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Normalize mixed date formats in a CSV

Convert a column of mixed dates to one format and review every ambiguous, conflicting, invalid or unrecognized value before downloading.

Runs on your device
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How to use

  1. Open a file and choose the date column.
  2. Check the detected order (DMY or MDY) and the flagged rows.
  3. Pick an output format and download.

Worked example

Audit column: 25/12/2026, 03/04/2026, 4 Mar 2026, 2026-03-04, 31/02/2026, soon. Because 25 can only be a day, the column is read day-first: output 2026-12-25, 2026-04-03, 2026-03-04, 2026-03-04. 31/02/2026 is flagged as no such date and “soon” as not recognized; both are kept as written.

Supported formats and limits

InputCSV, XLSX, Text list
OutputCSV, XLSX, Text
EngineMulti-pattern parser with column-level day/month order inference

Limitations

  • Two-digit years are mapped to 1950 to 2049 by default (configurable).
  • Times are kept as written: an offset such as +02:00 is not converted to another time zone.

Questions

How does the tool handle a date that could be day-first or month-first?

It looks at the whole column. A value such as 25/12/2026 can only be day-first, so 03/04/2026 is then read as 3 April. If no value settles it, dates are read with your fallback order (day-first by default) and flagged as ambiguous. You can also force day-first or month-first: 03/04/2026 becomes 2026-04-03 or 2026-03-04.

What happens to invalid or unrecognized dates?

They are flagged, for example 31/02/2026 as no such date, and kept as written by default. You can choose to blank them instead, and show only flagged values to review them.

Are time zones converted?

No. Dates and times are kept as wall-clock values, so 2026-03-04T23:30:00+02:00 stays on 4 March. Unix timestamps are read as UTC.

Guides

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