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SQL、Pandas 和 AI 代理：哪个能最好地解决分析问题？
相同的三个分析问题、三个工具、八个维度，通过真实执行时间和真实代理提示进行测量。
来源:KDnuggets简介
We gave the same three interview questions from StrataScratch to SQL,Pandas, and a Claude agent. Every piece of code executed against the same dataset, and every timing number is a median over 500 runs.代理的答案正是克劳德根据记录的提示生成的答案，而不是代理可能产生的假设示例。
比较涵盖八个维度：速度、准确性、可解释性、调试、可扩展性、灵活性、幻觉风险和生产准备情况。 The three questions span Easy, Medium, and Hard difficulty levels. The harder the question, the more the differences between SQL, Pandas, and the agent become visible.
How We Ran This Comparison
The three questions come from the StrataScratch interview bank and cover Easy, Medium, and Hard difficulty levels. SQL ran on SQLite in-memory, timed over 500 runs, with the median taken. Pandas ran on the same dataset in Python 3.12, also over 500 runs. The agent is Claude's claude-sonnet-4-6, called via the Anthropic API.
Each question got its own schema-grounded user prompt that included the table names, column names, and a few sample rows. The system prompt below stayed the same for all three calls. Agent response times are measured from the time the request is sent to the first token received.
Simple Retrieval: All Three Agree
对于 Meta 的第一个面试问题，用户被要求查找至少执行过一次 roll_up 事件的每个用户，并返回不同的用户 ID。 The data lives in a single table called facebook_web_log.
Data
Here's the facebook_web_log table.
SQL Coding Solution (0.002 ms)
SELECT DISTINCT user_id
FROM facebook_web_log
WHERE action = 'scroll_up';
Pandas Coding Solution (0.40 ms)
import pandas as pd
result = (