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Sampling Observations Base-100 Series and Explicit Missing-Value Fill
x402
Sampling Observations Base-100 Series and 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 2026-10-15).
Computes a base-100 index series and fills missing values (zero, mean, or median) for up to 200 rows of weighted sampling observation data.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — Base-100 series + Explicit missing-value fill: Base-100 series + Explicit missing-value fill for supplied sampling observations records (sample_value, sample_weight). 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 base-100 indexed series where each row's value is expressed relative to the reference row at base_index, and (2) an explicitly missing-value-filled version of the chosen field (sample_value or sample_weight) using the specified fill method (zero, mean, or median). Both reports are computed locally without external lookups.
Example use cases
Survey panel data normalization
“I've got 80 rows of survey panel observations across two groups, some sample_value fields are null — please index them to base-100 starting from position 0 and fill the missing values with the mean, working on the sample_value field.”
Market research sample rebase
“Can you take my 120 market research sampling records and convert the sample_value column to a base-100 index anchored at row 5, then fill any gaps using zero substitution?”
Weighted observation quality fill
“I have a dataset of 60 sampling observations where sample_weight has some nulls — rebase the sample_weight field to base-100 from index 0 and impute the missing weights using the median method.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-169?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to (1) normalize a time-series or grouped observation dataset to a common base-100 index anchored at a specific reference row, AND (2) simultaneously handle missing values via zero, mean, or median fill — all in a single cheap call ($0.01 USDC). Ideal for small-to-medium datasets (≤200 rows) of weighted sampling observations. Prefer alternatives if you need live data lookup, predictive modeling, deduplication, outlier flagging, or datasets larger than 200 rows.
Side effects
- Fewer than 1 row or more than 200 rows submitted — rejected by schema validation
- base_index out of range (0–199) or exceeds actual row count — may cause reference errors
- Non-numeric or out-of-range sample_value/sample_weight values — rejected by schema constraints
- Invalid date format (not YYYY-MM-DD) — schema pattern validation failure
- Missing required fields (row_id, dataset_group, date) — request rejected
- All values null in selected field — mean/median imputation may produce undefined results
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string | |
| base_index | integer |
Tags
x402
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