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Dataset Null Pattern Audit
x402
Dataset Null Pattern Audit is a paid API for AI agents from www.mahastrategies.com, paid per call via x402, $0.010063/call, status unknown (last checked 2026-10-19).
Counts missing fields, nulls, empty strings, and populated cells per column across a supplied dataset, without imputation or PII detection.
Price / call$0.010063
Success—
StatusUnknown
How this service works
Count missing fields, nulls, empty strings and populated cells separately. No imputation or private-data detection.
Output
Returns per-column counts of missing fields, null values, empty strings, and populated cells across the submitted dataset rows, enabling a clear picture of data completeness without any imputation or sensitive-data processing.
Example use cases
Pre-training data completeness check
“Before I fine-tune my model, can you audit this synthetic dataset of 300 rows across columns ['feature_1','feature_2','label','weight','split'] and tell me how many nulls, empty strings, and missing values exist per column?”
Reporting on public survey data gaps
“I have public survey results — 150 rows with columns ['respondent_id','age','gender','income','education','response_score'] — can you count the missing fields, nulls, and blank strings in each column so I know where the gaps are?”
Pipeline data quality gate
“Run a null pattern audit on this public dataset with 400 rows and columns ['transaction_id','amount','currency','merchant','timestamp','status','country'] — I need to know how many cells are populated versus missing or null before we push it downstream.”
Technical details
Endpoint
POSThttps://www.mahastrategies.com/api/v1/verify/datasets/null-patterns?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, lightweight null-pattern audit of a tabular dataset (up to 500 rows, 64 columns) and only want raw counts of missing, null, empty, and populated cells — without any data imputation, transformation, or PII/sensitive-data detection. It is ideal as a pre-processing quality gate or reporting step in data pipelines where auditability and simplicity matter more than remediation.
Side effects
- Exceeds 500-row limit — request rejected with a validation error
- Column list is empty or exceeds 64 columns — schema validation failure
- Invalid dataClass value (not 'synthetic' or 'public') — enum validation error
- Malformed row objects that don't match declared columns — partial or error response
- Network timeout for large payloads near the row/column limits
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| columns | array | |
| dataClass | string |
Tags
x402
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