Collect, Combine, and Transform Data Using Power Query in Excel and Power BI Business Skills Series

Using Power Query, you can import, reshape, and cleanse any data from a simple interface, so you can mine that data for all of its hidden insights. Power Query is embedded in Excel, Power BI, and other Microsoft products, and leading Power Query expert Gil Raviv will help you make the most of it. Discover how to eliminate time-consuming manual data preparation, solve common problems, avoid pitfalls, and more. Then, walk through several complete analytics challenges, and integrate all your skills in a realistic chapter-length final project. By the time you?re finished, you?ll be ready to wrangle any data?and transform it into actionable knowledge.

Prepare and analyze your data the easy way, with Power Query

·         Quickly prepare data for analysis with Power Query in Excel (also known as Get & Transform) and in Power BI

·         Solve common data preparation problems with a few mouse clicks and simple formula edits

·         Combine data from multiple sources, multiple queries, and mismatched tables

·         Master basic and advanced techniques for unpivoting tables

·         Customize transformations and build flexible data mashups with the M formula language

·         Address collaboration challenges with Power Query

·         Gain crucial insights into text feeds

·         Streamline complex social network analytics so you can do it yourself

For all information workers, analysts, and any Excel user who wants to solve their own business intelligence problems.

Introduction

Chapter 1 Introduction to Power Query

What Is Power Query?

    A Brief History of Power Query

    Where Can I Find Power Query?

Main Components of Power Query

    Get Data and Connectors

    The Main Panes of the Power Query Editor

Exercise 1-1: A First Look at Power Query

Summary

Chapter 2 Basic Data Preparation Challenges

Extracting Meaning from Encoded Columns

    AdventureWorks Challenge

    Exercise 2-1: The Old Way: Using Excel Formulas

    Exercise 2-2, Part 1: The New Way

    Exercise 2-2, Part 2: Merging Lookup Tables

    Exercise 2-2, Part 3: Fact and Lookup Tables

Using Column from Examples

    Exercise 2-3, Part 1: Introducing Column from Examples

    Practical Use of Column from Examples

    Exercise 2-3, Part 2: Converting Size to Buckets/Ranges

Extracting Information from Text Columns

    Exercise 2-4: Extracting Hyperlinks from Messages

Handling Dates

    Exercise 2-5: Handling Multiple Date Formats

    Exercise 2-6: Handling Dates with Two Locales

    Extracting Date and Time Elements

Preparing the Model

    Exercise 2-7: Splitting Data into Lookup Tables and Fact Tables

    Exercise 2-8: Splitting Delimiter-Separated Values into Rows

Summary

Chapter 3 Combining Data from Multiple Sources

Appending a Few Tables

    Appending Two Tables

    Exercise 3-1: Bikes and Accessories Example

    Exercise 3-2, Part 1: Using Append Queries as New

    Exercise 3-2, Part 2: Query Dependencies and References

    Appending Three or More Tables

    Exercise 3-2, Part 3: Bikes + Accessories + Components

    Exercise 3-2, Part 4: Bikes + Accessories + Components + Clothing

Appending Tables on a Larger Scale

    Appending Tables from a Folder

    Exercise 3-3: Appending AdventureWorks Products from a Folder

    Thoughts on Import from Folder

    Appending Worksheets from a Workbook

    Exercise 3-4: Appending Worksheets: The Solution

Summary

Chapter 4 Combining Mismatched Tables

The Problem of Mismatched Tables

    What Are Mismatched Tables?

    The Symptoms and Risks of Mismatched Tables

    Exercise 4-1: Resolving Mismatched Column Names: The Reactive Approach

Combining Mismatched Tables from a Folder

    Exercise 4-2, Part 1: Demonstrating the Missing Values Symptom

    Exercise 4-2, Part 2: The Same-Order Assumption and the Header Generalization Solution

