# Duplicate Observations — Grouped Numeric Summary + Explicit Missing-Value Fill

> Duplicate Observations — Grouped Numeric Summary + 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 2026-10-20).

Computes a grouped numeric summary and fills missing values for duplicate_rows and total_rows fields across up to 200 supplied observation records.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-04-099?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-20
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/duplicate-observations-grouped-numeric-summary-explicit-missing-value-34f326fe
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_iWt_03Ntopo5VHGL8jDdG

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 duplicate-observations-grouped-numeric-summary-explicit-missing-value-34f326fe -d '<json body>'
```

Example prompt: I have up to 200 rows of duplicate observation records with dataset_group, date, duplicate_rows, and total_rows fields — can you give me a grouped numeric summary of the duplicate_rows field and fill any missing values using the median method?

## When to prefer this

Use this endpoint when you have tabular duplicate-observation data (with duplicate_rows and total_rows fields) and need both a grouped statistical summary and missing-value imputation in a single call. It is purpose-built for data quality analytics on duplicate record metrics and handles up to 200 rows per call at very low cost. Prefer this over general analytics services when your data fits the duplicate_rows/total_rows schema and you need both aggregation and null-filling together.

## Known failure modes

- Exceeds 200-row limit — request rejected
- Missing required fields (row_id, dataset_group, date) — validation error
- Invalid date format (must be YYYY-MM-DD) — schema validation failure
- Invalid fill method enum value — rejected
- All values null for a group — mean/median fill may be undefined
- Numeric values outside allowed range — schema rejection

## How this service works

Duplicate observations — Grouped numeric summary + Explicit missing-value fill: Grouped numeric summary + Explicit missing-value fill for supplied duplicate observations records (duplicate_rows, total_rows). 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 grouped numeric summary of the specified field (duplicate_rows or total_rows) broken down by dataset_group, and (2) the same dataset with explicit missing-value fills applied using the chosen method (zero, mean, or median). Both reports are computed locally from the 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",
     "duplicate_rows",
     "total_rows"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "total_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "duplicate_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "total_rows": 1,
     "dataset_group": "group-a",
     "duplicate_rows": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "total_rows": 2,
     "dataset_group": "group-b",
     "duplicate_rows": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "total_rows": 3,
     "dataset_group": "group-a",
     "duplicate_rows": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "total_rows": 4,
     "dataset_group": "group-b",
     "duplicate_rows": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "duplicate_rows",
    "total_rows"
   ],
   "type": "string",
   "default": "duplicate_rows"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/duplicate-observations-grouped-numeric-summary-explicit-missing-value-34f326fe/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)