# Dataset Partition Sizes — Numeric Distribution & Category Frequencies

> Dataset Partition Sizes — Numeric Distribution & 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 2026-10-22).

Analyzes up to 200 dataset partition size records (row_count and file_mb) to produce numeric distribution statistics and category frequency reports for the dataset_group field.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-07-072?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-22
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/dataset-partition-sizes-numeric-distribution-category-frequencies-23770098
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_HoMN4dh9e8Q6NUccM3qKk

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 dataset-partition-sizes-numeric-distribution-category-frequencies-23770098 -d '<json body>'
```

Example prompt: I have a list of up to 200 dataset partition records with row_count and file_mb values across different dataset groups — can you run both a numeric distribution report on row_count and a category frequency report on the dataset_group field for me?

## When to prefer this

Choose this endpoint when you have raw dataset partition records (with row counts and file sizes) and need quick descriptive statistics plus group frequency analysis without building your own analytical pipeline. It is especially useful when you want two coordinated reports — numeric distribution and category breakdown — from the same batch of up to 200 rows at very low cost ($0.01 USDC). It does not perform live lookups, predictions, or external data enrichment, so it is best for purely local, supply-side analytics on data you already have.

## Known failure modes

- Exceeding 200 rows will be rejected by the schema
- Missing required fields (row_id, dataset_group, date, row_count, file_mb) will cause validation errors
- Invalid date format (not YYYY-MM-DD) will fail pattern validation
- Entirely null numeric fields may produce degenerate or empty distribution reports
- Payment failure via x402 protocol will block the request

## How this service works

Dataset partition sizes — Numeric distribution + Category frequencies: Numeric distribution + Category frequencies for supplied dataset partition sizes records (row_count, file_mb). 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: (1) a numeric distribution summary for the chosen field (row_count or file_mb) including statistics such as min, max, mean, median, percentiles, and spread; (2) a category frequency report showing how often each dataset_group value appears across the supplied rows. Both reports are generated from the supplied data with no external lookups.

## Request schema (JSON Schema)

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

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/dataset-partition-sizes-numeric-distribution-category-frequencies-23770098/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)