# Company Headcount Observations — String Normalisation & Missing-Value Fill

> Company Headcount Observations — String Normalisation & 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).

Normalises string fields and fills missing values (headcount or open_roles) in company headcount observation records using zero, mean, or median imputation across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-12-037?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-string-normalisation-missing-value-fill-e0879237
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Lr8bWxygGChFCtiVxKF1M

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-string-normalisation-missing-value-fill-e0879237 -d '<json body>'
```

Example prompt: Can you clean up my company headcount dataset — I have up to 50 rows with company_group, date, headcount, and open_roles fields, some of which have null headcount values — fill the missing headcount entries using median imputation and normalise the string fields?

## When to prefer this

Choose this endpoint when you have tabular company headcount or open_roles observation data that needs string field normalisation and missing-value imputation in a single lightweight call. It is ideal for pre-processing HR or workforce datasets of up to 200 rows before loading into analytics pipelines or BI tools. Prefer it over general-purpose data tools when you specifically need the combination of string normalisation and explicit null fill (zero, mean, or median) on headcount or open_roles columns without requiring live data lookup or external enrichment.

## Known failure modes

- Rows array exceeds 200 items — request rejected
- Missing required fields (row_id, company_group, date, headcount, open_roles) on any row — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Unknown imputation method value — enum mismatch error
- All values already present — imputation report will show no changes made
- Payment not included or insufficient — x402 payment required error

## How this service works

Company headcount observations — String normalisation + Explicit missing-value fill: String normalisation + 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 for the submitted rows: one covering string normalisation applied to the company_group and date fields, and one covering explicit missing-value imputation for the selected field (headcount or open_roles) using the chosen method (zero, mean, or median). Output reflects cleaned, standardised records ready for downstream analysis.

## 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-string-normalisation-missing-value-fill-e0879237/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)