{"uid":"cap_DqxYZgIupMyhIx-Be19EL","slug":"label-review-daily-sum-series-explicit-missing-value-fill-27b85408","name":"Label Review — Daily Sum Series + Explicit Missing-Value Fill","description":"Label review — Daily sum series + Explicit missing-value fill: Daily sum series + Explicit missing-value fill for supplied label review records (reviewed_records, changed_labels). 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-12-145?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","reviewed_records","changed_labels"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"changed_labels":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"reviewed_records":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","changed_labels":1,"reviewed_records":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","changed_labels":2,"reviewed_records":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","changed_labels":3,"reviewed_records":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","changed_labels":4,"reviewed_records":30}],"maxItems":200,"minItems":1},"field":{"enum":["reviewed_records","changed_labels"],"type":"string","default":"reviewed_records"},"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_Muy-mDQWykckSXzwLxrd4","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 a daily sum time series and fills missing values using a chosen strategy (zero, mean, or median) for label review records containing reviewed_records and changed_labels fields.","exampleAgentPrompt":"Can you compute the daily sum of reviewed_records for my label review dataset and fill any missing dates using median values? Here's my data: group-a on 2026-10-01 had 10 reviewed_records and 1 changed_label, group-a on 2026-10-03 had 15 reviewed_records and 3 changed_labels, group-b on 2026-10-02 had 20 reviewed_records and 2 changed_labels.","exampleUseCases":[{"title":"Daily label review throughput trending","prompt":"I have up to 200 rows of label review data across two dataset groups with dates, reviewed_records counts, and changed_labels counts. Can you generate a daily sum series for reviewed_records and use median to fill any missing days?"},{"title":"Changed labels gap-fill for ML pipeline audit","prompt":"My ML annotation pipeline has sporadic days with null changed_labels values — can you compute the daily sum of changed_labels across my dataset groups and fill the gaps with zero so I have a complete time series?"},{"title":"Multi-group label review rollup","prompt":"I need to roll up my label review records into daily totals for both group-a and group-b, filling any missing dates with the mean. The field I care about is reviewed_records. Here's my dataset with about 50 rows spanning October 2026."}],"resultDescription":"Returns two analytical reports: (1) a daily sum time series aggregating the chosen field (reviewed_records or changed_labels) per date, and (2) a version of that series with explicit missing-value fill applied using the selected strategy (zero, mean, or median). Covers up to 200 input rows with no external lookups or predictions.","failureModes":["Exceeding 200 rows returns a validation error","Invalid date format (not YYYY-MM-DD) causes schema rejection","Missing required fields (row_id, date, reviewed_records, changed_labels) cause item-level errors","Values outside the numeric range (-1e12 to 1e12) are rejected","Invalid method enum value (not zero/mean/median) or invalid field enum value causes a request error"],"whenToPreferThis":"Choose this endpoint when you have tabular label review data with date, reviewed_records, and changed_labels columns and need both a daily sum aggregation and explicit missing-value imputation in a single low-cost call. It is ideal for ML ops and data quality pipelines where label annotation throughput needs to be tracked over time and sparse or gappy data needs to be regularized before further analysis. Prefer it over general-purpose analytics tools when you want a lightweight, deterministic, local-engine solution without any external data access.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:39:08.219Z","isFirstParty":false,"canonicalSlug":"label-review-daily-sum-series-explicit-missing-value-fill-27b85408"}