How to Find an Email in the Name Column (and Other Misplaced Data in a CSV)

To find misplaced data in a CSV, classify each cell by what it looks like (email, phone number, website) and flag any cell whose type doesn't match what its column header says should be there. An address containing an @ sitting in the "Full name" column is wrong whatever the row looks like. The free CSV misplaced data finder does this for you: it lists every value found in the wrong column and, where the target cell allows, moves it or swaps it back.

Find misplaced values in a list →

How lists end up shifted

• Stacked exports. One file has Name, Email, Phone and the next has Email, Name, Phone. Pasting one under the other under a single header puts a third of the emails in the Name column.
• Embedded commas. A company called "Northwind, Inc." that isn't quoted in the file splits in two, and every cell to its right shifts one column. Fixing broken CSV rows before import deals with that cause.
• Web copy-paste. A directory page lists the phone, then the email, then the name. Your sheet expects the opposite.
• Manual edits. Someone typed an address into the first free cell.
• Delimiter mix-ups. A semicolon file opened as comma-separated puts a whole row in one cell; see converting semicolon CSV to comma.

The result is a list that looks complete. Row counts match, nothing is blank, and the problem shows up later as import errors, "Dear ana@northwind.example" in a mail merge, or mail sent to a column of phone numbers.

How a value is classified

The tool reads each cell and decides what it is, using plain patterns:

Looks like	Test
Email address	Has the shape of an address: text, an @, a domain
Phone number	Digits, spaces, brackets, dashes and an optional +, with between 7 and 15 digits
Website	Starts with http, https or www, or is a domain ending in a common extension such as .com, .net or .org
Plain text	Anything else

Dates written with slashes (9/11/2026) aren't mistaken for phone numbers. Then each column header sets an expectation: a header with "email" expects an address, "phone", "mobile" or "tel" a phone number, "website" or "url" a site, and name, company, title, role, city or country columns expect plain text. Columns with other headers aren't checked, which keeps notes and custom fields from being flagged.

A mismatch is a finding. A website in the email column, a phone in the company column and an address in the name column all show up with the row number, column name and value.

Move, swap or leave

Finding the value isn't the same as knowing where it goes. For each finding the tool takes one of three actions:

1. Move. The value goes into an empty cell of the right type in the same row. An email in the Name column, with the Email cell empty, moves to Email.
2. Swap. The target cell holds a value of the wrong-column kind that belongs where the first one sits. The two trade places.
3. Leave. The target cell holds something else. The row stays as it was and appears in the "left as they were" count for a person to decide on.

The conservative rule is the point. Overwriting a good address with a phone number to make a column tidy would destroy data. Leave is the correct answer whenever the fix isn't obvious.

A manual method for small files

1. Sort the sheet by the Name column. Cells starting with digits or containing @ cluster at the top or bottom.
2. Add a helper column with a formula that flags cells containing @ in the Name column and cells without @ in the Email column.
3. Filter on the flags and fix the rows one at a time.
4. Repeat for the phone column.

It works for a few hundred rows. Past that, a tool that applies the classification to every column in one pass saves time and misses fewer rows.

After the fix

Moving values back is about shape. Three checks still need doing before the file goes anywhere:

1. Re-count. The number of rows should match the number before the fix. If rows disappeared, stop and look.
2. Spot-check 20 rows at the start, middle and end. Look for names in a company column or a missing last name.
3. Look at what is left. A long "left as they were" list means the file has a structural problem (a column shift across many rows) that moves and swaps can't repair. Go back to the source export.

Then clean the rest: other columns that need work before import, duplicates, and how complete each column is.

Verify the recovered addresses

Addresses recovered from the wrong column are often the ones nobody has checked. They came from a different source, they may be older, and they were invisible to every check you ran on the Email column. Their risk is the same as any unverified address: the mailbox may not exist, the domain may not take mail, the person may have left.

Upload the fixed file at /verify before the import or the send:

• Valid: keep.
• Invalid: suppress and keep a record so they don't return in the next merge. Removing invalid emails from a CSV walks through it.
• Risky or catch-all: send in a smaller, slower group, after the valid rows have gone out cleanly. Catch-all emails: what to do explains the choice.

The finder passes the addresses in the repaired Email column to the verifier, so you don't have to export and re-upload.

Quick answers

• Does the tool change my original file? No. It gives you a fixed copy and a list of what changed.
• Does it run in my browser? Yes, like the other free CSV tools, so the list isn't uploaded just to be reshuffled.
• What about columns with unusual headers? They aren't checked, so rename a column (for example "Contact email") if you want it included.
• Will it fix a whole shifted row? Only value by value. A row shifted by one column in the middle needs the source fixed.

Try it on your own list

Upload a sample at /verify. The first 100 checks are free with a Google sign-in, no card.

Pro, $39 a month: Unlimited email verification.* Never metered, never cut off. See pricing.

*Fair use: the first 100,000 checks a month at full speed, then slower, never cut off.