| Description |
1 online resource (345 p.) |
| Note |
Description based upon print version of record. |
| Contents |
Cover -- Title Page -- Copyright and Credit -- Dedicated -- Contributors -- Table of Contents -- Preface -- Part 1: The Basics -- Reading and Writing Excel Files from R and Python -- Chapter 1: Reading Excel Spreadsheets -- Technical requirements -- Working with R packages for Excel manipulation -- Reading Excel files to R -- Installing and loading libraries -- Reading multiple sheets with readxl and a custom function -- Python packages for Excel manipulation -- Python packages for Excel manipulation -- Considerations -- Opening an Excel sheet from Python and reading the data -- Using pandas |
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Using openpyxl -- Reading in multiple sheets with Python (openpyxl and custom functions) -- The importance of reading multiple sheets -- Using openpyxl to access sheets -- Reading data from each sheet -- Retrieving sheet data with openpyxl -- Combining data from multiple sheets -- Custom function for reading multiple sheets -- Customizing the code -- Summary -- Chapter 2: Writing Excel Spreadsheets -- Technical requirements -- Packages to write into Excel files -- writexl -- openxlsx -- xlsx -- A comprehensive recap and insights -- Creating and manipulating Excel sheets using Python |
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Why export data to Excel? -- Keeping it simple -- exporting data to Excel with pandas -- Advanced mode -- openpyxl for Excel manipulation -- Creating a new workbook -- Adding sheets to the workbook -- Deleting a sheet -- Manipulating an existing workbook -- Choosing between openpyxl and pandas -- Other alternatives -- Summary -- Chapter 3: Executing VBA Code from R and Python -- Technical requirements -- Installing and explaining the RDCOMClient R library -- Installing RDCOMClient -- Executing sample VBA with RDCOMClient -- Integrating VBA with Python using pywin32 |
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Why execute VBA code from Python? -- Setting up the environment -- Error handling with the environment setup -- Writing and executing VBA code -- Automating Excel tasks -- Pros and cons of executing VBA from Python -- Summary -- Chapter 4: Automating Further -- Task Scheduling and Email -- Technical requirements -- Installing and understanding the tasksheduleR library -- Creating sample scripts -- RDCOMClient for Outlook -- Using the Microsoft365R and blastula packages -- Microsoft365R -- The blastula package -- Scheduling Python scripts -- Introduction to Python script scheduling |
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Built-in scheduling options -- Third-party scheduling libraries -- Best practices and considerations for robust automation -- Email notifications and automation with Python -- Introduction to email notifications in Python -- Setting up email services -- Sending basic emails -- Sending email notifications for script status -- Summary -- Part 2: Making It Pretty -- Formatting, Graphs, and More -- Chapter 5: Formatting Your Excel Sheet -- Technical requirements -- Installing and using styledTables in R -- Installing and using basictabler in R -- Advanced options for formatting with Python |
| Note |
Cell formatting |
| Summary |
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 For businesses, data analysis and visualization are crucial for informed decision-making; however, Excel's limitations can make these tasks time-consuming and challenging. 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, the company behind the ownR platform engineering solution for R, Python, and other data science languages. 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. Whether you're a beginner or an expert, this book has 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. It provides a comprehensive introduction to the topics covered, making it suitable for both beginners and intermediate learners. A basic understanding of Excel, Python, and R is all you need to get started. |
| Subject |
Data mining.
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Data mining -- Computer programs.
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| Added Author |
Kun, David, author.
|
| Other Form: |
Print version: Sanderson, Steven Extending Excel with Python and R Birmingham : Packt Publishing, Limited,c2024 |
| ISBN |
9781804615546 |
|
1804615544 |
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