Transpose Table

Overview

You send a rectangular table. We return the transpose: each original row becomes a column named row_1, row_2, and so on, and mixed source types become NULLABLE_ANY. firstColumn=SOURCE_COLUMN_NAMES prepends a STRING column of the original names. This is not a pivot, a spreadsheet formula, or sparse matrix algebra.

A silent reshape that invents column names or coerces types makes a later union undebuggable. Generated names and NULLABLE_ANY on conflicts stay explicit.

When to use it

Scope

Non-goals

• Does not pivot or aggregate
• Does not evaluate spreadsheet formulas
• Does not treat the table as a sparse matrix

Properties

Capabilities

Cost & billing

Flat rate per invocation

File handling

How we handle your data

Input schema

Output schema

API contract

curl -sS -X POST "$BASE/v1/execute" -H "Authorization: Bearer $KEY" -H 'Content-Type: application/json' -d '{"skillKey":"data.transpose_table","input":{"table":{"columns":[{"name":"id","type":"STRING"},{"name":"city","type":"STRING"}],"rows":[["1","Paris"],["2","Lyon"]]}}}'

import { Squillify } from "@squillify/sdk";

const sq = new Squillify({ apiKey: process.env.SQUILLIFY_API_KEY });

const { output } = await sq.execute("data.transpose_table", {"table":{"rows":[["1","Paris"],["2","Lyon"]],"columns":[{"type":"STRING","name":"id"},{"type":"STRING","name":"city"}]}});

Errors

Other limits

Limitations

• Does not pivot or aggregate
• Does not evaluate spreadsheet formulas
• Does not treat the table as a sparse matrix
• Building a pivot with aggregations
• Transposing a formula grid

How to call

POST /v1/execute (requires authentication)

{
  "skillKey": "data.transpose_table",
  "input": {
    "table": {
      "rows": [
        [
          "1",
          "Paris"
        ],
        [
          "2",
          "Lyon"
        ]
      ],
      "columns": [
        {
          "type": "STRING",
          "name": "id"
        },
        {
          "type": "STRING",
          "name": "city"
        }
      ]
    }
  }
}

Examples

{
  "table": {
    "rows": [
      [
        "1",
        "Paris"
      ],
      [
        "2",
        "Lyon"
      ]
    ],
    "columns": [
      {
        "type": "STRING",
        "name": "id"
      },
      {
        "type": "STRING",
        "name": "city"
      }
    ]
  }
}

{
  "columns": [
    {
      "type": "STRING",
      "name": "row_1"
    },
    {
      "type": "STRING",
      "name": "row_2"
    }
  ],
  "rowCount": 2,
  "rows": [
    [
      "1",
      "2"
    ],
    [
      "Paris",
      "Lyon"
    ]
  ],
  "columnCount": 2
}

{
  "table": {
    "rows": [],
    "columns": [
      {
        "type": "STRING",
        "name": "α"
      }
    ]
  },
  "firstColumn": "SOURCE_COLUMN_NAMES"
}

{
  "columns": [
    {
      "type": "STRING",
      "name": "source_column"
    }
  ],
  "rowCount": 1,
  "rows": [
    [
      "α"
    ]
  ],
  "columnCount": 1
}

Recommended use cases

• Flip a 1-by-N extract so each original row is a column for a later union
• Keep original column names as a first STRING column before a second pass

Avoid for

• Building a pivot with aggregations
• Transposing a formula grid

Related skills

Composable with

Safety & privacy

• Runs without calling any external service.
• Your payloads are never stored permanently.
• Telemetry captures metadata only — never your payload contents.
• We never train models on your payloads.
• No human reviews your payloads.

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