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

Calling web services in Power Query

Another way to interact with machine learning models within Power Query is to invoke web services. As you may already know, a machine learning model can be used to carry out the scoring of many observations in batch mode using a trained model (process described previously). Another option for being able to interact with a machine learning model is to deploy it to a web service so that it can be invoked via REST APIs. You've already learned how to work with external APIs in Chapter 9, Calling External APIs to Enrich Your Data. again, info boxes don't usually have lead-ins so either display the following information as body text or remove this lead-in

Important Note

Remember that you can't consume external services via REST API calls from a Python or R visual because internet access is blocked for security reasons. Therefore, you can only consume these services in Power Query.

As an example, in this section, you'll see...