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 learned about the most popular free Python distributions in the community and the best practices for their use.

Using the unique features of Power BI Desktop and the Power BI service, you have learned how to properly create specific Python environments.

You also learned that the most popular IDE in the R community (RStudio) can also run Python code. In addition, you have installed and configured VSCode, which is to date one of the most widely used advanced editors for Python.

You were also introduced to all of the best practices for properly configuring both Power BI Desktop and the Power BI service with Python, whether in a development or enterprise environment.

Finally, you've learned some of the limitations on using Python with Power BI, knowledge of which is critical to avoid making mistakes in developing and deploying reports.

In the next chapter, we'll finally start working with R and Python scripts in Power BI, doing data...