JSON to CSV Converter.

Convert JSON to CSV online — free and private

A JSON to CSV converter turns structured JSON — most often a JSON array of objects — into comma-separated rows you can open in Excel, Google Sheets, or any data tool. Paste your JSON above, press Convert to CSV, and the tool flattens each record into a row with a header built from your keys. Because the whole conversion runs client-side, you can convert JSON to CSV online without ever uploading sensitive data to a server.

How to convert JSON to CSV in three steps

1. Paste your JSON into the input, or click “Load sample” to see the format.
2. Choose your options — delimiter, how nested arrays should expand, whether to flatten nested objects, and a header row.
3. Convert and export — pick the columns you want, then copy the CSV or download it as data.csv.

Handle nested arrays and objects your way

Real JSON is rarely flat, and that is where most converters fall down — they only flatten with dot-notation and force one shape on you. This tool gives you the full set of array modes: expand to rows creates one CSV row per array item (great for one-to-many data), join collapses an array into a single cell with a separator, and keep as JSON preserves the raw array inside the cell. Nested objects flatten into readable parent.child columns, or you can switch flattening off to keep them as JSON.

Custom delimiters, TSV, and Excel

Choose a comma, semicolon, tab, or pipe — or type your own delimiter. Selecting tab produces a TSV file; semicolon suits European spreadsheets; and the Excel BOM option makes accented and Unicode characters open correctly in Microsoft Excel. A built-in column picker lets you export only the fields you care about without editing your JSON first.

Convert JSON to CSV in Python, JavaScript, or the terminal

If you need the conversion inside a script rather than a one-off export, here are the standard approaches. The tool above matches what these produce — it is simply faster when you just need the file once.

Python (pandas) — the shortest route for a flat array of objects, with json_normalize handling nested keys:

import pandas as pd

df = pd.read_json("data.json")          # flat array of objects
df = pd.json_normalize(records)         # or: flatten nested objects
df.to_csv("data.csv", index=False)

Python (standard library) — no dependencies, and you control the header explicitly:

import json, csv

with open("data.json") as f:
    records = json.load(f)

with open("data.csv", "w", newline="") as f:
    writer = csv.DictWriter(f, fieldnames=records[0].keys())
    writer.writeheader()
    writer.writerows(records)

JavaScript / Node.js — build the header from the union of all keys so records with missing fields still line up:

const rows = JSON.parse(input);
const cols = [...new Set(rows.flatMap(Object.keys))];
const esc = (v) => '"' + String(v ?? "").replace(/"/g, '""') + '"';

const csv = [
  cols.join(","),
  ...rows.map((r) => cols.map((c) => esc(r[c])).join(",")),
].join("\n");

Command line (jq) — handy in a pipeline or CI job:

jq -r '(.[0] | keys_unsorted) as $cols
       | ($cols | @csv), (.[] | [.[$cols[]]] | @csv)' data.json > data.csv

Each of these needs you to handle quoting, embedded commas, and missing keys yourself — exactly the edge cases the converter above already covers.

Common JSON to CSV problems (and fixes)

“My JSON is an object, not an array.” CSV is a table, so it needs a list of records. If your data is wrapped — { "results": [...] } — point the converter at the inner array, or use the JSON flatten tool to reshape it first.

“Every row has different keys.” That is fine: the header becomes the union of all keys and missing values are written as empty cells, so nothing is silently dropped.

“Excel mangles my accents or splits my dates.” Enable the Excel BOM option so Excel reads the file as UTF-8. For a true multi-sheet workbook, use the JSON to Excel converter instead.

“My file is one JSON object per line.” That is JSONL / NDJSON, not JSON. Run it through the JSONL to JSON converter first, then convert the result here.

“The converter says my JSON is invalid.” Fix it with the JSON repair tool, which handles trailing commas, single quotes, and unquoted keys — the usual damage from hand-edited or LLM-generated JSON.

Why use this JSON to CSV tool?

Unlike converters that quietly upload your file and cap you around 10 MB, this one processes everything locally with no file-size limit and no paywall. It validates your JSON before converting, so malformed input is caught with the exact line and column of the error. Need the structure first? Use the JSON viewer to explore it as a tree, the JSON formatter to pretty-print, or the JSON validator to check syntax. Want a different target format? Export a real workbook with the JSON to Excel converter, render a grid with the JSON to table tool, or produce a Markdown table for your README. Going the other way, the CSV to JSON converter turns spreadsheets back into JSON. Learn the details in our guide on how to convert JSON to CSV.