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

Installing an IDE for Python development

In Chapter 2, Configuring R with Power BI, you installed RStudio to conveniently develop your own R scripts. Did you know that, starting with version 1.4, you can write and run Python code directly in RStudio, making use of advanced tools for viewing instantiated Python objects?

Let's see how to configure your RStudio installation to also run Python code.

Configuring Python with RStudio

In order to allow RStudio to communicate with the Python world, you need to install a package called reticulate, which contains a comprehensive set of tools for interoperability between Python and R thanks to embedded Python sessions within R sessions. After that, it's a breeze to configure Python within RStudio. Let's see how to do it:

  1. Open RStudio and make sure the referenced engine is the latest one, in our case CRAN 4.0.2. As seen in Chapter 2, Configuring R with Power BI, you can set up your R engine in RStudio by going to...