Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Mar 2024
Publisher Packt
ISBN-13 9781837639533
Length 814 pages
Edition 2nd Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Luca Zavarella Luca Zavarella
Author Profile Icon Luca Zavarella
Luca Zavarella
Arrow right icon
View More author details
Toc

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

To get the most out of this book

  • You will need a working PC with a stable Internet connection. This setup will allow you to not only download the necessary software, but also access online resources that can enhance your learning experience. In addition, it is critical that you have Power BI Desktop installed on your computer. This software is the backbone of the concepts and labs that we will explore in this book.
  • It is best to have a basic understanding of Power BI. Familiarity with the interface and basic concepts will help you navigate through the exercises and understand the more advanced topics more easily.
  • If you are reading a digital version of this book, we recommend that you type the code examples yourself or access the code from the book’s GitHub repository. A link to the repository is provided in the next section. This practice will help you avoid potential errors that can result from copying and pasting code directly.

Download the example code files

The code bundle for the book is hosted on GitHub at https://github.com/PacktPublishing/Extending-Power-BI-with-Python-and-R-2nd-edition. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://packt.link/gbp/9781837639533.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. For example: “Activate the environment that gives you the error you saw before using the conda activate <your-environment-name> command.”

A block of code is set as follows:

re.search('test', 'TeSt', re.IGNORECASE)
re.match('test', 'TeSt', re.IGNORECASE)
re.sub('test', 'xxxx', 'TesTing', flags=re.IGNORECASE)

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

import pandas as pd
import numpy
df = pd.DataFrame(dir(numpy))

Any command-line input or output is written as follows:

successfully initialized (spaCy Version: 3.5.0, language model: en_core_web_lg)
(python options: type = "condaenv", value = "C:\ProgramData\Miniconda3\envs\presidio_env")

Bold: Indicates a new term, an important word, or words that you see on the screen. For instance, words in menus or dialog boxes appear in the text like this. For example: “Personally Identifiable Information (PII), also known as personal information or personal data, is any information about an identifiable individual.”

Warnings or important notes appear like this.

Tips and tricks appear like this.

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image