# Dataset Storage — String Normalisation + Explicit Missing-Value Fill

> Dataset Storage — String Normalisation + 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 2026-10-15).

Normalises string fields and fills missing numeric values (used_mb, row_count) in dataset storage records using zero, mean, or median imputation, returning two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-18-037?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/dataset-storage-string-normalisation-explicit-missing-value-fill-a13adfc1
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_FEGaoeP357cDxsHGA4FtH

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-string-normalisation-explicit-missing-value-fill-a13adfc1 -d '<json body>'
```

Example prompt: I have up to 150 dataset storage rows with some null used_mb values and inconsistent dataset_group strings — can you normalise the strings and fill those missing used_mb values using median imputation, then give me two analytical reports?

## When to prefer this

Choose this endpoint when you need lightweight, local string normalisation combined with explicit missing-value imputation specifically for dataset storage records (used_mb, row_count fields) and want two analytical reports back in a single low-cost call. Prefer it over general-purpose data cleaning tools when your data fits the dataset storage schema and you need a fast, deterministic, offline result with no external dependencies.

## Known failure modes

- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, dataset_group, date) — validation error
- Invalid date format (must match YYYY-MM-DD pattern) — 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
- Numeric values outside allowed range (±1,000,000,000,000) — validation error
- Empty rows array — rejected (minItems: 1)

## How this service works

Dataset storage — String normalisation + Explicit missing-value fill: String normalisation + 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

Two analytical reports covering the supplied dataset storage records (up to 200 rows), with string fields normalised and missing numeric values (used_mb or row_count) explicitly filled using the chosen method (zero, mean, or median). No external lookups or predictions are made; all processing uses local analytical engines.

## 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-string-normalisation-explicit-missing-value-fill-a13adfc1/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)