# Data Reconciliation — Date×Category Pivot + Missing-Value Fill

> Data Reconciliation — Date×Category Pivot + 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).

Pivots reconciliation rows by date and dataset group, then fills missing source_total or target_total 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-11-189?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-reconciliation-date-category-pivot-missing-value-fill-7b8879bf
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_tJq6bpOVH1nqFu6MeybBH

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-reconciliation-date-category-pivot-missing-value-fill-7b8879bf -d '<json body>'
```

Example prompt: I have 4 reconciliation rows with source and target totals across two groups (group-a and group-b) on dates 2026-10-01 through 2026-10-04 — can you pivot them by date and category and fill any missing source_total values using median imputation?

## When to prefer this

Choose this endpoint when you need to reshape reconciliation data into a date-by-category pivot AND handle missing values in a single call. It is ideal for pre-reporting data quality workflows where source/target totals must be compared across groups and dates without nulls. Prefer it over generic pivot tools when you specifically need the combined pivot + imputation output as two structured reports at low cost ($0.01 USDC for both). Not suitable for live data lookups, predictions, or datasets exceeding 200 rows.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, dataset_group, source_total, target_total) on any row — validation error
- Date strings not matching YYYY-MM-DD format — pattern validation failure
- Numeric values outside ±1,000,000,000,000 range — schema rejection
- Empty rows array (minItems: 1 not met) — validation error
- Invalid enum value for field or method — schema rejection

## How this service works

Data reconciliation — Date by category pivot + Explicit missing-value fill: Date by category pivot + Explicit missing-value fill for supplied data reconciliation records (source_total, target_total). 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 table of the selected field (source_total or target_total) across all dataset groups and dates, and (2) a missing-value fill report showing which cells were imputed and what values were used, based on the chosen method (zero, mean, or median). Up to 200 input rows supported.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-reconciliation-date-category-pivot-missing-value-fill-7b8879bf/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)