{"uid":"cap_TIRtvd2Oy1ItFGFqdcYhQ","slug":"geospatial-tile-processing-equal-width-histogram-explicit-missing-783d520b","name":"Geospatial Tile Processing — Equal-Width Histogram + Explicit Missing-Value Fill","description":"Geospatial tile processing — Equal-width histogram + Explicit missing-value fill: Equal-width histogram + 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-21-175?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"bins":{"type":"integer","default":5,"maximum":30,"minimum":1},"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_USy3gjTQ7CB1y5t4J3ulr","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":"Computes an equal-width histogram and fills missing values using a chosen strategy (zero, mean, or median) for geospatial tile processing records (tile_count, processing_seconds) across up to 200 rows.","exampleAgentPrompt":"I have 150 geospatial tile processing records with tile_count and processing_seconds — can you compute a 10-bin equal-width histogram on tile_count and fill any missing values using median imputation?","exampleUseCases":[{"title":"Distribution analysis of tile render times","prompt":"I've got 80 geospatial tile processing records with processing_seconds values — some are null. Can you build a 5-bin equal-width histogram on processing_seconds and fill the missing ones using mean imputation?"},{"title":"QA check on tile count data completeness","prompt":"Run a missing-value fill using zero on the tile_count field across these 120 geospatial tile records, and also give me a histogram with 8 bins so I can see the distribution before and after."},{"title":"Station group tile load profiling","prompt":"I have tile processing data from two station groups over the past week, up to 200 rows — can you histogram the tile_count into 15 equal-width bins and impute any nulls with the median so I can profile the load per group?"}],"resultDescription":"Returns two analytical reports: (1) an equal-width histogram showing bin boundaries and record counts for the chosen field, and (2) a record set with missing values filled using the specified strategy (zero, mean, or median). Both reports are computed locally from the supplied data with no external lookups.","failureModes":["More than 200 rows submitted — request rejected at schema validation","Invalid date format (not YYYY-MM-DD) causes row rejection","Bins value outside 1–30 range returns validation error","Field value not in enum ['tile_count','processing_seconds'] causes error","Method value not in enum ['zero','mean','median'] causes error","All values for chosen field are null — histogram may be empty or degenerate","Numeric values outside ±1,000,000,000,000 range rejected"],"whenToPreferThis":"Choose this endpoint when you need both a distribution summary (histogram) and missing-value imputation on geospatial tile processing metrics (tile_count or processing_seconds) in a single API call. Ideal for preprocessing pipelines where you want to understand data distribution and clean nulls before downstream analysis. Best when data is already collected locally (up to 200 rows) and you need fast, stateless computation without external lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:35:55.616Z","isFirstParty":false,"canonicalSlug":"geospatial-tile-processing-equal-width-histogram-explicit-missing-783d520b"}