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

Product type Paperback

Published in Apr 2024

Publisher Packt

ISBN-13 9781804610695

Length 344 pages

Edition 1st Edition

Languages

Python

Tools

Excel

Concepts

Data Analysis

Authors (2):

Steven Sanderson

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 FREE CHAPTER

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

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Other alternatives

Apart from pandas and openpyxl, there are other libraries available for exporting data to Excel from Python. Some popular alternatives include XlsxWriter, xlrd, and xlwt. These libraries offer different features and capabilities, and the choice depends on your specific requirements. XlsxWriter, for example, emphasizes performance and supports advanced Excel features, while xlrd and xlwt provide functionality for reading and writing older Excel file formats (.xls).

In this section, we explored the benefits of exporting data to Excel, demonstrated how to do so using pandas, went through the process of creating and manipulating Excel sheets using openpyxl, discussed the reasons for choosing openpyxl or pandas based on your needs, and mentioned other alternatives available. By leveraging the power of these libraries, you can seamlessly export your data from Python to Excel, enabling efficient analysis, reporting, and collaboration.