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

The distance between two geographic locations

It often happens that you have in your dataset coordinates expressed in longitude and latitude that identify points on the globe. Depending on the purpose of the analysis you need to complete, you can leverage these coordinates to calculate measures that best help to describe the scenario you want to deal with. For example, assuming you have the geographic coordinates of some hotels in a dataset, it might make sense to calculate the distance of each of them to the nearest airport if you want to give an additional value of interest to a visitor. Let's start by figuring out what types of distances to consider for our case.

Spherical trigonometry

The study of how to measure triangles (trigonometry) has been of great interest in the past. The ancient Egyptians and Babylonians had already addressed the issues between the relationships between sides, although they did not yet have the notion of an angle. It is thanks to Hellenistic...