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Extending Excel with Python and R

You're reading from  Extending Excel with Python and R

Product type Book
Published in Apr 2024
Publisher Packt
ISBN-13 9781804610695
Pages 344 pages
Edition 1st Edition
Languages
Authors (2):
Steven Sanderson Steven Sanderson
Profile icon Steven Sanderson
David Kun David Kun
Profile icon 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
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 19. Other Books You May Enjoy

Some basics

Before we dive into the core of this chapter, here are some things that it will help us understand:

  • Effective communication: Visualizations are a powerful communication tool that transcends language barriers and simplifies complex concepts. Presenting data in charts, graphs, and interactive dashboards allows for a more compelling and persuasive narrative during meetings, presentations, and reports.
  • Data-driven decision making: Visualizations empower decision-makers to base their judgments on data evidence. When data is presented visually, it becomes easier to identify potential opportunities, risks, and areas for improvement, leading to more informed and effective decisions.
  • Identifying trends and anomalies: Visualization tools enable programmers to spot trends, changes, and anomalies in data quickly. This is particularly valuable in fields such as finance, where spotting irregularities promptly can prevent significant financial losses.
  • Exploratory Data...
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