{"uid":"cap_Koynqq5oShCMiTjuQuTJJ","slug":"time-series-completeness-exact-row-deduplication-numeric-interval-288113d5","name":"Time Series Completeness — Exact Row Deduplication + Numeric Interval Filter","description":"Time series completeness — Exact row deduplication + Numeric interval filter: Exact row deduplication + Numeric interval filter 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-16-043?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"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_E8N1xUISMBzFuBAAMDeAP","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":"Deduplicates exact rows and filters by numeric interval in time series completeness records (observed_points, expected_points) for up to 200 rows, producing 2 analytical reports.","exampleAgentPrompt":"Can you deduplicate these time series completeness rows and filter them so only rows where observed_points falls between 0 and 100 are kept? I have up to 50 records with date, row_id, dataset_group, observed_points, and expected_points fields.","exampleUseCases":[{"title":"Daily pipeline completeness audit","prompt":"I have 120 time series completeness records from my daily data pipeline — each has a row_id, dataset_group, date, observed_points, and expected_points. Can you deduplicate exact duplicate rows and then filter to only keep rows where observed_points is between 0 and 500?"},{"title":"Sensor data gap detection cleanup","prompt":"Here are my sensor completeness records for October — about 80 rows with observed_points and expected_points per day per sensor group. Please remove any exact duplicate entries and filter out rows where expected_points falls outside the range of 1 to 1000."},{"title":"Multi-group completeness report prep","prompt":"I need to prepare two analytical reports on these 200 time series completeness rows across multiple dataset groups — first deduplicate exact rows, then filter so only records where observed_points is between 10 and 200 are included. Can you run both on the same dataset?"}],"resultDescription":"Returns 2 analytical reports derived from the submitted rows: one showing exact row deduplication results (unique vs duplicate rows identified) and one showing rows filtered by the specified numeric interval on the chosen field (observed_points or expected_points). No external data is fetched; all analysis is performed on the supplied rows only.","failureModes":["Submitting more than 200 rows results in validation rejection","Missing required fields (row_id, date, observed_points, expected_points) causes row-level errors","Invalid date format (not YYYY-MM-DD) fails pattern validation","minimum greater than maximum produces an empty or nonsensical filter result","Null numeric values may be excluded from interval filtering"],"whenToPreferThis":"Choose this endpoint when you need both exact row deduplication and numeric range filtering applied to time series completeness data (observed_points / expected_points) in a single low-cost call. It is ideal for lightweight local analytical checks on batches up to 200 rows without requiring any external data lookup, prediction, or live service. Prefer it over general-purpose data tools when your data is already structured with row_id, dataset_group, date, and numeric completeness fields.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:37:23.370Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-exact-row-deduplication-numeric-interval-288113d5"}