# Join Coverage Date-by-Category Pivot with Missing-Value Fill

> Join Coverage Date-by-Category Pivot with 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 2026-10-15).

Pivots join coverage data (matched_rows, source_rows) by date and category, then fills missing values using zero, mean, or median imputation — producing two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-189?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/join-coverage-date-by-category-pivot-with-missing-value-fill-a65c6365
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_AUbX3W3yue04Mb3IpXtpL

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-date-by-category-pivot-with-missing-value-fill-a65c6365 -d '<json body>'
```

Example prompt: I have up to 100 rows of join coverage data with matched_rows and source_rows across dataset groups and dates — can you pivot them by date and category and fill any missing matched_rows values using median imputation?

## When to prefer this

Choose this endpoint when you have raw join coverage records (matched_rows, source_rows) keyed by date and dataset group and need both a pivot-table view and missing-value imputation in a single call. It is ideal for data engineers auditing pipeline join quality across categories over time without needing external data sources, live lookups, or custom code execution. Prefer it over general analytics tools when your dataset fits within 200 rows and you want two pre-packaged report types (pivot + fill) for a flat $0.01 fee.

## Known failure modes

- Fewer than 1 row supplied — validation error
- More than 200 rows supplied — rejected, exceeds limit
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Invalid field enum value (not matched_rows or source_rows) — rejected
- Invalid method enum (not zero, mean, or median) — rejected
- Numeric values outside allowed range (±1,000,000,000,000) — rejected
- Missing required fields (row_id, dataset_group, date) — schema validation error

## How this service works

Join coverage — Date by category pivot + Explicit missing-value fill: Date by category pivot + 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

Two analytical reports: (1) a date-by-category pivot of the specified field (matched_rows or source_rows) across dataset groups, and (2) the same dataset with explicit missing-value imputation applied using the chosen fill method (zero, mean, or median). Both reports are computed locally 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",
     "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-date-by-category-pivot-with-missing-value-fill-a65c6365/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)