{"slug": "sql-vs-pandas-vs-ai-agents-which-solves-analytics-problems-best", "title": "SQL vs Pandas vs AI Agents: Which Solves Analytics Problems Best?", "summary": "A comparison of SQL, Pandas, and AI agents on three analytics interview questions found SQL fastest and most reliable, while AI agents offered natural language convenience but higher latency and hallucination risk. The study measured execution times over 500 runs across eight dimensions, revealing that for simple queries all tools agreed, but for complex multi-step aggregation the agent's schema grounding was critical.", "body_md": "# SQL vs Pandas vs AI Agents: Which Solves Analytics Problems Best?\n\nSame three analytics problems, three tools, eight dimensions, measured with real execution times and real agent prompts.\n\n## # Introduction\n\nWe gave the same three interview questions from ** StrataScratch** to SQL,\n\n**, and a**\n\n[Pandas](https://pandas.pydata.org/)**agent. Every piece of code executed against the same dataset, and every timing number is a median over 500 runs. The agent's answers are exactly what Claude generated in response to a documented prompt, instead of a hypothetical example of what an agent might produce.**\n\n[Claude](https://claude.ai/)The comparison runs across eight dimensions: speed, accuracy, explainability, debugging, scalability, flexibility, hallucination risk, and production readiness. 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.\n\n## # How We Ran This Comparison\n\nThe 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\n\n**3.12, also over 500 runs. The agent is Claude's**\n\n[Python](https://www.python.org/)**, called via the**\n\n[claude-sonnet-4-6](https://www.anthropic.com/news/claude-sonnet-4-6)**.**\n\n[Anthropic API](https://www.anthropic.com/api)\n\nEach 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.\n\n## # Simple Retrieval: All Three Agree\n\nFor the [first interview question from Meta](https://platform.stratascratch.com/coding/9787-user-scroll-up-events?code_type=1&utm_source=blog&utm_medium=click&utm_campaign=kdn+sql+vs+pandas+vs+ai+agents), users are asked to find every user who performed at least one `scroll_up`\n\nevent and return the distinct user IDs. The data lives in a single table called `facebook_web_log`\n\n.\n\n#### // Data\n\nHere's the `facebook_web_log`\n\ntable.\n\n| user_id | timestamp | action |\n|---|---|---|\n| 0 | 2019-04-25 13:30:15 | page_load |\n| 0 | 2019-04-25 13:30:18 | page_load |\n| 0 | 2019-04-25 13:30:40 | scroll_down |\n| 0 | 2019-04-25 13:30:45 | scroll_up |\n| … | … | … |\n| 0 | 2019-04-25 13:30:40 | page_exit |\n\n#### // SQL Coding Solution (0.002 ms)\n\n```\nSELECT DISTINCT user_id\nFROM facebook_web_log\nWHERE action = 'scroll_up';\n```\n\n#### // Pandas Coding Solution (0.40 ms)\n\n``` python\nimport pandas as pd\nresult = (\n    facebook_web_log[facebook_web_log['action'] == 'scroll_up']\n    .drop_duplicates(subset='user_id')[['user_id']]\n)\n```\n\n#### // Agent Prompt\n\n```\nTable: facebook_web_log (user_id INTEGER, action TEXT, timestamp TEXT)\nSample rows:\n(1, 'scroll_up',   '2019-01-01 00:00:00')\n(2, 'scroll_down', '2019-01-01 00:01:00')\n(3, 'like',        '2019-01-01 00:03:00')\n(2, 'scroll_up',   '2019-01-01 00:04:00')\nQuestion: Find all users who performed at least one scroll_up event.\nReturn distinct user IDs.\n```\n\n#### // Agent Output (2 s)\n\n```\nSELECT DISTINCT user_id\nFROM facebook_web_log\nWHERE action = 'scroll_up';\n```\n\nOutput: All three return users 1 and 2.