CSV to JSON Converter

A spreadsheet export is easy for a person to read but awkward for code to consume — most APIs, scripts, and no-code automation tools expect JSON, not rows and columns. This CSV to JSON converter parses pasted CSV text and turns it into a proper JSON array, using the first row as object keys by default so every row below becomes a structured record with named fields.

It handles the CSV details that trip up a naive comma-split: values wrapped in quotes, commas that appear inside a quoted field, and doubled quote characters used to escape a literal quote. You also get to choose the delimiter — comma, semicolon, or tab — since not every CSV export actually uses commas, especially files produced by spreadsheet tools set to a European locale.

Everything happens in your browser; there's no upload step, so you paste the CSV text directly into the input box. The output is a standard JSON array you can copy to your clipboard or download as a .json file, ready to drop into an API test, a script, or a database seed file.

Rather than blindly splitting each line on every comma, the parser reads character by character and tracks whether it's currently inside a quoted field. A value like "Miller, John" stays as one field even though it contains a comma, because the parser only treats the delimiter as a column break when it's outside a pair of quotes.

A literal quote character inside a quoted value is represented by two quote characters in a row — ""like this"" — and the parser collapses that pair back down to a single quote in the output. This is the same escaping convention used by Excel, Google Sheets, and most CSV libraries, so files exported from those tools should paste in and convert cleanly without manual cleanup.

Not every CSV file actually uses commas. Spreadsheet software set to certain regional formats exports semicolon-separated files instead, since a comma is already used as the decimal separator in those locales, and many database and BI tools export tab-separated data by default. This tool doesn't auto-detect which one your file uses — you select it manually with the delimiter buttons before converting.

If the JSON output comes back with every row squeezed into a single field, that's the first thing to check: the file most likely uses a different delimiter than the one currently selected. Opening the raw CSV in a plain text editor for a few seconds is usually enough to confirm which character is actually separating the columns.

With "First row as headers" checked, the first line of your CSV is treated as field names and every following row becomes an object keyed by those names — for example name,age,city on the first line turns each data row into {"name": ..., "age": ..., "city": ...}. This is the right setting for almost any normal spreadsheet export.

Unchecking it treats every line, including the first, as plain data, and each row instead becomes a plain array of string values in their original column order, with no field names attached at all. Use this mode for raw data that never had a header row to begin with, or when you specifically want positional arrays rather than named objects.

This converter doesn't try to guess data types: a column containing 28 in the spreadsheet becomes "age": "28" in the JSON, a quoted string, not a number. The same goes for anything that looks like a boolean or a date — everything stays text exactly as it appeared in the CSV cell.

That's usually fine for copying data into documentation or a quick API test, but if you're feeding the output into code that expects real numbers or booleans, plan on a small conversion step afterward — something like mapping Number(row.age) in JavaScript — rather than assuming the JSON types already match what a database column expects.

Fully blank lines in your pasted CSV are skipped automatically before parsing begins, so stray empty rows at the end of an export won't turn into empty objects. Rows with a mismatched number of values compared to the header row are handled silently rather than flagged: a row with fewer values than there are headers gets empty strings for the missing trailing fields, and a row with more values than headers has the extras dropped without a warning.

That silent handling means a spreadsheet with a ragged edge — one row accidentally missing a trailing comma, or an extra stray value typed into a cell past the last real column — won't produce an error message, only a JSON object that's subtly short a field or missing data you expected. Scanning a few converted rows against the original spreadsheet is worth doing before you trust the output for anything important.

There's no file-upload button on this tool — you paste the CSV content directly into the text box. If your data lives in an actual .csv file, open it first in a text editor, spreadsheet app, or your terminal, select all, and copy it in rather than looking for a browse-and-select option.

For very large files this means copy-paste is doing the heavy lifting, which can get slow or hit clipboard limits in some browsers well before it hits any limit in the parser itself; splitting a huge export into smaller chunks and converting each separately is a practical workaround if you run into that.