CSV Column Extractor

Exports are rarely shaped the way you need them. Paste a CSV and name the columns you want, by header, by number or by range, and this returns just those, optionally with duplicate rows removed. It's a quick way to trim a big contact or sales export without opening a spreadsheet.

How to use it

1. Paste your CSV.
2. List the columns to keep, such as email, 3 or 2-4.
3. Tick the header and duplicate options if needed.
4. Copy the new CSV.

When it helps

• Pulling the email column out of a contact export.
• Trimming a large data file.
• Removing duplicate rows.
• Preparing data for import.

Good to know

• Header names aren't case sensitive but must otherwise match.
• Columns come back in the order you list them, so this also reorders.
• If a name is wrong, the tool lists the available columns.

How columns are selected

The tool parses your CSV properly, respecting quoted fields, then reads your list of columns. Names are matched against the header row ignoring case, numbers count from 1, and ranges like 2-4 select several at once. The chosen columns are written out in your order.

Rows with fewer fields than the header get empty cells, so nothing shifts.

Worked example: a contact export

Say an export has name, email, city and plan columns. To get only the emails of paying customers' plans, list email and plan. Tick remove duplicates and repeated rows disappear. The output keeps the header, so it's ready to import elsewhere.

To reorder columns without dropping any, list them all in the new order.

Data handling tips

Contact exports contain personal data, so handle them carefully, share only the columns needed and delete copies you no longer require. Because the tool runs in your browser, the data isn't uploaded.

Check the output for stray quotes or odd characters before importing it.

When to use a spreadsheet instead

For filtering rows, editing values or calculations, a spreadsheet or a data tool is better. This extractor is for the narrow job of trimming columns quickly, especially in a workflow where you just need clean text to paste.

If your file is very large, a command-line tool or script is more efficient.

Cleaning data first

Before extracting, check that every row has the same number of fields, and fix stray quotes. Extra commas in unquoted text break the columns. If your data is messy, run it through the Delimiter Converter first to normalise it.

Look at the first few rows of the output to confirm the right columns came through.

A tip for large exports

If a file has dozens of columns, paste only the first few rows first, work out the column names or numbers, and then paste the whole file. It saves scrolling and avoids picking the wrong column.

Questions