JSON to CSV Converter

Convert JSON to CSV for Spreadsheets and Data Analysis

JSON is a natural format for API responses and application data, but spreadsheets work best with rows and columns. This JSON to CSV converter turns a JSON array of records or a single object into a table you can copy or download. You can choose the delimiter, decide how nested values appear, and set the column order before exporting.

Paste valid JSON or open a .json file, choose the options that fit the application you will use, and select Convert to CSV. Check the table preview and row and column counts, then copy the CSV or download it as converted.csv. Conversion happens in your browser; the JSON is not uploaded for conversion.

How to Convert JSON to CSV

1. Paste JSON into the input box, choose Open JSON, or use the sample to see the output format.
2. Choose a delimiter: comma, semicolon, or tab.
3. Set whether to include a header row and flatten nested objects. Choose how arrays should appear in a cell.
4. Optionally add a preferred column order, enable the UTF-8 BOM for Excel, or turn on spreadsheet-safe output.
5. Select Convert to CSV. Review the preview, then copy or download the result.

The input must be valid JSON. If conversion stops with a syntax error, use the JSON Validator to locate formatting problems before trying again.

Example: Flattening Nested JSON into CSV

For an array of support tickets, each object becomes a row. With Flatten nested objects enabled, fields inside an object become columns with dotted names:

Input JSON

[
  {"id": 1042, "status": "open", "customer": {"name": "Mina Patel", "city": "Pune"}},
  {"id": 1043, "status": "closed", "customer": {"name": "Luis Chen", "city": "Austin"}}
]

CSV output

id,status,customer.name,customer.city
1042,open,Mina Patel,Pune
1043,closed,Luis Chen,Austin

This layout makes nested values easy to filter or sort in a spreadsheet. If you turn flattening off, the nested customer object is kept as JSON text inside one cell instead.

What Happens to Different JSON Values?

Arrays of objects

An array of objects is the most convenient shape for a table: each item becomes a row, and the properties found across the records become columns. If one record lacks a property that another has, the corresponding CSV cell is blank. A standalone object is treated as one data row.

Nested objects

Flattening turns nested paths into column names, such as customer.city. This is useful when you need to work with individual fields. If you need to preserve the nested structure in a cell, switch flattening off; the object is then written as JSON text. CSV cannot preserve JSON nesting as a table structure, so consider whether the destination can accept a flattened representation.

Arrays inside a record

Nested arrays stay in a single cell; they do not create extra rows or columns. Stringify each value keeps the array in JSON notation, for example ["email","phone"]. Join with ; puts its items into one semicolon-separated value. Joining is convenient for simple lists, but if an item itself contains a semicolon, the result can be ambiguous. Keep the JSON-stringified option when preserving the array boundaries matters.

Nulls, missing keys, and inconsistent records

Null values and missing properties appear as empty cells. The converter builds its headers from keys found in the records, so records with different fields still line up under one set of columns. An empty array or a structure with no exportable fields produces no CSV table.

Choose CSV Options for the Destination

• Delimiter: Use comma for standard CSV, semicolon for software configured to use it as a list separator, or tab when you need tab-separated text.
• Header row: Keep it on when column names will help you or the receiving system identify fields. Turn it off when the destination expects data rows only.
• Column order: Enter existing header names separated by commas or new lines, such as id,customer.name,status. Listed columns move to the front; other columns follow in their original discovery order. Names must match the generated headers.
• UTF-8 BOM for Excel: Add a byte order mark that can help some Excel versions detect UTF-8 text when opening a CSV. If text still looks incorrect, import the file and select UTF-8 as the encoding.
• Spreadsheet-safe output: Prefixes string values beginning with whitespace, =, +, -, or @ with an apostrophe. This can reduce the chance that spreadsheet software interprets those strings as formulas. It changes the exported text, so review it before using the CSV in another system.

Fields containing the selected delimiter, a quotation mark, or a line break are enclosed in double quotes; quotation marks inside those fields are doubled. This keeps ordinary CSV records readable by compatible importers. Still, check the preview when your destination has a strict import format.

Before You Import the CSV

CSV is a table, not a complete copy of the original JSON. It does not carry JSON types such as number, boolean, or null in a way every spreadsheet will preserve, and nested structures need to be flattened or stored as text. Spreadsheet applications may also remove leading zeros, reformat long numbers, recognize dates, or interpret formula-like values. For identifiers such as postal codes, account numbers, or IDs, import the relevant columns as text if their exact characters must remain unchanged.

For large files, the file picker accepts JSON files up to 50 MiB. Large inputs can still take time and memory to parse in a browser. If a file is too large to handle comfortably, split it into smaller records before converting.

Frequently Asked Questions

Related JSON Tools

Use the JSON Formatter to make JSON easier to read, the JSON Validator to check syntax, the JSON Viewer to inspect nested data, or JSON Compare to identify differences between two documents.