Question
Given a list of records (each a dict), a `group_key`, and a numeric `value_key`, compute the SUM of `value_key` for each distinct value of `group_key` — the equivalent of `SELECT group_key, SUM(value_key) GROUP BY group_key`. Return a list of `[group_value, total]` pairs sorted alphabetically by group value. If a total is integral, return it as an int rather than a float.
group_totals(rows: list[dict], group_key: str, value_key: str) → list[list][[{"sales":10,"region":"west"},{"sales":5,"region":"east"},{"sales":3,"region":"west"}],"region","sales"]out[["east",5],["west",13]]State your approach and its time/space complexity out loud before you optimize. Handle the edge cases (empty input, duplicates, overflow), and say why you chose this over the brute force. Green tests are the floor, not the grade.
Vibe coding: describe the solution in plain language (or narrate it) and the coach grades your approach. Generating runnable code from your description is coming next.