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

Handling optimization problems with Python and R

As you’ve probably noticed, the large community that develops Python packages never stands still. Even in this case, it has provided a module that helps us solve linear optimization problems. Its name is PuLP (https://github.com/coin-or/pulp) and it is an LP modeler written in Python. It interfaces with the most common free and non-free engines that solve LP, Mixed-Integer Programming (MIP), and other related problems, such as the GNU Linear Programming Kit (GLPK), COIN-OR Branch and Cut (CBC), which is the default one, and IBM ILOG CPLEX. Using PuLP is fairly straightforward. Let’s put it into practice right away with the problem from the previous section.

Solving the LP problem in Python

The code that will be explained to you in this section can be found in the Python\01-linear-optimization-in-python.py file in the Chapter 14 folder of the repository.

First, you have to install the PuLP module in your environment...