Why this chapter matters
GroupBy tables power reporting and decisions, so they must be both accurate and easy for others to verify.
What you will learn
- Group data by one or more dimensions with deterministic output.
- Compute counts, sums, and averages using named aggregations.
- Validate grouped totals against baseline totals.
Lessons in this chapter
- Grouping patternsChoose dimensions that match the reporting question.
- Named aggregationsProduce stable, readable output column names.
- Time bucket summariesAggregate by week or month after date parsing.
- Guide: pandas groupbyCreate report-ready KPI tables from transaction data. Read the full guide →
Study task
Create weekly revenue, order count, and average order value by channel, then verify grouped totals match raw totals.
Chapter checkpoint
What is one benefit of named aggregations in pandas groupby?
They make output columns explicit, which improves readability and downstream reliability.