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Python in Excel for Data Analytics

Python in Excel for Data Analytics

By : George Mount
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Python in Excel for Data Analytics

Python in Excel for Data Analytics

By: George Mount

Overview of this book

Excel is one of the most widely used tools for business analysis, but many analytical tasks quickly reach the limits of formulas and built-in features. Python in Excel changes that by allowing you to perform advanced analysis directly within the spreadsheet environment. This book shows how to combine Excel’s structure and usability with Python’s analytical power. You will learn how to move data between Excel and Python DataFrames, clean and transform datasets efficiently, and explore data using modern visualization techniques. As you progress, you will apply Python to real analytical problems including statistical testing, regression modeling, forecasting, and simulation. You will also learn how to integrate Python outputs into Excel dashboards and reports, creating workflows that are both powerful and practical. Designed for Excel users with no prior programming experience, this book introduces Python concepts gradually and focuses on real-world applications rather than theory. By the end, you will be able to extend Excel in meaningful ways, helping you analyze data more effectively and support better decision-making.
Table of Contents (7 chapters)
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Testing differences across multiple groups with ANOVA

Analysis of Variance (ANOVA) provides a single test of whether at least one group mean differs from the others. Instead of running multiple pairwise comparisons, it asks a broader question: is the variation between groups large enough, relative to the variation within groups, to suggest the groups are not all the same?

The result is summarized by an F-statistic, which compares between-group variability to within-group variability. If all groups truly have the same mean, this ratio should be close to 1. Larger values indicate stronger evidence that at least one group mean differs.

Running one-way ANOVA

We already have two groups. We just need to create the third with the following code:

usa = mpg_df[mpg_df['origin'] == 'usa']['mpg']

Now run the test:

f_stat, p_value = stats.f_oneway(usa, europe, japan)

pd.DataFrame({
     'F-statistic': [round(f_stat, 2)],
     'p-value': [str(round...
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Python in Excel for Data Analytics
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