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)
1
Section 1: Best Practices for Using R and Python in Power BI
5
Section 2: Data Ingestion and Transformation with R and Python in Power BI
11
Section 3: Data Enrichment with R and Python in Power BI
17
Section 3: Data Visualization with R in Power BI

Pseudonymizing data in Power BI

Unlike anonymization, pseudonymization maintains the statistical characteristics of the dataset by transforming the same input string into the same output string, and keeps track of replacements that have occurred, allowing those with access to this mapping information to obtain the original dataset again.

Moreover, pseudonymization replaces sensitive data with fake strings (pseudonyms), having the same form as the original one, making the de-identified data more realistic.

Depending on the analytical language used, there are different solutions driven by the different packages available that lead to the same result. Let's see how to apply pseudonymization in Power BI to the contents of the same Excel file used in the previous sections with Python.

Pseudonymizing data using Python

The modules and the code structure you will use are quite similar to those already used for anonymization. One difference is that, once the sensitive entities...