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

The available Python engines

As with R, there are several distributions you can install for Python: standard Python, ActivePython, Anaconda, and so on. Typically, "pure" developers download the latest version of the Python engine from, and then install various community-developed packages useful for their projects from the Python Package Index (PyPI). Other vendors, such as ActiveState and Anaconda, pre-package a specific version of the Python engine with a set of packages for the purpose of accelerating a project's startup. While the standard Python and ActiveState distributions are more aimed at general-purpose developers, Anaconda is the distribution preferred by data scientists and by those who work more closely with machine learning projects. In turn, Anaconda comes in two distinct distributions itself: Anaconda and Miniconda.

The Anaconda distribution, with its more than 150 included packages, can be considered to be the best do-it-yourself...