# Dataset Storage Analytics: IQR Outlier Flags + Missing Value Fill

> Dataset Storage Analytics: IQR Outlier Flags + 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 2026-10-19).

Detects IQR-based outliers and fills missing values in dataset storage records (used_mb, row_count) for up to 200 rows, returning two analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-18-180?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/dataset-storage-analytics-iqr-outlier-flags-missing-value-fill-05ad8b6c
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_9x4eNNCt9yr6C90O5fsQk

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-storage-analytics-iqr-outlier-flags-missing-value-fill-05ad8b6c -d '<json body>'
```

Example prompt: Analyze these 12 storage records for IQR outliers and fill any missing used_mb values using median — the dataset has groups like 'warehouse-a' and 'warehouse-b' with dates in October 2026.

## When to prefer this

Choose this endpoint when you need lightweight, low-cost statistical data quality analysis on dataset storage metrics (used_mb, row_count) without any external API dependencies. It is ideal when you want both IQR outlier detection and missing value imputation in a single $0.01 call for up to 200 rows. Prefer it over heavier ML-based anomaly detection services when your data is tabular, small-to-medium scale, and interpretability of IQR bounds matters. Not suitable for real-time streaming, prediction, or datasets larger than 200 rows per call.

## Known failure modes

- Input rows exceed 200 item limit — request rejected
- Missing required fields (row_id, dataset_group, date, used_mb, row_count) — validation error
- Invalid date format (must match YYYY-MM-DD) — schema validation failure
- Invalid field enum value (must be 'used_mb' or 'row_count') — rejected
- Invalid method enum value (must be 'zero', 'mean', or 'median') — rejected
- Empty rows array — minItems violation
- Numeric values outside allowed range (-1e12 to 1e12) — schema error

## How this service works

Dataset storage — IQR outlier flags + Explicit missing-value fill: IQR outlier flags + Explicit missing-value fill for supplied dataset storage records (used_mb, row_count). 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 per row for the selected field (used_mb or row_count), identifying which rows fall outside the interquartile range bounds; (2) a missing-value fill report showing which null values were replaced and what fill value (zero, mean, or median) was used. 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",
     "used_mb",
     "row_count"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "used_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",
     "used_mb": 10,
     "row_count": 1,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "used_mb": 20,
     "row_count": 2,
     "dataset_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "used_mb": 15,
     "row_count": 3,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "used_mb": 30,
     "row_count": 4,
     "dataset_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "used_mb",
    "row_count"
   ],
   "type": "string",
   "default": "used_mb"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/dataset-storage-analytics-iqr-outlier-flags-missing-value-fill-05ad8b6c/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)