# Sampling Observations — String Normalisation + Category Frequencies

> Sampling Observations — String Normalisation + Category Frequencies is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).

Performs string normalisation and category frequency analysis on up to 200 sampling observation records (sample_value, sample_weight) in a single call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-15-024?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-24
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/sampling-observations-string-normalisation-category-frequencies-e4fe1ade
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_IDNzO-ltrDD9md20xKMYI

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-category-frequencies-e4fe1ade -d '<json body>'
```

Example prompt: I have 50 sampling observation rows — each with a row ID, dataset group name, date, sample value, and sample weight — and I need two reports: one that normalises the string category labels and one that shows how frequently each category appears across the dataset.

## When to prefer this

Choose this endpoint when you need both string normalisation of categorical labels and a frequency count of those categories from sampling observation data in a single low-cost API call. It is ideal for cleaning and auditing survey, lab, or market sample datasets with up to 200 rows, where no external data lookup or prediction is needed — just local analytical computation on the data you supply.

## Known failure modes

- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) cause row-level rejection
- Dates not matching YYYY-MM-DD pattern are rejected
- sample_value or sample_weight outside ±1,000,000,000,000 range are rejected
- Empty rows array (minItems: 1 not met) returns an error

## How this service works

Sampling observations — String normalisation + Category frequencies: String normalisation + Category frequencies 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 for the supplied rows: (1) a string normalisation report standardising the categorical/text fields across the dataset, and (2) a category frequency report showing how often each distinct dataset_group value appears, potentially weighted by sample_weight. No external lookups or live data are used — analysis is performed locally on the submitted rows.

## 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/sampling-observations-string-normalisation-category-frequencies-e4fe1ade/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)