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)
1
Section 1: Best Practices for Using R and Python in Power BI
5
Section 2: Data Ingestion and Transformation with R and Python in Power BI
11
Section 3: Data Enrichment with R and Python in Power BI
17
Section 3: Data Visualization with R in Power BI

Using trained models in script visuals

As you learned in Chapter 4, Importing Unhandled Data Objects, thanks to object serialization and its string representation, you can import any object into a Python or R visual in the form of a dataframe of strings. Once said dataframe is available in the script visual, you can revert it to the original object via inverse deserialization transformations. Since you can do what we described with any object, evidently you can also do it for machine learning models already trained outside of Power BI.

When the appropriately deserialized model is available in the script visual session, new observations can be predicted immediately using the scoring function described in the previous section.

The first thing you might ask yourself is what's the point of being able to score a dataset inside a script visual when the data must always be available first in the Power BI data model in order to use it in the visual. In fact, if the data of the observations...