nl-data-analysislisted

# Natural-Language Data Analysis ## Overview Turn natural-language questions about tabular data into executable analysis: profile the dataset, translate the question into pandas (or SQL), produce charts, and summarize insights in plain language. ## When to Use - "分析这个表", "画个图", "这数据说明什么", "analyze", "make a chart", "数据可视化", "跑个分析", "correlation", "trend". - The user uploads or points to a CSV, Excel, or database table. - The user wants a quick exploratory view of a dataset. ## Workflow 1. **Profile** — Load the data; report shape, columns, types, missing counts, head. Use `scripts/profile_data.py` (stdlib, no deps). 2. **Clarify** — If ambiguous (which column? what metric? time range?), ask one focused question. Do not guess silently. 3. **Translate** — Map the question to pandas/SQL (groupby, agg, join, pivot). Prefer pandas for files, SQL for databases. 4. **Visualize** — Generate a chart with `scripts/make_chart.py` (requires `matplotlib`). Choose by intent: trend→line, distribution→hist/box, composition→bar/pie, relationship→scatter. 5. **Summarize** — State the finding in 1-3 sentences; include the number that matters. ## Rules - Never fabricate statistics. Compute from the actual data. - One chart per question unless comparing. - Keep code minimal; load the data once, reuse the DataFrame. - Save outputs to the working directory; report the path. ## Bundled Resources - `scripts/profile_data.py` — profile a CSV (shape, columns, missing, head) with no third-party depe