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Key benefits

Get the most out of Python and R with Power BI by implementing non-trivial code

Leverage the toolset of Python and R chunks to inject scripts into your Power BI dashboards

Implement new techniques for ingesting, enriching, and visualizing data with Python and R in Power BI

Description

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.

Who is this book for?

This book is for business analysts, business intelligence professionals, and data scientists who already use Microsoft Power BI and want to add more value to their analysis using Python and R. Working knowledge of Power BI is required to make the most of this book. Basic knowledge of Python and R will also be helpful.

What you will learn

Discover best practices for using Python and R in Power BI products

Use Python and R to perform complex data manipulations in Power BI

Apply data anonymization and data pseudonymization in Power BI

Log data and load large datasets in Power BI using Python and R

Enrich your Power BI dashboards using external APIs and machine learning models

Extract insights from your data using linear optimization and other algorithms

Handle outliers and missing values for multivariate and time-series data

Create any visualization, as complex as you want, using R scripts

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Dr. Franco Arda Oct 21, 2022

I'm a Tableau developer and have no clue about Power BI. However, I code in R/Python, so I was curious to see if "we" could steal some ideas :-)I was particularly interested in the anonymizing data.It's a very good, and I love the author's eye for detail. The material feels novel to me and is not just another R/Python script doing classification problems.I found the optimization problems quite challenging (see screenshot).Kudos

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Nikola Ilic Nov 27, 2021

Luca is a well-known and proven expert in the community, he’s also a Microsoft MVP and someone who always had interesting things to share. Since I don’t know (almost) anything about Python and R, I’ve decided to read this book and learn how these two very popular and powerful languages integrate with my favorite tool – Power BI!Luca breaks down this wide topic into 16 chapters, spanning across ~520 pages! Some of the chapters that I liked most are:*Loading Large Datasets beyond the Available RAM in Power BI – didn’t have idea that this is possible:)*Anonymizing and Pseudonymizing Your Data in Power BI*Adding Statistics Insights: Associations*Exploratory Data AnalysisAdvanced Visualizations – circular barplot, for example!Have you heard about Dask?! Neither did I! But, I’ve learned from Luca’s book that you can use this Python library to increase the parallelism of your machine and manipulate 100+ GB datasets with one single laptop!At the end of each chapter, there is a short summary of the concepts and skills you’ve learned, which helps you to stay on track. Luca writes clearly and there are a lot of practical examples of how to apply gained knowledge.Personally, the book overcame my expectations – I’ve learned a lot about some new features and how Power BI can be extended beyond default functionalities using popular languages such as Python and R. I wholeheartedly recommend it to anyone interested in deepening knowledge about using Python and R to enhance your Power BI solutions.In my opinion, the book is not for the Power BI “newbies”, more for intermediate and advanced users.

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Jaouad Safouani Dec 31, 2021

I went through the book last week. some of the material used is aimilar to Udacity Nano degree. Book explains how to use Python and R with Power BI. Loved the fact that the author explains the theory and the code used.

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Paul Turley Dec 31, 2021

After reading the other reviews for this book, there is little I can add about the outstanding quality and value of this comprehensive technical reference. This is a complex and difficult topic to cover, but Luca and Francesca do an amazing job; with simple, complete and well-organized instructions. I learned not only why there are multiple choices and versions of each language, but when to consider using different implementations of R or Python for data transformation, analysis and advanced visualization. Beginners will have a clear understanding about how R and Python fit into the Power BI ecosystem and experienced analysts will have a useful set of tools to take their solutions to the next level, with several examples and patterns to follow.

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Markus Ehrenmueller-Jensen Nov 29, 2021

First impression: A book of over 500 pages!And it is spending the first 80 pages on how to configure R and Python to work with Power BI, which is very helpful, if you are new to R/Python or on their integration into Power BI Desktop.The book introduces techniques on how to build different virtual environments, which come in handy, when you plan to use different versions of libraries. This is especially important, when you want to publish a report to the Power BI Service (where you do not have control over the installed versions of R and Python and the available libraries).It then shows good use cases, where R and/or Python can give a hand to support solutions, which are not available in Power BI Desktop itself, covering certain techniques for data ingestion, transformation, data enrichment, and data visualization.The chapters are built on top of each other - therefore I would recommend to read it cover-to-cover, unless you already have experiences with some of the content described.While, for the review, I did not walk myself through the examples, I enjoyed having a detailed step-by-step description of what exactly to do to achieve the intended result.For reasons of brevity, I want to highlight only some of the chapters:To be honest, I was a bit lost in the complexity of chapter 10 “Calculating Columns Using Complex Algorithms”, as the use cases (calculating the distance between two geographic locations) are not very related to my previous or current projects. It was nice though, to see again, what’s indeed possible when you bring in R or Python.I especially enjoyed the explanations and examples in chapter 12 “Outliers and Missing Values”.Chapter 14 “Exploratory Data Analysis” is a good reminder, that it is so important that you get to know the data before you can do something useful with it – and the book gives you useful scripts to indeed explore your data.Chapter 15 "Advanced Visualizations" shows how to implement a circular bar plot. While I enjoyed reading and learning what's possible, I am not really convinced that I will ever build such a visual, as I don't see how such a visual would improve a report.Even if you do not intend to use Power BI Desktop, this book is a very good start into topics like regular expressions, anonymizing data, calling APIs, linear programming, correlation analysis, outlier detection, or exploratory data analysis with R and Python.I am pretty sure, that I will return to one or the other chapter in this book for some of my projects in the upcoming months.

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Extending Power BI with Python and R: Ingest, transform, enrich, and visualize data using the power of analytical languages

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