CSV ⇄ JSON Converter — RFC 4180 Compliant

Real-world use cases

• Turn a 2,000-row Excel export into JSON for a seed script

  You exported products.csv from Excel with 2,000 rows, and one column holds descriptions with commas and line breaks. Paste it, keep "First row is header" on, and copy the JSON array straight into your seed script. The quoted multi-line cells stay intact, so no row gets split in two the way a naive split-on-comma would do.

• Reshape an API's JSON response into a CSV for a stakeholder

  A teammate sends you 350 records as a JSON array but the finance lead only opens spreadsheets. Paste the JSON, switch to JSON→CSV, and the columns get auto-detected from the object keys. Open the result in Google Sheets in 10 seconds, no Python, no pandas, no "let me write a quick script" detour.

• Clean up a semicolon-delimited export from German Excel

  Your German colleague's export uses `;` as the separator because the comma is the decimal mark. Set the delimiter to semicolon and the parser reads it correctly, so 19,90 stays one field instead of becoming two. Convert to JSON, then back to comma-delimited CSV your US tools expect.

• Sanity-check a headerless log dump before importing

  A system dumped 500 lines with no header row. Toggle "First row is header" off so keys become col_1, col_2, col_3. Now you can eyeball the JSON, confirm column 3 is always a timestamp, and catch the 12 malformed rows before they break your import job downstream.

Common pitfalls

• Splitting CSV on every comma yourself instead of parsing it — a cell like `"Smith, John"` becomes two columns. Let the parser respect the quotes.

• Forgetting to turn off "First row is header" for headerless data, so your real first data row silently vanishes into keys. Check the first JSON object looks like data, not column names.

• Assuming JSON→CSV preserves nested objects — a value like `{"city":"NYC"}` flattens to `[object Object]`. Flatten nested fields in the JSON before converting.

Privacy