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

Python

Concepts

Business Intelligence

Author (1):

Luca Zavarella

Table of Contents (27) Chapters

Preface

1. Where and How to Use R and Python Scripts in Power BI

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

Dealing with dates/times in Python and R script steps

The handling of date, date/time, and time fields is native in Power BI. If you want to add a Python or R script step from a dataset containing a date/time field, you would expect to find that field in the default dataset dataframe with the corresponding data type depending on whether it is a Python or R script. Unfortunately, it is not so straightforward. But with a little forethought, it is possible to handle these data types in the right way.

The code used in both Python and R makes use of date-specific functions with which you may not be too familiar. It is not important to completely know them for the purposes of understanding the section; you can delve into them later when you need to.

In the folder pertaining to Chapter 4 in the GitHub repository associated with the book, you will find the date-time-fields.xlsx Excel file containing the dates sheet with only date, date/time, and time fields: