CSV to JSON

Convert CSV into JSON — objects (with headers) or arrays. Free, in your browser.

Parses CSV (quoted fields supported) into JSON, in your browser.

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  • Header or array mode
  • Quoted-field support
  • 100% private
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  • Type inference & custom delimiters

About the CSV to JSON

CSV to JSON turns a flat, comma-separated table into structured JSON your code can actually use. The standard conversion reads the first line of your file as the header row, then maps each header to the matching cell in every following row. The result is an array of objects, where each object is one record and each key is a column name. So a file beginning with name,email,age followed by Jane,jane@site.com,31 becomes a JSON object with name, email, and age keys. This is the shape almost every JavaScript framework, REST API, and document database expects, which is why a quick paste-and-convert beats hand-editing brackets by hand.

Reach for this tool whenever tabular data needs to cross into a code or API world. Common jobs include seeding a MongoDB collection from a spreadsheet export, feeding rows into a React or Vue table component, building a small JSON config or fixtures file from a CRM or analytics download, and prototyping an endpoint before a real database exists. Because CSV is what Excel and Google Sheets produce on export, this conversion is often the first step between a non-technical data source and an application. JSON's nesting and explicit types make the same data far easier to validate, query, and pass between services than a flat sheet.

Under the hood, conversion is mostly careful parsing. Each line is split on the delimiter, but a good parser does not split blindly: a field wrapped in double quotes can legally contain commas, line breaks, and even quotes of its own. Per RFC 4180, a literal quote inside a quoted field is written as two consecutive quotes, so ""Ada"", Lovelace" is one value, not two. After splitting, the tool can optionally infer types, turning the text 42 into a JSON number and true into a boolean, since CSV stores everything as plain text while JSON distinguishes numbers, booleans, strings, and null.

Type inference is powerful but worth treating with care. Numeric-looking strings such as ZIP codes (01234) or phone numbers lose their leading zeros or precision if forced into numbers, so identifiers should usually stay strings. When that matters, leave inference off and keep every value as text, then cast deliberately in your code. On privacy: this converter runs entirely in your browser. The CSV you paste or upload is parsed locally with JavaScript and never sent to a server, so customer lists, exports, and other sensitive tables stay on your machine while you work.

Frequently asked questions

How does the first row become the JSON keys?

By default the tool treats line one as the header row and uses each column name as an object key. Every later row becomes one object, mapping those keys to its cell values. If your file has no header, you can switch to an output that uses numeric indexes or arrays instead.

Will it handle commas inside a field?

Yes, as long as the field is wrapped in double quotes per the CSV convention. The parser keeps quoted commas, line breaks, and escaped quotes (written as two double quotes) inside a single value instead of splitting on them.

Are numbers converted to real numbers or kept as text?

You choose. With type inference on, values like 42 and true become a JSON number and boolean; with it off, every value stays a string. Turn inference off for ZIP codes, phone numbers, or any ID with leading zeros, since those break when treated as numbers.

My file uses semicolons, not commas. Will it work?

Set the delimiter to a semicolon and it will parse correctly. Semicolon-delimited files are common from Excel in European locales, where the comma is the decimal separator. Tab-separated (TSV) files are also supported by choosing the tab delimiter.

Is my data uploaded anywhere?

No. The conversion happens entirely in your browser using JavaScript, so your CSV is never transmitted to a server. You can convert sensitive exports offline and copy the JSON straight out.

From our blog

How to Compare Two Texts and Actually Trust the Differences

By the Super Simple Digital Tools Team · Updated June 2026

Comparing two versions of a document by eye is one of those tasks that feels easy and goes wrong constantly. The human eye is excellent at reading meaning and terrible at catching a single transposed digit, a removed "not," or a clause that changed from "may" to "shall." A text diff checker removes that guesswork by mechanically aligning the two versions and coloring in every place they disagree. The skill worth learning is not how to run it, but how to read the output and how to set up your inputs so the differences it reports are the ones you care about.

Start by deciding which view fits the job. A side-by-side view is best when you want to read both versions in context, for example reviewing a rewritten paragraph or comparing two contract drafts where surrounding wording matters. A unified view stacks the changes into one column with plus and minus markers, which is denser and closer to what you would paste into a chat or commit message. Unified shines for code and config because it shows only the changed regions plus a few lines of context, so a small edit in a large file does not bury you in unchanged text.

Understanding what the tool is doing helps you trust it. Internally it solves the longest common subsequence problem: it finds the largest ordering of lines (or words) that both texts share, then everything outside that shared backbone becomes an addition or a deletion. The dominant method is Eugene Myers' 1986 algorithm, which frames the comparison as finding the shortest route through an edit graph and runs in O(ND) time, where D is the number of differences. Because git, GNU diff, and most review tools use this same family, the diff you see here is consistent with the diff your teammates see.

The most common surprise is a diff that flags things you cannot visually distinguish. This is the algorithm doing its job, not a bug. Trailing whitespace, a tab swapped for spaces, Windows versus Unix line endings, and curly versus straight quotes are all real byte-level differences. When that noise gets in the way, normalize your inputs first: strip trailing spaces, standardize line endings, or paste plain text rather than copying from a formatted editor. When precision is the whole point, such as auditing a legal change, leave them in so nothing slips past.

Finally, treat the diff as evidence, not a verdict. It tells you exactly where two texts differ, but it cannot tell you whether a change is intentional, correct, or dangerous. A diff can show that a license URL changed or that a tax rate moved from 18 to 1.8, but you still have to judge whether that edit should have happened. Used that way, with a sensible view, clean inputs, and your own review on top, a diff checker turns version comparison from a slow, error-prone chore into a fast and reliable check.

  • Use side-by-side for prose and contracts where context matters; switch to unified for code, config, and anything you will paste into chat or a commit.
  • Normalize line endings and strip trailing whitespace before diffing if you only care about real content changes, to cut out invisible noise.
  • Diff two JSON or API responses to instantly spot an added, removed, or renamed field instead of scanning nested objects by hand.
  • When verifying a revised contract or config, leave whitespace and quote differences in place so subtle but real edits are not hidden.

Read the full guide →

Tool by the Super Simple Digital Tools Team. Reviewed by our editorial team. Free to use, no signup required.

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