Export records as CSV
Paste an array of JSON objects and download or copy CSV-style output.
Input
[{"name":"A","score":10}] Output
name,score A,10
Developer And Data Tools
JSON to CSV
Convert JSON arrays or objects to CSV locally in the browser.
Waiting
Runs in your browser. Files do not leave your device.
Input
JSON to CSV. Paste text, run the tool locally, and copy the result.
Guide
How to use JSON to CSV
Step-by-step
- Choose or enter json in the workbench.
- Run the conversion tool locally in your browser.
- Review the csv result, then copy or download it if the workbench offers that action.
- Use the related tools on this page for cleanup, validation, conversion, or the next step in the workflow.
Questions
Is JSON to CSV free to use?
Yes. The public tool is free to use in your browser.
Are my files uploaded?
No. This tool runs locally in your browser, so selected files or pasted input are not uploaded to Convurter.
What should I check before using the csv result?
CSV escaping is handled by Papa Parse. Review the final output before using it in production work.
What can I do after this?
Good next steps include CSV to JSON, JSON Formatter, and CSV to Markdown Table.
Workflow fit
Use JSON to CSV in the right place
If you are unsure, start from the data chooser and pick by shape: validate, convert, infer schema, export, decode, or clean.
Best for
- Developer and data cleanup where validation, formatting, schema inference, export, or local transformation is more useful than a static explanation.
- Preparing JSON, CSV, XML, YAML, TOML, NDJSON, URLs, hashes, certificates, or web text for another tool or system.
- A focused convert task where the expected output is csv.
Before you start
- This tool runs in the browser, so keep the tab open until the result is created and downloaded or copied.
- Validate syntax before conversion so malformed input does not become a confusing output problem.
- Remove secrets, credentials, production tokens, private customer data, and unnecessary identifiers before using any shared browser session.
- Know the target system requirements: delimiter, encoding, columns, date format, schema, or workbook expectations.
- Decide whether conversion should preserve fidelity, transparency, text, table shape, or only the usable final format.
Quality checks
- Compare source and output for formatting, data shape, metadata, or visual fidelity before using the converted result.
- Review row counts, keys, columns, nesting, encoding, and empty values after conversion.
- Use schema inference or validation before handing structured data to another workflow.
- For hashes and decoders, remember that readable output is not proof of trust or authenticity.
- Copy or download the result only after confirming the displayed output matches the task you intended.
Common mistakes
- Exporting to XLSX or CSV before flattening the data shape can hide nested values or create ambiguous columns.
- Treating JWT, certificate, or CSR decoding as verification. Decoding is not the same as validating trust.
- Assuming format conversion preserves comments, ordering expectations, or every data type nuance.
- Closing the tab before downloading or copying a browser-generated result.
- Using conversion as cleanup. Fix structure, metadata, dimensions, or data shape before final conversion when possible.
Continue the workflow
Use these when the current result is only one step in a longer task.
Verify or clean up
Use these when the output needs checking, cleanup, comparison, compression, or a final share-ready pass.