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

A brief introduction to regexes

A regular expression (usually shortened to regex) is defined by a series of characters that identify an abstract search pattern. Essentially, it is a mathematical technique that was developed in 1951 by experts of formal language and theoretical computer science. It is used to validate input data or to search for and extract information from texts.

If you don't know the syntax of a regex, at first glance, it might look really tricky:

Figure 5.1 – An example of a regex pattern

Fortunately, there are online regex visualization tools that make it easier to understand patterns (you can find one of them at For example, the regex highlighted in Figure 5.1 can be visualized as follows:

Figure 5.2 – A visualization of a regex

From Figure 5.2, we can see that the regex in Figure 5.1 will identify email addresses in a piece of text.

Learning how to use regexes like...