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Extending Excel with Python and R

You're reading from   Extending Excel with Python and R Unlock the potential of analytics languages for advanced data manipulation and visualization

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Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781804610695
Length 344 pages
Edition 1st Edition
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Authors (2):
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Steven Sanderson Steven Sanderson
Author Profile Icon Steven Sanderson
Steven Sanderson
David Kun David Kun
Author Profile Icon David Kun
David Kun
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Table of Contents (20) Chapters Close

Preface 1. Part 1:The Basics – Reading and Writing Excel Files from R and Python
2. Chapter 1: Reading Excel Spreadsheets FREE CHAPTER 3. Chapter 2: Writing Excel Spreadsheets 4. Chapter 3: Executing VBA Code from R and Python 5. Chapter 4: Automating Further – Task Scheduling and Email 6. Part 2: Making It Pretty – Formatting, Graphs, and More
7. Chapter 5: Formatting Your Excel Sheet 8. Chapter 6: Inserting ggplot2/matplotlib Graphs 9. Chapter 7: Pivot Tables and Summary Tables 10. Part 3: EDA, Statistical Analysis, and Time Series Analysis
11. Chapter 8: Exploratory Data Analysis with R and Python 12. Chapter 9: Statistical Analysis: Linear and Logistic Regression 13. Chapter 10: Time Series Analysis: Statistics, Plots, and Forecasting 14. Part 4: The Other Way Around – Calling R and Python from Excel
15. Chapter 11: Calling R/Python Locally from Excel Directly or via an API 16. Part 5: Data Analysis and Visualization with R and Python for Excel Data – A Case Study
17. Chapter 12: Data Analysis and Visualization with R and Python in Excel – A Case Study 18. Index 19. Other Books You May Enjoy

Summary

In this chapter, you have learned ways to call R and Python from Excel instead of the other way around to empower your end users even further. We have covered the reasons for doing so and two very different approaches: calling R and Python locally and via an API endpoint.

For locally calling R and Python, we covered BERT and xlwings in detail, from setting up and testing the environment via creating R and Python solutions up to and including how to call those solutions from Excel using the various methods provided by BERT and xlwings, such as VBA code and UDFs.

Next, you learned about API endpoints and the benefits of using an API endpoint to connect Python and R to Excel. We discussed the pros and cons of such a setup and then delved into the two flavors of API hosting: open source tools and commercial solutions. We have covered the two most used open source setups: plumber for R and FastAPI for Python. Finally, we had a look at commercial solutions for hosting R and...

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