# Dataset Storage Analytics — Numeric Row Ordering + Explicit Missing-Value Fill

> Dataset Storage Analytics — Numeric Row Ordering + 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 2026-10-23).

Sorts dataset storage records numerically and fills missing values using a chosen method (zero, mean, or median) for up to 200 rows across two fields.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-18-124?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-23
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/dataset-storage-analytics-numeric-row-ordering-explicit-missing-value-b166cd43
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_kw0_TzE1zWV_SIlAUD9S1

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-numeric-row-ordering-explicit-missing-value-b166cd43 -d '<json body>'
```

Example prompt: Sort my dataset storage rows numerically by used_mb and fill in any missing values using the median — here are up to 50 rows with row_id, date, dataset_group, used_mb, and row_count fields.

## When to prefer this

Choose this endpoint when you have up to 200 dataset storage records (with used_mb and/or row_count fields) that need both numeric ordering and missing-value imputation in a single low-cost call. It is ideal for lightweight data preprocessing pipelines where you want two complementary analytical views — sorted order and filled gaps — without spinning up a full data processing stack. Prefer this over general analytics services when your data fits the dataset-storage schema and you need a cheap, deterministic, no-external-lookup transformation.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, dataset_group, date) on any row — validation error
- Invalid date format not matching YYYY-MM-DD pattern — schema validation failure
- Field or method enum value not recognized — bad request
- Empty rows array — minimum 1 item required

## How this service works

Dataset storage — Numeric row ordering + Explicit missing-value fill: Numeric row ordering + 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 submitted dataset storage rows: one report showing records in numeric order by the chosen field, and one report where missing values in that field have been filled using the specified method (zero, mean, or median). Returns processed row data up to 200 records.

## 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-numeric-row-ordering-explicit-missing-value-b166cd43/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)