# Sampling Observations String Normalisation and Missing Value Fill

> Sampling Observations String Normalisation and 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).

Normalises strings and fills missing values for up to 200 sampling observation records (sample_value or sample_weight) using zero, mean, or median imputation, returning two analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-15-037?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/sampling-observations-string-normalisation-and-missing-value-fill-83283277
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_YCvVS-eeibWv7MdzKz2M1

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 sampling-observations-string-normalisation-and-missing-value-fill-83283277 -d '<json body>'
```

Example prompt: Can you clean up my sampling observations — I have up to 150 rows with sample_value and sample_weight fields, normalise the strings, and fill any missing sample_value entries using the median method?

## When to prefer this

Choose this endpoint when you need lightweight, local string normalisation combined with explicit missing-value imputation (zero, mean, or median) for small-to-medium sampling datasets of up to 200 rows with sample_value and sample_weight fields, and you want two bundled analytical reports for a flat $0.01 USDC fee with no external network dependencies.

## Known failure modes

- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, dataset_group, date) cause validation errors
- Date strings not matching YYYY-MM-DD pattern are rejected
- Values outside allowed numeric range (-1e12 to 1e12) are rejected
- Invalid method or field enum values cause schema validation failure
- Empty rows array (minItems: 1 not met) returns error

## How this service works

Sampling observations — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied sampling observations records (sample_value, sample_weight). 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 covering string-normalised and missing-value-filled versions of the supplied sampling observation rows, with imputed values for the chosen field (sample_value or sample_weight) using the selected method (zero, mean, or median). No external lookups or predictions are performed; results are based entirely on the supplied data.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/sampling-observations-string-normalisation-and-missing-value-fill-83283277/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)