How to clean duplicate email entries in a CSV

Your contact export has the same email address more than once, and you need to clean it up before importing or sharing the file. Deleting every repeated address can also remove useful details, such as a newer name, a different subscription status, or notes attached to a contact.

Use the CSV Editor to sort and inspect the email column, then remove only the rows that are genuinely redundant. Keep an untouched copy of the export so you can recover information if you change your mind.

Find and review duplicate email entries

1. Open the CSV Editor and load your CSV file. The file is opened in your browser and stays on your device.
2. Check that the columns are separated correctly and that the first row contains the expected headings. Find the column with email addresses. If there are several, decide which one is the address you want to use for matching.
3. Sort the rows by the email column. Matching addresses should now appear next to each other, making it easier to spot repeated values and compare the other columns.
4. Inspect each group of matching addresses. Decide which row to keep, or whether details from more than one row should be combined before you remove anything.
5. Use the editor's duplicate-removal feature only if its matching rule fits your goal. If it removes only identical rows, it will not necessarily remove different rows that share an email address.
6. Save or download the cleaned file as a new CSV. Open the new copy and check a few contacts, including some near the beginning and end of the file.

Do not overwrite your only copy. A CSV stores rows of plain text values, so removing a row can permanently remove details that were not repeated elsewhere.

What the results mean

The entire row appears more than once

If every field in two or more rows is identical, they are exact duplicate records. Removing the extra copy usually does not lose contact information. Still, check whether the file has hidden or easily missed columns, such as a record ID or update date, before deleting the row.

The email matches, but other fields differ

These are multiple records for one address, not necessarily duplicate rows. One row might have an old name, an unsubscribed status, or a newer date. Keep the correct record based on your task, or combine the details you need into one row before deleting the others. If subscription or consent status differs, do not assume the newest-looking row is safe to keep without checking your own records and rules.

The addresses look the same but are formatted differently

One value might have a space before or after it, or use uppercase letters where another uses lowercase. Trim spaces around an address when they are accidental. Email domains are not case-sensitive, but the part before the @ symbol can technically be case-sensitive, even though many email services treat it without regard to case. Compare uncertain matches rather than changing every address automatically.

Several rows have no email address

Blank email cells do not identify the same person. If you remove duplicates based on a blank email value, you could delete unrelated contacts. Leave blank-address rows alone unless another reliable field, such as a unique contact ID, gives you a safe way to match them.

How to clean the file safely

1. Make a working copy. Keep the original export unchanged and clean a separate copy. This gives you a fallback if an address or contact detail is removed by mistake.
2. Choose the matching rule first. Decide whether you want to remove identical full rows or keep only one row per email address. Those are different tasks. A whole-row comparison will leave records with the same email but different names or statuses.
3. Check how duplicate removal compares rows. Before applying it, look at the available controls and confirm whether you can select the email column or whether the tool compares every field. If it cannot match by email alone, sort the email column and review the repeated groups instead of assuming the automatic removal will do what you want.
4. Keep the most reliable details. Compare names, dates, and status fields within each group. If you need one row per email, copy any useful information into the row you keep before deleting the extras.
5. Normalize only obvious formatting problems. Remove accidental spaces around email values, but avoid deleting plus tags, changing dots, or stripping characters based on assumptions about a particular mail provider. Those changes can make a different address look like a duplicate.
6. Check the cleaned export. Sort by email again and look for repeated groups. Also check that the headers, commas, quotes, and row count look reasonable. Save the result under a different file name so it is clear which copy is cleaned.

Why duplicate emails appear in exports

Repeated addresses can come from importing the same contact list twice, combining exports from different systems, or keeping a separate row for each event or subscription. Some systems also store one contact in several lists. In those cases, repeated email values may represent different activity, not an accidental copy.

A CSV has no built-in rule that says which row is the correct contact. It only stores the fields it was given. The safest cleanup depends on what you will do with the file: a mailing list may need one contact per address, while an activity report may need every row preserved. The CSV Editor lets you inspect and edit the file in your browser; the file stays on your device.