An enrollment list contains the same name twice. Perhaps the form was submitted twice, or perhaps the person booked two different sessions. A duplicate-removal command cannot decide that question of meaning. Before deleting rows, establish what counts as a duplicate and which information should survive when records disagree.
Derive the key from the purpose
For a list with one row per booking, a unique booking identifier may be the right key. For an attendee list organized by session, you may need the combination of attendee identifier and session. Comparing only names could be too broad in either case.
Write the rule in one sentence, such as “Review multiple rows with the same booking identifier; retain separate sessions.” That makes the decision understandable later. Without a reliable identifier, similar-looking entries may require manual review.
Distinguish completely identical rows from rows that share a key but disagree elsewhere. Two different phone numbers for one identifier represent a conflict, not merely redundant repetition. Decide which source or confirmed version controls, rather than accidentally keeping whichever row appears first.
Identify candidates before deleting
Work on a copy. Highlighting duplicates or creating a sorted review view can help identify candidates. Remember that checking individual cells does not automatically implement a combined comparison rule. Matching session names alone, for example, do not indicate duplicate attendees.
In duplicate removal, the selected comparison columns determine which rows are considered equal. Inspect that selection deliberately. Leaving out a necessary column can merge distinct bookings; including an incidental note that differs can conceal a genuine repetition.
Spaces and inconsistent spelling can complicate comparisons. Normalize them only under a justified rule while retaining the original data. For identifiers and names, a supposed cleanup must not merge information that is genuinely distinct.
Make the result explainable
Record the row counts before and after processing and review the removed cases. A reported number of deleted duplicates is not proof that the result is correct. Pay particular attention to multiple sessions, missing identifiers, and conflicting contact details.
If differing details need to be combined, document that decision separately from deleting identical rows. For recurring lists, a repeatable rule with an exception list is useful. Uncertain cases remain visible instead of disappearing differently in every import.
Keep the unchanged source and the reviewed cleaned version separately. You can then answer a question about a removed booking without reconstructing the entire process from memory.