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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

Time series statistics and statistical forecasting

Data exploration and statistical analysis are crucial steps in understanding the characteristics of time series data. In this section, we’ll walk you through how to perform data exploration and apply statistical analysis techniques in Python to gain valuable insights into your time series.

Statistical analysis for time series data

After exploring the data using the plots in the previous section, let’s move on to statistical analysis to gain a deeper understanding. This section focuses on two areas:

  • The Augmented Dickey-Fuller (ADF) test: This statistical test is used to determine whether the time series data is stationary. Stationary data is easier to model and forecast.
  • Time series decomposition: Time series decomposition separates the data into its constituent components: trend, seasonality, and residuals. This decomposition aids in isolating patterns for forecasting.

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