About Parse CSV to JSON
Paste any CSV data and instantly convert it to a JSON array. Auto-detects comma, semicolon, and tab delimiters. Toggle Header row to use the first row as keys, and Infer types to automatically convert numbers and booleans. Handles quoted fields, embedded commas, and empty cells.
Frequently Asked Questions
What is CSV to JSON conversion?
CSV to JSON conversion parses a comma-separated values file and transforms each row into a JSON object, with column headers as keys. The result is a JSON array ready for use in a REST API, JavaScript application, or any system that consumes JSON data.
How does the converter handle CSV files with quoted fields containing commas?
Fields wrapped in double quotes are treated as a single value even if they contain commas: "Smith, John",42 correctly produces {"name":"Smith, John","age":"42"}. Escaped quotes inside a quoted field ("He said ""hello""") are unescaped to a single quote. Standard RFC 4180 quoting rules are followed.
Can I convert a CSV with a header row to an array of named objects?
Yes — the first row is treated as the header by default, and each subsequent row becomes a JSON object with keys from the header. If your CSV has no header, toggle the "No header" option and the output uses column indices (column_0, column_1, etc.) or you can specify custom column names.
What happens to empty cells in CSV when converting to JSON?
Empty cells convert to empty string values "" by default. Toggle "Convert empty to null" to output null instead — useful for databases where null and empty string have different semantics. Entirely empty rows are skipped.
How do I convert a CSV with thousands of rows without performance issues?
This tool processes CSV entirely in your browser and handles files up to a few megabytes without issues. For very large files (tens of MB), the browser may slow briefly during parsing. For production batch processing, use a server-side tool: Python pandas read_csv() and to_json(), or the Node.js csv-parse library with streaming.
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