{"uid":"cap_7jruXAs5KGjbeay9483Wn","slug":"sampling-observations-string-normalisation-numeric-distribution-aa5ceada","name":"Sampling Observations — String Normalisation + Numeric Distribution","description":"Sampling observations — String normalisation + Numeric distribution: String normalisation + Numeric distribution 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-15-022?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_5nENq4AuCx_sD1752EA7F","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":"Analyzes up to 200 sampling observation rows to produce two reports: string normalisation of labels/categories and numeric distribution statistics for a chosen numeric field.","exampleAgentPrompt":"Can you run string normalisation and numeric distribution analysis on my sampling observations? I have 4 rows: row-1 on 2026-10-01 with sample_value 10 and sample_weight 1 in group-a, row-2 on 2026-10-02 with sample_value 20 and sample_weight 2 in group-b, row-3 on 2026-10-03 with sample_value 15 and sample_weight 3 in group-a, and row-4 on 2026-10-04 with sample_value 30 and sample_weight 4 in group-b — analyze the sample_value field.","exampleUseCases":[{"title":"Survey response distribution audit","prompt":"I have 150 survey response rows with sample_value scores and sample_weight respondent counts across several dataset_groups — can you run both string normalisation on the group labels and a numeric distribution analysis on sample_value so I can see how scores are spread?"},{"title":"QC check on lab sample measurements","prompt":"Run a string normalisation and numeric distribution report on my lab sampling data: I have 80 rows each with a row_id, date, sample_value measurement, and sample_weight, grouped by experiment type — I want to check for label inconsistencies and see the full numeric spread of sample_weight."},{"title":"Weighted population sample analysis","prompt":"I've got 200 rows of population sampling observations with sample_value representing income estimates and sample_weight representing population weights across different region groups — can you give me string normalisation for the region labels and a distribution breakdown for sample_value?"}],"resultDescription":"Returns two analytical reports derived from the supplied rows: (1) a string normalisation report covering the dataset_group and other string fields, cleaning and standardising label variants; and (2) a numeric distribution report for the chosen field (sample_value or sample_weight), including statistics such as min, max, mean, median, percentiles, and spread metrics — all computed locally with no external lookups.","failureModes":["Empty or missing rows array returns a validation error","More than 200 rows submitted exceeds the maxItems limit and is rejected","Invalid date format (not YYYY-MM-DD) causes schema validation failure","Null values in sample_value or sample_weight may reduce distribution report completeness","Invalid field enum value (anything other than 'sample_value' or 'sample_weight') is rejected","row_id missing or empty string fails required field validation"],"whenToPreferThis":"Choose this endpoint when you have up to 200 structured sampling observation records and need both string normalisation of categorical/group labels and numeric distribution statistics in a single low-cost call. It is ideal for lightweight, local analytical work on tabular sample data where no external data lookup, prediction, or live enrichment is needed. At $0.01 USDC for two reports, it is well-suited for data QC pipelines, survey analysis, or exploratory statistics on small-to-medium datasets.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:59.255Z","isFirstParty":false,"canonicalSlug":"sampling-observations-string-normalisation-numeric-distribution-aa5ceada"}