# Duplicate Observations Analytics: Date-by-Category Pivot + Missing Value Fill

> Duplicate Observations Analytics: Date-by-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 2026-10-20).

Pivots duplicate-observation records by date and category, then fills missing values using zero, mean, or median imputation — returns two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-04-189?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-20
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/duplicate-observations-analytics-date-by-category-pivot-missing-value-fcd9b3d7
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_zbkxohDg6cZ7pvFxD2flZ

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 duplicate-observations-analytics-date-by-category-pivot-missing-value-fcd9b3d7 -d '<json body>'
```

Example prompt: I have 4 rows of duplicate observation data across two groups (group-a and group-b) dated 2026-10-01 through 2026-10-04 — can you pivot them by date and category and fill any missing duplicate_rows values using median imputation?

## When to prefer this

Choose this endpoint when you have tabular duplicate-observation data (with duplicate_rows and total_rows per date and group) that needs to be reshaped into a date-by-category pivot and have missing values imputed — all in a single low-cost call. It is ideal for data quality reporting, ETL monitoring dashboards, and pre-processing before downstream analysis when you need both the pivoted view and the imputed dataset together. It is not suitable for live data fetching, prediction, or analysis of non-duplicate-count datasets.

## Known failure modes

- Input array is empty or exceeds 200 rows — validation error
- date fields don't match YYYY-MM-DD pattern — schema validation failure
- duplicate_rows or total_rows values outside allowed numeric range — validation error
- Missing required fields (row_id, dataset_group, date) in any row — rejected
- Invalid fill method enum value — schema error

## How this service works

Duplicate observations — Date by category pivot + Explicit missing-value fill: Date by category pivot + Explicit missing-value fill for supplied duplicate observations records (duplicate_rows, total_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 date-by-category pivot table showing the selected field (duplicate_rows or total_rows) across groups and dates, and (2) the same data with missing values explicitly filled using the chosen method (zero, mean, or median). Both reports are computed locally from the supplied rows without external lookups.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "duplicate_rows",
     "total_rows"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "total_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "duplicate_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "total_rows": 1,
     "dataset_group": "group-a",
     "duplicate_rows": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "total_rows": 2,
     "dataset_group": "group-b",
     "duplicate_rows": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "total_rows": 3,
     "dataset_group": "group-a",
     "duplicate_rows": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "total_rows": 4,
     "dataset_group": "group-b",
     "duplicate_rows": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "duplicate_rows",
    "total_rows"
   ],
   "type": "string",
   "default": "duplicate_rows"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/duplicate-observations-analytics-date-by-category-pivot-missing-value-fcd9b3d7/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)