{"uid":"cap_tpTqzTN2BpEUSJWlbIrC6","slug":"join-coverage-analytics-weighted-mean-date-by-category-pivot-7b0a72de","name":"Join Coverage Analytics: Weighted Mean + Date-by-Category Pivot","description":"Join coverage — Secondary-measure weighted mean + Date by category pivot: Secondary-measure weighted mean + Date by category pivot 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-09-185?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"}}},"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_WyJQPkpypILmeuhD8OylD","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 a secondary-measure weighted mean and a date-by-category pivot table for supplied join coverage records (matched_rows, source_rows) — two local analytical reports in one call.","exampleAgentPrompt":"I have join coverage data for two groups across four dates — can you compute the weighted mean of matched_rows and also pivot the data by date and dataset_group? Here are my rows: row-1 on 2026-10-01 group-a with source_rows=1 matched_rows=10, row-2 on 2026-10-02 group-b source_rows=2 matched_rows=20, row-3 on 2026-10-03 group-a source_rows=3 matched_rows=15, row-4 on 2026-10-04 group-b source_rows=4 matched_rows=30.","exampleUseCases":[{"title":"ETL pipeline match-rate review","prompt":"I've got join coverage results from last week's ETL run — 6 rows across group-a and group-b with dates from 2026-10-01 to 2026-10-06, each with source_rows and matched_rows counts. Can you give me the weighted mean of matched_rows and a pivot of matched_rows by date and dataset group so I can see where coverage dropped?"},{"title":"Data quality audit across categories","prompt":"Run a weighted mean analysis on source_rows and a date-by-category pivot for these 8 join coverage records across three dataset groups — I need to quickly spot which group had the worst coverage on which days for our data quality report."},{"title":"Coverage trend comparison between datasets","prompt":"I have join coverage logs for two datasets over the past month — around 50 rows total with matched_rows and source_rows per day per group. Can you compute the secondary-measure weighted mean using source_rows as the field and also pivot matched_rows by date and dataset_group so I can compare coverage trends?"}],"resultDescription":"Returns two analytical reports: (1) a secondary-measure weighted mean of the selected field (matched_rows or source_rows) across the supplied rows, and (2) a pivot table cross-tabulating the measure by date and dataset_group — both computed locally from the supplied data without any external lookups.","failureModes":["Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) cause validation errors","Date strings not matching YYYY-MM-DD pattern are rejected","Supplying more than 200 rows exceeds the row limit and will fail","Invalid field enum value (not 'matched_rows' or 'source_rows') causes rejection","Null values in both measure fields may produce undefined weighted mean results"],"whenToPreferThis":"Choose this endpoint when you have tabular join coverage data (matched_rows, source_rows) and need both a weighted mean summary and a date-by-category pivot in a single low-cost call. It is ideal for data engineers auditing ETL match rates or data quality analysts who want quick local analytics without external data access or prediction.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:45.507Z","isFirstParty":false,"canonicalSlug":"join-coverage-analytics-weighted-mean-date-by-category-pivot-7b0a72de"}