{"uid":"cap_ibstd8ozVMYGbcURw9wIa","slug":"join-coverage-grouped-numeric-summary-with-missing-value-fill-a8cc7939","name":"Join Coverage Grouped Numeric Summary with Missing-Value Fill","description":"Join coverage — Grouped numeric summary + Explicit missing-value fill: Grouped numeric summary + 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-09-099?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_wwssXyu3oHzAUsrMwUJqH","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 grouped numeric statistics for join coverage data (matched_rows, source_rows) and fills missing values using a chosen imputation method, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have up to 200 join coverage records with matched_rows and source_rows per dataset group — can you run a grouped numeric summary on matched_rows and fill any missing values using median imputation?","exampleUseCases":[{"title":"Daily join coverage audit by group","prompt":"Here are my join coverage records for October — each row has a dataset_group, date, matched_rows, and source_rows. Can you give me a grouped numeric summary for matched_rows and fill any nulls with the median?"},{"title":"Zero-fill missing source rows before pipeline","prompt":"I need to prep my join coverage data before a pipeline run. Can you summarize source_rows by dataset group and replace any missing values with zero so nothing breaks downstream?"},{"title":"Comparing match rates across two groups","prompt":"I've got join coverage data for group-a and group-b across the past week. Summarize matched_rows by group and use mean imputation to handle any gaps — I want two clean reports back."}],"resultDescription":"Two analytical reports: (1) a grouped numeric summary of the chosen field (matched_rows or source_rows) broken down by dataset_group, and (2) an explicit missing-value fill report showing imputed values using the selected method (zero, mean, or median). Both reports are computed locally from supplied data with no external lookups.","failureModes":["Rows array empty or missing — minItems:1 not met","More than 200 rows submitted — maxItems:200 exceeded","Invalid date format — must match YYYY-MM-DD pattern","Unknown field value — must be 'matched_rows' or 'source_rows'","Unknown method value — must be 'zero', 'mean', or 'median'","row_id missing or empty string — minLength:1 required","Payment not included or insufficient — $0.01 USDC required per call"],"whenToPreferThis":"Choose this endpoint when you need both a grouped numeric summary AND missing-value imputation for join coverage data (matched_rows/source_rows) in a single $0.01 call, with up to 200 rows and no need for external data lookups or live predictions. Ideal for ETL pipeline QA, data quality checks on join operations, or pre-processing steps where nulls must be handled before downstream analysis.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-11T12:48:32.850Z","isFirstParty":false,"canonicalSlug":"join-coverage-grouped-numeric-summary-with-missing-value-fill-a8cc7939"}