{"uid":"cap_mI4VBtzkLfhiN1Xp7jnOv","slug":"join-coverage-analytics-numeric-row-ordering-explicit-missing-value-79a3e9c9","name":"Join Coverage Analytics: Numeric Row Ordering + Explicit Missing-Value Fill","description":"Join coverage — Numeric row ordering + Explicit missing-value fill: Numeric row ordering + Explicit missing-value fill for supplied join coverage records (matched_rows, source_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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-09-124?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","matched_rows","source_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"matched_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_rows":1,"matched_rows":10,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","source_rows":2,"matched_rows":20,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","source_rows":3,"matched_rows":15,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","source_rows":4,"matched_rows":30,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["matched_rows","source_rows"],"type":"string","default":"matched_rows"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_YHe7Oj3yNDhT3rwNzpxXI","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Sorts join coverage records by row order and fills missing matched_rows or source_rows values using zero, mean, or median imputation, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have join coverage records for groups A and B from October 2026 — some matched_rows values are null. Can you sort them numerically and fill the missing matched_rows using median imputation? Here are 4 rows: row-1 (2026-10-01, group-a, matched_rows=10, source_rows=1), row-2 (2026-10-02, group-b, matched_rows=null, source_rows=2), row-3 (2026-10-03, group-a, matched_rows=15, source_rows=3), row-4 (2026-10-04, group-b, matched_rows=null, source_rows=4).","exampleUseCases":[{"title":"ETL pipeline gap filling before reporting","prompt":"I'm running an ETL job and some source_rows values are missing in my join coverage table. Sort the rows numerically and fill the nulls in source_rows using mean imputation — here are my 6 records across dataset groups 'sales' and 'returns' from 2026-09-01 to 2026-09-06."},{"title":"Data quality audit for matched row completeness","prompt":"Can you take my join coverage data for groups 'orders' and 'shipments' — 10 rows from 2026-08-01 to 2026-08-10 — and give me two analytical reports after sorting and filling any missing matched_rows values with zero?"},{"title":"Pre-analysis dataset standardization across groups","prompt":"Before I run my coverage analysis, I need to standardize this 15-row join coverage dataset: sort the rows in numeric order and substitute any null matched_rows using median fill. The data spans groups 'inventory' and 'fulfillment' from 2026-07-01 to 2026-07-15."}],"resultDescription":"Returns two analytical reports covering the supplied join coverage rows after applying numeric row ordering and explicit missing-value imputation (zero, mean, or median) on the selected field (matched_rows or source_rows). Each report reflects the cleaned, ordered dataset with nulls replaced by the chosen fill strategy.","failureModes":["More than 200 rows submitted — request exceeds maxItems limit","Invalid date format not matching YYYY-MM-DD pattern","Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) on any row","Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000)","Invalid enum value for method or field parameters","Empty rows array (minItems:1 not satisfied)"],"whenToPreferThis":"Choose this endpoint when you have join coverage data (matched_rows and source_rows per dataset group and date) with missing values that need imputation before analysis, and you want numeric row ordering and two analytical reports in a single call for up to 200 rows at minimal cost ($0.01 USDC). It performs entirely on supplied data with no external lookups.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T18:54:46.992Z","isFirstParty":false,"canonicalSlug":"join-coverage-analytics-numeric-row-ordering-explicit-missing-value-79a3e9c9"}