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

Configuring the Power BI service to work with R

As you have already learned from Chapter 2, Configuring R with Power BI, in order to allow the Power BI service to use R in the data transformation steps with Power Query, you must install the on-premises data gateway in personal mode on an external machine, on which an R engine is installed. The same thing applies to Python with Power Query in the Power BI service. So, if you have not installed the on-premises data gateway yet, do it by following the steps in Chapter 2.

Important note

Python engines and R engines can be installed on the same external machine and referenced by a single data gateway. You must make sure, however, that the machine's resources are sufficient to handle the load of requests coming from the Power BI service.

Sharing reports that use Python scripts in the Power BI service

What was said about how to share reports that use R scripts for data transformations in the Power BI service in Chapter 2...