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 1: Where and How to Use R and Python Scripts in Power BI

Power BI is Microsoft's flagship self-service business intelligence product. It consists of a set of on-premises applications and cloud-based services that help organizations integrate, transform, and analyze data from a wide variety of source systems through a user-friendly interface.

The platform is not limited to data visualization. Power BI is much more than this, when you consider that its analytics engine (Vertipaq) is the same as SQL Server Analysis Services (SSAS) and Azure Analysis Services. It also uses Power Query as its data extraction and transformation engine, which we find in both Analysis Services and Excel. The engine comes with a very powerful and versatile formula language (M) and GUI, thanks to which you can "grind" and shape any type of data into any form.

Moreover, Power BI supports DAX as a data analytic formula language, which can be used for advanced calculations and queries on data that has already been loaded into tabular data models.

Such a versatile and powerful tool is a godsend for anyone who needs to do data ingestion and transformation in order to build dashboards and reports to summarize a company's business.

Recently, the availability of huge amounts of data, along with the ability to scale the computational power of machines, has made the area of advanced analytics more appealing. So, new mathematical and statistical tools have become necessary in order to provide rich insights. Hence the integration of analytical languages such as Python and R within Power BI.

R or Python scripts can only be used within Power BI with specific features. Knowing which Power BI tools can be used to inject R or Python scripts into Power BI is key to understanding whether the problem you want to address is achievable with these analytical languages.

This chapter will cover the following topics:

  • Injecting R or Python scripts into Power BI
  • Using R and Python to interact with your data
  • R and Python limitations on Power BI products