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