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CSV / JSON / Markdown Table Converter
The CSV / JSON / Markdown Table Converter turns tabular data between the three formats SEO and data work lives in: spreadsheet-friendly CSV, machine-readable JSON, and the Markdown tables that drop straight into docs, tickets, and GitHub readmes. It uses a real RFC 4180 parser, so quoted fields containing commas, line breaks, and escaped quotes survive the round trip instead of shredding the columns the way a naive split on commas would.
It is completely free and browser-based. Paste a keyword export, an API response, or a crawl extract, pick a direction, and the conversion happens locally on your machine. Nothing you paste is sent to a server, which makes it safe for client data, internal exports, and anything you would rather not upload.
🔒 Private: everything runs in your browser. Nothing you paste is uploaded.
How to use the CSV, JSON, and Markdown converter
Choose a direction with the tabs, paste your data, set the CSV delimiter and whether the first row is a header, then press Convert. Use “Use output as input” to chain conversions, for example to clean a CSV by routing it through JSON and back.
CSV to JSON with a parser that respects quotes
The CSV to JSON direction reads the first row as field names and emits an array of objects, one per data row. The parser follows RFC 4180, so a quoted cell like "Smith, John" stays a single value and a doubled quote inside a field is correctly unescaped, rather than splitting on every comma. Values that look like numbers, true, false, or null are typed automatically, while identifiers such as a leading-zero ZIP code are kept as strings so they are not silently corrupted into integers.
JSON to CSV and Markdown for reports and tickets
Going the other way, the tool accepts an array of objects, an array of arrays, or a single object, then builds the column set from the union of every key so that records with missing fields still line up. JSON to CSV re-quotes any value containing the delimiter, a quote, or a newline, producing a file Excel and Google Sheets open cleanly. JSON to Markdown and CSV to Markdown render a padded, pipe-delimited table with the separator row, escaping stray pipes so the table never breaks inside a pull request or document.
Delimiters, headers, and chaining conversions
European exports often use a semicolon instead of a comma, and database dumps frequently use tabs, so the delimiter selector covers all three for both reading and writing CSV. The header toggle handles files with no header row by generating column_1, column_2, and so on, so headerless data still converts cleanly. The “Use output as input” button feeds a result back into the input box and flips the direction, which is the fastest way to normalise a messy CSV by passing it through JSON and rebuilding it.
CSV, JSON, and Markdown converter frequently asked questions
Q1Does the converter handle commas inside quoted CSV fields?
Yes. The parser implements RFC 4180, the standard CSV specification, so a quoted field such as “Paris, France” is treated as one value rather than two columns. It also unescapes doubled quotes inside a field and preserves line breaks that appear inside quotes, which a simple split-on-comma approach would break apart and misalign.
Q2Can it convert JSON back into a CSV spreadsheet?
Yes. Switch to the JSON to CSV tab and paste an array of objects. The tool builds the column list from the union of all keys, fills missing fields with blanks, and quotes any value that contains the delimiter, a quote, or a newline. The result opens directly in Excel, Google Sheets, or LibreOffice without further cleanup.
Q3Is my data uploaded anywhere when I convert it?
No. Every conversion runs in your browser using client-side JavaScript, and nothing you paste is sent to a server, logged, or stored. The tool keeps working after the page has loaded, so it is safe for confidential client exports, internal datasets, and anything you would not want leaving your machine.
Q4What if my CSV has no header row?
Set the first-row option to “Is data” and the converter will generate generic column names, column_1, column_2, and so on, instead of consuming your first record as field names. This keeps every row of data intact and is useful for raw exports, log dumps, and pasted ranges that begin straight away with values.
Q5Why are some numbers kept as text in the JSON output?
The converter types clean numbers, booleans, and null automatically, but it deliberately keeps values like 007 or a leading-zero phone code as strings. Converting those to integers would drop the leading zero and corrupt the identifier, so the tool preserves the original text whenever turning it into a number would change its meaning.
