source: kdnuggets: sql vs pandas vs ai agents: which solves analytics problems best?
level: technical
we tested sql, pandas, and a claude agent on three analytics questions from stratascratch. the questions ranged from easy to hard. sql ran on sqlite in-memory, pandas on python 3.12, and the agent used claude-sonnet-4-6 via api. each tool got the same dataset and schema. we measured median execution time over 500 runs for sql and pandas. agent time was from request to first token. we compared speed, accuracy, explainability, debugging, scalability, flexibility, hallucination risk, and production readiness.
for a simple retrieval, all three returned correct distinct user ids. sql took 0.002 ms, pandas 0.40 ms, and the agent 2 seconds. the agent matched sql exactly because the prompt included column names. without schema grounding, it might guess wrong column names and return empty results silently. for a multi-step aggregation with two tables, all three got the right average completion percentages. sql took 0.007 ms, pandas 2.05 ms, and the agent 3 seconds. the prompt explicitly said to count non-starters as 0%, which prevented the agent from using an inner join and getting wrong averages.
for a hard problem with three tables and window functions, all three produced the same output. sql took 0.010 ms, pandas 1.84 ms, and the agent 4 seconds. the agent used a different valid window function pattern. without the full schema, the agent would likely hallucinate wrong results. the agent's main risks are reproducibility and schema dependency. each api call can generate different but equivalent sql. teams must verify outputs. the practical pattern is to provide the full schema, ask for sql, and review results before use.
why it matters: choosing the right tool depends on speed needs, data size, and tolerance for non-deterministic outputs in analytics workflows.
source: kdnuggets: sql vs pandas vs ai agents: which solves analytics problems best?