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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

Validating data using regex in Power BI

To date, Power BI has no native feature in Power Query to perform operations via regexes. There are cases when you can't avoid using regexes to extract useful information from data in text form. The only way to be able to use regexes is through R scripts or Python scripts. The only cons you have in this case is that, if you need to publish the report on the Power BI service, to allow Power Query to use external R or Python engines, you must also install the on-premises data gateway in personal mode.However, let's get right into it with real-world examples.Let's say you work for a retail company that has a team dedicated to identifying fraudulent customers. As soon as a team member identifies a fraudster, they fill out an Excel spreadsheet, in which the Email and BannedDate columns are included along with others. Your task is to load the data from this Excel file into Power BI and, from other data sources, select only the fraudster...

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