# Dataset Partition Sizes — Exact Row Deduplication + IQR Outlier Flags

> Dataset Partition Sizes — Exact Row Deduplication + IQR Outlier Flags 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).

Deduplicates dataset partition size records and flags IQR-based outliers across row_count and file_mb fields for up to 200 rows in two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-07-049?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/dataset-partition-sizes-exact-row-deduplication-iqr-outlier-flags-a5af53fe
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_xlc3LBrinfNhciTxwmxhv

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 dataset-partition-sizes-exact-row-deduplication-iqr-outlier-flags-a5af53fe -d '<json body>'
```

Example prompt: Can you deduplicate and flag IQR outliers for my dataset partition records? I have rows with row_id, dataset_group, date, row_count, and file_mb — here are up to 200 entries — and I want the analysis run on the row_count field.

## When to prefer this

Choose this endpoint when you need a quick, low-cost ($0.01 USDC) combined deduplication and IQR outlier detection pass on dataset partition metrics (row_count or file_mb) for batches up to 200 rows. It is ideal for data engineering pipelines that need automated data quality checks before ingestion, without requiring an external analytics platform or live data connection. Prefer alternatives if you need more than 200 rows, require prediction or forecasting, need professional statistical assessment, or require analysis beyond row_count and file_mb fields.

## Known failure modes

- More than 200 rows submitted — exceeds maxItems limit, request rejected
- Missing required fields (row_id, dataset_group, date, row_count, file_mb) — validation error
- Invalid date format not matching YYYY-MM-DD pattern — schema validation failure
- Empty rows array (minItems: 1 violated) — request rejected
- Numeric values outside allowed range (±1,000,000,000,000) — schema validation failure
- Invalid field selector (not 'row_count' or 'file_mb') — enum validation error

## How this service works

Dataset partition sizes — Exact row deduplication + IQR outlier flags: Exact row deduplication + IQR outlier flags for supplied dataset partition sizes records (row_count, file_mb). 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 independent analytical reports per call: (1) an exact row deduplication report identifying and removing duplicate records from the supplied dataset partition rows, and (2) an IQR-based outlier flagging report marking records whose selected metric (row_count or file_mb) falls outside the interquartile fence. 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",
     "row_count",
     "file_mb"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "file_mb": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "row_count": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "file_mb": 1,
     "row_count": 10,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "file_mb": 2,
     "row_count": 20,
     "dataset_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "file_mb": 3,
     "row_count": 15,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "file_mb": 4,
     "row_count": 30,
     "dataset_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "row_count",
    "file_mb"
   ],
   "type": "string",
   "default": "row_count"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/dataset-partition-sizes-exact-row-deduplication-iqr-outlier-flags-a5af53fe/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)