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

Section 2: Data Ingestion and Transformation with R and Python in Power BI

The data ingestion and data transformation phases are the most common in any system such as Power BI that allows the integration of data from heterogeneous external data sources. The introduction of analytical languages in Power BI allows analysts to add powerful and sophisticated tools to these phases, facilitating operations in scenarios that seem impossible to address with the common tools provided by Power BI. We are talking about tools such as some techniques of reading and writing files directly from analytical languages, the manipulation of serialized files through analytical languages, regular expressions, and sophisticated techniques of natural language processing and distributed systems that allow you to work with very large datasets without generating memory problems. All of this will be the subject of this section of the book and will grant the analyst knowledge of data manipulation above the standard...