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

Choosing a circular barplot

Very often, it happens that we need to represent the measures associated with various categorical entities using a bar chart (or barplot). However, when the number of entities to be represented exceeds 15 or 20, the graph starts to become unreadable, even though you arrange it vertically:

Figure 15.1 – A barplot of worldwide weapons sellers

In this case, as you have already seen in Chapter 14, Exploratory Data Analysis, it is often a good idea to represent a maximum number of entities, after which the subsequent individual entities are grouped into a single category (in our case, the Others category). In this way, the readability of the graph is preserved, but part of the information you want to represent is lost.

If it is strictly necessary to display all entities with their measure, we often resort to a more eye-catching organization of the space occupied by the barplot, wrapping it in a circular shape, thus obtaining...