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

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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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Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

accuracy of model 203

adjusted R-squared 208

Advanced Python Scheduler (APScheduler) 68, 69

features and benefits 68

aesthetic 122

Apache Kafka 69

Application Programming Interface (API) 268

calling, from Excel VBA 276-279

R/Python, calling from Excel via 268

Augmented Dickey-Fuller (ADF) test 239-241

components 240, 241

auto ARIMA modeling

with healthyR.ts 226-232

autocorrelation function (ACF) plot 223, 238

creating, in R 224-226

autoregressive (AR) 238

autoregressive integrated moving average (ARIMA) models 218

Azure Functions 279

con 279

pro 279

use cases 279

background color of cells

modifying 94

bar charts

cons 114

constructing, with matplotlib 136, 137

constructing, with plotnine 123

ggplot2, using...

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Table of Contents (20) Chapters