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Pivot table

As explained in The Well-Grounded Data Analyst

A pivot table is a summarized tabular view that groups and aggregates records across selected row and column dimensions.

A pivot table is a summarized tabular view that groups and aggregates records across selected row and column dimensions. It supports comparisons such as distributor market share by year. section 5.4.3

Use a pivot table to:

  • Place years in rows and distributors in columns so each distributor becomes a time-series line. section 5.4.3
  • Aggregate repeated observations with a grouping operation before reshaping the result. section 5.4.3
  • Cross-reference two categorical variables and optionally visualize their counts or values with a heatmap. section 7.3.1
  • Inspect category quality before interpretation by merging equivalent labels and deciding how to handle ambiguous categories. section 5.4.3

Distributor market share by year

Group distributors by Year and Distributor, sum Market share, and unstack Distributor into columns. Before plotting, merge labels such as “eOne Films” and “Entertainment One,” combine “Other” categories, and decide whether to retain only distributors represented in every year. section 5.4.3

How The Well-Grounded Data Analyst covers it

How deep it goes
Demonstrates grouping, summing, unstacking, category cleanup, and filtering, but does not develop pivot-table internals.
Examples
Uses distributor market share by year and categorical-variable cross-tabulation as concrete analytical examples.
What you can do after
Apply it via pandas to aggregate records, reshape dimensions, expose category problems, and prepare comparisons for visualization.
How it connects
Connects pivot tables with pandas, categorical data, aggregation, data cleaning, time-series plotting, and heatmaps.

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This explainer was generated by Manning's knowledge graph from the text of The Well-Grounded Data Analyst. The section links point to the parts of the book it draws on.