| Description |
1 online resource (814 pages) : illustrations |
| Series |
Expert insight |
|
Expert insight.
|
| Contents |
Cover -- Copyright -- Contributors -- Table of Contents -- Preface -- Chapter 1: Where and How to Use R and Python Scripts in Power BI -- Technical requirements -- Injecting R or Python scripts into Power BI -- Data loading -- Data transformation -- Data visualization -- Using R and Python to interact with your data -- Python and R compatibility across Power BI products -- Summary -- Test your knowledge -- Chapter 2: Configuring R with Power BI -- Technical requirements -- The available R engines -- The CRAN R distribution -- The Microsoft R Open distribution and MRAN |
|
Multi-threading in MRO -- Choosing an R engine to install -- The R engines used by Power BI -- Installing the suggested R engines -- The R engine for data transformation -- The R engine for R script visuals on the Power BI service -- What to do when the Power BI service upgrades the R engine -- Installing an IDE for R development -- Installing RStudio -- Installing RTools -- Linking Intel's MKL to R -- Configuring Power BI Desktop to work with R -- Debugging an R script visual -- Configuring the Power BI service to work with R -- Installing the on-premises data gateway in personal mode |
|
Sharing reports that use R scripts in the Power BI service -- R script visuals limitations -- Summary -- Test your knowledge -- Chapter 3: Configuring Python with Power BI -- Technical requirements -- The available Python engines -- Choosing a Python engine to install -- The Python engines used by Power BI -- Installing the suggested Python engines -- The Python engine for data transformation -- Creating an environment for data transformations using pip -- Creating an optimized environment for data transformations using conda |
|
Creating an environment for Python script visuals on the Power BI service -- What to do when the Power BI service upgrades the Python engine -- Installing an IDE for Python development -- Configuring Python with RStudio -- Configuring Python with Visual Studio Code -- Working with the Python Interactive window in Visual Studio Code -- Configuring Power BI Desktop to work with Python -- Configuring the Power BI service to work with Python -- Sharing reports that use Python scripts in the Power BI service -- Limitations of Python visuals -- Summary -- Test your knowledge |
|
Chapter 4: Solving Common Issues When Using Python and R in Power BI -- Technical requirements -- Avoiding the ADO.NET error when running a Python script in Power BI -- The real cause of the problem -- A practical solution to the problem -- Avoiding the Formula.Firewall error -- Incompatible privacy levels -- Indirect access to a data source -- The easy way -- Combining queries and/or transformations -- Encapsulating queries into functions -- Using multiple datasets in Python and R script steps -- Applying a full join with Merge -- Using arguments of the Python.Execute function |
| Summary |
The latest edition of this book delves deep into advanced analytics, focusing on enhancing Python and R proficiency within Power BI. New chapters cover optimizing Python and R settings, utilizing Intel's Math Kernel Library (MKL) for performance boosts, and addressing integration challenges. Techniques for managing large datasets beyond available RAM, employing the Parquet data format, and advanced fuzzy matching algorithms are explored. Additionally, it discusses leveraging SQL Server Language Extensions to overcome traditional Python and R limitations in Power BI. It also helps in crafting sophisticated visualizations using the Grammar of Graphics in both R and Python. This Power BI book will help you master data validation with regular expressions, import data from diverse sources, and apply advanced algorithms for transformation. You'll learn how to safeguard personal data in Power BI with techniques like pseudonymization, anonymization, and data masking. You'll also get to grips with the key statistical features of datasets by plotting multiple visual graphs in the process of building a machine learning model. The book will guide you on utilizing external APIs for enrichment, enhancing I/O performance, and leveraging Python and R for analysis. You'll reinforce your learning with questions at the end of each chapter |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Information visualization -- Computer programs
|
|
Visual analytics -- Data processing
|
|
Data mining -- Computer programs
|
|
Business intelligence -- Computer programs
|
|
R (Computer program language)
|
|
Python (Computer program language)
|
| Genre/Form |
Electronic books
|
| Form |
Electronic book
|
| Author |
Talwar, Rajat, writer of foreword
|
|
