# Join Coverage Analytics: Numeric Row Ordering + Explicit Missing-Value Fill

> Join Coverage 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 join coverage records by row order and fills missing matched_rows or source_rows values 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-09-124?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/join-coverage-analytics-numeric-row-ordering-explicit-missing-value-79a3e9c9
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_mI4VBtzkLfhiN1Xp7jnOv

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 join-coverage-analytics-numeric-row-ordering-explicit-missing-value-79a3e9c9 -d '<json body>'
```

Example prompt: I have join coverage records for groups A and B from October 2026 — some matched_rows values are null. Can you sort them numerically and fill the missing matched_rows using median imputation? Here are 4 rows: row-1 (2026-10-01, group-a, matched_rows=10, source_rows=1), row-2 (2026-10-02, group-b, matched_rows=null, source_rows=2), row-3 (2026-10-03, group-a, matched_rows=15, source_rows=3), row-4 (2026-10-04, group-b, matched_rows=null, source_rows=4).

## When to prefer this

Choose this endpoint when you have join coverage data (matched_rows and source_rows per dataset group and date) with missing values that need imputation before analysis, and you want numeric row ordering and two analytical reports in a single call for up to 200 rows at minimal cost ($0.01 USDC). It performs entirely on supplied data with no external lookups.

## Known failure modes

- More than 200 rows submitted — request exceeds maxItems limit
- Invalid date format not matching YYYY-MM-DD pattern
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) on any row
- Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000)
- Invalid enum value for method or field parameters
- Empty rows array (minItems:1 not satisfied)

## How this service works

Join coverage — Numeric row ordering + Explicit missing-value fill: Numeric row ordering + Explicit missing-value fill for supplied join coverage records (matched_rows, source_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 covering the supplied join coverage rows after applying numeric row ordering and explicit missing-value imputation (zero, mean, or median) on the selected field (matched_rows or source_rows). Each report reflects the cleaned, ordered dataset with nulls replaced by the chosen fill strategy.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "matched_rows",
     "source_rows"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "source_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "matched_rows": {
      "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",
     "source_rows": 1,
     "matched_rows": 10,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "source_rows": 2,
     "matched_rows": 20,
     "dataset_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "source_rows": 3,
     "matched_rows": 15,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "source_rows": 4,
     "matched_rows": 30,
     "dataset_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "matched_rows",
    "source_rows"
   ],
   "type": "string",
   "default": "matched_rows"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/join-coverage-analytics-numeric-row-ordering-explicit-missing-value-79a3e9c9/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)