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

Implementing outlier detection algorithms

The first thing you'll do is implement what you've just studied in Python.

Implementing outlier detection in Python

In this section, we will use the Wine Quality dataset created by Paulo Cortez et al. ( to show how to detect outliers in Python. The dataset contains as many observations as the different types of red wine, each described by the organoleptic properties measured by the variables, except for the quality one, which provides a measure of the quality of the product using a discrete grade scale from 1 to 10.

You'll find the code used in this section in the file into the Chapter12\Python folder.

Once you have loaded the data from the winequality-red.csv file directly from the web into the df variable, let's start by examining the sulphates variable. Let's check if it contains any outliers by displaying its boxplot...