Extending Power BI with Python and R - Second Edition
Extending Power BI with Python and R - Second Edition
Perform advanced analysis using the power of analytical languages
About the Book
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.
About the Author
Table of Contents
- Where and How to Use R and Python Scripts in Power BI
- Configuring R with Power BI
- Configuring Python with Power BI
- Solving Common Issues When Using Python and R in Power BI
- Importing Unhandled Data Objects
- Using Regular Expressions in Power BI
- Anonymizing and Pseudonymizing your Data in Power BI
- Logging Data from Power BI to External Sources
- Loading Large Datasets Also Beyond the Available RAM in Power BI
- Boosting Data Loading Speed in Power BI with Parquet Format
- Calling External APIs To Enrich Your Data
- Calculating Columns Using Complex Algorithms: Distances
- Calculating Columns Using Complex Algorithms: Fuzzy Matching
- Calculating Columns Using Complex Algorithms: Optimization Problems
- Adding Statistics Insights: Associations
- Adding Statistics Insights: Outliers and Missing Values
- Using Machine Learning Without Premium or Embedded Capacity
- Using SQL Server External Languages for Advanced Analytics and ML Integration in Power BI
- Exploratory Data Analysis
- Using the Grammar of Graphics in Python with plotnine
- Advanced Visualizations
- Interactive R Custom Visuals
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