# Transformation Throughput IQR Outlier Flags + Explicit Missing-Value Fill

> Transformation Throughput IQR Outlier Flags + Explicit 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).

Detects IQR-based outliers and fills missing values in transformation throughput records (processed_rows, duration_seconds) for up to 200 rows, returning two analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-13-180?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-iqr-outlier-flags-explicit-missing-value-fill-c2dea7d9
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_HjCvslHgCbcz-xEVL8OcY

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-iqr-outlier-flags-explicit-missing-value-fill-c2dea7d9 -d '<json body>'
```

Example prompt: Can you run IQR outlier detection and fill missing values using median on my transformation throughput data — here are 5 records with row_id, dataset_group, date, processed_rows, and duration_seconds — and give me both analytical reports?

## When to prefer this

Choose this endpoint when you need lightweight, local statistical analysis on transformation throughput data (processed_rows and duration_seconds) without any external API calls or predictions. Ideal for ETL monitoring, data quality pipelines, or pre-processing audits where you want both IQR outlier detection and missing-value imputation in a single low-cost call for up to 200 rows.

## Known failure modes

- More than 200 rows submitted — exceeds maximum batch size
- Missing required fields (row_id, date, dataset_group) cause row rejection
- Invalid date format (not YYYY-MM-DD) fails schema validation
- Invalid enum for field or method parameter causes request rejection
- All rows have no nulls — missing-value fill report will show no imputations
- All values within IQR — outlier flag report will show no flagged rows

## How this service works

Transformation throughput — IQR outlier flags + Explicit missing-value fill: IQR outlier flags + 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) IQR-based outlier flags for each row indicating whether processed_rows or duration_seconds values fall outside the interquartile range, and (2) a missing-value fill report showing which null values were imputed and what values were substituted using the chosen fill method (zero, mean, or median). All analysis is performed locally on supplied data 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-iqr-outlier-flags-explicit-missing-value-fill-c2dea7d9/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)