You're reading from Extending Power BI with Python and R Ingest, transform, enrich, and visualize data using the power of analytical languages

Product type Paperback

Published in Nov 2021

Publisher Packt

ISBN-13 9781801078207

Length 558 pages

Edition 1st Edition

Languages

Python

Tools

Power BI

Concepts

Business Intelligence

Author (1):

Luca Zavarella

View More author details

Table of Contents (22) Chapters

Preface

1. Section 1: Best Practices for Using R and Python in Power BI

2. Chapter 1: Where and How to Use R and Python Scripts in Power BI FREE CHAPTER

3. Chapter 2: Configuring R with Power BI

4. Chapter 3: Configuring Python with Power BI

5. Section 2: Data Ingestion and Transformation with R and Python in Power BI

6. Chapter 4: Importing Unhandled Data Objects

7. Chapter 5: Using Regular Expressions in Power BI

8. Chapter 6: Anonymizing and Pseudonymizing Your Data in Power BI

9. Chapter 7: Logging Data from Power BI to External Sources

10. Chapter 8: Loading Large Datasets beyond the Available RAM in Power BI

11. Section 3: Data Enrichment with R and Python in Power BI

12. Chapter 9: Calling External APIs to Enrich Your Data

13. Chapter 10: Calculating Columns Using Complex Algorithms

14. Chapter 11: Adding Statistics Insights: Associations

15. Chapter 12: Adding Statistics Insights: Outliers and Missing Values

16. Chapter 13: Using Machine Learning without Premium or Embedded Capacity

17. Section 3: Data Visualization with R in Power BI

18. Chapter 14: Exploratory Data Analysis

19. Chapter 15: Advanced Visualizations

20. Chapter 16: Interactive R Custom Visuals

21. Other Books You May Enjoy

Handling optimization problems with Python

As you've probably already figured out, the large community that develops Python packages never stands still. Even in this case, it 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 not-free engines that solve LP, Mixed Integer Programming (MIP), and other related problems, such as GNU Linear Programming Kit (GLPK), Coin-or Branch and Cut (CBC), which is the default one, and IBM ILOG CPLEX. Its use is quite 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 03-linear-optimizaiont-in-python.py file in the Chapter10\Python folder of the repository.

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

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