{"uid":"cap_1ILO9NoamfbKvTc6_EkBr","slug":"lighting-setups-string-normalisation-explicit-missing-value-fill-8aaf0dcc","name":"Lighting Setups — String Normalisation + Explicit Missing-Value Fill","description":"Lighting setups — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied lighting setups records (light_count, render_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-11-037?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","asset_group","date","light_count","render_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"asset_group":{"type":"string","default":"","maxLength":80},"light_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"render_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","asset_group":"group-a","light_count":10,"render_seconds":1},{"date":"2026-10-02","row_id":"row-2","asset_group":"group-b","light_count":20,"render_seconds":2},{"date":"2026-10-03","row_id":"row-3","asset_group":"group-a","light_count":15,"render_seconds":3},{"date":"2026-10-04","row_id":"row-4","asset_group":"group-b","light_count":30,"render_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["light_count","render_seconds"],"type":"string","default":"light_count"},"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_d9atrnH1KVs1FTplrc96D","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":"Normalises string fields and fills missing numeric values (light_count or render_seconds) in lighting setup records using zero, mean, or median imputation, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you clean up my lighting setup records — normalise the string fields and fill in any missing light_count values using the median method? I have about 50 rows with asset groups like 'group-a' and 'group-b' across dates in October 2026.","exampleUseCases":[{"title":"Imputing missing render seconds in VFX pipeline","prompt":"I've got 120 rows of lighting setup data from our VFX pipeline and some render_seconds values are null — can you fill them in using the mean and also normalise the string fields like asset_group?"},{"title":"Cleaning light count data before studio analysis","prompt":"Before I run analytics on our studio lighting rigs, I need to fill any missing light_count values with zero and normalise all the string columns — can you do that for these 80 rows?"},{"title":"Standardising lighting records across asset groups","prompt":"We have lighting setup records across two asset groups from late 2026 and some light_count entries are missing — please normalise the strings and use median imputation to fill those gaps across our 200 rows."}],"resultDescription":"Returns two analytical reports: one covering string normalisation results (standardised asset_group and other string fields) and one covering explicit missing-value fill results for the chosen numeric field (light_count or render_seconds) using the selected method (zero, mean, or median), applied to up to 200 input rows. No external data is fetched; all processing is local.","failureModes":["More than 200 rows submitted — request rejected with validation error","Invalid date format not matching YYYY-MM-DD pattern","Numeric values outside the allowed range (-1e12 to 1e12)","Missing required fields (row_id) causing row rejection","Invalid enum value for method or field parameters","Empty rows array returns validation error"],"whenToPreferThis":"Choose this endpoint when you need to simultaneously normalise string fields AND impute missing numeric values in lighting setup records (light_count or render_seconds) within a single $0.01 call that returns two reports. Prefer it over single-operation endpoints when both cleaning tasks are needed together, and when your dataset is under 200 rows and offline (no live lookup or prediction required).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-22T00:49:19.319Z","isFirstParty":false,"canonicalSlug":"lighting-setups-string-normalisation-explicit-missing-value-fill-8aaf0dcc"}