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Book Overview & Buying
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Table Of Contents
Python in Excel for Data Analytics
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In this chapter, you brought together the pandas skills from Chapter 2 and the seaborn skills from Chapter 3 to work through a full exploratory analysis. You built structured data quality summaries, handled missing values by dropping or filling them, created derived columns to support new questions, used cross-tabulations to examine how categorical variables interact, connected grouped calculations to visualizations, layered plots for richer comparisons, and identified and investigated outliers using both code and charts.
These techniques are not exotic or advanced. They are the everyday tools you reach for when working with a new dataset. Exploratory data analysis is an iterative process. You observe something in the data, follow up on it, and let each result guide what to look at next.
In the chapters ahead, these skills become the foundation for more formal analysis. Chapter 5 introduces statistical tests to evaluate whether the patterns you observe are meaningful. Chapters...