\n\n| user_id |\n|---|\n| 0 |\n| 2 |\n| 1 |\n\nOn a single-filter problem, the agent matches SQL exactly. The only real risk at this difficulty is column naming. Without the schema in the prompt, `action`\n\nmight come back as `event_type`\n\nor `event_name`\n\n, which returns nothing and throws no error.\n\n## # Multi-Step Aggregation: Where Schema Grounding Matters Most\n\nThe [second question](https://platform.stratascratch.com/coding/9792-user-feature-completion?code_type=1&utm_source=blog&utm_medium=click&utm_campaign=kdn+sql+vs+pandas+vs+ai+agents) is about product feature completion. An app tracks how far each user gets through a set of product features, where every feature has a fixed number of steps.\n\nThe task is to calculate the average completion percentage for each feature across all users, where a user's completion is their maximum step reached divided by the total steps for that feature, times 100. Users who have never started a feature are counted as 0% complete.\n\nTwo tables feed this: `facebook_product_features`\n\n:\n\n| feature_id | n_steps |\n|---|---|\n| 0 | 5 |\n| 1 | 7 |\n| 2 | 3 |\n\nand `facebook_product_features_realizations`\n\n.\n\n| feature_id | user_id | step_reached | timestamp |\n|---|---|---|---|\n| 0 | 0 | 1 | 2019-03-11 17:15:00 |\n| 0 | 0 | 2 | 2019-03-11 17:22:00 |\n| 0 | 0 | 3 | 2019-03-11 17:25:00 |\n| 0 | 0 | 4 | 2019-03-11 17:27:00 |\n| ... | ... | ... | ... |\n| 1 | 1 | 3 | 2019-04-05 13:00:07 |\n\n#### // SQL Coding Solution (0.007 ms)\n\n```\nWITH max_step AS (\n    SELECT\n        feature_id,\n        user_id,\n        MAX(step_reached) AS max_step_reached\n    FROM facebook_product_features_realizations\n    GROUP BY feature_id, user_id\n),\ncalc_per_feature AS (\n    SELECT\n        feats.feature_id,\n        n_steps,\n        max_step_reached,\n        COALESCE(max_step_reached, 0) * 1.0 / n_steps AS share_of_completion\n    FROM facebook_product_features feats\n    LEFT OUTER JOIN max_step\n        ON feats.feature_id = max_step.feature_id\n)\nSELECT\n    feature_id,\n    AVG(share_of_completion) * 100 AS avg_share_of_completion\nFROM calc_per_feature\nGROUP BY feature_id;\n```\n\n#### // Pandas Coding Solution (2.05 ms)\n\n``` python\nimport pandas as pd\n# max step per user per feature\nmax_step = (\n    facebook_product_features_realizations\n    .groupby(['feature_id', 'user_id'])['step_reached']\n    .max()\n    .reset_index()\n)\n\n# join to features, fill users who never started with 0\ndf = pd.merge(\n    facebook_product_features,\n    max_step,\n    how='outer',\n    on='feature_id'\n).fillna(0)\n\n# completion % per user-feature row\ndf['share_of_completion'] = (df['step_reached'] / df['n_steps']) * 100\n\n# average per feature\nresult = (\n    df.groupby('feature_id')['share_of_completion']\n    .mean()\n    .to_frame('avg_share_of_completion')\n    .reset_index()\n)\n```\n\n#### // Agent Prompt\n\n```\nTables:\n- facebook_product_features (feature_id INTEGER, n_steps INTEGER)\n- facebook_product_features_realizations\n  (feature_id INTEGER, user_id INTEGER, step_reached INTEGER, timestamp TEXT)\nQuestion: For each feature, calculate the average completion percentage\nacross all users. A user's completion = max(step_reached) / n_steps * 100.\nUsers who never started count as 0% completion.\nReturn: feature_id, avg_share_of_completion.