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

Importing PKL files in Python

Let's give you an overview of what you're going to implement using the Python code on GitHub. If you are not familiar with Python, you should familiarize yourself with the basic structures through this tutorial: For a more detailed study of how to implement algorithms and data structures in Python, we suggest this free e-book:

A very short introduction to the PyData world

The PyData world is made up of users and developers who are passionate about data analytics and love to use open source data tools. The PyData community also loves to share best practices, new approaches, and emerging technologies for managing, processing, analyzing, and visualizing data. The most important and popular packages used by the Python data management community are as follows:

  • NumPy: This is the main library for scientific computing in Python. It provides a high-performance multidimensional...