevic
4.5 (14)
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Power Query Cookbook

Power Query Cookbook

4.5 (14)
By: Janicijevic

Overview of this book

Power Query is a data preparation tool that enables data engineers and business users to connect, reshape, enrich, and transform their data to facilitate relevant business insights and analysis. With Power Query's wide range of features, you can perform no-code transformations and complex M code functions at the same time to get the most out of your data. This Power Query book will help you to connect to data sources, achieve intuitive transformations, and get to grips with preparation practices. Starting with a general overview of Power Query and what it can do, the book advances to cover more complex topics such as M code and performance optimization. You'll learn how to extend these capabilities by gradually stepping away from the Power Query GUI and into the M programming language. Additionally, the book also shows you how to use Power Query Online within Power BI Dataflows. By the end of the book, you'll be able to leverage your source data, understand your data better, and enrich it with a full stack of no-code and custom features that you'll learn to design by yourself for your business requirements.
Table of Contents (12 chapters)
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Chapter 4: Reshaping Your Data

When data grows in size and complexity, you need tools that allow you to make sense of your data and create views that can be relevant for reporting and presenting that data. In order to do this, they need features and options to reshape their data properly and to clean it if needed, especially when you can't edit data sources directly. Often, they connect to data that does not have a correct schema or a correct column name and they need to group and change how data is displayed.

In the recipes in this chapter, you will see how to leverage Power Query's built-in features that will allow you to reshape your data, to change its structure, and to adapt it to your reporting needs.

In this chapter, we will cover the following recipes:

  • Formatting data types
  • Using first rows as headers
  • Grouping data
  • Unpivoting and pivoting columns
  • Filling empty rows
  • Splitting columns
  • Extracting data
  • Parsing JSON or XML
  • ...

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