Extending Power BI with Python and R: Perform advanced analysis using the power of analytical languages

Table of Contents

1. Where and How to Use R and Python Scripts in Power BI
2. Configuring R with Power BI
3. Configuring Python with Power BI
4. Solving Common Issues When Using Python and R in Power BI
5. Importing Unhandled Data Objects
6. Using Regular Expressions in Power BI
7. Anonymizing and Pseudonymizing your Data in Power BI
8. Logging Data from Power BI to External Sources
9. Loading Large Datasets Also Beyond the Available RAM in Power BI
10. Boosting Data Loading Speed in Power BI with Parquet Format
11. Calling External APIs To Enrich Your Data
12. Calculating Columns Using Complex Algorithms: Distances
13. Calculating Columns Using Complex Algorithms: Fuzzy Matching
14. Calculating Columns Using Complex Algorithms: Optimization Problems
15. Adding Statistics Insights: Associations
16. Adding Statistics Insights: Outliers and Missing Values
17. Using Machine Learning Without Premium or Embedded Capacity
18. Using SQL Server External Languages for Advanced Analytics and ML Integration in Power BI
19. Exploratory Data Analysis
20. Using the Grammar of Graphics in Python with plotnine
21. Advanced Visualizations
22. Interactive R Custom Visuals