Paste your CSV data including the header row
Ready to extract
Paste CSV and specify columns to pull// pull exact columns from csv in one click
Extract specific columns from any CSV file instantly. Select columns by name or index, reorder them, and download the filtered output. Free, browser-based, no upload needed.
Ready to extract
Paste CSV and specify columns to pullPaste any CSV data with a header row into the input area.
Type column names or numbers, or click the detected column chips to add them.
Click Extract, then copy or download the filtered CSV output.
The CSV Column Extractor lets you pull specific columns out of any CSV dataset instantly. Instead of opening Excel or writing scripts, paste your data, specify the columns you want by name or position, and get a clean filtered CSV back in seconds. All processing happens in your browser — no data leaves your machine.
Yes. Enter column numbers (1-based) instead of names. For example, 1,3,5 will extract the first, third, and fifth columns regardless of their header names.
Absolutely. The output columns appear in the exact order you specify them. Enter city,name,email and the output will have those columns in that order, even if the source CSV has them differently.
Yes. Use the Delimiter dropdown to switch between comma, semicolon, tab, and pipe delimiters before extracting. The output always uses standard comma separation.
The extraction runs entirely in your browser via JavaScript. Your CSV data is never uploaded or stored anywhere. It is processed locally and disappears when you leave the page.
The tool returns a clear error listing the column that was not found along with the available header names so you can correct your input.
Since everything runs in the browser, practical limits depend on your device memory. Most CSVs up to a few MB paste and process without issue. For very large files, consider a command-line tool like csvcut.
A CSV Column Extractor is a tool that lets you select only the columns you need from a CSV (Comma-Separated Values) file and produce a new, trimmed-down CSV containing just those fields. Rather than manually opening a spreadsheet application, deleting rows, or writing a Python or awk script, you paste your data, specify which columns to keep, and get a clean result in seconds.
This tool is especially useful for data analysts, developers, and anyone who regularly works with exported reports or database dumps that contain far more columns than are actually needed for the task at hand.
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CSV files exported from databases, analytics platforms, CRMs, or e-commerce systems routinely contain dozens of columns. In practice, you often need only a handful. Stripping unnecessary columns serves several purposes:
The most intuitive approach is selecting columns by their header names. In the Columns to Extract field, type the header names exactly as they appear in your CSV, separated by commas. The tool performs a case-insensitive match, so Email and email both work. If the tool detects your CSV headers after you paste input, clickable column chips appear — click any chip to append it to your column selector automatically.
When column names are unknown, long, or contain special characters, you can address them by their position using 1-based indexing. Column 1 is the leftmost column. Enter 1,4,7 to pull the first, fourth, and seventh columns. You can mix names and numbers in the same request — for example, id, 3, email — and the tool resolves each one appropriately.
One often-overlooked feature of a column extractor is the ability to reorder fields. If your source CSV has columns in the order id, name, email, city, country but your import target expects email, name, country, simply enter them in that order in the Columns field and the output will follow exactly that sequence. No pivot tables, no VLOOKUP, no scripting required.
While the comma is the default delimiter in CSV files, many systems export data using alternative separators. European locales frequently use the semicolon (;) because the comma serves as the decimal separator in those regions. Tab-separated values (TSV) are common from spreadsheet exports. Pipe-delimited files appear frequently in legacy system integrations. This tool supports all four common delimiter types via a simple dropdown selector.
All processing in this tool is performed locally in your browser using JavaScript. Your CSV data is never transmitted to any server, stored in a database, or logged anywhere. This makes the tool suitable for handling sensitive data that cannot leave your local environment. Simply paste, extract, and close the tab — no traces remain.
Extracting email addresses from a user export: Paste the full user table CSV, type email in the columns field, and download the result. You get a single-column CSV containing only email addresses, ready for an email marketing import.
Preparing data for an API import: Many APIs accept CSV uploads but require a specific set of fields in a specific order. Extract and reorder the necessary columns from your source data to match the API's expected format exactly.
Creating a sanitised dataset for sharing: Remove columns containing names, emails, or addresses from an analytics export before passing the file to a colleague or embedding it in a report.
Debugging a data pipeline: When a downstream system reports unexpected values, extract a few columns of interest from a large CSV to focus your investigation without being distracted by unrelated fields.
If you work with CSV files programmatically, several command-line tools perform similar operations. csvcut from the csvkit library is a popular choice among data engineers. In Python, the pandas library handles column extraction in a single line. In the shell, awk or cut can extract columns from simpler CSV files. However, for a quick, one-off extraction without any setup, this browser-based tool is the fastest path to a result.
"Smith, John"), paste as-is — the parser handles RFC 4180-compliant quoting correctly.