CSV Filter

How to use

1. 1Paste your CSV into the input area, drop a .csv or .tsv file, or click Load sample to try it on a small dataset. The delimiter is auto-detected.
2. 2Confirm First row is header so column labels appear in the rule dropdowns, then click Add rule.
3. 3Pick a column, an operator, and a value. Numeric and date columns offer greater than, between, and other comparisons; text columns offer contains, regex, in-list, and more.
4. 4Add more rules and pick ALL (AND) to narrow, or ANY (OR) to widen. Tick Invert match to return rows that do NOT match your rules.
5. 5Use Case sensitive on any text rule if A and a should not match, and review per-rule warnings if a value cannot be parsed (such as a date that does not look like a date).
6. 6Read the matched count and preview, then click Copy CSV or Download to save the filtered output.

About this tool

CSV Filter answers the everyday question "give me only the rows where column X meets some condition" without spinning up a spreadsheet, a Python shell, or a database. Paste a CSV (or TSV, semicolon-, or pipe-separated file) on top, add one or more rules that pair a column with an operator and a value, and the matching rows appear below ready to copy or download. Operators are picked from a list that fits each column's detected type. Text columns get equals, does not equal, contains, does not contain, starts with, ends with, regex matches, does not match regex, is one of (comma-separated list), and the inverse. Number and date columns get equals, does not equal, greater than, greater than or equal to, less than, less than or equal to, and between (inclusive). Every column type can also test is empty and is not empty so blank-detection works the same way across the file. Stack up to eight rules with ALL (AND) logic to narrow your output, or switch to ANY (OR) logic to widen it. Toggle Invert match to flip the result and return the rows that do not match, which is the cleanest way to bulk-delete rows by criteria. Numeric and date comparisons understand $1,200, 1.234,56, percentages, ISO 8601 dates, and a handful of other common formats, so $1,200 compares as 1200 and 2024-12-31 sorts after 2024-01-01 without manual cleanup. The parser is RFC 4180 compliant: quoted fields can contain delimiters, escaped quotes, and embedded newlines without breaking the row. Header rows stay pinned at the top of the output, populate the column dropdowns by name, and never get caught by your rules. The output panel shows a live preview of the first 50 matched rows, a match count, and a dropped count, plus a Copy CSV button and a Download button that streams a .csv or .tsv to your computer. Useful for cleaning marketing exports, filtering analytics dumps to a single country or plan, slicing log files to one status code, pulling all customers above a spend threshold, dropping rows with empty required columns, narrowing GA exports to one device category, isolating Stripe payouts in a date range, and any other place a quick "where col = value" makes a 10,000-row sheet manageable. Everything runs locally on your device; the CSV is parsed, filtered, and re-emitted entirely in your browser, so customer lists, financial exports, and private data never leave the page.

Free to use. Works in your browser. No signup, no login.