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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

Python and R compatibility across Power BI products

The first question once you are clear on where to inject R and Python scripts in Power BI could be: “Is the use of R and Python code allowed in all Power BI products?” In order to cover that, let’s briefly recap the various Power BI products and their usage in general. Here is a concise list:

  • Power BI service: This is sometimes called Power BI Online, and it’s the Software as a Service (SaaS) version of Power BI. It was created to facilitate the sharing of visual analysis between users through dashboards and reports.
  • Power BI Report Server: This is the on-premises version of Power BI and it extends the capabilities of SQL Server Reporting Services, enabling the sharing of reports created in Power BI Desktop (for Report Server) and Power BI Report Builder for Power BI paginated reports.
  • Power BI embedded: A Microsoft Azure service that allows dashboards and reports to be embedded in an application for users who do not have a Power BI account.
  • Power BI Desktop: A free desktop application for Windows that allows you to use almost all of the features that Power BI offers. It is not the right tool for sharing results between users, but it allows you to share them on the Power BI service and Power BI Report Server. The desktop versions that allow publishing on the two mentioned services are distinct and support slightly different sets of features. They are named Power BI Desktop and Power BI Desktop for Power BI Report Server, respectively.
  • Power BI mobile: A mobile application, available on Windows, Android, and iOS, that allows secure access to the Power BI service and Power BI Report Server, and that allows you to browse and share dashboards and reports, but not edit them.
  • Power BI Report Builder: A free desktop application for Windows that allows you to create paginated reports. These can then be published and shared in the Power BI service and Power BI Report Server.

Apart from the licenses, which we will not go into here, a summary figure of the relationships between the previously mentioned products follows:

Diagram  Description automatically generated

Figure 1.15: Interactions between Power BI products

Unfortunately, of all these products, only the Power BI service, Power BI Embedded, and Power BI Desktop allow you to enrich data via code in R and Python:

Diagram  Description automatically generated with low confidence

Figure 1.16: Power BI products, compatibility with R and Python

IMPORTANT NOTE

From here on out, when we talk about the Power BI service in terms of compatibility with analytical languages, what we say will also apply to Power BI embedded.

So, if you need to develop reports using advanced analytics through R and Python, make sure the target platform supports them.

You have been reading a chapter from
Extending Power BI with Python and R - Second Edition
Published in: Mar 2024
Publisher: Packt
ISBN-13: 9781837639533
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