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

Embedding training code in Power Query

One of the easiest solutions to train a machine learning model is to write the code needed to do so directly in Power Query, right after importing a dataset on which you will build the model.

Training a model on a fairly large dataset typically takes quite a bit of time to complete. As you embed the code in Power Query, it will run every time the data is refreshed, and this may result in a non-negligible delay in getting the data online. Hence, the following applies:

Important Note

This solution is recommended when you are certain that the time required to complete the model training is acceptable.

Let's now look at an example of how to write some training code using PyCaret.

Training and using ML models with PyCaret

Let's take the Titanic disaster dataset to train a machine learning model. Specifically, we want to create a model that predicts whether a passenger survives (the Survived column) based on their attributes...