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

Using trained models in Power Query

As you already saw in Chapter 4, Importing Unhandled Data Objects, you used to share objects that were the result of complex, time-consuming processing (thus also a machine learning model) in a serialized format specific to the language you were using. At that point, it was very simple to deserialize the file and get the model ready to be used in Power Query to predict the target variable of new observations. However, it is important to know the dependencies needed by the scoring function (which gets the new observations as input and returns the predictions), since they are closely related to how the training of the model took place. For this reason, we recommend the following:

Important Note

When you need to use a serialized machine learning model provided by a third party, make sure that whoever developed it also provides you with a working scoring function in order to avoid unnecessary headaches when predicting target values for unknown...