{"uid":"cap_GNanO03cltra0Wud7ZmfV","slug":"sampling-observations-string-normalisation-date-by-category-pivot-705d5191","name":"Sampling Observations — String Normalisation + Date by Category Pivot","description":"Sampling observations — String normalisation + Date by category pivot: String normalisation + Date by category pivot for supplied sampling observations records (sample_value, sample_weight). 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-analytics-15-035?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","sample_value","sample_weight"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"sample_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"sample_weight":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","sample_value":10,"dataset_group":"group-a","sample_weight":1},{"date":"2026-10-02","row_id":"row-2","sample_value":20,"dataset_group":"group-b","sample_weight":2},{"date":"2026-10-03","row_id":"row-3","sample_value":15,"dataset_group":"group-a","sample_weight":3},{"date":"2026-10-04","row_id":"row-4","sample_value":30,"dataset_group":"group-b","sample_weight":4}],"maxItems":200,"minItems":1},"field":{"enum":["sample_value","sample_weight"],"type":"string","default":"sample_value"}}},"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_zBbLyN2TD6eRHQRY3R5xe","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":"Runs two local analytical reports on up to 200 sampling observation rows: string normalisation and a date-by-category pivot for sample_value or sample_weight fields.","exampleAgentPrompt":"I have 150 sampling observation rows with row_id, dataset_group, date, sample_value, and sample_weight — can you run the string normalisation and date-by-category pivot reports on sample_value for me?","exampleUseCases":[{"title":"Survey sample pivot by date and group","prompt":"I've got 80 survey sample records with group labels and dates — can you pivot the sample_value field by category and date, and also normalise the string fields in the dataset?"},{"title":"Weighted sample distribution by category","prompt":"Here are 120 sampling observations across three dataset groups over Q3 — run the string normalisation and date-by-category pivot on sample_weight so I can see how weights break down across groups over time."},{"title":"Data prep for sampling QA pipeline","prompt":"Before I hand off these 200 sampling observation rows to QA, can you do a string normalisation pass and generate the date-by-category pivot on sample_value to flag any grouping anomalies?"}],"resultDescription":"Two analytical reports generated locally from the supplied rows: (1) a string normalisation report cleaning and standardising text fields such as dataset_group, and (2) a date-by-category pivot table showing the selected field (sample_value or sample_weight) broken down by date and dataset_group — all computed without external lookups or live data.","failureModes":["Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) causes validation failure","More than 200 rows submitted exceeds the maxItems limit","Date strings not matching YYYY-MM-DD pattern are rejected","Invalid field selector value (not 'sample_value' or 'sample_weight') causes schema error","Numeric values outside ±1,000,000,000,000 are rejected","Empty rows array triggers minItems validation error"],"whenToPreferThis":"Choose this endpoint when you need two bundled local analytical operations — string normalisation and a date-by-category pivot — on sampling observation data (with sample_value and sample_weight fields) in a single low-cost call. It is ideal for lightweight, offline-style data prep or QA tasks on datasets up to 200 rows where no external lookup, prediction, or live enrichment is needed.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:53:17.269Z","isFirstParty":false,"canonicalSlug":"sampling-observations-string-normalisation-date-by-category-pivot-705d5191"}