*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*
The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. If you find this content useful, please consider supporting the work by buying the book!
No changes were made to the contents of this notebook from the original.
Pivot Tables
We have seen how the GroupBy abstraction lets us explore relationships within a dataset.
A pivot table is a similar operation that is commonly seen in spreadsheets and other programs that operate on tabular data.
The pivot table takes simple column-wise data as input, and groups the entries into a two-dimensional table that provides a multidimensional summarization of the data.
The difference between pivot tables and GroupBy can sometimes cause confusion; it helps me to think of pivot tables as essentially a multidimensional version of GroupBy aggregation.
That is, you split-apply-combine, but both the split and the combine happen across not a one-dimensional index, but across a two-dimensional grid.
Motivating Pivot Tables
For the examples in this section, we'll use the database of passengers on the Titanic, available through the Seaborn library (see Visualization With Seaborn):