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

Chapter 5: Using Regular Expressions in Power BI

Often, many data cleansing tasks involve carrying out complex searches and substitutions between strings. The usual search and replace tools are sometimes not enough to get the desired results. For instance, let's suppose you need to match strings, not in an exact way (for instance, via equality conditions) but using similar criteria between them. Knowing how to use tools such as regular expressions (alias regex) can make all the difference in projects that require high-quality data. Thanks to R and Python, you can add these tools to your arsenal.

In this chapter, we will cover the following topics:

  • A brief introduction to regexes
  • Validating data using regex in Power BI
  • Loading complex log files using regex in Power BI
  • Extracting values from text using regex in Power BI