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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
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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

Formatting Your Excel Sheet

In this chapter, we are going to go over two different libraries in R and various libraries in Python that can be used to format tables and data in an Excel worksheet.

There are several packages in R that we will be discussing, including the following:

  • styledTables
  • basictabler

We are going to create some fictitious data along with using one of R’s built-in datasets, Iris, and then we are going to apply styling to it from the aforementioned packages. Each package will have its methods to apply this styling, so it’s good to go over them and see which type of workflow you prefer.

In the Python section of this chapter, we will explore the advanced options available in pandas and openpyxl to create beautiful tables and pivot tables from Python in Excel. In particular, we will use pandas, openpyxl, and pywin32.

In this chapter, we will understand the following topics:

  • Installing and using styledTables in R
  • ...
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