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Extending Excel with Python and R

Extending Excel with Python and R

By : Steven Sanderson, Kun
5 (5)
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Extending Excel with Python and R

Extending Excel with Python and R

5 (5)
By: Steven Sanderson, Kun

Overview of this book

– Extending Excel with Python and R is a game changer resource written by experts Steven Sanderson, the author of the healthyverse suite of R packages, and David Kun, co-founder of Functional Analytics. – This comprehensive guide transforms the way you work with spreadsheet-based data by integrating Python and R with Excel to automate tasks, execute statistical analysis, and create powerful visualizations. – Working through the chapters, you’ll find out how to perform exploratory data analysis, time series analysis, and even integrate APIs for maximum efficiency. – Both beginners and experts will get everything you need to unlock Excel's full potential and take your data analysis skills to the next level. – By the end of this book, you’ll be able to import data from Excel, manipulate it in R or Python, and perform the data analysis tasks in your preferred framework while pushing the results back to Excel for sharing with others as needed.
Table of Contents (20 chapters)
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1
Part 1:The Basics – Reading and Writing Excel Files from R and Python
6
Part 2: Making It Pretty – Formatting, Graphs, and More
10
Part 3: EDA, Statistical Analysis, and Time Series Analysis
14
Part 4: The Other Way Around – Calling R and Python from Excel
16
Part 5: Data Analysis and Visualization with R and Python for Excel Data – A Case Study

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.

A

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

B

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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