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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 explored how Python in Excel expands what you can do beyond formulas, PivotTables, and native charts. You learned how to load tabular data into pandas DataFrames, navigate the Python Object and Excel Value display modes, and create calculated columns.
You also ran quick profiling checks using head(), tail(), sample(), and shape, computed descriptive statistics with describe(), and checked for missing values with isna(). From there, you moved into more hands-on pandas operations: filtering rows with boolean indexing, sorting with sort_values(), counting categories with value_counts(), and grouping and aggregating data with groupby().
These are foundational skills you will use throughout the rest of this book. By working through these examples step by step, you have built a clear sense of the workflow and how Python behaves in the grid. You have also seen that Python is not inherently more difficult than Excel. It is simply another way to express the same kinds...