JSON to CSV Converter

Paste a JSON array of objects and instantly convert it to CSV. Handles nested objects with dot-notation flattening. All processing happens in your browser.

3 rows × 5 cols

Why Convert JSON to CSV?

JSON is the standard data format for web APIs and modern applications, but CSV remains the lingua franca for spreadsheets, databases, and data analysis tools. Converting JSON to CSV lets you import API responses into Excel, Google Sheets, or pandas DataFrames without writing custom parsing code.

CSV is also significantly more compact than JSON for flat, tabular data — no repeated keys, no braces, no brackets — making it ideal for large datasets where file size matters.

Handling Nested JSON Structures

Real-world JSON often contains nested objects and arrays. Flattening nested objects with dot notation (e.g., address.city) is the most common approach, as it preserves the hierarchical relationship in the column name while keeping the CSV structure flat.

Arrays present a different challenge since CSV has no native array type. This tool serializes arrays as JSON strings within cells, which preserves all data and can be parsed later. Alternative approaches include creating numbered columns (tags.0, tags.1) or joining values with a separator.

CSV Delimiter Choices

While commas are the default CSV delimiter, tabs and semicolons are widely used alternatives. Tab-separated values (TSV) avoid conflicts with data containing commas and paste cleanly into spreadsheet applications. Semicolons are the standard in many European countries where the comma serves as a decimal separator.

When choosing a delimiter, consider your target application. Most spreadsheet software auto-detects delimiters, but command-line tools and programming libraries may need explicit configuration.

Best Practices for Data Conversion

When converting JSON to CSV, verify that all objects in your array share a consistent schema. Objects with different keys will still convert — missing values become empty cells — but inconsistent schemas can produce sparse CSV files that are hard to work with.

For production workflows, consider automating the conversion using tools like jq, pandas, or Node.js scripts. This online tool is ideal for quick, one-off conversions and for exploring data structure before writing automated pipelines.

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