# Company Headcount Observations — Date-by-Category Pivot + Missing-Value Fill

> Company Headcount Observations — 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 2026-10-19).

Transforms up to 200 company headcount observation rows into a date-by-category pivot table and fills missing values using zero, mean, or median imputation.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-12-189?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/company-headcount-observations-date-by-category-pivot-missing-value-945e56f7
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_fddlq0qfGlQ4slXTf2Os1

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 company-headcount-observations-date-by-category-pivot-missing-value-945e56f7 -d '<json body>'
```

Example prompt: I have up to 200 rows of company headcount observations with dates, company groups, headcount, and open_roles — can you pivot them by date and category and fill any missing headcount values using median imputation?

## When to prefer this

Choose this endpoint when you have pre-collected company headcount or open_roles observations (up to 200 rows) and need to reshape them into a date-by-category pivot table AND fill missing values in a single low-cost call. Ideal for HR analytics pipelines, recruiting dashboards, or workforce reporting where the data is already on hand and you want two complementary analytical views (pivot + imputed fill) without external lookups or predictions.

## Known failure modes

- Fewer than 1 row supplied — minimum input requirement not met
- Rows missing required fields (row_id, company_group, date, headcount, or open_roles) — validation error
- Date strings not matching YYYY-MM-DD pattern — schema rejection
- More than 200 rows supplied — exceeds maxItems limit
- Invalid field enum value (not headcount or open_roles) — rejected
- Invalid method enum value (not zero, mean, or median) — rejected
- Payment not completed — x402 payment required before processing

## How this service works

Company headcount observations — Date by category pivot + Explicit missing-value fill: Date by category pivot + Explicit missing-value fill for supplied company headcount observations records (headcount, open_roles). 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 of the selected field (headcount or open_roles) with rows as dates and columns as company_group values, and (2) the same dataset with explicit missing-value fill applied using the chosen method (zero, mean, or median). Both reports are computed locally from the supplied rows without external data.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/company-headcount-observations-date-by-category-pivot-missing-value-945e56f7/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)