List Data Cleaner - Normalise & Fix List Data
Apply one-click presets or mix operations to turn dirty exported data into clean, consistent output ready for databases, spreadsheets, and APIs.
Presets:
Input
Type or Drag & Drop a file
Output
Output Options
Whitespace & Characters
Null / Empty
Text
Numbers
Dates
Abbreviations
Frequently Asked Questions
What is this tool used for?
The List Data Cleaner normalises messy or inconsistent data before you import it into a database, spreadsheet, or API. Instead of configuring individual Text Fixer options, you pick a preset - like Database Ready or Numeric Data - and all relevant operations apply in one click. Use it when you have exported data from a system you didn't control: a CSV dump, a copy-pasted report, scraped text, or a spreadsheet full of null values, mixed case, and stray HTML.
What null values does the tool replace?
It replaces any line that contains only a null-like value:
NaN, N/A, NA, #N/A, null, NULL, None, undefined, nil, -, and -. Lines that contain a null word embedded in other text are not replaced - only cells that are entirely a null value. Set the replacement value to an empty string to effectively delete those lines after also enabling Remove blank lines.What address abbreviations are expanded?
The following street-type and compass abbreviations are expanded: St → Street, Ave → Avenue, Blvd → Boulevard, Dr → Drive, Ln → Lane, Rd → Road, Ct → Court, Pl → Place, Hwy → Highway, Fwy → Freeway, Pkwy → Parkway, Cir → Circle, Ter → Terrace, Trl → Trail, Apt → Apartment, Ste → Suite, NE → Northeast, NW → Northwest, SE → Southeast, SW → Southwest. Expansion is case-insensitive and uses whole-word matching so abbreviations inside other words are not affected.
What is the difference between List Data Cleaner and Text Fixer?
Text Fixer is for precise, manual transformation when you know exactly what you want: split on a delimiter, prepend text, format as JSON, etc. List Data Cleaner is for normalisation: when you have dirty imported data and want to make it consistent in one click using preset-based profiles. They complement each other - clean your data here first, then pass the output to Text Fixer for structural formatting.
How do I clean a list of email addresses?
Click the Email List preset, paste your list, and the tool will: trim whitespace from each address, convert everything to lowercase, remove blank lines, and deduplicate. This is the standard preparation step before importing email addresses into a CRM or mailing list tool. For extra safety, also enable Remove invisible characters to catch any hidden characters that could prevent email matching.
What date formats does the tool recognise?
Date normalisation works when each line is a date value. The following formats are all detected automatically:
Dates that don't match any pattern are left unchanged.
| Format | Examples |
|---|---|
| ISO / YYYY-MM-DD | 2026-05-19 2026-05-19 23:14:02 |
| Numeric (DMY / MDY) | 19/05/2026 05-19-2026 19.05.2026 |
| Month name first | January 15, 2024 Jan 15 2024 |
| Day first | 15 January 2024 15 Jan, 2024 |
| With ordinal suffix | May 19th, 2026 1st January 2024 |
| With day of week | Monday, May 19 2026 Mon 19 Jan 2026 |
| Hyphenated month name | 19-May-2026 May-19-2026 |
Dates that don't match any pattern are left unchanged.
How does date normalisation handle ambiguous formats?
When a date is ambiguous - like
01/02/2024, which could be 1 February or January 2 - the tool uses the Input Format you specify (European DD/MM or US MM/DD). Set this before running. Dates that can't be parsed are left unchanged. ISO dates (YYYY-MM-DD) are always detected unambiguously regardless of the input format setting. Month-name dates like 15 January 2024 or Jan 15, 2024 are also parsed automatically.Related reading