{"uid":"cap_dYg61yY-4zhlhBGJXYXGI","slug":"simulation-caches-date-category-pivot-explicit-missing-value-fill-fe319791","name":"Simulation Caches — Date×Category Pivot + Explicit Missing-Value Fill","description":"Simulation caches — Date by category pivot + Explicit missing-value fill: Date by category pivot + Explicit missing-value fill for supplied simulation caches records (cache_gb, simulation_seconds). 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-creative-08-189?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","asset_group","date","cache_gb","simulation_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"cache_gb":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"asset_group":{"type":"string","default":"","maxLength":80},"simulation_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","cache_gb":10,"asset_group":"group-a","simulation_seconds":1},{"date":"2026-10-02","row_id":"row-2","cache_gb":20,"asset_group":"group-b","simulation_seconds":2},{"date":"2026-10-03","row_id":"row-3","cache_gb":15,"asset_group":"group-a","simulation_seconds":3},{"date":"2026-10-04","row_id":"row-4","cache_gb":30,"asset_group":"group-b","simulation_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["cache_gb","simulation_seconds"],"type":"string","default":"cache_gb"},"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_44WxtufglMasyrB9m6Xn3","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 simulation cache records (cache_gb, simulation_seconds) by date and asset group category, then fills missing values using zero, mean, or median imputation — returns two analytical reports per call.","exampleAgentPrompt":"I have simulation cache records with columns row_id, asset_group, date, cache_gb, and simulation_seconds — can you pivot them by date and asset group, and fill any missing cache_gb values using median imputation? Here's my data: group-a had 10 GB on 2026-10-01 and 15 GB on 2026-10-03, group-b had 20 GB on 2026-10-02 and 30 GB on 2026-10-04.","exampleUseCases":[{"title":"Monthly cache usage pivot by team","prompt":"I have simulation cache logs for October 2026 across two asset groups — can you pivot the cache_gb values by date and group, and use median fill for any missing dates? Here are my 4 rows: row-1 group-a 2026-10-01 10GB 1s, row-2 group-b 2026-10-02 20GB 2s, row-3 group-a 2026-10-03 15GB 3s, row-4 group-b 2026-10-04 30GB 4s."},{"title":"Impute missing simulation runtimes before reporting","prompt":"My simulation_seconds column has some nulls in my cache records — can you fill those missing values using mean imputation and then pivot the results by date and asset group? I've got about 50 rows across groups alpha and beta spanning 2026-09-01 to 2026-09-30."},{"title":"Compare cache allocation across asset groups over time","prompt":"Can you reshape my simulation cache dataset into a date-by-category pivot table for cache_gb, using zero-fill for missing entries, so I can compare how group-x and group-y allocation evolved week by week? I'll give you up to 150 rows."}],"resultDescription":"Returns two analytical reports based on the supplied simulation cache records: (1) a date-by-category pivot table showing the selected metric (cache_gb or simulation_seconds) spread across asset groups on each date, and (2) the same dataset with missing values explicitly filled using the chosen method (zero, mean, or median). Both reports are computed locally with no external lookups. Up to 200 rows supported per call.","failureModes":["Input array is empty or missing — minItems:1 violation returns validation error","row_id field missing or blank — required field violation","date field does not match YYYY-MM-DD pattern — pattern validation error","More than 200 rows submitted — maxItems:200 exceeded","cache_gb or simulation_seconds out of allowed numeric range — schema rejection","Invalid enum value for 'field' or 'method' parameters — validation error","All values missing for the selected field — imputation may produce degenerate output"],"whenToPreferThis":"Choose this endpoint when you have simulation cache tabular data (cache_gb and/or simulation_seconds) keyed by date and asset group and need both a date-by-category pivot reshaping AND explicit missing-value imputation (zero, mean, or median) in a single low-cost call. Ideal for offline/batch analytical prep of up to 200 rows where no live data lookup, prediction, or external action is needed. At $0.01 USDC you get two independent analytical reports simultaneously, making it more efficient than running pivot and imputation separately.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-22T00:48:55.490Z","isFirstParty":false,"canonicalSlug":"simulation-caches-date-category-pivot-explicit-missing-value-fill-fe319791"}