{"uid":"cap_1e8rUbb5c_l8fxw1Z-7ly","slug":"road-segment-observations-string-normalisation-date-by-category-pivot-bcd911f8","name":"Road Segment Observations — String Normalisation + Date by Category Pivot","description":"Road segment observations — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied road segment observations records (length_m, travel_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-15-035?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","length_m","travel_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"length_m":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"station_group":{"type":"string","default":"","maxLength":80},"travel_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","length_m":10,"station_group":"group-a","travel_seconds":1},{"date":"2026-10-02","row_id":"row-2","length_m":20,"station_group":"group-b","travel_seconds":2},{"date":"2026-10-03","row_id":"row-3","length_m":15,"station_group":"group-a","travel_seconds":3},{"date":"2026-10-04","row_id":"row-4","length_m":30,"station_group":"group-b","travel_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["length_m","travel_seconds"],"type":"string","default":"length_m"}}},"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_X1baQSc-qmnuVS47w3tDk","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 produces a date-by-category pivot table for road segment observation records containing length_m and travel_seconds fields.","exampleAgentPrompt":"I have up to 200 road segment records with row IDs, station groups, dates, segment lengths in metres, and travel times in seconds — can you run string normalisation on the station group field and produce a date-by-category pivot for the length_m values?","exampleUseCases":[{"title":"Clean and pivot commute corridor data","prompt":"I've got a batch of road segment observations for our commute corridors — each row has a station group name, a date, length in metres, and travel seconds. Can you normalise the station group strings and give me a date-by-category pivot on travel_seconds so I can see how travel times vary by group over time?"},{"title":"Audit road sensor data quality","prompt":"We collected road sensor readings over the past month with up to 150 rows, each tagged with a station group, date, and length_m. Please run string normalisation on the station group labels and pivot the length_m values by date and category so I can spot inconsistencies."},{"title":"Summarise road works impact by segment group","prompt":"Here are 180 road segment observations from our road works monitoring project — row IDs, station groups, dates, lengths and travel times included. Run string normalisation on the station groups and produce a date-by-category pivot for length_m so we can compare segment behaviour across groups before and after works."}],"resultDescription":"Two analytical reports: (1) a string normalisation report for the station_group field, cleaning and standardising string values across up to 200 rows; (2) a date-by-category pivot table for the selected numeric field (length_m or travel_seconds), cross-tabulating values by date and station group category.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, station_group, date, length_m, travel_seconds) causes row rejection","Invalid date format (not YYYY-MM-DD) triggers schema validation failure","Null numeric values may affect pivot completeness","field enum value must be exactly 'length_m' or 'travel_seconds'"],"whenToPreferThis":"Choose this endpoint when you have road segment observation data (with station groups, dates, and numeric measurements) and need both string normalisation of categorical labels and a date-by-category pivot in a single low-cost call. Ideal for offline batch analysis of up to 200 rows where no live lookup, prediction, or external data enrichment is needed.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-30T19:10:22.044Z","isFirstParty":false,"canonicalSlug":"road-segment-observations-string-normalisation-date-by-category-pivot-bcd911f8"}