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

Python

Concepts

Business Intelligence

Author (1):

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

Test your knowledge

Q01. What is the most obvious disadvantage of anonymization?Q02. How does pseudonymization differ from anonymization?Q03. How does the architecture shown for pseudonymization ensure compliance with GDPR deletion requirements?Q04. Why is it necessary to use NLP techniques to identify PII instead of using the usual regexes?Q05. What is one of the best Python packages for de-identifying PPI? What NLP engines can be used behind the scenes?Q06. Which R package was used to de-identify PPI? What is special about this package as an engine for NLP?Q07. What are pseudonyms?Q08. Which Python and R packages were used to generate pseudonyms?

Answers

A01. The most obvious disadvantage of anonymization is that it removes significant value from the data involved. This is because once the anonymization process is complete, it becomes impossible to trace the identities that generated the data. This means that any information or insights that could be gained from analysing the data...