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

What outliers are and how to deal with them

Generally, outliers are defined as those observations that lie at an abnormal distance from other observations in a data sample. In other words, they are uncommon values in a dataset. The abnormal distance we're talking about obviously doesn't have a fixed measurement but is strictly dependent on the dataset you're analyzing. Simply put, it will be the analyst who decides the distance beyond which to consider others abnormal distances based on their experience and functional knowledge of the business reality represented by the dataset.

Important Note

It makes sense to talk about outliers for numeric variables or for numeric variables grouped by elements of categorical variables. It makes no sense to talk about outliers for categorical variables only.

But why is there so much focus on managing outliers? The answer is that very often they cause undesirable macroscopic effects on some statistical operations. The most...