JSON to CSV Converter

Example Input

[
  {"id": 1, "name": "Priya Singh", "email": "priya@learnhubly.com", "role": "Admin", "active": true, "joined": "2024-01-15"},
  {"id": 2, "name": "Jane Smith", "email": "jane@example.com", "role": "User", "active": true, "joined": "2024-03-22"},
  {"id": 3, "name": "Bob Johnson", "email": "bob@example.com", "role": "Editor", "active": false, "joined": "2024-05-10"}
]

Example Output

id,name,email,role,active,joined
1,Priya Singh,priya@learnhubly.com,Admin,true,2024-01-15
2,Jane Smith,jane@example.com,User,true,2024-03-22
3,Bob Johnson,bob@example.com,Editor,false,2024-05-10

Does it support MySQL and PostgreSQL export formats?

This tool converts JSON to standard CSV format — the universal format that MySQL, PostgreSQL, Excel, Google Sheets, and virtually every other data tool can read. If you have exported query results from MySQL or PostgreSQL as JSON, paste that JSON here to get a clean CSV back. Most databases also support exporting directly to CSV, but if you have the JSON and need the CSV, this is the fastest path.

Can it handle large JSON arrays with thousands of records?

Yes. The conversion runs locally in your browser using JavaScript and handles arrays with thousands of records in under a second for typical data shapes. For very large datasets — tens of thousands of records or objects with many deeply nested fields — performance depends on your device hardware, but the tool is designed to handle real-world API response sizes and database export sizes without issues.

What happens to nested objects and arrays in the JSON?

CSV is a flat format with no way to represent hierarchy. Nested objects are flattened using dot notation — a field like {"address": {"city": "Mumbai"}} becomes a column named address.city with the value Mumbai. Nested arrays are serialized as a quoted string in the cell — for example a tags field containing ["javascript", "nodejs"] becomes the string javascript,nodejs in that cell. If you need proper column separation for nested array values, flatten your JSON before converting.

Is my data safe when using this tool?

Yes. The conversion runs entirely in your browser — your JSON data is never transmitted to any server, never logged, and never stored. This matters because JSON data from real systems often contains user records, email addresses, internal IDs, financial figures, or other sensitive information. Browser-based processing means none of that data ever leaves your machine.

Why does Excel open my CSV with all data in one column?

This happens when Excel's regional settings use a semicolon as the list separator instead of a comma — common in European Windows locales where the comma is used as a decimal separator. To fix it, open Excel, go to Data, click From Text/CSV, select your file, and in the import dialog set the delimiter to comma explicitly. Alternatively, add sep=, as the very first line of the CSV file before the headers — Excel reads this as an instruction to use comma as the delimiter.

Can I convert the CSV back to JSON?

Not with this tool — JSON to CSV is a one-way conversion here. For converting CSV back to JSON, LearnHubly has a separate CSV to SQL tool in the related tools sidebar that may help depending on your use case. For a direct CSV to JSON conversion, most modern spreadsheet applications can export to JSON, or you can use a simple script in Python with the csv and json modules or in JavaScript with the Papa Parse library.

What does the Schema button do?

The Schema button analyzes your JSON array and shows you all the unique keys detected across every object in the array. This is useful before converting because it tells you exactly which column headers the CSV will have, and it reveals structural inconsistencies — for example if most objects have an email field but 10 records are missing it, the schema view shows that the email field exists but is not present in all records. Those 10 records will have an empty email cell in the CSV.

How do I convert JSON to CSV in Python or JavaScript without a tool?

In Python, use the built-in csv and json modules: load your JSON with json.loads(), then write the rows with csv.DictWriter which handles header detection and quoting automatically. In JavaScript, the most reliable approach is the Papa Parse library's unparse() function which handles all RFC 4180 edge cases including commas in values and escaped quotes. For one-off conversions this tool is faster, but for automated pipelines that run repeatedly, implementing it in code with a proper library is the right approach.