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

Chapter 10: Calculating Columns Using Complex Algorithms

The data ingestion phase allows you to gather all the information you need for your analysis from any data source. Once the various datasets have been imported, it may be that some of this information, taken as it is, isn't useful in describing a phenomenon from an analytical point of view. It is often necessary to apply non-trivial algorithms to the data you have in order to get measures or indicators that will do the trick and Power BI often doesn't have the tools to calculate them. Fortunately, thanks to R and Python, we have everything we need to calculate our measures.

In this chapter, you will learn about the following topics:

  • The distance between two geographic locations
  • Implementing distances using Python
  • Implementing distances using R
  • The basics of linear programming
  • Definition of the LP problem to solve
  • Handling optimization problems with Python
  • Solving LP problems with R...