Book Image

Learn Power Query

By : Linda Foulkes, Warren Sparrow
Book Image

Learn Power Query

By: Linda Foulkes, Warren Sparrow

Overview of this book

<p>Power Query is a data connection technology that allows you to connect, combine, and refine data from multiple sources to meet your business analysis requirements. With this Power Query book, you’ll be empowered to work with a variety of data sources to create interactive reports and dashboards using Excel and Power BI. </p><p>You’ll start by learning how to access Power Query across different versions of Excel and install the Power BI engine. After you've explored Power Pivot, you’ll see why Excel users find it challenging to clean data in Power Pivot and learn how Power Query can help to tackle the problem. The book will show you how to transform data using the Query Editor and write functions in Power Query. A dedicated section will focus on functions such as IF, Index, and Modulo, and creating parameters to alter query paths in a table. You’ll also work with dashboards, get to grips with multi-dimensional reporting, and create automated reports. As you advance, you'll cover the M formula language in Power Query, delve into the basic M syntax, and write the M query language with the help of examples such as loading all library functions offline in Excel and Power BI. Finally, the book will demonstrate the difference between M and DAX and show how results are produced in M. </p><p>By the end of this book, you’ll be ready to create impressive dashboards and multi-dimensional reports in Power Query and turn data into valuable insights.</p>
Table of Contents (17 chapters)
Free Chapter
1
Section 1: Overview of Power Pivot and Power Query
6
Section 2: Power Query Data Transformations
11
Section 3: Learning M

Chapter 5: Transforming Power Query Data

In this part of this book, you will learn how to transform data using Power Query in a multitude of ways and understand why it is important to prepare your dataset prior to analysis. You will apply Unpivot and Pivot to a dataset to structure data in the correct tabular format; work with row and column tools such as split, merge, duplicate, and extract; and use conditional columns to display the output you desire from if…then…else conditions. The automatic background refresh setting will also be discussed in this chapter. We will learn how to extract ages from a date column, which saves a huge amount of time as you would normally have to work this out for each individual column from a date column entry. In addition, we will look at using various delimiter constraints, as well as grouping data from various rows into a single value.

In this chapter, we're going to cover the following main topics:

  • Turning data with...