In this chapter, we examined various techniques for grouping and analyzing groups of data with pandas. An introduction to the split-apply-combine pattern was given along with an overview of how this pattern is implemented in pandas. Then we learned how to split data into groups based on data in both columns and in the levels of an index. We then examined how to process data within each group using aggregation functions and transformations. We finished with a quick examination of how to filter groups of data based on their contents.
In the next chapter, we will dive deep into one of the most powerful and robust capabilities of pandas - modeling of time series data.
