{"uid":"cap_IDNzO-ltrDD9md20xKMYI","slug":"sampling-observations-string-normalisation-category-frequencies-e4fe1ade","name":"Sampling Observations — String Normalisation + Category Frequencies","description":"Sampling observations — String normalisation + Category frequencies: String normalisation + Category frequencies 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-15-024?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}}},"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_-sDw3u7ktT96nbLR5Uaye","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 category frequency analysis on up to 200 sampling observation records (sample_value, sample_weight) in a single call.","exampleAgentPrompt":"I have 50 sampling observation rows — each with a row ID, dataset group name, date, sample value, and sample weight — and I need two reports: one that normalises the string category labels and one that shows how frequently each category appears across the dataset.","exampleUseCases":[{"title":"Survey response category cleanup","prompt":"I've got 120 survey observation records with messy group labels like 'Group A', 'group-a', and 'GROUP A' all meaning the same thing. Can you run string normalisation on the dataset_group field and show me how frequently each category appears? Each row has a row_id, date, sample_value, and sample_weight."},{"title":"Weighted sampling group frequency audit","prompt":"I have 80 rows of lab sampling data — row IDs, dates, sample values, and sample weights — split across groups like 'control', 'treatment-1', and 'treatment-2'. Can you give me a category frequency report alongside normalised group names so I can see how evenly distributed my sample groups are?"},{"title":"Market segment observation analysis","prompt":"Here are 150 market observation records with dataset groups like 'Segment B', 'segment_b', 'SEGMENT-B' that need standardising. Run string normalisation on the group labels and compute category frequencies so I can see which market segments are most represented in the sample."}],"resultDescription":"Two analytical reports for the supplied rows: (1) a string normalisation report standardising the categorical/text fields across the dataset, and (2) a category frequency report showing how often each distinct dataset_group value appears, potentially weighted by sample_weight. No external lookups or live data are used — analysis is performed locally on the submitted rows.","failureModes":["Submitting more than 200 rows returns a validation error","Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) cause row-level rejection","Dates not matching YYYY-MM-DD pattern are rejected","sample_value or sample_weight outside ±1,000,000,000,000 range are rejected","Empty rows array (minItems: 1 not met) returns an error"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation of categorical labels and a frequency count of those categories from sampling observation data in a single low-cost API call. It is ideal for cleaning and auditing survey, lab, or market sample datasets with up to 200 rows, where no external data lookup or prediction is needed — just local analytical computation on the data you supply.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:54:37.092Z","isFirstParty":false,"canonicalSlug":"sampling-observations-string-normalisation-category-frequencies-e4fe1ade"}