JSON to CSV Converter — Free, Instant, No Upload

JSON arrays of objects map naturally to the tabular structure of CSV. Each object in the array becomes one row, and the union of all keys across all objects becomes the set of column headers. The converter collects every unique key found across all objects, orders them consistently, then iterates through the array writing one CSV row per object. If an object is missing a key that other objects have, that cell is written as empty — ensuring the CSV grid is always rectangular even when objects have sparse or inconsistent keys.

Nested object flattening: Real-world API responses rarely consist of flat key-value pairs. The converter handles nested objects by flattening them with dot notation — a nested field { "address": { "city": "Austin" } } becomes a column named address.city. Arrays within objects are serialized to semicolon-separated strings so their values are preserved in a single cell without adding extra columns.

CSV escaping: Values that contain commas, double quotes, or newlines are wrapped in double quotes per RFC 4180. Double quotes within values are escaped by doubling them. This ensures the output opens correctly in Excel, Google Sheets, LibreOffice Calc, or any standards-compliant CSV reader without data corruption or column misalignment.

A developer exports a list of users from an API and needs to import them into a spreadsheet. The JSON response looks like this:

[
  { "name": "Alice", "email": "alice@example.com", "age": 30, "address": { "city": "Austin" } },
  { "name": "Bob",   "email": "bob@example.com",   "age": 25, "address": { "city": "Denver" } }
]

The converter produces this CSV output, ready to open directly in Excel or Google Sheets:

name,email,age,address.city
Alice,alice@example.com,30,Austin
Bob,bob@example.com,25,Denver

The nested address.city field is flattened into its own column using dot notation. The developer can import this CSV into Google Sheets via File → Import, open it in Excel, load it into a pandas DataFrame with pd.read_csv(), or import it into any database using a bulk import tool. This workflow is standard for reporting dashboards, data analysis tasks, sharing structured data with non-technical stakeholders, and seeding databases from API exports.

Flat JSON — arrays of simple key-value objects with no nesting — converts to CSV with no ambiguity. Each key maps to a column, each object maps to a row. This covers the majority of data export and reporting use cases.

Deeply nested JSON produces long dot-notation column names that can become unwieldy in a spreadsheet. If the nesting is more than 2–3 levels deep, consider whether a full CSV conversion is the right output format or whether the data should be restructured before converting. For deeply nested structures, the JSON Tree Viewer can help you understand the shape of the data before deciding on a conversion strategy.

Array values within objects are serialized as semicolon-separated strings. This preserves the data in a single cell but loses the array structure. If you need to query array elements as separate rows or columns, pre-process the JSON to normalize the arrays before converting — for example, exploding each tag in a tags: ["a", "b"] field into separate rows.

Null and missing values produce empty CSV cells. This is the correct behavior for most spreadsheet imports, where an empty cell represents an absent value. If your downstream tool requires explicit null strings, pre-process the JSON to replace null values with the string "null" before converting.