Wrangler
Wrangler profiles CSV, Excel, Parquet, or Google Sheets data, applies selected cleaning and aggregation steps, and produces a React and Plotly dashboard with retained session artifacts.
- Python
- FastAPI
- React
- Plotly
- Data quality
Before analysts can trust a dashboard, they must find missing values, duplicate rows, outlier candidates, and unsuitable aggregations in unfamiliar data.
Product direction, data-quality pipeline, FastAPI orchestration, dashboard specification, and the React interface used to review and refine the result.
Quality findings and transformation choices remain visible beside the charts, so the dashboard can be reviewed against the source data.
The visualization plan maps selected metrics to chart types, filters, aggregations, and layout sections before rendering the final dashboard.
The live product includes upload, review, results, and saved-session flows, with bundled datasets that require no private information.
