# Dataset Partition Sizes — Daily Sum Series + Explicit Missing-Value Fill

> Dataset Partition Sizes — Daily Sum 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 daily sum time series and fills missing values for dataset partition size metrics (row_count or file_mb) across groups, returning two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-07-145?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-daily-sum-series-explicit-missing-value-fill-1dc9cb16
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_sRYG4lMJI22np4H6iG5cV

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-daily-sum-series-explicit-missing-value-fill-1dc9cb16 -d '<json body>'
```

Example prompt: Can you compute the daily sum time series for row_count across these dataset partition records and fill any missing values using the median method? I have up to 50 rows covering October 2026 with groups like 'group-a' and 'group-b'.

## When to prefer this

Choose this endpoint when you need both a daily sum aggregation and missing-value imputation for dataset partition size metrics (row_count or file_mb) in a single low-cost call. It is ideal for data engineering and pipeline monitoring use cases where you supply your own records (no external lookup) and need two complementary analytical views — aggregated totals and gap-filled series — on time-series partition data of up to 200 rows.

## Known failure modes

- Input array is empty or exceeds 200 rows — rejected with validation error
- Invalid date format (not YYYY-MM-DD) in any row — schema validation failure
- Unknown fill method or field enum value — rejected at schema level
- All values for selected field are null — fill may produce degenerate or all-zero results
- Missing required row fields (row_id, dataset_group, date) — schema validation error

## How this service works

Dataset partition sizes — Daily sum series + Explicit missing-value fill: Daily sum 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: (1) a daily sum time series aggregating the chosen metric (row_count or file_mb) by date across dataset groups, and (2) an explicit missing-value-filled version of the series using the specified fill method (zero, mean, or median). No external data fetched; analysis is performed on the supplied rows only.

## 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"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/dataset-partition-sizes-daily-sum-series-explicit-missing-value-fill-1dc9cb16/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)