    Exercise 4-3: Simple Normalization Using Table.TransformColumnNames

    The Conversion Table

    Exercise 4-4: The Transpose Techniques Using a Conversion Table

    Exercise 4-5: Unpivot, Merge, and Pivot Back

    Exercise 4-6: Transposing Column Names Only

    Exercise 4-7: Using M to Normalize Column Names

Summary

Chapter 5 Preserving Context

Preserving Context in File Names and Worksheets

    Exercise 5-1, Part 1: Custom Column Technique

    Exercise 5-1, Part 2: Handling Context from File Names and Worksheet Names

Pre-Append Preservation of Titles

    Exercise 5-2: Preserving Titles Using Drill Down

    Exercise 5-3: Preserving Titles from a Folder

Post-Append Context Preservation of Titles

    Exercise 5-4: Preserving Titles from Worksheets in the same Workbook

Using Context Cues

    Exercise 5-5: Using an Index Column as a Cue

    Exercise 5-6: Identifying Context by Cell Proximity

Summary

Chapter 6 Unpivoting Tables

Identifying Badly Designed Tables

Introduction to Unpivot

    Exercise 6-1: Using Unpivot Columns and Unpivot Other Columns

    Exercise 6-2: Unpivoting Only Selected Columns

Handling Totals

    Exercise 6-3: Unpivoting Grand Totals

Unpivoting 2×2 Levels of Hierarchy

    Exercise 6-4: Unpivoting 2×2 Levels of Hierarchy with Dates

    Exercise 6-5: Unpivoting 2×2 Levels of Hierarchy

Handling Subtotals in Unpivoted Data

    Exercise 6-6: Handling Subtotals

Summary

Chapter 7 Advanced Unpivoting and Pivoting of Tables

Unpivoting Tables with Multiple Levels of Hierarchy

    The Virtual PivotTable, Row Fields, and Column Fields

    Exercise 7-1: Unpivoting the AdventureWorks N×M Levels of Hierarchy

Generalizing the Unpivot Sequence

    Exercise 7-2: Starting at the End

    Exercise 7-3: Creating FnUnpivotSummarizedTable

The Pivot Column Transformation

    Exercise 7-4: Reversing an Incorrectly Unpivoted Table

    Exercise 7-5: Pivoting Tables of Multiline Records

Summary

Chapter 8 Addressing Collaboration Challenges

Local Files, Parameters, and Templates

    Accessing Local Files–Incorrectly

    Exercise 8-1: Using a Parameter for a Path Name

    Exercise 8-2: Creating a Template in Power BI

    Exercise 8-3: Using Parameters in Excel

Working with Shared Files and Folders

    Importing Data from Files on OneDrive for Business or SharePoint

    Exercise 8-4: Migrating Your Queries to Connect to OneDrive for Business or SharePoint

