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Extending Power BI with Python and R - Second Edition

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
Author (1):
Luca Zavarella Luca Zavarella
Profile icon Luca Zavarella

Table of Contents (27) Chapters

Preface Where and How to Use R and Python Scripts in Power BI Configuring R with Power BI Configuring Python with Power BI Solving Common Issues When Using Python and R in Power BI Importing Unhandled Data Objects Using Regular Expressions in Power BI Anonymizing and Pseudonymizing Your Data in Power BI Logging Data from Power BI to External Sources Loading Large Datasets Beyond the Available RAM in Power BI Boosting Data Loading Speed in Power BI with Parquet Format Calling External APIs to Enrich Your Data Calculating Columns Using Complex Algorithms: Distances Calculating Columns Using Complex Algorithms: Fuzzy Matching Calculating Columns Using Complex Algorithms: Optimization Problems Adding Statistical Insights: Associations Adding Statistical Insights: Outliers and Missing Values Using Machine Learning without Premium or Embedded Capacity Using SQL Server External Languages for Advanced Analytics and ML Integration in Power BI Exploratory Data Analysis Using the Grammar of Graphics in Python with plotnine Advanced Visualizations Interactive R Custom Visuals Other Books You May Enjoy
Index
Appendix 1: Answers
Appendix 2: Glossary

Dealing with outliers

The most widely used approaches to deal with outliers are as follows:

  • Dropping them: The analyst concludes that eliminating the outliers altogether will guarantee better results in the final analysis.
  • Capping them: It is common to use the strategy of assigning a fixed extreme value (cap or winsorize) to all those observations that exceed it (in absolute value) when it is certain that all extreme observations behave in the same way as those with the cap value.
  • Assigning a new value: In this case, outliers are eliminated by replacing them with null values, and these null values are imputed using one of the simplest techniques: the replacement of null values with a fixed value that could be, for example, the mean or median of the variable in question. You’ll see more complex imputation strategies in the next sections.
  • Transforming the data: When the analyst is dealing with natural outliers, very often the histogram of the variable...
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