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

Exploring associations between variables

At first glance, you might wonder what the point of finding relationships between variables is. Being able to understand the behavior of a pair of variables and identify a pattern in their behavior helps business owners identify key factors to divert certain indicators of company health to their benefit. Knowing the pattern that binds the trend of two variables gives you the power to predict with some certainty one of them by knowing the other. So, knowing the tools to uncover these patterns gives you a kind of analytical superpower that is always appealing to business owners.

In general, two variables are associated with each other when the values of one of them are in some way related to the values of the other. When you can somehow measure the extent of the association of two variables, it is called correlation. The concept of correlation is immediately applicable in a case where the two variables are numerical. Let's see how.

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