DataDoctor

Runs entirely in your browser — no uploads, no saved snippets, no public links.

Paste a file, inspect the issues, export reviewed output.

CSV repair and export

CSV Cleaner

Prepare a reviewed CSV export by removing duplicate keys and optionally dropping incomplete rows.

Paste CSV, load the customer sample, or upload a local file.

Next workflow

Continue the preflight

CSV Duplicate Finder

Inspect duplicate keys before removing rows.

CSV Missing Values Checker

Locate blank cells before dropping incomplete rows.

CSV Data Quality Checker

Run a full data quality report.

After the tool runs

CSV Cleaner review guide

Use the tool above first. The supporting notes below help you interpret the result, fix the right issues in the right order, and choose the next DataDoctor tool without pushing SEO content above the actual task.

Best input

removing duplicate rows and optionally dropping incomplete rows before downstream checks.

Output to keep

Save the original file, the issue report and the reviewed export as separate files.

Next check

After structural and quality issues are visible, run a platform checker or schema validator before upload.

What it checks

CSV Cleaner for real data work

CSV Cleaner should sit before the import screen, not after a failed upload. It turns hidden spreadsheet problems into a checklist you can review row by row.

  • Header detection
  • Duplicate key column
  • Rows with missing values
  • Downloadable cleaned CSV

Fix these first

Common errors to review before downstream work

Most failures come from small file issues that become expensive only after an API call, import job or spreadsheet cleanup. Fix blocking errors first, then re-run the same tool before moving forward.

  • Cleaning before validating structure
  • Choosing an unstable key column
  • Dropping rows that need manual review
  • Forgetting to keep the original file

Recommended workflow

Run the check in this order

Treat any downloaded output as a reviewed candidate. Keep the source CSV unchanged so you can reconcile removed rows, duplicate groups or missing values later.

  1. Step 1

    Parse the CSV

  2. Step 2

    Select the duplicate key

  3. Step 3

    Choose whether to remove missing rows

  4. Step 4

    Download the cleaned CSV and validate it again

How to interpret a passing result

A pass means this specific preflight did not find the issues listed above. It is not a guarantee that the target system will accept every row, field, custom mapping or account-specific rule.

Do not clean, deduplicate or drop rows before parser errors, required columns and duplicate-key logic are clear.