{"uid":"cap_6ZIrTI2661KWpMjpv05-8","slug":"time-series-completeness-numeric-row-ordering-explicit-missing-value-df18085c","name":"Time Series Completeness — Numeric Row Ordering + Explicit Missing-Value Fill","description":"Time series completeness — Numeric row ordering + Explicit missing-value fill: Numeric row ordering + Explicit missing-value fill for supplied time series completeness records (observed_points, expected_points). 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-16-124?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","observed_points","expected_points"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"expected_points":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"observed_points":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","expected_points":1,"observed_points":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","expected_points":2,"observed_points":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_points":3,"observed_points":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_points":4,"observed_points":30}],"maxItems":200,"minItems":1},"field":{"enum":["observed_points","expected_points"],"type":"string","default":"observed_points"},"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_VAqAP7rL26sHXWB6DXVKt","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":"Orders time series completeness records numerically by row and fills missing observed_points or expected_points values using zero, mean, or median imputation, producing 2 analytical reports for up to 200 rows.","exampleAgentPrompt":"I have 50 daily completeness records for two dataset groups with some null observed_points values — can you sort them numerically by row and fill the missing observed_points using median imputation, then give me both analytical reports?","exampleUseCases":[{"title":"Daily sensor completeness gap-fill","prompt":"I have 120 rows of daily sensor completeness data with dates from 2025-01-01 to 2025-04-30, some observed_points are null — sort them by row and fill the missing observed_points using mean imputation, and give me both completeness reports."},{"title":"Multi-group expected points cleanup","prompt":"My dataset has two groups, 'production' and 'staging', with 80 completeness records total — some expected_points are missing. Can you order the rows numerically and fill the nulls in expected_points using zero fill, then produce the two analytical reports?"},{"title":"Monthly completeness audit imputation","prompt":"I need to audit completeness for 60 monthly records across my data pipeline — some observed_points entries are null. Please sort everything by row ID and use median to fill the missing observed_points, then give me the two reports."}],"resultDescription":"The endpoint returns 2 analytical reports covering the supplied time series completeness records after applying numeric row ordering and explicit missing-value fill (zero, mean, or median) to the specified field (observed_points or expected_points), for up to 200 input rows. No external data is fetched or predicted.","failureModes":["More than 200 rows submitted — exceeds maxItems limit","Missing required fields (row_id, date, observed_points, expected_points) on any row","Date field not in YYYY-MM-DD format — fails pattern validation","Invalid method enum value (not zero, mean, or median)","Invalid field enum value (not observed_points or expected_points)","Empty rows array — minItems 1 not met"],"whenToPreferThis":"Choose this endpoint when you have up to 200 time series completeness records (with observed_points and expected_points) that need numeric row ordering and explicit missing-value imputation (zero, mean, or median), and you want two local analytical reports without any external lookup, live data fetch, or predictive modeling. Prefer it over general-purpose analytics tools when working specifically with data completeness tracking across dated, grouped rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:39:13.895Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-numeric-row-ordering-explicit-missing-value-df18085c"}