{"uid":"cap_-z1OkFHsAwDObrhPH9qL-","slug":"sampling-observations-analytics-string-normalisation-grouped-numeric-b6bb9d42","name":"Sampling Observations Analytics — String Normalisation + Grouped Numeric Summary","description":"Sampling observations — String normalisation + Grouped numeric summary: String normalisation + Grouped numeric summary 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-15-023?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_A771QIwy_9avDMEjAnga6","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 grouped numeric summary analysis on up to 200 sampling observation records (sample_value, sample_weight) in two independent analytical reports.","exampleAgentPrompt":"Can you run a string normalisation and grouped numeric summary on my sampling observations? I have 150 rows with sample_value and sample_weight fields grouped into 'control' and 'treatment' — analyze the sample_value field and give me both reports.","exampleUseCases":[{"title":"Survey sample quality check by group","prompt":"I have 120 survey sampling records each with a sample_value and sample_weight, split across three groups: 'region-north', 'region-south', and 'region-east'. Can you normalize the strings and give me a grouped numeric summary for sample_value so I can compare across regions?"},{"title":"A/B test weighted sample analysis","prompt":"Run a grouped numeric summary and string normalisation on my A/B test sampling data — I've got 80 rows with sample_value and sample_weight across two groups, 'variant-a' and 'variant-b'. Analyze the sample_weight field and return both reports."},{"title":"Environmental monitoring data rollup","prompt":"I need to summarize 200 rows of environmental sampling observations by site group. Each row has a row_id, date, dataset_group, sample_value, and sample_weight. Please normalize the strings and produce a grouped numeric summary on sample_value."}],"resultDescription":"Two independent analytical reports: (1) a string normalisation report cleaning and standardising text fields across the supplied rows, and (2) a grouped numeric summary providing descriptive statistics (e.g. counts, means, totals) for the selected numeric field (sample_value or sample_weight) broken down by dataset_group.","failureModes":["Input array exceeds 200 rows — request will be rejected","Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) cause validation errors","Invalid date format (must match YYYY-MM-DD) triggers schema rejection","sample_value or sample_weight values outside ±1,000,000,000,000 are rejected","Empty rows array (minItems:1 not met) causes a 400-level error","Payment not completed — x402 payment of 0.01 USDC required before processing"],"whenToPreferThis":"Choose this endpoint when you have up to 200 sampling observation records and need both string normalisation and grouped numeric summary analytics in a single low-cost call ($0.01 USDC). It is ideal for offline/batch analytical tasks on pre-collected sample data where no live lookup, prediction, or external action is needed. Prefer it over general-purpose analytics tools when you specifically have sample_value and sample_weight fields grouped by dataset_group categories and want two structured reports without building your own pipeline.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:53:55.038Z","isFirstParty":false,"canonicalSlug":"sampling-observations-analytics-string-normalisation-grouped-numeric-b6bb9d42"}