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

Identifying outliers

There are different methods used to detect outliers depending on whether you are analyzing one variable at a time (univariate analysis) or multiple variables at once (multivariate analysis). In the univariate case, the analysis is fairly straightforward. The multivariate case, however, is more complex. Let's examine them in detail.

Univariate outliers

One of the most direct and widely used ways to identify outliers for a single variable is to make use of boxplots, which you learned about in Chapter 11, Adding Statistics Insights: Associations. Some of the key points of a boxplot are the interquartile range (IQR), defined as the distance from the first quartile (Q1) to the third quartile (Q3), the lower whisker (Q1 - 1.5 x IQR), and the upper whisker (Q3 + 1.5 x IQR):

Figure 12.2 – Boxplot's main characteristics

Specifically, all observations that are before the lower whisker and after the upper whisker are identified...