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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

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Product type Paperback
Published in Nov 2021
Publisher Packt
ISBN-13 9781801078207
Length 558 pages
Edition 1st 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 (22) Chapters Close

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

A brief introduction to regexes

A regular expression (usually shortened to regex) is defined by a series of characters that identify an abstract search pattern. Essentially, it is a mathematical technique that was developed in 1951 by experts of formal language and theoretical computer science. It is used to validate input data or to search for and extract information from texts.

If you don't know the syntax of a regex, at first glance, it might look really tricky:

Figure 5.1 – An example of a regex pattern

Fortunately, there are online regex visualization tools that make it easier to understand patterns (you can find one of them at https://regexper.com). For example, the regex highlighted in Figure 5.1 can be visualized as follows:

Figure 5.2 – A visualization of a regex

From Figure 5.2, we can see that the regex in Figure 5.1 will identify email addresses in a piece of text.

Learning how to use regexes like...

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