Extending Excel with Python and R

Excel is often where data arrives and where results need to be shared. This book shows how R and Python can extend that familiar spreadsheet workflow: read and reshape workbook data, automate repetitive tasks, analyze information with richer tools, and bring useful outputs back into Excel.

Steven Sanderson and David Kun take a practical, cross-language approach, moving between R packages and Python libraries rather than treating spreadsheet work as a one-language problem. The emphasis is on connecting tools and workflows, from everyday file handling to automation and analysis.

Work with Excel files in both R and Python

Start with the essentials: importing spreadsheets, handling multiple sheets, combining data, and writing results back to workbooks. The book discusses tools including readxl, openxlsx, pandas, and openpyxl, giving readers examples of how each language can participate in the spreadsheet workflow.

Make routine spreadsheet work more repeatable

From formatting workbooks and creating charts to running VBA code, scheduling scripts, and sending email notifications, the chapters explore ways to reduce manual steps. The result is a look at automation as part of a working analysis process—not simply a way to write code outside Excel.

Move from workbook data to analysis and visuals

The book also covers exploratory data analysis, statistical analysis, time-series work, and visualizations using tools such as ggplot2, matplotlib, plotnine, and plotly. These topics help connect data preparation with the charts and findings that readers may need to communicate.

Connect Excel to R and Python

Later material looks at calling R and Python from Excel, both locally and through APIs. That makes the book relevant to readers considering how spreadsheet users can access analysis or functions built in these languages.

A practical fit for data practitioners

Data analysts, data scientists, actuaries, and spreadsheet-focused practitioners may find this book useful when their work spans Excel and programming. It is positioned for beginner to intermediate readers, with basic familiarity with Excel, Python, and R recommended.

For readers who need spreadsheets to work alongside—not instead of—programming-based analysis, this book offers a structured view of the connections between them.