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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

Choosing a circular barplot

Very often, we need to display the measures associated with different categorical entities using a bar chart (or barplot). However, when the number of entities to be represented exceeds 15 or 20, the graph begins to become unreadable, even if it is arranged vertically:

A graph with a bar graph  Description automatically generated with medium confidence

Figure 21.1: A barplot of worldwide weapons sellers

In this case, as you saw in Chapter 19, Exploratory Data Analysis, it is often a good idea to plot a maximum number of entities and then group the subsequent entities into a single category (in our case, the Others category). This preserves the readability of the graph, but loses some of the information you want to represent.

If it is absolutely necessary to display all entities with all their dimensions, we often resort to a more eye-catching organization of the space occupied by the barplot, wrapping it in a circular shape, thus obtaining a circular barplot:

A graph with text on it  Description automatically generated with medium confidence

Figure 21.2: Circular barplot of worldwide weapons sellers...

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