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 4: Importing Unhandled Data Objects

In this chapter, you'll look at using R and Python in what is typically the first phase of report creation: data ingestion. Power BI is a very powerful tool from this point of view, because it has many connectors to various data sources out of the box. In addition to being able to import data directly by connecting to data sources, you can easily solve more complex data loading scenarios with Power BI. For example, you can merge multiple CSV files or multiple Excel workbook sheets dynamically directly from Power BI, even using the M language to apply special logic to the merge step. You can also scrape any web page by just clicking on the web page contents without using any code. All this is possible thanks to Power BI's standard features, without having to use R or Python.

There are, however, cases in which the data to be imported and used in Power BI comes from processing done on external systems, which persists data in formats...