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Pipeline Durations Analytics — Secondary-Measure Weighted Mean + Explicit Missing-Value Fill
x402
Pipeline Durations Analytics — Secondary-Measure Weighted Mean + Explicit 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 2026-10-18).
Computes a secondary-measure weighted mean and fills missing values for pipeline duration records (run_seconds, processed_rows) across up to 200 rows, returning 2 analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Pipeline durations — Secondary-measure weighted mean + Explicit missing-value fill: Secondary-measure weighted mean + Explicit missing-value fill for supplied pipeline durations records (run_seconds, processed_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 2 analytical reports: (1) a secondary-measure weighted mean of the specified field (run_seconds or processed_rows), and (2) an explicit missing-value fill report using the chosen strategy (zero, mean, or median). Results are computed locally from the supplied data rows without external lookups.
Example use cases
ETL pipeline performance benchmarking
“I've got 120 pipeline run records with run_seconds and processed_rows across two dataset groups, but about 15 run_seconds values are null. Can you compute the weighted mean of run_seconds (weighted by processed_rows) and fill those missing values using the mean fill method?”
Data volume-weighted duration analysis
“For my 80 pipeline runs tagged by date and dataset_group, can you calculate the weighted average of processed_rows using run_seconds as the secondary weight, filling any nulls with zero, so I can see which groups process the most data per second?”
Missing-value imputation before reporting
“I have a CSV of pipeline runs — row_id, date, dataset_group, run_seconds, processed_rows — with some null processed_rows entries. Can you fill those nulls using median imputation and then give me the weighted mean report for processed_rows weighted by run_seconds?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-05-187?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have pipeline duration data (run_seconds, processed_rows) with potential missing values and need both a secondary-measure weighted mean and explicit null imputation in a single call. It is ideal for small-to-medium datasets (≤200 rows) where you want offline, deterministic analytics without external data lookups or predictions.
Side effects
- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, dataset_group) cause validation errors
- Numeric values outside ±1,000,000,000,000 range rejected
- Date strings not matching YYYY-MM-DD pattern fail schema validation
- All values null for a field may produce degenerate weighted mean
- Payment of 0.01 USDC not received — x402 payment required error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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