CSV to JSON

Spreadsheet data often needs to become JSON: seed data for a web app, a mock API response, a config table for a static site, a dataset for a charting library, or a payload for a no-code automation. Writing that JSON by hand from a 300-row sheet is not an option.

This converter reads CSV or TSV in your browser and produces JSON in the shape you need: an array of objects using the header row as keys, an array of arrays, or NDJSON (one JSON object per line) for streaming and bulk-import tools. Numbers and true/false can become real JSON types, while codes with leading zeros stay as strings.

The parser follows the CSV standard properly, so quoted fields containing commas, escaped double quotes and line breaks inside cells all come through intact.

Empty header cells are named column_1, column_2 and so on, so every record ends up with a usable key even when a spreadsheet has an unlabelled column.

Processing happens in your browser tab, so exported user tables and internal reference data are not uploaded anywhere on their way to becoming JSON.

How It Works

1. Add CSV files — up to 20, delimiter detected automatically or set manually (comma, semicolon, tab, pipe).
2. Choose the JSON shape — objects keyed by header, arrays of rows, or NDJSON; enable type conversion and pretty-printing if wanted.
3. Download — each CSV becomes a .json or .ndjson file named after it. The result list shows how many records were created.

Compatibility & Support

Supported Input → Output Formats

• Input: .csv, .tsv, delimited .txt, UTF-8 (with or without BOM)
• Output: .json (array of objects or arrays) or .ndjson / JSON Lines
• Types: numbers, true/false converted when enabled; leading-zero values kept as strings

Unsupported Formats

Nested JSON (objects inside objects) can't be inferred from a flat CSV — every record is one level deep. Dates stay as strings exactly as written, because JSON has no date type. Excel files should be exported to CSV first with Excel to CSV.

File Size Limits

Up to 20 files per batch and 50 MB each. Pretty-printed JSON is roughly twice the size of the CSV it came from; turn pretty-printing off for large datasets.

The reason is repetition: in an array of objects every record repeats every key name, so a 10,000-row table with ten columns writes each column name 10,000 times. If size matters more than readability, the array-of-arrays shape stores the header once and is close to the CSV size.

For browser apps, JSON files over about 5 MB start to slow down page loads; consider splitting large datasets or loading them on demand.

Who Should Use This Tool?

• Students: A student converts a CSV of country statistics to JSON for a D3.js or Chart.js class project.
• Freelancers & Designers: A front-end freelancer turns a client's product spreadsheet into JSON for a Next.js static site.
• Office Professionals: An operations specialist converts a CSV of store locations to JSON for a map widget on the company website.
• Developers: A developer exports seed data as NDJSON for an Elasticsearch or BigQuery bulk load.

Why This Tool Beats the Alternatives

• Correct CSV parsing instead of naïve comma splitting.
• NDJSON output for bulk-load tools, which most converters lack.
• Type conversion that protects leading zeros.
• No upload of internal data.
• Free batch conversion.

Pro Tips

• Clean up header names before converting — they become your JSON keys, so prefer first_name over “First Name (required)”.
• Validate or reformat the result with the JSON Formatter before committing it to a repository or pasting it into an API client.
• For huge datasets choose NDJSON: tools such as BigQuery, Elasticsearch, MongoDB mongoimport and many log pipelines can stream it line by line instead of loading one giant array into memory.

Add your CSV files above, pick a JSON shape, and download.