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Extending Power BI with Python and R

You're reading from   Extending Power BI with Python and R Perform advanced analysis using the power of analytical languages

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Product type Paperback
Published in Mar 2024
Publisher Packt
ISBN-13 9781837639533
Length 814 pages
Edition 2nd Edition
Languages
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Author (1):
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Luca Zavarella Luca Zavarella
Author Profile Icon Luca Zavarella
Luca Zavarella
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Table of Contents (27) Chapters Close

Preface 1. Where and How to Use R and Python Scripts in Power BI FREE CHAPTER 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

Calling External APIs to Enrich Your Data

In the previous chapter, you saw an example of how to enrich existing data with external information. In that case, the data was exposed via CSV files, but this is not always the case. Very often, the data that is useful for enrichment is exposed through external application programming interfaces (APIs), most often in the form of web service endpoints. Power BI allows you to read data from a web service through a dedicated UI, but most of the time it is unusable. So, you have to resort to writing M code to get it done. Writing M code isn’t too difficult, but it’s not easy. You also have to be careful not to write code that causes refresh problems when you publish the report to the Power BI service. In addition, Power BI does not allow you to parallelize more than one call to the same web service to reduce latency when retrieving data. Using Python or R to retrieve data from a web service solves all of these issues very easily...

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