# Single-Workbook XLSX Cleaning and Field Mapping

A focused workflow for one workbook or one worksheet: select a range, map headers, convert the cleaned table to JSON, and review source, type, null, and duplicate behavior without expanding into batch ingestion or warehouse delivery.

> Canonical page: https://elysiatools.com/en/hubs/xlsx-single-workbook-cleaning-and-mapping

- **Keywords:** single-workbook XLSX cleaning, Excel range extraction, header mapping, XLSX to JSON, spreadsheet field mapping

## Frequently asked questions

### Who is this workflow for, and what should it produce?

It is for someone preparing one workbook or one worksheet for a JSON consumer. The usual result is a reviewed JSON array of row objects, with the source workbook and mapping decisions retained; a bounded range can instead be inspected as records or cell objects.

### When should I use an A1 range, a named range, or records mode?

Use an A1 range when the boundary is known, a named range when the workbook defines a reusable boundary, and records mode when the first selected row is a header. Choose cell_objects when coordinates are part of the review.

### Does the workflow remove duplicates or validate every type?

No. The tools extract, map, and transform values. Review keys or complete rows yourself, compare type-like values with the consumer's contract, and record whether blank or duplicate behavior is accepted.

### Can I use this for API or S3 batches, multiple workbooks, or warehouse delivery?

No. This hub is deliberately limited to one workbook or one worksheet. Batch ingestion, API or S3 intake, multi-workbook merging, SQL or Parquet delivery, and warehouse loading belong to other workflows.

## Related content

- [XLSX ETL Ingestion and Warehouse Handoff](https://elysiatools.com/en/hubs/xlsx-etl-ingestion-workflows): Turn mixed workbook and CSV sources into traceable, schema-aware outputs for SQL loading, Parquet pipelines, and object-storage batch handoff.
- [Data Quality and Anomaly Investigation Workflows](https://elysiatools.com/en/hubs/data-quality-anomaly-workflows): Profile tabular data, isolate duplicates, missingness, referential breaks, outliers, and time-series anomalies, and turn findings into defensible quality evidence.
- [JSON Normalization and Interchange Workflow](https://elysiatools.com/en/hubs/json-convert): Turn a structured source into a checked JSON representation, then export the exact table, configuration, document, API, XML, CSON, EDN, BSON, or UBJSON form required downstream.