Numerical Computing with Python

Restructuring Data into a Tidy Form

All the datasets used in the preceding chapters have not had much or any work done to change their structure. We immediately began processing the datasets in their original shape. Many datasets in the wild will need a significant amount of restructuring before commencing a more detailed analysis. In some cases, an entire project might only be concerned with formatting the data in such a way that it can be easily processed by someone else.

In this chapter, we will cover the following topics:

• Tidying variable values as column names with stack
• Tidying variable values as column names with melt
• Stacking multiple groups of variables simultaneously
• Inverting stacked data
• Unstacking after a groupby aggregation ...