CSV Row Filter

What this tool does

The CSV Row Filter takes a CSV file and keeps only the rows that match the conditions you set. You drop a file or paste CSV text, build one or more rules made of a column, an operator and a value, and the tool shows the matching rows in a live preview along with a count like “12 of 80 rows match”. You can then copy the filtered data or download it as filtered.csv, header included.

A CSV — comma-separated values — is a plain-text spreadsheet: each line is a row, and the comma-separated values on that line are the cells, with the first line usually naming the columns. Filtering means selecting a subset of those rows by a rule rather than by hand.

Why and when you’d use it

Most useful work starts by narrowing a big export down to the part you actually care about. Spreadsheets can filter, but the controls are fiddly, the results are hard to share, and it is easy to forget a filter is still applied. A dedicated filter that outputs a clean new file avoids all of that.

Common situations:

• Isolating a segment. Pull only the customers in one country, or only orders with a “pending” status, from a large export.
• Cleaning a marketing list. Keep rows where the email column is not empty, or drop rows that contain a test value.
• Focusing a report. Keep only transactions above a certain amount before handing the file to finance.
• Building a sample. Extract a small, representative slice of a dataset to share or test with.

How to use it

1. Drop a CSV file onto the upload area or paste CSV text into the box. A sample with an example condition loads automatically.
2. Set the Delimiter if the columns look wrong — Auto-detect handles most files, or pick Comma, Semicolon, Tab or Pipe. Toggle First row is a header if your file has no header line.
3. Build conditions. Each one is a column, an operator (equals, contains, greater than, is empty, matches regex and more) and a value. Operators like “is empty” need no value.
4. Choose Match all (AND) to require every condition, or Match any (OR) to require just one. Use Case-sensitive if upper and lower case should be treated as different.
5. Read the match count, check the preview, then Copy CSV or Download .csv to save filtered.csv.

Common pitfalls and tips

The most frequent surprise is case sensitivity. By default the text operators ignore case, so Shipped and shipped match — switch on Case-sensitive only when the distinction matters. Another is stray whitespace: a value of shipped with a trailing space will not equal shipped. The “is empty” and “is not empty” operators ignore surrounding spaces, but “equals” does not, so trim your data if exports are inconsistent.

Number comparisons depend on the data genuinely being numeric. If a total column contains a currency symbol or thousands separators — £1,200 rather than 1200 — it no longer reads as a number, and “greater than” falls back to alphabetical order. Clean such columns first. Watch, too, for Excel altering values: it strips leading zeros from ZIP codes and SKUs, which can make an “equals” condition miss rows you expected to keep.

If you also need to drop unwanted columns, the CSV Column Extractor trims and reorders them, and the CSV Merger combines several files before you filter them. All three share the same CSV parser.

Privacy

This tool runs entirely in your browser. The CSV you drop or paste is parsed and filtered by JavaScript on your own device. It is never uploaded, never stored between visits and never logged. When you close the tab the data is gone — which makes the filter safe for customer records, order exports and any file you would rather keep off the network.