{"uid":"cap_I6TGvnu0dbPsxRutix_Td","slug":"sampling-observations-string-normalisation-numeric-row-ordering-3ca46b30","name":"Sampling Observations — String Normalisation + Numeric Row Ordering","description":"Sampling observations — String normalisation + Numeric row ordering: String normalisation + Numeric row ordering 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-15-025?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_as7pVtMMOhhj2_8kx49bN","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 applies numeric row ordering to sampling observation records (sample_value, sample_weight) for up to 200 rows, returning 2 analytical reports.","exampleAgentPrompt":"Can you run string normalisation and numeric row ordering on my sampling observations? I have 4 rows — row-1 (group-a, 2026-10-01, sample_value=10, sample_weight=1), row-2 (group-b, 2026-10-02, sample_value=20, sample_weight=2), row-3 (group-a, 2026-10-03, sample_value=15, sample_weight=3), row-4 (group-b, 2026-10-04, sample_value=30, sample_weight=4) — and apply the reports to the sample_value field.","exampleUseCases":[{"title":"Ordering survey sample data numerically","prompt":"I have 50 survey sampling records with sample_value and sample_weight columns. Can you normalise the string fields like dataset_group and then order all rows numerically by sample_value? Here are the rows in JSON format with row_id, date, dataset_group, sample_value, and sample_weight filled in."},{"title":"Cleaning lab measurement group labels","prompt":"My lab has 120 sampling observation rows and the dataset_group labels are inconsistently cased and formatted. Can you run string normalisation on them and also give me a numeric row ordering report based on the sample_weight field?"},{"title":"Auditing weighted sample ordering for QA","prompt":"I need to QA my sampling dataset — can you normalise the string columns and produce a numeric ordering of the rows by sample_value so I can spot outliers? I'll send up to 180 rows with row_id, date, dataset_group, sample_value, and sample_weight."}],"resultDescription":"Two analytical reports: (1) a string normalisation report covering dataset_group and related string fields, and (2) a numeric row ordering report for the selected field (sample_value or sample_weight), applied across up to 200 supplied sampling observation rows.","failureModes":["More than 200 rows submitted — request rejected due to maxItems limit","Missing required fields (row_id, dataset_group, date, sample_value, or sample_weight) — validation error","Date field not matching YYYY-MM-DD pattern — schema validation failure","Numeric values outside allowed range (-1e12 to 1e12) — validation error","Empty rows array (minItems=1 violated) — rejected","Invalid field selector (must be 'sample_value' or 'sample_weight') — enum validation error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 sampling observation records (with sample_value and sample_weight) and need both string normalisation on group/label fields and a numeric row ordering report in a single low-cost call. It is ideal for pre-processing or QA of sampling datasets without needing live data lookup, prediction, or external integrations.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:53.026Z","isFirstParty":false,"canonicalSlug":"sampling-observations-string-normalisation-numeric-row-ordering-3ca46b30"}