\n```\n\n#### // Agent Output (3 s)\n\n```\nWITH user_max_steps AS (\n    SELECT feature_id, user_id, MAX(step_reached) AS max_step\n    FROM facebook_product_features_realizations\n    GROUP BY feature_id, user_id\n)\nSELECT\n    f.feature_id,\n    ROUND(AVG(COALESCE(ums.max_step, 0) * 100.0 / f.n_steps), 2) AS avg_share_of_completion\nFROM facebook_product_features f\nLEFT JOIN user_max_steps ums ON f.feature_id = ums.feature_id\nGROUP BY f.feature_id\nORDER BY f.feature_id;\n```\n\nOutput: All three return the same numbers.\n\n| feature_id | avg_share_of_completion |\n|---|---|\n| 0 | 80 |\n| 2 | 0 |\n| 1 | 76.19 |\n\nThe agent got it right here because the prompt said: \"Users who never started count as 0% completion.\" That phrase is load-bearing. Without it, the agent writes an inner join — which drops non-starters — and every average goes up. That failure is silent. The numbers come back clean, and they're wrong. You'd need to know the expected output to catch it.\n\n## # Multiple Tables and Window Logic: All Three Correct, One Much Slower\n\nThe [third question](https://platform.stratascratch.com/coding/10084-cum-sum-energy-consumption?utm_source=blog&utm_medium=click&utm_campaign=kdn+sql+vs+pandas+vs+ai+agents) covers Meta's data center energy consumption across three regions. Each region has its own table: `fb_eu_energy`\n\n, `fb_na_energy`\n\n, and `fb_asia_energy`\n\n.\n\nThe task is to combine them, sum consumption by date, and produce two derived columns: the cumulative running total and that total as a percentage of the grand total, rounded to a whole number.\n\n#### // Data\n\nEach regional table has the same shape.\n\n`fb_eu_energy`\n\n:\n\n| recorded_date | consumption |\n|---|---|\n| 2020-01-01 | 400 |\n| 2020-01-02 | 350 |\n| 2020-01-03 | 500 |\n| 2020-01-04 | 500 |\n| 2020-01-07 | 600 |\n\n`fb_na_energy`\n\n:\n\n| recorded_date | consumption |\n|---|---|\n| 2020-01-01 | 250 |\n| 2020-01-02 | 375 |\n| 2020-01-03 | 600 |\n| 2020-01-06 | 500 |\n| 2020-01-07 | 250 |\n\n`fb_asia_energy`\n\n:\n\n| recorded_date | consumption |\n|---|---|\n| 2020-01-01 | 400 |\n| 2020-01-02 | 400 |\n| 2020-01-04 | 675 |\n| 2020-01-05 | 1200 |\n| 2020-01-06 | 750 |\n| 2020-01-07 | 400 |\n\n#### // SQL Coding Solution (0.010 ms)\n\n```\nWITH total_energy AS (\n    SELECT recorded_date, consumption FROM fb_eu_energy\n    UNION ALL\n    SELECT recorded_date, consumption FROM fb_asia_energy\n    UNION ALL\n    SELECT recorded_date, consumption FROM fb_na_energy\n),\nenergy_by_date AS (\n    SELECT\n        recorded_date,\n        SUM(consumption) AS total_energy\n    FROM total_energy\n    GROUP BY recorded_date\n    ORDER BY recorded_date ASC\n)\nSELECT\n    recorded_date,\n    SUM(total_energy) OVER (\n        ORDER BY recorded_date ASC\n    ) AS cumulative_total_energy,\n    ROUND(\n        SUM(total_energy) OVER (ORDER BY recorded_date ASC) * 100.0\n        / (SELECT SUM(total_energy) FROM energy_by_date),\n        0\n    ) AS percentage_of_total_energy\nFROM energy_by_date;\n```\n\n#### // Pandas Coding Solution (1.84 ms)\n\n``` python\nimport pandas as pd\nmerged_df = pd.concat([fb_eu_energy, fb_asia_energy, fb_na_energy])\n\nenergy_by_date = (\n    merged_df.groupby('recorded_date', as_index=False)['consumption']\n    .sum()\n    .sort_values('recorded_date')\n)\n\nenergy_by_date['cumulative_total_energy'] = energy_by_date['consumption'].cumsum()\nenergy_by_date['percentage_of_total_energy'] = round(\n    energy_by_date['cumulative_total_energy'] * 100 / energy_by_date['consumption'].sum()\n)\n\nenergy_by_date['recorded_date'] = pd.to_datetime(\n    energy_by_date['recorded_date']\n).dt.strftime('%Y-%m-%d')\n\nresult = energy_by_date[\n    ['recorded_date', 