{"uid":"cap_IclS8wx8lps8tElR9PUv7","slug":"sampling-observations-adjacent-row-changes-explicit-missing-value-fill-30e30eb4","name":"Sampling Observations — Adjacent Row Changes + Explicit Missing-Value Fill","description":"Sampling observations — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + Explicit missing-value fill 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-15-162?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"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_N9K2wr2vhamcVVEIsFuE1","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":"Computes adjacent row changes and fills missing values (zero, mean, or median) for up to 200 rows of weighted sampling observation data, returning 2 analytical reports.","exampleAgentPrompt":"I have a dataset of 150 sampling observations with dates, sample values, and sample weights — some values are null. Can you compute the adjacent row changes and fill the missing sample_value entries using the median method?","exampleUseCases":[{"title":"Survey data gap imputation","prompt":"I have 80 rows of weekly survey observations with some null sample_value entries. Can you fill those missing values using the mean and also show me how much each row changed from the previous one?"},{"title":"Quality control on sensor readings","prompt":"I've got 120 sensor sampling records grouped by device ID, some with missing sample_weight readings. Please fill those gaps using zero and compute the adjacent row differences for sample_weight so I can spot anomalies."},{"title":"Longitudinal study missing data fix","prompt":"Here are 200 rows from our longitudinal study dataset with dates and sample values — several are null. Run both the adjacent row change analysis and the median missing-value fill on sample_value so we can clean the data before modeling."}],"resultDescription":"Two analytical reports: (1) adjacent row changes showing the delta between consecutive rows for the selected field, and (2) missing-value fill results where null entries are replaced using the chosen method (zero, mean, or median). Both reports are computed locally from the supplied data with no external lookups.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Invalid date format (not YYYY-MM-DD) — schema validation failure","sample_value or sample_weight out of allowed range (±1 trillion) — validation error","Empty rows array — minItems:1 violation","Unrecognized field or method enum value — schema rejection"],"whenToPreferThis":"Choose this endpoint when you have up to 200 rows of weighted sampling observations and need both adjacent row-change detection and missing-value imputation in a single call. Ideal for survey data, sensor readings, or longitudinal studies where nulls need filling and sequential deltas matter. Prefer this over general-purpose analytics tools when you want a low-cost ($0.01), self-contained computation that does not require any external data or live lookups.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:53.583Z","isFirstParty":false,"canonicalSlug":"sampling-observations-adjacent-row-changes-explicit-missing-value-fill-30e30eb4"}