    Exercise 8-5: From Local to SharePoint Folders

Security Considerations

    Removing All Queries Using the Document Inspector in Excel

Summary

Chapter 9 Introduction to the Power Query M Formula Language

Learning M

    Learning Maturity Stages

    Online Resources

    Offline Resources

    Exercise 9-1: Using #shared to Explore Built-in Functions

M Building Blocks

    Exercise 9-2: Hello World

    The let Expression

    Merging Expressions from Multiple Queries and Scope Considerations

    Types, Operators, and Built-in Functions in M

Basic M Types

    The Number Type

    The Time Type

    The Date Type

    The Duration Type

    The Text Type

    The Null Type

    The Logical Type

Complex Types

    The List Type

    The Record Type

    The Table Type

Conditions and If Expressions

    if-then-else

    An if Expression Inside a let Expression

Custom Functions

    Invoking Functions

    The each Expression

Advanced Topics

    Error Handling

    Lazy and Eager Evaluations

    Loops

    Recursion

    List.Generate

    List.Accumulate

Summary

Chapter 10 From Pitfalls to Robust Queries

The Causes and Effects of the Pitfalls

    Awareness

    Best Practices

    M Modifications

Pitfall 1: Ignoring the Formula Bar

    Exercise 10-1: Using the Formula Bar to Detect Static References to Column Names

Pitfall 2: Changed Types

Pitfall 3: Dangerous Filtering

    Exercise 10-2, Part 1: Filtering Out Black Products

    The Logic Behind the Filtering Condition

    Exercise 10-2, Part 2: Searching Values in the Filter Pane

Pitfall 4: Reordering Columns

    Exercise 10-3, Part 1: Reordering a Subset of Columns

    Exercise 10-3, Part 2: The Custom Function FnReorderSubsetOfColumns

Pitfall 5: Removing and Selecting Columns

    Exercise 10-4: Handling the Random Columns in the Wide World Importers Table

Pitfall 6: Renaming Columns

    Exercise 10-5: Renaming the Random Columns in the Wide World Importers Table

Pitfall 7: Splitting a Column into Columns

    Exercise 10-6: Making an Incorrect Split

Pitfall 8: Merging Columns

    More Pitfalls and Techniques for Robust Queries

Summary

Chapter 11 Basic Text Analytics

Searching for Keywords in Textual Columns

    Exercise 11-1: Basic Detection of Keywords

    Using a Cartesian Product to Detect Keywords

    Exercise 11-2: Implementing a Cartesian Product

    Exercise 11-3: Detecting Keywords by Using a Custom Function

    Which Method to Use: Static Search, Cartesian Product, or Custom Function?

Word Splits

    Exercise 11-4: Naïve Splitting of Words

    Exercise 11-5: Filtering Out Stop Words

    Exercise 11-6: Searching for Keywords by Using Split Words

    Exercise 11-7: Creating Word Clouds in Power BI

Summary

Chapter 12 Advanced Text Analytics: Extracting Meaning

Microsoft Azure Cognitive Services

    API Keys and Resources Deployment on Azure

    Pros and Cons of Cognitive Services via Power Query

Text Translation

    The Translator Text API Reference

    Exercise 12-1: Simple Translation

    Exercise 12-2: Translating Multiple Messages

Sentiment Analysis

    What Is the Sentiment Analysis API Call?

    Exercise 12-3: Implementing the FnGetSentiment Sentiment Analysis Custom Function

    Exercise 12-4: Running Sentiment Analysis on Large Datasets

Extracting Key Phrases

    Exercise 12-5: Converting Sentiment Logic to Key Phrases

Multi-Language Support

    Replacing the Language Code

    Dynamic Detection of Languages

    Exercise 12-6: Converting Sentiment Logic to Language Detection

Summary

Chapter 13 Social Network Analytics

Getting Started with the Facebook Connector

    Exercise 13-1: Finding the Pages You Liked

Analyzing Your Friends

    Exercise 13-2: Finding Your Power BI Friends and Their Friends

    Exercise 13-3: Find the Pages Your Friends Liked

Analyzing Facebook Pages

    Exercise 13-4: Extracting Posts and Comments from Facebook Pages–The Basic Way

    Short Detour: Filtering Results by Time

    Exercise 13-5: Analyzing User Engagement by Counting Comments and Shares

Exercise 13-6: Comparing Multiple Pages

Summary

Chapter 14 Final Project: Combining It All Together

Exercise 14-1: Saving the Day at Wide World Importers

    Clues

    Part 1: Starting the Solution

    Part 2: Invoking the Unpivot Function

    Part 3: The Pivot Sequence on 2018 Revenues

    Part 4: Combining the 2018 and 2015—2017 Revenues

Exercise 14-2: Comparing Tables and Tracking the Hacker

    Clues

    Exercise 14-2: The Solution

    Detecting the Hacker’s Footprints in the Compromised Table

Summary

9781509307951    TOC    9/6/2018

Gil Raviv is a Microsoft MVP and a Power BI blogger at https://DataChant.com. As a Senior Program Manager on the Microsoft Excel Product team, Gil led the design and integration of Power Query as the next-generation Get Data and data-wrangling technology in Excel 2016, and he has been a devoted M practitioner ever since.

With 20 years of software development experience, and four U.S. patents in the fi elds of social networks, cyber security, and analytics, Gil has held a variety of innovative roles in cyber security and data analytics, and he has delivered a wide range of software products, from advanced threat detection enterprise systems to protection of kids on Facebook.

In his blog, DataChant.com, Gil has been chanting about Power BI and Power Query since he moved to his new home in the Chicago area in early 2016. As a Group Manager in Avanade’s Analytics Practice, Gil is helping Fortune 500 clients create modern self-service analytics capability and solutions by leveraging Power BI and Azure.

You can contact Gil at gilra@datachant.com.