JSON to CSV Converter

What is JSON to CSV?

JSON dominates web APIs; CSV dominates spreadsheets and BI tools. Converting JSON → CSV bridges that gap, letting you take API responses, MongoDB exports, or log data and analyze them in Excel, Google Sheets, Tableau, or import into databases.

Common scenarios: API response analysis (paste API output into Excel for deeper analysis), NoSQL → SQL migration (MongoDB JSON → PostgreSQL via CSV import), data deliverables for non-technical stakeholders, backup/audit logs for spreadsheet review.

Conversion example

# JSON input
[
  { "name": "Alice", "age": 30, "city": "Seoul" },
  { "name": "Bob",   "age": 25, "city": "Tokyo" }
]

# CSV output
name,age,city
Alice,30,Seoul
Bob,25,Tokyo

Handling complex structures

• Different keys per object: tool collects all unique keys as headers; missing values are empty
• Nested objects: serialized as JSON strings inside cells (preserves data, but ugly in spreadsheet view)
• Arrays inside objects: same — serialized as JSON strings
• Nested array of objects: not flattened — keep at top level for clean CSV
• Boolean/null values: true/false kept as text; null becomes empty string
• Special characters in values: commas/quotes/newlines properly escaped per RFC 4180

For deeply nested JSON

If your JSON has many nested levels (3+ deep), CSV is the wrong format — flat tables can’t represent hierarchies cleanly. Options:

• Pre-flatten with jq: jq '[.users[] | {name, email, status}]' input.json
• Pre-flatten with Python: pandas’ json_normalize() handles nesting elegantly
• Use Parquet or Arrow instead: better for hierarchical analytics data
• Stay in JSON: use jq for filtering instead of converting

How to Use

1. Paste a JSON array of objects, or upload a .json file.
2. Click Convert.
3. Review the CSV — open in Excel/Sheets to verify columns and rows.
4. Copy or download as .csv.

Privacy: All conversion runs in your browser. No upload.

Flattening a Tree Into a Table

JSON nests and CSV does not, so this conversion is fundamentally about deciding what to do with structure that has nowhere to go. An array of flat objects converts perfectly — each object becomes a row, each property a column. Anything deeper requires a compromise.

That is why the conversion works beautifully for API responses that are essentially tabular, such as a list of orders or users, and awkwardly for deeply nested documents. If your JSON has objects inside arrays inside objects, expect to make choices about which parts become columns and which are dropped or stringified.

Why the Column Order and Set Can Surprise You

JSON objects in a list are not required to have the same properties. One record might include a middle name and the next might not, and the format is perfectly happy with that. CSV is not — every row must line up under the same header.

The practical consequence is that a column set has to be derived from the data, and any property missing from a given record becomes an empty cell. If the first few records happen to lack a field that appears later, a naive converter can miss the column entirely.

It is worth opening the result in a spreadsheet and checking the column headers against what you expected before treating the CSV as complete.

FAQ