Paste a JSON array of objects into the left panel and get a clean CSV in the right panel instantly. Each object becomes a row, and keys become column headers. Download the result as a .csv file ready for Excel or Google Sheets.

CSV is a flat, tabular format — rows and columns with no nesting. The converter works best with a JSON array of objects where every object has the same keys:

[
  { "id": 1, "name": "Alice", "role": "Engineer" },
  { "id": 2, "name": "Bob",   "role": "Designer" }
]

Each object becomes one CSV row, and the keys become column headers:

id,name,role
1,Alice,Engineer
2,Bob,Designer

If your JSON is a single object, wrap it in [] brackets first to make it a one-row array.

If objects have different keys, all unique keys are used as headers and missing values are left as empty cells.

Nested objects and arrays are serialized as JSON strings inside the cell — they cannot be represented as CSV columns without flattening first.

REST APIs return JSON arrays. Converting the response to CSV lets you open it in Excel or Google Sheets for analysis, filtering, or sharing with colleagues who don't work with raw JSON.

For database bulk imports, MySQL, PostgreSQL's COPY, and Airtable all accept CSV. Converting a JSON fixture file to CSV is faster than writing a custom import script.

Python's pandas, R, and most BI tools read CSV natively — no JSON parser needed. For data migrations, CSV is often the lowest common denominator that both the source and destination system understand without custom adapters.

CSV cannot represent nested structures natively. If your JSON has nested objects or arrays, you have two options:

Option 1 — flatten before converting. Restructure your JSON to move nested values to top-level keys:

// Before
{ "user": { "name": "Alice", "city": "NYC" } }

// After flattening
{ "user_name": "Alice", "user_city": "NYC" }

Option 2 — accept stringified cells. The converter will serialize nested values as JSON strings inside the CSV cell. This is lossy for analysis but acceptable if you just need a quick export and the nested fields are not critical.

For automated flattening, tools like flat (npm) or Python's pandas.json_normalize() handle this before you paste the result here.