# Transformation Throughput Analytics — Date-by-Category Pivot with Missing-Value Fill

> Transformation Throughput Analytics — Date-by-Category Pivot with Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-28).

Pivots transformation throughput records (processed_rows, duration_seconds) by date and category, then fills missing values using zero, mean, or median strategies — returning two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-13-189?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-28
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/transformation-throughput-analytics-date-by-category-pivot-with-8a91f880
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_WBGrZKZctiR6AABE_tGk9

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability transformation-throughput-analytics-date-by-category-pivot-with-8a91f880 -d '<json body>'
```

Example prompt: Pivot my transformation throughput data by date and dataset group — I have about 80 rows with processed_rows and duration_seconds fields — and fill in any missing processed_rows values using the median method.

## When to prefer this

Choose this endpoint when you have structured transformation throughput records (processed_rows and duration_seconds) that need to be reshaped into a date-by-category pivot table with controlled missing-value handling. It is ideal for lightweight, self-contained analytical reporting on ETL or data pipeline metrics without requiring an external analytics platform. Prefer it when you need two reports per call (pivot + fill) on datasets of up to 200 rows at minimal cost.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, dataset_group) in any row — validation error
- Invalid date format (non-YYYY-MM-DD) — schema validation failure
- Unsupported fill method or field enum value — rejected input
- Empty rows array — minimum 1 item required
- Numeric values outside allowed range (±1 trillion) — schema rejection

## How this service works

Transformation throughput — Date by category pivot + Explicit missing-value fill: Date by category pivot + Explicit missing-value fill for supplied transformation throughput records (processed_rows, duration_seconds). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.

## Output

Two analytical reports based on the supplied transformation throughput records: (1) a date-by-category pivot table for the chosen metric field (processed_rows or duration_seconds), and (2) a version of that pivot with missing values explicitly filled using the selected strategy (zero, mean, or median). All computation is local with no external lookups.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "processed_rows",
     "duration_seconds"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "processed_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "duration_seconds": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "processed_rows": 10,
     "duration_seconds": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "processed_rows": 20,
     "duration_seconds": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "processed_rows": 15,
     "duration_seconds": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "processed_rows": 30,
     "duration_seconds": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "processed_rows",
    "duration_seconds"
   ],
   "type": "string",
   "default": "processed_rows"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/transformation-throughput-analytics-date-by-category-pivot-with-8a91f880/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)