Profile and Detect CSV Anomalies
Profile CSV columns, detect anomalies, then clean flagged data.
Use case
Use this for quality checks before ingestion when data consistency is uncertain.
What to expect
Follow the steps from left to right for a quick overview, then use the inline stepper below to run each tool.
Profile CSV columns, detect anomalies, then clean flagged data.
CSV Column Profiler
CSV → JSON
Profile CSV columns
Review the result here before moving to the next step.
CSV Anomaly Detector
JSON → JSON
Detect anomalies
JSON report with missing values, type errors, outliers, and duplicate keys.
CSV Cleaner
CSV → CSV
Clean CSV
Normalized CSV ready for the next workflow step.
Workflow steps
Workflow shortcut
Next unlocked step: Step 1 · CSV Column Profiler
CSV Column Profiler
Profile CSV columns with inferred type, emptiness, uniqueness, top values, and numeric percentiles.
CSV input
Provide csv input for this workflow step.
Column profile report
Review the result here before moving to the next step.
Run this step to process the current input and prepare the next workflow stage.
CSV Anomaly Detector
Identify data quality issues in CSV: missing values, numeric outliers (IQR), type inconsistencies, and duplicate key rows.
Anomaly detector input (JSON envelope)
Provide { "csv": "...", "numericColumns"?: ["age"], "keyColumn"?: "id" }.
Anomaly report
JSON report with missing values, type errors, outliers, and duplicate keys.
Run this step to process the current input and prepare the next workflow stage.
CSV Cleaner
Trim whitespace and normalize CSV records before conversion.
CSV input
Paste the raw CSV you want to normalize.
Cleaned CSV
Normalized CSV ready for the next workflow step.
Run this step to process the current input and prepare the next workflow stage.