groupby splits data into groups, applies calculations, and combines results into a summary table. Define dimensions and measures first, then use named aggregations so output columns are clear and stable.
- Write the business question before writing the groupby statement.
- Use named aggregations for readable output column names.
- Sort and validate totals after aggregation to catch logic errors.
Build summaries from transaction-level data
For an online shop workflow, group by channel and week, then aggregate revenue, order count, and average order value. Keep the raw rows unchanged so summaries can be recomputed when assumptions change.
When date-based grouping is needed, convert date columns first and use dt.to_period or pd.Grouper for consistent calendar buckets.
Keep aggregation output production-friendly
After groupby, reset_index to flatten the result for export. Rename columns with business-friendly names and ensure numeric formatting is applied at presentation time, not in the calculation layer.
- Use as_index=False when tabular output is preferred.
- Check that subgroup totals reconcile to overall totals.
- Document filters applied before aggregation.
Compute weekly channel KPIs
A cleaned orders DataFrame contains order_date, channel, and amount fields.
- Create a week column with df["week"] = df["order_date"].dt.to_period("W").astype(str).
- Run groupby on ["week", "channel"] with named aggregations for total_revenue, orders, and avg_order_value.
- Sort by week and total_revenue, then export the summary table.
Common mistakes
- Grouping on unparsed date strings, which creates broken time buckets.
- Using unnamed aggregations that produce unclear column labels.
- Forgetting filters and comparing mismatched summary scopes.
- Assuming grouped totals are correct without reconciliation checks.
Try one
What is one reason to prefer named aggregations in pandas groupby outputs?
They produce explicit output column names, which makes downstream use and review easier.
Sources
- Python documentationOfficial reference for Python syntax, standard library modules, and tutorials.
- pandas documentationOfficial reference for pandas DataFrame operations, indexing, and analysis APIs.