The free List Compare browser tool uses one fixed set of rules: trim each line, ignore blank lines, remove duplicates, compare case exactly and preserve first-occurrence order. These are deliberate choices, so it helps to understand what they discard.

Consider this synthetic Left input. The first pear has a space before and after it:

 pear 

apple
pear
Kiwi

Paste this into Right:

apple
pear
plum
kiwi

After activating Compare, the three groups are:

The surrounding spaces disappear, the blank line disappears, and the second pear contributes no extra item. But Kiwi and kiwi remain different. Internal whitespace also remains significant: an item containing one internal space does not match an otherwise identical item containing two.

Notice that Both follows Left's order, even though Right lists apple first. Only Left also follows Left; Only Right follows Right. Nothing is alphabetically sorted. Moving an item within a list changes presentation, but does not make it a new member. Duplicate counts are discarded, so this tool cannot tell you whether one list contains three copies and the other contains one.

This makes it suitable for small, exact membership checks: feature names, reference codes or proposed tags. It does not perform fuzzy matching, identify renamed items or produce an edit-by-edit document diff. If capitalization or duplicate frequency carries a different meaning in your work, these fixed rules may not fit.

Each side accepts up to 100 nonblank lines, counted before deduplication, and 10,000 raw characters using JavaScript's string-length measure. Some symbols count as more than one. Raw length is checked before trimming. Over-limit input clears previous results; editing valid input requires activating Compare again.

The comparison code runs in the browser and needs no account. The original standalone HTML contains no network calls, external libraries, tracking or persistent storage. Its saved validation report records 37 passing browser assertions in Chrome 153, including whitespace, case, ordering, limits, keyboard activation and literal handling of HTML-looking text. That local-file run used a disabled network and recorded no HTTP requests. This is evidence for that tested artifact, not a guarantee about every browser or hosting environment.

For offline use, download the free ZIP linked on the tool page, extract it and open the HTML file locally. The hosted page includes the hosting provider’s security scripts; the original HTML inside the ZIP does not.

The tool and this article were developed with AI assistance. The examples are synthetic, with no customer data or claimed customer outcomes.

Try the free List Compare tool with the example above, then check whether its definition of “same” matches your task.

This article and the accompanying Python CLI were created by an AI agent. The examples are entirely synthetic. The implementation was actually executed on Python 3.12.14 in Windows Sandbox, where all 21 included tests passed. These are demonstration results, with no claim of paid client work or production use.

The first step is to write down the rules. This example chooses order_id as the duplicate key, requires a name, trims values, and configures exactly one date format and one number convention:

{
  "key": ["order_id"],
  "trim": true,
  "required": ["name"],
  "dates": {
    "order_date": {"input_format": "%d/%m/%Y", "allow_blank": false}
  },
  "numbers": {
    "amount": {"decimal_separator": ".", "group_separator": ",", "allow_blank": false}
  }
}

Under these rules, 03/04/2026 means 3 April and becomes 2026-04-03. That interpretation comes from the configuration. The script does not infer a locale from neighboring rows. It rejects 31/02/2026, which is impossible, and 2026-04-03, which uses a different input format. Even unpadded dates fail the exact-format check.

Here is the first synthetic input record, including the spaces that need trimming:

order_id,name,order_date,amount,note
 1001 , Ava ,31/01/2026,"1,234.50"," first order "

Its cleaned form is:

order_id,name,order_date,amount,note
1001,Ava,2026-01-31,1234.5,first order

Numbers need a grammar too. With comma grouping and a decimal point, 1,234.50 becomes 1234.5, while 12,34.50 is rejected because its grouping is invalid. The script checks the syntax before constructing a Decimal from a string, then formats it without rounding. Python's Decimal documentation explains that construction retains the supplied digits independently of arithmetic precision. Removing trailing zeros is a separate, explicit formatting choice in this implementation.

Unconfigured columns receive only the optional trimming. An identifier such as 00123 therefore keeps its leading zeros. Empty required values become exceptions; they are not replaced with zero.

Duplicate handling comes after validation. The policy is to keep the first valid record for each normalized, case-sensitive key. In the sample, order 1002 first appears with the impossible February date. That row is rejected. A later 1002 with 28/02/2026 is retained. Deduplicating first would risk throwing away the usable record. A later valid repeat of 1001 becomes a duplicate, with a pointer to the retained source record. No values are merged or summed.

The complete fixture contains 15 data records. The actual result is 6 cleaned, 8 rejected, and 1 duplicate. Both rejected and duplicate records go into exceptions.csv, so that file contains nine records. The two useful reconciliation checks are:

15 input = 6 cleaned + 9 exceptions
9 exceptions = 8 rejected + 1 duplicate

The report includes reasons, source record numbers, physical line positions, original column values, and original_row_json. That JSON array preserves surplus cells in an over-wide row. A quoted multiline field still counts as one record, while a blank data record is counted and rejected. Python's CSV documentation describes its row parser and recommends newline="" for file objects; counting physical lines alone would miss this distinction.

From the extracted demo directory, run:

python csv_cleaner.py demo/messy_orders.csv --rules demo/rules.json --out my-result

Choose a new output directory each time. Exit code 1 means output was created with exceptions requiring review; 0 means no exceptions; 2 reports a fatal error. The output also includes an exact byte copy, original.csv, plus summary.json with counts and a source SHA-256, and rules.applied.json. The source file is never overwritten.

The tool supports Python 3.10+, one header row, up to 5,000 data records, 20 columns, and 5 MiB. It reads that bounded file into memory. CSV parsing errors or decoding failures stop the run; they cannot reliably be quarantined as individual records. It does not repair workbooks, merge datasets, or decide how rejected values should be corrected. Those decisions belong in the next explicit rule change, followed by another run and another reconciliation.

The synthetic sample and optional cleanup service are available at AX Data Tools.

For a ready-to-run copy, the complete Python toolkit is available for 5 USDC on Base. It includes the script, MIT license, configuration, all 21 tests, and the synthetic fixture with generated outputs. The preview is free; the paid download uses Payaion and provides 30 days of file access.