[1] data-pre-inference
$cat clean --help
Upload a CSV (or import a Jupyter notebook's rendered DataFrame output) and CAT profiles columns, standardizes text, coerces numeric types, imputes missing values, removes outliers, and drops duplicates.
You get back a cleaned file, a quality report, and a row-level audit trail.
$cat clean upload <file.csv>
Drag a .csv onto the intake window, or browse to select one. Runs the full cleaning pipeline and redirects to the job's report.
$cat clean import --notebook <file.ipynb> [--table <name>]
Import a .ipynb that was run and saved with its outputs visible. CAT reads the rendered DataFrame only — notebook code never executes. Leave --table blank to use the first DataFrame found, or name a specific cell output.
$cat clean status <job_id>
Poll a job's progress while it's running — the report page uses this automatically, so you just watch it update.
$cat clean download <job_id>
Download the cleaned CSV once a job finishes.
$cat clean audit <job_id>
Download the row-level audit log — every cell CAT changed, and why.
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