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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

Performing a simple ML model with Python

In this section, we create a simple ML model in Python. Python has grown to be the primary go-to language for ML work (with R as the obvious alternative) and the number of packages implementing ML algorithms is difficult to overestimate. Having said that, sklearn remains the most widely used so we will also choose it for this section. Similarly to the R part of the chapter, we will use the xgboost model because it has a great balance between performance and explainability.

We will use the data loaded in the previous section.

Data preprocessing

The first thing to do for the modeling phase is to prepare the data. Fortunately, sklearn comes with a preprocessing functionality built-in!

Let’s review the steps involved in data preprocessing:

  • Handling missing values: Before training a model, it’s essential to address missing values in the dataset. sklearn provides methods for imputing missing values or removing rows...

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