# Duplicate Observations IQR Outlier Flags and Date-by-Category Pivot

> Duplicate Observations IQR Outlier Flags and Date-by-Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-18).

Analyzes supplied duplicate-observation records to produce two offline reports: IQR-based outlier flags and a date-by-category pivot table, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-04-178?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-18
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/duplicate-observations-iqr-outlier-flags-and-date-by-category-pivot-b49e42a5
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_U37nnnN9I6kYhvPG5ScSa

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-iqr-outlier-flags-and-date-by-category-pivot-b49e42a5 -d '<json body>'
```

Example prompt: I have up to 200 rows of duplicate-observation records with columns row_id, dataset_group, date, duplicate_rows, and total_rows — can you run the IQR outlier flagging and date-by-category pivot on the duplicate_rows field and show me which records are outliers and how the values break down across groups by date?

## When to prefer this

Choose this endpoint when you have batch duplicate-observation records (up to 200 rows) and need both IQR outlier detection and a date-by-category pivot in a single low-cost call. Prefer it over generic statistics APIs when your data is structured around dataset_group, date, duplicate_rows, and total_rows and you want two coordinated offline analytical reports without live lookups or external data enrichment.

## Known failure modes

- Fewer than 1 row submitted — returns a validation error
- More than 200 rows submitted — exceeds maxItems limit
- Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) — schema validation failure
- Malformed date format (not YYYY-MM-DD) — pattern validation rejection
- Null numeric values that reduce effective sample size for IQR computation — may produce degenerate outlier flags
- Invalid field enum value (not duplicate_rows or total_rows) — schema rejection

## How this service works

Duplicate observations — IQR outlier flags + Date by category pivot: IQR outlier flags + Date by category pivot 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 local analytical reports: (1) an IQR outlier flag report marking which input records have duplicate_rows or total_rows values outside the interquartile range, and (2) a date-by-category pivot table cross-tabulating the selected numeric field by date and dataset_group — all computed offline without 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-iqr-outlier-flags-and-date-by-category-pivot-b49e42a5/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)