# Transformation Throughput Analytics — Two-Measure Correlation + Missing-Value Fill

> Transformation Throughput Analytics — Two-Measure Correlation + 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 2026-10-24).

Computes Pearson correlation between processed_rows and duration_seconds, and fills missing values in a chosen field using zero, mean, or median imputation, for up to 200 transformation throughput records.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-13-184?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-24
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/transformation-throughput-analytics-two-measure-correlation-missing-79df4e4b
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_sVTxP2-3DcnU0PvJROtyG

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-two-measure-correlation-missing-79df4e4b -d '<json body>'
```

Example prompt: I have up to 200 rows of ETL throughput records with processed_rows and duration_seconds — can you compute the correlation between those two measures and fill any missing processed_rows values using the median?

## When to prefer this

Choose this endpoint when you need both a missing-value imputation pass and a correlation measurement between processed_rows and duration_seconds in a single paid call on up to 200 transformation throughput records. Ideal for lightweight, offline ETL diagnostics where no external data, predictions, or live lookups are needed and cost must be kept to $0.01 USDC for two bundled reports.

## Known failure modes

- Input array is empty or exceeds 200 rows — validation error
- Missing required fields (row_id, date, processed_rows, duration_seconds) on any row — schema rejection
- Date field does not match YYYY-MM-DD pattern — validation error
- All values in target field are null — imputation may return default or error
- Invalid method enum value other than zero/mean/median — schema rejection
- Insufficient non-null pairs to compute meaningful correlation — may return NaN or null correlation

## How this service works

Transformation throughput — Two-measure correlation + Explicit missing-value fill: Two-measure correlation + 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

Returns two analytical reports: (1) a Pearson correlation value between processed_rows and duration_seconds across all supplied rows, and (2) the dataset with missing values in the specified field filled using the chosen imputation method (zero, mean, or median). Results are computed locally with no external calls.

## 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-two-measure-correlation-missing-79df4e4b/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)