Clean CSV Columns Before an Import: Rename, Reorder, Delete and Find-and-Replace

Fix the columns in two passes: first the shape (which columns, what they're called, in what order), then the values (one spelling for each country, source or status). The CSV column editor does the first and find and replace in CSV the second, both in your browser and both without re-saving through Excel, so zip codes and long IDs stay as they were.

Why importers care about names and order

Most importers map columns by header name; a few read by position. A file with E-mail Address where the tool expects email, or with the email in column F when the uploader reads column A, imports blank or not at all. Sequencers also turn every header into a merge field, so First Name (Primary) becomes a variable nobody can type. Two columns with the same name are worse: most importers keep one and drop the other without saying so.

Pass 1: shape the columns

1. Paste or upload the CSV and press Load the columns. Each column is listed with its first value.
2. Delete what the importer doesn't need. A lead database export with 40 columns usually needs six. Deleted columns can be restored from the list underneath.
3. Rename. Type a new name, or rename every header to snake_case (first_name, company_name, linkedin_url) in one go.
4. Reorder with the arrows, or move the email column first.
5. Add a fixed-value column if you need one, such as lead_source = webinar-nov on every row, before files from several sources are combined.
6. Check the warnings for blank or repeated names, then download.

For a named destination such as Instantly, Smartlead or lemlist, the CSV import formatter maps the headers for you.

Pass 2: standardise the values

1. Load the CSV, type what to find and its replacement. Leave the replacement empty to delete the match.
2. Tick Whole cell only for codes, so US becomes United States but USB is left alone.
3. Tick only the columns to change, for example country. With none ticked, every column changes.
4. Add more rules; they run in order on each cell. A company cleanup might strip Inc. variants, then collapse double spaces.
5. Read the list of changed cells, then download the file and the change log.

Regular expressions help with patterns: \s+ replaced by a single space collapses repeated spaces, and ^(\d{4})$ replaced by 0$1 puts back a zip code zero a spreadsheet dropped. The syntax is JavaScript's; MDN's String.replace reference documents the $1 replacement patterns. For country and state columns specifically, normalize country and state columns has the full list of variants to map.

To see which spellings are in a column before you write rules, count them with count unique values in CSV.

Limits

The column editor works on one file at a time, moves columns one step per click, and previews the first 100 rows (the download has all of them). It doesn't change values or merge two columns into one. Find and replace has no row condition: a rule changes every matching cell in the chosen columns, so filter the file first if only some rows should change. Some PCRE regex features don't exist in JavaScript. Neither tool checks whether the addresses work.

Where SimpleVerifier fits

Once the file is shaped, upload it at /verify: every address gets a live mailbox check, and valid, risky and invalid download as separate files with your columns and names kept, so the valid file goes straight into the importer. The first 100 checks are free with a Google sign-in. Pro is $39 a month with the first 100,000 checks a month at full speed, then slower, never cut off. See pricing.