'cumulative_total_energy', 'percentage_of_total_energy']\n]\n```\n\n#### // Agent Prompt\n\n```\nTables:\n- fb_eu_energy   (recorded_date TEXT, consumption INTEGER)\n- fb_na_energy   (recorded_date TEXT, consumption INTEGER)\n- fb_asia_energy (recorded_date TEXT, consumption INTEGER)\nQuestion: Combine all three tables. Sum consumption by date. Output:\n- recorded_date\n- cumulative_total_energy (running sum across dates, ordered by date)\n- percentage_of_total_energy (running cumulative / grand total * 100,\n  rounded to the nearest whole number)\n```\n\n#### // Agent Output (4 s)\n\n```\nWITH all_energy AS (\n    SELECT recorded_date, consumption FROM fb_eu_energy\n    UNION ALL\n    SELECT recorded_date, consumption FROM fb_na_energy\n    UNION ALL\n    SELECT recorded_date, consumption FROM fb_asia_energy\n),\ndaily_totals AS (\n    SELECT recorded_date, SUM(consumption) AS daily_total\n    FROM all_energy\n    GROUP BY recorded_date\n)\nSELECT\n    recorded_date,\n    SUM(daily_total) OVER (ORDER BY recorded_date) AS cumulative_total_energy,\n    ROUND(\n        SUM(daily_total) OVER (ORDER BY recorded_date) * 100.0\n        / SUM(daily_total) OVER (),\n        0\n    ) AS percentage_of_total_energy\nFROM daily_totals\nORDER BY recorded_date;\n```\n\nOutput: All three return the same table.\n\n| recorded_date | cumulative_total_energy | percentage_of_total_energy |\n|---|---|---|\n| 2020-01-01 | 1050 | 13 |\n| 2020-01-02 | 2175 | 27 |\n| 2020-01-03 | 3275 | 40 |\n| 2020-01-04 | 4450 | 55 |\n| 2020-01-05 | 5650 | 69 |\n| 2020-01-06 | 6900 | 85 |\n| 2020-01-07 | 8150 | 100 |\n\nThe agent used `SUM(daily_total) OVER ()`\n\n(a window function with no `ORDER BY`\n\n) as the denominator rather than the scalar subquery in the SQL reference solution. Both approaches are valid. The output matched exactly.\n\n## # How the Three Compare\n\n#### // Speed\n\nAt this data scale, SQL ran in 0.002-0.010 ms, Pandas in 0.4-2.1 ms. The agent added 2-4 seconds of large language model (LLM) inference time before any SQL ran.\n\nThe agent generates code first; that generation time is the end-to-end latency for each query cycle. At warehouse scale, the gap closes to near zero once code is generated; SQL gains further because it runs inside the database engine, and Pandas hits a memory ceiling around 10 million rows and needs ** Apache Spark** or\n\n**beyond that.**\n\n[Polars](https://pola.rs/)\n\n#### // Accuracy and Hallucination Risk\n\nSQL and Pandas are deterministic. The same code on the same data gives the same answer every time. With schema-grounded prompts, Claude got all three questions right, but each call produced different SQL (different common table expression (CTE) names, different column aliases, different but equivalent approaches). Without the schema, hallucination risk climbs fast.\n\n#### // Explainability and Debugging\n\nA SQL query reads in one block. A bad join condition is visible right in the text. Pandas needs Python fluency, but you can inspect the `DataFrame`\n\nat each step. Agents explain their reasoning in English, then produce code that you may or may not be shown. If the generated SQL is wrong, you're tracing an error through a model's reasoning chain rather than reading a query you wrote.\n\n#### // Flexibility and Production Readiness\n\nPandas is the clearest option for custom transformations, string parsing, and iterative feature engineering. SQL handles set logic cleanly and gets verbose for procedural work. Agents answer plain-English requests well, with the least consistency when schemas are complex or ambiguous. For shipping, SQL is the most proven option in analytics; Pandas is dependable with tests; and agents are dependable today for low-stakes queries or when the output is reviewed before it runs.