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Extending Excel with Python and R

You're reading from  Extending Excel with Python and R

Product type Book
Published in Apr 2024
Publisher Packt
ISBN-13 9781804610695
Pages 344 pages
Edition 1st Edition
Languages
Authors (2):
Steven Sanderson Steven Sanderson
Profile icon Steven Sanderson
David Kun David Kun
Profile icon David Kun
View More author details

Table of Contents (20) Chapters

Preface 1. Part 1:The Basics – Reading and Writing Excel Files from R and Python
2. Chapter 1: Reading Excel Spreadsheets 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

Choosing between openpyxl and pandas

When it comes to exporting data to Excel, both openpyxl and pandas are excellent choices. openpyxl is a dedicated library for working with Excel files as it provides extensive functionality for creating, modifying, and saving Excel workbooks. On the other hand, pandas offers a high-level data manipulation interface with convenient methods for exporting data to Excel, which is ideal when a simple data dump is all you need.

If you require fine-grained control over the Excel file’s structure, such as adding formatting, formulas, or charts, openpyxl is a suitable option. It allows you to work directly with the underlying Excel objects, providing more flexibility. On the other hand, if you primarily focus on data manipulation and want a simpler way to export DataFrames to Excel without worrying about Excel-specific features, pandas is a convenient choice. It abstracts away some of the lower-level details and provides a more straightforward...

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