Extending Power BI with Python and R - Second Edition

Ingest, transform, manipulate, and visualize your data beyond Power BI's capabilities.

Purchase of the print or Kindle book includes a free eBook in PDF format.

Key Features

• Discover best practices for using Python and R in Power BI by implementing non-trivial code
• Enrich your Power BI dashboards using external APIs and machine learning models
• Create any visualization, as complex as you want, using Python and R scripts

Book Description

The latest edition of this book delves deeper into advanced analytics, focusing on enhancing Python and R proficiency within Power BI. New chapters cover optimizing Python and R settings, utilizing Intel's Math Kernel Library (MKL) for performance boosts, and addressing integration challenges. Techniques for managing large datasets beyond laptop RAM, employing parquet data format, and advanced fuzzy matching algorithms are explored. Additionally, it discusses leveraging SQL Server External Languages to overcome traditional Python and R limitations in Power BI. It also helps in crafting sophisticated visualizations using the grammar of graphics in both R and Python.

This PowerBI book will help you master data validation with regular expressions, import data from diverse sources, and apply advanced algorithms for transformation. Next, you'll learn to Safeguard personal data in Power BI with techniques like pseudonymization, anonymization, and data masking. You'll also get to grips with the key statistical features of data sets by plotting multiple visual graphs in the process of building a machine-learning model. The book will guide you to Utilize external APIs for enrichment, enhancing I/O performance, and leveraging Python and R for analysis.

You'll also be able to reinforce learning with questions at the end of each chapter.

What you will learn

• Configure optimal integration of Python and R with Power BI
• Perform complex data manipulations not possible by default in Power BI
• Boost Power BI logging and loading large datasets
• Extract insights from your data using algorithms like linear optimization
• Calculate string distances and learn how to use them for probabilistic fuzzy matching
• Handle outliers and missing values for multivariate and time-series data
• Apply Exploratory Data Analysis in Power BI with R
• Learn to use Grammar of Graphics in Python

Who this book is for

This book is for business analysts, business intelligence professionals, and data scientists who already use Microsoft Power BI and want to add more value to their analysis using Python and R. Working knowledge of Power BI is required to make the most of this book. Basic knowledge of Python and R will also be helpful.

Table of Contents

1. Where and How to Use R and Python Scripts in Power BI
2. Configuring R with Power BI
3. Configuring Python with Power BI
4. Solving Common Issues When Using Python and R in Power BI
5. Importing Unhandled Data Objects
6. Using Regular Expressions in Power BI
7. Anonymizing and Pseudonymizing your Data in Power BI
8. Logging Data from Power BI to External Sources
9. Loading Large Datasets Also Beyond the Available RAM in Power BI
10. Optimizing the Loading Time of Referenced Queries in Power BI
11. Calling External APIs To Enrich Your Data
12. Calculating Columns Using Complex Algorithms: Distances
13. Calculating Columns Using Complex Algorithms: Fuzzy Matching
14. Calculating Columns Using Complex Algorithms: Optimization Problems
15. Adding Statistics Insights: Associations
16. Adding Statistics Insights: Outliers and Missing Values

(N.B. Please use the Look Inside option to see further chapters)