{"uid":"cap_k95BH0LVy0bAuMa6QbkdG","slug":"tree-canopy-observations-date-category-pivot-missing-value-fill-72cb3862","name":"Tree Canopy Observations — Date×Category Pivot + Missing-Value Fill","description":"Tree canopy observations — Date by category pivot + Explicit missing-value fill: Date by category pivot + Explicit missing-value fill for supplied tree canopy observations records (canopy_m2, plot_m2). 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-17-189?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","canopy_m2","plot_m2"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"plot_m2":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"canopy_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","plot_m2":1,"canopy_m2":10,"station_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","plot_m2":2,"canopy_m2":20,"station_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","plot_m2":3,"canopy_m2":15,"station_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","plot_m2":4,"canopy_m2":30,"station_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["canopy_m2","plot_m2"],"type":"string","default":"canopy_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__9B97h2UCeqlzCnJyzQIU","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 tree canopy observation records by date and station group, then fills missing values using zero, mean, or median imputation — returning two analytical reports per call.","exampleAgentPrompt":"I have up to 200 rows of tree canopy observation records with fields row_id, station_group, date, canopy_m2, and plot_m2 — can you pivot them by date and station group, and fill any missing canopy_m2 values using the median method?","exampleUseCases":[{"title":"Urban tree survey gap filling","prompt":"I've got 150 rows of urban tree canopy survey records across three station groups with some missing canopy_m2 readings — can you pivot them by date and station group and fill in the missing values using median imputation?"},{"title":"Plot coverage matrix for city report","prompt":"I need a date-by-station pivot table for my plot_m2 measurements across all monitoring sites, and any gaps should be filled with zero — I have about 80 observation rows to analyze."},{"title":"Comparing canopy vs plot area trends","prompt":"Can you reshape my 120 tree canopy observation records into a date-by-category matrix for canopy_m2 and fill any nulls with the mean so I can spot trends across station groups over time?"}],"resultDescription":"Two analytical reports derived from the supplied data: (1) a date-by-station-group pivot table for the selected field (canopy_m2 or plot_m2), and (2) the same dataset with explicit missing-value fill applied using the chosen method (zero, mean, or median). No external data is fetched; all computation runs on the submitted rows.","failureModes":["Fewer than 1 row or more than 200 rows submitted — request rejected","Invalid date format (not YYYY-MM-DD) causes row-level validation failure","Unrecognized fill method value outside zero/mean/median enum","Missing required fields (row_id, station_group, date) cause schema rejection","All values for selected field are null — mean/median fill may return zero or undefined","Payment not confirmed — endpoint returns 402 status"],"whenToPreferThis":"Choose this endpoint when you have raw tree canopy observation records (canopy_m2, plot_m2) that need to be restructured into a date-by-category pivot and have missing values imputed in a single low-cost call. It is ideal for environmental monitoring workflows, urban forestry reporting, or any scenario where canopy and plot area measurements are collected across multiple station groups over time and gaps need to be handled analytically without external lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T06:47:24.952Z","isFirstParty":false,"canonicalSlug":"tree-canopy-observations-date-category-pivot-missing-value-fill-72cb3862"}