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.

Single-Workbook XLSX Cleaning and Field Mapping

Q: 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.

Keep the scope narrow

This workflow is for analysts, operations teams, and developers preparing one workbook or one worksheet for a JSON consumer. Keep an untouched source copy and produce a reviewed JSON array or a bounded range preview. Batch ingestion, API or S3 intake, multi-workbook merging, SQL, Parquet, and warehouse delivery are not part of this path.

Inspect the boundary before cleaning

Write down the worksheet name, the A1 or named range, the header row, and the expected number of data rows. Use xlsx-range-extractor in records mode when each row should become an object, or cell_objects mode when addresses and coordinates are evidence. Turn on blank dropping only when blank cells are not meaningful to the review. A partial range is an important boundary: the mapper and JSON transformer operate on a worksheet, so do not present them as range-aware stages.

Map fields with an explicit contract

On a working copy, use xlsx-column-mapper with the actual header row. Put canonical names in renameMapJson, aliases in aliasMapJson, the desired sequence in reorderColumns, and intentional removals in removeColumns. Decide whether keepUnmapped is true before running it. Inspect the new XLSX header row and keep the original file; the mapping output is a new workbook, not a replacement for the source.

Convert and review JSON behavior

Use xlsx-json-transformer in xlsx_to_json mode when the mapped worksheet has its header on row one. Keep the sheet name and path separator stable. restoreTypes can turn numeric-looking, boolean-looking, null-looking, array-looking, or object-looking text into other JSON values, while blank cells are omitted in XLSX-to-JSON mode. Compare the output with representative source rows, expected fields, types, blank behavior, and duplicate keys. The tools do not make duplicate or business-type decisions for you.

Accept the result only when every difference is explained and the excluded batch, external intake, multi-workbook, SQL, Parquet, and warehouse concerns have not slipped into this single-workbook handoff.

Before you start

Work from a copy of one workbook and identify the one worksheet that contains the table to review.

Record the worksheet name, A1 range or named range, header row, and whether coordinates or record objects are needed.

Define canonical field names and order, aliases, columns to remove, and whether unmapped columns should remain.

Prepare representative rows containing blanks, numeric-looking text, real numbers, and any key values that must be checked for duplicates.

Preserve the source and inspect the selected area

Make a working copy, record the worksheet and intended boundary, then run xlsx-range-extractor. Use records for a table-shaped JSON preview or cell_objects to retain row, column, and address evidence. If the range is only a section of a larger worksheet, do not imply that the other tools process that range automatically.

Convert the mapped table to JSON

Run xlsx-json-transformer with mode xlsx_to_json against the mapped worksheet. Select the sheet name, keep the path separator consistent with any nested keys, and use restoreTypes only when type-like text should be interpreted. Expect an array of row objects; blank cells are not emitted in this direction.

Choose the right path

Is the table a bounded range or the worksheet's main table?

Use xlsx-range-extractor with an A1 or named range. Choose records for header-based row objects or cell_objects when cell addresses matter. The mapper and transformer read a worksheet rather than an arbitrary range, so use the extractor result as the inspection or final records path when the range is only part of a larger sheet.

Which headers and columns should survive?

Use xlsx-column-mapper when the table can be processed as one worksheet. Set headerRow, then use renameMapJson or aliasMapJson for canonical names, reorderColumns for output order, removeColumns for deliberate drops, and keepUnmapped according to the field contract. Keep the source copy separate because the tool writes a new XLSX.

What JSON type and blank behavior is acceptable?

Use xlsx-json-transformer in xlsx_to_json mode after the mapped workbook has its header on row one. Choose restoreTypes carefully: it can parse numeric, boolean, null, array, and object-looking strings. Blank cells are omitted in this direction, and duplicate detection still requires comparison with the expected key or row policy.

Done when

The untouched source copy, worksheet name, selected range or named range, header row, and mapping decision are recorded.

The extracted or mapped field set has the intended headers and order; removed fields and retained unmapped fields are explainable.

The JSON output is checked against representative rows for row count, key names, numeric and text values, omitted blanks, and any nested path behavior.

Potential duplicate keys or rows are identified by review and resolved or explicitly accepted; no tool is treated as an automatic deduplication or type-validation guarantee.

Batch ingestion, API or S3 intake, multi-workbook merging, SQL, Parquet, and warehouse delivery are outside the accepted result.

Single-Workbook XLSX Cleaning and Field Mapping

Continue with adjacent topic clusters

Keep the scope narrow

Inspect the boundary before cleaning

Map fields with an explicit contract

Convert and review JSON behavior

Before you start

Clean and map one XLSX table before JSON handoff

Preserve the source and inspect the selected area

Normalize headers on the worksheet copy

Convert the mapped table to JSON

Compare the result with the source contract

Choose the right path

Is the table a bounded range or the worksheet's main table?

Which headers and columns should survive?

What JSON type and blank behavior is acceptable?

Done when

XLSX ETL Ingestion and Warehouse Handoff

Data Quality and Anomaly Investigation Workflows

JSON Normalization and Interchange Workflow

XLSX Range Extractor

XLSX Column Mapper

XLSX JSON Transformer

XLSX Range Extractor

XLSX Column Mapper

XLSX JSON Transformer