Seamlessly integrate the Python and R programming languages with spreadsheet-based data analysis to maximize productivity
Key Features
Perform advanced data analysis and visualization techniques with R and Python on Excel data
Use exploratory data analysis and pivot table analysis for deeper insights into your data
Integrate R and Python code directly into Excel using VBA or API endpoints
Purchase of the print or Kindle book includes a free PDF eBook
Book Description- 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. What you will learn
Read and write Excel files with R and Python libraries
Automate Excel tasks with R and Python scripts
Use R and Python to execute Excel VBA macros
Format Excel sheets using R and Python packages
Create graphs with ggplot2 and Matplotlib in Excel
Analyze Excel data with statistical methods and time series analysis
Explore various methods to call R and Python functions from Excel
Who this book is for- If you're a data analyst or data scientist, or a quants, actuaries, or data practitioner looking to enhance your Excel skills and expand your data analysis capabilities with R and Python, this book is for you.
- The comprehensive approach to the topics covered makes it suitable for both beginners and intermediate learners.
- A basic understanding of Excel, Python, and R is all you need to get started.
Sprache
Verlagsort
Zielgruppe
Maße
Höhe: 235 mm
Breite: 191 mm
Dicke: 19 mm
Gewicht
ISBN-13
978-1-80461-069-5 (9781804610695)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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. 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.
Table of Contents
Reading Excel Spreadsheets
Writing Excel Spreadsheets
Executing VBA Code from R and Python
Automating Further (Email Notifications and More)
Formatting Your Excel sheet
Inserting ggplot2/matplotlib Graphs
Pivot Tables (tidyquant in R and with win32com and pypiwin32 in Python)/Summary Table {gt}
Exploratory Data Analysis with R and Python
Statistical Analysis: Linear and Logistic Regression
Time Series Analysis: Statistics, Plots, and Forecasting
Calling R/Python Locally from Excel Directly or via an API
Data Analysis and Visualization with R and Python for Excel Data - A Case Study
