{"uid":"cap_UKgd8N25xFyzH-IvE3fH9","slug":"time-series-completeness-date-category-pivot-missing-value-fill-fbbcaac4","name":"Time Series Completeness — Date×Category Pivot + Missing-Value Fill","description":"Time series completeness — Date by category pivot + Explicit missing-value fill: Date by category pivot + 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-16-189?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__wcFpvz797qdtDpUk128q","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":"Pivots time series completeness records (observed vs expected data points) by date and category, then fills missing values using zero, mean, or median imputation — delivering two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have a completeness dataset with up to 150 rows — each row has a date, a dataset_group, and observed_points plus expected_points values. Can you pivot it by date and category and fill any missing observed_points using median imputation?","exampleUseCases":[{"title":"Daily pipeline completeness audit","prompt":"I'm tracking data pipeline completeness for two groups, 'prod' and 'staging', across 90 days. Can you pivot my records by date and group and fill any missing observed_points with the median so I can see where coverage is thin?"},{"title":"Impute missing expected points for reporting","prompt":"My QA dataset has expected_points values missing on some dates for certain categories. Fill those gaps using mean imputation and give me the pivot table so I can include it in the weekly report."},{"title":"Zero-fill gaps in sensor reading completeness","prompt":"I have sensor completeness records across 4 sensor groups over the past 60 days — some dates have null observed_points. Can you pivot the data by date and sensor group and zero-fill the missing values so I can spot which days had no readings?"}],"resultDescription":"Two analytical reports: (1) a date-by-category pivot table of the specified field (observed_points or expected_points) across all supplied rows, and (2) the same data with explicit missing values filled using the chosen method (zero, mean, or median). Both reports are computed locally from the supplied data with no external lookups.","failureModes":["Missing required fields (row_id, date, dataset_group, observed_points, expected_points) cause validation errors","More than 200 rows submitted — request rejected with row-count error","Invalid date format (not YYYY-MM-DD) causes parse failure","Invalid enum value for 'field' or 'method' returns schema validation error","Empty rows array returns an error","All values missing for a group may produce degenerate imputation results"],"whenToPreferThis":"Choose this endpoint when you have tabular time series completeness records with observed and expected point counts per date and category, and you need both a date×category pivot view and missing-value imputation in a single paid call. Prefer it over generic spreadsheet tools when you want a structured API response for up to 200 rows at low cost ($0.01 USDC for two reports). Not suitable for live data lookups, forecasting, or datasets exceeding 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:14.049Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-date-category-pivot-missing-value-fill-fbbcaac4"}