Book Image

Extending Power BI with Python and R

By : Luca Zavarella
Book Image

Extending Power BI with Python and R

By: Luca Zavarella

Overview of this book

Python and R allow you to extend Power BI capabilities to simplify ingestion and transformation activities, enhance dashboards, and highlight insights. With this book, you'll be able to make your artifacts far more interesting and rich in insights using analytical languages. You'll start by learning how to configure your Power BI environment to use your Python and R scripts. The book then explores data ingestion and data transformation extensions, and advances to focus on data augmentation and data visualization. You'll understand how to import data from external sources and transform them using complex algorithms. The book helps you implement personal data de-identification methods such as pseudonymization, anonymization, and masking in Power BI. You'll be able to call external APIs to enrich your data much more quickly using Python programming and R programming. Later, you'll learn advanced Python and R techniques to perform in-depth analysis and extract valuable information using statistics and machine learning. You'll also understand the main statistical features of datasets by plotting multiple visual graphs in the process of creating a machine learning model. By the end of this book, you’ll be able to enrich your Power BI data models and visualizations using complex algorithms in Python and R.
Table of Contents (22 chapters)
Section 1: Best Practices for Using R and Python in Power BI
Section 2: Data Ingestion and Transformation with R and Python in Power BI
Section 3: Data Enrichment with R and Python in Power BI
Section 3: Data Visualization with R in Power BI


In this chapter, you learned how Power BI interacts with Microsoft AI services by default through Power BI Desktop and data flow features. You also learned that by using AutoML platforms, you can get around the licensing problem (PPU license or Premium capacity) that Power BI needs to interface with Microsoft AI services. You used both an on-premises AutoML solution (PyCaret) and Azure AutoML on the cloud to solve a binary classification problem. You also used Cognitive Services' Text Analytics to do some sentiment analysis directly using a Python SDK.

You've learned that enrichment via AI mostly happens in Power Query (which allows access to the internet), although you've seen a case where it may be convenient to use a machine learning model directly within a Python visual.

In the next chapter, you will see how to implement data exploration of your dataset in Power BI.