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Duplicate Observations Analytics: Date-by-Category Pivot + Missing Value Fill
x402
Duplicate Observations Analytics: Date-by-Category Pivot + 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 2026-10-16).
Pivots duplicate-observation records by date and category, then fills missing values using zero, mean, or median imputation — returns two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Duplicate observations — Date by category pivot + Explicit missing-value fill: Date by category pivot + Explicit missing-value fill for supplied duplicate observations records (duplicate_rows, total_rows). 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 showing the selected field (duplicate_rows or total_rows) across groups and dates, and (2) the same data with missing values explicitly filled using the chosen method (zero, mean, or median). Both reports are computed locally from the supplied rows without external lookups.
Example use cases
Data quality audit across pipeline stages
“I've got records from two dataset groups showing duplicate_rows and total_rows counts by day for October — can you pivot them by date and group, and fill any missing duplicate_rows values with the median so I can see data quality trends clearly?”
ETL deduplication monitoring report
“I'm tracking duplicate rows in my ETL pipeline across groups alpha, beta, and gamma over the past week. Pivot the data by date and category and use zero-fill for any missing total_rows entries so I can spot which days had the most duplication.”
Missing value imputation for downstream analysis
“Some of my duplicate observation records have null duplicate_rows values — can you run a pivot by date and dataset group, then fill those nulls with the group mean so the data is ready for downstream reporting?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-04-189?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular duplicate-observation data (with duplicate_rows and total_rows per date and group) that needs to be reshaped into a date-by-category pivot and have missing values imputed — all in a single low-cost call. It is ideal for data quality reporting, ETL monitoring dashboards, and pre-processing before downstream analysis when you need both the pivoted view and the imputed dataset together. It is not suitable for live data fetching, prediction, or analysis of non-duplicate-count datasets.
Side effects
- Input array is empty or exceeds 200 rows — validation error
- date fields don't match YYYY-MM-DD pattern — schema validation failure
- duplicate_rows or total_rows values outside allowed numeric range — validation error
- Missing required fields (row_id, dataset_group, date) in any row — rejected
- Invalid fill method enum value — schema error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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