{"uid":"cap_sqSeXS61h4zjihts_Bp19","slug":"building-footprint-observations-string-normalisation-missing-value-fill-0ce0ad36","name":"Building Footprint Observations — String Normalisation & Missing-Value Fill","description":"Building footprint observations — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied building footprint observations records (footprint_m2, height_m). 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-weather-16-037?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","footprint_m2","height_m"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"height_m":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"footprint_m2":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"station_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","height_m":1,"footprint_m2":10,"station_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","height_m":2,"footprint_m2":20,"station_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","height_m":3,"footprint_m2":15,"station_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","height_m":4,"footprint_m2":30,"station_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["footprint_m2","height_m"],"type":"string","default":"footprint_m2"},"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_2AHsz-DZd-e9akW6YrqJ2","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 strings and fills missing values in building footprint observation records (footprint_m2, height_m) for up to 200 rows, returning two analytical reports.","exampleAgentPrompt":"Clean up my building footprint dataset — normalise the station group strings and fill any missing footprint_m2 values using median for these 5 rows: [{row_id: 'r1', station_group: 'grp A', date: '2025-01-10', footprint_m2: null, height_m: 12}, {row_id: 'r2', station_group: 'grp B', date: '2025-01-11', footprint_m2: 85.5, height_m: 9}].","exampleUseCases":[{"title":"Cleaning survey data before analysis","prompt":"I have 50 building survey records with messy station group labels and some null footprint_m2 fields — can you normalise the strings and fill the missing footprint_m2 values using the median of the provided rows?"},{"title":"Pre-processing height data for a model","prompt":"Before I feed my building dataset into a model, I need the height_m nulls filled using the mean and the string fields normalised — here are 120 rows with row_ids, dates, station groups, footprint and height values."},{"title":"Auditing urban building records","prompt":"Can you clean up these 30 urban building observation records? Normalise the station_group strings and fill any missing height_m values with zero so I have complete, consistent data to work with."}],"resultDescription":"Two analytical reports covering: (1) string normalisation results for station_group and other string fields across submitted rows, and (2) explicit missing-value fill results for the specified numeric field (footprint_m2 or height_m) using the chosen method (zero, mean, or median). Results are based entirely on the supplied rows with no external lookups.","failureModes":["Rows array empty or exceeds 200-item limit — validation error returned","Invalid date format (not YYYY-MM-DD) causes row rejection","Unknown fill method value outside zero/mean/median enum triggers schema error","Unknown field value outside footprint_m2/height_m enum triggers schema error","All values for chosen field are null — mean/median fill cannot be computed","row_id missing or blank causes required-field validation failure"],"whenToPreferThis":"Choose this endpoint when you have building footprint observation data (footprint_m2, height_m) that needs both string normalisation and explicit missing-value imputation in a single offline pass without external API calls or live predictions. It is ideal for small-to-medium batch jobs (up to 200 rows) where you need deterministic, reproducible cleaning using simple statistical fill methods (zero, mean, median) and want two structured reports for auditability.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-31T00:44:05.814Z","isFirstParty":false,"canonicalSlug":"building-footprint-observations-string-normalisation-missing-value-fill-0ce0ad36"}