CSV Viewer & Editor

How to use this tool

1. Paste CSV text or upload a .csv file.
2. Choose a delimiter if automatic detection does not match your file.
3. Search, sort, and edit records in the table.
4. Download all rows or only the filtered rows as CSV or JSON.

CSV format and delimiters

CSV (Comma-Separated Values) stores records as text, with one row per record and a delimiter between fields. This viewer detects comma, semicolon, tab, and pipe separators from the file's rows. Detection is a best guess: a file with inconsistent records or punctuation-heavy values can be ambiguous, so use the Delimiter menu to choose the separator yourself when the columns do not line up.

The common quoting rules from RFC 4180 also apply:

• Each record is on its own line (CRLF line breaks in strict RFC 4180)
• Fields are separated by commas (or another delimiter)
• Fields containing commas, double quotes, or newlines must be enclosed in double quotes
• A literal double quote is escaped by doubling it: "He said ""hello"""

Common CSV variants

• TSV: tab-separated values. Avoids comma-in-data issues. Common in bioinformatics.
• PSV: pipe-separated values (|). Less common but unambiguous.
• CSV with semicolons: default in European Excel due to comma being the decimal separator in many locales.

How to open a large CSV

This tool runs entirely in your browser and has no hard row/column limits, but browser memory constraints apply. Files under 50 MB typically load fine. For files over 100 MB or with millions of rows, consider:

• Python pandas: pd.read_csv("file.csv") can handle multi-GB files efficiently with chunking.
• DuckDB: an in-process SQL database that can query CSV files directly without loading them into memory: SELECT * FROM 'file.csv' WHERE ...
• csvkit: command-line suite (csvstat, csvcut, csvsql) for quick inspection and manipulation.

Sorting, searching, and exporting

Click any column header to sort ascending or descending. Search checks every cell in each data row without changing the original values. Download filtered CSV or JSON to keep only the matching rows, or choose the all-rows export buttons to include the complete table. JSON export uses the first row as object keys; blank headers get a Column label and duplicate headers gain a numeric suffix so values are not overwritten. For advanced conditions such as “show rows where column A is greater than 100,” export the data and use a spreadsheet, jq, or a SQL database.

Data validation tips

• Duplicate headers: Two columns with the same name cause key collisions in JSON conversion. Rename one.
• Empty rows: Trailing commas or blank lines at the end of the file create empty records. Remove them before processing.
• Mismatched column counts: The first record sets the table's columns. Short rows show blank cells; extra values can be hidden in the table and omitted from JSON export. Check the source file or normalize its rows before relying on the converted data.
• Encoding issues: If non-ASCII characters appear garbled, the file may be in ISO-8859-1 or Windows-1252 instead of UTF-8. Re-save as UTF-8 in a text editor.