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  • Book Overview & Buying Extending Excel with Python and R
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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

Getting visualizations with Python

In this section, we are going to go over visualizations of the data in Python, analogous to the preceding R section. We will use plotnine to have visualizations similar to those created in R using ggplot2 and provide interpretations of the results.

Getting the data

Like in the earlier chapters, we will load the data using pandas. Just like before, the path to the XLSX file may be different for you from what I have, so adjust the filepath accordingly:

import pandas as pd
# Define the file path (may be different for you)
file_path = "./Chapter 12/diamonds.xlsx"
# Load the dataset into a pandas DataFrame
df = pd.read_excel(file_path)
# Display the first few rows of the DataFrame
print(df.head())

Note that we use the raw diamonds dataset without spitting it first and then recombining it, as it was done in the R part of the chapter.

Visualizing the data

Once we have our data loaded, we can use plotnine to create visualizations...

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Extending Excel with Python and R
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