data-cleaner
A data cleaner applies a set of common normalization rules to messy tabular or list data β trimming whitespace, fixing inconsistent capitalization, removing duplicates, normalizing line endings, repairing common encoding glitches (mojibake), and standardizing date and email formats β so the data is fit for import into a database, spreadsheet, or downstream tool. The ZTools Data Cleaner offers a configurable pipeline of cleanup steps, previews each transformation, runs entirely in your browser, and reports the count of changes per rule so you can audit what was cleaned.
Use casesβ
Cleaning a CSV export before importβ
A vendor CSV with leading/trailing whitespace, mixed case names, and duplicate rows. Run the cleaner; the import succeeds first time instead of after three rounds of fix-and-retry.
Email list de-duplication and normalizationβ
"Alice@Example.com" and "alice@example.com " are the same person but two duplicate rows in your CRM. Lowercase + trim + dedupe collapses them.
Fixing copy-pasted text from PDFs or web pagesβ
PDF copy-paste leaves runs of weird whitespace, ligature glitches (ο¬, ο¬), and orphaned line breaks. The cleaner normalizes these in one pass.
Pre-processing form submissions for analysisβ
User-typed data has typos, mixed case, weird unicode characters. The cleaner standardizes before you analyze patterns, count distinct values, or generate reports.
How it worksβ
- Paste your data β CSV, list, or column from a spreadsheet. The cleaner detects format and shows row/column count.
- Toggle cleanup rules β Trim whitespace, normalize case (lower/upper/title), remove duplicates, normalize line endings, fix encoding glitches, replace tabs with spaces, validate emails, normalize phone numbers.
- Preview the diff β Side-by-side shows original vs cleaned. Each cell that changed is highlighted; the rule that triggered each change is annotated.
- Apply and export β Click apply to commit. Export as CSV, JSON, or copy to clipboard. Original is preserved in a separate panel for comparison.
Examplesβ
Input: " Alice ",30 "alice ",30
Output: "Alice",30 (after trim, lowercase emails, dedupe β 1 row out of 2)
Input: Mojibake: "cafΓ©" displayed as "cafΓΒ©"
Output: Decoded back to "cafΓ©" (UTF-8 misencoded as Latin-1 fix).
Input: Mixed line endings: "a\r\nb\nc\r"
Output: Normalized to a single line ending: "a\nb\nc"
Frequently asked questionsβ
What does "normalize case" mean?
Apply a consistent case rule across all values in a column. Options: lowercase (good for emails), UPPERCASE (good for codes), Title Case (good for names). Choose per-column based on the meaning.
How do I detect mojibake (encoding glitches)?
Mojibake is recognizable: "ΓΒ©" instead of "Γ©", "Γ’β¬β’" instead of "'", "Γ" instead of "ΓΒ©". The cleaner detects the most common UTF-8 β Latin-1 misinterpretations and offers to reverse them.
Can it preserve specific columns from cleaning?
Yes β toggle per-column rules. ID columns shouldn't be lowercased; description columns shouldn't have whitespace collapsed mid-content.
Does it dedupe across all columns or just one?
Configurable. By default, dedupe checks all columns (full row equality). For "dedupe by email", select only that column for the dedupe rule.
Will it modify my source file?
No β input stays unchanged. Cleaned output is a separate copy. Always preview before exporting; some rules have edge cases that need manual review.
Tipsβ
- Always preview before applying β a clean rule applied to the wrong column can corrupt data.
- For email lists, the standard cleanup is: trim β lowercase β dedupe β validate-email.
- Mojibake detection is heuristic; double-check important values manually.
- Run the cleaner before, not after, downstream analysis β clean inputs prevent compounding errors.
Try it nowβ
The full data-cleaner runs in your browser at https://ztools.zaions.com/data-cleaner β no signup, no upload, no data leaves your device.
Last updated: 2026-05-05 Β· Author: Ahsan Mahmood Β· Edit this page on GitHub