Table Row Column Label Coverage

Audit explicit table label counts and exact duplicate labels, without inventing missing identifiers.

Description

Audit explicit table label counts and exact duplicate labels, without inventing missing identifiers.

Table label counts and duplicate labels are separate consistency checks. Correct length does not establish unique identity.

When to use this tool

Audit explicit table label counts and exact duplicate labels, without inventing missing identifiers.

Prepare the supplied rows, columns, rowLabels, columnLabels fields for this operation.
Compare the complete example with the representation used by your own input before carrying the result into another step.

How to use the tool and how it works

Enter rows, columns, rowLabels, columnLabels. Use the exact text syntax, units and index conventions explained below.
Run the tool. If input validation fails, correct the supplied fields before interpreting any result.
Read result.rowCountMatches, result.columnCountMatches, result.duplicateRows, result.duplicateColumns. Interpret false, null and empty lists using the distinctions below; they can be valid outputs.

Require rows/columns from 0 to 128. Compare each label-list length with its declared dimension and report duplicate labels with source indexes independently.^[1]

Input field	Meaning and representation	Accepted input
rows	rows.	Integer from 0 to 128 inclusive
columns	columns.	Integer from 0 to 128 inclusive
rowLabels	row labels.	List of strings; at most 128 entries
columnLabels	column labels.	List of strings; at most 128 entries

Output field	Interpretation
result	Object containing the named outputs below.
result.rowCountMatches	Flag for row count matches under the exact condition described above.
result.columnCountMatches	Flag for column count matches under the exact condition described above.
result.duplicateRows	The duplicate rows list in the ordering and representation described above.
result.duplicateColumns	The duplicate columns list in the ordering and representation described above.

json

{
  "input": {
    "rows": 2,
    "columns": 1,
    "rowLabels": [
      "a",
      "a"
    ],
    "columnLabels": []
  },
  "output": {
    "result": {
      "rowCountMatches": true,
      "columnCountMatches": false,
      "duplicateRows": [
        {
          "key": "a",
          "indexes": [
            0,
            1
          ]
        }
      ],
      "duplicateColumns": []
    }
  }
}

Limitations and assumptions

No table data is inspected. Duplicate labels can be flagged even when dimension counts match.
Every declared top-level field is required and unexpected fields are rejected. Numeric inputs must be finite; ordinary text fields are bounded to 8,192 UTF-16 code units, with tighter operation-specific limits where stated.
Lists are limited to 128 entries. JSON text must meet bounded size and depth checks, and numeric outputs beyond the safe finite magnitude are rejected. Exact large integers use decimal strings.
Indexes are zero-based and spans have an exclusive end unless explicitly stated otherwise. A valid false, null or empty-list result is distinct from malformed input.

Alternative or Complementary methods

Choose the exact text grammar before combining serializers and parsers. UTF-16 record lengths, raw separators and custom backslash escapes differ from byte framing, CSV, Markdown and spreadsheet semantics.

References

Tool behavior and example: Table Row Column Label Coverage — tool.io

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