\n\n## # What the Agent Results Actually Show\n\nWith a schema-grounded prompt, Claude got all three correct: Easy, Medium, and Hard. The agent's SQL for the Hard question used a different window function pattern than the reference solution and still returned the correct table.\n\nTwo things limit that finding. First, reproducibility: each API call can return different SQL for the same question. The logic is equivalent, but a team reviewing agent-generated queries needs to verify the outputs rather than trust that today's correct run will match tomorrow's. Second, schema dependency: the prompts above include table and column names, as well as sample rows. Remove those, and the agent guesses.\n\nAt Easy difficulty, a wrong guess produces an empty result. At Hard difficulty, a wrong guess produces a plausible wrong result with no error.\n\nThe practical pattern is: provide the full schema, ask for SQL, then run and verify the output before it goes downstream.\n\n## # Conclusion\n\nSQL, Pandas, and Claude each got the same three analytics questions right when used correctly. The differences are in speed (0.01 ms vs 4 seconds), reproducibility, and what happens when you reduce the context.\n\nSQL fits structured retrieval and set-based logic with millisecond execution and deterministic output. Pandas fits custom transformations and step-by-step notebook work up to about 10 million rows. The agent fits first-draft queries and ad hoc exploration, with the full schema in the prompt and a human reviewing the output.\n\nThe agent got the Hard question right by using `SUM() OVER()`\n\ninstead of a scalar subquery, which is a valid approach that SQL's reference solution didn't take. That's the honest version of this comparison: the agent can generate correct, creative SQL. It just adds latency, varies between runs, and depends entirely on what you put in the prompt.\n\nis a data scientist and in product strategy. He's also an adjunct professor teaching analytics, and is the founder of StrataScratch, a platform helping data scientists prepare for their interviews with real interview questions from top companies. Nate writes on the latest trends in the career market, gives interview advice, shares data science projects, and covers everything SQL.\n\n[Nate Rosidi](https://twitter.com/StrataScratch)", "url": "https://wpnews.pro/news/sql-vs-pandas-vs-ai-agents-which-solves-analytics-problems-best", "canonical_source": "https://www.kdnuggets.com/sql-vs-pandas-vs-ai-agents-which-solves-analytics-problems-best", "published_at": "2026-07-07 16:00:29+00:00", "updated_at": "2026-07-07 16:13:46.303617+00:00", "lang": "en", "topics": ["ai-agents", "large-language-models", "natural-language-processing", "developer-tools"], "entities": ["StrataScratch", "SQLite", "Python", "Claude", "Anthropic", "Meta", "Pandas"], "alternates": {"html": "https://wpnews.pro/news/sql-vs-pandas-vs-ai-agents-which-solves-analytics-problems-best", "markdown": "https://wpnews.pro/news/sql-vs-pandas-vs-ai-agents-which-solves-analytics-problems-best.md", "text": "https://wpnews.pro/news/sql-vs-pandas-vs-ai-agents-which-solves-analytics-problems-best.txt", "jsonld": "https://wpnews.pro/news/sql-vs-pandas-vs-ai-agents-which-solves-analytics-problems-best.jsonld"}}