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Extending Power BI with Python and R - Second Edition

You're reading from  Extending Power BI with Python and R - Second Edition

Product type Book
Published in Mar 2024
Publisher Packt
ISBN-13 9781837639533
Pages 814 pages
Edition 2nd Edition
Languages
Author (1):
Luca Zavarella Luca Zavarella
Profile icon Luca Zavarella

Table of Contents (27) Chapters

Preface 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 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 Statistical Insights: Associations 16. Adding Statistical 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 23. Other Books You May Enjoy
24. Index
Appendix 1: Answers
1. Appendix 2: Glossary

Summary

In this chapter, you covered some of the types of issues that are common when integrating Python or R scripts into Power BI.In particular, you learned how to avoid the indecipherable ADO.NET error that can occur when you use Power BI with a Conda environment that is not properly enabled.You learned about the different levels of privacy that are managed in Power BI and how to resolve incompatibility issues between them that are caused by the Formula Firewall.You also learned new techniques for referencing more than one dataset in a Python or R script, even though the step stack in which the script is inserted references only one dataset.Finally, you learned how to properly handle data types for date and time fields in step scripts in Python and R.In the next chapter, you'll start working with Python and R scripts in Power BI to perform data ingestion and import data sources that Power BI doesn't handle by default.

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