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Dataset Partition Sizes — Exact Row Deduplication + Equal-Width Histogram
x402
Dataset Partition Sizes — Exact Row Deduplication + Equal-Width Histogram is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-23).
Runs exact row deduplication and equal-width histogram analysis on up to 200 dataset partition size records (row_count or file_mb), returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset partition sizes — Exact row deduplication + Equal-width histogram: Exact row deduplication + Equal-width histogram 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 independent analytical reports: (1) an exact row deduplication report identifying and removing duplicate records from the supplied partition size data, and (2) an equal-width histogram report showing frequency distribution of the selected field (row_count or file_mb) across the specified number of bins. Both reports are computed locally with no external lookups.
Example use cases
Audit partition table for duplicate rows
“I have 120 dataset partition records with row_id, dataset_group, date, row_count, and file_mb — can you run exact deduplication on them and also give me a 5-bin histogram on the file_mb field so I can see how storage is distributed?”
Distribution check before data pipeline run
“Before we kick off our pipeline, can you check these 80 partition size rows for duplicates and generate an equal-width histogram with 8 bins on row_count so I can spot any weird outliers?”
Compare partition size spread across dataset groups
“I've got 200 partition records tagged by dataset_group and date — run deduplication and a 15-bin equal-width histogram on row_count so I can understand the size distribution across groups.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-07-048?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both duplicate detection and distributional analysis on dataset partition size records in a single low-cost call. It is ideal for data engineers auditing partition tables before pipeline runs, or analysts who want a quick statistical summary of row counts and file sizes without setting up local tooling. Prefer this over general-purpose analytics tools when your data fits within 200 rows and you specifically need the row_count/file_mb domain schema.
Side effects
- Supplying more than 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, row_count, file_mb) causes a schema validation failure
- Bins value outside 1–30 range is rejected
- Null values in the selected numeric field (row_count or file_mb) may affect histogram bin calculations
- Malformed date strings not matching YYYY-MM-DD pattern cause validation errors
- Non-numeric row_count or file_mb values cause parsing failures
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| bins | integer | |
| rows | array | |
| field | string |
Tags
x402
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