Data Aggregation and Group Operations

Another important tool for data analysis, is the ability to group and categorize data, for computing some statistics with aggregate operations to each group, or maybe pivoting a table for statistical visualization purposes.

Both pandas and Kotlin DataFrame follow the split-apply-combine strategy ([Wic11]).

This strategy involves breaking down a dataset into smaller subsets, performing a specific analysis or transformation on each subset, and then combining the result into a single output.

In the following section we will see in depth how Kotlin DataFrame compute grouping operations, data aggregation and table pivoting in comparison with pandas.