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JSON to CSV Converter

Convert JSON arrays to CSV format online. Instant client-side conversion with download support.

What is a JSON to CSV Converter?

A JSON to CSV converter transforms structured JSON arrays into comma-separated value (CSV) format — the universal tabular data format supported by spreadsheets, databases, and data analysis tools. If you’ve ever needed to import API data into Excel or Google Sheets, this tool saves you from writing custom conversion scripts.

Our converter automatically detects the keys in your JSON objects and creates appropriate column headers. It handles nested values, special characters, and proper CSV escaping so your data imports cleanly every time.

How to Use This JSON to CSV Converter

  1. Paste a JSON array of objects into the input area (e.g., [{"name": "Alice", "age": 30}, {"name": "Bob", "age": 25}])
  2. Click “Convert to CSV” to transform the data
  3. Review the output — keys become column headers, values become rows
  4. Download the CSV file or copy the output directly
  5. Import into Excel, Google Sheets, or your database tool

Common Use Cases

  • Exporting API data to spreadsheets — Convert REST API JSON responses into CSV for analysis in Excel or Google Sheets
  • Database imports — Transform JSON data dumps into CSV for bulk import into SQL databases
  • Data analysis — Convert JSON datasets into CSV for use with pandas, R, or other analysis tools
  • Reporting — Generate CSV reports from JSON data sources for non-technical stakeholders
  • ETL pipelines — Quick format conversion as part of data processing workflows

Frequently Asked Questions

What JSON format does this tool accept?

The tool expects a JSON array of objects, where each object represents a row: [{"col1": "val1"}, {"col1": "val2"}]. All objects should have the same keys for best results, though the tool handles missing keys gracefully.

How does the tool handle nested JSON objects?

Nested objects are typically stringified or flattened. For deeply nested JSON, consider using the JSON Formatter first to understand the structure, then flatten the data before conversion.