JSON to CSV Converter

How to Use the JSON to CSV Converter

1. Paste a JSON array of objects into the input pane. Each object becomes one CSV row. Arrays of primitives alone will not convert - the top level must be an array whose items are objects.
2. Pick a delimiter: comma (the default), semicolon (common in European locales), or tab for TSV output.
3. Toggle the header row. Keep it on for most cases. Turn it off when appending to an existing CSV that already has a header.
4. Click Convert. Values that contain the delimiter, a quote, or a newline are automatically wrapped in double quotes per RFC 4180. Copy the output or download it as a .csv or .tsv file.

How the Conversion Works

The converter runs JSON.parse first, which guarantees well-formed input and converts duplicate keys into the last-one-wins value. It then walks every object in the array and collects the union of keys - so if one object has email and another has phone, the header row contains both and objects missing a key produce empty cells for that column. Key order in the header follows first-appearance across the object sequence.

Value serialisation handles the RFC 4180 quoting rules: any cell containing the active delimiter, a double quote, a carriage return, or a newline is wrapped in double quotes, and embedded double quotes are doubled (so say "hi" becomes "say ""hi"""). Nested objects and arrays serialise to JSON strings inside the cell because CSV has no nested structure concept; a value like {"address":{"city":"NY"}} becomes the literal text {"city":"NY"} quoted as a string. Booleans render as true/false, numbers as their JSON literal form, and null as an empty cell.

Real-World Uses

• Exporting an API response to a .csv that you can open in Excel or Google Sheets for stakeholder review.
• Dumping a list of users from a NoSQL database to CSV for import into a CRM or a data warehouse.
• Preparing data for bulk-upload into services that accept CSV but not JSON (Mailchimp imports, Salesforce Data Loader).
• Building a quick spreadsheet pivot over JSON log data without spinning up a full analytics pipeline.
• Generating a deterministic CSV test fixture from a JSON seed so both formats stay in sync.
• Converting a public JSON dataset into CSV for students or analysts who prefer spreadsheet tooling.

Gotchas to Watch For

• Heterogeneous objects. If your array mixes object shapes ("user" records and "event" records), the union-of-keys approach produces a sparse matrix with many empty cells. Consider filtering the array first so each conversion handles one shape.
• Deep nesting. Nested objects and arrays serialise as JSON strings. That means downstream readers (Excel, Sheets) see text like {"city":"NY"} rather than separate columns. Flatten the data with a map step before converting if you need one column per leaf field.
• Locale-specific delimiters. If your audience uses Excel in a locale where semicolon is the list separator, pick Semicolon. Otherwise the file opens with everything in column A. US/UK Excel uses comma by default.
• Line endings. The output uses \\r\\n per RFC 4180, which Excel prefers. Unix tools tolerate CRLF but may show ^M artefacts in old editors; convert with dos2unix if needed.
• Booleans and null. JavaScript null becomes empty; the string "null" becomes the literal text "null". Those are not the same thing downstream. Be explicit about which you want.

CSV Specification Background

RFC 4180 ("Common Format and MIME Type for CSV Files") is the closest thing to a canonical CSV spec. It requires CRLF line endings, double-quote escaping for special characters, and an optional header row. It also acknowledges that CSV in the wild varies widely. The RFC\'s quoting rules are the part worth memorising: wrap a field in double quotes if it contains a comma, a quote, or a line break; double any quotes inside the wrapped field. That algorithm - no more, no less - is what this converter implements. The MIME type is text/csv; for TSV the de facto type is text/tab-separated-values. Neither is used widely because browsers typically treat CSV/TSV as generic application/octet-stream downloads.

Compared to Other Converters

Command-line users reach for jq -r '.[] | [.a, .b, .c] | @csv' when they know the schema or csvkit\'s in2csv when they do not. Those tools scale to gigabytes and handle edge cases like reading from stdin with streaming. Spreadsheets have built-in CSV export but require loading the data first. Dedicated ETL frameworks like Airbyte or dbt handle JSON-to-CSV as one transformation among many. This in-browser tool wins on latency for ad hoc conversions of a few hundred to a few thousand rows; beyond that the memory pressure on a tab becomes noticeable and you should graduate to jq or a dedicated script.