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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

Keeping it simple – exporting data to Excel with pandas

pandas is a popular data manipulation library in Python that provides powerful tools for data analysis. It also offers excellent functionality for exporting data to Excel. Using pandas, you can effortlessly transform your data into Excel sheets or workbooks.

pandas provides the DataFrame.to_excel() method, allowing you to export data to an Excel file with just a few lines of code. Here’s an example:

import pandas as pd
# Create a DataFrame with sample data
data = {
    'Name': ['John', 'Jane', 'Mike'],
    'Age': [25, 30, 35],
    'City': ['New York', 'London', 'Sydney']
}
df = pd.DataFrame(data)
# Export the DataFrame to an Excel file
df.to_excel('data.xlsx', index=False)

The code doesn’t return anything, but it does have a side effect –...

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