# Duplicate Observations — Grouped Numeric Summary + Category Frequencies

> Duplicate Observations — Grouped Numeric Summary + Category Frequencies is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).

Runs two local analytical reports on duplicate observation data: a grouped numeric summary and category frequency breakdown for duplicate_rows and total_rows fields across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-04-086?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/duplicate-observations-grouped-numeric-summary-category-frequencies-23358901
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_KY_1gZx3D08kjJcdjlE1B

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-category-frequencies-23358901 -d '<json body>'
```

Example prompt: Can you run a grouped numeric summary and category frequency report on my duplicate observation data? I have 4 rows covering groups A and B from October 2026, with duplicate_rows counts like 10, 20, 15, and 30 against total_rows of 1 through 4 — analyze the duplicate_rows field.

## When to prefer this

Choose this endpoint when you have duplicate observation records with a dataset_group dimension and need both grouped numeric statistics and category frequency counts in a single call at low cost ($0.01 USDC for up to 200 rows). It is ideal for data quality workflows where you want to profile duplication rates across named groups without making any external API calls or predictions. Prefer it over general analytics tools when your data already conforms to the duplicate_rows/total_rows schema and you need lightweight, deterministic profiling.

## Known failure modes

- Fewer than 1 row submitted returns a validation error
- More than 200 rows submitted exceeds the maxItems limit
- Invalid date format (not YYYY-MM-DD) fails pattern validation
- Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) cause schema rejection
- Field enum value not in ['duplicate_rows','total_rows'] causes rejection
- Numeric values outside ±1,000,000,000,000 fail min/max validation

## How this service works

Duplicate observations — Grouped numeric summary + Category frequencies: Grouped numeric summary + Category frequencies 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

Two analytical reports: (1) a grouped numeric summary showing descriptive statistics (e.g. mean, min, max, count) for the selected numeric field (duplicate_rows or total_rows) broken down by dataset_group, and (2) a category frequency table showing how often each dataset_group value appears across the supplied rows. Both reports are computed locally on 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"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/duplicate-observations-grouped-numeric-summary-category-frequencies-23358901/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)