{"uid":"cap_NYubbKAcxTvYlmyXdWqdb","slug":"rainfall-observations-string-normalisation-and-date-by-category-pivot-a85105e0","name":"Rainfall Observations String Normalisation and Date-by-Category Pivot","description":"Rainfall observations — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied rainfall observations records (rainfall_mm, duration_hours). 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-02-035?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","rainfall_mm","duration_hours"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"rainfall_mm":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"station_group":{"type":"string","default":"","maxLength":80},"duration_hours":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","rainfall_mm":10,"station_group":"group-a","duration_hours":1},{"date":"2026-10-02","row_id":"row-2","rainfall_mm":20,"station_group":"group-b","duration_hours":2},{"date":"2026-10-03","row_id":"row-3","rainfall_mm":15,"station_group":"group-a","duration_hours":3},{"date":"2026-10-04","row_id":"row-4","rainfall_mm":30,"station_group":"group-b","duration_hours":4}],"maxItems":200,"minItems":1},"field":{"enum":["rainfall_mm","duration_hours"],"type":"string","default":"rainfall_mm"}}},"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_o1YgwB_Ph1htZNNp7FQa9","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":"Performs string normalisation and date-by-category pivot analysis on supplied rainfall observation records (rainfall_mm, duration_hours) for up to 200 rows, returning two analytical reports.","exampleAgentPrompt":"I have a list of up to 200 rainfall observation rows — each with a row ID, station group, date, rainfall_mm, and duration_hours — can you run string normalisation and a date-by-category pivot on the rainfall_mm field and give me both reports?","exampleUseCases":[{"title":"Station group data cleanup and pivot","prompt":"I've got 150 rows of rain gauge readings from multiple station groups with messy string labels and dates ranging from 2025-01-01 to 2025-06-30 — can you normalise the station_group strings and pivot the rainfall_mm values by date and category so I can see totals per group per day?"},{"title":"Hydrology report on duration observations","prompt":"Here are 80 rainfall observation records with row IDs, station groups, dates, and duration_hours readings — please run string normalisation and a date-by-category pivot on the duration_hours field and return the two analytical reports."},{"title":"Multi-site rainfall data restructuring","prompt":"I need to clean up and restructure my rainfall dataset — I have 200 rows from different monitoring stations across several dates. Can you normalise the station group labels and generate a date-by-category pivot on rainfall_mm so I can compare sites across time?"}],"resultDescription":"Two analytical reports derived from the supplied rainfall observation rows: (1) a string-normalised version of the records with standardised station_group labels, and (2) a date-by-category pivot table showing the selected field (rainfall_mm or duration_hours) aggregated by date and station group category. Both reports use local analytical engines with no external lookups.","failureModes":["Input array exceeds 200 rows — request rejected","Missing required fields (row_id, station_group, date, rainfall_mm, duration_hours) in any row — validation error","Date values not matching YYYY-MM-DD pattern — schema validation failure","Invalid field enum value (must be 'rainfall_mm' or 'duration_hours') — rejected","Numeric values outside allowed range — schema rejection","Empty rows array (minItems:1 violated) — error returned"],"whenToPreferThis":"Choose this endpoint when you have raw rainfall observation records (up to 200 rows) that need both string normalisation of categorical labels and a date-by-category pivot in a single low-cost call. It is ideal for preprocessing weather station data for downstream analysis without requiring live data lookups, predictions, or external integrations. Prefer it over generic analytics platforms when you need two bundled local reports at fixed low cost ($0.01 USDC).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-30T19:11:11.738Z","isFirstParty":false,"canonicalSlug":"rainfall-observations-string-normalisation-and-date-by-category-pivot-a85105e0"}