File Format Batch Converter — Free JSON, CSV & XML Tool

This tool converts small datasets between JSON, CSV, and XML — the three plain-text formats most research tools actually accept. JSON to CSV flattens a list of records into a spreadsheet-ready table. CSV to JSON does the reverse for web tools. JSON to XML and XML to JSON bridge older systems that still speak XML. One limit is stated plainly up front: Parquet is not supported. Parquet is a binary columnar format that needs a real parser and cannot be handled honestly by a small browser tool, so this converter does not pretend otherwise.

How to use it

Choose the conversion direction.
Paste your input data.
Click Convert and read the output box.
Copy the output into your target tool.
If conversion fails, check for a missing bracket, comma, or closing tag first.

Worked example

A collaborator sends a JSON list of participants: [{“name”:”Ana”,”age”:24},{“name”:”Bo”,”age”:31}]. Your spreadsheet workflow needs CSV, so you choose JSON to CSV and get name,age followed by Ana,24 and Bo,31 — ready to paste into a spreadsheet. Going the other way, a CSV export from a survey platform becomes JSON your web dashboard can read. Nested JSON objects are not flattened here; for nested data, use the API Response Flattener first, then convert the flat result.

Honest limits

This converter handles flat records well and nested data poorly: nested objects inside JSON become the text [object Object] in CSV, which is a warning sign, not a result. CSV parsing here is simple — it splits on commas and does not fully support quoted commas inside cells. Very large pastes will slow the page. And again: no Parquet, no Excel binary files, no database dumps. For those, use a desktop tool or a proper library in Python or R, where binary formats belong.

Who it is for

This tool is for researchers and analysts moving small datasets between a survey platform, a spreadsheet, and a web tool, and for students learning how the same data looks in three formats. It is a bridge for quick conversions, not a data pipeline: if you convert the same file every week, automate it with a script instead.

Going further

Always round-trip your conversion before trusting it. Convert the data, convert it back, and compare the two versions field by field: a clean round-trip returns the same values, while a lossy one drops fields, mangles accents, or turns numbers into text. Check the edges specifically — empty fields, values containing commas or quotes, accented characters, and leading zeros in codes like 007, which spreadsheets love to turn into 7. When a conversion loses information, the fix is usually to change the route, not to patch the output by hand: flatten nested data first, choose a format that supports your types, or keep an identifier column so rows can be re-joined to the original. Record the conversion in your processing log with the tool, the direction, and the date, because a dataset whose format history is unknown cannot be audited. Small conversions deserve the same provenance as big ones; the errors they hide are just as real. Choose the output format by what the next tool actually needs, not by habit. Spreadsheets want CSV with a header row and one record per line; web dashboards want JSON arrays; older institutional systems want XML with a declared schema. Sending JSON to a spreadsheet user, or CSV to an API, creates a second conversion downstream where errors multiply. When a collaborator asks for a format you do not recognize, ask what tool will open it before converting — five minutes of clarification beats a file that opens as gibberish and a week of confused emails about whose export is broken.

Embed this tool on your website — free

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