# Data Ingestion Batches — Numeric Distribution + Explicit Missing-Value Fill

> Data Ingestion Batches — Numeric Distribution + 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).

Analyzes up to 200 data ingestion batch records by computing a numeric distribution report and filling missing values in received_rows or accepted_rows using a chosen fill method (zero, mean, or median).

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-01-085?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/data-ingestion-batches-numeric-distribution-explicit-missing-value-fill-9058a032
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_OC01esAiu4qStJGxiJKHi

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 data-ingestion-batches-numeric-distribution-explicit-missing-value-fill-9058a032 -d '<json body>'
```

Example prompt: Can you analyze the numeric distribution of received_rows across these batch ingestion records and fill any missing values using the median method? I have about 50 rows with fields like row_id, dataset_group, date, received_rows, and accepted_rows.

## When to prefer this

Choose this endpoint when you need both a numeric distribution profile and missing-value imputation for data ingestion batch metrics (received_rows or accepted_rows) in a single $0.01 call covering up to 200 rows. Prefer it over general statistics APIs when you specifically work with ETL/ingestion pipeline batch records and need a combined two-report output without any external data lookups or predictions.

## Known failure modes

- Validation error if rows array is empty or exceeds 200 items
- Schema error if required fields (row_id, dataset_group, date, received_rows, accepted_rows) are missing from any row
- Invalid date format — must match YYYY-MM-DD pattern
- Invalid enum value for field or method parameters
- Payment failure via x402 protocol if USDC is not supplied
- Numeric values out of allowed range (-1e12 to 1e12) will be rejected

## How this service works

Data ingestion batches — Numeric distribution + Explicit missing-value fill: Numeric distribution + Explicit missing-value fill for supplied data ingestion batches records (received_rows, accepted_rows). 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) a numeric distribution summary (e.g. min, max, mean, percentiles, histogram bins) for the specified field (received_rows or accepted_rows), and (2) the same dataset with missing values in that field explicitly filled using the chosen method (zero, mean, or median). Both reports are computed locally against 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",
     "received_rows",
     "accepted_rows"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "accepted_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "received_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "accepted_rows": 1,
     "dataset_group": "group-a",
     "received_rows": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "accepted_rows": 2,
     "dataset_group": "group-b",
     "received_rows": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "accepted_rows": 3,
     "dataset_group": "group-a",
     "received_rows": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "accepted_rows": 4,
     "dataset_group": "group-b",
     "received_rows": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "received_rows",
    "accepted_rows"
   ],
   "type": "string",
   "default": "received_rows"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-ingestion-batches-numeric-distribution-explicit-missing-value-fill-9058a032/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)