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 were introduced to the basics of how to use regexes. Using the bare minimum, you were able to effectively validate strings representing email addresses and dates in Power BI, using both Python and R.

Additionally, you learned how to extract information from semi-structured log files through the use of regexes, and how to import the extracted information, in a structured way, into Power BI.

Finally, you learned how to use regex in Python and R to extract information from seemingly unprocessable free text thanks to the real-world case of notes associated with sales orders.

In the next chapter, you'll learn how to use some de-identification techniques in Power BI to anonymize or pseudonymize datasets that show sensitive data about individuals in plain text before they are imported into Power BI.