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

Handling optimization problems with Python

As you've probably already figured out, the large community that develops Python packages never stands still. Even in this case, it provided a module that helps us solve linear optimization problems. Its name is PuLP ( and it is an LP modeler written in Python. It interfaces with the most common free and not-free engines that solve LP, Mixed Integer Programming (MIP), and other related problems, such as GNU Linear Programming Kit (GLPK), Coin-or Branch and Cut (CBC), which is the default one, and IBM ILOG CPLEX. Its use is quite straightforward. Let's put it into practice right away with the problem from the previous section.

Solving the LP problem in Python

The code that will be explained to you in this section can be found in the file in the Chapter10\Python folder of the repository.

First, you have to install the PuLP module in your environment: