Point Spreadsheet Cleaner at a messy CSV or Excel file. Get a per-dimension data-quality report you can hand to your client before the migration runs. Everything happens on your machine. Nothing is uploaded.
Fully offline · No cloud, no account · A report you can sign off
Desktop app or command line · Python package too
Every finding maps to a recognized data-quality dimension (DAMA-UK / DMBOK), so the report reads as a standard scorecard, not a pile of ad-hoc warnings.
Missing required values, and placeholder text like N/A or - that really means blank.
Values that don't match the column's intended type: a word in a number column, a broken date.
Format drift: several date formats in one column, and stray or doubled whitespace.
The same value spelled and cased more than one way: Active / active / ACTIVE.
Duplicate rows, and duplicate values in a column that's supposed to be a primary key.
Blank rows, completely empty columns, and unlabeled headers that break a load.
It also infers each column's type, reports fill rate and distinct counts, flags primary-key candidates, and exports the whole audit as a self-contained HTML file, plus Markdown and JSON for your pipeline. When you're ready to fix it, spreadsheet-cleaner clean applies a deterministic recipe, logs every change, and writes a cleaned copy. Then validate runs the file against your target system's schema for a hard PASS/FAIL, and reconcile proves counts, keys, and control totals match before sign-off. Your source file is never touched.
Especially when the file holds real client data that can't be uploaded to a cloud tool.
Profile and vet source data, catch what would break the load, and hand the client a signed-off quality report before you migrate.
Understand and clean an inherited spreadsheet, without spinning up a data-engineering stack or a warehouse.
A fast, offline first pass on ad-hoc files that don't justify a full pipeline, with a CI-friendly exit code.
Python 3.10+. Install from source today; a PyPI package is coming.