{"uid":"cap_ZkKfluRFcvuibGZN7nY3P","slug":"geospatial-tile-processing-string-normalisation-explicit-missing-3708c8a8","name":"Geospatial Tile Processing — String Normalisation + Explicit Missing-Value Fill","description":"Geospatial tile processing — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied geospatial tile processing records (tile_count, processing_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-weather-21-037?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","tile_count","processing_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"tile_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"station_group":{"type":"string","default":"","maxLength":80},"processing_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","tile_count":10,"station_group":"group-a","processing_seconds":1},{"date":"2026-10-02","row_id":"row-2","tile_count":20,"station_group":"group-b","processing_seconds":2},{"date":"2026-10-03","row_id":"row-3","tile_count":15,"station_group":"group-a","processing_seconds":3},{"date":"2026-10-04","row_id":"row-4","tile_count":30,"station_group":"group-b","processing_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["tile_count","processing_seconds"],"type":"string","default":"tile_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_7j2hUOHGp86F9cvtbIC7e","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 (tile_count, processing_seconds) in geospatial tile processing records using zero, mean, or median imputation.","exampleAgentPrompt":"I have up to 200 geospatial tile processing rows with some null tile_count values — can you clean them up by normalising the station_group strings and filling in those missing tile_count values using median imputation?","exampleUseCases":[{"title":"Impute missing tile counts before pipeline","prompt":"I'm preparing my geospatial tile processing dataset for analysis — some of the tile_count values are null. Can you fill in those missing values using median imputation across my 150 rows so the pipeline doesn't break?"},{"title":"Standardise station group labels","prompt":"My station_group strings in these tile processing records are inconsistently formatted. Can you normalise them and also fill any missing processing_seconds values with the mean across all my records?"},{"title":"Zero-fill missing processing seconds","prompt":"I need to zero-fill any null processing_seconds entries in my 80-row geospatial tile dataset and clean up the string fields at the same time — can you run that for me?"}],"resultDescription":"Returns up to 2 analytical reports per call: cleaned records with normalised string fields and filled numeric values for the chosen field (tile_count or processing_seconds), using the selected imputation method (zero, mean, or median), covering up to 200 input rows.","failureModes":["Input array exceeds 200 rows — rejected with validation error","Invalid date format (not YYYY-MM-DD) — schema validation failure","Unknown imputation method enum value — rejected","Numeric values outside allowed range — validation error","Empty rows array — rejected (minItems: 1)","row_id missing or empty — required field error"],"whenToPreferThis":"Choose this endpoint when you have geospatial tile processing records (tile_count, processing_seconds) with missing numeric values and/or inconsistent string fields, and need a lightweight, low-cost ($0.01 USDC) local analytical clean-up covering up to 200 rows without any external lookup or live data dependency. Prefer it over heavier ETL services when you only need string normalisation and missing-value imputation on a small batch.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-03T12:41:55.137Z","isFirstParty":false,"canonicalSlug":"geospatial-tile-processing-string-normalisation-explicit-missing-3708c8a8"}