GroupBy with Multiple Aggregations

Pandas data engineering interview problem. Difficulty: beginner. Pattern: GroupBy. About 12 minutes. Free to practice.

Per-region totals, average, count, and max amount in pandas. Treat this as a production helper: match the contracted return shape, including empty and duplicate inputs.

A sales manager wants a short summary for each region. From the DataFrame sales, group the rows by region and calculate four numbers for each region: total_amount (the sum of the amounts), avg_amount (the average), txn_count (how many sales rows) and max_amount (the biggest single amount). Return 1 row per region, with the columns region, total_amount, avg_amount, txn_count and max_amount, sorted by region. Save the final table in result.

Requirements

• Return 1 row for each region.
• Calculate all 4 metrics: sum, mean, count and max of amount.
• Name the columns exactly total_amount, avg_amount, txn_count and max_amount.
• Make region a normal column, not the index.
• Sort by region.

Constraints

• sales has the columns date, product, region and amount.
• Use groupby('region').agg(...) with named aggregation, for example total_amount=('amount', 'sum').
• Call reset_index() after grouping so region is a column again.

Examples

Input: sales Output: region | total_amount | avg_amount | txn_count | max_amount EU | 70.0 | 35.0 | 2 | 40.0 US | 140.0 | 35.0 | 4 | 60.0 Named aggregations keep column contracts clear.

Topics: lakebench, pandas, groupby, agg.

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