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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

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

Why subscribe?

19. Other Books You May Enjoy

Creating and managing pivot tables in Python with win32com and pypiwin32

Pivot tables are powerful tools in data analysis, allowing you to summarize and explore large datasets quickly and efficiently. While they are a staple feature in spreadsheet software such as Microsoft Excel, you can also create and manipulate pivot tables programmatically using Python. In this section of the chapter, we will delve into the world of pivot tables and learn how to harness their potential with the win32com and pywin32 libraries.

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Authors (2)

Steven Sanderson

Steven Sanderson

Steven Sanderson, MPH, is an applications manager for the patient accounts department at Stony Brook Medicine. He received his bachelor's degree in economics and his master's in public health from Stony Brook University. He has worked in healthcare in some capacity for just shy of 20 years. He is the author and maintainer of the healthyverse set of R packages. He likes to read material related to social and labor economics and has recently turned his efforts back to his guitar with the hope that his kids will follow suit as a hobby they can enjoy together.

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Kun

Kun

David Kun is a mathematician and actuary who has always worked in the gray zone between quantitative teams and ICT, aiming to build a bridge. He is a co-founder and director of Functional Analytics and the creator of the ownR Infinity platform. As a data scientist, he also uses ownR for his daily work. His projects include time series analysis for demand forecasting, computer vision for design automation, and visualization.

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