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Extending Power BI with Python and R

You're reading from Extending Power BI with Python and R Ingest, transform, enrich, and visualize data using the power of analytical languages

Product type Paperback

Published in Nov 2021

Publisher Packt

ISBN-13 9781801078207

Length 558 pages

Edition 1st Edition

Languages

Python

Tools

Power BI

Concepts

Business Intelligence

Author (1):

Luca Zavarella

View More author details

Table of Contents (22) Chapters

Preface

1. Section 1: Best Practices for Using R and Python in Power BI

2. Chapter 1: Where and How to Use R and Python Scripts in Power BI FREE CHAPTER

3. Chapter 2: Configuring R with Power BI

4. Chapter 3: Configuring Python with Power BI

5. Section 2: Data Ingestion and Transformation with R and Python in Power BI

6. Chapter 4: Importing Unhandled Data Objects

7. Chapter 5: Using Regular Expressions in Power BI

8. Chapter 6: Anonymizing and Pseudonymizing Your Data in Power BI

9. Chapter 7: Logging Data from Power BI to External Sources

10. Chapter 8: Loading Large Datasets beyond the Available RAM in Power BI

11. Section 3: Data Enrichment with R and Python in Power BI

12. Chapter 9: Calling External APIs to Enrich Your Data

13. Chapter 10: Calculating Columns Using Complex Algorithms

14. Chapter 11: Adding Statistics Insights: Associations

15. Chapter 12: Adding Statistics Insights: Outliers and Missing Values

16. Chapter 13: Using Machine Learning without Premium or Embedded Capacity

17. Section 3: Data Visualization with R in Power BI

18. Chapter 14: Exploratory Data Analysis

19. Chapter 15: Advanced Visualizations

20. Chapter 16: Interactive R Custom Visuals

21. Other Books You May Enjoy

References

For additional reading, please refer to the following books and articles:

Regular Expressions: The Complete Tutorial, Jan Goyvaerts (https://www.princeton.edu/~mlovett/reference/Regular-Expressions.pdf)
Data Privacy Settings In Power BI/Power Query, Part 1: Performance Implications (https://blog.crossjoin.co.uk/2017/05/24/data-privacy-settings-in-power-bipower-query-part-1-performance-implications/)

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Authors (1)

Zavarella

Zavarella

Luca Zavarella has a rich background as an Azure Data Scientist Associate and Microsoft MVP, with a Computer Engineering degree from the University of L'Aquila. His decade-plus experience spans the Microsoft Data Platform, starting as a T-SQL developer on SQL Server 2000 and 2005, then mastering the full suite of Microsoft Business Intelligence tools (SSIS, SSAS, SSRS), and advancing into data warehousing. Recently, his focus has shifted to advanced analytics, data science, and AI, contributing to the community as a speaker and blogger, especially on Medium. Currently, he leads the Data & AI division at iCubed, and he also holds an honors degree in classical piano from the "Alfredo Casella" Conservatory in L'Aquila.

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