{"uid":"cap_-hsJ8c5xAyVgZUNA0PFbq","slug":"humidity-observations-string-normalisation-date-by-category-pivot-5d3c2ed4","name":"Humidity Observations — String Normalisation & Date by Category Pivot","description":"Humidity observations — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied humidity observations records (humidity_percent, observation_count). 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-04-035?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","humidity_percent","observation_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"station_group":{"type":"string","default":"","maxLength":80},"humidity_percent":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"observation_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","station_group":"group-a","humidity_percent":10,"observation_count":1},{"date":"2026-10-02","row_id":"row-2","station_group":"group-b","humidity_percent":20,"observation_count":2},{"date":"2026-10-03","row_id":"row-3","station_group":"group-a","humidity_percent":15,"observation_count":3},{"date":"2026-10-04","row_id":"row-4","station_group":"group-b","humidity_percent":30,"observation_count":4}],"maxItems":200,"minItems":1},"field":{"enum":["humidity_percent","observation_count"],"type":"string","default":"humidity_percent"}}},"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_vAxPvl00PMq1KUYrpS20n","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 up to 200 rows of humidity observation data, returning 2 analytical reports per call.","exampleAgentPrompt":"Can you run string normalisation and a date-by-category pivot on my humidity observation records? I have 4 rows: station group-a on 2026-10-01 with humidity_percent 10 and observation_count 1, group-b on 2026-10-02 with humidity_percent 20 and observation_count 2, group-a on 2026-10-03 with 15% humidity and 3 observations, and group-b on 2026-10-04 with 30% humidity and 4 observations — analyse the humidity_percent field.","exampleUseCases":[{"title":"Weather station humidity data cleanup","prompt":"I have humidity readings from two station groups collected over October — can you normalise the station group strings and pivot the humidity_percent values by date and category? Here are my 4 rows: row-1, group-a, 2026-10-01, 10%, 1 observation; row-2, group-b, 2026-10-02, 20%, 2 observations; row-3, group-a, 2026-10-03, 15%, 3 observations; row-4, group-b, 2026-10-04, 30%, 4 observations."},{"title":"Observation count pivot by station group","prompt":"Please run the two analytical reports — string normalisation plus date-by-category pivot — on my humidity dataset, but use observation_count as the field to analyse. I have rows from group-a and group-b across four dates in October 2026 with counts ranging from 1 to 4."},{"title":"Multi-station humidity trend summarisation","prompt":"I need to clean up and pivot humidity readings collected from multiple station groups over the past week — can you normalise the station group labels and produce a date-by-category pivot for humidity_percent across my 6 station records?"}],"resultDescription":"Returns 2 analytical reports: (1) a string normalisation report cleaning and standardising the station_group and other string fields in the supplied rows, and (2) a date-by-category pivot report cross-tabulating the selected numeric field (humidity_percent or observation_count) by date and station group category. Both reports are computed locally on the supplied data with no external lookups.","failureModes":["Exceeds 200 row limit — request rejected","Missing required fields (row_id, station_group, date, humidity_percent, observation_count) — validation error","Invalid date format (must match YYYY-MM-DD pattern) — schema validation failure","Invalid field enum value (must be 'humidity_percent' or 'observation_count') — rejected","Empty rows array — schema minItems violation","Payment not received or insufficient USDC — 402 response"],"whenToPreferThis":"Choose this endpoint when you have up to 200 rows of humidity observation records and need both string normalisation of categorical fields (like station groups) and a date-by-category pivot of a numeric measure in a single low-cost call. It is ideal for lightweight, self-contained analytical preprocessing of weather station data without requiring an external database, live lookup, or prediction engine. At $0.01 USDC for two reports, it is cost-effective for small-batch data quality and summarisation tasks on structured humidity datasets.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T12:50:45.177Z","isFirstParty":false,"canonicalSlug":"humidity-observations-string-normalisation-date-by-category-pivot-5d3c2ed4"}