# Dataset Partition Sizes — Trailing Mean Series + Explicit Missing-Value Fill

> Dataset Partition Sizes — Trailing Mean Series + 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 2026-10-19).

Computes a trailing mean time series and fills missing values for dataset partition size records (row_count or file_mb) over up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-07-154?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-partition-sizes-trailing-mean-series-explicit-missing-value-849ef52a
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_SokaKQxmKtRoZRpKI1qYP

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-trailing-mean-series-explicit-missing-value-849ef52a -d '<json body>'
```

Example prompt: Can you compute a trailing mean series with a window of 3 and fill any missing values using the median method for these partition records — I want to analyze row_count across groups?

## When to prefer this

Use this endpoint when you have up to 200 dataset partition size records (row counts or file sizes in MB) and need both a trailing moving average series and explicit null/missing-value imputation in a single call, without any external data fetching, prediction, or professional assessment. Ideal for data engineers or analysts who want lightweight, deterministic time-series smoothing and gap-filling on partition metrics.

## Known failure modes

- Fewer than 1 row or more than 200 rows submitted — request rejected
- Invalid date format (not YYYY-MM-DD) — validation error
- Window size outside 2–200 range — validation error
- All values null for the chosen field — fill/mean computation may be undefined
- Field value outside allowed numeric range — validation error
- Missing required row properties (row_id, dataset_group, date) — validation error

## How this service works

Dataset partition sizes — Trailing mean series + Explicit missing-value fill: Trailing mean series + Explicit missing-value fill 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

Two independent analytical reports covering: (1) a trailing mean time series computed over the specified window for the chosen field (row_count or file_mb), and (2) an explicit missing-value fill for the same field using the specified method (zero, mean, or median). Results are derived entirely from the supplied rows 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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  },
  "window": {
   "type": "integer",
   "default": 2,
   "maximum": 200,
   "minimum": 2
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/dataset-partition-sizes-trailing-mean-series-explicit-missing-value-849ef52a/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)