This example visualizes class performance by computing the average score for each student and then the overall class average. The Python functions `get_class_average`, `get_average`, and `average` process a list of student dictionaries, iterating through each student to accumulate individual results in a buffer before reducing them to a final numeric summary. The code accesses per-student score data via a list and calls `results.append()` to build the dataset, ultimately returning a single aggregate value through `average()`. The visualization would plot these calculated averages, with the data source being the hardcoded student list and the rendering dependent on an external plotting library.