{"uid":"cap_zFW56dO-P_SZNhq5jGDfb","slug":"join-coverage-numeric-distribution-explicit-missing-value-fill-87909c6e","name":"Join Coverage Numeric Distribution + Explicit Missing-Value Fill","description":"Join coverage — Numeric distribution + Explicit missing-value fill: Numeric distribution + 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-09-085?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_yhjI8ZN9BM_uzRa056ape","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":"Computes numeric distribution statistics and fills missing values (zero, mean, or median) for join coverage records tracking matched_rows and source_rows across dataset groups.","exampleAgentPrompt":"Analyze the numeric distribution of matched_rows and fill any missing values using median imputation for this join coverage dataset — I have up to 50 rows with row_id, date, dataset_group, matched_rows, and source_rows fields.","exampleUseCases":[{"title":"ETL pipeline join audit","prompt":"I have daily join coverage records for two dataset groups over the past month, some matched_rows values are null — can you run a numeric distribution report on matched_rows and fill those nulls using the median?"},{"title":"Data quality check for source rows","prompt":"Run a distribution analysis on source_rows across my join coverage data and replace any missing source_rows values with zero so I can see the full picture."},{"title":"Cross-group join coverage comparison","prompt":"I have join coverage data for group-a and group-b across 60 dates, some values are missing — give me the numeric distribution of matched_rows and impute the missing ones using the mean."}],"resultDescription":"Returns two analytical reports: (1) a numeric distribution report summarizing the spread, min, max, percentiles, and shape of the selected field (matched_rows or source_rows); and (2) an explicit missing-value fill report showing which rows had nulls and what values were substituted using the chosen method (zero, mean, or median). Both reports are computed locally from the supplied data with no external lookups.","failureModes":["More than 200 rows submitted — endpoint rejects input exceeding maxItems limit","Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) cause validation errors","Invalid date format (not YYYY-MM-DD) triggers pattern validation failure","Invalid enum value for field or method parameter returns schema error","All values for selected field are null — mean/median imputation may produce undefined result","Payment not completed or insufficient USDC balance blocks the request"],"whenToPreferThis":"Choose this endpoint when you have up to 200 join coverage records with matched_rows and source_rows metrics and need both a statistical distribution summary and explicit null-value imputation in a single call for $0.01 USDC. It is purpose-built for join coverage data quality workflows where you want to understand numeric spread and handle missing values without any external data lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:37.475Z","isFirstParty":false,"canonicalSlug":"join-coverage-numeric-distribution-explicit-missing-value-fill-87909c6e"}