JSON to CSV Converter (Flattens Nested JSON)

How the conversion works

JSON is a tree; CSV is a grid. Converting between them means deciding how the tree maps onto rows and columns. This tool uses the convention most developers expect: the top-level array is the list of rows, each object in it is one row, and every distinct key across all objects becomes a column.

The header row is built from the union of keys in the order they first appear. If the first record has id, name, and email, and the third record adds phone, phone becomes the fourth column and earlier rows get an empty cell there. Nothing is dropped just because records are inconsistent, which is normal for API data.

Flattening nested JSON

Real JSON is rarely flat. A user record from an API often looks like {"id": 7, "address": {"city": "Austin", "zip": "78701"}}. Without flattening, the address column would hold the raw text {"city":"Austin","zip":"78701"}, which is useless in a spreadsheet.

With Flatten on, the converter walks each object recursively and joins the path with dots, producing columns id, address.city, and address.zip. That is the same convention used by pandas json_normalize and most BI import tools, so the column names will look familiar downstream.

Arrays are the exception. A tags field of ["math", "code"] could mean one column per tag, one row per tag, or a joined string, and the right answer depends on your analysis. The tool keeps arrays as compact JSON in a single cell so no information is lost; split them later in your spreadsheet if you need to.

Worked example

Input: [{"id": 1, "name": "Ada Lovelace", "address": {"city": "London"}, "tags": ["math", "code"]}, {"id": 2, "name": "Grace Hopper", "address": {"city": "Arlington"}, "note": "Said \"hi\""}].

Output header: id,name,address.city,tags,note. Row 1: 1,Ada Lovelace,London,"[""math"",""code""]", followed by an empty note cell. Row 2: 2,Grace Hopper,Arlington, an empty tags cell, then "Said ""hi""". The tags cell and the note cell are quoted because they contain double quotes, and each inner quote is doubled. That is exactly what RFC 4180 requires and what Excel and Google Sheets expect when reading the file back.

Escaping rules that prevent broken files

A CSV cell must be wrapped in double quotes if it contains the delimiter, a double quote, a carriage return, or a line feed. Inside a quoted cell, every double quote is written twice. Rows are separated with CRLF line endings, the RFC 4180 default, which every major spreadsheet reads correctly.

The most common hand-rolled converter bug is forgetting one of those cases. A single street address with a comma, like 123 Main St, Apt 4, then shifts every following column one place to the right. If you have ever opened a CSV and seen data in the wrong columns, that is usually why.

Opening the result in Excel and Google Sheets

The downloaded file starts with a UTF-8 byte order mark. Without it, Excel on Windows guesses the encoding and turns names like José or Zoë into garbage characters. Google Sheets handles UTF-8 either way.

Excel in many European locales uses the comma as a decimal separator and expects semicolons between fields. If your whole file lands in column A, re-export with the semicolon delimiter. Tab-separated output is handy for pasting straight into a spreadsheet grid.

Watch leading zeros. ZIP codes like 02134 and IDs like 007 are strings in JSON, but Excel converts them to numbers on open and drops the zeros. Import through Data > From Text/CSV and set those columns to Text to keep them.

Security note: formula injection and data privacy

If a cell value starts with =, +, -, or @, spreadsheet apps may treat it as a formula. Data scraped from user input can carry a malicious formula this way, a problem known as CSV injection. Review untrusted data before opening it, or prefix risky cells with a single quote in your pipeline.

On privacy: many online converters upload your file to their server. This one parses and writes the CSV inside your browser tab, so customer lists, exports, and logs stay on your machine.