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

Product type Paperback

Published in Mar 2024

Publisher Packt

ISBN-13 9781837639533

Length 814 pages

Edition 2nd Edition

Languages

Python

Tools

Power BI

Concepts

Business Intelligence

Author (1):

Luca Zavarella

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

Answers

The stages are data loading, data transforming, and data viewing.
You can’t use a Python dictionary as a data source. Only pandas DataFrames are recognized by Power BI as allowed data sources.
You can’t use an R list as a data source. Only R data frames are recognized by Power BI as allowed data sources.
In the case of both the Python script and R script, the name of the variable that contains the transformation data obtained up to the previous step is dataset.
You must drag the fields you want to use in your script to the Values area.
You must refresh the data in order to re-run a Python or R script added as a Power Query step (either in the loading phase or in the transforming phase) – or set a Page Refresh property for DirectQuery imports of data frames.
Python and R script visuals are updated whenever you interact with other visuals in the report (cross-filtering).
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