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

Validating data using regex in Power BI

To date, Power BI has no native feature in Power Query to perform operations via regexes. There are cases when you can't avoid using regexes to extract useful information from data in text form. The only way to be able to use regexes is through R scripts or Python scripts. The only cons you have in this case is that, if you need to publish the report on the Power BI service, to allow Power Query to use external R or Python engines, you must also install the on-premises data gateway in personal mode.

However, let's get right into it with real-world examples.

Let's suppose you work at a retail company where there is a team dedicated to identifying fraudulent customers. As soon as a team member identifies a fraudster, they fill out an Excel spreadsheet, in which the Email and BannedDate columns are included along with others. Your task is to load the data from this Excel file into Power BI and, from other data sources, select...