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Dataset Storage Analytics — Date×Category Pivot with Missing-Value Fill
x402
Dataset Storage Analytics — Date×Category Pivot with 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).
Pivots dataset storage records (used_mb or row_count) by date and category, filling missing values via zero, mean, or median, returning two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Dataset storage — Date by category pivot + Explicit missing-value fill: Date by category pivot + Explicit missing-value fill for supplied dataset storage records (used_mb, row_count). 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: (1) a date-by-category pivot table of the selected metric (used_mb or row_count) with missing values explicitly filled using the chosen method (zero, mean, or median), and (2) a supplementary summary report — both computed locally from the supplied rows with no external data lookup.
Example use cases
Storage usage trend by team
“I have 120 rows of dataset storage logs with columns for date, dataset_group, and used_mb. Can you pivot them into a date-by-group table and fill any missing cells with the median so I can see which team is using the most storage each day?”
Row count cross-tab with zero-fill
“Take my dataset records — up to 180 rows — and create a pivot table of row_count grouped by date and dataset_group. Fill any missing date-group combinations with zero rather than leaving blanks.”
Missing-value imputation before reporting
“My storage dataset has gaps where some groups didn't log data on certain dates. Pivot the used_mb values by date and category, using mean imputation for the missing cells, so I get a clean matrix across all 90 rows.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-18-189?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have raw dataset storage records (with used_mb or row_count fields) that need to be reshaped into a date-by-category pivot table with explicit missing-value handling, all within a single low-cost call. It is ideal for offline/batch analytical reshaping of up to 200 rows when no live data lookup, prediction, or external enrichment is needed.
Side effects
- Fewer than 1 row supplied returns a validation error
- More than 200 rows submitted exceeds the row limit
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- Unknown fill method outside zero/mean/median is rejected
- Unknown field outside used_mb/row_count is rejected
- Null-only columns may produce degenerate mean/median fill results
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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