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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

Creating a Brownian motion with healthyR.ts

The final time series plot that we are going to showcase is the Brownian motion. Brownian motion, also known as the Wiener process, is a fundamental concept in finance and mathematics that describes the random movement of particles in a fluid. In the context of finance, it is often used to model the price movement of financial instruments such as stocks, commodities, and currencies.

Here are some of the key characteristics of Brownian motion:

  • Randomness: Brownian motion is inherently random. The future direction and magnitude of movement at any point in time cannot be predicted with certainty.
  • Continuous path: The path of a Brownian motion is continuous, meaning that the asset’s price can move smoothly without sudden jumps or gaps.
  • Independent increments: The changes in the asset’s price over non-overlapping time intervals are independent of each other. In other words, the price movement in one interval does...

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