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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

The causes of outliers

Before considering any action to be taken on the outliers of a variable, it is necessary to consider what may have caused them. Once the cause is identified, it may be possible to correct the outliers immediately. Here is a possible categorization of the causes of outliers:

  • Data entry errors: There may be an analyst collecting the data who made a mistake in compiling the data. For example, if the analyst is collecting the birth dates of a group of people, the analyst may write 177 instead of 1977. If the dates collected are in the 1900-2100 range, it is easy to correct the outlier created by the data entry error. Other times, it is not possible to recover the correct value.
  • Intentional outliers: Very often, the introduction of “errors” is intentional on the part of the individuals to whom the measurements apply. For example, adolescents typically do not accurately report the amount of alcohol they consume.
  • Data processing...
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