How JSON to CSV Conversion Works

This tool converts JSON arrays into CSV (Comma-Separated Values) format. Each object in the array becomes a row, and each property becomes a column. It's the fastest way to get JSON data into Excel, Google Sheets, or any spreadsheet application.

What JSON Structure Works Best?

The ideal input is an array of flat objects:

[
  { "name": "Alice", "email": "alice@example.com", "age": 30 },
  { "name": "Bob", "email": "bob@example.com", "age": 25 }
]

Or an object containing an array:

{
  "users": [
    { "name": "Alice", "email": "alice@example.com" },
    { "name": "Bob", "email": "bob@example.com" }
  ]
}

Handling Nested Objects

When your JSON contains nested objects or arrays, they're converted to JSON strings in the CSV output. For example:

{ "name": "Alice", "address": { "city": "NYC", "zip": "10001" } }

Becomes:

name,address
Alice,"{""city"":""NYC"",""zip"":""10001""}"

Delimiter Options

Common Use Cases

• API response to spreadsheet — Export API data for analysis in Excel
• Database export — Convert MongoDB/JSON exports to CSV for reporting
• Data migration — Move data between systems that expect different formats
• Quick data review — CSV is easier to scan than nested JSON

Programmatic Conversion

JavaScript

function jsonToCsv(jsonArray) {
  if (!jsonArray.length) return '';
  
  const headers = Object.keys(jsonArray[0]);
  const rows = jsonArray.map(obj => 
    headers.map(h => JSON.stringify(obj[h] ?? '')).join(',')
  );
  
  return [headers.join(','), ...rows].join('\n');
}

Python

import json
import csv
import io

def json_to_csv(json_data):
    output = io.StringIO()
    writer = csv.DictWriter(output, fieldnames=json_data[0].keys())
    writer.writeheader()
    writer.writerows(json_data)
    return output.getvalue()

Pro Tips

• 💡 Flatten first — For deeply nested JSON, consider using our JSON Flatten tool before converting
• 💡 Check encoding — If you see garbled characters, ensure your JSON is UTF-8 encoded
• 💡 Large files — This tool handles files up to a few MB. For larger datasets, use a programmatic approach

Related Tools

• CSV to JSON — Convert CSV back to JSON
• JSON to Excel — Direct Excel export
• JSON Validator — Validate your JSON first
• JSON Path — Extract specific data before converting

Common Mistakes & Pro Tips

• Nested objects and arrays don't flatten automatically — CSV is a flat, two-dimensional format, so a value like {"address":{"city":"NYC"}} can't map to a single cell cleanly. Most converters either serialize the nested value back to a JSON string in the cell (e.g. {"city":"NYC"}) or flatten keys with dot notation (address.city). Check which behavior your output uses before importing into a tool that expects scalar columns.
• Commas, quotes, and newlines inside values must be quoted and escaped — Per RFC 4180, any field containing a comma, a double quote, or a line break must be wrapped in double quotes, and literal double quotes inside it are doubled (" becomes ""). So the value She said "hi" becomes "She said ""hi""". If a converter omits this, a single value with a comma will shift every following column and silently corrupt the row.
• Headers come from the union of all keys, not just the first object — If your array has objects with different shapes, the column list should be the union of every key across every row, not only the keys of the first record. Rows missing a given key get an empty cell for that column. If a converter only reads keys from the first object, later fields like a phone that only some records have will be dropped entirely.
• null, undefined, and missing keys are not the same as empty string — CSV has no way to distinguish a JSON null from an absent key or from an empty string "" — all three usually render as an empty cell. If that distinction matters downstream (for example, NULL vs '' in a database load), CSV is lossy and you may need a sentinel value or a format like JSON Lines instead.
• Excel may mangle CSV on open even when the file is correct — A correct CSV can still look wrong because Excel auto-converts cell contents: leading zeros in ZIP codes or IDs get stripped, long numbers go to scientific notation, and strings like 1/2 or SEPT1 become dates. The CSV file itself is fine — to preserve values, import via Data > From Text/CSV and set column types to Text rather than double-clicking the file.