Paste your CSV with header row. Quoted fields supported.
Ready to filter
Paste CSV, set your condition, and click Apply Filter// keep or remove rows matching any condition
Filter CSV rows instantly by column value. Keep or remove rows matching any condition — equals, contains, starts with, regex. Free, browser-based, no upload.
Ready to filter
Paste CSV, set your condition, and click Apply FilterInclude a header row. Quoted fields and commas inside values are handled automatically.
Pick a column, choose a condition (contains, equals, regex…), enter a value, and decide to keep or remove matches.
Copy the filtered CSV to clipboard or download it as a .csv file ready to use.
CSV Row Filter lets you instantly keep or remove rows from any CSV file based on a condition applied to any column. No spreadsheet software needed — just paste, configure, and download. All processing happens in your browser; your data never leaves your machine.
No. All filtering is done entirely in your browser using JavaScript and a local PHP API call on the same server. Your data is never sent to any third-party service and is not stored anywhere.
Currently the tool supports one condition at a time. For multi-condition filtering, you can run the tool twice — apply the first filter, copy the result, paste it back, and apply a second filter.
Enter a valid JavaScript/PHP compatible regular expression pattern (without delimiters). For example, ^(US|UK|DE)$ will match rows where the cell value is exactly US, UK, or DE. The case-sensitive toggle also applies to regex.
The parser fully supports RFC 4180 CSV — fields wrapped in double quotes, including those containing commas, newlines, or escaped double quotes (""), are parsed correctly before filtering.
Yes. The header row is always preserved in the output regardless of whether it matches the filter condition. Only data rows are filtered.
Since processing is browser-based, practical limits depend on your device memory. Files up to a few MB (tens of thousands of rows) work well. For very large datasets, consider splitting your file first using a dedicated CSV splitter.
A CSV Row Filter is an online tool that lets you selectively keep or remove rows from a CSV (Comma-Separated Values) file based on a condition you define. Instead of opening a large file in Excel or writing a script, you can paste your data, configure a rule in seconds, and download a clean result instantly.
Whether you're a data analyst cleaning up exports, a developer testing API payloads, or a marketer managing contact lists, a row filter saves significant manual work whenever you need to segment, clean, or narrow down tabular data.
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Every CSV file is structured as rows and columns. The first row is typically a header that names each column (e.g., name, status, country). A row filter evaluates each data row by extracting the value in a specified column and checking it against a condition:
^(active|pending)$).The tool offers two complementary modes. In Keep Matches mode, only rows that satisfy your condition are included in the output — all other rows are discarded. In Remove Matches mode, rows that match are excluded and everything else is kept. Both modes preserve the header row untouched.
For example, to extract only active users from a user export, you would select the status column, choose equals, enter active, and use Keep Matches. To clean out test accounts, you might select the email column, choose contains, enter @test., and use Remove Matches.
By default, the filter is case-insensitive, so Active, ACTIVE, and active are all treated as equal. When you enable the Case Sensitive toggle, comparisons become exact. This is important when working with data that uses capitalization as meaningful differentiation — for instance, product codes or status flags like OK vs ok.
CSV files often contain fields wrapped in double quotes, especially when values include commas or line breaks. This tool's parser is fully RFC 4180 compliant, meaning it correctly handles quoted fields, escaped quotes ("" inside a quoted field), and embedded commas. You don't need to pre-process or clean your CSV before filtering.
The ability to filter CSV rows on the fly is useful across many workflows:
Traditional approaches to CSV row filtering require either a spreadsheet application (time-consuming for large files), command-line tools like awk or grep (requires technical knowledge), or writing custom scripts in Python or JavaScript. A browser-based tool eliminates all of these barriers. There's nothing to install, no code to write, and no data to upload to a third-party cloud service.
Since the entire operation runs locally in your browser, your sensitive data — customer records, financial exports, internal reports — stays on your machine. This makes the tool safe to use even with confidential datasets.
A few practices make row filtering faster and more reliable. Always ensure your CSV has a proper header row — the column selector uses the header to help you identify which column to target. For complex multi-condition filters, chain multiple passes: filter once, copy the output, paste it back, and filter again. When using regex, test your pattern on a small sample first before running it on a full dataset. For numeric comparisons, use equals with exact values, or regex patterns like ^[0-9]{4}$ for four-digit years.