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

Section 3: Data Enrichment with R and Python in Power BI

With the introduction of the R and Python analytical languages, data enrichment is a more-than-achievable goal thanks to the wide range of features introduced by packages developed by the community. You can easily invoke web services to add contextual information to your data, use complex mathematical algorithms to derive measures not provided directly, leverage some knowledge of statistics to detect patterns between variables or abnormal behaviors, and finally, use the power of artificial intelligence and machine learning to derive valuable insights from your data. In this section, you'll learn a little bit about all of this, enriching your background to become an advanced analyst.

This section comprises the following chapters:

  • Chapter 9, Calling External APIs to Enrich Your Data
  • Chapter 10, Calculating Columns Using Complex Algorithms
  • Chapter 11, Adding Statistics Insights – Associations
  • Chapter...