Paste your CSV to convert it to JSON, or switch directions to convert JSON back to CSV. Supports auto-detection of commas, tabs, and semicolons as delimiters, and handles quoted fields with embedded commas and newlines.
CSV (Comma-Separated Values, defined by RFC 4180) is a plain-text tabular format: each line is a row, and values within a row are separated by a delimiter — typically a comma, though tabs and semicolons are common. The first row usually contains column headers. JSON (JavaScript Object Notation, RFC 8259) is a structured format that represents data as nested objects and arrays of key–value pairs. CSV is optimized for spreadsheets and bulk exports; JSON is optimized for APIs, configuration, and JavaScript-native data interchange.
Many workflows involve moving data between systems that expect different formats. A database export might give you CSV, but your frontend application or API needs JSON. Conversely, JSON API responses sometimes need to be imported into spreadsheets or BI tools that work best with CSV. Common use cases include:
Not all CSV files use commas. European data often uses semicolons because commas serve as decimal separators. Tab-separated values (TSV) are common in database exports. This tool auto-detects the delimiter by analyzing the first row, or you can choose manually. Quoted fields — values wrapped in double quotes — allow commas, newlines, and other special characters to appear inside a single field without breaking the structure, as required by RFC 4180.
When converting CSV to JSON, the first row becomes the set of header keys. Each subsequent row becomes a JSON object mapping those keys to their values. The final result is a JSON array of objects. When converting JSON → CSV, the tool unions all keys across every object in the array to form the header row, then writes each object as a CSV line. Keys missing in some objects produce empty cells for those rows.
A browser-based converter is the fastest path for ad-hoc conversions and data you can see on screen. It is the wrong tool for:
csv module, or jq on the command line.csvkit, jq, csv-parser in Node) instead of a web page.pandas.read_csv, csv.DictReader) gives you dtype control.address.city) before conversion. If your JSON has arrays inside arrays, you need custom flattening logic, not a generic converter.Working with complex data pipelines, ETL workflows, or API integrations? Our web development team builds robust data handling solutions with proper validation, streaming parsers, and error handling. Get in touch for a consultation.
Yes. Fields wrapped in double quotes are parsed correctly, including when they contain commas, newlines, or escaped quotes. The parser follows RFC 4180, the canonical CSV specification, so output is compatible with Excel, Google Sheets, and most database exports.
The first row is always treated as the header. If subsequent rows have fewer columns, the missing fields are emitted as empty strings in the JSON output. Extra columns beyond the header count are dropped rather than silently added as unnamed keys.
No. All parsing happens in your browser via client-side JavaScript. Your CSV and JSON data never leave your device and no network requests are made after the page loads. This makes the tool safe for sensitive data like customer exports or PII.
Performance depends on your device, but most desktops handle files up to ~10 MB without noticeable lag. Files above 50 MB may slow down the browser — for batch ETL workloads, use a streaming parser (Papa Parse, Python csv module, Node.js csv-parser) instead.
Comma (standard CSV), tab (TSV, common in database exports), and semicolon (common in European locales where comma is the decimal separator). The tool auto-detects by scanning the first row, or you can pick manually.
By default, all CSV values are strings, matching RFC 4180. Numbers like "42" become "42" (string) in JSON. If you need numeric coercion, run the output through a JSON.parse + type-cast step in your downstream code — it is a single-line transform.